ArticleCurrent medicinal chemistry2025
Machine Learning-based Gene Biomarker Identification for Improving Prognosis and Therapy in Hepatocellular Carcinoma.
Article in Current medicinal chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Identification and validation of BLK and OSBPL10 as diagnostic and prognostic biomarkers for nasopharyngeal carcinoma through machine learning algorithms.Scientific reports · 2026Article
- Comparative Tumor Microenvironment Analysis for HCC and PDAC Using KMplotter.International journal of molecular sciences · 2025Article
- Recognition of molecular clusters and a novel prognostic signature based on natural killer cell-related genes in skin cutaneous melanoma.World journal of surgical oncology · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionTraditional clinical evaluations based on pathological classification have shown limited effectiveness in predicting prognosis and guiding treatment for patients with hepatocellular carcinoma (HCC). This study aims to identify a robust molecular biomarker for improving prognosis and therapy in HCC.
methodsThe International Cancer Genome Consortium (ICGC), Gene Expression Omnibus (GEO), and The Cancer Genome Atlas (TCGA) provided expression data and clinicopathological information for several cohorts. First, Cox regression analysis and differentially expressed analysis were performed to identify robust prognostic genes. Next, machine learning algorithms, including 101 statistical models, were employed to pinpoint key genes in HCC. Single-cell sequencing analysis was conducted to explore the potential subcellular functions of each key gene. Based on these findings, an HCC Prognosis-Related Index (HPRI) was developed from the identified key genes, and HPRI-based nomogram models were validated across multiple cohorts. Additionally, tumor microenvironment analysis and drug sensitivity analysis were performed further to assess the clinical significance of the HPRI in HCC.
resultsA total of 36 robust prognostic genes with differential expression in HCC were identified, from which seven key genes-
conclusionOur study demonstrates a well-rounded approach by integrating gene expression, machine learning, tumor microenvironment analysis, and drug sensitivity profiling. HPRI may serve as a promising predictor for guiding prognosis and personalized treatment in HCC.
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