Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Original Article
  • Published:

Identification of differentially expressed genes and their upstream regulators in colorectal cancer

Cancer Gene Therapy volume 24, pages 244–250 (2017)Cite this article

Subjects

Abstract

To identify the differentially expressed genes (DEGs) and their transcription factors (TFs) in colorectal cancer (CRC). We performed an integrated analysis of microarray studies. Functional annotation and CRC-specific transcriptional regulatory network construction were performed. Expression of selected DEGs and TFs was verified with The Cancer Genome Atlas (TCGA) data sets and qRT-PCR. SurvMicro was used to analyze the correlation between the overall survival time of CRC patients and the expression of DEGs and TFs. Seven data sets were obtained and 2014 DEGs in CRC were identified. Pathways in cancer and fatty acid metabolism were significantly enriched pathways of upregulated and downregulated DEGs, respectively. Expression of five DEGs (RERGL, ESM1, CA1, ANGPTL7 and TMEFF2) and their five TFs (ZNF354C, ARID3A, NFIC, BRCA1 and ZEB1) was verified by TCGA data sets and qRT-PCR. Their expression in TCGA data sets was same as that in our integrated analysis. Only the expression of EMS1, NFIC, BRCA1 and ZEB1 was inconsistent with integrated analysis and TCGA data sets. Expression of RERGL and BRCA1 was significantly correlated with the overall survival time of CRC patients. These five DEGs may have roles in CRC regulated by their five upstream TFs, which may make a contribution in uncovering the mechanism and providing new strategy of diagnosis and therapies for CRC.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5

Similar content being viewed by others

References

  1. Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM . Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer 2010; 127: 2893–2917.

    Article  CAS  Google Scholar 

  2. Yang R, Chen B, Pfütze K, Buch S, Steinke V, Holinski-Feder E et al. Genome-wide analysis associates familial colorectal cancer with increases in copy number variations and a rare structural variation at 12p12.3. Carcinogenesis 2014; 35: 315–323.

    Article  CAS  Google Scholar 

  3. Nakayama T, Hirakawa H, Shibata K, Nazneen A, Abe K, Nagayasu T et al. Expression of angiopoietin-like 4 (ANGPTL4) in human colorectal cancer: ANGPTL4 promotes venous invasion and distant metastasis. Oncol Rep 2011; 25: 929–935.

    Article  CAS  Google Scholar 

  4. Su CY, Lin TC, Lin YF, Chen MH, Lee CH, Wang HY et al. DDX3 as a strongest prognosis marker and its downregulation promotes metastasis in colorectalcancer. Oncotarget 2015; 6: 18602–18612.

    PubMed  PubMed Central  Google Scholar 

  5. Rudolph A, Shi H, Forsti A, Hoffmeister M, Sainz J, Jansen L et al. Repeat polymorphisms in ESR2 and AR and colorectal cancer risk and prognosis: results from a German population-based case–control study. BMC Cancer 2014; 14: 817.

    Article  Google Scholar 

  6. Wang ZS, Cong ZJ, Luo Y, Mu YF, Qin SL, Zhong M et al. Decreased expression of interleukin-36alpha predicts poor prognosis in colorectal cancer patients. Int J Clin Exp Pathol 2014; 7: 8077–8081.

    PubMed  PubMed Central  Google Scholar 

  7. Li Y, Li Y, Chen W, He F, Tan Z, Zheng J et al. NEAT expression is associated with tumor recurrence and unfavorable prognosis in colorectal cancer. Oncotarget 2015; 6: 27641–27650.

    PubMed  PubMed Central  Google Scholar 

  8. Kunizaki M, Sawai T, Takeshita H, Tominaga T, Hidaka S, To K et al. Clinical value of serum p53 antibody in the diagnosis and prognosis of colorectal cancer. Anticancer Res 2016; 36: 4171–4175.

    CAS  PubMed  Google Scholar 

  9. Letellier E, Schmitz M, Baig K, Beaume N, Schwartz C, Frasquilho S et al. Identification of SOCS2 and SOCS6 as biomarkers in human colorectal cancer. Br J Cancer 2014; 111: 726–735.

    Article  CAS  Google Scholar 

  10. Lee KW, Lee SS, Kim SB, Sohn BH, Lee HS, Jang HJ et al. Significant association of oncogene YAP1 with poor prognosis and cetuximab resistance in colorectal cancer patients. Clin Cancer Res 2015; 21: 357–364.

    Article  CAS  Google Scholar 

  11. Ahmadi S, Sharifi M . Locked nucleic acid inhibits miR-92a-3p in human colorectal cancer, induces apoptosis and inhibits cell proliferation. Cancer Gene Ther 2016; 23: 199–205.

    Article  CAS  Google Scholar 

  12. Akao Y, Nakagawa Y, Hirata I, Iio A, Itoh T, Kojima K et al. Role of anti-oncomirs miR-143 and -145 in human colorectal tumors. Cancer Gene Ther 2010; 17: 398–408.

    Article  CAS  Google Scholar 

  13. Zhang X, Zhang Y, Yang J, Li S, Chen J . Upregulation of miR-582-5p inhibits cell proliferation, cell cycle progression and invasion by targeting Rab27a in human colorectal carcinoma. Cancer Gene Ther 2015; 22: 475–480.

    Article  Google Scholar 

  14. Wang R, Du L, Yang X, Jiang X, Duan W, Yan S et al. Identification of long noncoding RNAs as potential novel diagnosis and prognosis biomarkers in colorectal cancer. J Cancer Res Clin Oncol 2016; 142: 2291–2301.

    Article  CAS  Google Scholar 

  15. He Q, Chen HY, Bai EQ, Luo YX, Fu RJ, He YS et al. Development of a multiplex MethyLight assay for the detection ofmultigene methylation in human colorectal cancer. Cancer Genet Cytogenet 2010; 202: 1–10.

    Article  CAS  Google Scholar 

  16. Kondo Y, Issa JPJ . Epigenetic changes in colorectal cancer. Cancer Metastasis Rev 2004; 23: 29–39.

    Article  CAS  Google Scholar 

  17. Church TR, Wandell M, Lofton-Day C, Mongin SJ, Burger M, Payne SR et al. Prospective evaluation of methylated SEPT9 in plasma for detection of asymptomatic colorectal cancer. Gut 2014; 63: 317–325.

    Article  CAS  Google Scholar 

  18. Pedersen SK, Baker RT, McEvoy A, Murray DH, Thomas M, Molloy PL et al. A two-gene blood test for methylated DNA sensitive for colorectal cancer. PLoS ONE 2015; 10: e0125041.

    Article  Google Scholar 

  19. Lanza G, Ferracin M, Gafà R, Veronese A, Spizzo R, Pichiorri F et al. mRNA/microRNA gene expression profile in microsatellite unstable colorectal cancer. Mol Cancer 2007; 6: 1–11.

    Article  Google Scholar 

  20. Zhao H, Cai W, Su S, Zhi D, Lu J, Liu S . Screening genes crucial for pediatric pilocytic astrocytoma using weighted gene coexpression network analysis combined with methylation data analysis. Cancer Gene Ther 2014; 21: 448–455.

    Article  CAS  Google Scholar 

  21. Xiao WH, Qu XL, Li XM, Sun YL, Zhao HX, Wang S et al. Identification of commonly dysregulated genes in colorectal cancer by integrating analysis of RNA-Seq data and qRT-PCR validation. Cancer Gene Ther 2015; 22: 278–284.

    Article  CAS  Google Scholar 

  22. Xiong Y, Wu S, Du Q, Wang A, Wang Z . Integrated analysis of gene expression and genomic aberration data in osteosarcoma (OS). Cancer Gene Ther 2015; 22: 524–529.

    Article  CAS  Google Scholar 

  23. Hippo Y, Taniguchi H, Tsutsumi S, Machida N, Chong JM, Fukayama M et al. Global gene expression analysis of gastric cancer by oligonucleotide microarrays. Cancer Res 2002; 62: 233–240.

    CAS  PubMed  Google Scholar 

  24. Clarke PA, Poele RT, Wooster R, Workman P . Gene expression microarray analysis in cancer biology, pharmacology, and drug development: progress and potential 2. Biochem Pharmacol 2001; 62: 1311–1336.

    Article  CAS  Google Scholar 

  25. Koga Y, Yamazaki N, Takizawa S, Kawauchi J, Nomura O, Yamamoto S et al. Gene expression analysis using a highly sensitive DNA microarray for colorectal cancer screening. Anticancer Res 2014; 34: 169–176.

    CAS  PubMed  Google Scholar 

  26. Bianchini M, Levy EM, Zucchini C, Pinsky V, Macagno C, Valvassori L et al. Analysis of gene expression in human colorectal cancer tissues by cDNA microarray: implications for novel immunotherapy strategies. Cancer Res 2005; 65: 841-.

    Google Scholar 

  27. Jorissen RN, Gibbs P, Christie M, Prakash S, Lipton L, Desai J et al. Metastasis-associated gene expression changes predict poor outcomes in patients with dukes stage B and C colorectal cancer. Clin Cancer Res 2009; 15: 7642–7651.

    Article  CAS  Google Scholar 

  28. Andersen CL, Thorsen K, Kruhøffer M, Ørntoft TF . Transcription factors dysregulated in colorectal cancer. Cancer Res 2006; 66: 407-.

    Google Scholar 

  29. Kryczek I, Lin Y, Nagarsheth N, Peng D, Zhao L, Zhao E et al. IL-22+CD4+T cells promote colorectal cancer stemness via STAT3 transcription factor activation and induction of the methyltransferase DOT1L. Immunity 2014; 40: 772–784.

    Article  CAS  Google Scholar 

  30. Sakuma K, Aoki M, Kannagi R . Transcription factors c-Myc and CDX2 mediate E-selectin ligand expression in colon cancer cells undergoing EGF/bFGF-induced epithelial-mesenchymal transition. Proc Natl Acad Sci USA 2012; 109: 7776–7781.

    Article  CAS  Google Scholar 

  31. Jiang H, Fu XG, Chen YT . Serum level of endothelial cell-specific molecule-1 and prognosis of colorectal cancer. Genet Mol Res 2015; 14: 5519–5526.

    Article  CAS  Google Scholar 

  32. Na YJ, Kim YH, Ye JJ, Yun HK, Lee CI . Identification of endothelial cell-specific molecule-1 as a potential serum marker for colorectal cancer. Cancer Sci 2010; 101: 2248–2253.

    Article  Google Scholar 

  33. Kang UB, Yeom J, Kim HJ, Kim H, Lee C . Expression profiling of more than 3500 proteins of MSS-type colorectal cancer by stable isotope labeling and mass spectrometry. J Proteomics 2012; 75: 3050–3062.

    Article  CAS  Google Scholar 

  34. Song M, Kim H, Kim WK, Hong SP, Lee C, Kim H . High expression of AT-rich interactive domain 3A (ARID3A) is associated with good prognosis in colorectal carcinoma. Ann Surg Oncol 2014; 21: 481–489.

    Article  Google Scholar 

  35. Covarrubias AS, Bergfors T, Jones TA, Högbom M . Structural mechanics of the pH-dependent activity of beta-carbonic anhydrase from Mycobacterium tuberculosis. J Biol Chem 2006; 281: 4993–4999.

    Article  Google Scholar 

  36. Kummola L, Hämäläinen JM, Kivelä J, Kivelä AJ, Saarnio J, Karttunen T et al. Expression of a novel carbonic anhydrase, CA XIII, in normal and neoplastic colorectal mucosa. BMC Cancer 2005; 5: 41.

    Article  Google Scholar 

  37. Parri M, Pietrovito L, Grandi A, Campagnoli S, Camilli ED, Bianchini F et al. Angiopoietin-like 7, a novel pro-angiogenetic factor over-expressed in cancer. Angiogenesis 2014; 17: 881–896.

    Article  CAS  Google Scholar 

  38. Moutinho C, Martinez-Cardús A, Santos C, Navarro-Pérez V, Martínez-Balibrea E, Musulen E et al. Epigenetic inactivation of the BRCA1 interactor SRBC and resistance to oxaliplatin in colorectal cancer. J Natl Cancer Inst 2013; 106: 91–103.

    Google Scholar 

  39. Sopik V, Phelan C, Cybulski C, Narod SA . BRCA1 and BRCA2 mutations and the risk for colorectal cancer. Clin Genet 2015; 87: 411–418.

    Article  CAS  Google Scholar 

  40. Niell BL, Rennert G, Bonner JD, Almog R, Tomsho LP, Gruber SB . BRCA1 and BRCA2 founder mutations and the risk of colorectal cancer. J Natl Cancer Inst 2004; 96: 15–21.

    Article  CAS  Google Scholar 

  41. Finlin BS, Gau CL, Murphy GA, Chiu YF, Botstein D, Brown PO et al. RERG Is a novel ras-related, estrogen-regulated and Growth-inhibitory Gene in Breast Cancer. J Biol Chem 2001; 276: 42259–42267.

    Article  CAS  Google Scholar 

  42. Mitchell SM, Ross JP, Drew HR, Ho T, Brown GS, Saunders NF et al. A panel of genes methylated with high frequency in colorectal cancer. BMC Cancer 2014; 14: 740–745.

    Article  Google Scholar 

  43. Zou H, Harrington JJ, Shire AM, Rego RL, Liang W, Campbell ME et al. Highly methylated genes in colorectal neoplasia: implications for screening. Cancer Epidemiol Biomark Prev 2007; 16: 2686–2696.

    Article  CAS  Google Scholar 

  44. Lin K, Taylor JR, Wu TD, Gutierrez J, Elliott JM, Vernes JM et al. TMEFF2 is a PDGF-AA binding protein with methylation-associated gene silencing in multiple cancer types including glioma. PLoS ONE 2011; 6: e18608–e18608.

    Article  CAS  Google Scholar 

  45. Sun TT, Tang JY, Du W, Zhao HJ, Zhao G, Yang SL et al. Bidirectional regulation between TMEFF2 and STAT3 may contribute to H. pylori-associated gastric carcinogenesis. Int J Cancer 2014; 136: 1053–1064.

    Article  Google Scholar 

  46. Barsky SH, Siegal GP, Jannotta F, Liotta LA . Loss of basement membrane components by invasive tumors but not by their benign counterparts. Lab Invest 1983; 49: 140–147.

    CAS  PubMed  Google Scholar 

  47. Thiery JP . Epithelial-mesenchymal transitions in tumour progression. Nat Rev Cancer 2002; 2: 442–454.

    Article  CAS  Google Scholar 

  48. Brabletz T, Jung A, Spaderna S, Hlubek F, Kirchner T . Opinion: migrating cancer stem cells—an integrated concept of malignant tumour progression. Nat Rev Cancer 2005; 5: 744–749.

    Article  CAS  Google Scholar 

  49. Spaderna S, Schmalhofer O, Hlubek F, Berx G, Eger A, Merkel S et al. A transient, EMT-linked loss of basement membranes indicates metastasis and poor survival in colorectal cancer. Gastroenterology 2006; 131: 830–840.

    Article  CAS  Google Scholar 

  50. Xiong H, Hong J, Du W, Lin Y, Ren L, Wang Y et al. Roles of STAT3 and ZEB1 proteins in E-cadherin down-regulation and human colorectal cancer epithelial-mesenchymal transition. J Biol Chem 2011; 287: 5819–5832.

    Article  Google Scholar 

  51. Spaderna S, Schmalhofer O, Wahlbuhl M, Dimmler A, Bauer K, Sultan A et al. The transcriptional repressor ZEB1 promotes metastasis and loss of cell polarity in cancer. Cancer Res 2008; 68: 537–544.

    Article  CAS  Google Scholar 

  52. Zhang G, Zhou T, Tian H, Liu Z, Xia S . High expression of ZEB1 correlates with liver metastasis and poor prognosis in colorectal cancer. Oncol Lett 2013; 5: 564–568.

    Article  CAS  Google Scholar 

  53. Crous-Bou M, Rennert G, Salazar R, Rodriguez-Moranta F, Rennert HS, Lejbkowicz F et al. Genetic polymorphisms in fatty acid metabolism genes and colorectal cancer. Mutagenesis 2012; 27: 169–176.

    Article  CAS  Google Scholar 

  54. Yeh CS, Wang JY, Cheng TL, Juan CH, Wu CH, Lin SR . Fatty acid metabolism pathway play an important role in carcinogenesis of human colorectal cancers by microarray-bioinformatics analysis. Cancer Lett 2006; 233: 297–308.

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Oncology, Linyi People’ Hospital, Linyi, China

    H Y Liu & C J Zhang

Authors
  1. H Y Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C J Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to C J Zhang.

Ethics declarations

Competing interests

The authors declare no conflict of interest.

Additional information

Supplementary Information accompanies the paper on Cancer Gene Therapy website

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, H., Zhang, C. Identification of differentially expressed genes and their upstream regulators in colorectal cancer. Cancer Gene Ther 24, 244–250 (2017). https://doi.org/10.1038/cgt.2017.8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/cgt.2017.8

This article is cited by

Search

Advanced search

Quick links