{"title":"Reliable estrogen-related prognostic signature for uterine corpus endometrial carcinoma","authors":"Mojuan Li , Shuai Wang , Hao Huang , Li Li","doi":"10.1016/j.compbiolchem.2024.108216","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>Uterine corpus endometrial carcinoma (UCEC) is a predominant gynecological malignancy worldwide. Overdosed estrogen exposure has been widely known as a crucial risk factor for UCEC patients. The purpose of this work is to explore crucial estrogen-related genes (ERGs) in UCEC.</div></div><div><h3>Methods</h3><div>UCEC scRNA-seq data, bulk RNA data, and ERGs were obtained from GEO, TCGA, and Molecular Signature Database, respectively. Differential expression analysis and cross analysis determined the candidate genes, and optimal genes in risk score were obtained after univariate Cox regression analysis, LASSO Cox regression analysis, and multivariate Cox regression analysis. The functional information was revealed by GO, KEGG, and GSVA enrichment analyses. CCK8 assay was used to detect the drug sensitivity.</div></div><div><h3>Results</h3><div>After cross analysis of the differentially expressed genes and the 8734 ERGs, 86 differentially expressed ERGs were identified in UCEC, which were significantly enriched in some immune related pathways and microbiota related pathways. Of them, the most optimal 8 ERGs were obtained to build prognostic risk score, including GAL, PHGDH, SLC7A2, HNMT, CLU, AREG, MACC1, and HMGA1. The risk score could reliably predict patient prognosis, and high-risk patients had worse prognosis. Higher HMGA1 gene expression exhibited higher sensitivity to Osimertinib.</div></div><div><h3>Conclusions</h3><div>Predictive risk score based on 8 ERGs exhibited excellent prognostic value in UCEC patients, and high-risk patients had inferior survival. UCEC patients with distinct prognoses showed different tumor immune microenvironment.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108216"},"PeriodicalIF":2.6000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Biology and Chemistry","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1476927124002044","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Background
Uterine corpus endometrial carcinoma (UCEC) is a predominant gynecological malignancy worldwide. Overdosed estrogen exposure has been widely known as a crucial risk factor for UCEC patients. The purpose of this work is to explore crucial estrogen-related genes (ERGs) in UCEC.
Methods
UCEC scRNA-seq data, bulk RNA data, and ERGs were obtained from GEO, TCGA, and Molecular Signature Database, respectively. Differential expression analysis and cross analysis determined the candidate genes, and optimal genes in risk score were obtained after univariate Cox regression analysis, LASSO Cox regression analysis, and multivariate Cox regression analysis. The functional information was revealed by GO, KEGG, and GSVA enrichment analyses. CCK8 assay was used to detect the drug sensitivity.
Results
After cross analysis of the differentially expressed genes and the 8734 ERGs, 86 differentially expressed ERGs were identified in UCEC, which were significantly enriched in some immune related pathways and microbiota related pathways. Of them, the most optimal 8 ERGs were obtained to build prognostic risk score, including GAL, PHGDH, SLC7A2, HNMT, CLU, AREG, MACC1, and HMGA1. The risk score could reliably predict patient prognosis, and high-risk patients had worse prognosis. Higher HMGA1 gene expression exhibited higher sensitivity to Osimertinib.
Conclusions
Predictive risk score based on 8 ERGs exhibited excellent prognostic value in UCEC patients, and high-risk patients had inferior survival. UCEC patients with distinct prognoses showed different tumor immune microenvironment.
期刊介绍：
Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions with a major computational component in the areas of nucleic acid and protein sequence research, molecular evolution, molecular genetics (functional genomics and proteomics), theory and practice of either biology-specific or chemical-biology-specific modeling, and structural biology of nucleic acids and proteins are particularly welcome. Exceptionally high quality research work in bioinformatics, systems biology, ecology, computational pharmacology, metabolism, biomedical engineering, epidemiology, and statistical genetics will also be considered.
Given their inherent uncertainty, protein modeling and molecular docking studies should be thoroughly validated. In the absence of experimental results for validation, the use of molecular dynamics simulations along with detailed free energy calculations, for example, should be used as complementary techniques to support the major conclusions. Submissions of premature modeling exercises without additional biological insights will not be considered.
Review articles will generally be commissioned by the editors and should not be submitted to the journal without explicit invitation. However prospective authors are welcome to send a brief (one to three pages) synopsis, which will be evaluated by the editors.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
