{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T18:29:26Z","timestamp":1767205766415,"version":"3.48.0"},"reference-count":32,"publisher":"Public Library of Science (PLoS)","issue":"12","license":[{"start":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T00:00:00Z","timestamp":1767139200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"\n Background<\/jats:title>\n Obstructive sleep apnea (OSA) and interstitial cystitis\/bladder pain syndrome (IC\/BPS) are chronic conditions that significantly impact patients\u2019 quality of life. OSA involves recurrent upper airway obstruction during sleep, causing hypoxia and fragmented sleep linked to cardiovascular and metabolic issues. IC\/BPS is defined by chronic pelvic pain and urinary symptoms; its pathophysiology is complex and poorly understood. The overlap in the prevalence of OSA and IC\/BPS suggests a possible shared pathophysiological link. This study aimed to identify shared molecular mechanisms and diagnostic biomarkers between OSA and IC\/BPS through integrated bioinformatics approaches.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n This study used bioinformatics and machine learning to analyze transcriptomic data for OSA and IC\/BPS, identifying differential expressed genes (DEGs) and enriched pathways from Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) constructed gene co-expression networks and identified hub genes, while immune infiltration analysis characterized the immune microenvironment. Four machine learning algorithms developed diagnostic models and also identified key markers.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n A total of 2,233 DEGs were identified in OSA and 1,183 in IC\/BPS, with 93 overlapping genes. Among these, machine learning algorithms identified DUSP9 as the single common gene linking both disorders, forming two-gene signatures for each condition (\n DUSP9<\/jats:italic>\n \/\n CCDC68<\/jats:italic>\n for OSA and\n DUSP9<\/jats:italic>\n \/\n KPNA2<\/jats:italic>\n for IC\/BPS). Key pathways for OSA included RIG-I-like and NOD-like receptor signaling. In contrast, IC\/BPS was linked to cytokine interactions and JAK-STAT signaling. Immune infiltration analysis showed that\n DUSP9<\/jats:italic>\n expression was correlated with CD56dim natural killer cells in OSA and with activated CD4 T cells in IC\/BPS, further supporting its role in the immune response associated with these disorders.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n \n This study established\n DUSP9<\/jats:italic>\n as a pivotal shared biomarker and central regulator linking OSA and IC\/BPS through integrated bioinformatics analysis.\n <\/jats:p>\n <\/jats:sec>","DOI":"10.1371\/journal.pone.0339824","type":"journal-article","created":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T18:24:41Z","timestamp":1767205481000},"page":"e0339824","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Exploring the molecular link between obstructive sleep apnea and interstitial cystitis\/bladder pain syndrome: A bioinformatics and machine learning 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