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

BACKGROUND AND 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 AND RESULTS: In total, 85 PANoptosis-related genes that possess both PANoptosis and multi-omics 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 5 machine learning algorithms was developed to identify the 5 hub genes (POSTN, SFN, MYOF, HOGA1, and PECR). The subsequently constructed PANoptosis risk score demonstrates outstanding performance in predicting prognosis and clinical translation across multicenter cohorts with multi-omics profiling. Bulk and single-cell transcriptome profiling were used to investigate the tumor microenvironment, emphasizing the crucial role of macrophages in the tumor microenvironment of ICCs. Moreover, a positive spatial correlation of cancer-associated fibroblasts-derived POSTN expression with tumor-associated macrophages infiltration and PD-L1/PD-L2 expression in ICC patients was observed, suggesting that overexpression of POSTN may lead to resistance to immune checkpoint blockade therapy in ICC patients.

CONCLUSIONS: 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 PANoptosis risk score in predicting prognosis and therapy response. These findings will help guide clinical treatment strategies for ICC.