A necroptosis-related prognostic model for predicting prognosis, immune landscape, and drug sensitivity in hepatocellular carcinoma based on single-cell sequencing analysis and weighted co-expression network

Abstract

Background: Hepatocellular carcinoma (HCC) is a highly lethal cancer and is the second leading cause of cancer-related deaths worldwide. Unlike apoptosis, necroptosis (NCPS) triggers an immune response by releasing damage-related molecular factors. However, the clinical prognostic features of necroptosis-associated genes in HCC are still not fully explored.Methods: We analyzed the single-cell datasets GSE125449 and GSE151530 from the GEO database and performed weighted co-expression network analysis on the TCGA data to identify the necroptosis genes. A prognostic model was built using COX and Lasso regression. In addition, we performed an analysis of survival, immunity microenvironment, and mutation. Furthermore, the hub genes and pathways associated with HCC were localized within the single-cell atlas.Results: Patients with HCC in the TCGA and ICGC cohorts were classified using a necroptosis-related model with significant differences in survival times between high- and low-NCPS groups (p < 0.05). High-NCPS patients expressed more immune checkpoint-related genes, suggesting immunotherapy and some chemotherapies might prove beneficial to them. In addition, a single-cell sequencing approach was conducted to investigate the expression of hub genes and associated signaling pathways in different cell types.Conclusion: Through the analysis of single-cell and bulk multi-omics sequencing data, we constructed a prognostic model related to necroptosis and explored the relationship between high- and low-NCPS groups and immune cell infiltration in HCC. This provides a new reference for further understanding the role of necroptosis in HCC.

Multi-omics landscape analysis reveals the pan-cancer association of arginine biosynthesis genes with tumor immune evasion and therapy resistance

“…Among the remaining genes, GPT, GLS, GLS2, GLUL, GLUD1, GLUD2, GOT1, and GOT2 are mainly involved in the synthesis and metabolism of glutamate, which can be used to synthesize arginine through the ornithine cycle, so the synthesis and metabolism of glutamate is associated with the synthesis of arginine as well. On the basis of the study by Li et al [ 24 ], we calculated arginine biosynthesis scores to comprehensively evaluate the contribution of arginine biosynthesis genes in multiple cancers and found that arginine biosynthesis scores were risk factors for SKCM, THYM, and MESO, and patients with high-score SKCM had worse OS, DSS, and PFI. Wu et al [ 21 ] reported that arginine deficiency enhances the sensitivity of melanoma to tumor necrosis factor-related apoptosis-inducing ligand therapy, suggesting that arginine metabolism is associated with tumor prognosis and drug resistance.…”

The necroptosis related gene LGALS3 can be used as a biomarker for the adverse progression from chronic HBV infection to HCC

“…NRGs play a vital role in the development of HCC, and many studies have used them to establish risk-prediction models for prognosis prediction of patients with HCC ( 10 – 15 ). However, currently established prognostic models using NRGs for HCC are not applicable to patients with HBV-HCC, and the role of NRGs in the adverse progression of chronic HBV infection remains unclear.…”

“…We collected several studies (10)(11)(12)(13)(14)(15) on the construction of prognostic models of HCC patients using NRGs, and conducted a Kaplan-Meier curve analysis of 89 patients with HBV-HCC in the TCGA database using the prognostic models in the literature to determine whether the prognostic models in the literature are suitable for HBV-HCC.…”