Differential Expression Analysis Identifies Novel Regulatory Genes in Endometriosis
Keywords:
Endometriosis, Differential gene expression, Protein–protein interaction network, Progesterone resistance, Inflammatory signalingAbstract
Background: Endometriosis is a chronic estrogen-dependent inflammatory disorder associated with pelvic pain and infertility. Despite its high prevalence, the molecular regulatory mechanisms underlying disease progression remain incompletely understood. Objective: This study aimed to identify differentially expressed genes (DEGs) and prioritize key regulatory candidates involved in endometriosis pathogenesis using integrative bioinformatics analysis. Methods: Publicly available human transcriptomic datasets comparing endometriotic lesions and normal endometrial tissues were analyzed. Data were normalized and subjected to differential expression analysis using adjusted p-value < 0.05 and |log2 fold change| ≥ 1 as thresholds. Functional enrichment analysis and protein–protein interaction (PPI) network construction were performed to identify enriched pathways and hub genes. Results: A total of 326 significant DEGs were identified, including 189 upregulated and 137 downregulated genes. Enrichment analysis revealed predominant involvement of inflammatory signaling, angiogenesis, extracellular matrix remodeling, and steroid hormone response pathways. Network analysis identified IL6, STAT3, VEGFA, and MMP9 as central upregulated hub genes, while PGR and HOXA10 were among the key downregulated regulators associated with progesterone resistance. Conclusion: Endometriosis exhibits coordinated activation of inflammatory–angiogenic networks and suppression of steroid signaling pathways. The identified hub genes represent potential biomarkers and therapeutic targets, warranting further experimental validation.
Downloads
References
Abdollahi, S., Izadi, P., & Azizi-Tabesh, G. (2023). Bioinformatics analysis reveals novel differentially expressed genes between ectopic and eutopic endometrium in women with endometriosis. Journal of Obstetrics and Gynaecology of India, 73(Suppl 1), 115–123. https://doi.org/10.1007/s13224-023-01749-9
Cano-Herrera, G., Salmun Nehmad, S., Ruiz de Chávez Gascón, J., Méndez Vionet, A., van Tienhoven, X. A., Osorio Martínez, M. F., et al. (2024). Endometriosis: A comprehensive analysis of the pathophysiology, treatment, and nutritional aspects, and its repercussions on the quality of life of patients. Biomedicines, 12(7), 1476. https://doi.org/10.3390/biomedicines12071476
Chauhan, S., More, A., Chauhan, V., & Kathane, A. (2022). Endometriosis: A review of clinical diagnosis, treatment, and pathogenesis. Cureus, 14(9), e28864. https://doi.org/10.7759/cureus.28864
Clarina, S., Siswanto, F. M., Pranandi, I., Handayani, M. D. N., Dewi, R., & Regina. (2025). Identification of miR-103a/PLEKHA1 pair as candidate biomarkers and therapeutic targets for skin aging by bioinformatics analysis. Frontiers in Health Informatics, 14(2), 2245–2254.
Geng, R., Huang, X., Li, L., Guo, X., Wang, Q., & Zheng, Y., et al. (2022). Gene expression analysis in endometriosis: Immunopathology insights, transcription factors and therapeutic targets. Frontiers in Immunology, 13, 1037504. https://doi.org/10.3389/fimmu.2022.1037504
Kaliaperumal, K., Govindarajan, S., Ravi, K., Pranandi, I., Kumar, P. V., & Gobinath, V. M. (2025). AI-assisted design and biochemical optimization of protein structures for enhanced drug delivery in chemotherapy. Journal of Neonatal Surgery, 14(7), 147–151.
Kanehisa, M., & Goto, S. (2026). Gene Expression Omnibus (GEO) [Internet]. National Center for Biotechnology Information. https://www.ncbi.nlm.nih.gov/geo/
Kanehisa, M., & Goto, S. (2026). Kyoto Encyclopedia of Genes and Genomes (KEGG) [Internet]. Kanehisa Laboratories. https://www.genome.jp/kegg/
Mariadas, H., Chen, J.-H., & Chen, K.-H. (2025). The molecular and cellular mechanisms of endometriosis: From basic pathophysiology to clinical implications. International Journal of Molecular Sciences, 26(6), 2458. https://doi.org/10.3390/ijms26062458
NCBI Staff. (2023). RNA-Seq archives. NCBI Insights [Internet]. https://ncbiinsights.ncbi.nlm.nih.gov/tag/rna-seq/
Petraglia, F., Vannuccini, S., Santulli, P., Marcellin, L., & Chapron, C. (2024). An update for endometriosis management: A position statement. Journal of Endometriosis and Uterine Disorders, 100062. https://doi.org/10.1016/j.jeud.2024.100062
Pranandi, I. (2025). Biochemical pathways in neonatal intestinal atresia: Vascular and genetic perspectives. Journal of Neonatal Surgery, 14(7s), 713–719.
Pranandi, I. (2025). Bioinformatics exploration of biochemical traits associated with culturally distinct populations: Between genetics and identity. Journal of Bio Advanced Science Research, 1(3), 1–19. https://doi.org/10.63721/25JBASR0127
Pranandi, I. (2025). Integrative biochemical diagnostics: From prenatal genomics to environmental and behavioral biomarkers. Journal of Natural Science Research Review, 1(5), 123–130. https://doi.org/10.65150/EP-jnsrr/V1E5/2025-05
Pranandi, I., & Arieselia, Z. (2026). Integrative transcriptomic profiling of human neural tissues reveals core molecular signatures of neurodegeneration. Journal of Natural Science Research Review, 2(2), 62–66. https://doi.org/10.65150/EP-jnsrr/V2E2/2026-02
Rahmioglu, N., Mortlock, S., Ghiasi, M., Møller, P. L., Stefansdottir, L., Galarneau, G., et al. (2023). The genetic basis of endometriosis and comorbidity with other pain and inflammatory conditions. Nature Genetics, 55(3), 423–436. https://doi.org/10.1038/s41588-023-01323-z
Szklarczyk, D., Gable, A. L., Nastou, K. C., Lyon, D., Kirsch, R., Pyysalo, S., et al. (2025). STRING protein–protein association networks [Internet]. STRING Consortium. https://string-db.org/
Yin, H., Duo, H., Li, S., Qin, D., Xie, L., & Xiao, Y., et al. (2024). Unlocking biological insights from differentially expressed genes: Concepts, methods, and future perspectives. Journal of Advanced Research, 76, 135–157. https://doi.org/10.1016/j.jare.2024.12.004
Zhou, J., Xiong, W., Wang, Y., & Guan, J. (2021). Protein function prediction based on PPI networks: Network reconstruction vs edge enrichment. Frontiers in Genetics, 12, 758131. https://doi.org/10.3389/fgene.2021.758131
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Ian Pranandi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under aCreative Commons Attribution-ShareAlike 4.0 International (CC-BY-SA). that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
