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Introduction <p>Clear cell renal cell carcinoma (ccRCC) is a prevalent malignancy of the kidney, known for its aggressive growth and tendency to metastasize. Cellular senescence (CS), regarded as a characteristic feature of the natural aging process, is strongly linked to the initiation and advancement of several diseases. This research seeks to develop an integrated prognostic model utilizing senescence markers in ccRCC, with the goal of evaluating clinical outcomes and clarifying the tumor’s immune microenvironment.</p> Methods <p>The transcriptome profiles from ccRCC patients were acquired from TCGA database, and the senescence-related genetic model (SRGM) was developed through Cox proportional hazards analysis integrated with multiple machine learning approaches. In addition, immune microenvironment and drug sensitivity analysis were performed. EIF4EBP1 expression and its role in epithelial cell senescence were analyzed using single-cell RNA sequencing, spatial transcriptomics, and trajectory analysis. Finally, cellular experiments were conducted to confirm the role of silencing EIF4EBP1 in the biological behavior of ccRCC cells.</p> Results <p>Through a comprehensive framework incorporating 117 machine learning algorithm combinations, we established an SRGM consisting of 9 genes. The optimal model was constructed using CoxBoost + StepCox[forward] and achieved a concordance index (C-index) of 0.743, demonstrating robust prognostic predictive capacity across independent datasets. EIF4EBP1 is a central gene in the model. Silencing EIF4EBP1 reduced cell migration, proliferation, and invasion, while inducing senescence-associated phenotypes and apoptosis in ccRCC cells. Additionally, it significantly enhanced the responsiveness of ccRCC cells to sunitinib treatment.</p> Conclusions <p>In summary, we developed a novel SRGM that effectively stratifies prognosis in ccRCC. Furthermore, EIF4EBP1 has been validated at the cellular level as a promising therapeutic target, providing innovative insights into the personalized treatment of ccRCC.</p>

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Multi-omics integration constructs a senescence-based prognostic model and identifies EIF4EBP1 as a therapeutic target in clear cell renal cell carcinoma

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  • 发表日期：

    2026年3月11日

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  • He Duan,
  • Ning Li,
  • Dingming Song,
  • Yongzhuo Li,
  • Xin Liang,
  • Yongxue Ding,
  • Ming Tong

摘要

Introduction

Clear cell renal cell carcinoma (ccRCC) is a prevalent malignancy of the kidney, known for its aggressive growth and tendency to metastasize. Cellular senescence (CS), regarded as a characteristic feature of the natural aging process, is strongly linked to the initiation and advancement of several diseases. This research seeks to develop an integrated prognostic model utilizing senescence markers in ccRCC, with the goal of evaluating clinical outcomes and clarifying the tumor’s immune microenvironment.

Methods

The transcriptome profiles from ccRCC patients were acquired from TCGA database, and the senescence-related genetic model (SRGM) was developed through Cox proportional hazards analysis integrated with multiple machine learning approaches. In addition, immune microenvironment and drug sensitivity analysis were performed. EIF4EBP1 expression and its role in epithelial cell senescence were analyzed using single-cell RNA sequencing, spatial transcriptomics, and trajectory analysis. Finally, cellular experiments were conducted to confirm the role of silencing EIF4EBP1 in the biological behavior of ccRCC cells.

Results

Through a comprehensive framework incorporating 117 machine learning algorithm combinations, we established an SRGM consisting of 9 genes. The optimal model was constructed using CoxBoost + StepCox[forward] and achieved a concordance index (C-index) of 0.743, demonstrating robust prognostic predictive capacity across independent datasets. EIF4EBP1 is a central gene in the model. Silencing EIF4EBP1 reduced cell migration, proliferation, and invasion, while inducing senescence-associated phenotypes and apoptosis in ccRCC cells. Additionally, it significantly enhanced the responsiveness of ccRCC cells to sunitinib treatment.

Conclusions

In summary, we developed a novel SRGM that effectively stratifies prognosis in ccRCC. Furthermore, EIF4EBP1 has been validated at the cellular level as a promising therapeutic target, providing innovative insights into the personalized treatment of ccRCC.