ArticleBioMed research international2022
A New Inflammation-Related Risk Model for Predicting Hepatocellular Carcinoma Prognosis.
Article in BioMed research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Targeting the E2F6-TOP2A-DKK1 axis: a novel therapeutic strategy for EMT-driven hepatocellular carcinoma progression.Frontiers in immunology · 2026Article
- Identification and Analysis of Key Immune- and Inflammation-Related Genes in Idiopathic Pulmonary Fibrosis.Journal of inflammation research · 2025Article
- Machine learning approach identifies inflammatory gene signature for predicting survival outcomes in hepatocellular carcinoma.Scientific reports · 2024Article
- Nomogram built based on machine learning to predict recurrence in early-stage hepatocellular carcinoma patients treated with ablation.Frontiers in oncology · 2024Article
- Identification of Immuno-Inflammation-Related Biomarkers for Acute Myocardial Infarction Based on Bioinformatics.Journal of inflammation research · 2023Article
- An Inflammation-Associated Prognosis Model for Hepatocellular Carcinoma Based on Adenylate Uridylate- (AU-) Rich Element Genes.Mediators of inflammation · 2023Article
- Integrated Multi-Omics Data Analysis Reveals Associations Between Glycosylation and Stemness in Hepatocellular Carcinoma.Frontiers in oncology · 2022Article
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Authors and funding
2 authors.
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Abstract
Background: Hepatocellular carcinoma (HCC) is characterized by a poor prognosis. Inflammation has a vital role in the formation and development of HCC. However, the prediction of HCC prognosis using inflammation-related genes (IRGs) remains elusive. In this study, we constructed a new IRG risk model to predict the HCC prognosis. Results: HCC-related RNA expression profiles and their corresponding clinical data were downloaded from TCGA and ICGC databases to explore the IRGs' predicting ability. Seven hundred thirty-seven IRGs from GeneCards were used as candidate genes to construct the model. The associations of overall survival (OS) with IRGs were evaluated using the log-rank test and univariate Cox analysis, and 32 out of 737 IRGs showed predicting the potential for HCC prognosis. These IRGs were further analyzed using the least absolute shrinkage and selection operator (LASSO) and multivariate Cox analyses. Finally, 6 IRGs were included in an IRG risk model. Based on the cut-off of the risk score calculated according to the IRG risk model, HCC samples were divided into the high-risk and the low-risk groups. The OS of patients was lower in the high-risk group than in the low-risk group ( Conclusions: The new IRG risk score was an independent risk factor for HCC prognosis and could be used to assess the immune status of the HCC microenvironment.
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