Scholarlink

Background <p>Senescence plays a critical role in prostate cancer, influencing disease onset and progression. However, the alterations of senescence-associated genes during prostate cancer progression and their potential value in predicting disease advancement remain to be further elucidated.</p> Methods <p>117 machine learning methods were applied to construct the senescence-related gene signature (SRGS). Temporal trajectory analysis based on bulk and single-cell transcriptomic datasets was performed to link SRGS with prostate cancer progression. Functional validations of PCNA were conducted both in vitro and in vivo to support our analytical findings.</p> Results <p>Using 117 machine learning methods, we developed the SRGS, which demonstrated robust predictive capability across multiple cohorts, including our own cohort of 90 patients. The SRGS also showed strong potential in predicting overall survival in patients treated with second-generation AR inhibitors. Temporal trajectory analysis of bulk RNA-seq and single-cell data revealed the biological significance of SRGS and identified Proliferating Cell Nuclear Antigen (PCNA) as a potential driver of PCa progression. Pharmacological inhibition of PCNA with AOH1996 significantly suppressed tumor growth and enhanced the efficacy of androgen deprivation therapy.</p> Conclusion <p>We developed the SRGS that effectively predicts prostate cancer prognosis and progression. Moreover, our findings highlight PCNA as a promising therapeutic target in PCa.</p> <p></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Senescence-related gene signature predicts prostate cancer progression and identifies PCNA as a therapeutic target via multi-omics machine learning integration

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月18日

查看全文
  • Renxuan Lin,
  • Hiocheng Un,
  • Youmei Kang,
  • Jiahao Lei,
  • Lingwu Chen,
  • Ren Liu,
  • Zongren Wang

摘要

Background

Senescence plays a critical role in prostate cancer, influencing disease onset and progression. However, the alterations of senescence-associated genes during prostate cancer progression and their potential value in predicting disease advancement remain to be further elucidated.

Methods

117 machine learning methods were applied to construct the senescence-related gene signature (SRGS). Temporal trajectory analysis based on bulk and single-cell transcriptomic datasets was performed to link SRGS with prostate cancer progression. Functional validations of PCNA were conducted both in vitro and in vivo to support our analytical findings.

Results

Using 117 machine learning methods, we developed the SRGS, which demonstrated robust predictive capability across multiple cohorts, including our own cohort of 90 patients. The SRGS also showed strong potential in predicting overall survival in patients treated with second-generation AR inhibitors. Temporal trajectory analysis of bulk RNA-seq and single-cell data revealed the biological significance of SRGS and identified Proliferating Cell Nuclear Antigen (PCNA) as a potential driver of PCa progression. Pharmacological inhibition of PCNA with AOH1996 significantly suppressed tumor growth and enhanced the efficacy of androgen deprivation therapy.

Conclusion

We developed the SRGS that effectively predicts prostate cancer prognosis and progression. Moreover, our findings highlight PCNA as a promising therapeutic target in PCa.