ArticleFrontiers in genetics2021
Identification of a Four-Gene Signature With Prognostic Significance in Endometrial Cancer Using Weighted-Gene Correlation Network Analysis.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Article
- Gene Co-Expression Network Analysis Associated with Endometrial Cancer Tumorigenesis and Survival Outcomes.International journal of molecular sciences · 2024Article
- Lentinan has a beneficial effect on cognitive deficits induced by chronic Toxoplasma gondii infection in mice.Parasites & vectors · 2023Article
- FAM family gene prediction model reveals heterogeneity, stemness and immune microenvironment of UCEC.Frontiers in molecular biosciences · 2023Article
- Investigation of Transcriptome Patterns in Endometrial Cancers from Obese and Lean Women.International journal of molecular sciences · 2022Article
- Protein-based prognostic signature for predicting the survival and immunotherapeutic efficiency of endometrial carcinoma.BMC cancer · 2022Article
- Study on the Predictive Value of P53 Protein Expression in Brain Metastasis in NSCLC and the Mechanism of miR-424 Reversing Platinum Resistance in NSCLC.Contrast media & molecular imaging · 2022Article
- Ferroptosis: Opportunities and Challenges in Treating Endometrial Cancer.Frontiers in molecular biosciences · 2022Review
- Lentinan Supplementation Protects the Gut-Liver Axis and Prevents Steatohepatitis: The Role of Gut Microbiota Involved.Frontiers in nutrition · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Endometrial hyperplasia (EH) is a precursor for endometrial cancer (EC). However, biomarkers for the progression from EH to EC and standard prognostic biomarkers for EC have not been identified. In this study, we aimed to identify key genes with prognostic significance for the progression from EH to EC. Weighted-gene correlation network analysis (WGCNA) was used to identify hub genes utilizing microarray data (GSE106191) downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified from the Uterine Corpus Endometrial Carcinoma (UCEC) dataset of The Cancer Genome Atlas database. The Limma-Voom R package was applied to detect differentially expressed genes (DEGs; mRNAs) between cancer and normal samples. Genes with |log2 (fold change [FC])| > 1.0 and
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