Purpose: DNA repair and DNA damage responses in cancer cells are regulated by metabolic reprogramming, which is increasingly recognized as a key factor contributing to PARP inhibitor (PARPi) treatment failure. This study aims to explore the metabolic mechanisms underlying PARPi resistance in PTEN-deficient prostate cancer and identify clinically viable metabolic interventions to overcome therapy failure. Experimental Design: A multicenter retrospective cohort was analyzed to evaluate the efficacy of combined metformin-PARPi therapy. Mechanistic studies utilized molecular assays to elucidate PARPi resistance and its critical determinants. Machine learning models predicting PARPi response were developed using clinical datasets and interpreted via SHAP analysis. Results: In PTEN-deficient cancer cells, lactate accumulation activated the NHE1/PKC/NOX1 axis, sustaining elevated NADP(+) levels. NADP(+) competitively inhibited the formation of PARPi-PARP-DNA complexes, leading to PARPi resistance. However, metformin administration significantly elevated NADP(+) levels, inducing allosteric effects on PARP structures and enhancing PARPi efficacy. Based on these findings, we developed and validated a predictive machine learning model for PARPi response, which was interpreted using SHAP and deployed on a web platform. Conclusions: Metformin modulates NADP(+) levels to influence PARPi sensitivity in PTEN-deficient prostate cancer. Additionally, we developed a machine learning model to provide clinicians with personalized predictions for PARPi response.
Metformin Sensitizes PTEN-deficient Prostate Cancer to PARP Inhibitors by Rebuilding NADP(+) Homeostasis.
二甲双胍通过重建 NADP(+) 稳态,使 PTEN 缺陷型前列腺癌对 PARP 抑制剂敏感。
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| 期刊: | International Journal of Biological Sciences | 影响因子: | 10.000 |
| 时间: | 2026 | 起止号: | 2026 Jan 8; 22(3):1346-1368 |
| doi: | 10.7150/ijbs.121033 | ||
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