A senescence-based machine learning model prognosticates and personalizes therapy in cervical cancer
Gong Chen1,2, Qianqian Jiang1, Jingyuan Xu1
1Nanjing University of Chinese Medicine, Nanjing, China.
Translational Cancer Research
|July 14, 2026
Summary
Cellular senescence in cervical cancer creates a tumor microenvironment linked to aggressive disease. A new senescence-related signature (SRS) predicts patient risk and therapy response, with a combination drug therapy showing promise.
Area of Science:
- Oncology
- Cell Biology
- Bioinformatics
Background:
- Cervical cancer presents a significant global health challenge.
- Understanding the role of cellular senescence in the cervical cancer tumor microenvironment (TME) is crucial for improving patient outcomes.
- This study addresses the gap in knowledge regarding senescence landscapes in cervical cancer.
Purpose of the Study:
- To characterize the role of cellular senescence in the cervical cancer TME.
- To identify clinical implications of senescence in cervical cancer.
- To develop a prognostic and predictive biomarker for cervical cancer.
Main Methods:
- Integrated single-cell RNA sequencing (scRNA-seq), bulk transcriptomics, and multi-omics analyses.
- Developed a 17-gene senescence-related signature (SRS) using Boruta feature selection.
- Validated a machine learning model (RSF + GBM) for prognostic stratification in TCGA-CESC and GSE44001 cohorts.
- Investigated the anti-tumor efficacy of LY3177833 and paclitaxel combination using in vitro and in vivo models.
Main Results:
- Single-cell analysis revealed fibroblasts and cancer cells as key senescent populations influencing stromal remodeling.
- The SRS model stratified patients into high- and low-risk groups with distinct survival outcomes and differential TME characteristics.
- High-risk patients exhibited metabolic reprogramming, pro-tumorigenic interactions, increased co-mutations, and reduced immune checkpoint expression.
- The LY3177833-paclitaxel combination suppressed proliferation pathways and demonstrated synergistic anti-tumor effects via apoptosis in vitro and in vivo.
Conclusions:
- A senescence-rich TME is associated with aggressive cervical cancer biology.
- The SRS is a robust prognostic and predictive biomarker for personalized risk assessment in cervical cancer.
- The LY3177833-paclitaxel combination represents a promising therapeutic strategy with synergistic anti-tumor effects.
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