A novel necroptosis-related lncRNA signature predicts the prognosis and immune microenvironment of hepatocellular carcinoma

Jianguo Wang; Bingbing Shen; Xinyuan Liu; Jianxin Jiang

doi:10.3389/fgene.2022.985191

A novel necroptosis-related lncRNA signature predicts the prognosis and immune microenvironment of hepatocellular carcinoma

Front Genet. 2022 Oct 4:13:985191. doi: 10.3389/fgene.2022.985191. eCollection 2022.

Authors

Jianguo Wang¹, Bingbing Shen¹, Xinyuan Liu², Jianxin Jiang¹

Affiliations

¹ Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.
² Department of Hepatic-Biliary-Pancreatic Surgery, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, China.

Abstract

Hepatocellular carcinoma (HCC) is one of the malignant tumors with high mortality and a worse prognosis globally. Necroptosis is a programmed death mediated by receptor-interacting Protein 1 (RIP1), receptor-interacting Protein 1 (RIP3), and Mixed Lineage Kinase Domain-Like (MLKL). Our study aimed to create a new Necroptosis-related lncRNAs (NRlncRNAs) risk model that can predict survival and tumor immunity in HCC patients. The RNA expression and clinical data originated from the TCGA database. Pearson correlation analysis was applied to identify the NRlncRNAs. The LASSO-Cox regression analysis was employed to build the risk model. Next, the ROC curve and the area under the Kaplan-Meier curve were utilized to evaluate the accuracy of the risk model. In addition, based on the two groups of risk model, we performed the following analysis: clinical correlation, differential expression, PCA, TMB, GSEA analysis, immune cells infiltration, and clinical drug prediction analysis. Plus, qRT-PCR was applied to test the expression of genes in the risk model. Finally, a prognosis model covering six necroptosis-related lncRNAs was constructed to predict the survival of HCC patients. The ROC curve results showed that the risk model possesses better accuracy. The 1, 3, and 5-years AUC values were 0.746, 0.712, and 0.670, respectively. Of course, we also observed that significant differences exist in the following analysis, such as functional signaling pathways, immunological state, mutation profiles, and medication sensitivity between high-risk and low-risk groups of HCC patients. The result of qRT-PCR confirmed that three NRlncRNAs were more highly expressed in HCC cell lines than in the normal cell line. In conclusion, based on the bioinformatics analysis, we constructed an NRlncRNAs associated risk model, which predicts the prognosis of HCC patients. Although our study has some limitations, it may greatly contribute to the treatment of HCC and medical progression.

Keywords: bioinformatics; hepatocellular carcinoma; immune infiltration; lncRNA; necroptosis; risk model.