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Four targeted genes for predicting the prognosis of colorectal cancer: A bioinformatics analysis case.

ABSTRACT: The molecular mechanisms underlying the development and progression of colorectal cancer (CRC) have not been clarified. The purpose of the present study was to identify key genes that may serve as novel therapeutic targets or prognostic predictors in patients with CRC using bioinformatics analysis. Four gene expression datasets were downloaded from the Gene Expression Omnibus database, which revealed 19 upregulated and 34 downregulated differentially expressed genes (DEGs). The downregulated DEGs were significantly enriched in eight pathways according to Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. A protein-protein interaction network was constructed with 52 DEGs and 458 edges. Ten key genes were identified according to the degree value, betweenness centrality and closeness centrality. Survival analysis revealed that low expression of four of the ten genes, carcinoembryonic antigen related cell adhesion molecule 7 (CEACAM7), solute carrier family 4 member 4 (SLC4A4), glucagon (GCG) and chloride channel accessory 1 (CLCA1) genes, were associated with unfavorable prognosis in CRC. Furthermore, gene set enrichment analysis revealed that two pathways were significantly enriched in the CEACAM7 low-expression group. Thus, CEACAM7, SLC4A4, GCG and CLCA1 may be prognostic markers or therapeutic targets of CRC. Low CEACAM7 expression may be associated with the activation of glycosaminoglycan biosynthesis-chondroitin sulfate and extracellular matrix receptor interaction pathways and may affect the prognosis of CRC.

SUBMITTER: Bian Q

PROVIDER: S-EPMC6781647 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Four targeted genes for predicting the prognosis of colorectal cancer: A bioinformatics analysis case.

Bian Qinglai Q Chen Jiaxu J Qiu Wenqi W Peng Chenxi C Song Meifang M Sun Xuebin X Liu Yueyun Y Ding Fengmin F Chen Jianbei J Zhang Liqing L

Oncology letters 20190913 5

The molecular mechanisms underlying the development and progression of colorectal cancer (CRC) have not been clarified. The purpose of the present study was to identify key genes that may serve as novel therapeutic targets or prognostic predictors in patients with CRC using bioinformatics analysis. Four gene expression datasets were downloaded from the Gene Expression Omnibus database, which revealed 19 upregulated and 34 downregulated differentially expressed genes (DEGs). The downregulated DEG ...[more]

PMID: 31612015

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Project description:Colorectal cancer (CRC) is a life-threatening disease with a poor prognosis. Therefore, it is crucial to identify molecular prognostic biomarkers for CRC. The present study aimed to identify potential key genes that could be used to predict the prognosis of patients with CRC. Three CRC microarray datasets (GSE20916, GSE73360 and GSE44861) were downloaded from the Gene Expression Omnibus (GEO) database, and one dataset was obtained from The Cancer Genome Atlas (TCGA) database. The three GEO datasets were analyzed to detect differentially expressed genes (DEGs) using the BRB-ArrayTools software. Functional and pathway enrichment analyses of these DEGs were performed using the Database for Annotation, Visualization and Integrated Discovery tool. A protein-protein interaction (PPI) network of DEGs was constructed, hub genes were extracted, and modules of the PPI network were analyzed. To investigate the prognostic values of the hub genes in CRC, data from the CRC datasets of TCGA were used to perform the survival analyses based on the sample splitting method and Cox regression model. Correlation among the hub genes was evaluated using Spearman's correlation analysis. In the three GEO datasets, a total of 105 common DEGs were identified, including 51 down- and 54 up-regulated genes in CRC compared with normal colorectal tissues. A PPI network consisting of 100 DEGs and 551 edges was constructed, and 44 nodes were identified as hub genes. Among these 44 genes, the four hub genes TIMP metallopeptidase inhibitor 1 (TIMP1), solute carrier family 4 member 4 (SLC4A4), aldo-keto reductase family 1 member B10 (AKR1B10) and ATP binding cassette subfamily E member 1 (ABCE1) were associated with overall survival (OS) in patients with CRC. Three significant modules were extracted from the PPI network. The hub gene TIMP1 was present in Module 1, ABCE1 was involved in Module 2 and SLC4A4 was identified in Module 3. Univariate analysis revealed that TIMP1, SLC4A4, AKR1B10 and ABCE1 were associated with the OS of patients with CRC. Multivariate analysis demonstrated that SLC4A4 may be an independent prognostic factor associated with OS. Furthermore, the results from correlation analysis revealed that there was no correlation between TIMP1, SLC4A4 and ABCE1, whereas AKR1B10 was positively correlated with SLC4A4. In conclusion, the four key genes TIMP1, SLC4A4, AKR1B10 and ABCE1 associated with the OS of patients with CRC were identified by integrated bioinformatics analysis. These key genes may be used as prognostic biomarkers to predict the survival of patients with CRC, and may therefore represent novel therapeutic targets for CRC.

Project description:Colorectal cancer (CRC) is known to display a high risk of metastasis and recurrence. The main objective of our investigation was to shed more light on CRC pathogenesis by screening CRC datasets for the identification of key genes and signaling pathways, possibly leading to new approaches for the diagnosis and treatment of CRC. We downloaded the colorectal cancer datasets from the Gene Expression Omnibus (GEO) database site. We used GEO2R to screen for differentially expressed genes (DEGs) of which those with a fold change >1 were considered as up-regulated and those with a fold change <-1 were considered as down-regulated on the basis of a P < .05. "Gene ontology (GO)" and "Kyoto Encyclopedia of Genes and Genomes (KEGG)" data were analyzed by the "DAVID" software. The online search tool "STRING" was used to search for interacting genes or proteins and we used Cytoscape (v3.8.0) to generate a PPI network map and to identify key genes. Finally, survival analysis and stage mapping of key genes were performed using "GEPIA" with the aim of elucidating their potential impact on CRC. Our study revealed 120 intersecting genes of which 55 were up- and 65 were downregulated, respectively. GO analysis revealed that these genes were involved in cell proliferation, exosome secretion, G2/M transition, cytosol, protein binding, and protein kinase activity. KEGG pathway analysis showed that these genes were involved in cell cycle and mineral absorption. The Cytoscape PPI map showed 17 nodes and 262 edges, and 10 hub genes were identified by top 10 degrees. Survival analysis demonstrated that the AURKA, CCNB1, and CCNA2 genes were strongly associated with the survival rate of CRC patients. In addition, CCNB1, CCNA2, CDK1, CKS2, MAD2L1, and DLGAP5 could be correlated to pathological CRC staging. In this research, we identified key genes that may explain the molecular mechanism of occurrence and progression of CRC but may also contribute to an improvement in the clinical staging and prognosis of CRC patients.

Dataset Information

Four targeted genes for predicting the prognosis of colorectal cancer: A bioinformatics analysis case.

Publications

Four targeted genes for predicting the prognosis of colorectal cancer: A bioinformatics analysis case.

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