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Background <p>Hepatocellular 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.</p> Methods <p>Gene 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.</p> Results <p>A four-gene signature (<i>EFNA4</i>, <i>MMP1</i>, <i>NEIL3</i>, <i>TXNRD1</i>) was developed for predicting HCC prognosis. The GSE14520 database confirmed these genes were overexpressed in HCC tissues (<i>p</i> &lt; 0.001). Immunohistochemical (IHC) analysis revealed that EFNA4, MMP1, and TXNRD1 protein levels were significantly elevated in HCC compared to nearby normal tissues (<i>p</i> &lt; 0.05), and the risk score successfully pinpointed high-risk patients with lower overall survival (<i>p</i> &lt; 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 &gt; 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.</p> Conclusion <p>Our 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.</p>

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Development of a four-gene prognostic signature for hepatocellular carcinoma using TCGA and GEO datasets

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月1日

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  • Wanhong Gu,
  • Weiwei Chen,
  • Yaqiong Zhang,
  • Xiaoyuan Yan,
  • Ouyang Zhang,
  • Peijie Wang

摘要

Background

Hepatocellular 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.

Methods

Gene 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.

Results

A 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.

Conclusion

Our 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.