Prediction of genomic biomarkers for endometriosis using the transcriptomic dataset

Zeynep Kucukakcali, Sami Akbulut, Cemil Colak, Cemil Çolak
World journal of clinical cases · 2025 · vol. 13(20) , pp. 104556 · doi:10.12998/wjcc.v13.i20.104556 · PMID:40671745 · W4409275450
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This study utilized machine learning on transcriptomic data from endometriosis patients and controls to identify potential genomic biomarkers, with Bagged CART achieving high classification accuracy.

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Abstract

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 model. Future genomic studies could explain the underlying pathology of endometriosis, and a non-invasive diagnostic method could replace the invasive ones.

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endometriosis

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