Development and validation of subtype and risk models for kinase-related genes as novel prognostic features in breast...

Protein kinases are essential regulators of cellular signaling pathways and are deeply involved in breast cancer (BC) progression. This study aimed to identify BC molecular subtypes and construct a prognostic model based on kinase-related genes (KRGs).

BC transcriptome profiles from The Cancer Genome Atlas (TCGA) were analyzed to detect differentially expressed KRGs between tumor and adjacent non-cancerous tissues. Univariate Cox regression and ConsensusClusterPlus were used to categorize molecular subtypes. Key prognostic genes were selected by integrating Cox regression with the least absolute shrinkage and selection operator (LASSO), and the resulting risk model was validated using independent Gene Expression Omnibus (GEO) datasets. Comparative analyses of clinical features, immune cell infiltration, tumor mutational burden (TMB), and drug sensitivity (via pRRophetic) were performed between risk groups.

Two distinct molecular subtypes were identified based on KRG expression. A 7-gene prognostic model reliably estimated survival in both the training cohort and external validation datasets. Patients were stratified into high- and low-risk groups, with the low-risk group showing significantly improved survival, elevated immune cell infiltration, and higher immune scores. These patients also exhibited more robust responses to a range of anticancer drugs, and signature genes were correlated with therapeutic response, indicating their potential as targets.

We established a KRG-based BC molecular subtyping and prognostic model that effectively stratifies patients by survival risk. This model may improve diagnostic accuracy and support the identification of novel therapeutic targets in future clinical practice.