Integrating bulk and single-cell RNA sequencing data to establish necroptosis-related lncRNA risk model and analyze the immune microenvironment in hepatocellular carcinoma.

Background: The increasing evidence suggests that necroptosis mediates many behaviors of tumors, as well as the regulation of the tumor microenvironment. Long non-coding RNAs (lncRNAs) are involved in a variety of regulatory processes during tumor development and are significantly associated with patient prognosis. It suggests that necroptosis-related lncRNAs (NRlncRNAs) may serve as biomarkers for the prognosis of hepatocellular carcinoma (HCC). Methods: lncRNA expression profiles of HCC were obtained from TCGA database. LncRNAs associated with necroptosis were extracted using correlation analysis. Prognostic models were constructed based on least absolute shrinkage and selection operator algorithm (LASSO) and multivariate Cox regression analysis. The differences of tumor microenvironment between high-risk and low-risk groups were further analyzed. Single-cell RNA sequencing data of HCC was performed to assess the enrichment of necroptosis-related genes in immune cell subsets. Finally, real-time RT-PCR was used to detect the prognosis-related lncRNAs expression in different HCC cell lines. Results: We constructed a prognostic signature based on 8 NRlncRNAs, which also showed good predictive accuracy. The model showed that the prognosis of patients with high-risk score was significantly worse than that of patients with low-risk score ( Conclusion: This prognostic signature based on the necroptosis-related lncRNAs may provide meaningful clinical insights for the prognosis and immunotherapy responses in patients with HCC.