Functional gene clustering via gene annotation sentences, MeSH and GO keywords from biomedical literature

Gene function annotation remains a key challenge in modern biology. This is especially true for high-throughput techniques such as gene expression experiments. Vital information about genes is available electronically from biomedical literature in the form of full texts and abstracts. In addition, various publicly available databases (such as GenBank, Gene Ontology and Entrez) provide access to gene-related information at different levels of biological organization, granularity and data format. This information is being used to assess and interpret the results from high-throughput experiments. To improve keyword extraction for annotational clustering and other types of analyses, we have developed a novel text mining approach, which is based on keywords identified at the level of gene annotation sentences (in particular sentences characterizing biological function) instead of entire abstracts. Further, to improve the expressiveness and usefulness of gene annotation terms, we investigated the combination of sentence-level keywords with terms from the Medical Subject Headings (MeSH) and Gene Ontology (GO) resources. We find that sentence-level keywords combined with MeSH terms outperforms the typical ‘baseline’ set-up (term frequencies at the level of abstracts) by a significant margin, whereas the addition of GO terms improves matters only marginally. We validated our approach on the basis of a manually annotated corpus of 200 abstracts generated on the basis of 2 cancer categories and 10 genes per category. We applied the method in the context of three sets of differentially expressed genes obtained from pediatric brain tumor samples. This analysis suggests novel interpretations of discovered gene expression patterns.
Background
In recent years, increasing amounts of biological data have become available through techniques such as DNA microarrays and other high-throughput gene and
protein assays. [1,2] As large numbers of genes can be included in such studies, the task of assigning meaningful biological function to gene patterns or gene clusters
is a considerable challenge. Typical analyses using supervised (classification) or unsupervised (clustering) methods require the user to incorporate the necessary background
knowledge. [3] This ability to incorporate background knowledge is fundamental to effective and efficient scientific discovery. A substantial amount of biomedical knowledge is
captured in free-text form in abstracts and full-text articles and also in specialized biological information systems such as Gene Ontology (GO) [4], Medical Subject Headings
(MeSH) [5], Database of Interacting Proteins (DIP) [6] etc. Until only a few years ago, human reasoning was the primary method for the extracting, synthesizing and interpreting
the information contained in the biomedical literature and supporting biological information systems.
However, in recent years the number of online documents (and other biological information repositories) has grown tremendously. This is both an opportunity and a challenge.
On one hand, such resources facilitate automated processing of the knowledge and information contained in these documents. On the other hand, such processing poses considerable
algorithmic and computational challenges [7]. For example, the biomedical abstract database MEDLINE [8] currently contains about 15 million citations and about 40 000 citations
are added monthly.
Text mining is the application of techniques from machine learning, natural language processing (NLP), information extraction and statistical/mathematical approaches to
automated extraction of useful knowledge from text [9]. Text mining of biomedical literature has been applied successfully to various biological problems. Many studies focus
on protein-protein [10–13] and gene-protein interactions. [14] Other specific relationships between biological entities such as sub-cellular localization of proteins [15,16],
molecular binding relationships [17] and interaction between genes and drugs [18] are also explored. Text analysis of biomedical literature has also been applied successfully
to incorporate functional information of gene expression data [19–23]. For example, MedMOLE [19] identifies the functions among a group of genes by simple text clustering of
entire MEDLINE documents associated with the genes. Blaschke et al. [20] extracted information about the common biological characteristics of gene clusters from MEDLINE using
a statistical term weighting approach. This method returns an ordered set of keywords with a high probability of occurrence in abstracts. Liu et al. [24] extended this approach
by clustering such keywords to find gene-to-gene relationships. Clustering genes by functional keyword association can provide direct information about the nature of genes and
their functional association [25]. However, the quality of the keyword lists extracted from the biomedical literature for each gene significantly affects the clustering results.
Commonly, these approaches represent genes by extracting keywords from entire abstracts [25]. These keywords may undergo transformations such as weighting or dimension reduction
with the goal of improving clustering quality and efficiency. However, gene clustering using entire abstracts has the following main drawbacks. (a) Abstracts normally contain a
large number of irrelevant sentences. These sentences may influence the clustering process and are likely to obscure information useful for gene annotation. (b) The number of
unique terms in abstracts is typically very large. This requires the ability to deal with sparse data spaces or methods for dimensionality reduction. (c) Dimension reduction
methods such as principal component analysis or signal-to-noise methods increase the computational complexity, may lead to the loss of important keywords and do not guarantee
that the reduced dimensionality will yield better clustering/annotation information. Also, the composite features may be hard to interpret.
To avoid the above drawbacks and improve the clustering process, we decided to use gene annotation sentences from abstracts instead of using full abstracts to extract the
keywords. Current NLP techniques allow such sentence extraction from documents. Using clustering on the basis of sentence-extraction techniques has the advantage of avoiding
complex dimensionality reduction and term weighting techniques. Further, this approach is likely to yield more specific terms which are easier to interpret. We first extracted
the potential sentences describing gene annotation information from abstracts using a NLP method utilizing gene/protein name dictionaries and pattern-matching-based rules. In
addition to this sentence-keywords approach, we carried out two further experiments involving MeSH terms and GO terms as supplementary keywords. Hence, in our method, each gene
is represented by set of keywords extracted from sentences, MeSH terms and GO terms. To demonstrate the usefulness of the proposed text mining methods, we performed hierarchical
clustering of a gene × keyword matrix to find functionally discrete sub-groups of genes. The overall experimental design and its components are illustrated in the Figure 1.
We validated the performance of keywords extracted by our method using a manually annotated corpus of 200 abstracts. We also evaluated the usefulness of our method by sorting
differentially expressed genes from a microarray experiment into functional sub-groups. The objective of our gene clustering process using functional keywords is to identify and
summarize potential functional gene groups and to complement the conventional gene expression data clustering tasks.
Methodology
Gene/Protein name and synonym dictionary creation
One of the major obstacles in biomedical literature processing is the variety of names each gene or protein is known by. To address this problem in the present study, we
developed a gene/protein name dictionary. Essentially, each entry of this dictionary consists of a preferred (or canonical) name for a gene/protein and a list of synonyms used for
this gene/protein. The dictionary was created on the basis of the Entrez Gene [26] (previously LocusLink) database, one the most stable and complete sources of information on genes.
Since our study is part of a wider investigation in the context of human brain tumor research, we focused specifically on human genes/proteins. We developed PERL scripts to extract
and select from the Entrez Gene entries the official symbols as preferred name of the gene and other aliases as known synonyms. In addition, we augmented the dictionary with relevant
synonyms from other publicly available databases including GeneCards [27], SwissProt [28], GoldenPath [29] and HUGO [30]. The final dictionary contains 26 731 unique human gene/protein
names and 274 845 synonym names.
Keywords extraction from biomedical literature
In our study each gene is represented by a list of keywords extracted from MEDLINE abstract sentences, MeSH terms and GO terms. The procedure for extracting keywords from each data
source is discussed below.
MEDLINE abstracts keywords extraction
To extract the keywords associated with each abstract, we decided to use gene annotation sentences from the abstracts instead of constructing a large keyword vector based on the entire
abstract. The assumption is that the information given on sentence-level is much more specific and therefore useful to characterize the function of the genes. Only sentences that contain
one or more genes reference from our gene lists will be considered as gene annotation sentences, all other sentences are discarded from the analysis. We applied the following three steps
to extract sentence-level keywords (1) gene-name normalization, (2) sentence filtering, and (3) keyword extraction.
Gene-name normalization
This process replaces all the gene names in the abstract with its unique canonical identifier (Entrez gene ID) using the gene-synonym dictionary specially constructed for this study.
Sentence filtering
This process extracts all the gene annotation sentences from abstracts that contain one or more gene names from our gene lists using regular-expression pattern matching rules. We used different
regular expressions (which rely on matching of pre-defined patterns or rules such as arrangement of gene/protein names with articles, prepositions and other keywords) to filter sentences containing
one to three genes. We defined our regular expressions as nouns describing agents, passive verbs, active verbs and nouns describing actions. Table 1 (in supplementary material) depicts an example
for each type of expression.  For example, the regular expression
($gene @{0,6} $action (of|with) @{0,2} $gene)
extracts sentences that match the structure shown below the expression. The notational construct ‘A → B → ...’ is interpreted as ‘A followed by B followed by ...’.
gene name → 0-6 words → action verb → ‘of’ or ‘with’ → 0-2 words → gene name
Sentence keyword extraction
Sentences containing one or more gene names were parsed using the Brill part-of-speech tagger. [31] This program labels each word in a sentence with its part-of-speech information such as
word category like noun, verb, adjective, preposition, etc. This information plays a critical role in identifying corresponding noun and verb phrases. Then, with a simple PERL program, noun
phrases containing gene names were filtered out and the remaining noun phrases and verb phrases were extracted as keywords. Initial tests showed that certain keywords were common for most of
the genes in the list (e.g., activates, associates, stimulates etc.). We manually removed these common keyword words from the list. The following example illustrates this process:
Sentence
BRCA1 physically associates with p53 and stimulates its transcriptional activity.
Brill-POS-tagged sentence
BRCA1/NNP physically/RB associates/VBZ with/IN p53/NN and/CC stimulates/VBZ its/PRP$ transcriptional/JJ activity/NN. /.
Sentence keywords
associates, stimulates, transcription activity
Sentence keywords after manual curation
transcription activity
MeSH keywords extraction
To extract MeSH keywords, we searched for the gene names in our gene lists in the title and abstract of MEDLINE citations related to each gene and extracted the associated MeSH terms for
each gene. The extracted gene-MeSH term list was represented by scores indicating the frequency of gene-MeSH term co-occurrence. Initial tests showed that certain MeSH keywords in the list
were common biological terms and less useful from the point of view of gene annotation (e.g., human, DNA, animal, Support U.S Govt etc.). A collection of MeSH stop words was created manually
and these terms were removed from the gene-MeSH term lists. Finally, from the thus filtered gene-MeSH lists, the 20 highest-frequency MeSH terms associated with each gene were taken as MeSH
keywords associated with each gene. For example the MeSH keywords associated with a gene “FOS” in our gene list are oncogene, felypressin, transcription-factor, thermoreceptors, DNA-binding,
antibiosis, inflammatory-response, zinc-fingers, gene-regulation, and neuronal-plasticity.
GO keyword extraction
We used the GO keywords information incorporated in Gene Ontology [Error! Bookmark not defined.] to extract GO keywords associated with each gene. Out of the three GO annotation categories
we included only molecular function and biological process as we believe that cellular component (e.g. nucleus, cell membrane etc.) is less important for characterizing genes in the context
of this study. Further, due to the hierarchical nature of GO and multiple inheritance in the GO structure, we consider with every ancestor up to level 2 in the GO tree in assigning GO keywords.
This enables us to use more generalized GO terms. For example the GO keywords associated with the gene “FOS” in our gene list are protein-dimerization, DNA binding, RNA polymerase, transcription
factor, DNA methylation, inflammatory-response, and nucleus.
Keyword representation and calculation of numeric vectors
After the keyword extraction phase, each gene was described by a list of keywords extracted from MEDLINE abstract sentences, MeSH terms and GO terms. These keyword vectors then served as a basis
for clustering (i.e., unsupervised class discovery). To do this, each term vector needed to be represented by a numeric vector representing the relative importance of keywords for each gene. This
process is concerned with computing the numeric weight, wij, for each gene-term pair (gi, tj) (i = 1, 2,…n and j = 1, 2, … k) to represent the gene's characteristics in terms of the associated keywords.
Common techniques for such numeric encoding includes (1) Binary, the presence or absence of a keyword relative to a gene, (2) Term frequency, he frequency of occurrence of a keyword with a gene (3)
Term frequency × inverse document frequency (TF*IDF), the relative frequency of occurrence of a keyword with a gene compared to other genes.
As we derived the keywords from gene annotation sentences but not from full abstracts, we found the number of keywords associated with each gene is small. We noticed also that absolute frequency of
most keywords tended be one. Therefore, we adopted the binary encoding scheme as illustrated in Table 2 in supplementary material, in which each gene is represented by a vector of ‘normalized’ absolute
keywords frequencies. The ‘normalized’ absolute frequency of each vector element (keyword) is either zero or one.
Gene clustering
Clustering is a data mining technique that groups or clusters data components (typically represented as numeric vectors) according to their similarity or dissimilarity. The goal is to maximize
intra-cluster and minimize inter-cluster similarity among the components. [32,33] Clustering is typically used to identify sample groups in data. Unlike supervised learning methods that require explicit
class label information, clustering is unsupervised and no information about target groups (classes) is used. Two basic approaches to clustering can be distinguished, hierarchical clustering (e.g., agglomerative
and divisive) and non-hierarchical clustering (e.g., k-means/c-means clustering). Agglomerative hierarchical clustering starts with each object representing a cluster and then merges the clusters in sequence.
Divisive hierarchical clustering starts with all samples in one cluster and successively split clusters. In hierarchical clustering the distance (similarity) between clusters is measured using different
techniques such as single linkage, average linkage or complete linkage [32] and basic distance and similarity metrics (e.g., Euclidean, Minkowski, Hamming distance). K-means clustering requires a priori
specification of the desired number of clusters, k. This method clusters data into groups by iteratively optimizing the positions of cluster centers (means) so that the sum of within-cluster similarities
(the similarity between data points and their cluster centers) is maximized.
Essentially, the sentence-level binary coding scheme adopted in this study consists of numeric row vectors representing genes (via the associated biological function/process terms), and numeric column
vectors representing annotation terms (via the associated genes). These two sets of vectors can be independently clustered using available clustering algorithms and tools. This approach can produce useful
and specific information about the biological characteristics of sets of genes. In this study, we have used average linkage hierarchical clustering algorithm. [33] Using this algorithm has two advantages
for this study. First, clustograms, a visualization of the substructures contained in a gene collection are produced, and second, individual clusters of genes are identified by clustogram splits at different
levels. Clustering was performed using Cluto [34] and Cluster/Treeview [35] facilitates visualization of the clustograms.
Results and discussion
Evaluation
To obtain a quantitative measure on the performance of the various keyword encoding schemes, we developed a text corpus of 200 manually annotated abstracts based on two cancer categories brain tumor and breast
cancer of our interest (see Table 4 under supplementary material). We used the following procedure to establish the corpus: (1)Determine randomly two cancer categories (brain tumor and breast cancer ), (2) For
each cancer category, select randomly 10 genes from Entrez such that species = human and number of associated abstracts ≥ 50, (3)For each gene identified in this way, select randomly 10 abstracts, resulting in
a total of 200 abstracts; 10 abstracts for each of the 10 genes associated with each of the two cancer categories, (4) For each of the 200 abstracts, identify manually the keywords characterizing biological
function and processes from abstracts, MeSH terms and GO terms.
With this text corpus we were able to construct a matrix containing all 20 genes and their associated keywords and keyword frequencies from abstracts, MeSH terms and Go terms. The manually annotated corpus
of 200 abstracts and the matrix of 20 annotated genes served as gold standard for our evaluation experiments. We carried our four evaluation experiments: (1) Abstract keywords (baseline). Extracts gene annotation
terms based on term frequencies * inverse document frequencies (TF*IDF) within the entire abstract without regard to sentence structure, (2) Sentence keywords. Extracts gene annotation terms based sentence-level
keywords, (3) Sentence + MeSH keywords. As in (2) above plus MeSH terms (see Section MeSH keywords extraction), (4) Sentence + MeSH + GO keywords. As in (2) above plus MeSH terms (see Section MeSH keywords
extraction) and GO terms (see Section GO keyword extraction).
Essentially, in each evaluation experiment the input is the text corpus of 200 abstracts and the output is a list of genes with its predicted annotation terms. Informally, the closer the predicted annotation
terms match the manually established annotation terms, the better is the method. Performance is measured via commonly used criteria such a recall (analogous to sensitivity), precision (analogous to positive
predictive value) and the F-measure (a score that combines recall and precision). The results we obtained are shown in Table 5 (below in supplementary material).
We notice that the baseline method comprising TF*IDF keywords fares worst among all four approaches. We interpret this as evidence for the validity of the methods involving sentence-level processing as this
information is likely to carry most specific characterizing terms. The ‘brute-force’ abstract-level processing will have difficulty in extracting these terms correctly and consistently. We further notice that
the substantial improvements of precision and recall when we include MeSH terms and GO terms. This may be because these two categories are more specific and MeSH and GO annotations were done using full-papers
and these biological functions and process are not described in all abstracts.
Clustering of genes resulting from microarray experiment
To demonstrate the usefulness of the presented keyword-extraction techniques to microarray data analysis, this method was applied to annotate and cluster gene lists that were found differentially expressed in
a microarray experiment investigating the impact of two mitogenic proteins, Epidermal growth factor (EGF) and Sphingosine 1-phosphate (S1P), on glioblastoma cell lines [36]. The microarray data set reveals three
sets of differentially expressed genes (p<0.05), namely, genes differentially expressed with response to EGF, G(EGF), genes differentially expressed with respect to S1P, G(S1P) and genes differently expressed in
response to both, G(COM).
Genes were considered differentially expressed if their p-value is smaller than 0.05. We found that, when compared to the resting state, 19 genes were significantly differentially expressed as a response to EGF,
35 genes as a response to S1P and 30 genes as a response to COM, i.e., combined stimuli of S1P and EGF. The three gene lists are referred to as G(EGF), G(S1P) and G(COM), respectively (see Table 6 in supplementary material).
Using these the three gene lists obtained from the microarray experiment (Table 6 shown in supplementary material) as query in MEDLINE returned the three corresponding sets of abstracts A(EGF), A(S1P) and
A(COM), respectively. The abstracts were processed with the keyword extraction method involving sentence-level, MeSH and GO terms and the resulting representations were clustered using average linkage hierarchical
clustering algorithm. Our gene clustering strategy and clustering algorithms are explained in the Methodology section. The resulting clustograms are presented in Figure 2, Figure 3, and Figure 4, respectively.
The clustograms depict associations between genes and biological function/process terms derived from the abstracts obtained with the various gene lists. For the investigating scientist, the clustograms fulfill the
following main functions: (1) Squares highlighted in a horizontal line link a gene to one or more biological functions or processes. This is useful to see which genes are associated with which functions/processes and
which genes have few or many associations. The interpretation of many and few is very much dependent on the associated biological function/process categories, the particular scientific question under investigation,
and also on how extensively a particular gene has been researched and reported in the literature. (2) Users may visually delineate clusters, i.e., rectangular areas with many highlighted squares in them and few
highlighted squares around them. Any cluster, small or large, is potentially very useful to have discovered. Each cluster identified in this way relates a set of genes to a group of biological functions and processes.
In a sense, each gene in the clustered is characterized by the same set of biological function and process concepts, a kind of ‘guilt by association’. This information is extremely useful as it provides clues as to
the roles genes may play collectively in pathways and functions, processes, and possible phenotypes, that are associated with these pathways.
Summary of analysis of EGF cluster, G(EGF)
The clustograms in Figure 2 show the results obtained from extracting the sentence-level function/process keywords (plus MeSH and GO terms) from 28,913 abstracts (for the 19 genes detected in response to EGF
stimulus) and the subsequent clustering. In Figure 2a several individual genes with very many (e.g., CALD1, CLU, FOS) and very few (e.g., HRY, DUSP6) associations stand out. Another interesting feature is the
large cluster at the lower left corner of Figure 2a (reproduced in more detail in Figure 2b) containing the genes DUSP, ID1, KLF2, CALD1, ABCA, CLU, FOS, JUN and SLC5A3. Many genes in this cluster are associated
with the same set of keywords (transcription factor, cell death and secretion).
Summary of analysis of S1P cluster, G(S1P)
The clustograms in Figure 3 show the results obtained from extracting the sentence-level function/process keywords (plus MeSH and GO terms) from 19,705 abstracts (for the 30 genes detected in response to S1P
stimulus) and the subsequent clustering. In Figure 3a several individual genes with very many (e.g., CCL3, IL6, IL8, F3) and very few (e.g., HERB2, DOC1) associations stand out. Another interesting feature is the
large cluster at the upper left corner of Figure 3a (reproduced in more detail in Figure 3b) containing the genes TNAIP, KLF5, BCL6, NAB1, BTG1, NFKBIA, NR4A1, SOCS5, CITED2, NRG1, JAG1, PLAU, CCL2, IL8, IL6, GLIPR1,
F3, MAP2K3, and EHD1. Many genes in this cluster are associated with the same set of keywords (atherogenesis, mitogenesis, assemble, inflammation, focal-contact, …, and protein-binding).
Summary of analysis of the common gene cluster, G(COM)
The clustograms in Figure 4 show the results obtained from extracting the sentence-level function/process keywords (plus MeSH and GO terms) from 39,890 abstracts (for the 30 genes detected in response to EFG and
S1P stimuli) and the subsequent clustering. In Figure 4a several individual genes with very many (e.g., MYC, MAFF, ATF3) and very few (e.g., DIPA, UGCG, SNARK) associations stand out. Another interesting feature is
the large cluster at the upper left corner of Figure 4a (reproduced in more detail in Figure 4b) containing the genes SPRY2, GEM, ZYX, NEDD9, MYC, LIF, SERPINE1, DTR, MUCL1, C8FW, MAFF, ATF3, RTP801, EGR1, JUNB,
FOSL1, CEPED, TIEG, EGR2, EGR3, and ZFP36. Many genes in this cluster are associated with the same set of keywords (DNA binding, zinc fingers, repressor proteins, …, and mitosis).
An important aim in microarray data mining is to bind transcriptionally modulated genes to functional pathways or to understand how transcriptional modulation can be associated with specific biological events
such as genetic disease phenotype, molecular mechanism of drug action, cell differentiation etc. However, the amount of functional annotation available with each transcriptionaly modulated genes is still a limiting
factor because not all genes are well annotated. Our functional clustering/grouping will enable to select literally informative genes (Figure 2b, Figure 3b, and Figure 4b) for further investigations in the above
data mining and knowledge discovery pipeline. Our evaluation suggests that this approach will provide more specific and useful information than typical approaches using abstract-level information. This is particularly
the case when the sentence-level terms are augmented by MeSH and GO keywords.
Conclusion
The sequencing of whole genomes and the introduction high throughput analysis (e.g., oligonucleotide and cDNA chips, MALDI/SELDI-TOF MS) provides biomedical research with a global perspective, which necessitates
the development of novel mining tools to explore and interpret data in timely manner. This paper presents a novel approach to combine sentence-level keywords with GO and MeSH terms. In our evaluation experiment, this
approach has shown promising results. The present evaluation suggests that this approach will provide more specific information than typical approaches using abstract-level information. This is particularly the case
when the sentence-level terms are complemented by MeSH and GO terms. Further, clustering of genes into different functional groups based on literature keywords has the potential to help biologists identify and characterize
literally informative genes of interest for further investigations.
Future work
Future enhancements of the system will include additional data resources (OMIM. DIP, KEGG) and the generation of association rules to identify correlations among genes in the same cluster. Association rules between
the genes in the same cluster seem particularly interesting because it allows one to find the presence of regularities between gene groups. Finally, abstracts were used in this study as they are readily and easily available
but they are limited in content. As full-text contains large number of irrelevent sentences compared to abstracts this approach may be useful for full-text analysis too, as it performs filtering of irrelevant sentences
before clustering. The plan to perform the current study with full-text articles and compare the results with that of abstracts is on the way.
Supplementary material
Data 1

text mining
microarray data analysis
functional clustering


Small-cell lung cancer patients are just ‘a little bit’ tired: response shift and self-presentation in the measurement of fatigue

Background Response shift has gained increasing attention in the measurement of health-related quality of life (QoL) as it may explain counter-intuitive findings as a result of adaptation to deteriorating health.
Introduction
Quality of life (QoL) is considered an important treatment outcome when the treatment intent is not curative but palliative. However, the expected deterioration in QoL often does not occur, even in cases of serious illness. For example, Groen et al. studied patients with inoperable non-small cell lung cancer treated by radiation with and without chemotherapy [7]. QoL was measured with The European Organization for Research and Treatment of Cancer Core Quality of Life Questionnaire (EORTC QLQ-C30) [1], which has been designed specifically for use in clinical trials focusing on cancer patients. However, contrary to expectation, they did not find significant deterioration in the scale scores over the treatment period of 6 weeks. Although cancer patients are willing to undergo risky and toxic treatments [11], it seemed implausible that the side-effects of treatment had not affected their QoL. Other studies also reported counter-intuitive results. For example, patients with a life-threatening disease or disability were found to report stable QoL, and patients with a severe chronic illness reported QoL levels that were not inferior to that of patients with a less severe illness or to healthy patients [2, 3, 5]. Such counter-intuitive findings, labelled by Breetvelt and Van Dam as ‘underreporting of problems’ suggest that patients report less distress and dissatisfaction than they actually feel [5].
In recent years, response shift theory has gained increasing attention in explaining paradoxical and counter-intuitive findings. Response shift refers to a change in internal standards, values and conceptualization of QoL and is recognized as an important mediator in adaptation to changing health [17]. However, despite the explanatory power of response shift theory, our current understanding of phenomena that can complicate the interpretation of QoL scores is still limited. Therefore, we investigated QoL measurement in small-cell lung cancer (SCLC) patients during 1st line chemotherapy. We were quite surprised when we noticed discrepancies between levels of fatigue measured with the questionnaire and answers spontaneously reported during the interview. We therefore investigated these ‘conflicting’ findings in the measurement of QoL in more depth.
This paper reports the results of an exploratory longitudinal multiple-case study, in which we focused on how patients responded to the EORTC QLQ-C30 question ‘were you tired’ at different points in their treatment trajectory. We aimed to describe the patients’ explanations when answering the question, and to search for explanations of counter-intuitive findings.
Methods
Procedures and study sample
Between March 2001 and September 2003, we recruited newly-diagnosed patients with SCLC who were evaluated for 1st line chemotherapy. The patients were attending one of five outpatient clinics for chest diseases in the Netherlands. To maximize the likelihood that we would interview patients from the beginning of their treatment, we were informed about new patients immediately after diagnosis. No restrictions were made with regard to age or treatment (chemotherapy or a combination of chemotherapy and radiotherapy). Participating patients gave written consent and were interviewed at equivalent points in the treatment trajectory. The first interview (T1) was carried out at the start of chemotherapy. In the original plan the second interview was planned after completion of the course of chemotherapy. However, after inclusion and first interviews of 3 patients we made a decision to interview the patients during treatment as well. Therefore the second interview (T2) was conducted 4 weeks after T1 and the third (T3) 7–10 days after completion of the treatment with chemotherapy and the fourth (T4) 6 weeks later. Approval for this study was obtained from the Medical Ethics Committees of the research site and the participating hospitals.
During the course of the study, 41 eligible patients were invited to the study. Four patients were unwilling to participate, 3 died before informed consent could be obtained, and 3 were not interviewed because of imminent death. Of the 31 respondents who were interviewed, 8 were excluded from further analysis because their data were incomplete, i.e. they were only interviewed once (six died within a month after T1 and two were too sick at T2 and died before the end of the planned chemotherapy). Consequently, the final study sample consisted of 23 SCLC patients, of whom 12 had limited (3 male and 9 female, mean age 55, range 42–69) and 11 had extended disease (8 male and 3 female, mean age 64, range 39–72). All patients received standard chemotherapy, except for 7 patients whose chemotherapy was combined with local radiation of the tumour. The majority of the patients were married (19, 83%), and had children (17, 74%).
Of the 23 patients in our study sample, 15 were interviewed four times, 7 were interviewed three times, and one patient was only interviewed twice resulting in a total of 83 interviews. The interviews were conducted by MW in the homes of the patients. In three cases the 1st interview was held in the hospital. Interviews averaged 80–110 min.
Materials and qualitative method
In this exploratory, longitudinal multiple-case study, QoL was assessed with EORTC QLQ-C30 (version 3.0) [1] and the lung cancer module QLQ-CL13 [4]. The EORTC QLQ-C30 is the most widely used cancer-specific QoL instrument in European clinical trials. The questionnaire is composed of several scales, which measure among others physical function, mental health, general health and global QoL. Furthermore, it measures different symptoms such as pain, dyspnoea, nausea and fatigue. The Fatigue Scale consists of 3 items: ‘did you need to rest’, ‘have you felt weak’ and ‘were you tired’ (respectively questions 10, 12 and 18), for which there are 4 response categories: ‘not at all’, ‘a little’, ‘quite a bit’ and ‘very much’ (respectively scores 1, 2, 3 and 4).
The EORTC QLQ-C30 and CL13 were completed in combination with the Three-Step Test-Interview (TSTI) to investigate how respondents interpreted the items and how they responded to them. The TSTI consists of the following steps [8]: (1) concurrent think aloud, aimed at collecting observational data on how a respondent completes the questionnaire, expressing his thoughts aloud; (2) focused interview, aimed at clarifying respondents’ previous expressions while completing the questionnaire; (3) semi-structured interview, aimed at eliciting respondents’ experiences and opinions with regard to the questionnaire.
Interview protocol
Each interview was conducted in an identical format. At T1, QoL was measured with the EORTC QLQ-C30, followed by the lung cancer module QLQ-CL13. The questionnaire was conducted in a concurrent think aloud manner and after completion, respondents were asked to clarify previous hesitations, expressions when rating certain items and experiences (i.e. second and third step of the TSTI). Individual QoL was then measured with the Schedule for the Evaluation of Individual Quality of Life—Direct Weighting (SEIQoL-DW) [13, 14]. Finally, we encouraged patients to talk freely about the impact of diagnosis and treatment. At follow-up (i.e. T2, T3 and T4), after the SEIQoL-DW assessment, the EORTC questionnaires were administered a second time as a so-called ‘then-test’ [15, 16, 18] (i.e. the patients filled out the questionnaire in reference to how they perceived themselves as they were in the previous interview). In these 2nd and following interviews, EORTC assessments were conducted in a concurrent think aloud manner and with the second step of TSTI integrated in the assessment. In fact, we encouraged patients to think aloud and we probed for clarification after each item in the case of extra information was considered useful to understand patients’ answer. We used a flexible approach in order not to interrupt the natural flow of both the assessment as well as the patient-interviewer communication.
The interviews were audio-taped and transcribed verbatim. In this article we focus on the EORTC QLQ-C30 question ‘were you tired’.
Analysis
Three types of data were collected and used for analysis [12]: (1) completed EORTC questionnaires (T1–T4), (2) observed respondent behaviour recorded in field notes, and (3) transcriptions of the interviews, including ‘think aloud’. The analysis was aimed at identifying discrepancies, response strategies and explanations of response behaviour. We used the qualitative computer package Kwalitan 5.0 (http://www.kwalitan.net) to extract relevant parts of the transcriptions (1) ‘think aloud’ of the question ‘were you tired’ and of other items that were useful in understanding the response behaviour to the question on fatigue (i.e. two other items of the fatigue scale, the general health and the global QoL question of the QLQ-C30), and (2) ‘comments’ related to fatigue symptoms, impact of treatment on perceived QoL, and attitudes towards life. In order to deal with the still remaining large amount of extracted data, two of the authors (MW, AT) condensed extracted transcripts of the ‘comments’ into core texts. For each patient, the data (i.e. think aloud combined with scores and core texts of comments) were organized per interview in one mind map (see example in Fig. 1) by means of the computer package Mindjet Mindmanager Pro 6 (http://www.mindjet.com).
Fig. 1Analysis by means of a mind map. Branch EORTC: scores of GH/QOL and the fatigue scale are organized per interview and complemented by the think aloud data. Branch Comments: core texts of relevant parts of transcripts are organized per code and per interview. Note: (+) not all branches of the different interview moments are shown
Furthermore, a different mind map was made to organize think aloud data related to the question ‘were you tired’ for all patients per response category per assessment, including then-test. For the analysis, three authors (MW, AT, TH) each independently read the mind maps of each patient. They studied patients’ scores, their think aloud responses, and examined whether response shift type explanations would be provided: recalibration (i.e., using different standards of comparison to assess fatigue over time), reprioritization (i.e., changes in the importance attached to fatigue over time) and reconceptualization (i.e., changes in the meaning of fatigue over time). Two researchers (MW, AT) searched for additional explanations in the core texts to account for the response behaviour and the discrepancies. The research team (MW, AT, TH, MS) discussed critically the different response strategies used by the patients and the robustness of the interpretations of response shift.
Results
Patients with or without discrepancies
Of the 23 patients, 15 (5 male and 10 female, age 46–72) showed discrepancies at least at one measurement point, i.e. differences between their answer to the EORTC question ‘were you tired’ and their level of fatigue spontaneously reported during the interview. In their answers to the EORTC questionnaire they all presented themselves positively and said that they were not tired. Mary, for example, was 60 years old at the time of the first interview. During all her interviews she reported that she was tired, but she consistently answered ‘not at all’ to the EORTC question (Box 1). Only once she scored ‘a little bit’. This was at T2 when filling in her questionnaire as a then-test for T1 and she commented “a little, more than at the moment”.
Box 1Example of a patient with discrepancies in reported level of fatiguePatient MaryMary was 60 years old and married. She had two sons and two grandchildren. Her answer to the EORTC question was consistently ‘not at all’, except for the then-test concerning the interview T1. This suggests that she did not suffer from fatigue in the week prior to the interviews and that fatigue due to chemotherapy did not have any impact at all on her energy level. But, during the interview she spontaneously provided information that indicated that chemotherapy had an impact on her life and that she regularly suffered from fatigue.T1 EORTC score ‘not at all’Think aloud: Were you tired... no not at all, no, no difference compared to the pastInterview: I’m getting tired at the least little thingT2 EORTC score ‘not at all’Think aloud T2: Not tired, last week, not at allThink aloud then-test T1: a little, more than at the moment.Interview: According to the doctors, the X-rays were very good. I’m very optimistic, sometimes I’m tired but that’s my own fault. I don’t have as much energy as I did before I became ill.T3 EORTC score ‘not at all’Think aloud: Last week I wasn’t tired, it’s the second week after my chemo, not at all tiredThink aloud then-test for T2:not at allInterview: Yes, it was my last cycle of the chemo, I was afraid that I wasn’t going to be able to carry on through the treatment. But I managed, okay; I’m tired but apart from that... nothing at all.T4 EORTC score ‘not at all’Think aloud: No I wasn’t tired last week, not at allThink aloud then-test for T3:not at allInterview: Now and then, I’m tired. It’s different to before my chemotherapy. Sometimes I’m so tired, so tired, more than in the past. It comes suddenly...in the middle of the day.
For many of our respondents, every new cycle of chemotherapy had a more severe impact on their energy level. Therefore, the highest level of fatigue was to be expected after the 5th and last cycle of chemotherapy, at T3. However, the 15 patients with discrepancies all answered the question ‘were you tired’ with ‘not at all’ or ‘a little’. For example, during her interviews at T2 and T3, Ann reported the growing impact of every cycle of chemotherapy, but her answer to the EORTC question at both interviews was ‘a little’ (Box 2).
Box 2Example of a patient using different response strategies, comparison with more sick patients, response shift and self-presentationPatient AnnAnn was 47 years old and living with a partner. She didn’t have any children.Her scores suggest that the chemotherapy had a slight impact on her energy level during her treatment and a greater impact 6 weeks after completion of the treatment. But, the interview and the think aloud provided information that indicated that the chemotherapy had a growing impact on her life, and the score ‘quite a bit’ at T4 was the result of bad news (i.e. a recurrence of the tumour). Furthermore, her data show examples of different response strategies, comparison with more sick patients and self-presentation.T1 EORTC score ‘not at all’Think aloud: Were you tired.. in principle I wasn’t tired. I was mentally tired, it costs me a lot of energy to talk with my relatives. I think you mean physically tired. You’re tired in the sense that you can hardly put one foot in front of the other. That’s being tired. No I’m not tired.Interview: I want to be realistic, think positively. I try not to worry; there is nothing I can do. I don’t know when I will die. I get angry when people are surprised...if I say I’m doing fine...it’s my decision how I’m feeling.T2 EORTC score ‘a little bit’Think aloud: I feel it a little bit, compared to other people who are very sick. So, if I have pain or when I’m tired I say to myself don’t complain, so everything I feel, I only feel a little. I’m doing fine.Think aloud then-test for T1:a littleInterview: Yesterday, I worked for three hours and I was exhausted. I went to bed in the middle of the day in order to be able to show my friend that I’m doing fine. She has trouble in coping...me... having cancer.T3 EORTC score ‘a little bit’Think aloud:A little, yes because I have the feeling that I was able to get over itThink aloud then-test for T2: I think that I’ve said a little last time, it felt a little, but I shift my limit. Interview: It’s a kind of tiredness, I don’t know. I’ve never been like that. To allow yourself to be tired. I think that the story they all tell, that the last cycle of chemo has the most impact, I think that’s very very true.T4 EORTC score ‘quite a bit’Think aloud: Yes, I was quite a bit tired, but only mentally tired. I have to adjust to the idea of a new course of treatment and radiation. My health is excellent. I’m able to do everything I like, better than 6 weeks ago. But, with all the medicine I’m taking to suppress the epileptic fits... I’m scared, just like after the start of chemo.Think aloud then-test for T3:a little but I am not really sure, the pain which I had in my ankels made me tired.Interview: The radiation will make me tired, but it has not started yet, so I am not tired yet.
Patients with discrepancies were identified in both stages of disease and with both treatment regimens, i.e. 12 patients (LD n = 5, ED n = 7) treated with chemotherapy and 3 LD patients treated with chemotherapy and radiotherapy. During the course of the treatment (T1–T4, 55 interviews) they answered the question “were you tired” 20 times with ‘not at all, 29 times with ‘a little’, 4 times with ‘quite a bit’ and twice with ‘very much’ (see individual scores per interview in Table 1)
Table 1Individual (then-test) scores of patients answering the EORTC QLQ-C30 question ‘were you tired’. Response categories 1, 2, 3 and 4 are representing respectively the category ‘not at all’, ‘a little’, ‘quite a bit’ and ‘very much’. Small-cell lung cancer patients (n = 23), limited (LD) and extended (ED) disease receiving 1st line chemotherapy were interviewed at equivalent points in treatment: at start of chemotherapy (T1), 4 weeks later (T2), at end of chemotherapy (T3), and 6 weeks later (T4). T1t, T2t and T3t are representing then-test scores obtained at respectively T2, T3 and T4, when patients are asked to provide a renewed evaluation of their fatigue at the previous assessment. Two groups were identified: patients with (n = 15) and without (n = 8) discrepancies between their questionnaire answer and fatigue spontaneously reported in the interviewPatients’ characteristicsNr.M/FAgeLD/EDT1T1tT2T2tT3T3tT4DiscrepanciesP 02Male57ED33––1––P 04Female50LD1223232P 08Female69ED2212122P 09Male66ED3222222P 10Male46LD2222222P 12Female47LD1222223P 15Female69LD1223232P 17Female64ED22222––P 18Male72ED112–1–3P 21Male69ED2223222P 22Male55LD411–2––P 24Female56LD1112121P 26Female59LD42122––P 32Female60LD1211111P 34Female51LD1121211No discrepanciesP 01Female42LD22––443P 03Female64ED31––334P 13Male72ED332332P 14Male39LD43223––P 16Male68LD1133332P 20Female44LD1–4–4–4P 27Male69LD322––42P 29Male63ED2332332
No discrepancies were identified in 8 of the 23 patients (LD n = 4, ED n = 4, age 39–72). They answered the question ‘were you tired’ at the end of chemotherapy (T3) with ‘quite a bit’ or ‘very much’. During the course of treatment (T1–T4, 28 interviews) these patients answered the question twice with ‘not at all’, 9 times with ‘a little’, 11 times with ‘quite a bit’ and 6 times with ‘very much’ (Table 1).
Then-test scores were dissimilar with scores of the previous assessment in 25 out of 52 cases, with higher then-test scores in 16 cases. Transcripts showed that patients had difficulty remembering either the previous measurement point and/or their fatigue at that time.
Response strategies for the four response options
The think aloud texts for the response categories ‘quite a bit’ and ‘very much’ were minimal in the entire study population. Only a few patients reacted briefly during the think aloud, e.g. “quite a bit, too tired to keep my eyes open”, “yes quite a bit, very tired” and “next week it will be better...very much”. The same pattern was found in all patients for all four response options when filling in the questionnaire as a then-test, e.g. “a little bit, I think”, “tired then, no” and “very much”. However, patients in the group with discrepancies had much more to say in the conventional QoL measurement when choosing the options ‘not at all’ and ‘a little’. During ‘think aloud’, they seemed to justify the chosen response category. They used various strategies to moderate the impact of fatigue on their life. We summarized their strategies in four categories: (1) I am not tired all the time (e.g. “only in the afternoon”); (2) I am not really tired, it’s something else (e.g. “ it’s the flu”); (3) I have no problems with it (e.g. “I can still cope with it; I don’t want to exaggerate”); (4) I am a little bit tired but it is due to something else (e.g. “I didn’t have a proper meal”).
Many of our respondents said that they had expected to become very tired as a result of the treatment, but that they were not as sick as they had expected. They were very happy that they were able to cope with the treatment, and had adjusted to the situation. A male patient, for example, had a score of ‘not at all’ at T2, and explained: “I’m currently doing nothing, so I’m not tired”. The respondents indicated that they wanted to be honest when filling in the questionnaire and did not want to lie or to exaggerate their fatigue and, because many patients were not tired all the time they considered a score of ‘not at all’ or ‘a little’ to be a suitable score (Box 3).
Box 3Examples of different response strategies used by patients with discrepancies (n = 15)Think aloud about the question ‘were you tired’I’m not tired all the timeI’m only tired in the afternoonNo not at all, I’m not tired at the moment, it comes suddenlyI have to be honest, sometimes I’m tired, I can’t say not at all, otherwise I would be lyingI’m not really tired, it’s something elseNo not tired, it’s the flue, that’s why I’m tiredIt’s not being tired you know, it’s more like being restlessI’m not physically tired, I’m mentally tiredActually, I can’t be tired because the Hb level in my blood is okayI’ve no problems with itI’m currently doing nothing, so I’ve no problems, I’m not tiredOf course, you can make yourself tired, but I’ don’tI can still cope with it; I don’t want to exaggerateI’m a little bit tired but it’s due to something elseA little, but it was my own fault, I did too muchI didn’t have a proper meal, that’s why I was tiredI didn’t have my lady working for me in the house, she went on holidayIt’s because I’ve got problems with my voice caused by the radiation
Optimism
Most patients told the interviewer regularly that they had adapted to the situation and had changed their attitude towards a more optimistic perspective. They were not hopeful immediately after diagnosis, but optimism about recovery increased when the tumour was shrinking. Of the 23 patients 17 reported spontaneously that they were optimistic: e.g. “I’ve got good news, I’m as optimistic as can be”, “I’m full of hope because I was diagnosed in an early stage, so I’m good in time”. Furthermore, they said that they felt better off than expected, compared to patients who were worse off: e.g. “I’m lucky not to be very sick, compared to the patients I saw at the hospital”. Although they experienced the impact of every new cycle of treatment as more severe, they said to be able to cope with the treatment and to accept the side-effects: e.g. “it’s part of the package, I’m willing to put up with, knowing the chemo is doing the job properly”. Some patients were actually feeling better after each cycle and happy that they were still alive: e.g. “It was much worse than I wanted to admit last time, I’m feeling much better”, “I’ve already a couple of months extra”.
In contrast, 5 of the 23 patients expressed pessimistic feelings: e.g. “I’m a broken man, hard work all my life and now...I don’t think I’ve much time left”, “I’m a bit depressed, when does it stop, if it doesn’t stop it would be better if my life was over”, “No plans for the future, you never know when the tumour will come back”. These patients all reported high levels of fatigue during the course of the treatment. One patient did not provide specific comments about optimism or pessimism.
Response shift and self presentation
In both groups—with and without discrepancies—we found patients who had reported to have changed their reference point after T1 (i.e. recalibration in contrast to T1). They compared their fatigue at the second and following interviews with that of other patients e.g. “I was tired, yes, but compared to the patients I’ve seen in the hospital, I’m just a little bit tired” or, with the period in which they were more tired (e.g. “Compared to the first week after chemo, it’s the second now ... I’m not tired”). In one case, a patient spontaneously re-evaluated her previous measurement: “I told you that I was really tired then, but compared to how I’m feeling now, it was then just a piece of a cake”. Another one spoke about a shift of limits which also suggests recalibration: “I already told you that I would change my standards”.
We did not find indications of reconceptualization and reprioritization of fatigue. The only exception was Ann who made a distinction between being physically and mentally tired (see Box 2). At T1, she said that she was mentally tired but not physically and her answer was ‘not at all’: “Tired means that you can hardly put one foot in front of the other”. At T4, she said that she had recovered from chemotherapy and was physically able to do anything she wanted but, unfortunately, suffered from sudden epileptic attacks caused by metastases. She was feeling anxious in the same way as at the start of her chemotherapy, and had to consider further treatment options. Just like in the interview at T1, she said that she was mentally tired but not physically. However, this time her score was ‘quite a bit’ instead of ‘not at all’: “Purely, because I was mentally tired last week. I have to adjust to the idea of a new course of treatment. Actually, I have to admit that I really am the cancer patient I never wanted to be”. This response pattern might be interpreted as reprioritization (i.e., changes in the importance attached to mental fatigue over time).
Box 4Examples of coping strategies used by patients with discrepancies (n = 15)Spontaneously reported coping behaviourProtective behaviourI’m trying to avoid or minimize pessimistic thoughtsI don’t think about it, otherwise I can’t cope with itWe don’t talk about it, just follow my every day routineI’m building a wall around myselfAssertive behaviour/power displayI’ll show others that I’m managing all rightYou have to be positiveYou have to believe in yourself, otherwise you can’t manage it anymoreFighting the stigmaI’m not the cancer patient my neighbour thinks I amThey think I’m lying on my bed all dayPeople look at me, and give advice that I don’t wantI have to admit that I really am a cancer patient... I didn’t want to be (see patient Ann, Box 2).
Because the above mentioned response shift type explanations could not adequately explain our conflicting findings in the discrepancy group we questioned: “Why are patients presenting themselves in the questionnaire more positively than in the informal interview”. In our search for an other explanation, we found that 13 of the 15 patients with discrepancies had spontaneously reported how they dealt with having cancer and the perspective of a short life-expectancy. We summarized their comments in three categories (see examples of coping strategies in Box 4): (1) Protective behaviour (e.g. protecting themselves from harmful thoughts); (2) Assertive behaviour/power display (e.g. projecting the image of being positive and managing all right); (3) Fighting the stigma (e.g. fighting against being stigmatized).
Taking these strategies into account, we concluded that a possible mechanism underlying the discrepancies in this group was ‘self-presentation’. As the questionnaires are explicitly related to cancer and since these patients want to distance themselves from being reduced to only a cancer patient, they want to present themselves as a person who just happened to have cancer. Therefore, they applied various strategies to respond to the question on fatigue in order to produce a score that was as favourable as possible and presented themselves as positive and managing their fatigue.
Discussion
Two third of the patients showed discrepancies in their reported level of fatigue. They reported a gradual decrease in energy at the end of chemotherapy, but they were ‘not at all’ or just ‘a little bit’ tired according to their answer to the EORTC questionnaire, with underreporting as a result. They presented a positive image of themselves and used various strategies to explain their choice of response category. A predominant finding was that patients adopted a more optimistic perspective on the treatment. Interestingly, this was not exclusively found in the discrepancy group. The same was true for recalibration and for the only indication of reprioritization. These response shift type explanations did not sufficiently account for the conflicting findings in our discrepancy group.
Self-presentation was found to be an additional (coping) mechanism underlying the discrepancies. Our results suggest that patients are not only concerned about the impression they make on others. They try to protect themselves from negative thoughts and they also feel the need to be positive and to distance themselves from the stereotypical cancer patient. With this strategy they are more capable of coping with a situation that they cannot change.
The suggestion that self-presentation is an underlying mechanism is supported, for example in the case of Ann. After a recurrence of the tumour she adopted the realistic perspective by admitting that she really was ‘the cancer patient’, which she did not want to be before. It seems that she had given up her attitude of showing others that everything was all right, and for the first time she did not present her self as more positive than she actually was as she did before.
Self-presentation (also called impression management [6, 9]) is a phenomenon described by Leary et al. in relation to health behaviour [10]. They discussed its implications for research in health psychology. Our study shows that, in addition to response shift, self-presentation may explain unexpected results, at least in SCLC patients. The question ‘were you tired’ in the EORTC-QLQ-C30 does not unequivocally measure the impact of chemotherapy on the energy level of patients; in fact, with their responses, patients seem to show how that they are managing the situation. From our results we cannot conclude that in the group without discrepancies self-presentation is not present at all, or that whenever self-presentation occurs discrepancies will also be present. However, our study does show that self-presentation affects QoL measurement. These findings must be taken into account when investigating and interpreting QoL data, also in other study populations. Especially after diagnosis and in the initial phase of treatment, self-presentation might be an important coping strategy. In fact, during each phase in which a new equilibrium and a new identity has to be found (e.g. after a recurrence of the tumour, or metastases) self-presentation might affect QoL measurement.

small-cell lung cancer
response shift
self-presentation
fatigue
quality of life


Expression microarray analysis of papillary thyroid carcinoma and benign thyroid tissue: emphasis on the follicular variant and potential markers of malignancy

The most common sub-variant of papillary thyroid carcinoma (PTC) is the so-called follicular variant (FVPTC), which is a particularly problematic lesion and can be challenging from a diagnostic viewpoint even in resected lesions. Although fine needle aspiration cytology is very useful in the diagnosis of PTC, its accuracy and utility would be greatly facilitated by the development of specific markers for PTC and its common variants. We used the recently developed Applied Biosystems 1700 microarray system to interrogate a series of 11 benign thyroid lesions and conditions and 14 samples of PTC (six with classic morphology and eight with follicular variant morphology). TaqMan® reverse transcriptase-polymerase chain reaction was used to validate the expression portfolios of 50 selected transcripts. Our data corroborates potential biomarkers previously identified in the literature, such as LGALS3, S100A11, LYN, BAX, and cluster of differentiation 44 (CD44). However, we have also identified numerous transcripts never previously implicated in thyroid carcinogenesis, and many of which are not represented on other microarray platforms. Diminished expression of metallothioneins featured strongly among these and suggests a possible role for this family as tumour suppressors in PTC. Fifteen transcripts were significantly associated with FVPTC morphology. Surprisingly, these genes were associated with an extremely narrow repertoire of functions, including the major histocompatibility complex and cathepsin families.
Introduction
Papillary thyroid carcinoma (PTC) is the most common endocrine malignancy and encompasses a variety of morphological/architectural variants, all of which are characterized by a distinctive nuclear appearance. In recent years, PTC has become an important paradigm of solid tumour molecular pathogenesis principally arising from intensive investigation prompted by the Chernobyl accident.
The discovery of ret rearrangements [12, 13, 35] and their association with radiation [42] was followed by the demonstration of the BRAF V600E mutation [21, 30] found more commonly associated with sporadic PTC in non-radiation exposed populations [22, 32]. In the past, our group and others have noted an association between classic morphology and the BRAF V600E mutation and between variant morphology and ret rearrangements particularly ret/PTC-3 [11, 39]. Similarly ret/PTC-3 appears to strongly correlate with the solid/follicular variant seen commonly in children exposed to the Chernobyl fallout [42]. Nevertheless, the utility of these genetic lesions to diagnostic pathology and clinical practice has remained negligible.
Recently, gene expression microarray technology has been used to attempt to identify clinically relevant biomarkers of malignancy related to the thyroid [2, 6, 9, 10, 20, 28]. The discovery of such a biomarker or panel of biomarkers allied to the gold standard triage method of fine needle aspiration cytology (FNAC) would represent a significant advancement in the treatment of the solitary thyroid nodule.
An intriguing but commonly occurring variant of PTC is known as follicular variant (FVPTC). This lesion, which by definition retains the classic nuclear features of PTC, shows no evidence of the architectural papillae. FVPTC may be a controversial lesion due to interobserver variation in its pathological diagnosis [25]. Further, the occurrence of follicular patterned lesions with poorly or incompletely developed nuclear features may occur, which are easily dismissed as benign thyroid nodules. This has led to the controversial designation “well-differentiated tumour of uncertain malignant potential” for tumours of this type [45].
It is clear that this is a complex and contentious area, and that further work needs to be done to ascertain the underlying molecular biology of this particular variant. Recently, inroads into elucidation of molecular pathways underpinning PTC have been carried out using microarray studies. The overriding objective of these investigations was to identify clinically useful biomarkers. However, the majority of these studies have analysed PTC as though it were a homogenous singular entity without deference in a detailed manner to sub-variants and, in particular, the most common variant (FVPTC). The identification of specific biomarkers of FVPTC and a deeper understanding of its origins are clearly warranted.
The aim of this expression microarray study using a novel microarray platform was twofold: to identify markers that distinguish PTC from benign thyroid tissue and lesions and, secondly, to identify potential markers and further explore the molecular pathology of FVPTC.
Materials and methods
Patients and tissue samples
Tissue from 25 thyroid resections was collected prospectively from patients undergoing partial or total thyroidectomy for a variety of reasons at St. James’s Hospital, Dublin. The study had approval of the local ethics committee and informed consent was obtained from each patient by the clinical team before surgery. Small samples (<1 cm) were divided and immediately snap-frozen in liquid nitrogen for storage at −80°C until use. Histopathological examination of formalin-fixed paraffin-embedded sections was performed by two pathologists (SF and MT) for diagnostic categorisation. Classification of neoplastic tissue was made according to a recognised system [24]. The cohort comprised 11 benign lesions or conditions including follicular adenoma, nodular goitre, normal thyroid tissue, and Graves’s thyroiditis. The remaining 14 samples were diagnosed as PTC and comprised six classical morphology PTC and eight FVPTC (see Table 1). Immediately before RNA extraction, frozen sections were cut and stained to confirm the presence of representative lesional tissue with morphology corresponding to that noted in the diagnostic formalin-fixed paraffin-embedded sections.
Table 1Sample cohortIdentifierDiagnosisN1Normal thyroid tissueN2Normal thyroid tissueN3Lymphocytic thyroiditisN4Nodular hyperplasiaN5Follicular adenomaN6Nodular hyperplasia with focal lymphocytic thyroiditisN7Nodular hyperplasiaN8Follicular adenomaN9Nodular hyperplasiaN10Follicular adenomaN11Grave’s thyroiditisT1Solid/FVPTCT2FVPTCT3PTC classic morphologyT4FVPTC-oxyphilT5FVPTCT6FVPTCT7PTC classic morphologyT8FVPTCT9PTC classic morphologyT10FVPTCT11PTC classic morphologyT12FVPTCT13PTC classic morphologyT14PTC classic morphologyList of the 11 benign and 14 malignant lesions that were used in this study.FV follicular variant; PTC papillary thyroid carcinoma
RNA isolation and characterization
Samples were ground in liquid nitrogen and homogenised in RLT buffer (Qiagen, UK). RNA was then extracted using the RNeasy Mini Kit with optional on-column RNase-free DNase digestion (Qiagen) according to the manufacturer’s instructions. RNA quantity was determined using UV spectrophotometry. RNA quality was assessed using the RNA 6000 Nano LabChip® Kit in conjunction with the Agilent 2100 Bioanalyzer (Agilent Technologies, Waldbronn, Germany).
Microarray analysis
Applied Biosystems Human Genome Survey Arrays were used to analyse the transcriptional profiles of thyroid RNA samples in this study. Digoxigenin-UTP-labelled cRNA was generated and linearly amplified from 5 μg of total RNA using Applied Biosystems Chemiluminescent RT-IVT Labelling Kit v 2.0 using manufacturer’s protocol. 10 μg of labelled cRNA were hybridized to each pre-hybed microarray in a 1.5-ml volume at 55°C for 16 h. Array hybridization and chemiluminescence detection were performed using Applied Biosystems Chemiluminescence Detection Kit following manufacturer’s protocol. Images were collected for each microarray using the 1700 analyser. Images were auto-gridded and the chemiluminescent signals were quantified, corrected for background and spot, and spatially normalized.
TaqMan® PCR validation
Sufficient RNA remained from 20 of the initial 25 samples for TaqMan® polymerase chain reaction (PCR) validation in a series of 50 targets. RNA was reverse transcribed using a High Capacity cDNA Archive Kit (Applied Biosystems, CA, USA). Primers and probes for TaqMan® PCR were obtained by using Applied Biosystems’ pre-designed TaqMan® Gene Expression Assays. PCR was carried out using an ABI PRISM 7900 Sequence Detection System (Applied Biosystems). Analysis of relative gene expression data was performed using the ΔΔCT method [23] with glyceraldehyde-3-phosphate dehydrogenase (GAPDH) as an endogenous control/reference assay.
Statistical analysis
Microarrays were analysed using Spotfire DecisionSite™ for Functional Genomics (Spotfire AB, Goteborg, Sweden) and R version 1.9.1, a free language and environment for statistical computing and graphics (R Development Core Team, 2004). Arrays were initially normalized, and genes were deemed undetectable and, therefore, excluded from final gene lists if they had a signal-to-noise ratio of less than three (S/N < 3) in greater than 18 of the 25 arrays.
An ANOVA test was used to generate p values for statistical differences between groups. Their p values were then adjusted for multiple comparisons using the technique described by Benjamini and Yekutieli [3]. Genes were deemed statistically different between groups if they had an adjusted p < 0.05 and an average fold-change difference of greater than 2. Hierarchical clustering was performed based on the statistically different genes to determine whether samples grouped appropriately. Gene ontology analysis was performed using an online database known as the Panther classification system (http://www.pantherdb.org). Correlations between microarray and TaqMan® expression data were measured using the Pearson coefficient.
Results
Unsupervised clustering of all 25 samples demonstrated clustering into two major groups (data not shown) comprising the N group and the T group (see Table 1). There was no tendency for FVPTC to cluster independently with classic morphology PTC, confirming the close relationship of these variants of PTC. To identify potential markers of malignancy, ANOVA with false discovery rate correction was used to compare the benign and malignant thyroid cohorts. A p value cut-off of <0.05 and fold-change difference of ≥2 yielded 236 statistically significant probes. Of these, 172 corresponded to fully annotated probes and are listed in Table 2.
Table 2Genes differentially expressed in malignant vs benign thyroid tissueGene NameGene SymbolAdjusted p value1700 probe IDGenes up-regulated in malignant vs benign Active BCR-related geneABR0.014482154399 Adaptor-related protein complex 2, alpha 1 subunitAP2A10.0312115368 Apoptosis, caspase activation inhibitorAVEN0.030998203738 BCL2-associated X proteinBAX0.009782146510 BH3 interacting domain death agonistBID0.021424131216 Brain abundant, membrane attached signal protein 1BASP10.024214198318 Brain acyl-CoA hydrolaseBACH0.014566133876 Bromodomain adjacent to zinc finger domain, 1ABAZ1A0.03538209809 Calcium/calmodulin-dependent protein kinase ICAMK10.041887157712 Cathepsin SCTSS0.046544105790 CD44 antigen (homing function and Indian blood group system)CD440.044181133604 Chemokine (C-X-C motif) ligand 16CXCL160.043234199059 Chromosome 1 open reading frame 38C1orf380.049072202924 CLIP-170-related proteinCLIPR-590.023629102205 Docking protein 1, 62 kDa (downstream of tyrosine kinase 1)DOK10.041898204989 Epidermodysplasia verruciformis 1EVER10.003348175569 FXYD domain containing ion transport regulator 5FXYD50.01444154607 FXYD domain containing ion transport regulator 5FXYD50.023629112771 Galactose-4-epimerase, UDPGALE0.047363141143 Genethonin 1GENX-34140.016836124360 Hypothetical gene BC008967BC0089670.015683108526 Hypothetical protein FLJ10849FLJ108490.013822224983 Hypothetical protein FLJ22531FLJ225310.024391145918 Hypothetical protein MGC4607MGC46070.006507211836 Intercellular adhesion molecule 1 (CD54), human rhinovirus receptorICAM10.028746109070 Jun dimerization protein p21SNFTSNFT0.043301144215 Lectin, galactoside-binding, soluble, 3 (galectin 3)LGALS30.034491179836 Major vault proteinMVP0.0312212354 Matrix metalloproteinase 14 (membrane-inserted)MMP140.038682152076 Milk fat globule-EGF factor 8 proteinMFGE80.02392144588 Mst3 and SOK1-related kinaseMST40.042028112198 neuronal cell adhesion moleculeNRCAM0.011178106462 Phospholipase D3PLD30.034491143388 Promyelocytic leukemiaPML0.016018217558 Protein inhibitor of activated STAT protein PIASyPIASY0.00536153434 Protein tyrosine phosphatase, receptor type, EPTPRE0.048653221568 Rho GDP dissociation inhibitor (GDI) betaARHGDIB0.043853143589 S100 calcium binding protein A11 (calgizzarin)S100A110.019933145550 Similar to rat tricarboxylate carrier-like proteinBA108L7.20.025387179870 SP110 nuclear body proteinSP1100.0312113484 Stimulated by retinoic acid gene 6FLJ125410.043234193986 Syndecan 3 (N-syndecan)SDC30.048804143980 Tax interaction protein 1TIP-10.006673119665 TBC1 domain family, member 2TBC1D20.029907205982 Tenascin C (hexabrachion)TNC0.032355143831 Thymosin, beta 4, Y chromosomeTMSB4Y0.040937193911 Tissue inhibitor of metalloproteinase 1 (erythroid potentiating activity, collagenase  inhibitor)TIMP10.032306134692 Topoisomerase (DNA) II alpha 170 kDaTOP2A0.040937135302 Transforming growth factor, beta 1TGFB10.016836170749 Transgelin 2TAGLN20.031971172296 Tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, eta  polypeptideYWHAH0.039009188379 v-yes-1 Yamaguchi sarcoma viral related oncogene homologLYN0.016175194134Genes down-regulated in malignant vs benign Aldehyde oxidase 1AOX10.003227106573 Ankyrin 2, neuronalANK20.035357155780 Aspartate beta-hydroxylaseASPH0.002064221656 Aspartate beta-hydroxylaseASPH0.019666114180 ATPase, Cu++ transporting, beta polypeptideATP7B0.044511198852 Brain-specific protein p25 alphap250.023629120622 Casein kinaseLOC1494200.022382149347 Cellular retinoic acid binding protein 1CRABP10.008315100295 Centromere protein JCENPJ0.0312164563 Ceroid-lipofuscinosis, neuronal 5CLN50.011021205999 Chloride channel KbCLCNKB0.040418176266 Chondroitin beta1,4 N-acetylgalactosaminyltransferaseChGn0.013148101140 Chromosome 11 open reading frame 8C11orf80.001977174025 Chromosome 11 open reading frame 8C11orf80.004148108279 Chromosome 21 open reading frame 4C21orf40.0042156895 Clusterin-like 1 (retinal)CLUL10.019631186062 Component of oligomeric golgi complex 3COG30.003664129212 Coxsackie virus and adenovirus receptorCXADR0.004648108284 Crystallin, alpha BCRYAB0.030418190274 Cytosolic sialic acid 9-O-acetylesterase homologCSE-C0.040993213856 Dicarbonyl/l-xylulose reductaseDCXR0.001977103350 DnaJ (Hsp40) homolog, subfamily B, member 4DNAJB40.043853103618 ERO1-like beta (S. cerevisiae)ERO1LB0.013962207998 Extracellular link domain containing 1XLKD10.039738195865 Family with sequence similarity 13, member A1FAM13A10.019631116936 Fatty acid binding protein 4, adipocyteFABP40.014832150137 Fc fragment of IgG binding proteinFCGBP0.001965118361 Fibroblast growth factor receptor 2FGFR20.0042110548 FLJ35740 proteinFLJ357400.020224101102 Friedreich ataxia region gene X123X1230.032602133505 Glutamate-ammonia ligase (glutamine synthase)GLUL0.014649175147 Glycine amidinotransferase (l-arginine/glycine amidinotransferase)GATM0.013962111904 Glycoprotein M6AGPM6A0.011739215326 Growth hormone receptorGHR0.017721190306 HLA complex group 4HCG40.025807191199 Hypothetical protein BC009561LOC1197100.003227211319 Hypothetical protein BC019238LOC1203790.013438201200 Hypothetical protein FLJ13204FLJ132040.003227145066 Hypothetical protein FLJ13842FLJ138420.016448208504 Hypothetical protein FLJ14054FLJ140540.049072202017 Hypothetical protein FLJ20154FLJ201540.014428143310 Hypothetical protein FLJ20513FLJ205130.019493154130 Hypothetical protein FLJ32110FLJ321100.015507229492 Hypothetical protein FLJ32343FLJ323430.012208116902 Hypothetical protein FLJ33516FLJ335160.03965224600 Hypothetical protein FLJ37549FLJ375490.001956218577 Hypothetical protein FLJ39378FLJ393780.005853163223 Hypothetical protein FLJ40021FLJ400210.023629174198 Hypothetical protein LOC134285LOC1342850.018694163671 Hypothetical protein MGC10946MGC109460.022382195982 Hypothetical protein MGC14425MGC144250.015445161569 Hypothetical protein MGC17299MGC172990.026062168452 Hypothetical protein MGC17943MGC179430.0042147296 Hypothetical protein MGC23980MGC239800.018694224619 Hypothetical protein MGC24047MGC240470.001956138122 Hypothetical protein MGC33607MGC336070.033547100645 Ionized calcium binding adapter molecule 2IBA20.0042179489 KIAA0390 gene productKIAA03900.014832119936 KIAA0703 gene productKIAA07030.032602146652 Lectin, mannose-binding, 1LMAN10.031092179632 Leiomodin 1 (smooth muscle)LMOD10.022352120404 Likely ortholog of rat SNF1/AMP-activated protein kinaseSNARK0.044605157942 LIM domain kinase 2LIMK20.002409151439 Low density lipoprotein-related protein 1B (deleted in tumors)LRP1B0.00536209464 Low density lipoprotein-related protein 2LRP20.040937114919 Matrilin 2MATN20.0042167316 Metallothionein 1A (functional)MT1A0.013822204773 Metallothionein 1A (functional)|metallothionein 1E (functional)|metallothionein 1K| metallothionein 2AMT1A|MT2A|MT1E|MT1K0.027037146368 Metallothionein 1A (functional)|metallothionein 1E (functional)|metallothionein 2A| metallothionein 1KMT1A|MT2A|MT1K|MT1E0.043841182305 Metallothionein 1A (functional)|metallothionein 2A|metallothionein 1K| metallothionein 1E (functional)MT1A|MT1K|MT1E|MT2A0.011739223856 Metallothionein 1B (functional)MT1B0.019631174119 Metallothionein 1F (functional)MT1F0.024726144569 Metallothionein 1GMT1G0.0192164525 Metallothionein 1GMT1G0.03965171539 Metallothionein 1JMT1J0.008315227956 Metallothionein 1XMT1X0.008335119685 Metallothionein 1XMT1X0.010748173072 Metallothionein IVMT40.007447223241 Methionine adenosyltransferase II, alphaMAT2A0.014428158350 Mitogen-activated protein kinase 4MAPK40.042306131252 Myc-induced nuclear antigen, 53 kDaMINA530.011959130284 NIMA (never in mitosis gene a)- related kinase 11NEK110.001965194628 OtospiralinLOC1506770.018694182360 PDZ/coiled-coil domain binding partner for the rho-family GTPase TC10PIST0.013822103651 Phospholipase A2 receptor 1, 180 kDaPLA2R10.004029134379 Phospholipase C-like 1PLCL10.022657206894 Phosphotidylinositol transfer protein, betaPITPNB0.014428122698 Polycystic kidney and hepatic disease 1 (autosomal recessive)-like 1PKHD1L10.0042199896 Polymerase (DNA directed) iotaPOLI0.003227167492 Potassium channel, subfamily K, member 9KCNK90.000849108648 Potassium channel-interacting protein 4KCNIP40.011447147058 Potassium inwardly-rectifying channel, subfamily J, member 13KCNJ130.008972124187 pp21 HomologLOC511860.004326127636 Pre-B cell leukemia transcription factor 4PBX40.030311199118 Protein kinase, cAMP-dependent, catalytic, betaPRKACB0.023863198878 Protein phosphatase 4, regulatory subunit 2|hypothetical protein LOC151987PPP4R2|LOC1519870.011338200919 RAB23, member RAS oncogene familyRAB230.000659122394 Ras association (RalGDS/AF-6) domain family 6RASSF60.048804119072 Sarcoglycan, delta (35 kDa dystrophin-associated glycoprotein)SGCD0.011178120415 Serum deprivation response (phosphatidylserine binding protein)SDPR0.011021156433 SH3 and multiple ankyrin repeat domains 2SHANK20.043996193906 Solute carrier family 26, member 7SLC26A70.0042225067 Solute carrier family 26, member 7SLC26A70.005853213530 Solute carrier family 5 (iodide transporter), member 8SLC5A80.031284231731 SPARC related modular calcium binding 2SMOC20.021505135930 Syndecan 2 (heparan sulfate proteoglycan 1, cell surface-associated, fibroglycan)SDC20.001258209676 Syntaxin 12STX120.01117199949 T-box 22TBX220.024297177517 Thioredoxin-like, 32 kDaTXNL0.001102192552 Thyroid stimulating hormone receptorTSHR0.02176108606 Tissue inhibitor of metalloproteinase 4TIMP40.023629184795 Trefoil factor 3 (intestinal)TFF30.004648114445 Trefoil factor 3 (intestinal)TFF30.014428100949 UDP-N-acetyl-alpha-d-galactosamine/polypeptide N-acetylgalactosaminyltransferase 9 (GalNAc-T9)GALNT90.031284161042 WEE1 homolog (S. pombe)WEE10.024101123533 WW domain containing oxidoreductaseWWOX0.012208224298 WW domain containing oxidoreductaseWWOX0.024101135080 Zinc finger protein 258ZNF2580.013962225961 Zinc finger protein 36, C3H type-like 2ZFP36L20.018837210469Two-tail ANOVA with p value correction yielded 173 probes (52 up-, 121 down-regulated in PTC) significantly different (p < 0.05) between the malignant and benign thyroid tissues.
Supervised hierarchical clustering was performed on the 25 arrays based on the 236 statistically significant probes to determine whether the samples would segregate appropriately. The resulting heat map can be observed in Fig. 1. Benign lesions and tumours clustered together with the exception of one tumour sample that clustered with the benign group (T5).
Fig. 1Hierarchical clustering of samples. This heat map shows the clustering of the 25 samples based on the 236 probes found to be differentially regulated in benign vs malignant thyroid tissue. Clustering was performed using the unweighted pair group method with arithmetic mean, with Euclidian distance as the similarity measure. Average value was used as the ordering function
A binomial statistics tool was used to compare classifications of multiple clusters of lists to a reference list (i.e. the complete human genome) to statistically determine over- or under-representation of Panther classification categories. Biological processes over-represented in the up-regulated PTC cohort included tumour suppressor, oncogenesis, DNA replication, cell cycle, and cell adhesion (p < 0.0001). Genes involved in homeostasis and other homeostasis activities were highly over-represented in the down-regulated cohort (p < 0.000001).
ANOVA tests were used to determine which genes were differentially regulated in the FVPTC cohort only. Fifteen genes were identified, including cluster of differentiation 14 (CD14), CD74, CTSC, CTSH, CTSS, DPP6, ETHE1, human leucocyte antigen A (HLA-A), HLA-DMA, HLA-DPB1, HLA-DQB1, HLA-DRA, osteoclast stimulating factor 1 (OSTF1), TDO2, and a previously uncharacterized gene (noname).
Microarray results were validated using a reverse transcription reaction followed by TaqMan® PCR for 50 gene targets. The ΔΔCT method [23] was used to analyse relative gene expression data. GAPDH was used as an endogenous control, and T12 was chosen as an arbitrary calibrator sample. Gene expression profiles for TaqMan® PCR were plotted in conjunction with those for microarray results in Fig. 2. Pearson co-efficient was used to directly compare data from microarray analysis and TaqMan® RT-PCR. Table 3 depicts genes differentially expressed in both benign vs malignant and FVPTC vs classic morphology PTC.
Fig. 2TaqMan® PCR validation of microarray experiments. Profile charts of gene expression levels comparing results obtained by microarray analysis (n = 25) to TaqMan® PCR analysis (n = 20) for six genes. Plots for those genes with multiple probes are also displayed where appropriateTable 3Correlation between TaqMan® and microarray dataGenePearson’s r coefficienttwo-tailed pGenes differentially expressed in FVPTC vs classic PTCCD140.83<0.0001CD74a0.87<0.00010.740.0002CTSC0.530.0170CTSH0.710.0005CTSS0.620.0037DPP60.760.0001ETHE10.300.2008HLA-A0.77<0.0001HLA-DMA0.750.0001HLA-DPB10.96<0.0001HLA-DQB1a0.700.00060.630.0031HLA-DRA0.82<0.0001NONAME−0.020.9323OSTF10.150.5171TDO20.680.0009Genes differentially expressed in benign vs malignantBAX0.200.3997CAMK10.170.4771CD440.570.0094CTSS0.620.0037CXADR0.360.1152FGFR20.84<0.0001GALE0.540.0138ICAM10.620.0038LYN0.450.0483MAPK40.76<0.0001MMP140.360.1175MT1F0.92<0.0001MT1Ka0.680.00110.690.00070.700.0006MT1Xa0.88<0.00010.88<0.0001RAB230.700.0006S100A110.610.0041SDC20.340.1415SDC30.570.0087TFF3a0.97<0.00010.96<0.0001TGFB10.560.0098TIMP10.730.0003TIMP40.80<0.0001TOP2A0.070.7769TSHR0.180.4440Gene expression profiles for TaqMan® PCR and microarray results were compared using the Pearson coefficient.aGenes have more than one probe ID on microarray
Discussion
The primary aim of this study was to generate an overview of molecular markers of malignancy in PTC with a view to identifying discriminators between common sub-types (classic PTC and FVPTC), using genome-wide expression microarray technology validated by TaqMan® RT-PCR. To this end, lesions that were well characterized histologically were selected.
The application of microarray analysis designed to identify transcripts strongly associated with each group of interest yielded a gene list of 173 genes that were differentially expressed between cohorts. Significant down-regulation of Coxsackie virus receptor was recorded in the malignant cohort of thyroid lesions. The Coxsackievirus B and adenovirus receptor (CAR) plays a dual role as a homotypic junctional adhesion protein and as a viral receptor. It is biologically plausible that altered expression may impact on the morphology peculiar to PTC given its association with cellular adhesion. CAR has been shown to be differentially expressed in various human adenocarcinomas, and differential expression may represent a new factor in thyroid tumourigenesisigenesis [27].
Rab 23 expression was also down regulated in the malignant cohort. The Rab small G protein family is composed of approximately 40 members. Many of them are ubiquitous and are expressed and participate in transport processes, such as endocytosis and exocytosis [26]. Other gene targets demonstrating significant down-regulation in the malignant group were syndecans 2 and 3. Syndecans are transmembrane proteoglycans expressed on adherent cells. Changes in syndecan expression have been postulated to influence cell adhesion, migration, and the structure of focal contacts and the cytoskeleton [8].
The abundance of metallothionein genes in the cohort of genes down-regulated in PTC is interesting partly because of the sheer number of isoforms detected (Table 2). There have been many studies showing increased metallothionein expression in a plethora of cancer types but few showing decreased expression [4, 7, 33]. Although previous microarray experiments showed metallothionein genes to be down-regulated in thyroid tumours to a certain extent [2, 9, 16], none have detected so many as the current study.
Apart from microarray experiments, there has been little reported in the literature regarding metallothioneins in thyroid cancer. An early report by Nartey et al. [31] showed metallothioneins to be expressed actively in certain human thyroid neoplastic tissues but not in normal thyroid tissue, which would seem to contradict the current authors’ findings. In contrast to this, a later immunohistochemical study showed an absence of metallothionein expression in 13 of 20 PTCs [36]. Interestingly, in three of the seven positive PTCs, metallothionein positivity was restricted to areas of follicular differentiation. In one of the only recent studies, Huang et al. [17] followed up their initial microarray experiment by showing that MT1G is down regulated in PTC via hypermethylation. The biological significance of low metallothionein expression in thyroid tumours is therefore still poorly understood; however, it is interesting to speculate that metallothioneins may have roles as tumour suppressors in thyroid carcinoma.
Many genes identified, such as LGALS3 [9, 16, 20], LYN [46], TFF3 [2, 9, 16], CRABP1 [9, 16], BAX [2], MAPK4 [28], CD44 [16], TIMP1 [20], FGFR2 [9], and S100A11 [20, 40], have been previously reported in both microarray and conventional experiments in thyroid cancer. The data generated in this study corroborates the importance of several biological processes in the progression of thyroid neoplasia. For example, S100A11 expression was up regulated in the PTC cohort compared with benign lesions, paralleling the increased protein expression of this gene target identified at the protein level using immunohistochemistry [29]. S100A11 has also been suggested as a biomarker of malignancy in the context of colorectal carcinoma as long ago as 1995 [41].
Correlation of highlighted features with the current state of knowledge of the molecular pathology of thyroid neoplasia goes some way towards providing an external validation of the data obtained from the AB1700 system. However, additional validation using TaqMan® RT-PCR was performed.
In general, microarray and TaqMan® data correlated well with approx. 80% of comparisons having p < 0.05. Candidate genes were selected contingent on results identified as over-represented biological processes (oncogenesis, cell cycling, DNA replication, and homeostasis) using the Panther binomial statistics tool as opposed to the more traditional method of selecting the most highly disregulated genes. This may account for the poor correlation observed with certain genes. Some genes, such as TFF3 and FGFR2, had excellent correlation between microarray and TaqMan® results, whereas others, such as BAX and TSHR, showed poor correlation despite previous studies implicating them in thyroid cancer [15, 38]. Where there was discordance in the data, it could be accounted for by differences in the sequences targeted by the TaqMan® target sequences and the microarray probe. In those cases, the array and pre-designed TaqMan assays interrogated different exons or alternative splice variants. This finding highlights the importance of matching the targets to be validated from microarray data sets.
Analysis of differentially expressed transcripts in the FVPTC revealed many transcripts showing similar expression level patterns in both FVPTC and classic morphology PTC. Examples of these transcripts are included in Table 2. In addition, somewhat surprisingly, unsupervised clustering of all samples showed no tendency for FVPTC to cluster independently of classic morphology PTC, emphasizing the very close relationship of these PTC variants (data not shown). However, analysis of differentially expressed genes in FVPTC exclusive of those identified in classic morphology PTC revealed 15 genes exhibiting differential expression in FVPTC compared with benign lesions and conditions outwith of classic morphology PTC. These genes displayed a narrow gamut of function represented by the transcripts involved (Table 3). TaqMan® RT-PCR was performed for all of these targets to confirm the array findings and correlation with the array data was strong (see Table 3).
Aberrant expression of two major groups of transcripts was noted in FVPTC. Relatively increased expression of class 1 major histocompatibility complex (MHC) genes (HLA-A) and aberrant expression of class 2 MHC genes (HLA-DMA, HLA-DPB-1, HLA-DQB-1, HLA DRA) and associated genes (e.g. CD74 represents the invariant membrane bound moiety of class II HLA molecules and regulates the biology and functions of MHC class II molecules and CD14 is a surface marker of monocytes/macrophages) were the most significant findings. Additionally, relatively up-regulated expression of members of the cathepsin family (cathepsin C, cathepsin H, cathepsin S, and TDO2) was striking in the FVPTC group. HLA expression is generally associated with immune functions such as T cell interaction and antigen presentation. The presence of prominent HLA transcript expression, especially among class 2 in FVPTC, is intriguing. One potential cause of this was that tumour-infiltrating leucocytes were responsible for this finding. However, haematoxylin–eosin (H&E)-stained slides of each case were reviewed to specifically identify the degree of tumour infiltration by leucocytes. Although a mild lymphocytic infiltrate was noted in some cases, there was no apparent over representation of lymphocytes in the follicular variant compared to benign lesions and classic morphology PTC. This raises the clear possibility that the findings represent aberrant increased expression of class 2 HLA transcripts by the epithelium of FVPTC. This is an unexpected finding, as over-expression of MHC class 2 molecules would be expected to increase tumour immunogenicity. A similar aberrant expression of HLA transcripts has been recently described in ovarian neoplasms [34]. Rangel et al. [34] concluded over-expression of HLA-DRA might represent a novel biomarker for malignancy, and this also seems biologically plausible in the FVPTC setting. A recent paper has also described HLA-DRA expression in ret/PTC-activated papillary thyroid carcinoma but not in surrounding normal thyroid follicles [19]. Yu et al. [47] showed discordant expression of (CD74Ii) and HLA-DR in Hashimoto thyroiditis, an autoimmune condition associated with increased incidence of PTC and sharing molecular features such as ret/PTC expression [18, 37]. Hwang et al. [19] have drawn attention to aberrant expression of HLA-DRA in ret/PTC-activated PTC. Expression of HLA-DRA may in some way explain the propensity for PTC to metastasize to lymph nodes and often apparently reside there without markedly worsening prognosis.
Cathepsins C (dipeptidyl-peptidase I), H, and S showed up-regulation in FVPTC compared to benign lesions. Cathepsins are a family of proteases that play an important role in protein degradation. They are key players in the proliferative, invasive, and metastatic potential of malignant tumour cells. Their expression in the relatively biologically indolent FVPTC is intriguing, and it remains possible that cathepsins have cellular roles outside of those involved in invasion and dissemination of tumour cells as indeed has been suggested by others [43]. For example, cathepsin L has recently been shown to play a role in nuclear transcriptional activation, and cathepsins are now recognized to play a role in MHC class II antigen presentation [44]. OSTF1 has no defined role in carcinogenesis, although outside of its role in ossification, it is also known to have roles in signal transduction and protein binding, which may be relevant to carcinogenesis and, particularly, FVPTC. A recent paper highlights the role of bone mineralization proteins including osteopontin and osteoclast stimulating factors as potential biomarkers of malignant tumours in general [1]. Indeed, in addition to elevated OSTF1 expression, increased expression of osteopontin was seen in PTC (data not shown); however, in our data, osteopontin does not appear to be specifically up-regulated in FVPTC, and this finding has also been noted by other researchers [14]. In any case, osteopontin is known to be a downstream effector of ret/PTC [5] and mutated BRAF [14], where it acts in association with CD44, another transcript showing increased expression in both classic morphology PTC and FVPTC.
A particular focus of this study was to compare transcriptome profiles for PTCs with classic morphology and FVPTC given the propensity for FVPTC lesions to prove problematic from a diagnostic perspective. Although the study confirms the close relationship between the two most common variants of PTC, a narrow portfolio of genes and, in particular, gene functions was elucidated in the FVPTC cohort. The targets identified are easily amenable to analysis by more established techniques such as TaqMan® RT-PCR, with associated potential as additional markers for application in the FNAC setting. Clearly, the potential biomarkers identified in this study will require prospective evaluation in the context of real clinical diagnostic situations in the future to consolidate their merit as adjunctive tests in the diagnostic setting and to validate their altered expression states in the pathobiology of PTC development.

microarray
papillary thyroid carcinoma
follicular variant
biomarker
taqman® pcr