Dataset Information

Identification of Hub Genes in High-Grade Serous Ovarian Cancer Using Weighted Gene Co-Expression Network Analysis.

ABSTRACT: BACKGROUND High-grade serous ovarian cancer (HGSOC) is the most malignant gynecologic tumor. This study reveals biomarkers related to HGSOC incidence and progression using the bioinformatics method. MATERIAL AND METHODS Five gene expression profiles were downloaded from GEO. Differentially-expressed genes (DEGs) in HGSOC and normal ovarian tissue samples were screened using limma and the function of DEGs was annotated by KEGG and GO analysis using clusterProfiler. A co-expression network utilizing the WGCNA package was established to define several hub genes from the key module. Furthermore, survival analysis was performed, followed by expression validation with datasets from TCGA and GTEx. Finally, we used single-gene GSEA to detect the function of prognostic hub genes. RESULTS Out of the 1874 DEGs detected from 114 HGSOC versus 49 normal tissue samples, 956 were upregulated and 919 were downregulated. The functional annotation indicated that upregulated DEGs were mostly enriched in cell cycle, whereas the downregulated DEGs were enriched in the MAPK or Ras signaling pathway. Two modules significantly associated with HGSOC were excavated through WGCNA. After survival analysis and expression validation of hub genes, we found that 2 upregulated genes (MAD2L1 and PKD2) and 3 downregulated genes (DOCK5, FANCD2 and TBRG1) were positively correlated with HGSOC prognosis. GSEA for single-hub genes revealed that MAD2L1 and PKD2 were associated with proliferation, while DOCK5, FANCD2, and TBRG1 were associated with immune response. CONCLUSIONS We found that FANCD2, PKD2, TBRG1, and DOCK5 had prognostic value and could be used as potential biomarkers for HGSOC treatment.

SUBMITTER: Wu M

PROVIDER: S-EPMC7101203 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Identification of Hub Genes in High-Grade Serous Ovarian Cancer Using Weighted Gene Co-Expression Network Analysis.

Wu Meijing M Sun Yue Y Wu Jing J Liu Guoyan G

Medical science monitor : international medical journal of experimental and clinical research 20200317

BACKGROUND High-grade serous ovarian cancer (HGSOC) is the most malignant gynecologic tumor. This study reveals biomarkers related to HGSOC incidence and progression using the bioinformatics method. MATERIAL AND METHODS Five gene expression profiles were downloaded from GEO. Differentially-expressed genes (DEGs) in HGSOC and normal ovarian tissue samples were screened using limma and the function of DEGs was annotated by KEGG and GO analysis using clusterProfiler. A co-expression network utilizi ...[more]

PMID: 32180586

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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:Evidence indicates that high-grade serous ovarian carcinoma arises from the fallopian tube, rather than ovarian surface epithelium. This is termed the 'tubal origin' theory. The aim of the present study was to compare the immunophenotype and gene expression profiling among high-grade serous ovarian carcinoma (HGSOC), fallopian tube epithelium (FTE) and ovarian surface epithelium (OSE) based on tubal origin theory, and identify the differential genes associated with ovarian carcinogenesis. A total of 61 cases of fresh tissue samples including 21 cases of HGSOC, 20 cases of OSE, and 20 cases of FTE were obtained following surgical resection. Immunostaining was performed to detect the expression of PAX8, which has been considered as a potential immunophenotype marker of Müllerian origin. Illumina BeadChip was applied for gene expression profiling. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was performed to confirm the differential expression of candidate genes between HGSOC and FTE. The results of the present study demonstrated that PAX8 was highly expressed in HGSOC (19/21, 90.4%) and FTE (20/20, 100%), but not in OSE (3/20, 14.3%). A dendrogram generated by cluster analysis indicated a higher similarity of gene expression profile between HGSOC and FTE than OSE. A total of 2,412 differentially expressed genes were identified (absolute fold change >2) between HGSOC and FTE, including 822 upregulated genes in cancer and 1,590 downregulated genes. S100 calcium binding protein P, Ras-interacting protein 1, Wnt family member 5A, tumor-associated calcium signal transducer 2, Dickkopf Wnt signaling pathway inhibitor 3 and tumor suppressor candidate 3 genes were identified as candidate markers, of which the differential gene expression in HGSOC and FTE was confirmed by RT-qPCR (P<0.05). The results indicate the presence of a greater similarity in the immunophenotype and gene expression profile of HGSOC and FTE, when compared with OSE, which was consistent with the tubal origin theory of HGSOC.

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 in High-Grade Serous Ovarian Cancer Using Weighted Gene Co-Expression Network Analysis.

Publications

Identification of Hub Genes in High-Grade Serous Ovarian Cancer Using Weighted Gene Co-Expression Network Analysis.

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