ArticleBioFactors (Oxford, England)
A Multi-Omics and Single-Cell Framework Identifies ITGA1 as a Candidate Predictive Biomarker of Neoadjuvant Chemotherapy Response in Epithelial Ovarian Cancer.
Article in BioFactors (Oxford, England). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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-throughput multi-omics technologies have accelerated biomarker discovery, the translation of candidate markers into clinically actionable predictors remains limited. Here, we developed an integrative multi-omics framework combining single-cell RNA sequencing, bulk transcriptomic profiling, and machine-learning-based modeling to identify biomarkers associated with chemotherapy response in EOC. Single-cell analyses were used to delineate cellular heterogeneity and intercellular communication landscapes before and after NACT, while bulk cohorts were leveraged to validate response-associated molecular signatures. Network-based and pathway analyses were applied to prioritize functionally relevant candidates. We identified integrin subunit alpha 1 (ITGA1) as a response-associated candidate with notable discriminatory performance for chemotherapy response and functional relevance in cisplatin-resistant ovarian cancer models. ITGA1-related signatures stratified patients by therapeutic response and were associated with immune and stress-response pathways, extracellular matrix signaling, and altered cell-cell 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-discriminatory and functionally relevant biomarker candidate for chemotherapy response in EOC and highlights the value of integrating single-cell and bulk multi-omics data with machine-learning approaches. These findings provide a translational framework for patient stratification and experimental prioritization in ovarian cancer.
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