A programmed cell death-related gene signature to predict prognosis and therapeutic responses in liver hepatocellular carcinoma

• DOI: 10.1007/s12672-024-00924-2
• Publication Date: 2024-03-11T05:01:51Z
• Authors: Xinyu Gu; Jie Pan; Yanle Li; Liushun Feng
• Keywords: Gene signature

Abstract Background Programmed cell death (PCD) functions critically in cancers and PCD-related genes are associated with tumor microenvironment (TME), prognosis therapeutic responses of cancer patients. This study stratified hepatocellular carcinoma (HCC) patients develop a prognostic model for predicting responses. Methods Consensus clustering analysis was performed to subtype HCC The Cancer Genome Atlas (TCGA) database. Differentially expressed (DEGs) among the subtypes were filtered subjected least absolute shrinkage selection operator (LASSO) regression univariate Cox filter genes. A gene signature TCGA constructed validated ICGC-LIRI-JP GSE14520 datasets. TME analyzed using CIBERSORT, MCP-counter, TIMER EPIC algorithms. Drug sensitivity predicted by oncoPredict package. Spearman used detect correlation. Results Four molecular categorized based on Subtype C1 showed poorest prognosis, most infiltration Fibroblasts, dentritic (DC) cancer-associated fibroblasts (CAFs), highest TIDE score. C4 had better survival outcome, lowest immune infiltration. outcomes C2 C3 intermediate. Next, total 69 co-DEGs screened four subsequently we identified five (MCM2, SPP1, S100A9, MSC EPO) developing model. High-risk not only unfavorable higher clinical stage grade, more inflammatory pathway enrichment, but also possessed possibility escape sensitive Cisplatin 5. Fluorouracil. robustness external Conclusion provides new insights into subtyping may serve as useful tool predict guide treatments HCC.