Construction and evaluation of a prognostic risk model for PANoptosis-related long non-coding RNA in hepatocellular carcinoma
Objective Using bioinformatics,this study aimed to explore and identify long non-coding RNAs(lncRNAs)related to PANoptosis with significant prognostic impact on hepatocellular carcino-ma(HCC)patients by mining the cancer genome atlas(TCGA)database.Subsequently,a prognostic risk model for PANoptosis-related long non-coding RNAs(PANRlncRNAs)in HCC was constructed.Methods Clinical data of 377 HCC patients were downloaded from the TCGA database.Keyword"PANoptosis"was searched in various databases,and relevant genes were extracted from the literature.Co-expression analysis of PANoptosis-related genes and lncRNAs was performed to determine PANRlncRNAs.Differential analysis was conducted in cancer and adjacent tissues.After preprocessing clinical data for 377 patients,343 sam-ples meeting the criteria(entire dataset)were selected.The entire dataset was randomly divided into a training set(172 cases)and a test set(171 cases).A prognostic risk model for the training set was estab-lished through univariate Cox,LASSO,and multivariate Cox regression analyses.Each patient's risk score was obtained,and they were classified into high or low-risk groups based on the median risk score.Kaplan-Meier analysis was used for survival analysis,and the model's performance was assessed using the receiver operating characteristic(ROC)curve.Analysis of survival time and immune function in high and low-risk groups was conducted,with group comparisons performed using the Wilcoxon test.Results A total of 147 PANRlncRNAs were screened,of which 108 PANRlncRNAs were differentially expressed,and 4 PANRln-cRNAs were identified to constitute a prognostic risk model for HCC.The area under curve(AUC)values for the model's performance in predicting 1-,3-,and 5-year survival in patients from the training set were 0.740,0.774,and 0.791,respectively.The overall survival of high-risk group patients was significantly lower than that of the low-risk group(P＜0.05).The model also exhibited good predictive efficacy in the test set and the entire set.Further analysis revealed differences in immune cells and their functions between high and low-risk groups.Conclusion The prognostic risk model based on 4 PANRlncRNAs exhibits ex-cellent predictive capability and can effectively assess the survival prognosis of HCC patients,and its risk score has the potential to be an independent prognostic factor for HCC.
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