- Open Access
Dysregulated transcription across diverse cancer types reveals the importance of RNA-binding protein in carcinogenesis
© Wang et al.; licensee BioMed Central Ltd. 2015
Published: 11 June 2015
It is well known that carcinogenesis is in part dictated by dysregulated transcription events and signal pathways. Large-scale transcriptional profiling studies in each cancer type have reported aberrant gene expression associated with cancer development. However, common and specific patterns altered across cancer types, especially the contribution of transcriptional and post-transcriptional regulators, are rarely explored.
Using transcriptional profiles from matched tumor and normal samples in the Cancer Genome Atlas pan-cancer dataset, we performed a comprehensive analysis on the altered expression across 9 cancer types, focusing on transcriptional and post-transcriptional regulators and cancer-related genes. As we expected, the transcription of cancer-related genes was significantly deregulated in tumor vs. normal across all cancer types. Surprisingly, the expression of RNA-binding proteins (RBPs), master regulators of post-transcriptional gene expression, was also significantly changed across most studied cancer types. Although the expression of RBPs was not as strongly deregulated as cancer-related genes, their direct interaction partners are enriched by cancer-related genes, suggesting the cascade regulation effect of RBPs. Integrating genetic and epigenetic profiles found that deregulated RBPs were frequently caused by genetic rather than epigenetic alterations. Furthermore, tissue-specific genes were under-expressed in tumor vs. normal across all cancer types except prostate cancer.
Dysregulated transcription across cancer types reveals the importance of RBPs in carcinogenesis. The aberrant expression of RBPs is caused by genetic alterations and spreads their effect to cancer-related genes. In addition, disruption of tissue-specific genes contributes to the corresponding cancer pathology.
Cancer development is characterized by uncontrolled cell proliferation, which is in part due to expression alteration of genes which regulate cell growth and differentiation, such as the improper over-expression of oncogenes, or the under-expression or disabling of tumor suppressor genes . Comparative analysis of expression alterations between tumor and matched normal samples in each individual cancer type has identified many transcriptional and post-transcriptional regulators associated with carcinogenesis [2–13]. For instance, compared to normal mucosa, transcription factor (TF) NRF2 was found over-expressed in head and neck squamous cell carcinoma . Using transcriptional data of 17 adenomas and paired samples of normal mucosa, the transcription-regulating network of colorectal adenomas is characterized by significantly altered expression of over 250 TF genes . Compared to TFs, expression alteration of RNA-binding proteins (RBPs), master regulators at the post-transcriptional level, was less studied but deregulated transcriptions of several RBPs also have been reported to play a critical role in human cancers [9–12]. For example, QKI was frequently down-regulated in lung cancer, and QKI-5 inhibited the proliferation and transformation of lung cancer cells . Transcription profiling analysis of RBPs uncovered their aberrant function associated with prostate adenocarcinoma, colon adenocarcinoma, and breast carcinoma as well [9, 12, 15]. Additionally, aberrant expression of microRNAs (miRNAs) and long non-coding RNAs' (lncRNAs') also led to cancer development [16–20]. However, common and specific patterns altered across different cancer types, especially the contribution of transcriptional and post-transcriptional regulators, are rarely known.
Large-scale genomics projects, such as the Cancer Genome Atlas (TCGA), provided various omics data for thousands of tumors with matched normal samples, including genetic, epigenetic, transcriptomics and proteomics data , which gave us a great opportunity to perform pan-cancer studies for understanding the common and specific profiles across multiple cancer types. Recently, landscapes of somatic mutation, copy number alterations and oncogenic signatures across major cancer types have been studied [22–24], as well as microRNA-target interaction and functional proteomics data analysis [25, 26]. However, as far as we know, comparative analysis of expression alterations of transcriptional and post-transcriptional regulators across cancer types has never been explored.
In this study, we characterized the expression perturbation of TFs, RBPs, lncRNAs, cancer related genes (allOnco) and other genes on 522 matched tumor and normal tissue pairs across 9 cancer types. We first analyzed the differential expression between matched tumor and normal for each type of gene sets across all studied cancer types, and compared their amplitude of alterations. Then we integrated genetic and epigenetic data and protein-protein interaction network (PPI) to explain the upstream cause and downstream effect of dysregulated transcription. Finally we compared expression changes of tissue-specific genes with non-specific ones and investigated the consistent pathway changes across different cancer types.
Results and discussion
Expression alteration of RBPs contributes to cancer development
Number and significance of differently expressed genes.
Surprisingly, RBPs were significantly changed in 6 of the 9 cancer types. Marginal significance was observed in PRAD (p-value = 0.01) and HNSC (p-value = 0.04), while highly statistical significance was detected in COADREAD, LUAD and LUSC (p-value = 3.82e-13, 1e-15 and 4.04e-16 respectively) (Figure 1). Consistent expression alterations of RBPs across different cancer types suggested that they play an important role in carcinogenesis. Compared to RBPs, TFs only showed marginally significant enrichment in HNSC, possibly due to the fact that the activity changes of TF are at the protein level which cannot be reflected at the transcription level (Figure 1). lncRNAs were significantly depleted across all cancer types (Figure 1), which are possibly biased because only 264 of 9227 lncRNAs were included in the standardized mRNA-Seq data in Firehose (see Materials and Methods). Additionally the expression level of lncRNAs is especially low compared to other regulation factors .
Tissue-specific genes lost function in tumor
Functional similarity across different cancer types
Dysregulated transcription of RBPs plays an important role in cancer development. The aberrant expression of RBPs is caused by genetic alterations and spreads their effect to cancer-related genes. In addition, disruption of tissue-specific genes contributes to the corresponding cancer pathology.
Methods and materials
Genetic, epigenetic and transcriptomics data for 9 cancer types
The mRNA-Seq data of 522 matched tumor and adjacent normal samples for 9 cancer types, the copy number alterations, and the DNA methylation data were downloaded from Firehose developed by the Broad GDAC (https://confluence.broadinstitute.org/display/GDAC/Dashboard-Stddata). The nine cancer types are BRCA (Breast cancer carcinoma), COADREAD (colon/ rectum adenocarcinoma), HNSC (head and neck squamous cell carcinoma), LUAD (lung adenocarcinoma), KIRC (kidney renal clear cell carcinoma), LIHC (liver hepatocellular carcinoma), LUSC (lung squamous cell carcinoma), THCA (thyroid carcinoma) and PRAD (prostate adenocarcinoma). There are 111 paired samples for BRCA, 32 for COADREAD, 41 for HNSC, 72 for KIRC, 57 for LUAD, 59 pairs for THCA and 50 for each type of LUSC, LIHC and PRAD, respectively.
Different gene sets
1889 TFs were collected from TRANSFAC , and 799 experimentally characterized RBPs were obtained from a recent publication dissecting transcriptional profiles of RNA-binding protein in cancer . Over 9,000 lncRNAs were downloaded from Genecode [58, 59], but only 264 of these were included in mRNA-Seq data from the Broad GDAC standardized data packages. A comprehensive list of 2102 cancer related genes (allOnco), which is a non-redundant union of 8 studies [33, 60–64], was downloaded from Bushman Lab (http://www.bushmanlab.org/links/genelists). About 2570 tissue-specific genes were collected from PaGenBase, which defines genes to be tissue-specific if they are dominantly expressed in one tissue. There are 145 breast-specific, 364 colon-specific, 480 kidney-specific, 628 liver-specific, 643 lung-specific, 263 prostate-specific and 227 thyroid-specific genes, respectively . Different types of somatic mutations, including frameshift mutations, germline mutations, missense mutations, large deletions, splicing mutations and translocations were collected from COSMIC .
Statistical evaluation of differential expression
Paired t-test was used to detect differentially expressed genes between matched tumor and normal tissue pairs. Bonferroni method was used to adjust p-values for multiple testing. Hypergeometric test was used to evaluate the enrichment of different types of genes in the set of differentially expressed genes. All statistical tests in this study were implemented in R (version 3.0.3) .
Pairwise Spearman correlations were calculated between the copy number alterations/DNA methylation alterations and gene expression changes for differentially and non-differentially expressed RBPs. The statistical difference of the correlation coefficients were assessed by Wilcoxon Rank Sum test .
Clustering by biological pathways
KEGG pathways are wiring diagrams of molecular interactions, reactions, and relations, and mainly used for biological interpretation of higher-level systemic functions. Different cancers may have consistent changes in some cancer related pathways. To find those pathways similarly altered across different cancers, we performed hierarchal clustering under some specific pathways, including cell cycle, cell proliferation, pathways in cancer and etc. The distance matrix was calculated by Spearman correlation coefficient of expression alteration between different cancer types.
This work was supported by grants CCSG (P30 CA068485), BETRNet (U01 CA163056), Breast (P50 CA098131), and GI (P50 CA095103).
The publication charges for this article have been funded by the corresponding authors.
This article has been published as part of BMC Genomics Volume 16 Supplement 7, 2015: Selected articles from The International Conference on Intelligent Biology and Medicine (ICIBM) 2014: Genomics. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcgenomics/supplements/16/S7.
- Croce CM: Oncogenes and cancer. The New England journal of medicine. 2008, 358 (5): 502-511. 10.1056/NEJMra072367.View ArticlePubMedGoogle Scholar
- Stacy DR, Ely K, Massion PP, Yarbrough WG, Hallahan DE, Sekhar KR, Freeman ML: Increased expression of nuclear factor E2 p45-related factor 2 (NRF2) in head and neck squamous cell carcinomas. Head & neck. 2006, 28 (9): 813-818. 10.1002/hed.20430.View ArticleGoogle Scholar
- Khor TO, Huang MT, Prawan A, Liu Y, Hao X, Yu S, Cheung WK, Chan JY, Reddy BS, Yang CS, et al: Increased susceptibility of Nrf2 knockout mice to colitis-associated colorectal cancer. Cancer prevention research. 2008, 1 (3): 187-191. 10.1158/1940-6207.CAPR-08-0028.PubMed CentralView ArticlePubMedGoogle Scholar
- Citterio C, Menacho-Marquez M, Garcia-Escudero R, Larive RM, Barreiro O, Sanchez-Madrid F, Paramio JM, Bustelo XR: The rho exchange factors vav2 and vav3 control a lung metastasis-specific transcriptional program in breast cancer cells. Science signaling. 2012, 5 (244): ra71-View ArticlePubMedGoogle Scholar
- Ell B, Kang Y: Transcriptional control of cancer metastasis. Trends in cell biology. 2013, 23 (12): 603-611. 10.1016/j.tcb.2013.06.001.View ArticlePubMedGoogle Scholar
- Maddox J, Shakya A, South S, Shelton D, Andersen JN, Chidester S, Kang J, Gligorich KM, Jones DA, Spangrude GJ, et al: Transcription factor Oct1 is a somatic and cancer stem cell determinant. PLoS genetics. 2012, 8 (11): e1003048-10.1371/journal.pgen.1003048.PubMed CentralView ArticlePubMedGoogle Scholar
- Wolford CC, McConoughey SJ, Jalgaonkar SP, Leon M, Merchant AS, Dominick JL, Yin X, Chang Y, Zmuda EJ, O'Toole SA, et al: Transcription factor ATF3 links host adaptive response to breast cancer metastasis. The Journal of clinical investigation. 2013, 123 (7): 2893-2906. 10.1172/JCI64410.PubMed CentralView ArticlePubMedGoogle Scholar
- Frank DA: Transcription factor STAT3 as a prognostic marker and therapeutic target in cancer. Journal of clinical oncology : official journal of the American Society of Clinical Oncology. 2013, 31 (36): 4560-4561. 10.1200/JCO.2013.52.8414.View ArticleGoogle Scholar
- Lukong KE, Chang KW, Khandjian EW, Richard S: RNA-binding proteins in human genetic disease. Trends in genetics : TIG. 2008, 24 (8): 416-425. 10.1016/j.tig.2008.05.004.View ArticlePubMedGoogle Scholar
- Kim MY, Hur J, Jeong S: Emerging roles of RNA and RNA-binding protein network in cancer cells. BMB reports. 2009, 42 (3): 125-130. 10.5483/BMBRep.2009.42.3.125.View ArticlePubMedGoogle Scholar
- Castello A, Fischer B, Hentze MW, Preiss T: RNA-binding proteins in Mendelian disease. Trends in genetics : TIG. 2013, 29 (5): 318-327. 10.1016/j.tig.2013.01.004.View ArticlePubMedGoogle Scholar
- Wurth L: Versatility of RNA-Binding Proteins in Cancer. Comparative and functional genomics. 2012, 2012: 178525-PubMed CentralView ArticlePubMedGoogle Scholar
- Vonlanthen J, Okoniewski MJ, Menigatti M, Cattaneo E, Pellegrini-Ochsner D, Haider R, Jiricny J, Staiano T, Buffoli F, Marra G: A comprehensive look at transcription factor gene expression changes in colorectal adenomas. BMC cancer. 2014, 14: 46-10.1186/1471-2407-14-46.PubMed CentralView ArticlePubMedGoogle Scholar
- Zong FY, Fu X, Wei WJ, Luo YG, Heiner M, Cao LJ, Fang Z, Fang R, Lu D, Ji H, et al: The RNA-binding protein QKI suppresses cancer-associated aberrant splicing. PLoS genetics. 2014, 10 (4): e1004289-10.1371/journal.pgen.1004289.PubMed CentralView ArticlePubMedGoogle Scholar
- Kechavarzi B, Janga SC: Dissecting the expression landscape of RNA-binding proteins in human cancers. Genome biology. 2014, 15 (1): R14-10.1186/gb-2014-15-1-r14.PubMed CentralView ArticlePubMedGoogle Scholar
- Sassen S, Miska EA, Caldas C: MicroRNA: implications for cancer. Virchows Archiv : an international journal of pathology. 2008, 452 (1): 1-10. 10.1007/s00428-007-0532-2.View ArticleGoogle Scholar
- Vincent K, Pichler M, Lee GW, Ling H: MicroRNAs, Genomic Instability and Cancer. International journal of molecular sciences. 2014, 15 (8): 14475-14491. 10.3390/ijms150814475.PubMed CentralView ArticlePubMedGoogle Scholar
- Prensner JR, Chinnaiyan AM: The emergence of lncRNAs in cancer biology. Cancer discovery. 2011, 1 (5): 391-407. 10.1158/2159-8290.CD-11-0209.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhang H, Chen Z, Wang X, Huang Z, He Z, Chen Y: Long non-coding RNA: a new player in cancer. Journal of hematology & oncology. 2013, 6: 37-10.1186/1756-8722-6-37.View ArticleGoogle Scholar
- Cheetham SW, Gruhl F, Mattick JS, Dinger ME: Long noncoding RNAs and the genetics of cancer. British journal of cancer. 2013, 108 (12): 2419-2425. 10.1038/bjc.2013.233.PubMed CentralView ArticlePubMedGoogle Scholar
- Cancer Genome Atlas Research N, Weinstein JN, Collisson EA, Mills GB, Shaw KR, Ozenberger BA, Ellrott K, Shmulevich I, Sander C, Stuart JM: The Cancer Genome Atlas Pan-Cancer analysis project. Nature genetics. 2013, 45 (10): 1113-1120. 10.1038/ng.2764.View ArticleGoogle Scholar
- Zack TI, Schumacher SE, Carter SL, Cherniack AD, Saksena G, Tabak B, Lawrence MS, Zhang CZ, Wala J, Mermel CH, et al: Pan-cancer patterns of somatic copy number alteration. Nature genetics. 2013, 45 (10): 1134-1140. 10.1038/ng.2760.PubMed CentralView ArticlePubMedGoogle Scholar
- Kandoth C, McLellan MD, Vandin F, Ye K, Niu B, Lu C, Xie M, Zhang Q, McMichael JF, Wyczalkowski MA, et al: Mutational landscape and significance across 12 major cancer types. Nature. 2013, 502 (7471): 333-339. 10.1038/nature12634.PubMed CentralView ArticlePubMedGoogle Scholar
- Ciriello G, Miller ML, Aksoy BA, Senbabaoglu Y, Schultz N, Sander C: Emerging landscape of oncogenic signatures across human cancers. Nature genetics. 2013, 45 (10): 1127-1133. 10.1038/ng.2762.PubMed CentralView ArticlePubMedGoogle Scholar
- Jacobsen A, Silber J, Harinath G, Huse JT, Schultz N, Sander C: Analysis of microRNA-target interactions across diverse cancer types. Nature structural & molecular biology. 2013, 20 (11): 1325-1332. 10.1038/nsmb.2678.View ArticleGoogle Scholar
- Li J, Lu Y, Akbani R, Ju Z, Roebuck PL, Liu W, Yang JY, Broom BM, Verhaak RG, Kane DW, et al: TCPA: a resource for cancer functional proteomics data. Nature methods. 2013, 10 (11): 1046-1047.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhang L, Zhou W, Velculescu VE, Kern SE, Hruban RH, Hamilton SR, Vogelstein B, Kinzler KW: Gene expression profiles in normal and cancer cells. Science. 1997, 276 (5316): 1268-1272. 10.1126/science.276.5316.1268.View ArticlePubMedGoogle Scholar
- Li ML, Zhang JC, Li SG, Wu WG, Rao LH, Dong P, Gu J, Lu JH, Zhang L, Ding QC, et al: Characteristic gene expression profiles in the progression from normal gastric epithelial cells to moderate gastric epithelial dysplasia and to gastric cancer. Chinese medical journal. 2012, 125 (10): 1777-1783.PubMedGoogle Scholar
- Hu YC, Lam KY, Law S, Wong J, Srivastava G: Profiling of differentially expressed cancer-related genes in esophageal squamous cell carcinoma (ESCC) using human cancer cDNA arrays: overexpression of oncogene MET correlates with tumor differentiation in ESCC. Clinical cancer research : an official journal of the American Association for Cancer Research. 2001, 7 (11): 3519-3525.Google Scholar
- Hu J, Locasale JW, Bielas JH, O'Sullivan J, Sheahan K, Cantley LC, Vander Heiden MG, Vitkup D: Heterogeneity of tumor-induced gene expression changes in the human metabolic network. Nature biotechnology. 2013, 31 (6): 522-529. 10.1038/nbt.2530.PubMed CentralView ArticlePubMedGoogle Scholar
- Lowe AW, Olsen M, Hao Y, Lee SP, Taek Lee K, Chen X, van de Rijn M, Brown PO: Gene expression patterns in pancreatic tumors, cells and tissues. PloS one. 2007, 2 (3): e323-10.1371/journal.pone.0000323.PubMed CentralView ArticlePubMedGoogle Scholar
- Santarius T, Shipley J, Brewer D, Stratton MR, Cooper CS: A census of amplified and overexpressed human cancer genes. Nature reviews Cancer. 2010, 10 (1): 59-64. 10.1038/nrc2771.View ArticlePubMedGoogle Scholar
- Futreal PA, Coin L, Marshall M, Down T, Hubbard T, Wooster R, Rahman N, Stratton MR: A census of human cancer genes. Nature reviews Cancer. 2004, 4 (3): 177-183. 10.1038/nrc1299.PubMed CentralView ArticlePubMedGoogle Scholar
- Liu J, Kouzine F, Nie Z, Chung HJ, Elisha-Feil Z, Weber A, Zhao K, Levens D: The FUSE/FBP/FIR/TFIIH system is a molecular machine programming a pulse of c-myc expression. The EMBO journal. 2006, 25 (10): 2119-2130. 10.1038/sj.emboj.7601101.PubMed CentralView ArticlePubMedGoogle Scholar
- Page-McCaw PS, Amonlirdviman K, Sharp PA: PUF60: a novel U2AF65-related splicing activity. Rna. 1999, 5 (12): 1548-1560. 10.1017/S1355838299991938.PubMed CentralView ArticlePubMedGoogle Scholar
- Edwards RA, Lee MS, Tsutakawa SE, Williams RS, Nazeer I, Kleiman FE, Tainer JA, Glover JN: The BARD1 C-terminal domain structure and interactions with polyadenylation factor CstF-50. Biochemistry. 2008, 47 (44): 11446-11456. 10.1021/bi801115g.PubMed CentralView ArticlePubMedGoogle Scholar
- Fabbro M, Savage K, Hobson K, Deans AJ, Powell SN, McArthur GA, Khanna KK: BRCA1-BARD1 complexes are required for p53Ser-15 phosphorylation and a G1/S arrest following ionizing radiation-induced DNA damage. The Journal of biological chemistry. 2004, 279 (30): 31251-31258. 10.1074/jbc.M405372200.View ArticlePubMedGoogle Scholar
- Ryser S, Dizin E, Jefford CE, Delaval B, Gagos S, Christodoulidou A, Krause KH, Birnbaum D, Irminger-Finger I: Distinct roles of BARD1 isoforms in mitosis: full-length BARD1 mediates Aurora B degradation, cancer-associated BARD1beta scaffolds Aurora B and BRCA2. Cancer research. 2009, 69 (3): 1125-1134. 10.1158/0008-5472.CAN-08-2134.View ArticlePubMedGoogle Scholar
- Zhang YQ, Bianco A, Malkinson AM, Leoni VP, Frau G, De Rosa N, Andre PA, Versace R, Boulvain M, Laurent GJ, et al: BARD1: an independent predictor of survival in non-small cell lung cancer. International journal of cancer Journal international du cancer. 2012, 131 (1): 83-94. 10.1002/ijc.26346.View ArticlePubMedGoogle Scholar
- Yamamoto H, Arakaki K, Morimatsu K, Zaitsu Y, Fujita A, Kohashi K, Hirahashi M, Motoshita J, Oshiro Y, Oda Y: Insulin-like growth factor II messenger RNA-binding protein 3 expression in gastrointestinal mesenchymal tumors. Human pathology. 2014, 45 (3): 481-487. 10.1016/j.humpath.2013.10.010.View ArticlePubMedGoogle Scholar
- Lin L, Zhang J, Wang Y, Zheng L, Lin Z, Cai Y: Expression of insulin-like growth factor 2 mRNA-binding protein 3 expression and analysis of prognosis in the patients with lung squamous cell carcinoma. Xi bao yu fen zi mian yi xue za zhi = Chinese journal of cellular and molecular immunology. 2013, 29 (7): 694-697.PubMedGoogle Scholar
- Lin CY, Chen ST, Jeng YM, Yeh CC, Chou HY, Deng YT, Chang CC, Kuo MY: Insulin-like growth factor II mRNA-binding protein 3 expression promotes tumor formation and invasion and predicts poor prognosis in oral squamous cell carcinoma. Journal of oral pathology & medicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology. 2011, 40 (9): 699-705. 10.1111/j.1600-0714.2011.01019.x.View ArticleGoogle Scholar
- Flajollet S, Tian TV, Flourens A, Tomavo N, Villers A, Bonnelye E, Aubert S, Leroy X, Duterque-Coquillaud M: Abnormal expression of the ERG transcription factor in prostate cancer cells activates osteopontin. Molecular cancer research : MCR. 2011, 16 (7): 914-924.View ArticleGoogle Scholar
- Baumgarten P, Harter PN, Tonjes M, Capper D, Blank AE, Sahm F, von Deimling A, Kolluru V, Schwamb B, Rabenhorst U, et al: Loss of FUBP1 expression in gliomas predicts FUBP1 mutation and is associated with oligodendroglial differentiation, IDH1 mutation and 1p/19q loss of heterozygosity. Neuropathology and applied neurobiology. 2014, 40 (2): 205-216. 10.1111/nan.12088.View ArticlePubMedGoogle Scholar
- Martin BT, Kleiber K, Wixler V, Raab M, Zimmer B, Kaufmann M, Strebhardt K: FHL2 regulates cell cycle-dependent and doxorubicin-induced p21Cip1/Waf1 expression in breast cancer cells. Cell cycle. 2007, 6 (14): 1779-1788. 10.4161/cc.6.14.4448.View ArticlePubMedGoogle Scholar
- Xiao SJ, Zhang C, Zou Q, Ji ZL: TiSGeD: a database for tissue-specific genes. Bioinformatics. 2010, 26 (9): 1273-1275. 10.1093/bioinformatics/btq109.PubMed CentralView ArticlePubMedGoogle Scholar
- Lage K, Hansen NT, Karlberg EO, Eklund AC, Roque FS, Donahoe PK, Szallasi Z, Jensen TS, Brunak S: A large-scale analysis of tissue-specific pathology and gene expression of human disease genes and complexes. Proceedings of the National Academy of Sciences of the United States of America. 2008, 105 (52): 20870-20875. 10.1073/pnas.0810772105.PubMed CentralView ArticlePubMedGoogle Scholar
- Pan JB, Hu SC, Shi D, Cai MC, Li YB, Zou Q, Ji ZL: PaGenBase: a pattern gene database for the global and dynamic understanding of gene function. PloS one. 2013, 8 (12): e80747-10.1371/journal.pone.0080747.PubMed CentralView ArticlePubMedGoogle Scholar
- Kanehisa M: Post-genome informatics. 2000, Oxford; New York: Oxford University PressGoogle Scholar
- Kanehisa M, Goto S: KEGG: kyoto encyclopedia of genes and genomes. Nucleic acids research. 2000, 28 (1): 27-30. 10.1093/nar/28.1.27.PubMed CentralView ArticlePubMedGoogle Scholar
- Kanehisa M, Goto S, Sato Y, Kawashima M, Furumichi M, Tanabe M: Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic acids research. 2014, 42 (Database): D199-205.PubMed CentralView ArticlePubMedGoogle Scholar
- Bisteau X, Caldez MJ, Kaldis P: The Complex Relationship between Liver Cancer and the Cell Cycle: A Story of Multiple Regulations. Cancers. 2014, 6 (1): 79-111. 10.3390/cancers6010079.PubMed CentralView ArticlePubMedGoogle Scholar
- Vincenzi B, Schiavon G, Silletta M, Santini D, Perrone G, Di Marino M, Angeletti S, Baldi A, Tonini G: Cell cycle alterations and lung cancer. Histology and histopathology. 2006, 21 (4): 423-435.PubMedGoogle Scholar
- Caldon CE, Daly RJ, Sutherland RL, Musgrove EA: Cell cycle control in breast cancer cells. Journal of cellular biochemistry. 2006, 97 (2): 261-274. 10.1002/jcb.20690.View ArticlePubMedGoogle Scholar
- Ma Q, Wang X, Li Z, Li B, Ma F, Peng L, Zhang Y, Xu A, Jiang B: microRNA-16 represses colorectal cancer cell growth in vitro by regulating the p53/survivin signaling pathway. Oncology reports. 2013, 29 (4): 1652-1658.PubMedGoogle Scholar
- Rothenberg SM, Ellisen LW: The molecular pathogenesis of head and neck squamous cell carcinoma. The Journal of clinical investigation. 2012, 122 (6): 1951-1957. 10.1172/JCI59889.PubMed CentralView ArticlePubMedGoogle Scholar
- Matys V, Fricke E, Geffers R, Gossling E, Haubrock M, Hehl R, Hornischer K, Karas D, Kel AE, Kel-Margoulis OV, et al: TRANSFAC: transcriptional regulation, from patterns to profiles. Nucleic acids research. 2003, 31 (1): 374-378. 10.1093/nar/gkg108.PubMed CentralView ArticlePubMedGoogle Scholar
- Derrien T, Johnson R, Bussotti G, Tanzer A, Djebali S, Tilgner H, Guernec G, Martin D, Merkel A, Knowles DG, et al: The GENCODE v7 catalog of human long noncoding RNAs: analysis of their gene structure, evolution, and expression. Genome research. 2012, 22 (9): 1775-1789. 10.1101/gr.132159.111.PubMed CentralView ArticlePubMedGoogle Scholar
- Harrow J, Frankish A, Gonzalez JM, Tapanari E, Diekhans M, Kokocinski F, Aken BL, Barrell D, Zadissa A, Searle S, et al: GENCODE: the reference human genome annotation for The ENCODE Project. Genome research. 2012, 22 (9): 1760-1774. 10.1101/gr.135350.111.PubMed CentralView ArticlePubMedGoogle Scholar
- Huret JL, Ahmad M, Arsaban M, Bernheim A, Cigna J, Desangles F, Guignard JC, Jacquemot-Perbal MC, Labarussias M, Leberre V, et al: Atlas of genetics and cytogenetics in oncology and haematology in 2013. Nucleic acids research. 2013, 41 (Database): D920-924.PubMed CentralView ArticlePubMedGoogle 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
- Akagi K, Suzuki T, Stephens RM, Jenkins NA, Copeland NG: RTCGD: retroviral tagged cancer gene database. Nucleic acids research. 2004, 32 (Database): D523-527.PubMed CentralView ArticlePubMedGoogle Scholar
- Coffin JM: Retroviruses. 2002, Cold Spring Harbor, NY: Cold Spring Harbor Laboratory Press, 1 CD-ROMGoogle Scholar
- Vogelstein B, Papadopoulos N, Velculescu VE, Zhou S, Diaz LA, Kinzler KW: Cancer genome landscapes. Science. 2013, 339 (6127): 1546-1558. 10.1126/science.1235122.PubMed CentralView ArticlePubMedGoogle Scholar
- Team RC: R: A Language and Environment for Statistical Computing. 2014Google Scholar
- Saito R, Smoot ME, Ono K, Ruscheinski J, Wang PL, Lotia S, Pico AR, Bader GD, Ideker T: A travel guide to Cytoscape plugins. Nature methods. 2012, 16 (11): 1069-1076.View ArticleGoogle Scholar
- Hollander M, Wolfe DA: Nonparametric statistical methods. 1973, New York: WileyGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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.