Prediction of genomic biomarkers for endometriosis using the transcriptomic dataset  

作　　者：Zeynep Kucukakcali Sami Akbulut Cemil Colak 

机构地区：[1]Department of Biostatistics and Medical Informatics,Inonu University Faculty of Medicine,Malatya 44280,Türkiye [2]Surgery and Liver Transplant Institute,Inonu University Faculty of Medicine,Malatya 44280,Türkiye

出　　处：《World Journal of Clinical Cases》2025年第20期6-19,共14页世界临床病例杂志(英文)

基　　金：approved by the Inonu University institutional review board for noninterventional studies(Approval No:2022/3842).

摘　　要：BACKGROUND Endometriosis is a clinical condition characterized by the presence of endometrial glands outside the uterine cavity.While its incidence remains mostly uncertain,endometriosis impacts around 180 million women worldwide.Despite the presentation of several epidemiological and clinical explanations,the precise mechanism underlying the disease remains ambiguous.In recent years,researchers have examined the hereditary dimension of the disease.Genetic research has aimed to discover the gene or genes responsible for the disease through association or linkage studies involving candidate genes or DNA mapping techniques.AIM To identify genetic biomarkers linked to endometriosis by the application of machine learning(ML)approaches.METHODS This case-control study accounted for the open-access transcriptomic data set of endometriosis and the control group.We included data from 22 controls and 16 endometriosis patients for this purpose.We used AdaBoost,XGBoost,Stochasting Gradient Boosting,Bagged Classification and Regression Trees(CART)for classification using five-fold cross validation.We evaluated the performance of the models using the performance measures of accuracy,balanced accuracy,sensitivity,specificity,positive predictive value,negative predictive value and F1 score.RESULTS Bagged CART gave the best classification metrics.The metrics obtained from this model are 85.7%,85.7%,100%,75%,75%,100%and 85.7%for accuracy,balanced accuracy,sensitivity,specificity,positive predictive value,negative predictive value and F1 score,respectively.Based on the variable importance of modeling,we can use the genes CUX2,CLMP,CEP131,EHD4,CDH24,ILRUN,LINC01709,HOTAIR,SLC30A2 and NKG7 and other transcripts with inaccessible gene names as potential biomarkers for endometriosis.CONCLUSION This study determined possible genomic biomarkers for endometriosis using transcriptomic data from patients with/without endometriosis.The applied ML model successfully classified endometriosis and created a highly accurate diagnostic prediction

关 键 词：ENDOMETRIOSIS RNA-SEQ TRANSCRIPTOMICS Machine learning Classification 

分 类 号：R711.71[医药卫生—妇产科学]