Background: While platinum sensitivity and resistance have long been central to treatment decisions in high-grade serous ovarian cancer (HGSOC), these categories are increasingly questioned in real-world clinical settings. This study seeks to develop a prognostic model based on platinum-free interval (PFI) as a reliable indicator of patient prognosis, with additional exploration of chemotherapy resistance-related genes and pathways. Methods: 70 HGSOC patients with varied gene expression profiles and corresponding clinical information of platinum-based chemotherapy responses were analysed. We first identified PFI-related genes (PRGs) that constituted a predictive signature for HGSOC by using univariate COX and LASSO regression analysis. We determined the optimal PFI indicative using linear correlation equations between gene expression levels and PFI. This time point was then employed to categorize patients into cohorts with good and poor prognosis, followed by an analysis of differentially expressed genes (DEGs) and their enriched pathways. Additionally, we utilized public available drug database to evaluate chemotherapeutic agents effective against the poor prognosis group. Results: A signature comprising 10 PRGs (TUBA4A, ENSG00000232325.3, ENSG00000268080.1, KCNK9, ENSG00000230567.3, CST6, KNTC1, LINC02167, ENSG00000267469.1, NKAIN4) was established. Patients within the high-risk category defined by this signature exhibited a poorer prognosis and earlier recurrence than low-risk group. The prognostic model had a robust accuracy in predicting prognosis with an area under curve value >0.90. We estimated a PFI threshold of 22.37 months, which serves as a cutoff point to further differentiate groups with good and poor prognosis. KEGG pathways enrichment analysis revealed that taurine and hypotaurine metabolism, melanogenesis, Cushing syndrome, and mTOR signaling pathways were enriched in the poor prognosis group. We also performed drug resistance assessment and found that patients from the poor prognosis group were more sensitive to anti-cancer drugs such as Pevonedistat and GDC0810 than the good prognosis group. Conclusions: Our study constructed a prognostic model based on PFI for HGSOC and further explored its implications for chemotherapy resistance. These findings could enhance clinical applications and inform novel anticancer therapeutic strategies targeting HGSOC.

The authors have declared no competing interest.

This study was supported by the National Key R&D Program of China (2022YFC2704200, 2022YFC2704201), and the National Nature Science Foundation of China (81772769).

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the Gene Expression Omnibus (GEO) database (GSE102073)https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE102073

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