Bin et al. BMC Medical Genetics 2019, 20(Suppl 2):190
https://doi.org/10.1186/s12881-019-0926-4

RESEARCH

Open Access

An analysis of mutational signatures ofsynonymous mutations across 15 cancer
types
Yannan Bin†, Xiaojuan Wang†, Le Zhao, Pengbo Wen and Junfeng Xia*
From 2018 International Conference on Intelligent Computing (ICIC 2018) and Intelligent Computing and Biomedical Informatics (ICBI) 2018 conference
Wuhan and Shanghai, China. 15-18 August 2018, 3-4 November 2018

Abstract
Background: Synonymous mutations have been identified to play important roles in cancer development,
although they do not modify the protein sequences. However, relatively little research has specifically delineatedthe functionality of synonymous mutations in cancer.
Results: We investigated the nucleotide-based and amino acid-based features of synonymous mutations across 15
cancer types from The Cancer Genome Atlas (TCGA), and revealed novel driver candidates by identifying hotspot
mutations. Firstly, synonymous mutations were analyzed between TCGA and 1000 Genomes Project at nucleotide
and amino acid levels. We found that C:G → T:A transitions were the most frequent single-base substitutions, andleucine underwent the largest number of synonymous mutations in TCGA due to prevalent C → T transition, which
induced the transformation between optimal and non-optimal codons. Next, 97 synonymous hotspot mutations in
86 genes were nominated as candidate drivers with potential cancer risk by considering the mutational rates acrossdifferent sequence contexts. We observed that non-CpG-island GC transition sequence context was positively
selected across most of cancer types, and different sequence contexts under which hotspot mutations occur could
be significance for genetic differences and functional features. We also found that the hotspots were moreconserved than neutral mutations of hotspot-mutation-containing-genes and frequently happened at leucine. In
addition, we mapped hotspots, neutral and non-hotspot mutations of hotspot-mutation-containing-genes to theirrespective protein domains and found ion transport domain was the most frequent one, which could mediate thecell interaction and had relevant implication for tumor therapy. And the signatures of synonymous hotspots werequalitatively similar with those of harmful missense variants.
Conclusions: We illustrated the preferences of cancer associated synonymous mutations, especially hotspots, andlaid the groundwork for understanding the synonymous mutations act as drivers in cancer.
Keywords: Cancer, Synonymous mutations, Hotspot, Driver* Correspondence: jfxia@ahu.edu.cn†
Yannan Bin and Xiaojuan Wang contributed equally to this work.
Institutes of Physical Science and Information Technology, School ofComputer Science and Technology, Anhui University, Hefei 230601, Anhui,
China© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Bin et al. BMC Medical Genetics 2019, 20(Suppl 2):190

Background
Synonymous mutations, which occur in the gene-coding
regions without changing the encoded amino acids, havelong been supposed to be silent for the fitness of organisms and neutral during evolution [1]. However, thisconservative concept begins to be rebutted by two lines
of evidence: first one is the understanding of synonymous mutational effect on protein synthesis and folding;second, codon usage bias reveals that synonymous codons are under evolutionary pressure [2]. Because of thedegeneracy of the genetic codons, synonymous mutations don’t changing the encoded amino acids, butchange the DNA and RNA sequence. Nevertheless, there
are growing evidences that the significant impact of synonymous mutations on RNA splicing, stability and folding [3, 4], translation or co-translational protein folding[5–8]. Chen et al. conducted a broad survey of 21,429
disease-related single nucleotide polymorphisms (SNPs)
to indicate that synonymous SNPs and non-synonymous
SNPs showed similar probability and effect size for human diseases [9]. In addition, some studies have identified that synonymous mutations frequently act as driver
mutations in human cancers [10, 11] and can affect clinical outcome or treatment response [12–14].
As a complex genetic disease, cancer was affected by alarge number of variants. But to date, the targets ofdrugs and treatments associated with cancer are limited
on a few genes, therefore, it is difficult to achieve curesfor cancer. Next-generation sequencing technology has
enabled the systemic analyses of huge variants in large
cohorts of cancer cases, e.g., The Cancer Genome Atlas(TCGA) [15] and International Cancer Genome Consortium [16]. Cancer genomes not only contain cancercausing driver mutations, but also many additional accumulated passenger mutations without direct relation to
the tumor phenotype. It is a key step to identify driver
mutations for understanding cancer biology and evolving
targeted treatments. There were several methods focusedon predicting driver mutations, such as E-Driver [17],
MuSiC [18] and OncodriveCLUST [19]. Nevertheless,those studies mainly focused on the missense mutations
and ignored the potential functions of synonymous mutations. Although Silent Variant Analyzer [20] is a tool
for the annotation and prediction of pathogenic synonymous mutations, the small datasets for training andvalidation restrict its applicability. TCGA, using the latest sequencing and analysis methods to identify somatic
variants across thousands of tumors, is found to meetthe data needs in this work [15, 21]. Additionally, more
recent studies have indicated that different substitution
types, codon usage bias and hotspot mutational positions
in base sequence could be associated with different biological processes and cancer types [2, 22, 23]. The hotspot mentioned in this work is not the hotspot inPage 2 of 10

protein-protein interfaces [24], and is defined as the mutation that occurs significantly more frequently than the
background frequency characterized by genes, cancer
types and mutation subtypes.
In this study, we documented the full repertoire ofcancer associated synonymous mutations, especially synonymous hotspot mutations, to investigate the mutational signatures in cancer. To acquire insight into thecharacters of pathogenic and neutral synonymous mutations between cancer and benign samples, the differences of synonymous mutations at nucleotide and aminoacid levels (such as nucleotide substitutions, mutational
positions of codon and distribution of amino acids at
which synonymous mutations happened) were investigated between datasets in TCGA and the 1000 Genomes
Project (1000G) (as neutral samples) [16]. And then, we
nominated synonymous hotspot mutations as candidate
drivers based on the mutational rates across different sequence contexts and investigated the features (such asconservation, distribution of amino acids and protein
domains undergo mutations) of hotspots, neutral synonymous mutations and non-hotspots in the hotspotmutation-containing-genes (HMCGs). For the comprehensiveness of analysis, this study not only highlights thenucleotide level preferences, but also amino acids level,
and especially hotspot mutations. The observation could
add perspective to understand cancer-related synonymous mutations. The procedure is illustrated in Additional file 1: Figure S1.

Material and methods
Synonymous mutation dataset

The cancer related synonymous mutations in TCGA
were downloaded from COSMIC v79 (Catalogue ofSomatic Mutations in Cancer) [25]. We got 373,434 cancer related synonymous mutations obtained from 5749
tumor samples across 15 types of cancer: breast cancer(BRCA), central nervous system tumor (CNST), cervicaladenocarcinoma (CEAD), endometrial adenocarcinoma(ENAD), haematopoietic and lymphoid tumor (HLTU),
kidney carcinoma (KICA), large intestine adenocarcinoma (INAD), liver carcinoma (LICA), lung adenocarcinoma (LUAD), ovarian carcinoma (OVCA), prostateadenocarcinoma (PRAD), skin cancer (SKCA), stomach
adenocarcinoma (STAD), thyroid carcinoma (THCA)
and urinary tract carcinoma (UTCA).
The aim of 1000G is to discover variants with a frequency of occurrence > 1% in multiple human populations worldwide. In this study, 21,121 putatively benignsynonymous mutations were derived from 1000G (thephase 3 version 5b, 20,130,502) and this dataset will bereferred to as the neutral synonymous mutations dataset
for the comparative analysis of cancer related synonymous mutations.

Bin et al. BMC Medical Genetics 2019, 20(Suppl 2):190

Statistical analyses

The majority of statistical analyses in this work werecompleted by using R (https://www.r-project.org/), e.g.,
the distributions of synonymous variants across different
cancer types, nucleotide substitutions and amino acids.
Other statistical analyses were performed by GraphPad
Prism 5 (GraphPad Software). A p-value < 0.05 was considered statistically significant.
Hotspot mutations identification

Here we used the Hot-Driver package [26] to identify
the hotspot mutations that are positively correlated with
the number of mutations across all cancer samples forall 15 cancer types. The Hot-Driver suite assigns mutations to six subtypes: AT transition (ATts), AT transversion (ATtv), non-CpG-island GC transition (NC_GCts),
non-CpG-island GC transversion (NC_GCtv), CpGisland GC transition (C_GCts) and CpG-island GC
transversion (C_GCtv). Based on mutational significanceof each mutation subtype on amino acid position, a statistical method combines the significance level of different mutation subtypes to calculate the overall p-values(Poisson distribution and Fisher’s test). We reported hotspot mutations in amino acid position with adjusted pvalues < 0.05 corrected by false discovery rate. Lastly, inthis study, to avoid the bias of background number ofpassenger mutations, we only selected the hotspot mutations that predicted as the pathogenic mutations by
Functional Analysis through Hidden Markov Models[25].
To further investigate the mutational signatures of
hotspot mutations, neutral synonymous mutations of
HMCGs in 1000G and the non-hotspot mutation of
HMCGs in TCGA were used as control datasets. These
three datasets were applied for the further functional
analyses, for example, conservation, amino acids andprotein domains under which mutations occurred.
Conservation comparison

The conservation of nucleotide sequence for each gene
was assessed by rejected substitution (RS) score, computed by GERP++ [27]. In this work, RS scores wereextracted from the nucleotide bases that belong tohotspot mutations, neutral synonymous mutations of
HMCGs in 1000G and non-hotspot mutations of
HMCGs in TCGA, respectively. Single-tailed unpaired
t-test was used to test significantly difference between
hotspot mutations, neutral synonymous mutations andnon-hotspot mutations.
Protein domain annotation

We mapped the hotspot mutations to conserved protein
domains obtained from Pfam-A (version 29.0), a database of protein domain families [28], and manually

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curated data were used in this work. Since genes thatshared a common domain are more likely to share related functions, the important mutations in certain genes
tend to cluster in close proximity within functional domains [18, 29, 30].

Results and discussion
Synonymous mutation distribution across cancer types

From TCGA, we obtained 373,434 synonymous mutationsof 5749 tumor samples from 15 types of cancer. As seen in
the upper panel of Fig. 1, with regard to synonymous mutation proportion (blue bar), SKCA is the largest and HLTU
the smallest. For the proportion of tumor samples (yellowbar), BRCA is the most and UTCA the fewest. Moreover,
the average numbers of variants per sample across 15 cancer types are different with each other (the lower panel ofFig. 1), and exhibit many more or many less synonymous
mutations per sample than the average number of 15 cancer types. Notable among these outliers are SKCA, ENAD,
STAD and LUAD, which contain more than 100 synonymous mutations per sample. These larger numbers ofmutations reflect the participation of potential factors(ultraviolet light, hyperestrogenism, Helicobacter pylori infection and cigarette smoke, respectively) in the pathogenesis of these cancer types [22, 31–33]. Due to ultraviolet
light and deamination processes, the majority of SKCA mutations are C:G → T:A transitions [22]. Additionally, it has
been reported that the mutations occur at methylated CpG
dinucleotide, majority of which are C:G → T:A transitions,would significantly cause human genetic diseases [34].
Studies have shown that nucleotide substitutions, including
synonymous mutations, could be related to carcinogen exposures and DNA repair processes [35–37].
Synonymous mutational nucleotide substitutions

Distribution of 12 possible mutational nucleotide substitution patterns of synonymous mutations across the 15
cancer types was shown in Fig. 2. The greatest frequently
occurring is C → T transitional substitution (with average proportions of 44.01% across 15 cancer types), whichis possible to associate with the aberrant DNA methylation [38–40]. SKCA contains the largest proportion of
C → T transitions than other cancer types, owing to thesignatures of ultraviolet light exposure and deamination
processes [22]. Among transversions, the C → A substitution is the most frequent one (6.18%). As a result of
signature smoke exposure, LUAD has an increased C →A transversions [33]. At 5-methylcytosine in CpG dinucleotides, C:G → T:A transitions and C → A transversion
are associated with the most common epigenetic modifications of DNA [34, 35]. Moreover, due to the overabundance of synonymous sites involved in CpG
dinucleotides, the mutation rate in exons is 30~60%
higher than that in the non-coding regions [41]. It isBin et al. BMC Medical Genetics 2019, 20(Suppl 2):190

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Fig. 1 Distribution of synonymous variants, tumor samples across different cancer types. Upper panel: blue and yellow bars represent the
proportions of synonymous variants and corresponding tumor samples, respectively. Lower panel: Green bar represents the average numbers ofvariants per sample. The x-axis represents the cancer types

found that the percentage of transitions between C and
T preceded that between A and G, which is known to bea general property of DNA sequence change and evolution [42]. Moreover, the most frequently substitutedbases are C and G, and the most frequently mutated tobases are T and A. Cancer associated synonymous mutations have the tendency to become A/T-rich. Previous
study has proposed that special A/T-rich sequence binding protein acts as a global chromatin organizer formetastatic activity by controlling gene expression [43].

Comparisons between TCGA and 1000G datasets

At nucleotide level, owing to the degeneracy of geneticcodons, the nucleotide substitutions of synonymous codons occur at the third codon position (pos3), except
some L and R codons (only T↔C transition and A↔C
transversion) vary at the first codon position (pos1)(Additional file 1: Table S1). Similar with the distributions of mutational types in TCGA dataset, the mostfrequency mutational nucleotide changes in 1000G dataset are also C → T and G → A transitions (Fig. 3a).

Fig. 2 Distribution of 12 possible mutation patterns of synonymous mutations in different cancer types. The x-axis represents the cancer types
and the y-axis represents the 12 mutation patterns. Each bar represents the percentage of mutational types. ‘All’ in this figure represents theaverage proportions of mutational types across 15 cancer types

Bin et al. BMC Medical Genetics 2019, 20(Suppl 2):190

However, there are some differences for proportions ofmutational nucleotide changes between TCGA and
1000G datasets. Firstly, we investigated the differencesby performing a one-sample t-test, and the average proportion of each substitution in 1000G dataset was usedas hypothetical value. A p-value < 0.05 is considered tobe significant. The distributions of single-base mutational nucleotide changes in TCGA dataset are significant different from those in 1000G datasets (p-value <0.001) except T → G transversion at pos3 (p-value =0.8066). The non-significant different may be due to theless effect of T → G transversion on the transformation
between efficient codon and lower efficient codon, which
can affect protein production. Secondly, based on thesignificant differences, the single-base substitutions in
TCGA dataset are apt to G:C → A:T transitions, in contrast, these substitutions in 1000G are T:A → C:G transitions. Synonymous mutations in TCGA are prone tobecome A/T-rich. In a previous study, it was reported
that A/T-rich sequence could affect gene expression andbe more important for cancer development [43].
Besides nucleotide level, the analysis of synonymous
mutations was also performed at the amino acid level.
Except Met (M) and Trp (W), all amino acids are
encoded by two or more codons. The correlation between the percentages of mutation and the codon numbers of amino acids in TCGA (r2 = 0.67) is stronger thanthat in 1000G (r2 = 0.54) (Additional file 1: Figure S2).
DNA sequences of diverse organisms have shown thatsynonymous codons of amino acids are used with unequal frequency [44]. The codon usage bias is related tovarious biological processes, such as gene expression
level, protein structure, mutation frequency and GC

Page 5 of 10composition [45]. However, it is probable that the usesof synonymous codons in TCGA tend to be at equal frequencies, and are less affected by codon usage bias thanthose in 1000G. Furthermore, the efficient codon replacing with a less efficient one could affect protein synthesis. Because the abundance of cognate tRNAs
involved in preferred codons are available within the cell,
the use of efficient codons could increase the gene expression [6]. Therefore, it is proposed that the cancer related synonymous mutations prefer to influence thegene expression and are more pathogenic than neutral
ones in TCGA.
In TCGA and 1000G datasets, the mutational proportions of 18 amino acids with two, three, four or six-folddegenerate codons are different with each other (Fig.
3b). In 1000G, Ala is the most frequently mutated aminoacid due to G → A transition. And this transition is associated with two of four Ala’s codons, but independent of
the transformation between optimal and non-optimal
codons [10]. In TCGA, synonymous mutations are dominated by Leu (L) due to prevalent C:G → T:A transitions. It is similar with the character of pathogenic
missense mutations, the substitutions under L are also
the most frequent [46]. It is notable that among thethree amino acids with six synonymous codons, Arg (R)shows the fewest number of mutations not only in
TCGA but also in 1000G, which may be associated withthe synonymous codon usage bias (R has only one optimal codon while Ser and L both have two optimal codons). In summary, it is possible that synonymous
mutations under L may have more important effect on
gene expression and protein production than the mutations of other amino acids during biological processes.

Fig. 3 Mutational signatures of synonymous mutations in TCGA and 1000G. a Distribution of synonymous mutations for the 12 mutation patterns
at pos1 and pos3 of codons in TCGA and 1000G. b Proportions of 18 amino acids with two, three, four or six-fold degenerate codons underwhich synonymous mutations occurred. Orange bar represents the proportion of mutations in TCGA and grey bar represents that in 1000G. 2, 3,
4, and 6 codon represent the numbers of synonymous codons, respectively

Bin et al. BMC Medical Genetics 2019, 20(Suppl 2):190

Synonymous hotspot mutations for cancer

In this work, the hotspot mutation is defined as the mutation that occurs significantly more frequently than the
background frequency characterized by genes, cancer
types and mutation subtypes. In this study, we identified
97 hotspot mutations in 86 HMCGs associated with 14
cancer types (Additional file 1: Table S2). There is none
hotspot mutation in HLTU due to the lowest mutation
frequency (Fig. 1). To investigate the differences across
cancers, we compared the number of synonymous hotspot mutations in different cancer types. From the distribution of hotspot mutations across cancers (Additional
file 1: Figure S3A), it was found that the number of hotspot mutations varies largely from one cancer to another, many more or many fewer mutations than
average. For example, INAD has the largest number of
hotspots (39 hotspots), but ENAD and STAD only have
13 hotspots with the smallest number, and HLTU has
none. The enrichment of hotspot mutations reflects thegenetic heterogeneity of INAD, which has been discussed in previous research [47]. And heterogeneity may
affect the progression of INAD from the early to the advanced stages, drive phenotypic variations and present asignificant challenge to personalized medicine. By contrast, HLTU has none hotspot and THCA has only one
hotspot mutation in ILF3 (N192). In addition, to estimate the important synonymous mutations for pancancer, the distribution of hotspot mutations across different genes was analyzed (Additional file 1: Figure S3B).
T125 in TP53 is the most prevalently occurred mutationin nine different cancer types. It has been identified tobe pathogenic for its detrimental role in TP53 splicing[48]. It is also found that most hotspots are unique for
only one cancer type. The common and diverse

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mutational signatures of hotspots across different cancer
types may promote the understanding of the positive selection in the human genome, and facilitate the cancer
target therapy [26].
The mutational characters of 97 hotspot mutations
across 14 cancer types were also investigated under different sequence contexts, including ATts, ATtv, C_GCts,
C_GCtv, NC_GCts and NC_GCtv six subtypes. From
the distribution of hotspot mutations under different
subtypes of sequence contexts (Fig. 4a), the number of
hotspot mutations under NC_GCts sequence context(consists of C:G → T:A transitions) is the largest one
compared with the other types, and that under ATtv
(consists of A:T → C:G and A↔T transversions) is theleast one. This phenomenon is due to the most frequency of C → T and G → A transitions and least frequency of A:T → C:G and A↔T transversions (Fig. 3a),which correspond to the NC_GCts and ATtv sequence
contexts, respectively. As seen in Fig. 4b, the most widespread sequence context undergoes hotspots is NC_
GCts sequence context, which presents in 14 cancer
types. And it is also the most prevalent sequence context in nine cancer types (CEAD, CNST, ENAD,
INAD, KICA, PRAD, SKCA, STAD and THCA).
Moreover, in CEAD and THCA, the hotspots are only
enriched in NC_GCts. However, in LUAD, the hotspot mutations are enriched in NC_GCtv sequence
context. In LICA, OVCA and UTCA, the sequence
contexts under which hotspot mutations occur are
equal. In summary, NC_GCts sequence context is
positively selected across most cancer types, and different sequence contexts on which hotspots happen
are significant for considering their genetic differences
and functional features.

Fig. 4 Mutational signatures of synonymous hotspot mutation in 15 cancer typers. a Histogram of the number of hotspot mutations under
different sequence contexts. b Bar diagram where the value 100% represents the total number of mutations under different sequence contexts
distributed in each cancer type. ATts, AT transition (coral bar in a and b); ATtv, AT transversion (blue bar in a and b); C_GCts, CpG-island GC
transition (yellow bar in a and b); C_GCtv, CpG-island GC transversion (rose bar in a and b); NC_GCts, non-CpG-island GC transition (green bar ina and b); NC_GCtv, non-CpG-island GC transversion (grey bar in a and b)

Bin et al. BMC Medical Genetics 2019, 20(Suppl 2):190

Conservation comparison

It is customary for mutations with important functionaland evolutionary implications located in highly conservative region and protein domains. To evaluate the conservation of hotspot mutations, neutral synonymous
mutations of 86 HMCGs in 1000G (235 neutral synonymous mutations were shown in Additional file 2:
Table S3) and non-hotspot mutations of HMCGs in
TCGA (1358 non-hotspot mutations were shown in
Additional file 3: Table S4), we computed their RS
scores to estimate the evolutionary constraints acrossdifferent genome sites. As shown in Fig. 5, the RS scoresof hotspot mutations are significantly higher than thoseof neutral synonymous mutations in 1000G (p-value <2e-16). In contrast, there is no significantly different between hotspot and non-hotspot mutations (p-value =0.93), it is possible that the non-hotspot in TCGA might
influence cancer processes, but their harmfulness is lessthan that of hotspots. The result suggests that the sites
which hotspots occur on are more conservative thanthose of neutral synonymous mutations. As potential
driver mutations, these hotspots may be more importantfor cancer development.
Amino acids analysis

To further investigate the difference among hotspot mutations, neutral synonymous mutations and non-hotspot
mutations of HMCGs, the distributions of amino acids
under which the mutations occurred were investigated(Fig. 6). There is none synonymous mutation under Met
and Trp due to the lack of synonymous codons. Clearly,

Fig. 5 Comparison of RS scores between the datasets of hotspots
and neutral synonymous mutations, non-hotspots. Hotspot
represents hotspot mutations; 1000G represents neutral synonymous
mutations of HMCGs in 1000G dataset; Non-hotspot represents nonhotspot mutations of HMCGs in TCGA dataset. The RS score of
hotspot mutation dataset is significant higher than that neutral
synonymous mutation dataset (p-value <2e-16). But there is no
significant different between hotspot and non-hotspot datasets (pvalue = 0.93) for RS score

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the distributions of amino acids are different for thethree synonymous mutation datasets. In hotspot mutation dataset, L and Phe (12.37 and 11.34%, respectively)
are the most mutated amino acids due to prevalent C →T transition, which is involved the transformation between optimal and non-optimal codons. As an importantamino acid of leucine-rich repeats, L is associated with aversatile structural framework for the formation ofprotein-protein interactions [49]. However, for neutral
mutation dataset, the most frequent substitutions areunder Ala (14.44%), which is largely tolerated outsidefunctional site as the smallest residues can be fitted into
structures easily. Among non-hotspot mutations, themost frequent substitutions are under L (14.51%). The
distributions of the hotspot and neutral synonymous
mutations are quantitatively similar to those of missense
mutations published previously [46].
Domain characterization

We also investigated the domain compositions of theproteins, under which hotspot mutations, neutral synonymous mutations of HMCGs in 1000G and nonhotspot mutations of HMCGs in TCGA occurred.
Thirty-five different Pfam domains were detected in theproteins under which the hotspot mutations occurred,
whereas 29 and 91 protein domains under which neutral
synonymous and non-hotspot mutations happened, respectively. It is found that nine domain types are common to hotspot, neutral synonymous and non-hotspot
mutations (Fig. 7a), including 7 transmembrane receptor
domain (7tm_1, PF00001), cytidine and deoxycytidylate
deaminase zinc-binding region domain (dCMP_cyt_
deam_1 domain, PF00383), nucleotide-binding domain(cobW domain, PF02492), Hsp70 protein domain(HSP70 domain, PF00012), ion transport protein domain(Ion_trans domain, PF00520), immunoglobulin I-set domain (I-set domain, PF07679), laminin N-terminal domain (Laminin_N domain, PF00055), membrane-boundO-acyltransferase family domain (MBOAT domain,
PF03062) and transmembrane protein 67 domain(Meckelin domain, PF09773). The functions of these domains are different from each other. As rhodopsin-likereceptor, 7tm_1 domain comprises the group of G
protein-coupled receptor and encompasses a wide rangeof functions such as various autocrine, paracrine, andendocrine processes. dCMP_cyt_deam_1 domain is thecytidine and deoxycytidylate deaminase zinc-binding
region, which is associated with the catalytic activity ofcytidine deaminase. cobW domain contains a
nucleotide-binding loop and a histidine-rich region thatplays an important role in metal binding. HSP70 domain
is strongly upregulated by heat stress and toxic chemicals, particularly heavy metals. Ion_trans domain contains sodium, potassium and calcium ion channels, andBin et al. BMC Medical Genetics 2019, 20(Suppl 2):190

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Fig. 6 Radar diagrams showing the distributions of amino acid at which mutations occurred. a Hotspot mutations; b Neutral synonymous
mutations of HMCGs in 1000G dataset; c Non-hotspot mutations of HMCGs in TCGA dataset

a loop flanked by two helices determines ion selectivity.
I-set domain is not only frequent in cell adhesion protein, but also appears in many other types of proteins[50]. Laminin_N domain is extracellular matrix moleculeand MBOAT domain contains various acyltransferase
enzymes. Then we analyzed the distributions of hotspot,
neutral synonymous and non-hotspot mutations under
the nine common protein domains. Among these nine
domains, the Ion_trans domain is the most frequent one(28 items), but the proportion of hotspots is less thanthose of neutral synonymous mutations and nonhotspots (Fig. 7b). As an important target for tumor
therapy [51], Ion_trans domain is critical for cell-to-cellcommunication and regulates multiple biological processes. However, the analysis of synonymous mutation

distribution in Ion_trans domain is opposite with theprevious analysis of missense mutations [46], which may
be due to the different pathogenic mechanisms of synonymous and non-synonymous mutations. 7tm_1 domain is the highest proportion of hotspot mutations andconsists of the group of G protein-coupled receptor,
which could promote cancer metastasis [52].

Conclusions
In this study, we not only investigated the distribution
and mutational nucleotide changes of synonymous mutations across 15 cancer types, but also made the comparison of synonymous mutational signatures between
TCGA and 1000G at nucleotide and amino acid levels.
Meanwhile, we nominated 97 hotspot mutations in 86

Fig. 7 Analysis of mutation distribution in protein domains. a Venn diagram showing the number of domain types for the multiple intersections
among hotspot mutations, neutral synonymous mutations and non-hotspot mutations. b Bar diagram where the value 100% represents the total
number of mutations in the nine protein domains common to hotspot, neutral synonymous and non-hotspot mutations. Blue, salmon and green
sections on the bar represent which proportions of the total mutations are hotspot, neutral synonymous and non-hotspot mutations,
respectively. PF00001, 7tm_1 domain; PF00383, dCMP_cyt_deam_1 domain; PF02492, cobW domain; PF00012, HSP70 domain; PF00520, Ion_transdomain; PF07679, I-set domain; PF00055, Laminin_N domain; PF03062, MBOAT domain; PF09773, Meckelin domain

Bin et al. BMC Medical Genetics 2019, 20(Suppl 2):190

genes in TCGA as potential drivers by considering theirmutational rates across different mutational subtypes.
And the common and diverse mutational signatures
among hotspot mutations, neutral synonymous mutations of HCMGs in 1000G and non-hotspot mutationsof HCMGs in TCGA were also detected. The result indicated that there were significant differences in conservation, amino acids and domain characterization between
hotspots and neutral synonymous mutations. But there
are some limitations in this study. Firstly, it needs moreexperimental work to investigate the effects of these hotspots on protein folding, RNA splicing, stability andfolding, and whether they are drivers in cancers, and therelationship with cancer clinical outcome or treatment
response. Secondly, the consistent patterns and specificity of hotspots in individual cancer are important andshould be explored. But in this study, we just performed
a pan-cancer analyzed of the hotspots. We will attack
these problems in our further work. In summary, thepresent study would help to better understand the function of synonymous mutations in different cancer types
and depicting their roles in carcinogenesis.

Supplementary information
Supplementary information accompanies this paper at https://doi.org/10.
1186/s12881-019-0926-4.
Additional file 1: Figure S1. Illustration of analysis procedure of cancer
associated synonymous mutations. Figure S2. Correlation between
percentages of synonymous mutations and codon numbers of aminoacids in TCGA and 1000G. Figure S3. Distribution of synonymous
hotspot mutations across cancer types and genes. Table S1.
Synonymous codons of amino acids with optimal and non-optimal codons for human genome. Table S2. Hotspot synonymous mutations
across different cancer types in TCGA dataset.
Additional file 2: Table S3. List of neutral synonymous mutations of
HMCGs in 1000G dataset.
Additional file 3: Table S4. List of non-hotspot synonymous mutationsof HMCGs in TCAGA dataset.
Abbreviations
1000G: 1000 Genomes Project; ATts: AT transition; ATtv: AT transversion;
BRCA: Breast cancer; C_GCts: CpG-island GC transition; C_GCtv: CpG-island
GC transversion; CEAD: Cervical adenocarcinoma; CNST: Central nervous
system tumor; ENAD: Endometrial adenocarcinoma; HLTU: Haematopoietic
and lymphoid tumor; HMCGs: Hotspot-mutation-containing-genes;
INAD: Large intestine adenocarcinoma; KICA: Kidney carcinoma; LICA: Livercarcinoma; LUAD: Lung adenocarcinoma; NC_GCts: Non-CpG-island GC
transition; NC_GCtv: Non-CpG-island GC transversion; OVAD: Ovariancarcinoma; pos1: The first codon position; pos3: The third codon position;
PRAD: Prostate adenocarcinoma; RS: Rejected substitution; SKCA: Skin cancer;
SNPs: Single nucleotide polymorphisms; STAD: Stomach adenocarcinoma;
TCGA: The Cancer Genome Atlas; THCA: Thyroid carcinoma; UTCA: Urinarytract
Acknowledgements
The authors thank all members of our laboratory for their valuable
discussions.
About this supplement
This article has been published as part of BMC Medical Genetics Volume 20
Supplement 2, 2019: Proceedings of the 2018 International Conference on

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Intelligent Computing (ICIC 2018) and Intelligent Computing and Biomedical
Informatics (ICBI) 2018 conference: medical genetics. The full contents of the
supplement are available online at https://bmcmedgenet.biomedcentral.
com/articles/supplements/volume-20-supplement-2.
Authors’ contributions
YB performed the analysis and drafted the manuscript. XW, LZ and PW
helped perform the analysis. JX designed the study, performed the analysis,
and drafted the manuscript. All authors read and approved the final
manuscript.
Funding
Publication costs are funded by the grants from the National Natural Science
Foundation of China (61672037 and 21601001), the Anhui Provincial
Outstanding Young Talent Support Plan (gxyqZD2017005), the Young
Wanjiang Scholar Program of Anhui Province, the China Postdoctoral Science
Foundation Grant (2018 M630699) and the Anhui Provincial Postdoctoral
Science Foundation Grant (2017B325).
Availability of data and materials
The datasets supporting the conclusions of this article are included within
the article and its additional files.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Published: 9 December 2019
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