Investigating the Prognostic Role of Telomerase-Related Cellular Senescence Gene Signatures in Breast Cancer Using...

Area of Science:

• Oncology
• Genomics
• Cellular Biology

Background:

• Telomeres and cellular senescence are crucial in cancer development, impacting genomic stability and the tumor microenvironment.
• These processes are particularly relevant in breast cancer progression.
• Understanding their role is key to developing new therapeutic strategies.

Purpose of the Study:

• To develop a prognostic gene signature for breast cancer based on telomere maintenance and cellular senescence.
• To leverage machine learning for robust model development and validation.
• To explore tumor heterogeneity and the tumor microenvironment using advanced transcriptomic analyses.

Main Methods:

• Integrated bulk and single-cell RNA sequencing (scRNA-seq) data.
• Curated telomere-related genes (TEGs) and senescence-associated genes from TCGA and GEO databases.
• Employed a machine learning framework with 101 algorithmic combinations across 10 survival modeling approaches.

Main Results:

• Identified a 19-gene signature with optimal prognostic predictive accuracy, validated across independent cohorts.
• Functional enrichment revealed associations with immune responses and aging pathways.
• scRNA-seq highlighted cellular heterogeneity, with distinct subpopulations exhibiting varied risk scores and pathway activity; IHC confirmed protein expression differences.

Conclusions:

• A novel 19-gene signature for telomere and senescence provides significant prognostic value in breast cancer.
• Findings deepen the understanding of breast cancer tumor heterogeneity.
• This signature may guide precision oncology and future therapeutic interventions.