ArticleScientific reports2025
Construction of a prognostic model and identification of key genes in liver hepatocellular carcinoma based on multi-omics data.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Identification ofEndocrine, metabolic & immune disorders drug targets · 2026Article
- Transcriptomic Analysis Reveals the Role ofHuman mutation · 2026Article
- A Histone Deacetylase Activity Model for the Discovery and Validation of Sepsis Biomarkers.Endocrine, metabolic & immune disorders drug targets · 2026Article
- Construction and validation of a lung adenocarcinoma prognostic model based on neutrophil extracellular traps and oxidative stress-related genes.European journal of medical research · 2025Article
- Endothelial-specific genes TMTC1, RPS6KA2, and F8 are downregulated in hypertrophic cardiomyopathy.European journal of medical research · 2025Article
- Multi-Omics and Functional Analysis of BFSP1 as a Prognostic and Therapeutic Target in Liver Hepatocellular Carcinoma.Medicina (Kaunas, Lithuania) · 2025Article
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Authors and funding
3 authors.
Funding
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Abstract
Liver hepatocellular carcinoma (LIHC) strongly contributes to global cancer mortality, highlighting the need for a deeper understanding of its molecular mechanisms to enhance patient prognosis and treatment approaches. We aimed to investigate the differential expression of immunogenic cell death-related genes (ICDRGs) and cellular senescence-related genes (CSRGs) in LIHC and their effects on patient prognosis. We combined the GSE25097, GSE46408, and GSE121248 datasets by eliminating batch effects and standardizing the data. After processing, 16 genes were identified as ICDR&CSR differentially expressed genes (ICDR&CSRDEGs), including UBE2T, HJURP, PTTG1, CENPA, and FOXM1. Gene set enrichment analysis indicated a strong enrichment of these genes in pre-Notch expression and processing. Gene set variation analysis revealed 20 pathways with significant differences between the LIHC and control groups. Mutation analysis identified TP53 as the most commonly mutated gene in LIHC samples. A prognostic risk model integrating 12 ICDR&CSRDEGs was developed, showing high precision at 1 year but diminished accuracy at 2 and 3 years. Our constructed prognostic risk model provides valuable insights for predicting patient outcomes and may guide future therapeutic interventions targeting these specific genes. Further research is needed to explore the mechanistic roles of these genes in LIHC progression and treatment response.
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