ArticleDiscover oncology2026
Development of a four-gene prognostic signature for hepatocellular carcinoma using TCGA and GEO datasets.
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
backgroundHepatocellular carcinoma (HCC) poses a major global health challenge due to its high mortality and recurrence, necessitating continuous research into biomarkers for better diagnosis and treatment.
methodsGene expression and clinical data from HCC patients were sourced from TCGA, with the GSE14520 dataset used for validation. Differentially expressed genes (DEGs) between HCC and normal tissues were identified. Patients were randomly divided into training and testing groups using a repeated sampling strategy, and various regression analyses were conducted to develop a prognostic gene signature. The four-gene signature's prognostic ability was assessed using ROC curves, Kaplan-Meier analysis, multivariate Cox regression, and a nomogram, with further validation in the GSE76427 and ICGC cohorts.
resultsA four-gene signature (EFNA4, MMP1, NEIL3, TXNRD1) was developed for predicting HCC prognosis. The GSE14520 database confirmed these genes were overexpressed in HCC tissues (p < 0.001). Immunohistochemical (IHC) analysis revealed that EFNA4, MMP1, and TXNRD1 protein levels were significantly elevated in HCC compared to nearby normal tissues (p < 0.05), and the risk score successfully pinpointed high-risk patients with lower overall survival (p < 0.001). As an independent prognostic factor for HCC, the risk score was used alongside the stage to develop a nomogram, which surpassed the predictive ability of each factor individually for 1-, 3-, and 5-year survival rates (AUCs > 0.7). The nomogram's predictions matched observed outcomes well in the entire cohort. Functional analysis linked cell cycle and pyrimidine metabolism pathways to HCC development.
conclusionOur study developed a new four-gene model combined with clinical parameters to effectively predict HCC prognosis, offering a reliable tool for clinicians and a foundation for future mechanistic investigation.
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