Identification of SCAMP2 as a regulator of NOTCH signaling in cisplatin resistance through a novel prognostic model for bladder cancer - Longjun Cai; Shaoqi Zhang; Fangfang Zheng; Furong Ji; Jin Wang; Long Shi; Liu Chao; Xiangyu Wang; Jianjun Zhang; Zhiyong Chen (Frontiers in Immunology)
IntroductionBladder cancer remains a major challenge in clinical oncology, particularly due to the development of platinum resistance, which severely impacts patient prognosis. Despite numerous attempts to create effective prognostic models, their clinical applicability has often been limited.MethodsIn this study, we utilized a robust statistical approach, LASSO-COX regression analysis, to develop a novel prognostic model for bladder cancer based on cisplatin sensitivity-related genes (CSRGs). The model was validated using both the TCGA-BLCA dataset and an independent validation set, GSE32894. Additionally, we employed various in vitro assays, including CCK-8 and EdU assays for cell proliferation, transwell assays for migration, and flow cytometry for apoptosis analysis, to investigate the biological function of the identified genes.ResultsOur prognostic model demonstrated superior predictive performance, with high AUC values. SCAMP2 was identified as a critical gene with elevated expression in bladder cancer, showing strong correlation with sensitivity to multiple anti-cancer drugs, including cisplatin. Further functional assays revealed that SCAMP2 mediates drug resistance in bladder cancer cells via the NOTCH signaling pathway. Additionally, in vivo experiments showed that SCAMP2 overexpression significantly enhanced cisplatin sensitivity in bladder cancer tissues.DiscussionThese findings underscore the potential of CSRGs, particularly SCAMP2, as critical biomarkers for bladder cancer prognosis. The identification of SCAMP2 as a regulator of NOTCH signaling in cisplatin resistance offers new insights into the molecular mechanisms of chemotherapy resistance and suggests potential therapeutic targets for overcoming drug resistance. Our model could guide personalized treatment strategies and improve bladder cancer patient outcomes.