A Senescence‐Related Signature for Risk Stratification and Predicted Immunotherapy Response in Breast Cancer: Integrative Pathology and Multi‐Omics Analysis

ABSTRACT Cellular senescence exerts context‐dependent effects on breast cancer progression and therapeutic response through dynamic interactions with the tumor microenvironment; however, its pathological manifestations, cellular organization, and clinical significance remain incompletely understood. In this study, we integrated whole‐slide pathology images, bulk transcriptomic profiles, single‐cell ribonucleic acid (RNA) sequencing, spatial transcriptomics, and clinical data to systematically characterize senescence‐associated features in breast cancer. Quantitative pathology and deep‐learning features were extracted from hematoxylin and eosin‐stained images, and a senescence‐related signature (SRS) was constructed using machine‐learning survival models. The SRS effectively stratified patients into distinct prognostic groups and showed consistent performance across the internally divided TCGA‐BRCA subsets, with high‐SRS patients exhibiting significantly poorer survival. Single‐cell analyses revealed that senescence activity was predominantly enriched in epithelial and endothelial cells, whereas fibroblasts acted as major signal‐sending populations and mediated enhanced collagen‐related communication with tumor cells. Low‐SRS tumors displayed greater immune‐cell infiltration, higher immune‐checkpoint expression, higher immunophenoscores, and more favorable Tumor Immune Dysfunction and Exclusion (TIDE)/immunophenoscore (IPS)‐based predicted response profiles, while also exhibiting distinct drug‐sensitivity profiles. Integrative pathological, transcriptomic, and spatial analyses further identified STARD10 as a senescence‐associated candidate enriched in tumor regions and spatially concordant with senescence‐marker expression at the spot level. Functional experiments showed that STARD10 knockdown reduced cellular metabolic activity in CCK‐8 assays and suppressed migration and invasion, attenuated senescence‐associated β‐galactosidase staining and senescence‐associated secretory phenotype (SASP)‐related cytokine expression, and suppressed tumor growth in vivo. Collectively, these findings establish an integrated pathology‐ and multi‐omics‐derived senescence‐related signature for prognostic stratification and characterization of TIDE/IPS‐based predicted immunotherapy‐response profiles in breast cancer, while identifying STARD10 as a candidate biomarker and a potential target for further mechanistic and therapeutic investigation.

Authors

Institutions

Publication Details

Journal
The FASEB Journal
Published
2026-10-22
DOI
https://doi.org/10.1096/fj.202603669r
Primary Topic
Telomeres, Telomerase, and Senescence
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Senescence‐Related Signature for Risk Stratification and Predicted Immunotherapy Response in Breast Cancer: Integrative Pathology and Multi‐Omics Analysis

Yucang He, Songyun Zhao, Zijian Lin, Yanming Chen et al.
The FASEB Journal
Telomeres, Telomerase, and Senescence
article

A Senescence‐Related Signature for Risk Stratification and Predicted Immunotherapy Response in Breast Cancer: Integrative Pathology and Multi‐Omics Analysis

Yucang He, Songyun Zhao, Zijian Lin, Yanming Chen, Liqun Li
article en

Abstract

ABSTRACT Cellular senescence exerts context‐dependent effects on breast cancer progression and therapeutic response through dynamic interactions with the tumor microenvironment; however, its pathological manifestations, cellular organization, and clinical significance remain incompletely understood. In this study, we integrated whole‐slide pathology images, bulk transcriptomic profiles, single‐cell ribonucleic acid (RNA) sequencing, spatial transcriptomics, and clinical data to systematically characterize senescence‐associated features in breast cancer. Quantitative pathology and deep‐learning features were extracted from hematoxylin and eosin‐stained images, and a senescence‐related signature (SRS) was constructed using machine‐learning survival models. The SRS effectively stratified patients into distinct prognostic groups and showed consistent performance across the internally divided TCGA‐BRCA subsets, with high‐SRS patients exhibiting significantly poorer survival. Single‐cell analyses revealed that senescence activity was predominantly enriched in epithelial and endothelial cells, whereas fibroblasts acted as major signal‐sending populations and mediated enhanced collagen‐related communication with tumor cells. Low‐SRS tumors displayed greater immune‐cell infiltration, higher immune‐checkpoint expression, higher immunophenoscores, and more favorable Tumor Immune Dysfunction and Exclusion (TIDE)/immunophenoscore (IPS)‐based predicted response profiles, while also exhibiting distinct drug‐sensitivity profiles. Integrative pathological, transcriptomic, and spatial analyses further identified STARD10 as a senescence‐associated candidate enriched in tumor regions and spatially concordant with senescence‐marker expression at the spot level. Functional experiments showed that STARD10 knockdown reduced cellular metabolic activity in CCK‐8 assays and suppressed migration and invasion, attenuated senescence‐associated β‐galactosidase staining and senescence‐associated secretory phenotype (SASP)‐related cytokine expression, and suppressed tumor growth in vivo. Collectively, these findings establish an integrated pathology‐ and multi‐omics‐derived senescence‐related signature for prognostic stratification and characterization of TIDE/IPS‐based predicted immunotherapy‐response profiles in breast cancer, while identifying STARD10 as a candidate biomarker and a potential target for further mechanistic and therapeutic investigation.

The FASEB JournalVol. 40(20)
First Affiliated Hospital of Wenzhou Medical University (CN)
Openalex Percentile: Top 13%
Telomeres, Telomerase, and Senescence
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon