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Background <p>Hepatocellular carcinoma (HCC) is an aggressive malignancy associated with an unfavorable prognosis. Telomeres and telomere-related genes are central to tumorigenesis, but their systematic integration into prognostic modeling for HCC remains insufficiently explored. This study presents a telomere-based prognostic model aimed at improving risk stratification and informing therapeutic decision-making in HCC.</p> Methods <p>Transcriptomic profiles and clinical data were collected from The Cancer Genome Atlas (369 HCC and 50 normal samples) and Hepatocellular Carcinoma Gene Expression Database (203 cases). A curated panel of 2,093 telomere-related genes was retrieved from TelNet. Hub genes were identified using an integrated strategy combining ssGSEA, WGCNA, and differential expression analysis. A prognostic risk model was established using univariate Cox, LASSO, and multivariate Cox regression analyses, followed by external validation.</p> Results <p>We developed a robust telomere-based prognostic model featuring six key genes: <i>E2F1</i>, <i>MYCN</i>, <i>VPS72</i>, <i>CFAP53</i>, <i>OR8A1</i>, and <i>TXNRD1</i>. This six-gene signature stratified patients with HCC into high- and low-risk subgroups with significantly different overall survival (<i>P</i> &lt; 0.05) across training and validation cohorts. Multivariate analyses confirmed the risk score as an independent prognostic factor. Functional analysis revealed significant enrichment of DNA replication and cell cycle pathways in high-risk patients. Immune profiling showed distinct infiltration patterns and elevated immune evasion potential in the high-risk group. Drug sensitivity analyses highlighted potential therapeutic vulnerabilities to specific inhibitors.</p> Conclusion <p>This study established a telomere-based prognostic model for HCC. This model provides reliable survival prediction, captures key tumor microenvironment features, and yields insights to support personalized therapeutic strategies, offering a valuable tool for clinical decision-making in HCC.</p>

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A telomere-based prognostic model incorporating E2F1, MYCN, VPS72, CFAP53, OR8A1, and TXNRD1 for hepatocellular carcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月20日

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  • Yafei Wang,
  • Leiya Fu,
  • Zihan Yang,
  • Weizheng Wang,
  • Jiachun Sun,
  • Xinyu Gu

摘要

Background

Hepatocellular carcinoma (HCC) is an aggressive malignancy associated with an unfavorable prognosis. Telomeres and telomere-related genes are central to tumorigenesis, but their systematic integration into prognostic modeling for HCC remains insufficiently explored. This study presents a telomere-based prognostic model aimed at improving risk stratification and informing therapeutic decision-making in HCC.

Methods

Transcriptomic profiles and clinical data were collected from The Cancer Genome Atlas (369 HCC and 50 normal samples) and Hepatocellular Carcinoma Gene Expression Database (203 cases). A curated panel of 2,093 telomere-related genes was retrieved from TelNet. Hub genes were identified using an integrated strategy combining ssGSEA, WGCNA, and differential expression analysis. A prognostic risk model was established using univariate Cox, LASSO, and multivariate Cox regression analyses, followed by external validation.

Results

We developed a robust telomere-based prognostic model featuring six key genes: E2F1, MYCN, VPS72, CFAP53, OR8A1, and TXNRD1. This six-gene signature stratified patients with HCC into high- and low-risk subgroups with significantly different overall survival (P < 0.05) across training and validation cohorts. Multivariate analyses confirmed the risk score as an independent prognostic factor. Functional analysis revealed significant enrichment of DNA replication and cell cycle pathways in high-risk patients. Immune profiling showed distinct infiltration patterns and elevated immune evasion potential in the high-risk group. Drug sensitivity analyses highlighted potential therapeutic vulnerabilities to specific inhibitors.

Conclusion

This study established a telomere-based prognostic model for HCC. This model provides reliable survival prediction, captures key tumor microenvironment features, and yields insights to support personalized therapeutic strategies, offering a valuable tool for clinical decision-making in HCC.