Mining phase separation-related diagnostic biomarkers for endometriosis through WGCNA and multiple machine learning techniques: a retrospective and nomogram study

Objective The objective of this study was to investigate the role of phase separation-related genes in the development of endometriosis (EMs) and to identify potential characteristic genes associated with the condition.

We used GEO database data, including 74 non-endometriosis and 74 varying-degree EMs patients. Our approach involved identifying significant gene modules, exploring gene intersections, identifying core genes, and screening for potential EMs biomarkers using weighted gene co-expression network analysis (WGCNA) and various machine learning approaches. We also performed gene set enrichment analysis (GSEA) to understand relevant pathways. This comprehensive approach helps investigate EMs genetics and potential biomarkers.

Nine genes were identified at the intersection, suggesting their involvement in EMs. GSEA linked DEGs to pathways like complement and coagulation cascades, DNA replication, chemokines, apical plasma membrane processes, and diseases such as Hepatitis B, Human T-cell leukemia virus 1 infection, and COVID-19. Five feature genes (FOS, CFD, CCNA1, CA4, CST1) were selected by machine learning for an effective EMs diagnostic nomogram. GSEA indicated their roles in mismatch repair, cell cycle regulation, complement and coagulation cascades, and IL-17 inflammation. Notable differences in immune cell proportions (CD4 T cells, CD8 T cells, DCs, macrophages) were observed between normal and disease groups, suggesting immune involvement.

This study suggests the potential involvement of phase separation-related genes in the pathogenesis of endometriosis (EMs) and identifies promising biomarkers for diagnosis. These findings have implications for further research and the development of new therapeutic strategies for EMs. Similar content being viewed by others Data availability All data generated or analyzed during this study are included in this published article and its supplementary materials.

Sampson JAJAJoO, Gynecology. Peritoneal endometriosis due to the menstrual dissemination of endometrial tissue into the peritoneal cavity. 1927;14:422–69. Macer ML, Taylor HS. Endometriosis and infertility: a review of the pathogenesis and treatment of endometriosis-associated infertility. Obstet Gynecol Clin North Am. 2012;39(4):535–49. Zondervan KT, Becker CM, Koga K, Missmer SA, Taylor RN, Viganò P. Endometriosis. Nat Rev Dis Primers. 2018;4(1):9. Simoens S, Dunselman G, Dirksen C, Hummelshoj L, Bokor A, Brandes I, et al. The burden of endometriosis: costs and quality of life of women with endometriosis and treated in referral centres. Hum Reprod. 2012;27(5):1292–9. Brangwynne CP, Eckmann CR, Courson DS, Rybarska A, Hoege C, Gharakhani J, et al. Germline P granules are liquid droplets that localize by controlled dissolution/condensation. Science. 2009;324(5935):1729–32. Banani SF, Lee HO, Hyman AA, Rosen MK. Biomolecular condensates: organizers of cellular biochemistry. Nat Rev Mol Cell Biol. 2017;18(5):285–98. Boeynaems S, Alberti S, Fawzi NL, Mittag T, Polymenidou M, Rousseau F, et al. Protein phase separation: a new phase in cell biology. Trends Cell Biol. 2018;28(6):420–35. Xiao Q, McAtee CK, Su X. Phase separation in immune signalling. Nat Rev Immunol. 2022;22(3):188–99. Zhou W, Mohr L, Maciejowski J, Kranzusch PJ. cGAS phase separation inhibits TREX1-mediated DNA degradation and enhances cytosolic DNA sensing. Mol Cell. 2021;81(4):739-55.e7. Xia S, Chen Z, Shen C, Fu TM. Higher-order assemblies in immune signaling: supramolecular complexes and phase separation. Protein Cell. 2021;12(9):680–94. Yu X, Zhang L, Shen J, Zhai Y, Jiang Q, Yi M, et al. The STING phase-separator suppresses innate immune signalling. Nat Cell Biol. 2021;23(4):330–40. Wang B, Zhang L, Dai T, Qin Z, Lu H, Zhang L, et al. Liquid-liquid phase separation in human health and diseases. Signal Transduct Target Ther. 2021;6(1):290. Du M, Ea CK, Fang Y, Chen ZJ. Liquid phase separation of NEMO induced by polyubiquitin chains activates NF-κB. J Mol Cell. 2022;82(13):2415–26.e5. Pant A, Moar K, Arora TK, Maurya PK. Biomarkers of endometriosis. Clin Chim Acta; Int J Clin Chem. 2023;549:117563. Tamaresis JS, Irwin JC, Goldfien GA, Rabban JT, Burney RO, Nezhat C, et al. Molecular classification of endometriosis and disease stage using high-dimensional genomic data. Endocrinology. 2014;155(12):4986–99. Horne AW, Missmer SA. Pathophysiology, diagnosis, and management of endometriosis. BMJ (Clin Res ed). 2022;379:e070750. Mirza Z, Abdel-Dayem UA. Uncovering potential roles of differentially expressed genes, upstream regulators, and canonical pathways in endometriosis using an in silico genomics approach. Diagnostics (Basel). 2020;10(6):416. Gupta S, Agarwal A, Krajcir N, Alvarez JG. Role of oxidative stress in endometriosis. Reprod Biomed Online. 2006;13(1):126–34. Tan A, Luo R, Liang H, Li M, Ruan P. Bioinformatics approach reveals the key role of C-X-C motif chemokine receptor 2 in endometriosis development. Mol Med Rep. 2018;18(3):2841–9. Cote LE, Feldman JL. Won’t you be my neighbor: how epithelial cells connect together to build global tissue polarity. Front Cell Dev Biol. 2022;10:887107. Prechapanich J, Kajihara T, Fujita K, Sato K, Uchino S, Tanaka K, et al. Effect of a dienogest for an experimental three-dimensional endometrial culture model for endometriosis. Med Mol Morphol. 2014;47(4):189–95. Morsch DM, Carneiro MM, Lecke SB, Araújo FC, Camargos AF, Reis FM, et al. c-fos gene and protein expression in pelvic endometriosis: a local marker of estrogen action. J Mol Histol. 2009;40(1):53–8. Ye N, Ding Y, Wild C, Shen Q, Zhou J. Small molecule inhibitors targeting activator protein 1 (AP-1). J Med Chem. 2014;57(16):6930–48. Schraml BU, Hildner K, Ise W, Lee WL, Smith WA, Solomon B, et al. The AP-1 transcription factor Batf controls T(H)17 differentiation. Nature. 2009;460(7253):405–9. Cai C, Chen DZ, Tu HX, Chen WK, Ge LC, Fu TT, et al. MicroRNA-29c acting on FOS plays a significant role in nonalcoholic steatohepatitis through the interleukin-17 signaling pathway. Front Physiol. 2021;12:597449. Zhao L, Gu C, Ye M, Zhang Z, Han W, Fan W, et al. Identification of global transcriptome abnormalities and potential biomarkers in eutopic endometria of women with endometriosis: a preliminary study. Biomed Rep. 2017;6(6):654–62. Tan BJ, Sugata K, Reda O, Matsuo M, Uchiyama K, Miyazato P, et al. HTLV-1 infection promotes excessive T cell activation and transformation into adult T cell leukemia/lymphoma. J Clin Investig. 2021;131(24):e150472. Merling R, Chen C, Hong S, Zhang L, Liu M, Kuo YL, et al. HTLV-1 Tax mutants that do not induce G1 arrest are disabled in activating the anaphase promoting complex. Retrovirology. 2007;4:35. Dai Y, Jin F, Wu W, Kumar SK. Cell cycle regulation and hematologic malignancies. Blood Sci (Baltimore, Md). 2019;1(1):34–43. Cao J, Dong J, Wang Y, Chen Y. The expressions of DNA methyltransferase 1 (DNMT1) and cyclin A1 (CCNA1) in cervical carcinogenesis. Int J Clin Exp Pathol. 2019;12(1):40–9. Malik YS, Sircar S, Bhat S, Sharun K, Dhama K, Dadar M, et al. Emerging novel coronavirus (2019-nCoV)-current scenario, evolutionary perspective based on genome analysis and recent developments. Vet Q. 2020;40(1):68–76. Popescu I, Snyder ME, Iasella CJ, Hannan SJ, Koshy R, Burke R, et al. CD4(+) T-cell dysfunction in severe COVID-19 disease is tumor necrosis factor-α/tumor necrosis factor receptor 1-dependent. Am J Respir Crit Care Med. 2022;205(12):1403–18. Li X, Zhang Z, Wang Z, Gutiérrez-Castrellón P, Shi H. Cell deaths: involvement in the pathogenesis and intervention therapy of COVID-19. Signal Transduct Target Ther. 2022;7(1):186. Nanda A, Thangapandi K, Banerjee P, Dutta M, Wangdi T, Sharma P, et al. Cytokines, angiogenesis, and extracellular matrix degradation are augmented by oxidative stress in endometriosis. Ann Lab Med. 2020;40(5):390–7. Cao XL, Chai J, Yu YY, Tian X, Zhao JY, Yu LY, et al. Association of TNF-α gene T-1031C polymorphism with endometriosis: a meta-analysis. Am J Reprod Immunol. 2020;84(6):e13305. Kabani Z, Ramos-Nino ME, Ramdass P. Endometriosis and COVID-19: a systematic review and meta-analysis. Int J Mol Sci. 2022;23(21):12951. Králíčková M, Fiala L, Losan P, Tomes P, Vetvicka V. Altered immunity in endometriosis: what came first? Immunol Invest. 2018;47(6):569–82. Vallvé-Juanico J, Houshdaran S, Giudice LC. The endometrial immune environment of women with endometriosis. Hum Reprod Update. 2019;25(5):564–91. Chen S, Liu Y, Zhong Z, Wei C, Liu Y, Zhu X. Peritoneal immune microenvironment of endometriosis: role and therapeutic perspectives. Front Immunol. 2023;14:1134663. Laginha PA, Arcoverde FVL, Riccio LGC, Andres MP, Abrão MS. The role of dendritic cells in endometriosis: a systematic review. J Reprod Immunol. 2022;149:103462. Chen S, Chai X, Wu X. Bioinformatical analysis of the key differentially expressed genes and associations with immune cell infiltration in development of endometriosis. BMC Genom Data. 2022;23(1):20. Author information Authors and Affiliations Contributions XZ conceived the work. QL studied and drafted the manuscript. SY and XZ discussed and edited the manuscript. MM and XC assisted study. XZ checked the statistical and bioinformatic accuracy as an expert in statistics and bioinformatics. All authors read and approved the final version of the manuscript. Corresponding author Ethics declarations Ethical approval and consent to participate This is a retrospective study of publicly available databases, and the database developers obtained appropriate ethical review standards. Informed consent forms are not required for patient data extracted from public databases. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Additional information Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Highlights • Exploring EMs mechanisms: This study delves into the mechanisms of endometriosis (EMs) by investigating the role of phase separation-related genes. • Identification of candidate genes: The research identifies nine genes potentially linked to EMs, shedding light on their involvement in the condition’s development. • Pathway insights: Gene set enrichment analysis reveals associations of DEGs with pathways like complement and coagulation cascades and DNA replication, providing a deeper understanding of EMs. • Machine learning biomarkers: Five feature genes—FOS, CFD, CCNA1, CA4, and CST1—were selected through machine learning, offering potential diagnostic biomarkers. • Effective diagnostic model: A nomogram-based diagnostic model utilizing FOS, CFD, and CCNA1 demonstrates high accuracy in distinguishing EMs from normal cases. • Immunological signatures: Significant differences in immune cell proportions between EMs and normal groups suggest potential immune involvement in the disease. • Clinical implications: These findings open avenues for further research and the development of novel therapeutic strategies for endometriosis. Supplementary Information Below is the link to the electronic supplementary material. Rights and permissions Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. About this article Cite this article Liang, Q., Yang, S., Mai, M. et al. Mining phase separation-related diagnostic biomarkers for endometriosis through WGCNA and multiple machine learning techniques: a retrospective and nomogram study. J Assist Reprod Genet 41, 1433–1447 (2024). https://doi.org/10.1007/s10815-024-03079-9 Received: Accepted: Published: Version of record: Issue date: DOI: https://doi.org/10.1007/s10815-024-03079-9