Identify the PANoptosis signature and prognostic model via a multimachine-learning computational framework for bladder urothelial carcinoma.

backgroundAs accumulating evidence suggests that PANoptosis plays a significant role in tumour progression, it is essential to elucidate its implications for tumour prognosis and treatment. We aimed to characterize the PANoptotic features of patients with bladder urothelial carcinoma (BLCA) and to develop a novel model to guide clinical diagnosis and treatment, while further investigating the associated molecular mechanisms underlying tumour progression.

methodsFirst, samples with BLCA were divided into two clusters based on the expression of PANoptosis genes. Subsequently, 369 PANoptosis-associated genes were identified through differential expression gene analysis. A novel model was then developed by integrating Cox regression analysis with four machine learning algorithms to compute a PANscore (PANS) and quantify the PANoptotic features of each participant. Further, immunohistochemistry, 5-ethynyl-2′-deoxyuridine cell proliferation assay, Quantitative Reverse Transcriptase-Polymerase Chain Reaction, and immunoblotting experiments were employed to validate the model.

resultsWe developed a PANoptosis model that demonstrated robust performance in prognostic prediction. The high PANS group had higher Tumour Immune Dysfunction and Exclusion scores than the low PANS group, which suggested that the low PANS group obtained more benefit from the Immune Checkpoint Blockade treatment than the high PANS group. Moreover, our study revealed high expression of GNLY in Natural Killer cells and VSIG2 in tumour cells. Notably, VSIG2 expression positively correlated with the degree of malignancy in BLCA. Additionally, we explored VSIG2 function in BLCA to reveal that the proliferation capacity of BLCA cells diminished following VSIG2 knockdown. Finally, our research identified compounds or drugs targeting VSIG2 through molecular docking techniques. The small-molecule compound quercetin was found to target the VSIG2 protein, effectively reversing the enhanced proliferative capacity of BLCA induced by VSIG2 overexpression.

conclusionsThe PANoptosis model could accurately predict the prognosis of patients with BLCA and guide BLCA treatment. Additionally, our treatment of patients with high VSIG2 expression by the small-molecule compound quercetin has opened up a new direction for clinical treatment of BLCA.