{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T00:08:08Z","timestamp":1789171688093,"version":"build-2803163510"},"reference-count":76,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T00:00:00Z","timestamp":1779321600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Genes"],"abstract":"<jats:p>Background: Ovarian cancer (OVCA) remains the most lethal gynecologic malignancy, with poor prognosis largely due to late-stage diagnosis and therapy resistance. Pyroptosis, a pro-inflammatory form of programmed cell death, has recently emerged as a regulator of tumor progression and immune regulation. This study aimed to systematically profile pyroptosis-related genes and identify robust biomarkers for OVCA. Methods: Microarray data from the GSE54388 dataset were analyzed to characterize pyroptosis-related gene expression. Immune cell infiltration was assessed using xCell, and pathway enrichment was performed via Gene Set Enrichment Analysis (GSEA). Weighted Gene Co-expression Network Analysis (WGCNA) identified hub genes, followed by Gene Ontology (GO) and Reactome enrichment. Machine learning algorithms (Support Vector Machine, XGBoost, and Generalized Linear Model) were employed for feature selection and biomarker identification. Validation was conducted across independent bulk and scRNA-seq datasets, with GEPIA2 used to compare OVCA and normal samples and KMplot for survival analysis. Results: OVCA samples showed significantly reduced infiltration of CD4+ and CD8+ T cells, mast cells, monocytes, neutrophils, and immature dendritic cells compared to normal samples. GSEA revealed enrichment of cell cycle-related pathways, implicating pyroptosis-related genes as key regulators of mitotic progression. From 1097 differentially expressed genes, 22 pyroptosis-related DEGs (PYRDEGs) were identified, with nine hub genes (CASP1, CEP55, CHMP4C, HTRA1, IL18, MELK, PKM, PTX3, TNFSF13B) strongly associated with OVCA. Functional enrichment linked these genes to cytokinesis, inflammasome activity, and immune signaling. Machine learning consistently identified CEP55 as the core biomarker, demonstrating high diagnostic accuracy (AUC up to 0.972) and significant upregulation in OVCA samples. Correlation analysis linked CEP55 expression to altered immune cell populations, including positive associations with Th1 and class-switched memory B-cells and negative associations with iDCs, Tregs, and M2 macrophages. CEP55 was highly expressed across bulk and scRNA-seq datasets (cancer epithelial and CD8+ TEMRA cells) and negatively correlated with overall survival (OS) and progression-free survival (PFS). Conclusions: Pyroptosis-related genes play pivotal roles in OVCA pathogenesis. CEP55 emerges as a promising biomarker for early detection and a potential therapeutic target, bridging cell cycle regulation with immune modulation.<\/jats:p>","DOI":"10.3390\/genes17050595","type":"journal-article","created":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T16:46:18Z","timestamp":1779381978000},"page":"595","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Pyroptosis-Related Gene Signatures and Immune Modulation in Ovarian Cancer: Insights from Multi-Omics and Machine Learning"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2089-0891","authenticated-orcid":false,"given":"Rakesh","family":"Arya","sequence":"first","affiliation":[{"name":"Department of Biotechnology, Yeungnam University, Gyeongsan 38541, Gyeongbuk, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3030-0242","authenticated-orcid":false,"given":"Viplov Kumar","family":"Biswas","sequence":"additional","affiliation":[{"name":"Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Whitehead Biomedical Research, 615 Michael Street, Atlanta, GA 30322, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7314-7148","authenticated-orcid":false,"given":"Hemlata","family":"Shakya","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Shri G. S. Institute of Technology and Science, Indore 452003, Madhya Pradesh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jong-Joo","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Biotechnology, Yeungnam University, Gyeongsan 38541, Gyeongbuk, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.annonc.2021.12.007","article-title":"European Cancer Mortality Predictions for the Year 2022 with Focus on Ovarian Cancer","volume":"33","author":"Dalmartello","year":"2022","journal-title":"Ann. Oncol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Polaj\u017eer, S., and \u010cerne, K. (2025). Precision Medicine in High-Grade Serous Ovarian Cancer: Targeted Therapies and the Challenge of Chemoresistance. Int. J. Mol. 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