Research Article
Bioinformatics Analysis of Colorectal Cancer Risk Related Genes and Candidate Pathways Based on GEO Database
Junhua Li1, Bulin Baila3, Huricha Zhang3, Tian Xiang Xu2, Song Jiang2, Su Rina2, Tegexibaiyin Wang3 and Wu Ji1,*
1PLA Research Institute of General Surgery, Jinling Hospital, The First School of Clinical Medicine, Southern Medical University, Nanjing 210002, Jiangsu, China. 2Abdominal Tumor Surgery, Inner Mongolia People’s Hospital, No. 20 Zhaowuda Road, Hohhot, 010017, China. 3Pharmacy Laboratory, Inner Mongolia International Mongolian Hospital, NO. 83 DaXueDong Road, Hohhot 010065, China.
*, Correspondence
Wu Ji, PLA Research Institute of General Surgery, Jinling Hospital, The First School of Clinical Medicine, Southern Medical University, Nanjing 210002, Jiangsu, China. [email protected].
Tegexibaiyin Wang, Pharmacy Laboratory, Inner Mongolia International Mongolian Hospital, NO. 83 DaXueDong Road, Hohhot 010065, China. [email protected].
Received: April 11, 2023; Accepted: August 18, 2024; Published online: September 4, 2024.
Cite this paper: Junhua Li, Bulin Baila, Huricha Zhang, Tian Xiang Xu, Song Jiang, Su Rina, Tegexibaiyin Wang and Wu Ji. (2024) Bioinformatics Analysis of Colorectal Cancer Risk Related Genes and Candidate Pathways Based on GEO Database. Global Journal of Life Sciences, 5(1):28-39. http://naturescholars.com/gjls.050104. https://doi.org/10.46633/gjlsm.050104.
Copyright © 2024 by Scholars Publishing, LLC.
Abstract
Colorectal cancer (CRC) is one of the most common digestive tract cancers in the world. The incidence rate of cancer is increasing year by year around the world. At present, the exact mechanism of colorectal cancer has not been fully elucidated leading to poor diagnosis and treatment methodologies, therefore, it is necessary to understand the molecular mechanism of colorectal cancer. Applying bioinformatics to analyze the differentially expressed genes derived from CRC tissues or tissues adjacent to carcinoma has been a popular research methodology for investigating the underlining pathology of cancers. In this study, we downloaded the CRC related microarray data set (GSE32323) from the Gene Expression Omnibus (GEO). 33 samples were analyzed by microarray and 410 genes were up-regulated and 499 genes were down-regulated inCRC. Using gene chip prediction analysis (PAM) method and gsea-msigdb resource, the expressions of 1135 genes were compared and analyzed. Among them, prkcb, pik3cg, camk2d, CCND1, Cdk6, CDK4, CDKN1A were found to be potential biomarkers for CRC diagnosis. In addition, the PAM method revealed a better resolution of calculation, which provides confirmative data mining results for CRC related diagnostic marker prediction. these results will provide the basis for new research projects in clinical practice, and provide the basis for rapid risk assessment of colorectal cancer by microarray gene expression analysis.
Key words: bioinformatics analysis, colorectal cancer, risk related genes, GEO database.