{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T08:49:17Z","timestamp":1791449357433,"version":"4.3.3"},"reference-count":44,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T00:00:00Z","timestamp":1784073600000},"content-version":"vor","delay-in-days":14,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["iubmb.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["BioFactors"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Reliable biomarkers that predict therapeutic response remain a major unmet need in epithelial ovarian cancer (EOC), particularly for patients receiving neoadjuvant chemotherapy (NACT). Although high\u2010throughput multi\u2010omics technologies have accelerated biomarker discovery, the translation of candidate markers into clinically actionable predictors remains limited. Here, we developed an integrative multi\u2010omics framework combining single\u2010cell RNA sequencing, bulk transcriptomic profiling, and machine\u2010learning\u2010based modeling to identify biomarkers associated with chemotherapy response in EOC. Single\u2010cell analyses were used to delineate cellular heterogeneity and intercellular communication landscapes before and after NACT, while bulk cohorts were leveraged to validate response\u2010associated molecular signatures. Network\u2010based and pathway analyses were applied to prioritize functionally relevant candidates. We identified integrin subunit alpha 1 (ITGA1) as a response\u2010associated candidate with notable discriminatory performance for chemotherapy response and functional relevance in cisplatin\u2010resistant ovarian cancer models. ITGA1\u2010related signatures stratified patients by therapeutic response and were associated with immune and stress\u2010response pathways, extracellular matrix signaling, and altered cell\u2013cell communication patterns. Functional experiments showed that ITGA1 knockdown restored cisplatin sensitivity and suppressed clonogenic survival, migration, adhesion, and apoptosis evasion in resistant ovarian cancer cells. Together, our study supports ITGA1 as a response\u2010discriminatory and functionally relevant biomarker candidate for chemotherapy response in EOC and highlights the value of integrating single\u2010cell and bulk multi\u2010omics data with machine\u2010learning approaches. These findings provide a translational framework for patient stratification and experimental prioritization in ovarian cancer.<\/jats:p>","DOI":"10.1002\/biof.70135","type":"journal-article","created":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T02:06:28Z","timestamp":1784167588000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Multi\u2010Omics and Single\u2010Cell Framework Identifies\n                    <scp>ITGA1<\/scp>\n                    as a Candidate Predictive Biomarker of Neoadjuvant Chemotherapy Response in Epithelial Ovarian Cancer"],"prefix":"10.1002","volume":"52","author":[{"given":"Xue","family":"Xu","sequence":"first","affiliation":[{"name":"Department of Pathology First Affiliated Hospital of Anhui Medical University  Hefei Anhui Province 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