Identification of differentially expressed genes and biological characteristics of colorectal cancer by integrated bioinformatics analysis

Abstract

Colorectal cancer (CRC) ranks as one of the most common malignant tumors worldwide. Its mortality rate has remained high in recent years. Therefore, the aim of this study was to identify significant differentially expressed genes (DEGs) involved in its pathogenesis, which may be used as novel biomarkers or potential therapeutic targets for CRC. The gene expression profiles of GSE21510, GSE32323, GSE89076, and GSE113513 were downloaded from the Gene Expression Omnibus (GEO) database. After screening DEGs in each GEO data set, we further used the robust rank aggregation method to identify 494 significant DEGs including 212 upregulated and 282 downregulated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed by DAVID and the KOBAS online database, respectively. These DEGs were shown to be significantly enriched in different cancer‐related functions and pathways. Then, the STRING database was used to construct the protein–protein interaction network. The module analysis was performed by the MCODE plug‐in of Cytoscape based on the whole network. We finally filtered out seven hub genes by the cytoHubba plug‐in, including PPBP, CCL28, CXCL12, INSL5, CXCL3, CXCL10, and CXCL11. The expression validation and survival analysis of these hub genes were analyzed based on The Cancer Genome Atlas database. In conclusion, the robust DEGs associated with the carcinogenesis of CRC were screened through the GEO database, and integrated bioinformatics analysis was conducted. Our study provides reliable molecular biomarkers for screening and diagnosis, prognosis as well as novel therapeutic targets for CRC.

The promising novel biomarkers and candidate small molecule drugs in kidney renal clear cell carcinoma: Evidence from bioinformatics analysis of high‐throughput data

“…(Chowdhury et al, ) In the past decade, with the rapid development of microarray and RNA‐sequencing technology, more and more biomarkers of tumor initiation, progression, and prognosis have been identified using bioinformatics analysis. (Sun et al, ; Yan et al, ) In this study, we integrated three microarray datasets from gene expression omnibus (GEO) database (GSE781, GSE6344, and GSE100666) to identify the differentially expressed genes (DEGs) between KIRC and adjacent normal tissues, aiming to explore and determine the promising novel biomarkers associated with pathogenesis and prognosis of KIRC. Meanwhile, we revealed some candidate small molecule drugs that could reverse the gene expression of KIRC based on the CMap database.…”

Identification of prognostic-associated genes in colorectal cancer based on genome-wide expression profiles

“…C-X-C motif chemokine ligand 3 (CXCL3) is a member of the CXC chemokine family, and its expression is associated with various cancers, such as liver cancer [19], gastric cancer [20], breast cancer [21], prostate cancer [22], and non-small cell cancer [23]. Meanwhile, some scholars have explored CXCL3 in the CRC, and their ndings consistently reveal that the expression of CXCL3 in cancer tissues is signi cantly higher than that in normal tissues [24,25].…”

DNA methylation‐based diagnostic and prognostic biomarkers of nonsmoking lung adenocarcinoma patients

“…The PPI network with an interaction score of 0.4 as the criterion could well explain the mechanism of carcinogenesis at the molecular level. PPI networks were downloaded, then analyzed using Cytoscape Molecular Complex Detection . The selected modules had scores greater than 3 and nodes greater than or equal to 4.…”