Doris M. Kupfer1

Vicky L. White1

Marita C. Jenkins2

Dennis Burian1

1

Civil Aerospace Medical Institute

Federal Aviation Administration

Oklahoma City, OK 73125

2

Advancia, Inc.

Oklahoma City, OK 73104

January 2010

Final Report

Examining Smoking-Induced 

Differential Gene Expression 

Changes in Buccal Mucosa

DOT/FAA/AM-10/2

Office of Aerospace Medicine

Washington, DC 20591

OK-10-0077-JAH

Federal Aviation

Administration
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i 

Technical Report Documentation Page 

1. Report No. 2. Government Accession No. 3. Recipient's Catalog No. 

DOT/FAA/AM-10/2 

4. Title and Subtitle 5. Report Date 

Examining Smoking-Induced Differential Gene Expression Changes in January 2010 

Buccal Mucosa 6. Performing Organization Code 

 

7. Author(s) 8. Performing Organization Report No. 

Kupfer DM,1 White VL,1 Jenkins MC,2 Burian D1 

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS) 

 

11. Contract or Grant No. 

1

FAA Civil Aerospace Medical Institute 

P.O. Box 25082 

Oklahoma City, OK 73125 

2

Advancia, Inc. 

Oklahoma City, OK 73104 

 

12. Sponsoring Agency name and Address 13. Type of Report and Period Covered 

Office of Aerospace Medicine 

Federal Aviation Administration 

800 Independence Ave., S.W. 

Washington, DC 20591 14. Sponsoring Agency Code 

15. Supplemental Notes 

Work was accomplished under approved task AM-TOXLAB 

16. Abstract 

Gene expression changes resulting from conditions such as disease, environmental stimuli, and drug use can be 

monitored in the blood. However, a less invasive method of sample collection is of interest because of the 

discomfort and specialized personnel necessary for blood sampling, especially if multiple samples are being 

collected. Buccal mucosa (cheek swabs) are easily collected and may be an alternative sample material for 

biomarker testing. A limited number of studies, primarily in the smoker/oral cancer literature, address this 

tissue’s efficacy as an RNA source for expression analysis. The current study was undertaken to determine if total 

RNA isolated from buccal mucosa could be used as an alternative tissue source to assay relative gene expression. 

In this study, qPCR and microarray analyses were used to evaluate gene expression in buccal cells. Initially, 

qPCR was used to assess relative transcript levels of four genes from whole blood and buccal cells collected from 

the same seven individuals at the same time. The RNA isolated from buccal cells was degraded but was of 

sufficient quality to be used with RT-qPCR to detect expression of specific genes. Second, buccal cell RNA was 

used for microarray-based differential gene expression studies by comparing gene expression between smokers 

and nonsmokers. An amplification protocol allowed use of 150-fold less buccal cell RNA than had been reported 

previously with human microarrays. We report here the finding of a small number of statistically significant 

differentially expressed genes between smokers and nonsmokers, using buccal cells as starting material. Gene Set 

Enrichment Analysis confirmed that these genes had a similar expression pattern as results from another study. 

Our results suggest that despite a high degree of degradation, RNA from buccal cells from cheek mucosa could 

be used to detect differential gene expression between smokers and nonsmokers. However, the RNA degradation, 

increase in sample variability, and microarray failure rate show that buccal samples should be used with caution 

as source material in expression studies. 

 

17. Key Words 18. Distribution Statement 

 

 

Buccal Cells, Smoking, Differential Gene Expression, 

qPCR, Microarray 

Document is available to the public through the 

Defense Technical Information Center, Ft. Belvoir, VA 

22060; and the National Technical Information 

Service, Springfield, VA 22161 

19. Security Classif. (of this report) 20. Security Classif. (of this page) 21. No. of Pages 22. Price 

Unclassified Unclassified 27 

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized 

iii 

Authors’ Contributions

DMK participated in design of study, data analysis, and drafted the manuscript; VLW isolated RNA and performed amplifications; MCJ performed qPCR and microarray hybridizations; DB conceived the study and reviewed 

the manuscript.

Acknowledgments 

Our thanks to Kristina Holton and Dana Farber, Cancer Research Institute, Boston, MA, for assistance on analysis 

approaches, especially Rank Product.

v 

Contents

Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Quality Assessment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

qPCR Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Microarray Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Quality Assessment of the Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Microarray Data Analysis for Differential Expression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Function Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Sample Collection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

RNA Purification. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

1 

Examining Smoking-Induced Differential Gene

Expression Changes in Buccal Mucosa

Background 

Blood has been shown to be a responsive tissue that 

is useful for monitoring gene expression changes due 

to disease, environmental, biological or drug effects. 

However, for studies performed in human subjects, a 

less invasive tissue source for biomarker monitoring is of 

interest due to the discomfort, required skill level, and 

cost of blood collection, especially for repeated-measures 

studies. Buccal mucosa (from cheek swabs) is an easily 

accessed tissue and has been used successfully to obtain 

DNA for genotyping studies (1). However, the literature 

is limited as to the usefulness of RNA from buccal cells 

as a substrate for gene expression testing, presumably 

due to concern regarding high concentrations of RNases 

in saliva which are known to rapidly degrade RNA in 

these cells (2). qPCR has been used to detect expression changes in genes from the P450 family using snap 

frozen surgical samples (3) and from brushed exfoliated 

buccal cells (4, 5). These studies suggested that buccal 

cells might serve as an alternative to blood in qPCR assays examining gene expression profiles after exposure to 

environmental toxins, tobacco smoke, drugs, nutrients, 

or the presence of certain cancers. With RNA purified 

from brushed exfoliated buccal cells, Sridhar et al. (6) 

used microarrays to compare expression levels between 

smokers and nonsmokers, and to compare expression 

patterns between buccal cells and bronchial epithelium 

in smokers and nonsmokers (7) by Gene Set Enrichment 

Analysis (GSEA) (8).To our knowledge, buccal cells have 

not been used with a whole transcriptome approach to 

investigate differential gene expression. A successful study 

of this type would suggest that buccal cells have efficacy 

as source material for biomarker discovery or in a gene 

expression monitoring system. 

We describe here both qPCR and microarray approaches. The qPCR study used matched blood and 

brushed buccal samples from the same subjects. Relative 

expression levels of four genes allowed comparison of 

tissue sources and subject differences. RNA from buccal 

cells was highly degraded; nonetheless, expression could 

be detected by qPCR for all four transcripts tested. This 

was sufficient evidence of the potential of buccal cells 

to follow up on the work of Sridhar et al. (6) and use 

microarrays for differential gene expression analysis on 

the transcriptome level in smokers and nonsmokers. 

An important consideration was the availability of the 

Smoking Induced Epithelial Gene Expression Database, 

(SEIGE) (7) and smoker buccal mucosa-specific gene 

lists (6), against which results from this study could be 

compared to confirm our method. 

Our data were first analyzed for differences between 

smokers and nonsmokers using Significance Analysis 

of Microarray (9) and Rank Product (10) for detection 

of significant gene expression differences between the 

smoker and the nonsmokers in our study. These analyses 

resulted in a list of candidate marker genes from each 

method. Ingenuity Pathway Analysis (11) was used to 

find functional networks containing the differentially 

expressed genes. The gene lists were also examined for 

transcriptional coregulation by searching the promoters 

of differentially expressed genes for transcription factor 

binding sites (TFBS) using PAINT (12) to access the 

TRANSFAC database of known TFBS. Specifically, we 

identified 103 genes with Rank Product analysis that 

had increased expression in smokers. Pathway analysis 

showed five function networks involving 91 of the 103 

target genes. Network functions included cell cycle, cell 

growth, proliferation and movement, gene expression, 

and immunological disease. Upstream sequence analysis 

showed 41 target genes containing binding sites for at 

least one of three widely expressed transcription factors. 

Twenty-five genes were identified using SAM analysis. 

Similar to the RP results, 13 of these genes fell into one 

of two functional networks that had shared roles in tumor 

morphology, metabolic disease, lipid and carbohydrate 

metabolism, and which contained binding sites for at least 

one of two widely expressed transcription factors. These 

results suggest that many of these genes are co-regulated 

and that the transcriptional response affects numerous 

cellular functions. 

Both gene lists were further analyzed using GSEA to 

compare the buccal dataset against the Sridhar gene sets. 

The comparisons showed that the genes in the published 

sets changed expression in the same direction in our 

buccal array data. 

The results of the study suggest that buccal mucosa may 

indeed be useful for factors selected carefully for optimum 

expression change in buccal tissue. However, the extensive 

random degradation, which may vary between subjects, 

suggests a loss of sensitivity and possibly the need for 

multiple sampling, which is costly. It also suggests that 

due to the extensive degradation found, it seems unlikely 

to be a reliable source for biomarker discovery.
2 

Results 

Quality Assessment

Initially, we determined the quality of RNA purified 

from buccal mucosa. Matched blood and buccal total 

RNAs from seven subjects were purified (see Materials 

and Methods). RNA quality was assessed on the Agilent 

Bioanalyzer RNA using Nano 6000 chips (Figure1). Buccal RNA samples were found to be severely degraded with 

RNA Integrity Numbers (RINs) routinely less than three. 

In contrast, RINs from the blood samples were greater 

than seven in all cases (Table 1). These results indicate 

that the buccal RNA was not of high quality.

qPCR Validation 

To determine if RNA from buccal cells could be useful 

for marker analysis, we chose to perform qPCR on these 

paired samples. To test whether RNA degradation was 

non-specific or directional, we chose an amplification 

method that primed reverse transcription from random 

primed hexamers. Primers to four genes were used: ITGA5, 

ANKRD28, TMEM8, and RPS3A. BioGPS (13) values 

for these four genes indicated an approximate expected 

ratio of buccal cells (salivary gland used for estimate) 

versus blood (Table 2). Primers were made to the 3 

prime (3') ends of all four genes. To determine whether 

RNA degradation was random or specific by gene region, 

primers to upstream regions of ITGA5, ANKRD28 and 

TMEM8 were also designed (Table 3). 

The WT Ovation Pico kit was used for amplification 

of all 14 samples, both blood and buccal, and the subsequent product used for qPCR with the primer pairs 

detailed above. 

An average over the seven subjects showed that there 

was a lower apparent transcript copy-number for each 

tested gene in buccal mucosa RNA than in blood RNA. 

In some subjects, no Ct was calculated, and the differences 

between apparent transcript levels were greater than the 

mean value indicates. As seen from the increased standard 

deviations, RNA from buccal cells had greater variability 

in Cts, suggesting that buccal RNA quality is also more 

variable than blood RNA (Table 4). 

When specificity of degradation was investigated, no 

clear pattern was evident. ITGA5 showed a 32-fold difference from 5' to 3' in buccal mucosa compared to an 

approximately three-fold difference in blood, but most 

reactions with ITGA5 primers with buccal RNA failed. 

ANKRD28 showed no change in 5'/3' ratio in either RNA 

source. TMEM8 showed an increase in 3' signal over 5' 

signal in buccal mucosa but a 3' preferential loss of target 

in blood. Due to the short transcript length of RPS3A, 

no 5' primer set was designed. This initial analysis of the 

quality of buccal RNA shows that, in general, there were 

lower but detectable levels of target mRNA in buccal 

mucosa when compared to blood (Table 4). These results 

do not differentiate between tissue-specific expression 

differences or degradation; however, when the expression 

data from BioGPS and the RINs were factored into our 

analysis, the differences in Cts were greater than expected 

from expression data and likely due to degradation. The 

variability of results from buccal cells suggests that the 

degradation seen in the buccal samples is not predictably 

occurring in a directional fashion but randomly such that 

transcript size has no effect. 

The reduced signal detected in buccal versus blood 

samples with the WT amplification method led us to 

hypothesize that a 3'-specific amplification of sample 

assayed with 3'-specific primers would increase signal-tonoise ratios and have increased sensitivity in expression 

assays. To investigate this possibility, the same samples were 

amplified with the Ovation RNA Amplification System 

V2, a 3' specific method. Table 5 shows a comparison of 

the amplification results using the 3' targeted primers and 

both buccal mucosa and blood derived RNA template. For 

all three genes, 3' amplification resulted in a Ct decrease, 

i.e., an apparent increase in copy number, although Cts 

from buccal mucosa RNA tested with primers to ITGA5, 

remained greater than 31. Relative Cts from ANKRD28 

and TMEM8 between buccal RNA and blood RNA 

compare favourably with data from BioGPS comparing 

salivary gland to whole blood. However, ITGA5 values 

did not correspond particularly well, suggesting that 

ITGA5 was more sensitive to degradation than the other 

genes tested. 

Microarray Study 

Our ability to detect expression of genes by qPCR, most 

at levels well above background, in 3' amplified samples 

lead us to hypothesize that buccal samples could be used 

for differential expression testing by microarray analysis. 

Amplification of buccal sample RNA has the advantage 

of not requiring repeated sample collection and/or pooling of material from multiple collections. The previous 

work of others (5, 6) led to the further hypothesis that 

a comparison of smokers and nonsmokers was a model 

system likely to allow detection of differentially expressed 

genes. Affymetrix Human U133 plus 2.0 arrays were 

used for a global evaluation of gene expression changes 

between four smokers and four nonsmokers. All female 

subjects were used to prevent any gender bias in the data, 

and both cheeks from each subject were sampled. Total 

RNA was isolated and evaluated for quality as for the 

qPCR samples. One cheek sample from each subject was 

arbitrarily assigned to one of two groups, a or b (Materials 
3 

RIN=NA

RIN= 8.5 18S

28S

Sample 1-blood

Sample 1-cheek

Figure 1. Representative qPCR matched blood and buccal mucosa samples. The buccal 

RNA (cheek) appears to be heavily degraded compared to the blood RNA since there is no 

evidence of 18 or 28S rRNA peaks and the bulk of material is migrating rapidly, indicating 

small size. RIN, RNA integrity number. NA, No RIN could be determined. 
4 

Table 1. Total RNA yield from blood and buccal samples used in study.

 RNA 

qPCR Samples Conc. Vol Total RIN 

 ng/uL ul ng 

1-Cheek 1.45 30 43.5 2.5 

2-Cheek 3.12 30 93.6 N/A 

3-Cheek 3.22 30 96.6 N/A 

4-Cheek 4.57 30 137.1 2.5 

5-Cheek 5.07 30 152.1 2.6 

6-Cheek 5.63 30 168.9 2.7 

7-Cheek 3.99 30 119.7 N/A 

1-Blood 149.76 80 11980.8 8.5 

2-Blood 71.83 80 5746.4 8.7 

3-Blood 51.38 80 4110.4 8.1 

4-Blood 34.15 80 2732.0 7.4 

5-Blood 90.19 80 7215.2 8.0 

6-Blood 388.5 80 31080.0 7.8 

7-Blood 130.93 80 10474.4 8.1 

 

Microarray Samples RNA Vol Total RIN 

 ng/uL ul ng 

NS21a 13.94 30 418.2 2.8 

NS22a 6.43 30 192.9 2.5 

NS23a 9.12 30 273.6 N/A 

NS24a 13.23 30 396.9 N/A 

Sm25a 2.90 18 52.2 N/A 

Sm26a 6.55 30 196.5 N/A 

Sm27a 9.20 30 276.0 2.3 

Sm28a 9.30 30 279.0 2.5 

11Sma 2.93 30 87.9 ND 

12NSa 10.15 30 304.5 ND 

NS21b 3.91 30 117.3 N/A 

NS22b 25.43 30 762.9 2.3 

NS23b 7.19 30 215.7 N/A 

NS24b 12.90 30 387.0 N/A 

Sm25b 6.18 30 185.4 N/A 

Sm26b 13.46 30 403.8 2.1 

Sm27b 24.43 30 732.9 N/A 

Sm28b 11.79 30 353.7 N/A 

11Smb 5.99 30 179.7 ND 

12NSb 11.51 30 345.3 ND 

N/A No RIN could be calculated 

ND Not done 

Table 2. BioGPS approximate expression values for blood and salivary gland for the genes tested via qPCR.

 Ankrd28 Tmem8 Rps3a Itga5 

Salivary gland 130 2000 60,000 2000 

Blood 130 7000 >100,000 7000 

Values are approximate signal strength values from BioGPS Human U133A gcRMA dataset, accessed 2009-07-16 [13]. 
5 

Table 3. Primers used for qPCR portion of study.Gene

Refseq 

Accession 

ID

Affymetrix 

Probeset

ID

Transcript

Length

(bp) Sense Primer Anti-sense Primer

Position 

5' (bp)

Cycling

Temperature

degC

Concentration (nM) 

sense, anti-sense

% 

Efficiency

Amplicon 

Size(bp)

ANKRD28 NM_015199 226025_at 6339 

CAGATGATTTTGGC AGGACTTG 

GTTGGCAGCAGC GTAGTG 1345 50 250,150 96 143 

TGCTGAGATGTTAA TTGATAC 

TCCACAGAATTG ACTTGAG 2552 56 300,300 94 150 

CATAAATTAAGCA TCACTAAAGTCTC 

CCGAAATATCAG CCTTCTCTC 4379 54 300,250 91 199 

ITGA5 NM_002205 201389_at 4267 

GCTGGACTGTGGA GAAGAC 

AAGTGAGGTTCA GGGCATTC 1994 56 300,250 95 107 

TCAGACATTGGCA CCTAATC 

TTCCTGGCTTCT CCTAAATC 3879 54 300,300 94 104 

TMEM8 NM_021259 222718_at 2561 

CTTCCAGAGGTTT CTCATAC 

GCGTCATACTT CTCATCC 584 57 250,300 95 118 

CGTCAGCAGAAT GTATGTG 

TCCTGGTTCCA TAGTCTCC 1372 54 300,300 98 110 

CCCTGCCTCTTTG CCTTC 

TAGTAGTAGTT GTCGCTAGTCATC 2067 57 250,250 95 179 

RPS3A NM_001006 200099_s_at 930 

CACCAGGACCCAAGGAACC 

CCAACACAGAA CAGACGAAGC 198 60 200,200 94 276 
6 

Table 4. qPCR results comparing blood and buccal RNA across four genes with whole transcriptome amplified template.

 Tmem8_2067* Tmem8_1372 Tmem8_584 Ankrd_4387 Ankrd_2552 Ankrd_1345 Itga5_3879 Itga5_1994 RPS3A_198

Cycle temp 57deg 57deg 54deg 54 deg 56 deg 50 deg 54 deg 56 deg 60 deg 

Product 179bp 110bp 118bp 199bp 150bp 143bp 104bp 107bp 276bp 

Subject 

Buccal 1 33.76 No Ct 34.93 No Ct 30.33 28.91 31.7 No Ct 31.86 Buccal 2 29.72 29.15 26.83 31.64 29.52 27.47 No Ct No Ct 28.65 Buccal 3 28.18 30.52 29.27 30.81 35.73 30.13 No Ct 38.64 28.49 Buccal 4 24.96 27.74 28.09 31.8 33.55 31.98 35.71 No Ct 26.82 Buccal 5 25.31 26.56 26.39 31.36 32.8 34.02 No Ct No Ct 27.38 Buccal 6 35.22 34.85 34.1 No Ct No Ct 37.42 No Ct No Ct ND Buccal 7 29.05 29.24 35.31 No Ct No Ct No Ct No Ct No Ct 33.19 

Mean 29.46 29.68 30.7 31.4 32.39 31.66 33.71 38.64 29.4 

StDev 3.61 2.63 3.65 0.38 2.24 3.32 2.01 0 2.33 

Blood 1 27.03 26.49 25.78 30.53 29.28 30.36 24.86 26.67 26.31 Blood 2 28.5 28.28 26.58 29.04 28.87 28.86 24.41 26.42 26.66 Blood 3 28.08 27.71 26.7 29.03 29.93 28.33 24.28 25.34 26.1 Blood 4 27.64 26.53 26.57 29.43 29.61 29.68 25.14 25.62 27.11 Blood 5 27.3 27.63 25.32 29.82 28.71 28.89 24.72 25.29 26 Blood 6 27.96 27.67 26.02 29.68 29.09 29.45 26.69 28.92 ND Blood 7 27.8 28.48 27.62 29.49 28.73 28.06 24.34 27.14 26.82 

Mean 27.76 27.54 26.37 29.57 29.17 29.09 24.92 26.49 26.5 

StDev 0.46 0.78 0.69 0.48 0.43 0.74 0.78 1.19 0.4 

mRNA size 2529 bp 2529bp 2529bp 6339bp 6339bp 6339bp 4248bp 4248bp 903bp 

Controls^

NTC 39.4 No Ct No Ct No Ct No Ct No Ct No Ct No Ct No Ct 

WT 

amplification 28.4 27.28 26.45 29.47 29.23 29.48 24.63 26.59 25.94 

3' 

amplification 26 22.9 24.4 20.5 

Results are in Ct values * Gene names are followed by 5’ position of amplification on mRNA. ^WT and 3’ Controls are RNA from pooled blood samples NTC, No template control ND, not done Bp, basepair Deg, degree centigrade StDev, Standard deviation
7 

Table 5. qPCR results comparing methods of template amplification. 

Template Itga5b 3' Itga5b WT Tmem8 3' Tmem8 WT Ankrd28 3' Ankrd28 WT 

Buccal 4 31.25 39.15 20.38 24.96 20.68 31.8 

Buccal 5 35.81 No Ct 20.36 25.31 20.92 31.36 

Blood 4 21.43 23.96 21.87 30.68 20.33 29.43 

Blood 5 20.56 23.9 21.11 30.23 20.22 29.82 

Control 21.18 23.8 21.63 28.4 20.23 29.47 

All amplifications were performed using Nugen Kits, see Materials and Methods 

3’- RNA 3’ amplified via a poly T primer 

WT- RNA whole transcriptome amplified with random hexamers and poly T primers 

Control is a pooled sample from blood, see Materials and Methods

and Methods). Figure 2 shows the BioAnalyzer traces 

from all 16 samples with a trace representative of the 

quality of RNA usually purified from blood. As seen with 

the samples used in the qPCR study, the samples show 

no evidence of rRNA peaks and a range of degradation 

product sizes; an RIN be calculated in only a third of 

the samples could. 

Quality Assessment of the Arrays 

Following hybridization, each array was examined for 

quality. Table 6 lists the percent present (%p) and scaling factor (SF) values determined using the Gene Chip 

Operating Software (Affymetrix, Inc.; Materials and 

Methods). Two arrays, NS21a and Sm27a, had remarkably 

low %p and especially high SFs, both indicators of arrays 

that are suspect for data quality. Additionally, the same 

two arrays had much lower signal intensities (Figure3). 

The normalized unscaled standard error (NUSE) (14) 

calculations had high median values and large interquartile range for these two arrays (Table 6). Samples from 

the same subject’s opposite cheek did not show the same 

set of quality control issues, further evidence that RNA 

quality from buccal cells is inconsistent. Neither sample 

could have been predicted to be of lesser quality from 

the BioAnalyzer traces (Figure2A). Due to the poor quality of these two arrays, they were removed from further 

analysis. Two other arrays, Sm28a and b, had elevated 

NUSE parameters compared to other subjects but did 

not have a low %p or high SF, and so were not removed 

as the observed differences were likely subject-dependent 

and were more likely due to biological diversity between 

subjects.

Microarray Data Analysis for Differential Expression 

A study using Affymetrix hgU133A arrays to compare 

gene expression in smokers and never-smokers using RNA 

from buccal mucosa and nasal swabs was published by 

Sridhar et al. (6). This group performed an extensive 

microarray analysis of gene expression in bronchial lavage samples from smokers, former-smokers, and neversmokers and developed a list of 314 genes differentially 

expressed in smokers in this tissue (7, 15). Using Gene 

Set Enrichment Analysis (GSEA), Sridhar and associates examined the smoker buccal and nasal microarray 

data to determine whether the genes on the bronchial 

314 gene list showed the same direction of change and 

identified three leading-edge subsets of genes from the 

bronchial 314 list that were changing expression in the 

buccal or nasal data in the same direction as seen in the 

bronchial data. These were a 74 gene subset of genes 

up-regulated in buccal mucosa of smokers, a 120 gene 

subset up-regulated in the nasal mucosa of smokers, and 

a 50 gene subset down-regulated in nasal mucosa. The

buccal microarray cel files were downloaded from GEO 

and analyzed in parallel with the data from the current 

study (Materials and Methods). Initially, unsupervised 

hierarchical clustering was performed with the summarized data from the current study, termed SmvsNS, and 

BuccalCompare for the Sridhar study. Neither dataset 

showed any pattern of clustering by replicate sample (a 

vs b) in the case of the SmvNS data, nor by smokers and 

non-smokers in either dataset.

T-tests comparing the a samples to the b samples in the 

SmvsNS data were done to evaluate the within-subject 

variability. There were 871 significant probesets from 

53,800, or 1.62%. Comparing smokers to nonsmokers 

using the same test gave 178 probesets, or 0.33%. A 

T-test for the BuccalCompare data gave 65 probesets 

comparing never smokers to smokers and 66 probesets 

comparing a random grouping of odd numbered arrays 

against even. Taken together, these results suggest that 

there is as much or greater variability among subjects 

than smoking introduces between the two subject types. 

SAM (9) and RP (10) were used to develop lists 

of differentially expressed genes between smokers and 

nonsmokers. With the SmvNS data, SAM returned 30 

significant probesets with a Q value of 0 at a 10% FDR. 

All 30 probesets were up-regulated in smokers. For the 

BuccalCompare dataset, there were no significant results 

from the SAM analysis. With RP analysis, 17 genes were 
8 

RIN: 8.5

18S

28S

RIN: NA RIN: NA

RIN: 2.5 RIN: NA RIN: NA

RIN: 2.3 RIN: 2.5 RIN: 2.8

Sm25a Sm26a

NS21a

NS22a NS23a NS24a

Sm27a Sm28a

Figure 2A. Buccal mucosa total RNA from smokers and nonsmokers. A. Group a buccal cell samples. 

Note variation between the isolates in peak heights and species. Sm Smokers, NS nonsmoker. Sample 

1, whole blood total RNA, as seen in Figure 1 for comparison, showing 18S and 28S ribosomal peaks. 

RIN, RNA integrity number, NA RIN not determined. 

Figure 2B. The group b buccal samples. Sample 1, total RNA from whole blood, is added for 

comparison. Compare to Fig 2A. For example, Sm26a and Sm26b are from opposite cheeks of same 

subject and show some similarity in migration pattern. The same variation in peak heights and species 

between samples is seen here as in Figure 2A. RIN, RNA integrity Number, NA, RIN not determined.

RIN: 8.5 RIN: NA RIN:2.1

RIN:NA RIN:NA

RIN:2.3 RIN:NA

Sm25b

NS21b

NS23b NS24b

Sm27b Sm28b

NS22b

Sm26b

RIN:NA

RIN:NA

RIN: 8.5 RIN: NA RIN:2.1

RIN:NA RIN:NA

RIN:2.3 RIN:NA

Sm25b

NS21b

NS23b NS24b

Sm27b Sm28b

NS22b

Sm26b

RIN:NA

RIN:NA
9 

Table 6. Microarray quality metrics. 

Samples 

Scaling 

Factor 

% 

Present

NUSE 

Median 

NUSE 

IQR 

Smokers 

25a 24.6444 35.9 0.989 0.021 

25b 4.532 47.8 0.991 0.021 

 

26a 19.022 31.8 0.989 0.02 

26b 3.451 30 1.013 0.04 

 

27a 255.647 6 1.101 0.089 

27b 3.713 49.9 0.985 0.021 

 

28a 12.806 22.5 1.027 0.046 

28b 4.674 24 1.061 0.073 

NonSmokers 

21a 307.934 3.5 1.12 0.097 

21b 6.852 35 1 0.024 

 

22a 22.185 40.7 0.998 0.019 

22b 4.808 47.6 0.993 0.022 

 

23a 20.057 33.6 0.987 0.02 

23b 4.689 44.5 0.991 0.022 

 

24a 21.886 39.5 0.988 0.02 

24b 3.926 50.9 0.988 0.021 

Scaling Factor and % Present were determined with GCOS. 

NUSE, [14] and Materials and Methods 

IQR, Interquartile range
10 

Replicate A arrays Replicate B arrays

NS24a

NS21a*

NS22a

NS23a

Sm25a

Sm26a

Sm27a*

Sm28a

NS24b

NS21b

NS22b

NS23b

Sm25b

Sm26b

Sm27b

Sm28b

Histograms of raw values

Density

Log (base2) Intensity Log (base 2) Intensity

Replicate A arrays Replicate B arrays

NS24a

NS21a*

NS22a

NS23a

Sm25a

Sm26a

Sm27a*

Sm28a

NS24b

NS21b

NS22b

NS23b

Sm25b

Sm26b

Sm27b

Sm28b

NS24b

NS21b

NS22b

NS23b

Sm25b

Sm26b

Sm27b

Sm28b

Histograms of raw values

Density

Log (base2) Intensity Log (base 2) Intensity

Figure 3. Replicate Samples a and b Raw Signal Value Histograms. Two arrays, NS21a and Sm27a, had 

low overall signal strength as shown by the intensity plot. The arrays from the matching b cheek NS21b and 

Sm27b show acceptable values. Note the difference in y-axis density scale. NS nonsmoker, Sm smoker.

Figure 4. Venn diagram showing overlap between the four gene lists upregulated in smokers.
11 

Figure 5. Two graphics showing PAINT TREs with color added to indicate membership in a particular IPA 

functional network. The ovals represent target genes identified by PAINT as having transcription factor 

binding sites upstream of the gene. The color of the oval corresponds to the functional networks in which IPA 

placed the gene. The gray ovals represent genes not included in the IPA network. The rectangles indicate 

transcription factors. Arrows connect the transcription factors to genes with corresponding upstream binding 

sites. 

A. (Above) Merged results for the SAM_upSm gene list. Twelve of 25 genes are contained in both IPA and 

PAINT analyses.

B. (Below) Merged results for the RP_upSm gene list. Thirty-eight of 103 target genes are contained in both 

IPA and PAINT analyses.

cRel

KIDINS220

HNF-4

Gfi-1 CRE-BP1 VBP

TXNDC4 NAGA

TRAK2 GOLGA1

PHF10

ANKrd44

SKP1

QK1 BNC2 SWAP70

AMY1B

PCCB

SRGAP2

GALNT7

Tumor morphology, lipid metab.

Carbohydrate metab., small 

molecule biochemistry

No IPA Network

Transcription Factor

IPA Network Function

C-Rel

Ap1/FosJun

CREB

SPINK7

MAFF MUC20

DUSP5

KRT4

SPNS2

RAB7A

CSTA

CTSB CD59

DEFB4

C20orf24 STK24

A2ML1

GLUL

RFFL

WDR26

SCEL

ANXA2

TMOD3

ALDJ1A3

MT1X

ANXA11

EIF4G2 KRT19 S100A11

ATP6V1D

SAT1

MUC1

SLP1

UPP1

MYO6

S100A10

ESS8L1

PERP

MT2A

GADD45B

PRss27

ANKrd37

MPZL3

Cell death, immunol. disease

Cell movement, proliferation

Cell cycle, organ morphology

No IPA Network

Gene expression, cell develop.

Gene expression, cell proliferation

Transcription Factor

IPA Network Function

KRT4

TPT1

SKP1

MYO6
12 

found to be down-regulated and 118 genes up-regulated in 

smokers (Table 7).RP analysis could not be performed on 

the BuccalCompare dataset since there were no replicates.

Only a few genes were found to be in common between the up-regulated gene lists (Figure 4) (16). The 

RP_downSm gene list had no overlap with the corresponding Sridhar Nasal_downSm leading edge set. Note 

that the probesets for the genes on the SAM_upSm and 

the RP_upSM lists have similar fold change ranges and 

medians, but probesets in the RP_downSm differed in 

having overall low signal strength (Tables 8a and b).

Using a similar analysis approach to Sridhar, both the 

SmvsNS and the BuccalCompare datasets were compared 

against six gene lists in a GSEA enrichment analysis. The 

gene lists were the 74 genes in Buccal_upSm , the 120 

genes in Nasal_upSm and the 49 genes in Nasal_downSm 

defined as leading edge subsets by Sridhar ((6), the 25 

genes in SAM_upSm, the 107 RP_upSm genes , and the 

17 genes in RP_downSm all three lists from the current 

study (Table 7). 

When GSEA analysis of the SmvsNS dataset was performed against all six gene lists, the four lists up-regulated 

in smokers showed the same expression patterns in the 

SMvsNS dataset, and the two down-regulated gene lists 

likewise were down-regulated in the SMvsNS dataset. The 

same analysis was performed using the BuccalCompare 

data against the same six gene lists, with the same results. 

This showed correlation between the SMvsNS and BuccalCompare datasets in terms of the direction of gene 

expression change for genes in the six sets. However, in 

the SmvsNS comparison only the SAM_upSm list genes 

were significantly enriched in the smoker phenotype with 

FDR q-value 0.029 and p-value 0.025, not the RP_upSm 

genes. This was unexpected since the RP_upSm gene 

list was, in fact, derived from the SmvsNS dataset. The 

BuccalCompare data behaved similarly, with only the 

Buccal_upSm gene list significantly enriched. This was 

expected since it was derived from this dataset. 

As a check for reproducibility, two subjects (one 

smoker and one nonsmoker—both cheeks) were retested 

several months after the initial sampling was performed. 

Four arrays were generated (11Sm a, b and 12NS a, b). 

This small dataset was examined with GSEA against the 

same six gene sets. The results showed that this repeated 

subset had significant gene enrichment for smokers with 

the RP_upSm, Nasal_upSm, and Buccal_upSm gene 

lists with a nominal P-value of 0, an indication of good 

reproducibility. 

Function Analysis 

To further evaluate the SmvsNS gene lists for biological 

coherence, the SAM and RP gene lists were evaluated for 

over-representation of transcription factor binding sites in 

the promoters of these genes using the Promoter Analysis 

and Interactive Tool Set, (PAINT) (12, 17), Materials 

and Methods, and for shared functional interactions 

using Ingenuity Pathways Analysis, (IPA Ingenuity IPA 

version 7.0, Copyright 2009 Ingenuity Systems, Inc., 

Redwood City CA). Statistically significant transcriptional 

regulation elements (TREs) were found with 15 of the 

SAM_upSm and 42 RP_upSm genes. No TREs were 

found for genes in the RP_downSm genes. 

In IPA, 17 of the 25 genes from SAM_upSm could 

form a single network from two smaller networks sharing 

broad functional categories including tumor morphology, 

lipid metabolism, carbohydrate metabolism, and small 

molecule biochemistry. The RP_downSm genes did not 

result in any functional networks when examined in IPA. 

However, 91 of the genes on the RP_upSm list fell into 

five networks that could be merged into a single large 

network, indicating shared function. Functional categories for this network included: cell growth, movement, 

development and death; cell cycle; gene expression, cancer 

and immunological system development and function.

As a final step in the analysis, genes in TRE networks 

from PAINT were coded for network function from 

IPA (Figures 5 a and b). This analysis strongly suggests 

co-regulation within functional networks and speaks to 

the transcriptional affects of smoking on buccal cells.

Discussion

This study was focused on determining whether the 

buccal mucosa could serve as a tissue source for total 

RNA to be used in relative gene expression studies and 

biomarker detection by qPCR and microarray analyses. 

Two previous studies had suggested that buccal cells 

had efficacy for measuring responses to tobacco smoke 

exposure (5, 6) and suggested extrapolation of this tissue 

source to other inhalation or ingestion exposures (5).

Our initial RNA isolations from matched blood and 

buccal RNA showed a marked difference in the quality of 

the isolated material between the two sources and showed 

that there was significant degradation in buccal mucosa 

RNA. The qPCR results from the matched samples 

showed an average lower copy number in buccal RNA 

than blood RNA for all four genes tested and greater 

variability between subjects (Table 4). The lower copynumber was expected as salivary glands express all four 

genes at the same or lower level as blood on microarrays; 

however, the increased variability found between buccal 

samples over blood is a concern. 

The amplification protocols we utilized allowed buccal 

cell samples to be used in repeated measures experiments, 

removing the necessity to repeatedly sample to obtain 

enough RNA for a single microarray. The 50 ng of RNA 
13 

Table 7. Gene lists used for the GSEA assay. 

Buccal_upSm Nasal_upSm Nasal_downSm SAM_upSm RP_upSm RP_downSm 

up in smokers 

up in 

smokers 

down in 

smokers up in smokers up in smokers 

down in 

smokers 

AACS AACS PEX14 AMY1A, 2A A2ML1 ASMTL 

AKR1B1 ABHD2 SIX2 ANKRD44 ACTG1 DMRTC1 

AKR1B10 ADH7 TU3A BNC2 ADM FLJ33706 

ALDH3A1 AKR1B1 PPAP2B CGGBP1 ALDH1A3 FLJ40243 

APLP2 ALDH3A1 ANXA6 FLJ12595 ALG10 H19 

ARHE ANXA3 PECI GALNT7 ANKRD37 KCNA5 

BCL2L13 AP2B1 PDE8B GOLGA4 ANXA1 LCE1E 

BECN1 APLP2 HRIHFB2122 KIDINS220 ANXA11 LOC100131941 

C14orf1 ARHE RUTBC1 LCMT2 ANXA2 LOC158402 

CABYR ARL1 TSAP6 LRMP ATP6V1D LOC285708 

CAP1 ARPC3 SHARP NAGA BCL8 LOC401312 

CBLB AZGP1 BCL11A PAK2 C14orf129 MEX3D 

CCPG1 BECN1 SYNGR1 PCCB C18orf25 

NBPF1,8-11,14-

16,20 

CDC14B C14orf1 SEC14L3 PHF10 C20orf24 RGS12 

CEACAM5 C1orf8 TLR5 QKI C4orf7 TMEM107 

COPB2 CANX AK1 RBBP6 CAPN2 TSFM 

COX5A CAP1 AMACR RNF34 CD59 WNT6 

CTSC CCNG2 LU SGIP1 CPNE3 

CYP4F11 CCPG1 SERPINI2 SKP1 CRISP3 

CYP4F3 CEACAM5 TNFSF12, 13 SPTBN1 CRNN 

DIAPH2 CEACAM6 TLE2 SRGAP2 CSTA 

DKFZP566E144 CHP SLIT1 SWAP70 CSTB 

EDEM1 CLDN10 TENS1 TRA2 CTSB 

ENTPD4 COX5A GGA1 TRAK2 DEFB4 

ERP70 CPNE3 GAS6 TXNDC4 DUSP5 

FLJ13052 CREB3L1 SSH3 ECM1 

FOLH1 CSTA JAG2 EIF4G2 

GALNT1 CTSC EPOR EMP1 

GFPT1 CYP1A1 COL9A2 EPS8L1 

GHITM CYP1B1 CX3CL1 ERO1L 

GNE CYP4F11 HNMT FLG 

GPX2 CYP4F3 C3 FLJ22662 

GSN DAZ2, 4 FLRT3 FTH1 

GTF3C1 DHRS3 NCOR2 GADD45B 

HIG1 DPYSL3 PCDH7 GLUL 

HTATIP2 DSCR5 SFRS14 GPBP1l1 

JTB EDEM1 HLF GPR110 

LAMP2 ERP70 FLJ23514 GRHL1 

LYPLA1 FKBP11 CYFIP2 H3F3A B 

ME1 FKBP1A FGFR3 HIG2 

MTMR6 FLJ13052 TNS HOPX 

MUC5AC FOLH1 FMO2 ITGB1 

The lists in the first three columns are the leading edge gene sets identified in the Sridhar study [6]. 

The remaining three lists were derived from the current SmvsNS study using SAM or RP analysis as labeled. 

(Continued) 
14 

Table 7. (Continued) Gene lists used for the GSEA assay. 

Buccal_upSm Nasal_upSm Nasal_downSm SAM_upSm RP_upSm RP_downSm 

up in smokers 

up in 

smokers 

down in 

smokers up in smokers up in smokers 

down in 

smokers 

NQO1 FTH1 CUGBP1 KLK10 

PSMB5 GALNT1 ITM2A KRT13 

PSMD14 GALNT12 SCGB1A1 KRT19 

PTP4A1 GALNT3 TCF7L1 KRT4 

PTS GALNT7 SLIT2 LCE3D 

RAB11A GCLM NFIB LCN2 

RGC32 GCNT3 KRT15 LGALS3 

RNP24 GFPT1 MAFF 

RPN2 GMDS MAL 

S100P GNE MALAT1 

SEC31L1 GRP58 MALL 

SEPX1 GSN MPZL3 

SLC35A3 GUK1 MT1F 

SLC3A2 HGD MT1G 

SLC7A11 HIST1H2BK MT1H 

SMPDL3A HMGN4 MT1X 

SORL1 HTATIP2 MT2A 

SPDEF IDS MUC1 

SPINT2 IMPA2 MUC20 

SSR4 JTB MYO6 

TACSTD2 KATNB1 NAMPT 

TALDO1 KIAA0367 NDFIP2 

TARS KLF4 PADI1 

TIAM1 LAMP2 PER1 

TKT LOC92482 PERP 

TTC9 LOC92689 PLXNC1 

TXNL1 LRRC5 PPARD 

TXNRD1 LYPLA1 PPL 

UBE2J1 ME1 PPP1CB 

VPS13D MSMB PPP1R3C 

WBP5 NKX3-1 PRSS27 

XPOT NQO1 RAB7A 

NUDT4 RANBP9 

P4HB RFFL 

PGD RIOK3 

PIR RNF34 

PLA2G10 S100A10 

PRDX4 S100A11 

PTK9 S100P 

PTP4A1 SAT1 

RAB11A SCEL 

RAB2 SFRS5 

The lists in the first three columns are the leading edge gene sets identified in the Sridhar study [6]. 

The remaining three lists were derived from the current SmvsNS study using SAM or RP analysis as labeled. 

(Continued) 
15 

Table 7. (Continued) Gene lists used for the GSEA assay. 

Buccal_upSm Nasal_upSm Nasal_downSm SAM_upSm RP_upSm RP_downSm 

up in smokers 

up in 

smokers 

down in 

smokers up in smokers up in smokers 

down in 

smokers 

RAB7 SGIP1 

 RAP1GA1 SKP1 

RGC32 SLC12A6 

RNP24 SLPI 

RPN2 SPINK5 

S100A10 SPINK7 

 SCGB2A1 SPNS2 

SCP2 SPRR1B 

SEC31L1 STK24 

SEPX1 TACSTD2 

SLC17A5 TAX1BP3 

SLC35A1 

TMEM49, 

MIRN21 

SLC35A3 TMOD3 

SLC7A11 TMPRSS11B 

SLC7A11 

TMPRSS11E, 

11E2 

 SMPDL3A TncRNA 

SORL1 TPT1 

SPDEF TUFT1 

TACSTD2 UBAP1 

TAGLN2 UBB, UBC 

TCN1 UPP1 

TIMP1 WDR26 

TIPARP ZNF185 

TKT 

TLE1 

TM4SF13 

TM4SF3 

TMP21 

TOM1L1 

TRA1 

TTC9 

TXNDC5 

UBE2J1 

 UGT1A3, 6 

UPK1B 

 WBP5 

The lists in the first three columns are the leading edge gene sets identified in the Sridhar study [6]. 

The remaining three lists were derived from the current SmvsNS study using SAM or RP analysis as labeled. 
16 

Table 8a. Fold change of probesets included in the RP and SAM upregulated gene lists. 

RP_upSm SAM_upSm 

Probeset ID Signal Strength Probeset ID Signal Strength 

 AveSm AveNS Fold change AveSm AveNS Fold 

change 

1560263_at 2839.6 945.0 3.0 1556202_at 235.3 92.4 2.5 

1560538_at 307.7 98.4 3.1 1557502_at 564.7 435.0 1.3 

1560683_at 1280.0 432.6 3.0 1569603_at 203.8 79.8 2.6 

1560684_x_at 1293.2 532.6 2.4 1569854_at 406.3 200.6 2.0 

1560712_at 1625.7 349.6 4.7 200719_at 457.6 259.2 1.8 

1564307_a_at 885.7 333.1 2.7 201567_s_at 804.1 675.2 1.2 

1569603_at 203.8 79.8 2.6 202125_s_at 63.5 46.4 1.4 

1570233_at 81.4 36.2 2.2 202944_at 109.2 60.1 1.8 

200004_at 338.3 110.4 3.1 204013_s_at 101.8 55.8 1.8 

200648_s_at 696.7 261.3 2.7 206861_s_at 592.1 254.5 2.3 

200660_at 717.3 203.3 3.5 208498_s_at 382.2 178.5 2.1 

200718_s_at 293.8 93.8 3.1 208958_at 154.4 66.5 2.3 

200748_s_at 26221.4 8805.6 3.0 210369_at 130.3 69.8 1.9 

200839_s_at 1941.2 515.8 3.8 212162_at 319.0 171.1 1.9 

200872_at 1805.3 725.3 2.5 216757_at 440.2 259.9 1.7 

200983_x_at 800.6 213.5 3.7 219126_at 1249.2 867.4 1.4 

200985_s_at 1693.5 468.0 3.6 220716_at 839.0 541.2 1.6 

201012_at 7017.5 2488.8 2.8 226641_at 602.1 302.8 2.0 

201201_at 16345.2 7844.0 2.1 227635_at 166.5 95.9 1.7 

201324_at 29145.9 16135.9 1.8 229942_at 460.7 244.9 1.9 

201325_s_at 7452.1 3769.0 2.0 234849_at 103.3 51.9 2.0 

201407_s_at 3061.4 1246.2 2.5 235242_at 242.2 140.5 1.7 

201550_x_at 11101.9 4826.8 2.3 236288_at 381.5 103.9 3.7 

201590_x_at 1778.7 428.6 4.2 236650_at 1783.5 998.6 1.8 

201650_at 945.3 300.1 3.1 240670_at 575.4 365.5 1.6 

202119_s_at 320.8 94.9 3.4 242220_at 385.7 186.3 2.1 

202129_s_at 1658.8 515.0 3.2 242299_at 356.7 171.1 2.1 

202286_s_at 1550.2 313.3 4.9 244268_x_at 295.8 126.8 2.3 

202582_s_at 263.2 140.4 1.9 244348_at 288.2 129.0 2.2 

202912_at 1427.0 623.3 2.3 35974_at 39.4 27.7 1.4

203021_at 2057.7 655.3 3.1 424.5 241.9 1.9 

203180_at 484.5 181.8 2.7 Average Average Median 

203234_at 1313.2 431.8 3.0 

203380_x_at 1947.2 663.5 2.9 

203407_at 15025.0 4823.6 3.1 

203455_s_at 885.6 406.4 2.2 

203585_at 568.9 168.3 3.4 

204284_at 622.7 172.1 3.6 

204326_x_at 1850.8 486.1 3.8 

204351_at 1263.1 398.4 3.2 

204745_x_at 4730.1 1257.1 3.8 

204777_s_at 33731.3 13004.8 2.6 

204971_at 20307.1 9778.8 2.1 

205064_at 19747.9 6197.7 3.2 

205185_at 5303.8 2123.4 2.5 

205807_s_at 1933.3 934.6 2.1 

206200_s_at 1156.9 360.8 3.2 
17 

206461_x_at 4076.3 1131.3 3.6 

206471_s_at 3389.5 1431.7 2.4 

206884_s_at 8023.2 2087.3 3.8 

207356_at 2732.4 785.7 3.5 

207802_at 4651.9 1121.0 4.1 

207935_s_at 13501.1 6624.8 2.0 

208581_x_at 2077.0 524.4 4.0 

208683_at 449.6 164.8 2.7 

208854_s_at 736.9 218.5 3.4 

208855_s_at 2800.6 961.9 2.9 

208898_at 753.5 231.4 3.3 

208949_s_at 3260.5 1643.8 2.0 

209069_s_at 2829.4 992.1 2.9 

209154_at 156.9 48.3 3.2 

209365_s_at 8156.6 2827.3 2.9 

209373_at 1992.7 631.5 3.2 

209457_at 1721.0 759.6 2.3 

209792_s_at 255.8 69.1 3.7 

210427_x_at 1533.4 368.6 4.2 

210480_s_at 909.4 322.8 2.8 

210592_s_at 1631.9 600.0 2.7 

211296_x_at 2773.5 1111.9 2.5 

211597_s_at 2493.3 888.5 2.8 

211945_s_at 381.4 99.7 3.8 

211960_s_at 89.0 34.4 2.6 

211970_x_at 12242.4 5876.6 2.1 

211983_x_at 30559.4 13369.9 2.3 

212185_x_at 6117.9 2063.0 3.0 

212266_s_at 2409.3 647.6 3.7 

212284_x_at 13069.8 5894.7 2.2 

212531_at 1313.8 283.7 4.6 

213240_s_at 10862.7 4773.7 2.3 

213503_x_at 1746.2 452.3 3.9 

213560_at 1431.4 468.8 3.1 

213693_s_at 6514.1 2001.7 3.3 

214399_s_at 24445.3 12004.8 2.0 

214657_s_at 401.5 163.4 2.5 

215704_at 753.0 272.4 2.8 

217165_x_at 1979.5 454.2 4.4 

217508_s_at 251.1 90.1 2.8 

217739_s_at 761.8 292.3 2.6 

217835_x_at 651.7 277.9 2.3 

218107_at 339.6 112.3 3.0 

218454_at 696.3 220.9 3.2 

218507_at 586.8 198.5 3.0 

218779_x_at 1644.2 365.1 4.5 

220090_at 13668.3 4995.6 2.7 

220431_at 11097.6 4104.3 2.7 

220990_s_at 308.5 97.7 3.2 

221655_x_at 1918.8 394.5 4.9 

221665_s_at 1156.7 265.7 4.4 

222392_x_at 2353.1 1000.3 2.4 
18 

222646_s_at 8991.7 3433.9 2.6 

222830_at 199.0 57.5 3.5 

223077_at 330.1 93.6 3.5 

223239_at 118.7 33.2 3.6 

223596_at 64.7 21.8 3.0 

223720_at 1360.4 602.1 2.3 

223739_at 1066.5 279.9 3.8 

224328_s_at 989.6 359.8 2.8 

224565_at 2330.1 980.9 2.4 

224566_at 1424.5 471.3 3.0 

224567_x_at 12920.9 7753.9 1.7 

224585_x_at 15711.4 6478.1 2.4 

224799_at 821.4 269.1 3.1 

225671_at 570.2 189.0 3.0 

225750_at 1694.4 622.7 2.7 

226622_at 840.0 251.9 3.3 

226675_s_at 11677.6 8529.3 1.4 

227337_at 1919.1 823.5 2.3 

227747_at 4257.5 1262.4 3.4 

229152_at 690.4 166.6 4.1 

231735_s_at 31993.2 13536.9 2.4 

232056_at 220.4 85.8 2.6 

232074_at 675.6 226.4 3.0 

233513_at 215.3 79.3 2.7 

234989_at 15563.5 4119.7 3.8 

236288_at 381.5 103.9 3.7 

237919_at 360.7 164.0 2.2 

238689_at 258.8 92.8 2.8 

244677_at 940.0 271.3 3.5 

36711_at 1626.5 463.2 3.5 

37152_at 999.2 304.9 3.3 

46270_at 527.3 154.8 3.4 

91826_at 3230.9 1056.8 3.1 

 4421.6 1781.3 3.0 

 Average Average Median 
19 

we used for amplification can routinely be isolated from 

a single swab (Table 1), in contrast to the 8 ug required 

by Sridhar et al. (6), which was pooled from multiple 

sampling of the same individual over a period of 6 weeks. 

In most cases, the array quality was acceptable, but with 

buccal RNA, arrays did have a higher failure rate than 

is typical for arrays hybridized with target material from 

blood RNA. Two samples from 16 failed in hybridization, 

where matching samples from the other cheek passed. This 

opens the possibility that samples from both cheeks would 

be required to insure that every sample was collected in 

a study, but we found the intra-subject variability to be 

high as well. The availability of the Sridhar buccal dataset 

provided comparison data and, along with the previous 

work from this group (7), provided published lists of 

genes from buccal and nasal cells that change expression 

levels due to smoking. Gene lists developed from the current study did not overlap extensively with each other or 

with the Sridhar lists. However, by using the independent analysis tools, PAINT and IPA, 

a cohesive function/cotranscription 

network was generated, suggesting two 

non-random sets of genes upregulated 

in smokers. TFBS analysis is a good 

complement to a functional analysis 

such as IPA because it has no a priori

assumptions about gene function, 

relying instead on promoter sequence 

alone. The analysis results suggested 

that using an approach that included 

these two complementary methods is 

useful for evaluating candidate genes. 

The analysis conducted with GSEA 

was significant because there was 

perfect concordance between gene 

lists derived from each of the two 

datasets for the direction of change in 

expression between smokers and nonsmokers. The results from the small 

repeated dataset were an indication 

of reproducibility with this system. 

This validated the methods used in 

the current study to discover differentially expressed genes. However, 

the lack of consistent, statistically 

significant enrichment for the smoker 

phenotype with GSEA analysis, the 

within-subject variability of RNA 

quality, and degradation in RNA 

derived from buccal cells highlight 

the difficulties to be expected when 

using buccal-cell RNA for differential 

expression testing.

Conclusions 

This study was a straightforward evaluation of buccal mucosa as a tissue useful for evaluating relative gene 

expression changes using an analysis scheme containing well-validated and commonly used analysis tools. 

Isolation and amplification techniques were successfully 

modified from those used with whole blood. The level 

of degradation found was not unexpected, and we were 

able to successfully perform qPCR with the buccal RNA. 

Somewhat surprising was that, given the poor quality of 

the RNA, the quality of the majority of the microarrays 

was acceptable and that several lists of genes changing 

expression in smokers, compared to nonsmokers, resulted 

from statistical analysis of the arrays. There was evidence 

of reproducibility in expression change, but the borderline significance level of the lists questions the validity 

of the findings. Therefore, using buccal tissue RNA 

Table 8b. Fold change of probesets included in the RP down-regulated 

gene list. 

RP_downSm 

Probeset ID Signal Strength 

AveSm AveNS Fold change 

1/fold 

change 

1553998_at 19.5 57.5 0.3 2.9 

1555032_at 190.7 413.2 0.5 2.2 

1556721_at 24.3 64.4 0.4 2.7 

1557818_x_at 149.8 334.7 0.4 2.2 

1559224_at 185.0 431.1 0.4 2.3 

1560520_at 30.4 71.7 0.4 2.4 

1561061_at 87.7 199.2 0.4 2.3 

1564281_at 23.0 66.6 0.3 2.9 

1564996_at 86.4 280.3 0.3 3.2 

1566999_at 45.1 99.3 0.5 2.2 

1567139_at 53.7 127.4 0.4 2.4 

1567697_at 17.5 42.5 0.4 2.4 

1568365_at 73.1 188.6 0.4 2.6 

206762_at 72.5 209.9 0.3 2.9 

214331_at 37.1 95.6 0.4 2.6 

224495_at 21.4 53.0 0.4 2.5 

224997_x_at 96.4 419.1 0.2 4.3 

227926_s_at 53.2 121.4 0.4 2.3 

233891_at 207.2 450.6 0.5 2.2 

236769_at 69.1 151.6 0.5 2.2 

240411_at 139.9 322.6 0.4 2.3 

36554_at 101.2 230.4 0.4 2.3 

71933_at 88.4 318.6 0.3 3.6 

91816_f_at 39.8 118.5 0.3 3.0

79.7 202.8 0.4 2.4 

Average Average Median 
20 

with 3' amplification may be a suitable tissue choice and 

preparation approach when assaying specific, highly differentially expressed gene targets that could overcome the 

limitations of subject variability and sample degradation. 

However, our findings suggest that this may be a difficult 

tissue to use, requiring replicate sampling and arrays, and 

possibly a different technology such as an array format 

or amplification method designed for heavily degraded 

template material.

Methods

Sample Collection

All sample collection was performed with the informed 

consent of the study participants under the auspices of the 

local IRB. Blood samples were collected in PAXgeneTM 

Blood RNA tubes (PreAnalytix/Qiagen; Valencia, CA) 

according to the manufacturer’s published protocol. Urine 

samples for nicotine and cotinine testing were collected 

in urine cups without preservative and refrigerated until 

shipping to a clinical lab (Diagnostic Laboratory of 

Oklahoma; Oklahoma City, OK). All nonsmokers were 

below the level of detection for both nicotine (10 ng/ml) 

and cotinine (40 ng/ml). All smokers were greater than 

500 ng/ml for nicotine and 900 ng/ml for cotinine. The 

expected levels for smokers are a concentration greater 

than 100 ng/ml for nicotine and 200 ng/ml for cotinine.

Buccal samples were collected using sterile Cytobrush 

Plus® (Medscand Medical; Guttenberg, NJ). Subjects 

were asked not to eat for the 30 minutes prior to sampling 

and rinsed their mouths with a minimum of 20mL of 

water before sample collection. Two buccal samples were 

collected from each subject and processed separately as 

either “a” or “b” samples. Cheeks were brushed for 30 

seconds, then brushes were plunged into 2-mL tubes 

containing 1.0 ml of RNA-Later (Invitrogen; Carlsbad, 

CA). The brush ends were cut off with sterile surgical 

scissors such that the 2-ml tubes could be capped. RNA 

was purified from buccal cell swabs immediately after 

collection.

RNA Purification

RNA isolation from blood samples was performed 

according to the protocol in the PAXgeneTM Blood RNA 

Purification Kit (18) with the optional on-column DNase 

treatment. A blood total RNA control sample was created 

by pooling purified RNA samples from three individuals 

not participating in either study. 

Buccal-cell RNA was purified using the RNeasy Micro 

Kit (Qiagen; Valencia CA) with the modifications found 

in Spivack et al. (5) and here. Cells were pelleted by centrifugation at 4,000 X g. The brush was removed from 

the tube by scraping the bristles against the lip of the tube 

to remove any adhered cells and the pellet reformed by 

centrifugation as above. RNAlater was pipetted off the 

pellet and the pellet washed with ice-cold PBS and the 

PBS removed after centrifugation, as above. Two microliters of polyC (Sigma Chemical; St. Louis, MO) and 350 

ul Buffer RLT (RNeasy Micro Kit) containing 10ul/ml 

beta-mercaptoethanol was added and the pellet passed 

through a 25 ga needle to lyse the cells. The lysate was 

centrifuged at 20,000 xg for 3 minutes and the supernatant 

transferred to a fresh microfuge tube. Then 350 ul 70% 

ethanol was added, mixed well by pipetting, and the sample 

applied to a MinElute column (RNeasy Micro Kit) and 

centrifuged at 8000 xg for 30 seconds. The column was 

washed twice with 350 ul of RW1 buffer (RNeasy Kit) 

followed by centrifugation at 8000 xg for 15 seconds. 

The column was placed in a fresh 2 ml collection tube 

and 500ul RPE buffer (RNeasy Micro Kit) was added. 

The column was centrifuged at 8000xg for 30 seconds; 

500 ul of freshly prepared 80% ethanol was added to the 

column followed by centrifugation for 2 minutes at 8000 

xg. The column was transferred to a fresh 2 ml collection 

tube, with the cap open, and centrifuged at 16,000 xg 

for 5 minutes. The RNA was eluted by adding 30ul prewarmed (50-55° C) RNase-free water to the membrane. 

After 2 minutes incubation, the column was centrifuged 

at 16,000 xg for 2 minutes. Spectrophotometric analysis 

showed a large 230nM component, potentially salt carryover. To reduce this, the RNeasy Micro Kit protocol 

for RNA cleanup and concentration (December 2007 

version) was used as written by the manufacturer for 

sample volumes less than 100 ul.

RNA quality was assessed from Agilent Bioanalyzer 

2100 (Agilent; Santa Clara, CA) traces using the Agilent 

RNA 6000 Nano Series II kit following manufacturer’s 

directions with 1 ul of sample. Yield was determined on a 

Nanodrop 1000 spectrophotometer (Thermo Scientific; 

Waltham, MA) (Table 1). 

qPCR. Primers for qPCR were designed using Beacon 

Designer 7.0 (PREMIER Biosoft International; Palo Alto, 

CA). Primers were synthesized and HPLC purified (Integrated DNA Technologies; Coralville IA). For three genes, 

ITAG5, ANKRD28, and TMEM8, three sets of primers 

were designed to span the mRNA. See Table 3 for the 

sequences, positions, of the primer sets on the respective 

transcript, concentrations, and annealing temperatures.

Template material for qPCR was prepared from 50 

ng aliquots of total RNA that were reversed transcribed 

and amplified using either the WT-OvationTM Pico System or the OvationTM RNA Amplification System V2, 

#3300, 3100, respectively (Nugen Technologies, Inc.; 

San Carlos, CA). All qPCR reactions were 25 ul and 
21 

performed in triplicate with a SYBR® green based assay, 

PerfeCta SYBR Green FastMix, Low ROX, #95074-05k 

(Quanta Biosciences; Gaithersburg, MD) using 1 ng of 

amplified template material per reaction except in the 

amplification comparison series, where 5 ng/reaction was 

used. Cycling was performed on a Stratagene MX3005p 

(Agilent Technologies; La Jolla, CA). Cycling parameters 

were one cycle of 2 min 95° C, 40 cycles of 15 sec 95° 

C, 30 sec optimum annealing temperature, 15 sec 72° C 

extension, then a dissociation curve with 1 min 95° C, 30 

sec at optimum annealing temperature, and dissociation 

ramp rate at 0.01 degree/sec with all points data collection 

on. qPCR data were analyzed using qBase version 1.3.5 

(19). qPCR product size was assessed with Agilent DNA 

1000 Series II (Agilent Technologies) microfluidics chips.

Microarray target preparation. For microarray target 

material, 50 ng total RNA was reverse transcribed and 

amplified, per the manufacturer’s protocols using the 

OvationTM RNA Amplification System V2 (Nugen Technologies, Inc.), fragmented and biotin-labeled, using the 

FL-OvationTM cDNA Biotin Module V2, #4200 (Nugen 

Technologies, Inc.). Gene expression was determined by 

hybridization of the labelled template to hgU133 Plus 2.0 

human microarrays (Affymetrix, Inc.; Santa Clara, CA). 

Hybridization cocktail synthesis and post-hybridization 

processing was performed according to the “Affymetrix 

GeneChip Eukaryotic Array Analysis” protocol found in 

the appendix of the protocol book for the fragmentation 

kit. Arrays were hybridized for 18 hours and washed using 

fluidics protocol FS450_0004 on a GeneChip Fluidic 

Station 450 (Affymetrix, Inc.).

Microarray pre-processing. Quality assessment of 

the arrays was performed with the tools available in the 

Gene Chip Operating Software, version 1.4 (Affymetrix, 

Inc.) and the Bioconductor packages AffyQCReport (20) 

and AffyPLM (21), R version 2.8, Bioconductor version 

2.3 (22). The microarray data have been assigned series 

number GSE16149 in the Gene Expression Omnibus 

(GEO). 

Microarray data analysis. Array data were processed 

with Robust Multiarray Average (RMA) (23) using the 

package available at the Automated Microarray Pipeline 

(AMP) (24) and quantile normalized. Differential expression analysis comparing smokers to nonsmokers was 

performed with both Significance Analysis of Microarrays, SAM, (9) and Rank Product Analysis, RP, (10). For 

RP analysis, the samples matching the two poor quality 

arrays were removed, as this analysis utilizes the ranked 

expression values from replicate samples. This left 12 arrays, six in each replicate group, a and b, for this analysis. 

Unsupervised hierarchical clustering, T-tests, SAM and 

RP were performed using the packages available on the 

MultiExperiment Viewer, version 4.3.01 (MeV) (25, 26) 

with default settings. Gene Set Enrichment Analysis, 

GSEA version 2.04 (8, 27), was used to test the array 

data for enrichment of differentially expressed genes. The 

default settings were used, except the minimum size for 

gene sets was decreased to ten to allow analysis against 

the RP_downSm list, which GSEA reduced from 17. The 

same microarray differential expression analysis pipeline 

was used on the data from series GSE8987 from the 

GEO database (6), which were designated mouth and 

current smokers.

The output gene lists of differentially expressed genes 

from RP and SAM were evaluated for biological significance using Ingenuity Pathway Analysis, IPA, (Ingenuity 

Systems, Inc.; Redwood City, CA) for a core analysis. 

PAINT, promoter analysis and interaction network version 

3.6 (12) analysis using the TRANSFAC public database 

(28), was used with the same gene lists examining both 

strands to 2000 bases upstream looking for transcription 

factor binding sites and summing in TREs any potentially 

co-regulated genes. 

References

1. Thompson MD, Bowen RA, Wong BY, et al.: 

Whole genome amplification of buccal cell DNA: 

genotyping concordance before and after multiple 

displacement amplification. Clinical Chemical 

Laboratory Medicine 2005, 43(2):157-62.

2. Ceder O, van Dijken J, Ericson T, Kollberg H: 

Ribonuclease in different types of saliva from cystic 

fibrosis patients. Acta Paediatrica Scandinavica 

1985, 74:102-6.

3. Vondracek M, Xi Z, Larsson P, et al.: Cytochrome 

P450 expression and related metabolism in human 

buccal mucosa. Carcinogenesis 2001, 22(3):481-8.

4. Spira A, Beane J, Schembri F, et al.: Noninvasive 

method for obtaining RNA from buccal mucosa 

epithelial cells for gene expression profiling. BioTechniques 2004, 36:484-7.

5. Spivack SD, Hurteau GJ, Jain R, et al.: Geneenvironment interaction signatures by quantitative 

mRNA profiling in exfoliated buccal mucosal cells. 

Cancer Research 2004, 64:6805-6813.

6. Sridhar S, Schembri F, Zeskind J, et al.: Smokinginduced gene expression changes in the bronchial 

airway are reflected in nasal and buccal epithelium. 

BMC Genomics 2008, 9:259-.
22 

7. Shah V, Sridhar S, Beane J, et al.: SIEGE: Smoking 

induced epithelial gene expression database. Nucleic 

Acids Research 2005, 33(database):D573-9.

8. Subramanian A, Tamayo P, Mootha VK, et al.: 

Gene set enrichment analysis: A knowledge-based 

approach for interpreting genome-wide expression 

profiles. Proceedings of the National Academy of Sciences 2005, 102(43):15545-50.

9. Tusher VG, Tibshirani R, Chu G: Significance 

analysis of microarrays applied to the ionizing radiation response. Proceedings of the National Academy 

of Sciences 2001, 98(9):5116-21.

10. Breitling R, Armengaud P, Amtmann A, Herzyk 

P: Rank products: a simple, yet powerful, new 

method to detect differentially regulated genes in 

replicated microarray experiments. FEBS Letters 

2004, 573:83-92.

11. Ingenuity Pathway Analysis (IPA) [http://www.

ingenuity.com/] (access date 09.25.09).

12. Vadigepalli R, Chakravarthula P, Zak DE, et al.: 

PAINT: A promoter analysis and interaction network generation tool for gene regulatory network 

identification. OMICS 2003, 7(3):235-52.

13. SymAtlas/BioGPS gene atlas database [http://

biogps.gnf.org] (access date 09.25.09).

14. Bolstad BM, Collin F, Brettschneider J, et al.: 

Quality assessment of Affymetrix GeneChip data. 

Bioinformatics and computational biology solutions using R and Bioconductor. In: Bioinformatics 

and Computational Biology Solutions Using R and 

Bioconductor. Gentleman R, Carey V, Huber W, 

Irizarry RA, Dudoit S eds.: New York, Springer; 

2005: 33-47.

15. SIEGE-Smoking induced epithelial gene expression 

database [http://pulm.bumc.bu.edu/siegeDB/].

16. Oliveros JC: VENNY. An interactive tool 

for comparing lists with Venn Diagrams

[http://bioinfogp.cnb.csic.es/tools/venny/index.

html] (access date 09.25.09).

17. PAINT: Promoter analysis and interaction network 

toolset (V 3.6) [http://www.dbi.tju.edu/dbi/tools/

paint/] (access date 09.25.09).

18. PreAnalytiX Technical Notes [http://www.preanalytix.com/Tech_Notes.asp#] (access date 09.25.09).

19. Hellemans J, Mortier G, De Paepe A, et al.: qBase 

relative quantification framework and software for 

management and automated analysis of real-time 

quantitative PCR data. Genome Biology 2007, 

8(2):R19.

20. Parman C, Hallman C, Gentleman R: AffyQCreport [http://www.bioconductor.org/packages/2.4/

bioc/html/affyQCReport.html] (access date 

09.25.09).

21. Bolstad BM: affyPLM [http://www.bioconductor.

org/packages/bioc/1.6/src/contrib/html/affyPLM.

html] (access date 09.25.09).

22. Bioconductor: open source software for bioinformatics [http://www.bioconductor.org/] (access date 

09.25.09).

23. Irizarry RA, Hobbs B, Collin F, et al.: Exploration, 

normalization, and summaries of high density 

oligonucleotide array probe level data. Biostatistics 

2003, 4(2):249-64. 

24. AMP: Automated Microarray Pipeline [http://

compbio.dfci.harvard.edu/amp] (access date 

09.25.09).

25. TM4 Microarray Software Suite [http://www.tm4.

org] (access date 09.25.09).

26. Saeed AI, Sharov V, White J, et al.: TM4: a free, 

open-source system for microarray data management and analysis. Biotechniques 2003, 34(2):374-8.

27. Gene Set Enrichment Analysis [http://www.broad.

mit.edu/gsea] (access date 09.25.09).

28. TRANSFAC database of conserved transcription 

factor binding sites [http://www.gene-regulation.

com/pub/databases.html] (access date 09.25.09).