A novel PANoptosis-related lncRNA model for forecasting prognosis and therapeutic response in hepatocellular carcinoma

Author:

Lan Chenlu1,Qin Haifei1,Huang Zaida1,Huang Xinlei1,Peng Kai1,Li Yuhua1,Qin Chongjiu1,Wei Yongguang1,Zhou Xin1,Liao Xiwen1,Zhu Guangzhi1,Peng Tao1

Affiliation:

1. The First Affiliated Hospital of Guangxi Medical University

Abstract

Abstract

Some studies have shown PANoptosis-related genes were related to the prognosis for hepatocellular carcinoma (HCC), but efforts for PANoptosis-related lncRNAs are scarce. Data of The Cancer Genome Atlas (TCGA) was used to identify prognostic PANoptosis-related lncRNAs, risk model and nomogram were constructed for predicting the prognosis of HCC. The clinical characteristic, mutation landscape, immune response, drug sensitivity, enriched biological process and pathway between low and high risk groups were analyzed. The Polymerase Chain Reaction (PCR) was performed to verify the expression of lncRNAs. Risk models displayed good predictive performance in TCGA, train and test cohorts with the area under the receiver operator characteristic curves (AUC) of 1- and 3- year OS > 0.7. Notably, the performance of nomogram and risk model was better than TNM stage (AUC: 0.717 and 0.673 vs 0.660). The risk group was proved to be an independent prognostic factor (p < 0.05). Furthermore, we found that patients of high risk group had a larger tumor size, higher AFP level and advanced TNM stage than the low group (p < 0.05). The functional enrichment analysis suggested that high risk group was related to the upregulated molecular characteristics of cell division, cell proliferation, cell cycle and p53 signaling pathway, and downregulated in metabolic pathway. The mutation analysis revealed an obvious difference of TP53 and CTNNB1 mutation between high and low risk groups. Immune response and drug sensitivity analysis discovered that high risk group was likely to benefit from immunotherapy and some molecular targeted drugs. In conclusion, the PANoptosis-related lncRNA model may be used to predict the prognosis and therapeutic response for HCC.

Publisher

Springer Science and Business Media LLC

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