Dataset Information

[Identification of Hub Genes for Ovarian Cancer Stem Cell Properties with Weighted Gene Co-expression Network Analysis].

ABSTRACT:

Objective

To investigate the significance of stemness-related genes in the diagnosis and treatment of ovarian cancer.

Methods

Key modules and genes were identified with weighted gene co-expression network analysis (WGCNA). The signal pathways of high expression of key genes were analyzed by gene set enrichment analysis (GSEA) and single cell sequencing data. The chemosensitivity of ovarian cancer to chemotherapy drugs was estimated with pRRophetic. Flow cytometry was used to examine the expression of CD44 ⁺CD117 ⁺in SKOV3 cells and cancer stem cells. The expression of key genes in ovarian cancer stem cells was confirmed by qRT-PCR. The core genes were identified by GeneMANIA analysis.

Results

According to the WGCNA results, 15 key genes were identified at the transcription level, all being highly expressed in many kinds of tumors. They were involved in the cell cycle, DNA repair, E2 target and G2M checkpoint pathway, and had significant correlation with chemosensitivity. The proportion of CD44 ⁺ CD117 ⁺ cells in SKOV3 cells and ovarian cancer stem cells were (1.20±0.34)% and (37.17±1.80)% respectively, with statistically significant difference ( P<0.05). qRT-PCR confirmed that seven key genes ( BUB1, CDC20, CCNB2, DLGAP5, KIF4 A, NEK2, NUSAP1) in the WGCNA results were highly expressed in ovarian cancer stem cells, and BUB1 might have played a core role.

Conclusion

Seven hub genes, especially BUB1, were identified by constructing gene co-expression network, which may become potential biomarkers of ovarian cancer gene.

SUBMITTER: Luo M

PROVIDER: S-EPMC10408925 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Publications

[Identification of Hub Genes for Ovarian Cancer Stem Cell Properties with Weighted Gene Co-expression Network Analysis].

Luo Meng M Zeng Hao H Ma Xin-Yu XY Ma Xue-Lei XL

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition 20210301 2

<h4>Objective</h4>To investigate the significance of stemness-related genes in the diagnosis and treatment of ovarian cancer.<h4>Methods</h4>Key modules and genes were identified with weighted gene co-expression network analysis (WGCNA). The signal pathways of high expression of key genes were analyzed by gene set enrichment analysis (GSEA) and single cell sequencing data. The chemosensitivity of ovarian cancer to chemotherapy drugs was estimated with pRRophetic. Flow cytometry was used to exami ...[more]

PMID: 33829699

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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:Purpose: Lung adenocarcinoma is one of the most common malignancies. Though some historic breakthroughs have been made in lung adenocarcinoma, its molecular mechanisms of development remain elusive. The aim of this study was to identify the potential genes associated with the lung adenocarcinoma progression and to provide new ideas for the prognosis evaluation of lung adenocarcinoma. Methods: The transcriptional profiles of ten pairs of snap-frozen tumor and adjacent normal lung tissues were obtained by performing RNA-seq. Weighted gene co-expression network analysis (WGCNA) was used to construct free-scale gene co-expression networks in order to explore the associations of gene sets with the clinical features and to investigate the functional enrichment analysis of co-expression genes. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and Gene Set Enrichment Analysis (GSEA) analyses were performed using clusterProfiler. The protein-protein network (PPI) was established using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and hub genes were identified using Cytohubba in Cytoscape. Transcription factor enrichment analysis was performed by the RcisTarget program in R language. Results: Based on RNA-seq data, 1,545 differentially expressed genes (DEGs) were found. Eight co-expression modules were identified among these DEGs. The blue module exhibited a strong correlation with LUAD, in which ADCY4, RXFP1, AVPR2, CALCRL, ADRB1, RAMP3, RAMP2 and VIPR1 were hub genes. A low expression level of RXFP1, AVPR2, ADRB1 and VIPR1 was detrimental to the survival of LUAD patients. Genes in the blue module enriched in 86 Gene Ontology terms and five KEGG pathways. We also found that transcription factors EGR3 and EXOSC3 were related to the biological function of the blue module. Overall, this study brings a new perspective to the understanding of LUAD and provides possible molecular biomarkers for prognosis evaluation of LUAD.

Dataset Information

[Identification of Hub Genes for Ovarian Cancer Stem Cell Properties with Weighted Gene Co-expression Network Analysis].

Objective

Methods

Results

Conclusion

Publications

[Identification of Hub Genes for Ovarian Cancer Stem Cell Properties with Weighted Gene Co-expression Network Analysis].

Similar Datasets

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