Identification of crucial genes and pathways associated with colorectal cancer by bioinformatics analysis

Liu,Xiaoqun; Liu,Xiangdong; Qiao,Tiankui; Chen,Wei

doi:10.3892/ol.2020.11278

Identification of crucial genes and pathways associated with colorectal cancer by bioinformatics analysis

Authors:
- Xiaoqun Liu
- Xiangdong Liu
- Tiankui Qiao
- Wei Chen
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Affiliations: Center for Tumor Diagnosis and Therapy, Jinshan Hospital, Fudan University, Shanghai 201508, P.R. China, Department of Ophthalmology, Jinshan Hospital, Fudan University, Shanghai 201508, P.R. China
Published online on: January 9, 2020 https://doi.org/10.3892/ol.2020.11278
Pages: 1881-1889
Copyright: © Liu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Colorectal cancer (CRC) is a prevalent malignant tumour type arising from the colon and rectum. The present study aimed to explore the molecular mechanisms of the development and progression of CRC. Initially, differentially expressed genes (DEGs) between CRC tissues and corresponding non‑cancerous tissues were obtained by analysing the GSE15781 microarray dataset. The Database for Annotation, Visualization and Integrated Discovery was then utilized for functional and pathway enrichment analysis of the DEGs. Subsequently, a protein‑protein interaction (PPI) network was created using the Search Tool for the Retrieval of Interacting Genes and Proteins database and visualized by Cytoscape software. Furthermore, CytoNCA, a Cytoscape plugin, was used for centrality analysis of the PPI network to identify crucial genes. Finally, UALCAN was employed to validate the expression of the crucial genes and to estimate their effect on the survival of patients with colon cancer by Kaplan‑Meier curves and log‑rank tests. A total of 1,085 DEGs, including 496 upregulated and 589 downregulated genes, were screened out. The DEGs identified were enriched in various pathways, including ‘metabolic pathway’, ‘cell cycle’, ‘DNA replication’, ‘nitrogen metabolism’, ‘p53 signalling’ and ‘fatty acid degradation’. PPI network analysis suggested that interleukin‑6, MYC, NOTCH1, inhibin subunit βA (INHBA), CDK1, cyclin (CCN)B1 and CCNA2 were crucial genes, and their expression levels were markedly upregulated. Survival analysis suggested that upregulated INHBA significantly decreased the survival probability of patients with CRC. Conversely, upregulation of CCNB1 and CCNA2 expression levels were associated with increased survival probabalities. The identified DEGs, particularly the crucial genes, may enhance the current understanding of the genesis and progression of CRC, and certain genes, including INHBA, CCNB1 and CCNA2, may be candidate diagnostic and prognostic markers, as well as targets for the treatment of CRC.

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1	Siegel RL, Miller KD and Jemal A: Cancer statistics, 2019. CA Cancer J Clin. 69:7–34. 2019. View Article : Google Scholar : PubMed/NCBI
2	Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA and Jemal A: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 68:394–424. 2018. View Article : Google Scholar : PubMed/NCBI
3	Al Bandar MH and Kim NK: Current status and future perspectives on treatment of liver metastasis in colorectal cancer (Review). Oncol Rep. 37:2553–2564. 2017. View Article : Google Scholar : PubMed/NCBI
4	Kim SC, Shin YK, Kim YA, Jang SG and Ku JL: Identification of genes inducing resistance to ionizing radiation in human rectal cancer cell lines: Re-sensitization of radio-resistant rectal cancer cells through down regulating NDRG1. BMC Cancer. 18:5942018. View Article : Google Scholar : PubMed/NCBI
5	Andersen SE, Andersen IB, Jensen BV, Pfeiffer P, Ota T and Larsen JS: A systematic review of observational studies of trifluridine/tipiracil (TAS-102) for metastatic colorectal cancer. Acta Oncol. 58:1149–1157. 2019. View Article : Google Scholar : PubMed/NCBI
6	Jin S, Mu Y, Wang X, Liu Z, Wan L, Xiong Y, Zhang Y, Zhou L and Li L: Overexpressed RACK1 is positively correlated with malignant degree of human colorectal carcinoma. Mol Biol Rep. 41:3393–3399. 2014. View Article : Google Scholar : PubMed/NCBI
7	Li XY, Hu Y, Li NS, Wan JH, Zhu Y and Lu NH: RACK1 acts as a potential tumor promoter in colorectal cancer. Gastroenterol Res Pract. 2019:56250262019. View Article : Google Scholar : PubMed/NCBI
8	Ouyang S, Zhou X, Chen Z, Wang M, Zheng X and Xie M: LncRNA BCAR4, targeting to miR-665/STAT3 signaling, maintains cancer stem cells stemness and promotes tumorigenicity in colorectal cancer. Cancer Cell Int. 19:722019. View Article : Google Scholar : PubMed/NCBI
9	Shangguan H, Tan SY and Zhang JR: Bioinformatics analysis of gene expression profiles in hepatocellular carcinoma. Eur Rev Med Pharmacol Sci. 19:2054–2061. 2015.PubMed/NCBI
10	Kosti A, Harry Chen HI, Mohan S, Liang S, Chen Y and Habib SL: Microarray profile of human kidney from diabetes, renal cell carcinoma and renal cell carcinoma with diabetes. Genes Cancer. 6:62–70. 2015.PubMed/NCBI
11	Christgen M, Geffers R, Kreipe H and Lehmann U: IPH-926 lobular breast cancer cells are triple-negative but their microarray profile uncovers a luminal subtype. Cancer Sci. 104:1726–1730. 2013. View Article : Google Scholar : PubMed/NCBI
12	Yu J, Li X, Zhong C, Li D, Zhai X, Hu W, Guo C, Yuan Y and Zheng S: High-throughput proteomics integrated with gene microarray for discovery of colorectal cancer potential biomarkers. Oncotarget. 7:75279–75292. 2016. View Article : Google Scholar : PubMed/NCBI
13	Shen X, Yue M, Meng F, Zhu J, Zhu X and Jiang Y: Microarray analysis of differentially-expressed genes and linker genes associated with the molecular mechanism of colorectal cancer. Oncol Lett. 12:3250–3258. 2016. View Article : Google Scholar : PubMed/NCBI
14	Snipstad K, Fenton CG, Kjaeve J, Cui G, Anderssen E and Paulssen RH: New specific molecular targets for radio-chemotherapy of rectal cancer. Mol Oncol. 4:52–64. 2010. View Article : Google Scholar : PubMed/NCBI
15	Gautier L, Cope L, Bolstad BM and Irizarry RA: Affy-analysis of Affymetrix GeneChip data at the probe level. Bioinformatics. 20:307–315. 2004. View Article : Google Scholar : PubMed/NCBI
16	Hardcastle TJ: Generalised empirical Bayesian methods for discovery of differential data in high-throughput biology. Bioinformatics. 32:195–202. 2016.PubMed/NCBI
17	Huang DW, Sherman BT, Tan Q, Kir J, Liu D, Bryant D, Guo Y, Stephens R, Baseler MW, Lane HC and Lempicki RA: DAVID Bioinformatics Resources: Expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res. 35:W169–W175. 2007. View Article : Google Scholar : PubMed/NCBI
18	Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva NT, Roth A, Bork P, et al: The STRING database in 2017: Quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res. 45:D362–D368. 2017. View Article : Google Scholar : PubMed/NCBI
19	Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B and Ideker T: Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13:2498–2504. 2003. View Article : Google Scholar : PubMed/NCBI
20	Tang Y, Li M, Wang J, Pan Y and Wu FX: CytoNCA: A cytoscape plugin for centrality analysis and evaluation of protein interaction networks. Biosystems. 127:67–72. 2015. View Article : Google Scholar : PubMed/NCBI
21	Chandrashekar DS, Bashel B, Balasubramanya SAH, Creighton CJ, Ponce-Rodriguez I, Chakravarthi BVSK and Varambally S: UALCAN: A portal for facilitating tumor subgroup gene expression and survival analyses. Neoplasia. 19:649–658. 2017. View Article : Google Scholar : PubMed/NCBI
22	Gao X, Wang X and Zhang S: Bioinformatics identification of crucial genes and pathways associated with hepatocellular carcinoma. Biosci Rep. 38:BSR201814412018. View Article : Google Scholar : PubMed/NCBI
23	Hassanain M, Al-Alem F, Simoneau E, Traiki TA, Alsaif F, Alsharabi A, Al-Faris H and Al-Saleh K: Colorectal cancer liver metastasis trends in the kingdom of Saudi Arabia. Saudi J Gastroenterol. 22:370–374. 2016. View Article : Google Scholar : PubMed/NCBI
24	Katsidzira L, Ocvirk S, Wilson A, Li J, Mahachi CB, Soni D, DeLany J, Nicholson JK, Zoetendal EG and O'Keefe SJD: Differences in fecal gut microbiota, short-chain fatty acids and bile acids link colorectal cancer risk to dietary changes associated with urbanization among zimbabweans. Nutr Cancer. 71:1313–1324. 2019. View Article : Google Scholar : PubMed/NCBI
25	Park Y, Park SJ, Cheon JH, Kim WH and Kim TI: Association of family history with cancer recurrence, survival, and the incidence of colorectal adenoma in patients with colorectal cancer. J Cancer Prev. 24:1–10. 2019. View Article : Google Scholar : PubMed/NCBI
26	Cordier-Bussat M, Thibert C, Sujobert P, Genestier L, Fontaine É and Billaud M: Even the Warburg effect can be oxidized: Metabolic cooperation and tumor development. Med Sci (Paris). 34:701–708. 2018.(In French). View Article : Google Scholar : PubMed/NCBI
27	Subramanian C and Cohen MS: Over expression of DNA damage and cell cycle dependent proteins are associated with poor survival in patients with adrenocortical carcinoma. Surgery. 165:202–210. 2019. View Article : Google Scholar : PubMed/NCBI
28	Suehiro Y, Takemoto Y, Nishimoto A, Ueno K, Shirasawa B, Tanaka T, Kugimiya N, Suga A, Harada E and Hamano K: Dclk1 Inhibition cancels 5-FU-induced cell-cycle arrest and decreases cell survival in colorectal cancer. Anticancer Res. 38:6225–6230. 2018. View Article : Google Scholar : PubMed/NCBI
29	Thorenoor N, Faltejskova-Vychytilova P, Hombach S, Mlcochova J, Kretz M, Svoboda M and Slaby O: Long non-coding RNA ZFAS1 interacts with CDK1 and is involved in p53-dependent cell cycle control and apoptosis in colorectal cancer. Oncotarget. 7:622–637. 2016. View Article : Google Scholar : PubMed/NCBI
30	Fang Y, Yu H, Liang X, Xu J and Cai X: Chk1-induced CCNB1 overexpression promotes cell proliferation and tumor growth in human colorectal cancer. Cancer Biol Ther. 15:1268–1279. 2014. View Article : Google Scholar : PubMed/NCBI
31	Gan Y, Li Y, Li T, Shu G and Yin G: CCNA2 acts as a novel biomarker in regulating the growth and apoptosis of colorectal cancer. Cancer Manag Res. 10:5113–5124. 2018. View Article : Google Scholar : PubMed/NCBI
32	Patel SA and Gooderham NJ: IL6 mediates immune and colorectal cancer cell cross-talk via miR-21 and miR-29b. Mol Cancer Res. 13:1502–1508. 2015. View Article : Google Scholar : PubMed/NCBI
33	Miteva LD, Stanilov NS, Cirovski GМ and Stanilova SA: Upregulation of Treg-related genes in addition with IL6 showed the significant role for the distant metastasis in colorectal cancer. Cancer Microenviron. 10:69–76. 2017. View Article : Google Scholar : PubMed/NCBI
34	Lv Z, Wei J, You W, Wang R, Shang J, Xiong Y, Yang H, Yang X and Fu Z: Disruption of the c-Myc/miR-200b-3p/PRDX2 regulatory loop enhances tumor metastasis and chemotherapeutic resistance in colorectal cancer. J Transl Med. 15:2572017. View Article : Google Scholar : PubMed/NCBI
35	Okano M, Yamamoto H, Ohkuma H, Kano Y, Kim H, Nishikawa S, Konno M, Kawamoto K, Haraguchi N, Takemasa I, et al: Significance of INHBA expression in human colorectal cancer. Oncol Rep. 30:2903–2908. 2013. View Article : Google Scholar : PubMed/NCBI
36	Zhang H, Jiang H, Chen L, Liu J, Hu X and Zhang H: Inhibition of Notch1/Hes1 signaling pathway improves radiosensitivity of colorectal cancer cells. Eur J Pharmacol. 818:364–370. 2018. View Article : Google Scholar : PubMed/NCBI
37	Sajadimajd S and Yazdanparast R: Differential behaviors of trastuzumab-sensitive and -resistant SKBR3 cells treated with menadione reveal the involvement of Notch1/Akt/FOXO1 signaling elements. Mol Cell Biochem. 408:89–102. 2015. View Article : Google Scholar : PubMed/NCBI
38	Prabakaran DS, Muthusami S, Sivaraman T, Yu JR and Park WY: Silencing of FTS increases radiosensitivity by blocking radiation-induced Notch1 activation and spheroid formation in cervical cancer cells. Int J Biol Macromol. 126:1318–1325. 2019. View Article : Google Scholar : PubMed/NCBI