Ovarian cancer remains the most lethal gynecological malignancy and comprises a heterogeneous group of neoplasms with distinct etiologies, molecular profiles, histopathological features, and clinical characteristics. Therapeutic resistance, particularly in homologous recombination (HR)-proficient tumors treated with PARP inhibitors, represents a major clinical challenge. This study aimed to identify novel resistance mechanisms, focusing on metabolic reprogramming and autophagy. A genome-wide CRISPR-Cas9 knockout screen was performed in ovarian cancer cell lines treated with Niraparib in vitro and in vivo xenograft models. Resistance-associated genes were analyzed using the model-based analysis of genome-wide CRISPR-Cas9 knockout algorithm and gene set enrichment analysis. Lipidomic profiling was conducted by UPLC-MS/MS, and transcriptomic changes were assessed by RNA sequencing. Functional validation involved UGCG overexpression/knockdown, cell viability assays, colony formation, autophagy marker quantification, inhibitor treatments, xenograft models in mice, and patient-derived organoids. Statistical tests included t-tests, ANOVA, and Kaplan-Meier survival analysis. UGCG was identified as a key driver of Niraparib resistance, with enrichment of lipid metabolism and autophagy pathways. Multi-omics analyses revealed increased sphingolipid accumulation and upregulation of autophagy-related genes in resistant cells, positively correlated with UGCG expression. UGCG overexpression enhanced autophagy and drug resistance, whereas genetic or pharmacologic inhibition with Eliglustat restored Niraparib sensitivity. Combination treatments with Metformin or hydroxychloroquine further suppressed autophagy and tumor growth in vitro and in vivo. High UGCG expression was associated with poorer overall and progression-free survival. UGCG-driven sphingolipid metabolism promotes protective autophagy and contributes to Niraparib resistance in ovarian cancer. Targeting this pathway represents a promising therapeutic strategy, particularly for HR-proficient tumors. Future clinical trials should validate UGCG as a biomarker and explore broader applications.
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Zhang et al. (2026) studied this question.
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