Transcriptomic-based analysis of endometrial tissues from adenomyosis patients reveals significant inflammation biomarkers: A bioinformatics study

Published date: Jul 29 2025

Journal Title: International Journal of Reproductive BioMedicine

Issue title: International Journal of Reproductive BioMedicine (IJRM): Volume 23, Issue No. 5

Pages: 425 – 436

DOI: 10.18502/ijrm.v23i5.19265

Marni SianturiMaster’s Program in Biomedical Sciences, Faculty of Medicine, Universitas Riau, Pekanbaru

Alauddin SyaifulanwarMaster’s Program in Biomedical Sciences, Faculty of Medicine, Universitas Riau, Pekanbaru

Darmawi DarmawiDepartment of Histology, Faculty of Medicine, Universitas Riau, Pekanbaru

Wirawan AdikusumaDepartment of Pharmacy, Universitas Muhammadiyah Mataram, Mataram

Lalu Muhammad IrhamDepartment of Pharmacology and Clinical Pharmacy, Universitas Ahmad Dahlan, Yogyakarta

Muhammad YusufDepartment of Obstetrics and Gynecology, Faculty of Medicine, Universitas Riau, Pekanbaru

Rifia Tiara FaniDepartment of Veterinary, Faculty of Medicine, Universitas Riau, Pekanbaru

Febriani Febrianifebriani@lecturer.unri.ac.idDepartment of Obstetrics and Gynecology, Faculty of Medicine, Universitas Riau, Pekanbaru

Background: Adenomyosis is a gynecological disorder characterized by the presence of endometrial tissue within the myometrium, with incidence rates ranging from 10–65% among women of reproductive age.

Objective: This study utilized transcriptomic analysis to identify significant biomarkers associated with inflammation in endometrial tissue from patients with adenomyosis.

Materials and Methods: In this bioinformatics study, we utilized publicly available transcriptomic datasets. The research involved the systematic analysis of RNA sequencing data obtained from the NCBI-GEO database. Using a high-throughput RNA sequencing database from GSE190580 and GSE157718, we compared gene expression profiles between endometrium tissues of adenomyosis patients and healthy controls. Subsequently, pathways implicated in adenomyosis were analyzed through the Kyoto Encyclopedia of Genes and Genomes and gene ontology.

Results: Pathway analysis revealed the aberration of inflammation-related pathways, including tumor necrosis factor (TNF) and Ras-related protein 1 signaling. Furthermore, gene ontology analysis uncovered key biological processes, such as macrophage differentiation and extracellular matrix organization, which are central to the inflammatory response in adenomyosis. Candidate biomarkers, including transmembrane protein kinases, were identified as potential therapeutic targets. We found the top 5 genes that play a role in inflammation in adenomyosis, including TNF-α-induced protein 6, matrix metalloproteinase 7, TNF-α-induced protein 3, leukemia inhibitory factor, and serum and glucocorticoid-regulated kinase 1. Statistical significance was determined with adjusted p < 0.05.

Conclusion: These findings enhance our understanding of the molecular mechanisms of adenomyosis and propose novel biomarkers for more effective diagnostic and therapeutic strategies.

Keywords: Adenomyosis, Bioinformatics, Biomarker, Inflammation

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