Whole transcriptome sequencing identifies tumor-specific mutations in human oral squamous cell carcinoma
© Zhang et al.; licensee BioMed Central Ltd. 2013
Received: 18 April 2013
Accepted: 26 August 2013
Published: 4 September 2013
The accumulation of somatic mutations in genes and molecular pathways is a major factor in the evolution of oral squamous cell carcinoma (OSCC), which sparks studies to identify somatic mutations with clinical potentials. Recently, massively parallel sequencing technique has started to revolutionize biomedical studies, due to the rapid increase in its throughput and drop in cost. Hence sequencing of whole transcriptome (RNA-Seq) becomes a superior approach in cancer studies, which enables the detection of somatic mutations and accurate measurement of gene expression simultaneously.
We used RNA-Seq data from tumor and matched normal samples to investigate somatic mutation spectrum in OSCC.
By applying a sophisticated bioinformatic pipeline, we interrogated two tumor samples and their matched normal tissues and identified 70,472 tumor somatic mutations in protein-coding regions. We further identified 515 significantly mutated genes (SMGs) and 156 tumor-specific disruptive genes (TDGs), with six genes in both sets, including ANKRA2, GTF2H5, STOML1, NUP37, PPP1R26, and TAF1L. Pathway analysis suggested that SMGs were enriched in cell adhesion pathways, which are frequently indicated in tumor development. We also found that SMGs tend to be differentially expressed between tumors and normal tissues, implying a regulatory role of accumulation of genetic aberrations in these genes.
Our finding of known tumor genes proves of the utility of RNA-Seq in mutation screening, and functional analysis of genes detected here would help understand the molecular mechanism of OSCC.
KeywordsRNA-Seq Oral squamous cell carcinoma Somatic mutations Significantly mutated genes Differential expression Disruptive genes
Squamous cell carcinoma is one of the most commonly observed cancers worldwide , which is often diagnosed in the oropharynx and oral cavity. It is highly invasive and metastatic at the advanced stage, and presents a substantial threat to human health . Evidence from various molecular and genetic studies suggests an association between squamous cell carcinoma initiation and development and the accumulation of genetic alterations at both the DNA and RNA levels . Genomic alterations such as point mutations and copy number variations, epigenetic changes such as methylation and histone modifications, as well as gene expression changes have been previously revealed in oral squamous cell carcinoma (OSCC), which could facilitate biomarker development and make clinical decisions . Among them, mutations only occurring in tumor tissues, often referred as somatic mutations, are given particular attention. It is widely accepted that tumors develop through the accumulation of somatic mutations in specific genes, depending on their types . Various studies have found a higher than expected mutation frequency of candidate cancer genes, and that the tumor properties could be influenced by different combinations of mutations [5–8]. However, the high cost of Sanger sequencing prevents global profiling of somatic mutations in OSCC, and further understanding in mechanisms and clinical treatments.
Remarkable advances in sequencing technology over the last several years make possible to identify genetic alterations in a genome-wide scale. RNA-Seq is a newly developed deep sequencing technology, which is extensively applied in transcriptomic profiling due to its affordable cost. Compared with long standing methods such as microarray, RNA-Seq gives a far more precise measurement of transcript expression levels and a far more sophisticated characterization of transcript isoforms [9, 10]. Therefore it has been successfully applied to identify differentially expressed genes  and to characterize allele-specific expression patterns [12, 13]. Moreover, it is also an efficient and cost-effective way to study genomic alterations, such as somatic mutations in transcribed regions [14–17] or gene fusions [12–14]. Herein, we conducted a genome-wide study to investigate the somatic mutation spectrum in OSCC by interrogating RNA-Seq data from two tumor samples and their matched normal samples. We developed a sophisticated pipeline to identify somatic mutations, and then identified significantly mutated genes (SMG) and tumor-specific disruptive genes (TDG). By comparing with gene expression pattern, we also found a correlation between differentially expressed genes and SMGs. These findings demonstrate the ability of RNA-Seq to characterize global pattern of somatic mutations and suggest the potential mechanism on how somatic mutations could affect tumor development.
Deep sequencing data
Whole transcriptome short reads of three paired tumor and normal tissues from patients with oral squamous cell carcinoma (OSCC) were downloaded from European Nucleotide Archive (ENA, http://www.ebi.ac.uk/ena) with the accession number SRP002009. As described in the original paper, the study was conducted according to the Declaration of Helsinki, and was approved by the Institutional Review Board of the Mayo Clinic . Written informed consent for the collection of samples and subsequent analysis was available for all patients. 50-bp sequence reads were generated by using Applied Biosystems SOLiD System (V3 chemistry), following the manufacturer’s instructions. More details can be found in the original study .
We first excluded low quality reads in which one or a few bases have Q-score lower than 20. The initial quality check suggests that all reads from one patient have an average quality score < 20 and were excluded from further analysis. Then qualified short reads were aligned to 18,462 transcripts of UCSC consensus coding sequences (CCDS) in current human genome assembly (hg19, http://genome.ucsc.edu/). The alignment was carried out using BFAST , using options for color-space reads.
Variant calling and identification of somatic variants
Filter 1 Variants were removed if they are supported by less than two reads or mistakenly called with a probability greater than 0.01. This was done by requiring a value ≥ 20 for the ‘QUAL’ column in vcf files generated by SAMtools.
Filter 2 Somatic variants were called by comparing matched normal and tumor tissues. We first excluded variants located in genomic regions of poor quality, which were defined as regions with read coverage in only one of a sample pair, probably due to randomness in sequencing process.
Filter 3 Variants that are found in both of the matched normal and tumor samples were discarded. Also, variants found in dbSNP build 132  were also excluded.
Identification of significantly mutated genes
To find significantly mutated genes, we adopted approaches implemented in MuSiC analysis tool suites . Briefly, we counted the number of bases with at least three read depth in six categories, including A bases, T bases, C bases in CpG, G bases in CpG, C bases not in CpG, and G bases not in CpG. Then the discovered mutations were categorized as AT transitions, AT transversions, CpG transitions, CpG transversions, CG (non-CpG) transitions, CG transversions, and indels. Next, we calculated the background mutation rate (BMR) for each mutation category, which was done by dividing the total count of such category by the total number of available bases within such category. For indels, BMR is calculated as the total number of bases covered by indels divided by the total number of high quality bases. Since we used RNA-Seq data and the read depth depends on the expression level, tests that consider mutation coverage to identify significantly mutated genes may not be appropriate due to confounding factors such as allele-specific expression. Instead, we calculated the possibility of finding more mutations than the observation for each mutation category and combined then by the simple Fisher’s combined P-value test (FCPT) to generate a statistic , and the final P-value can be calculated according to a χ 2 distribution with two times the number of categories as the degrees of freedom.
Functional and pathway enrichment analysis
To identify enriched gene functions, we extracted functional annotations from gene ontology (GO)  using bioconductor (http://www.bioconductor.org) package “org.Hs.eg.db”. Then we used “topGO” package in R software  to perform hypergeometric tests. Kyoto Encyclopedia of Genes and Genomes pathway information [25, 26] was extracted using bioconductor package “KEGG.db”, and hypergeometric tests were used to identify enriched pathways.
Differential gene expression
To estimate gene expression abundance, we counted the number of reads that were aligned to each gene transcript. We then used bioconductor package “DESeq”  to identify differentially expressed genes. DESeq assumes a negative binomial distribution to estimate variance and mean for each group, and performs statistical test based on it. Multiple-testing was corrected by Benjamini and Hochberg procedure .
Read alignments and mutation spectrum
Summary statistics of whole transcriptome sequencing data used in this study
HQ reads (%)a
68.9 M (30.0)
47.0 M (18.3)
53.1 M (23.4)
17.8 M (9.0)
HQ mapped (%)b
6.4 M (9.3)
4.4 M (9.3)
9.1 M (17.1)
3.4 M (18.8)
Summary statistics of variants or genes after each bioinformatic filter
After filter 1
After filter 2
After filter 3
Significantly mutated genes
One distinguishable feature of tumor driver genes is the unexpectedly high somatic mutation rate, which leads to rapid accumulation of genetic aberrations and thus radical modification or disruption of gene functions. In hopes of finding tumor driver genes, we adopted an approach developed elsewhere  to identify significantly mutated genes (SMGs). Since the number of sequencing reads is highly variable among samples, we applied the pipeline to each sample separately. Significantly mutated genes in tumors were defined as genes that have ≥ 100 base pairs covered by least three reads and have a FCPT P-value < 0.01 in the two tumor samples but not in neither of the normal samples. In total, 515 significantly mutated genes were identified among 11,065 genes expressed in all samples (Additional file 1), and their average mutation rate (0.0018 per base) is significantly higher than that of other genes (0.0008 per base, P = 2.89 × 10-15).
Genes with disruptive mutations
Genes with disruptive mutations in tumor samples are also of great interest, as they embrace the potential to radically change gene functions. To identify disruptive mutations, we annotated 70,472 somatic mutations identified above and searched for nonsynonymous mutations and indels. In total, 27,310 disruptive mutations were found in all samples (Table 2). Since our purpose was to identify tumor-specific disruptions, we only focused on tumor-specific disruptive genes (TDGs) which contain disruptive mutations in the two tumor samples but not in any normal samples. As a result, 156 genes were found as TDGs, of which six genes were also identified as SMGs.
Gene ontology and pathway analysis
Enriched GO and pathway categories
voltage-gated cation channel activity
intrinsic to membrane
integral to membrane
integral to plasma membrane
intrinsic to plasma membrane
Neuroactive ligand-receptor interaction
Cell adhesion molecules (CAMs)
Complement and coagulation cascades
Aldosterone-regulated sodium reabsorption
Somatic mutations and gene expression
To understand the potential consequence of SMGs and TDGs, especially in gene expression, we estimated gene expression abundance as the number of high-quality reads mapped to each gene, and used “DESeq” to identify genes with significantly differential expression between tumors and normal samples. In total, we found 41 differentially expressed genes (DEGs) with an adjusted P < 0.05. Among them, five genes are SMGs, and one is TDG. The number of shared genes between DEGs and SMGs was highly unexpected (P = 0.002, hypergeometric test), while no such pattern was observed for TDGs (P = 0.07), indicating that SMGs may function through transcriptional regulation.
Functional consequence of candidate genes
It is well accepted that the accumulation of multiple genetic events in different genes and molecular pathways is the main cause of OSCC evolution [3, 32]. Previous studies have identified various types of genetic aberrations in OSCCs and oral dysplasias, the precursors of OSCCs, including somatic mutations in the D-loop of mtDNA sequence  and in exons nine and 20 of Phosphatidylinositol 3-kinase gene (PI3K) , common deletions on chromosome 3p such as the 3p14 locus that harbors FHIT (Fragile Histidine Triad) [35–37], as well as gene copy number increases in certain oncogenes such as EGFR and CCND1[31, 38, 39]. Evidence from microarray studies has also revealed differentially expressed genes in oral cavity tumors [40–44], suggesting multiple dimensions of genetic aberrations contributing to OSCC development. Here, we presented a whole transcriptome analysis to identify exonic somatic mutations in two OSCC samples. To overcome the small sample size, we have developed a stringent bioinformatic pipeline with multiple filters to reduce false positives. In total, we have identified 515 SMGs which were significantly mutated, and 156 TDGs with disruptive mutations in both tumor samples. We also measured gene expression and found SMGs were enriched in differentially expressed genes, implying that the accumulation of genetic aberrations may regulate corresponding gene expression and further affect tumor evolution.
Five of six genes identified in both SMGs and TDGs are known driver genes in COSMIC database, and the remaining gene GTF2H5 stimulates the ATPase activity of ERCC3, a nucleotide excision repair gene, to trigger DNA opening during DNA repair. Since genes involved in DNA repair functions are commonly associated with oral cancer [45–47], it is very likely that GTF2H5 is also related to carcinogenesis. Collectively, these observations indicate our bioinformatic pipeline has substantial power to identify tumor-related genes.
Of 515 SMGs, several membrane-related GO terms were enriched, including intrinsic to membrane, integral to membrane, intrinsic to plasma membrane and integral to plasma membrane. Interestingly, the original study found that the term of intrinsic to plasma membrane was enriched in mis-regulated genes in tumor samples , suggesting that disruption or mis-expression of genes related to plasma membrane may be involved in tumor development. We also found six tumor related genes, TUSC2, TP53I3, TSSC4, RAB23, RAB39A, and ERG, which function as either tumor suppressor genes or oncogenes. Additionally, we identified FGF2, a fibroblast growth factor in the FGF signaling pathway, which was reported to be important in OSCCs . Another pathway potentially associated with OSCC is the cell adhesion molecules (CAMs), which is also enriched in SMGs. CAMs are essential component to maintain the structure of stratified squamous epithelium and a critical mediator of tumor progression in OSCC [48, 49], and mis-expression or dysfunction of CAMs are shown to contribute to malignant tumors . The excess of CAMs in SMGs further suggests the critical role of the CAM pathway in OSCCs. Again, cell adhesion was found to be enriched in mis-regulated genes in the original study , confirming that tumor development may involve both mis-expression and dysfunction of CAMs.
Tumor driver genes are normally considered as with high somatic mutation rate, thus 156 TDGs identified without information from mutation rate are intriguing. Besides six genes also identified as SMGs, we also found that 57 (37%) TDGs significantly mutated in one tumor sample but not in the other tumor sample. Considering that only two patients were used in this study and a large proportion of TDGs were significantly mutated in only one sample, it is possible that some TDGs are in fact SMGs, but failed to be identified here due to the small sample size. We thus suggest that screening TDGs may be an alternative way to identify candidate cancer driver genes when sample size is limited.
Although a few pioneer studies demonstrated that RNA-Seq is suitable for identifying somatic mutations [14–17, 50, 51], there is a concern that RNA-Seq is prone to error  and may generate a high false discovery rate due to incorrect alignment of reads, sequencing errors or extremely high or low read coverage. To minimize the false positive rate, we have applied a series of stringent filters. First, we only used reads in which each base has a Q-score ≥ 20, which reduces the influence of sequencing errors. Next we filtered out read alignments with a mapping quality lower than 30, which avoids reads mapped to multiple locations alignments with low similarity. Then we required each qualified variant must have a read depth between three and 500. Our strategy to identify somatic mutations also automatically removed the effect of systematically incorrect alignments which present in both tumor and matched samples. Hence we believe that somatic mutations identified in this study provide a substantial list of candidates for biomarker development. However, it should also be noted that we only focused on exonic regions captured by RNA-Seq, somatic mutations in regulatory regions will not be identified here, therefore out list also presents a portion of somatic mutations in OSCCs.
In this study, we have developed a stringent bioinformatic pipeline to identify somatic mutations in tumors and applied it to two OSCC paired samples. By using multiple filters and calling candidate disruptive genes through two different ways, we minimized both false positives and false negatives due to the small sample size. The resulting candidate genes with both statistical and biological significance would help understand the molecular mechanism of OSCC and develop clinical biomarkers and drug targets.
We thank Dr. Yinglei Lai , Dr. Luiz De Marco and Dr. Yuji Miyazaki for helpful comments on this manuscript. This work was supported by the Introduction of Innovative R&D Team Program of Guangdong Province (China, NO. 2009010029).
- Parkin DM, Bray F, Ferlay J, Pisani P: Global cancer statistics, 2002. CA Cancer J Clin. 2005, 55 (2): 74-108. 10.3322/canjclin.55.2.74.View ArticlePubMedGoogle Scholar
- Jemal A, Siegel R, Ward E, Hao Y, Xu J, Thun MJ: Cancer statistics, 2009. CA Cancer J Clin. 2009, 59 (4): 225-249. 10.3322/caac.20006.View ArticlePubMedGoogle Scholar
- Gibb EA, Enfield KS, Tsui IF, Chari R, Lam S, Alvarez CE, Lam WL: Deciphering squamous cell carcinoma using multidimensional genomic approaches. J Skin Cancer. 2011, 2011: 541405.View ArticlePubMedGoogle Scholar
- Vogelstein B, Kinzler KW: Cancer genes and the pathways they control. Nat Med. 2004, 10 (8): 789-799. 10.1038/nm1087.View ArticlePubMedGoogle Scholar
- Greenman C, Stephens P, Smith R, Dalgliesh GL, Hunter C, Bignell G, Davies H, Teague J, Butler A, Stevens C, et al: Patterns of somatic mutation in human cancer genomes. Nature. 2007, 446 (7132): 153-158. 10.1038/nature05610.View ArticlePubMedPubMed CentralGoogle Scholar
- Jones S, Zhang X, Parsons DW, Lin JC, Leary RJ, Angenendt P, Mankoo P, Carter H, Kamiyama H, Jimeno A, et al: Core signaling pathways in human pancreatic cancers revealed by global genomic analyses. Science. 2008, 321 (5897): 1801-1806. 10.1126/science.1164368.View ArticlePubMedPubMed CentralGoogle Scholar
- Sjoblom T, Jones S, Wood LD, Parsons DW, Lin J, Barber TD, Mandelker D, Leary RJ, Ptak J, Silliman N, et al: The consensus coding sequences of human breast and colorectal cancers. Science. 2006, 314 (5797): 268-274. 10.1126/science.1133427.View ArticlePubMedGoogle Scholar
- Wood LD, Parsons DW, Jones S, Lin J, Sjoblom T, Leary RJ, Shen D, Boca SM, Barber T, Ptak J, et al: The genomic landscapes of human breast and colorectal cancers. Science. 2007, 318 (5853): 1108-1113. 10.1126/science.1145720.View ArticlePubMedGoogle Scholar
- Wang Z, Gerstein M, Snyder M: RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009, 10 (1): 57-63. 10.1038/nrg2484.View ArticlePubMedPubMed CentralGoogle Scholar
- Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B: Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008, 5 (7): 621-628. 10.1038/nmeth.1226.View ArticlePubMedGoogle Scholar
- Zhang LQ, Cheranova D, Gibson M, Ding S, Heruth DP, Fang D, Ye SQ: RNA-seq Reveals Novel Transcriptome of Genes and Their Isoforms in Human Pulmonary Microvascular Endothelial Cells Treated with Thrombin. PLoS One. 2012, 7 (2): e31229-10.1371/journal.pone.0031229.View ArticlePubMedPubMed CentralGoogle Scholar
- Gregg C, Zhang J, Butler JE, Haig D, Dulac C: Sex-specific parent-of-origin allelic expression in the mouse brain. Science. 2010, 329 (5992): 682-685. 10.1126/science.1190831.View ArticlePubMedPubMed CentralGoogle Scholar
- Gregg C, Zhang J, Weissbourd B, Luo S, Schroth GP, Haig D, Dulac C: High-resolution analysis of parent-of-origin allelic expression in the mouse brain. Science. 2010, 329 (5992): 643-648. 10.1126/science.1190830.View ArticlePubMedPubMed CentralGoogle Scholar
- Cloonan N, Forrest AR, Kolle G, Gardiner BB, Faulkner GJ, Brown MK, Taylor DF, Steptoe AL, Wani S, Bethel G, et al: Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nat Methods. 2008, 5 (7): 613-619. 10.1038/nmeth.1223.View ArticlePubMedGoogle Scholar
- Cirulli ET, Singh A, Shianna KV, Ge D, Smith JP, Maia JM, Heinzen EL, Goedert JJ, Goldstein DB: Screening the human exome: a comparison of whole genome and whole transcriptome sequencing. Genome Biol. 2010, 11 (5): R57-10.1186/gb-2010-11-5-r57.View ArticlePubMedPubMed CentralGoogle Scholar
- Kridel R, Meissner B, Rogic S, Boyle M, Telenius A, Woolcock B, Gunawardana J, Jenkins C, Cochrane C, Ben-Neriah S, et al: Whole transcriptome sequencing reveals recurrent NOTCH1 mutations in mantle cell lymphoma. Blood. 2012, 119 (9): 1963-1971. 10.1182/blood-2011-11-391474.View ArticlePubMedGoogle Scholar
- Canovas A, Rincon G, Islas-Trejo A, Wickramasinghe S, Medrano JF: SNP discovery in the bovine milk transcriptome using RNA-Seq technology. Mamm Genome. 2010, 21 (11–12): 592-598.View ArticlePubMedPubMed CentralGoogle Scholar
- Tuch BB, Laborde RR, Xu X, Gu J, Chung CB, Monighetti CK, Stanley SJ, Olsen KD, Kasperbauer JL, Moore EJ, et al: Tumor transcriptome sequencing reveals allelic expression imbalances associated with copy number alterations. PLoS One. 2010, 5 (2): e9317-10.1371/journal.pone.0009317.View ArticlePubMedPubMed CentralGoogle Scholar
- Homer N, Merriman B, Nelson SF: BFAST: an alignment tool for large scale genome resequencing. PLoS One. 2009, 4 (11): e7767-10.1371/journal.pone.0007767.View ArticlePubMedPubMed CentralGoogle Scholar
- Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R: The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009, 25 (16): 2078-2079. 10.1093/bioinformatics/btp352.View ArticlePubMedPubMed CentralGoogle Scholar
- Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K: dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001, 29 (1): 308-311. 10.1093/nar/29.1.308.View ArticlePubMedPubMed CentralGoogle Scholar
- Dees ND, Zhang Q, Kandoth C, Wendl MC, Schierding W, Koboldt DC, Mooney TB, Callaway MB, Dooling D, Mardis ER, et al: MuSiC: identifying mutational significance in cancer genomes. Genome Res. 2012, 22 (8): 1589-1598. 10.1101/gr.134635.111.View ArticlePubMedPubMed CentralGoogle Scholar
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al: Gene ontology: tool for the unification of biology, The Gene Ontology Consortium. Nat Genet. 2000, 25 (1): 25-29. 10.1038/75556.View ArticlePubMedPubMed CentralGoogle Scholar
- Alexa A, Rahnenfuhrer J, Lengauer T: Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics. 2006, 22 (13): 1600-1607. 10.1093/bioinformatics/btl140.View ArticlePubMedGoogle Scholar
- Kanehisa M, Goto S: KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000, 28 (1): 27-30. 10.1093/nar/28.1.27.View ArticlePubMedPubMed CentralGoogle Scholar
- Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M: KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 2012, 40 (Database issue): D109-D114.View ArticlePubMedGoogle Scholar
- Anders S, Huber W: Differential expression analysis for sequence count data. Genome Biol. 2010, 11 (10): R106-10.1186/gb-2010-11-10-r106.View ArticlePubMedPubMed CentralGoogle Scholar
- Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B. 1995, 57 (1): 12.Google Scholar
- Moslehi R, Kumar A, Mills JL, Ambroggio X, Signore C, Dzutsev A: Phenotype-specific adverse effects of XPD mutations on human prenatal development implicate impairment of TFIIH-mediated functions in placenta. Eur J Hum Genet. 2012, 20 (6): 626-631. 10.1038/ejhg.2011.249.View ArticlePubMedPubMed CentralGoogle Scholar
- Kumar P, Henikoff S, Ng PC: Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc. 2009, 4 (7): 1073-1081.View ArticlePubMedGoogle Scholar
- Garnis C, Chari R, Buys TP, Zhang L, Ng RT, Rosin MP, Lam WL: Genomic imbalances in precancerous tissues signal oral cancer risk. Mol Cancer. 2009, 8: 50-10.1186/1476-4598-8-50.View ArticlePubMedPubMed CentralGoogle Scholar
- Mao L, Hong WK, Papadimitrakopoulou VA: Focus on head and neck cancer. Cancer Cell. 2004, 5 (4): 311-316. 10.1016/S1535-6108(04)00090-X.View ArticlePubMedGoogle Scholar
- Liu SA, Jiang RS, Chen FJ, Wang WY, Lin JC: Somatic mutations in the D-loop of mitochondrial DNA in oral squamous cell carcinoma. Eur Arch Otorhinolaryngol. 2012, 269 (6): 1665-1670. 10.1007/s00405-011-1806-5.View ArticlePubMedGoogle Scholar
- Kozaki K, Imoto I, Pimkhaokham A, Hasegawa S, Tsuda H, Omura K, Inazawa J: PIK3CA mutation is an oncogenic aberration at advanced stages of oral squamous cell carcinoma. Cancer Sci. 2006, 97 (12): 1351-1358. 10.1111/j.1349-7006.2006.00343.x.View ArticlePubMedGoogle Scholar
- Califano J, van der Riet P, Westra W, Nawroz H, Clayman G, Piantadosi S, Corio R, Lee D, Greenberg B, Koch W, et al: Genetic progression model for head and neck cancer: implications for field cancerization. Cancer Res. 1996, 56 (11): 2488-2492.PubMedGoogle Scholar
- Tsui IF, Rosin MP, Zhang L, Ng RT, Lam WL: Multiple aberrations of chromosome 3p detected in oral premalignant lesions. Cancer Prev Res (Phila). 2008, 1 (6): 424-429. 10.1158/1940-6207.CAPR-08-0123.View ArticleGoogle Scholar
- Rosin MP, Cheng X, Poh C, Lam WL, Huang Y, Lovas J, Berean K, Epstein JB, Priddy R, Le ND, et al: Use of allelic loss to predict malignant risk for low-grade oral epithelial dysplasia. Clin Cancer Res. 2000, 6 (2): 357-362.PubMedGoogle Scholar
- Tsui IF, Poh CF, Garnis C, Rosin MP, Zhang L, Lam WL: Multiple pathways in the FGF signaling network are frequently deregulated by gene amplification in oral dysplasias. Int J Cancer. 2009, 125 (9): 2219-2228. 10.1002/ijc.24611.View ArticlePubMedPubMed CentralGoogle Scholar
- Myllykangas S, Bohling T, Knuutila S: Specificity, selection and significance of gene amplifications in cancer. Semin Cancer Biol. 2007, 17 (1): 42-55. 10.1016/j.semcancer.2006.10.005.View ArticlePubMedGoogle Scholar
- Leethanakul C, Patel V, Gillespie J, Pallente M, Ensley JF, Koontongkaew S, Liotta LA, Emmert-Buck M, Gutkind JS: Distinct pattern of expression of differentiation and growth-related genes in squamous cell carcinomas of the head and neck revealed by the use of laser capture microdissection and cDNA arrays. Oncogene. 2000, 19 (28): 3220-3224. 10.1038/sj.onc.1203703.View ArticlePubMedGoogle Scholar
- Alevizos I, Mahadevappa M, Zhang X, Ohyama H, Kohno Y, Posner M, Gallagher GT, Varvares M, Cohen D, Kim D, et al: Oral cancer in vivo gene expression profiling assisted by laser capture microdissection and microarray analysis. Oncogene. 2001, 20 (43): 6196-6204. 10.1038/sj.onc.1204685.View ArticlePubMedGoogle Scholar
- Belbin TJ, Singh B, Barber I, Socci N, Wenig B, Smith R, Prystowsky MB, Childs G: Molecular classification of head and neck squamous cell carcinoma using cDNA microarrays. Cancer Res. 2002, 62 (4): 1184-1190.PubMedGoogle Scholar
- Al Moustafa AE, Alaoui-Jamali MA, Batist G, Hernandez-Perez M, Serruya C, Alpert L, Black MJ, Sladek R, Foulkes WD: Identification of genes associated with head and neck carcinogenesis by cDNA microarray comparison between matched primary normal epithelial and squamous carcinoma cells. Oncogene. 2002, 21 (17): 2634-2640. 10.1038/sj.onc.1205351.View ArticlePubMedGoogle Scholar
- Yu YH, Kuo HK, Chang KW: The evolving transcriptome of head and neck squamous cell carcinoma: a systematic review. PLoS One. 2008, 3 (9): e3215-10.1371/journal.pone.0003215.View ArticlePubMedPubMed CentralGoogle Scholar
- Zavras AI, Yoon AJ, Chen MK, Lin CW, Yang SF: Association between polymorphisms of DNA repair gene ERCC5 and oral squamous cell carcinoma. Oral Surg Oral Med Oral Pathol Oral Radiol. 2012, 114 (5): 624-629. 10.1016/j.oooo.2012.05.013.View ArticlePubMedGoogle Scholar
- Mukherjee S, Bhowmik AD, Roychoudhury P, Mukhopadhyay K, Ray JG, Chaudhuri K: Association of XRCC1, XRCC3, and NAT2 polymorphisms with the risk of oral submucous fibrosis among eastern Indian population. J Oral Pathol Med. 2012, 41 (4): 292-302. 10.1111/j.1600-0714.2011.01097.x.View ArticlePubMedGoogle Scholar
- Vaezi A, Wang X, Buch S, Gooding W, Wang L, Seethala RR, Weaver DT, D’Andrea AD, Argiris A, Romkes M, et al: XPF expression correlates with clinical outcome in squamous cell carcinoma of the head and neck. Clin Cancer Res. 2011, 17 (16): 5513-5522. 10.1158/1078-0432.CCR-11-0086.View ArticlePubMedPubMed CentralGoogle Scholar
- Thomas GJ, Speight PM: Cell adhesion molecules and oral cancer. Crit Rev Oral Biol Med. 2001, 12 (6): 479-498. 10.1177/10454411010120060301.View ArticlePubMedGoogle Scholar
- Hung SC, Wu IH, Hsue SS, Liao CH, Wang HC, Chuang PH, Sung SY, Hsieh CL: Targeting l1 cell adhesion molecule using lentivirus-mediated short hairpin RNA interference reverses aggressiveness of oral squamous cell carcinoma. Mol Pharm. 2010, 7 (6): 2312-2323. 10.1021/mp1002834.View ArticlePubMedGoogle Scholar
- Chepelev I, Wei G, Tang Q, Zhao K: Detection of single nucleotide variations in expressed exons of the human genome using RNA-Seq. Nucleic Acids Res. 2009, 37 (16): e106-10.1093/nar/gkp507.View ArticlePubMedPubMed CentralGoogle Scholar
- Morin R, Bainbridge M, Fejes A, Hirst M, Krzywinski M, Pugh T, McDonald H, Varhol R, Jones S, Marra M: Profiling the HeLa S3 transcriptome using randomly primed cDNA and massively parallel short-read sequencing. Biotechniques. 2008, 45 (1): 81-94. 10.2144/000112900.View ArticlePubMedGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1755-8794/6/28/prepub
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