A chemotherapy response prediction model derived from tumor-promoting B and Tregs and proinflammatory macrophages in HGSOC

Background Most patients with high-grade serous ovarian cancer (HGSOC) experienced disease recurrence cumulative chemoresistance, leading to treatment failure. However, few biomarkers are currently available in clinical practice that can accurately predict chemotherapy response. The tumor immune microenvironment is critical for development, and its transcriptomic profile may be associated response differential outcomes. aim of this study was develop a new predictive signature HGSOC. Methods Two HGSOC single-cell RNA sequencing datasets from receiving were reinvestigated. subtypes endoplasmic reticulum stress-related XBP1 + B cells, invasive metastasis-related ACTB Tregs, proinflammatory-related macrophage good power identified. These results verified an independent bulk RNA-seq dataset chemotherapy. Further validation cohorts used quantitative real-time PCR (qRT-PCR). Results By combining cluster-specific genes the aforementioned cell subtypes, we constructed prediction model containing 43 achieved area under receiver operator curve (AUC) 0.97 ( p = 2.1e-07) GSE156699 cohort (88 samples). A huge improvement compared existing models maximum AUC 0.74. In addition, capability validated multiple datasets. qRT-PCR demonstrate expression six has highest diagnostic value, consistent trend observed analysis public data. Conclusions developed as valuable decision tool guide patients.