ArticleScientific reports2024
Machine learning approach identifies inflammatory gene signature for predicting survival outcomes in hepatocellular carcinoma.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Artificial intelligence-based prognostic modeling of immunoradiotherapy in Barcelona clinic liver cancer stage C hepatocellular carcinoma: a multicenter retrospective study.Frontiers in oncology · 2026Article
- Machine Learning Approach and Bioinformatics Analysis Discovered Key Genomic Signatures for Hepatitis B Virus-Associated Hepatocyte Remodeling and Hepatocellular Carcinoma.Cancer informatics · 2025Review
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Authors and funding
6 authors.
Funding
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Abstract
backgroundHepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide, often linked to chronic inflammation. Our study aimed to probe inflammation pathways at the genetic level and pinpoint biomarkers linked to HCC patient survival.
methodsWe analyzed gene transcriptome data from 246 resectable stage I and II HCC patients from The Cancer Genome Atlas (TCGA). After selecting 917 inflammation-related genes (IRGs), we identified 104 differentially expressed genes (DEGs) through differential expression analysis. Two significant prognostic DEGs, S100A9 and PBK, were identified using LASSO and Cox regression, forming the basis of a risk score model. We conducted functional enrichment and immune landscape analyses, validated our findings on 170 patients from the GSE14520 dataset, and performed mutational analysis using TCGA somatic mutation data.
resultsWe analyzed 296 samples (246 HCC, 50 normal liver), showing significant survival differences between high and low-risk groups based on our risk score model. Functional enrichment analysis unveiled inflammation-associated pathways. Validation using the GSE14520 dataset confirmed our risk score's predictive ability, and we explored clinical correlations.
conclusionOur study delineates inflammation-related genomic changes in HCC, unveiling prognostic biomarkers with potential therapeutic implications. These findings deepen our understanding of HCC molecular mechanisms and may guide personalized therapeutic approaches, ultimately improving patient outcomes.
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