Dataset Information

Systematic prediction of key genes for ovarian cancer by co-expression network analysis.

ABSTRACT: Ovarian cancer (OC) is the most lethal gynaecological malignancy, characterized by high recurrence and mortality. However, the mechanisms of its pathogenesis remain largely unknown, hindering the investigation of the functional roles. This study sought to identify key hub genes that may serve as biomarkers correlated with prognosis. Here, we conduct an integrated analysis using the weighted gene co-expression network analysis (WGCNA) to explore the clinically significant gene sets and identify candidate hub genes associated with OC clinical phenotypes. The gene expression profiles were obtained from the MERAV database. Validations of candidate hub genes were performed with RNASeqV2 data and the corresponding clinical information available from The Cancer Genome Atlas (TCGA) database. In addition, we examined the candidate genes in ovarian cancer cells. Totally, 19 modules were identified and 26 hub genes were extracted from the most significant module (R2 = .53) in clinical stages. Through the validation of TCGA data, we found that five hub genes (COL1A1, DCN, LUM, POSTN and THBS2) predicted poor prognosis. Receiver operating characteristic (ROC) curves demonstrated that these five genes exhibited diagnostic efficiency for early-stage and advanced-stage cancer. The protein expression of these five genes in tumour tissues was significantly higher than that in normal tissues. Besides, the expression of COL1A1 was associated with the TAX resistance of tumours and could be affected by the autophagy level in OC cell line. In conclusion, our findings identified five genes could serve as biomarkers related to the prognosis of OC and may be helpful for revealing pathogenic mechanism and developing further research.

SUBMITTER: Wang M

PROVIDER: S-EPMC7294139 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Systematic prediction of key genes for ovarian cancer by co-expression network analysis.

Wang Mingyuan M Wang Jinjin J Liu Jinglan J Zhu Lili L Ma Heng H Zou Jiang J Wu Wei W Wang Kangkai K

Journal of cellular and molecular medicine 20200421 11

Ovarian cancer (OC) is the most lethal gynaecological malignancy, characterized by high recurrence and mortality. However, the mechanisms of its pathogenesis remain largely unknown, hindering the investigation of the functional roles. This study sought to identify key hub genes that may serve as biomarkers correlated with prognosis. Here, we conduct an integrated analysis using the weighted gene co-expression network analysis (WGCNA) to explore the clinically significant gene sets and identify c ...[more]

PMID: 32319226

Similar Datasets

Project description:BACKGROUND:Aroma is an important organoleptic quality for fruit and has a large influence on consumer preference. Kiwifruit esters undergo rapid and substantial changes contributing to the flavor during fruit ripening. Part of enzymes and their coding genes have been indicated potential candidates for flavor-related esters synthesis. However, there still exist obvious gaps in the biosynthetic pathways of esters and the mechanisms regulating ester biosynthesis in kiwifruit remain unknown. RESULTS:Using gas chromatography-mass spectrometry (GC-MS), volatile compounds of kiwifruit were quantified in response to ethylene (ETH, 100??l/l, 24?h, 20?°C) and 1-methylcyclopropene (1-MCP, 1??l/l, 24?h, 20?°C). The results indicated that esters showed the most substantial changes enhanced by ethylene and were inhibited by 1-MCP. Correlations between RNA-seq results and concentrations of esters, constructed using Weighted Gene Co-Expression Network Analysis (WGCNA) indicated that three structural genes (fatty acid desaturase, AdFAD1; aldehyde dehydrogenase, AdALDH2; alcohol acyltransferase, AdAT17) had similar expression patterns that paralled the changes in total ester content, and AdFAD1 transcripts exhibited the highest correlation. In order to search for potential regulators for ester biosynthesis, 14 previously reported ethylene-responsive transcription factors (TFs) were included in the correlation analysis with esters and their biosynthetic genes. Using dual-luciferase assay, the in vivo regulatory activities of TFs on ester biosynthetic gene promoters were investigated and the results indicated that AdNAC5 and AdDof4 (DNA binding with one finger) trans-activated and trans-suppressed the AdFAD1 promoter. CONCLUSIONS:The present study advanced the molecular basis of ripening-related ester biosynthesis in kiwifruit by identifying three biosynthetic related genes AdFAD1, AdALDH2 and AdAT17 by transcriptome analysis, and highlighted the function of two TFs by transactivation studies.

Dataset Information

Systematic prediction of key genes for ovarian cancer by co-expression network analysis.

Publications

Systematic prediction of key genes for ovarian cancer by co-expression network analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets