Genome-Wide Analysis of Alternative Splicing Identifies a Prognostic Signature in ER-Positive Breast Cancer

Alternative splicing (AS) contributes substantially to transcriptomic diversity and has emerged as an important regulator of cancer progression. However, the genome-wide characterization of AS events specific to estrogen receptor (ER)-positive breast cancer remains limited. This study aims to comprehensively profile AS in ER-positive breast cancer and identify a prognostic AS signature associated with patient outcome.

Clinical and splicing data (Percent Spliced In values) were obtained from The Cancer Genome Atlas (TCGA) for 737 ER-positive samples. Prognostic AS events were identified using Cox regression analysis. The Least Absolute Shrinkage Selection Operator (LASSO) model was used to construct an AS-based prognostic signature, and a standardized risk score was calculated for each sample. The signature was then evaluated by Kaplan-Meier (KM) analysis and receiver operating characteristic (ROC) curves, in addition to other methods to validate model performance. Furthermore, transcript-level annotation and RNA expression correlation were performed to evaluate biological relevance.

Profiling identified 6276 AS events across 4457 genes, with exon skipping (ES) representing the most prevalent class (34.4%). Model analysis established a novel five-event AS prognostic signature (comprising DNAJC14, BAZ2B, PCDHAC1, PCDHA7, and DAPL1). The signature significantly stratified patients into high-risk and low-risk groups for both disease-free survival (DFS; p < 0.001) and overall survival (OS; p < 0.001). HER2-specific analysis demonstrated more consistent performance in HER2-negative patients for both DFS (p < 0.001) and OS (p = 0.018). The model achieved area under the curve (AUC) of 0.804 for 60-month follow-up supporting long-term prognostic performance. Additionally, the signature demonstrated stable and reliable discrimination with a concordance index (C-index) of approximately 0.73 across multiple validation methods.

The study identified AS signature with promising prognostic value in ER-positive breast cancer. This highlights the potential of splicing-based models to refine risk stratification beyond conventional gene expression analysis.