ArticleDiscover oncology2026
Identification and validation of NETs-related biomarkers in hepatocellular carcinoma through bioinformatics analysis and machine learning algorithms.
Article in Discover oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundEmerging evidence highlights the significant role of Neutrophil Extracellular Traps (NETs) in hepatocellular carcinoma (HCC), but the underlying molecular mechanisms involving NETs formation remain incompletely understood. This study aims to identify key biomarkers related to NETs in HCC through bioinformatic analysis.
methodsWe obtained RNA sequencing data of hepatocellular carcinoma tissues and adjacent normal liver tissues from the Gene Expression Omnibus (GEO) databases, followed by data correction, integration, and annotation. Subsequently, differentially expressed genes (DEGs) were identified, and Weighted Gene Co-expression Network Analysis (WGCNA) was used to screen for disease-related genes. The intersection with NETs-related genes (NRGs) yielded differentially expressed NET-related genes (DENRGs), which were subjected to single-sample Gene Set Enrichment Analysis (ssGSEA). Three machine learning models (LASSO, SVM-RFE, and RF) were further employed to screen for key biomarkers. Receiver Operating Characteristic (ROC) curves and a nomogram model were used to validate the diagnostic and predictive efficacy of those key biomarkers, with further validation using an external dataset. Unsupervised clustering and Gene Set Variation Analysis (GSVA) were performed on the key biomarkers.
resultsWe conducted a comprehensive bioinformatic analysis on 223 HCC samples and 127 normal liver tissue samples from 5 datasets. Transcriptomic analysis identified 826 DEGs. WGCNA revealed key gene modules associated with HCC, including 362 genes. By intersecting these with 627 NRGs, we identified 18 DENRGs. The results of ssGSEA showed that most of immune cells were significantly downregulated in HCC. Machine learning models (LASSO, SVM-RFE, and RF) identified three downregulated biomarkers (ECM1, DNASE1L3, JUN). A nomogram and ROC curves confirmed the diagnostic accuracy of these biomarkers. Cluster analysis revealed two distinct HCC subtypes with different immune microenvironment characteristics. Drug-gene interaction analysis identified potential inhibitors targeting DNASE1L3 and JUN.
conclusionThis study identified NET-related key biomarkers (ECM1, DNASE1L3, JUN) as reliable diagnostic tools for HCC, highlighting their diagnostic and therapeutic potential, and providing insights for HCC diagnostic tools and immunotherapy strategies.
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