Identification Target Genes for Potential Biomarkers in Endometriosis from Transcriptomics Database

Dian Zahlina, Benni Iskandar, Wirawan Adikusuma, Muhammad Yusuf, Rifia Tiara Fani, Ocktariyana Ocktariyana, Darmawi Darmawi
In: Jurnal Kesehatan · 2026 · vol. 17(1) , pp. 11–21 · doi:10.35730/jk.v17i1.1281 · W7160824864
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⚙ AI-generated summary by claude@2026-06, 2026-06-08 ⓘ

This study analyzed transcriptomic data from three endometriosis datasets to identify 339 differentially expressed genes, highlighting pathways involved in cell proliferation and angiogenesis, and proposing ten genes as potential biomarkers.

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The paper analyzed three publicly available endometriosis transcriptomic datasets (GSE7307, GSE23339, and GSE25628) from the GEO database to identify differentially expressed genes and underlying pathways. After intersecting DEGs across datasets, the authors reported 339 genes and found enrichment for processes such as epithelial cell proliferation and angiogenesis, with components involving lysosomal lumen and extracellular matrix, and molecular functions including Wnt-activated receptor activity and low-density lipoprotein particle binding. Ten genes (TAGLN, C7, TCF21, GATA6, GPC3, FZD7, TCEAL2, KLF2, FMO1, and HOXC6) were proposed as candidate biomarker targets. This paper is centrally about endometriosis — it derives candidate biomarker genes and enriched pathways from transcriptomic data.

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Abstract

Endometriosis is a common benign gynecological disease characterized by the ectopic growth of endometrial tissue. Its pathogenesis is influenced by complex genetic and epigenetic factors, making diagnosis and treatment challenging. This study aimed to identify molecular pathways and candidate genes associated with endometriosis using transcriptomic data. Three datasets (GSE7307, GSE23339, and GSE25628) were retrieved from the Gene Expression Omnibus (GEO) database and analyzed to identify differentially expressed genes (DEGs). A total of 339 intersecting DEGs were obtained and subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. The results indicated enrichment in biological processes related to epithelial cell proliferation and angiogenesis, cellular components associated with the lysosomal lumen and extracellular matrix, and molecular functions involving Wnt-activated receptor activity and low-density lipoprotein particle binding. Ten genes (TAGLN, C7, TCF21, GATA6, GPC3, FZD7, TCEAL2, KLF2, FMO1, and HOXC6) were identified as potential candidate biomarkers. These findings provide preliminary molecular insights into endometriosis and may support future experimental and clinical studies for biomarker development.
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Identification Target Genes for Potential Biomarkers in Endometriosis from Transcriptomics Database DOI: https://doi.org/10.35730/jk.v17i1.1281Abstract Endometriosis is a common benign gynecological disease characterized by the ectopic growth of endometrial tissue. Its pathogenesis is influenced by complex genetic and epigenetic factors, making diagnosis and treatment challenging. This study aimed to identify molecular pathways and candidate genes associated with endometriosis using transcriptomic data. Three datasets (GSE7307, GSE23339, and GSE25628) were retrieved from the Gene Expression Omnibus (GEO) database and analyzed to identify differentially expressed genes (DEGs). A total of 339 intersecting DEGs were obtained and subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. The results indicated enrichment in biological processes related to epithelial cell proliferation and angiogenesis, cellular components associated with the lysosomal lumen and extracellular matrix, and molecular functions involving Wnt-activated receptor activity and low-density lipoprotein particle binding. Ten genes (TAGLN, C7, TCF21, GATA6, GPC3, FZD7, TCEAL2, KLF2, FMO1, and HOXC6) were identified as potential candidate biomarkers. These findings provide preliminary molecular insights into endometriosis and may support future experimental and clinical studies for biomarker development. Downloads Published Issue Section License Copyright (c) 2026 Dian Zahlina, Benni Iskandar, Wirawan Adikusuma, Muhammad Yusuf, Rifia Tiara Fani, Ocktariyana Ocktariyana, Darmawi Darmawi This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

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