Literature DB >> 31211595

Comprehensive Analysis of Core Genes and Potential Mechanisms in Rectal Cancer.

Bao-Xinzi Liu1, Guan-Jiang Huang2, Hai-Bo Cheng3.   

Abstract

Rectal cancer is a common type of colorectal cancer with high mortality and morbidity. The objective of this study was to identify gene signatures and uncover the potential mechanisms during rectal cancer samples. The gene expression profiles of GSE87211 data set were downloaded from GEO (Gene Expression Omnibus) database. The GSE87211 data set contained 2363 samples, including 203 rectal cancer samples and 160 matched mucosa control samples. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted, and protein-protein interaction network of differentially expressed genes (DEGs) was performed by Cytoscape. Then, Gene Expression Profiling Interactive Analysis (GEPIA) was applied to get the hub genes expression level and survival analysis between rectum adenocarcinoma (READ) tissues and normal tissues. In total, 846 DEGs were identified, including 402 upregulated genes and 444 downregulated genes. GO analysis showed that upregulated DEGs were enriched in inflammatory response, signal transduction, cell adhesion, immune response, and positive regulation of cell proliferation. KEGG pathway analysis showed that upregulated DEGs were enriched in cytokine-cytokine receptor interaction, Pi3K-Akt signaling pathway, and chemokine signaling pathway. The top 20 hub genes contained IL8, CXCR1, SSTR2, SST, CXCR2, GALR1, GAL, CXCL1, SSTR1, NPY1R, NPY, AGT, PPY, PPBP, CXCL2, CXCL6, CXCL11, CXCL3, GNG4, and GNGT1, and only four genes significantly increased expression levels with obvious changes of survival analysis in READ tissues based on GEPIA. Our study indicated that identified DEGs might promote our understanding of molecular mechanisms, which might be used as molecular targets or diagnostic biomarkers for the treatment of rectal cancer.

Entities:  

Keywords:  bioinformatics analysis; gene; pathway; rectal cancer

Year:  2019        PMID: 31211595     DOI: 10.1089/cmb.2019.0073

Source DB:  PubMed          Journal:  J Comput Biol        ISSN: 1066-5277            Impact factor:   1.479


  7 in total

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2.  Identified GNGT1 and NMU as Combined Diagnosis Biomarker of Non-Small-Cell Lung Cancer Utilizing Bioinformatics and Logistic Regression.

Authors:  Jia-Jia Zhang; Jiang Hong; Yu-Shui Ma; Yi Shi; Dan-Dan Zhang; Xiao-Li Yang; Cheng-You Jia; Yu-Zhen Yin; Geng-Xi Jiang; Da Fu; Fei Yu
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3.  Identification of a 5-Gene-Based Scoring System by WGCNA and LASSO to Predict Prognosis for Rectal Cancer Patients.

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Journal:  Anal Cell Pathol (Amst)       Date:  2021-03-23       Impact factor: 2.916

4.  Identification of gene biomarkers and immune cell infiltration characteristics in rectal cancer.

Authors:  Lina Wen; Zongqiang Han; Yanlin Du
Journal:  J Gastrointest Oncol       Date:  2021-06

5.  Hub Genes and Key Pathway Identification in Colorectal Cancer Based on Bioinformatic Analysis.

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Journal:  Biomed Res Int       Date:  2019-11-06       Impact factor: 3.411

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7.  Molecular heterogeneity of guanine nucleotide binding-protein γ subunit 4 in left- and right-sided colon cancer.

Authors:  Jintian Song; Jianwei Yang; Rongbo Lin; Xiongchao Cai; Liang Zheng; Yigui Chen
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  7 in total

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