Identification of PANoptosis-related lncRNAs in hepatocellular carcinoma based on bioinformatics and construction of a prognostic model.

Abstract

PANoptosis, a novel form of pro-inflammatory programmed cell death, plays a role in the progression of various cancers. However, its mechanisms in hepatocellular carcinoma (HCC) remain unclear. Recent studies have highlighted the critical role of long non-coding RNAs (lncRNAs) in the development and progression of multiple cancers. In this study, we retrieve HCC datasets from the TCGA and GEO databases. We identify PANoptosis-related lncRNAs through correlation analysis based on HCC datasets and previous research. Consistent clustering analysis reveals two distinct subtypes of HCC patients: Cluster 1 and Cluster 2. Compared with the Cluster 2 subtype, Cluster 1 shows a better prognosis and higher levels of immune infiltration. We then perform a Lasso-Cox regression analysis of PANoptosis-related lncRNAs to construct a risk assessment model for predicting the prognosis of HCC patients. Kaplan-Meier analysis indicates that patients in the low-risk group have higher survival rates, while ROC (receiver operating characteristic curve) and calibration curves demonstrate the model's good predictive performance. These findings provide deeper insights into the critical role of PANoptosis-related lncRNAs in developing HCC, offering potential biomarkers and therapeutic targets for future HCC treatment.