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Identification of Hub Genes and Construction of a Transcriptional Regulatory Network Associated With Tumor Recurrence in Colorectal Cancer by Weighted Gene Co-expression Network Analysis.

ABSTRACT: Tumor recurrence is one of the most important risk factors that can negatively affect the survival rate of colorectal cancer (CRC) patients. However, the key regulators dictating this process and their exact mechanisms are understudied. This study aimed to construct a gene co-expression network to predict the hub genes affecting CRC recurrence and to inspect the regulatory network of hub genes and transcription factors (TFs). A total of 177 cases from the GSE17536 dataset were analyzed via weighted gene co-expression network analysis to explore the modules related to CRC recurrence. Functional annotation of the key module genes was assessed through Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. The protein and protein interaction network was then built to screen hub genes. Samples from the Cancer Genome Atlas (TCGA) were further used to validate the hub genes. Construction of a TFs-miRNAs-hub genes network was also conducted using StarBase and Cytoscape approaches. After identification and validation, a total of five genes (TIMP1, SPARCL1, MYL9, TPM2, and CNN1) were selected as hub genes. A regulatory network of TFs-miRNAs-targets with 29 TFs, 58 miRNAs, and five hub genes was instituted, including model GATA6-MIR106A-CNN1, SP4-MIR424-TPM2, SP4-MIR326-MYL9, ETS1-MIR22-TIMP1, and ETS1-MIR22-SPARCL1. In conclusion, the identification of these hub genes and the prediction of the Regulatory relationship of TFs-miRNAs-hub genes may provide a novel insight for understanding the underlying mechanism for CRC recurrence.

SUBMITTER: Liu S

PROVIDER: S-EPMC8058478 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectiveTo investigate the differential expression gene modules and hub genes associated with Alzheimer's disease (AD) by weighted gene co-expression network analysis (WGCNA) and annotate the biological functions of these modules.MethodsWe downloaded transcriptome sequencing data from the GEO database, and according to the correlation of the genes, a gene co-expression network was constructed with the parameter setting of β=8 and a correlation coefficient threshold of 0.85. Pearson correlation test was used to calculate the correlation between the module genes and clinical traits to screen the gene modules significantly associated with AD and identify the hub genes according to the connectivity within modules. GO functional enrichment analysis and KEGG pathway analysis were used to annotate the functions of the modules. A cell model of AD was established in SH-SY5Y cells by Aβ1-42 treatment, and the mRNA expression levels of the hub genes were compared between the Aβ1-42-treated cells and the control cells.ResultsTen gene co-expression modules were constructed based on the correlations of gene expression, in which the brown (r=0.66, P < 0.001) and turquoise modules (r=-0.68, P < 0.001) were significantly correlated with the AD group. Forty-eight genes were identified as the hub genes in the co-expression network. Function annotation revealed that the genes in both modules were mainly enriched in DNA damage and repair pathways and metabolism-related pathways. Differential expression analysis of the genes revealed that the genes DNASE1, TEKT2 and MTSS1L were highly expressed while ACP2, LANCL2 and GMPR2 were lowly expressed in AD group. The results of cell experiment confirmed the up-regulation of DNASE1, TEKT2 and MTSS1L genes and the down-regulation of ACP2, LANCL2, and GMPR2 in Aβ1-42-treated SH-SY5Y cells (P < 0.01).ConclusionThe brown and turquoise modules are closely correlated with AD. The hub genes including MTSS1L, GMPR2, ACP2, ACTG1 and LANCL2 selected from the modules may participate in AD pathogenesis by regulating DNA damage and repair.

Project description:BackgroundCardiotoxicity is a common complication following anthracycline chemotherapy and represents one of the serious adverse reactions affecting life, which severely limits the effective use of anthracyclines in cancer therapy. Although some genes have been investigated by individual studies, the comprehensive analysis of key genes and molecular regulatory network in anthracyclines-induced cardiotoxicity (AIC) is lacking but urgently needed.MethodsThe present study integrating several transcription profiling datasets aimed to identify key genes associated with AIC by weighted correlation network analysis (WGCNA) and differentially expressed analysis (DEA) and also constructed miRNA-transcription factor-gene regulatory network. A total of three transcription profiling datasets involving 47 samples comprising 41 rat heart tissues and 6 human induced pluripotent stem cell-derived cardiomyocytes (hiPSCMs) samples were enrolled.ResultsThe WGCNA and DEA with E-MTAB-1168 identified 14 common genes affected by doxorubicin administrated by 4 weeks or 6 weeks. Functional and signal enrichment analyses revealed that these genes were mainly enriched in the regulation of heart contraction, muscle contraction, heart process, and oxytocin signaling pathway. Ten (Ryr2, Casq1, Fcgr2b, Postn, Tceal5, Ccn2, Tnfrsf12a, Mybpc2, Ankrd23, Scn3b) of the 14 genes were verified by another gene expression profile GSE154603. Importantly, three key genes (Ryr2, Tnfrsf12a, Scn3b) were further validated in a hiPSCMs-based in-vitro model. Additionally, the miRNA-transcription factor-gene regulatory revealed several top-ranked transcription factors including Tcf12, Ctcf, Spdef, Ebf1, Sp1, Rcor1 and miRNAs including miR-124-3p, miR-195-5p, miR-146a-5p, miR-17-5p, miR-15b-5p, miR-424-5p which may be involved in the regulation of genes associated with AIC.ConclusionsCollectively, the current study suggested the important role of the key genes, oxytocin signaling pathway, and the miRNA-transcription factor-gene regulatory network in elucidating the molecular mechanism of AIC.

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Identification of Hub Genes and Construction of a Transcriptional Regulatory Network Associated With Tumor Recurrence in Colorectal Cancer by Weighted Gene Co-expression Network Analysis.

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