Multiomics approaches for identifying the PANoptosis signature and prognostic model via a multimachine-learning computational framework for intrahepatic cholangiocarcinoma

Background & Aims: The aims of the present study were to characterize the PANoptosis signature in intrahepatic cholangiocarcinoma (ICC) patients, construct a novel model to guide clinical diagnosis and treatment, and further explore the associated molecular mechanisms of drug resistance. Approach & Results: In total, 85 PANoptosis-related genes that possess both PANoptosis and multiomics features were respectively screened from transcriptomic data from the OEP001105 public cohort and from transcriptomic and proteomic sequencing data from The First Affiliated Hospital of Chongqing Medical University. A novel framework integrating Cox regression analysis and five machine learning algorithms was developed to identify the five hub genes (POSTN, SFN, MYOF, HOGA1, and PECR), The subsequently constructed PANoptosis risk score (PANRS) demonstrate outstanding performance in predicting prognosis and clinical translation across multicenter cohorts with multiomics profiling. Bulk and single-cell transcriptome profiling were used to investigate the tumor microenvironment (TME), emphasizing the crucial role of macrophages in the TME of ICCs. Moreover, a positive spatial correlation of CAFs-derived POSTN expression with TAMs infiltration and PD-L1/PD-L2 expression in ICC patients was observed, suggesting that overexpression of POSTN may lead to resistance to ICB therapy in ICC patients. Conclusion: The present study identified a precise prognostic and treatment strategy for ICC patients prone to PANoptosis, investigated the molecular mechanisms of PANoptosis in ICC cells, and highlighted the potential clinical relevance of the PANRS in predicting prognosis and therapy response. These findings will help guide clinical treatment strategies for ICC.