Construction of a stromal cell-related prognostic signature based on a 101-combination machine learning framework for predicting prognosis and immunotherapy response in triple-negative breast cancer DOI Creative Commons
Fanrong Li,

Congnan Jin,

Yacheng Pan

et al.

Frontiers in Immunology, Journal Year: 2025, Volume and Issue: 16

Published: May 14, 2025

Background Triple-negative breast cancer (TNBC) is a highly aggressive subtype with limited therapeutic targets and poor immunotherapy outcomes. The tumor microenvironment (TME) plays key role in progression. Advances single-cell transcriptomics have highlighted the impact of stromal cells on progression, immune suppression, immunotherapy. This study aims to identify cell marker genes develop prognostic signature for predicting TNBC survival outcomes response. Methods Single-cell RNA sequencing (scRNA-seq) datasets were retrieved from Gene Expression Omnibus (GEO) database annotated using known genes. Cell types preferentially distributed identified odds ratios (OR). Bulk transcriptome data analyzed Weighted correlation network analysis (WGCNA) myCAF-, VSMC-, Pericyte-related (MVPRGs). A consensus MVP cell-related (MVPRS) was developed 10 machine learning algorithms 101 model combinations validated training validation cohorts. Immune infiltration response assessed CIBERSORT, ssGSEA, TIDE, IPS scores, an independent cohort (GSE91061). FN1, gene model, through qRT-PCR, immunohistochemistry, interference, CCK-8 assay, apoptosis assay wound-healing assay. Results In TNBC, three subpopulations—myofibroblastic cancer-associated fibroblasts (myCAF), vascular smooth muscle (VSMCs), pericytes—were enriched, exhibiting high interaction frequencies strong associations prognosis. nine-gene (MVPRS), 23 prognostically significant among 259 MVPRGs, demonstrated excellent predictive performance as factor. nomogram integrating MVPRS, age, stage, grade offered clinical utility. High-risk group showed reduced increased activity tumor-related pathways like ANGIOGENESIS HYPOXIA, while low-risk groups responded better based TIDE scores. oncogene, expressed tissues lines, promoting proliferation migration inhibiting apoptosis. Conclusion reveals heterogeneity introduces myCAF, VSMC, Pericyte MVPRS effectively predicts prognosis response, providing guidance personalized treatment. FN1 oncogene impacting progression malignant phenotype, potential target.

Language: Английский

Construction of a stromal cell-related prognostic signature based on a 101-combination machine learning framework for predicting prognosis and immunotherapy response in triple-negative breast cancer DOI Creative Commons
Fanrong Li,

Congnan Jin,

Yacheng Pan

et al.

Frontiers in Immunology, Journal Year: 2025, Volume and Issue: 16

Published: May 14, 2025

Background Triple-negative breast cancer (TNBC) is a highly aggressive subtype with limited therapeutic targets and poor immunotherapy outcomes. The tumor microenvironment (TME) plays key role in progression. Advances single-cell transcriptomics have highlighted the impact of stromal cells on progression, immune suppression, immunotherapy. This study aims to identify cell marker genes develop prognostic signature for predicting TNBC survival outcomes response. Methods Single-cell RNA sequencing (scRNA-seq) datasets were retrieved from Gene Expression Omnibus (GEO) database annotated using known genes. Cell types preferentially distributed identified odds ratios (OR). Bulk transcriptome data analyzed Weighted correlation network analysis (WGCNA) myCAF-, VSMC-, Pericyte-related (MVPRGs). A consensus MVP cell-related (MVPRS) was developed 10 machine learning algorithms 101 model combinations validated training validation cohorts. Immune infiltration response assessed CIBERSORT, ssGSEA, TIDE, IPS scores, an independent cohort (GSE91061). FN1, gene model, through qRT-PCR, immunohistochemistry, interference, CCK-8 assay, apoptosis assay wound-healing assay. Results In TNBC, three subpopulations—myofibroblastic cancer-associated fibroblasts (myCAF), vascular smooth muscle (VSMCs), pericytes—were enriched, exhibiting high interaction frequencies strong associations prognosis. nine-gene (MVPRS), 23 prognostically significant among 259 MVPRGs, demonstrated excellent predictive performance as factor. nomogram integrating MVPRS, age, stage, grade offered clinical utility. High-risk group showed reduced increased activity tumor-related pathways like ANGIOGENESIS HYPOXIA, while low-risk groups responded better based TIDE scores. oncogene, expressed tissues lines, promoting proliferation migration inhibiting apoptosis. Conclusion reveals heterogeneity introduces myCAF, VSMC, Pericyte MVPRS effectively predicts prognosis response, providing guidance personalized treatment. FN1 oncogene impacting progression malignant phenotype, potential target.

Language: Английский

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