Integrating bulk and single-cell RNA sequencing analysis to reveal characterization of mechanical stimulus-related genes and prognostic signatures in breast cancer

TL;DR

This study identifies genes related to mechanical stimuli in breast cancer and develops a model to predict patient survival and treatment response.

Contribution

A novel prognostic signature based on mechanical stimulus-related genes is developed to predict breast cancer outcomes and inform treatment strategies.

Findings

A prognostic signature with 15 mechanical stimulus-related genes was developed and validated for breast cancer survival prediction.

Low-risk patients showed increased immune cell infiltration, while high-risk patients may benefit from Sepantronium bromide.

Knockdown of TEX19 suppressed breast tumor proliferation in in vitro and in vivo experiments.

Abstract

To identify molecular clusters and establish a scoring model based on mechanical stimulus-related genes (MSRGs) for predicting the prognosis of breast cancer patients and understanding the role of mechanical stimuli in the breast tumor microenvironment (TME).

We utilized bulk and single-cell RNA sequencing analysis to characterize MSRGs associated with breast cancer prognosis. Unsupervised consensus molecular clustering was applied to identify distinct clusters based on overall survival-associated MSRGs from The Cancer Genome Atlas (TCGA) database. The scoring model was constructed by LASSO-Cox method and validated. Additionally, single-cell RNA sequencing analysis, along with in vitro and in vivo experiments, were conducted to further investigate the role of the model in breast cancer.

We identified 23 overall survival-associated MSRGs and established two molecular subgroups with distinct survival outcomes. A prognostic signature incorporating 15 MSRGs was developed and validated, demonstrating its predictive capability for overall survival of breast cancer patients. The nomogram integrating clinical characteristics and the mechanical stimulus-related risk score exhibited promising predictive accuracy. The low-risk group displayed an immune "hot" phenotype with increased immune cell infiltration, while the high-risk group exhibited resistance to conventional chemotherapy but potential sensitivity to Sepantronium bromide. By using the SCISSOR algorithm, we provide evidence at single-cell resolution for the impact of mechanical stimulation on tumor immune microenvironment. The in vivo and in vitro assays demonstrated that knockdown of TEX19 significantly suppressed breast tumor proliferation.

We developed a pioneering prognostic signature incorporating MSRGs in breast cancer, with a particular focus on mechanical stimuli may influence breast cancer prognosis by remodeling the immune microenvironment. The findings highlighted the importance of personalized treatment strategies and provide new insights into the role of mechanical forces in breast tumor biology.

The online version contains supplementary material available at 10.1186/s13058-025-02130-6.

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