Objective: Alternative splicing (AS) is the mechanism by which a few genes encode numerous proteins, and it redefines the concept of gene expression regulation. Recent studies showed that dysregulation of AS was an important cause of tumorigenesis and microenvironment formation. Therefore, we performed a systematic analysis to examine the role of AS in breast cancer (Breast Cancer, BrCa) progression.Methods: The present study included 993 BrCa patients from The Cancer Genome Atlas (TCGA) database in the genome-wide analysis of AS events. We used differential and prognostic analyses and found differentially expressed alternative splicing (DEAS) events and independent prognostic factors related to patients’ overall survival (OS) and disease-free survival (DFS). We divided the patients into two groups based on these AS events and analyzed their clinical features, molecular subtyping and immune characteristics. We also constructed a splicing factor (SF) regulation network for key AS events and verified the existence of AS events in tissue samples using real-time quantitative PCR.Results: A total of 678 AS events were identified as differentially expressed, of which 13 and 10 AS events were independent prognostic factors of patients’ OS and DFS, respectively. Unsupervised clustering analysis based on these prognostic factors indicated that the Cluster 1 group had a better prognosis and more immune cell infiltration. SFs were significantly related to the expression of AS events, and AA-RPS21 was significantly upregulated in tumors.Conclusion: Alternative splicing expands the mechanism of breast cancer progression from a new perspective. Notably, alternative splicing may affect the patient’s prognosis by affecting the infiltration of immune cells. Our research provides important guidance for subsequent studies of AS in breast cancer.

Tumor immune microenvironment is associated with tumor progression. However, previous studies have not fully explored the breast cancer (BC) immune microenvironment. All the data analyzed in this study were obtained from the open-access database, including The Cancer Genome Atlas, Gene Expression Omnibus (TCGA), and cBioPortal databases. R software v4.0 and SPSS 13.0 were used to perform all the statistical analysis. Firstly, the clinical and expression profile information of TCGA, GSE20685, GSE20711, GSE48390, GSE58812, and METABRIC cohorts was collected. Then, 53 immune terms were quantified using the single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm. A prognosis model based on HER2_Immune_PCA, IL12_score, IL13_score, IL4_score, and IR7_score was established, which showed great prognosis prediction efficiency in both training group and validation group. A nomogram was then established for a better clinical application. Clinical correlation showed that elderly BC patients might have a higher riskscore. Pathway enrichment analysis showed that the pathway of oxidative phosphorylation, E2F targets, hedgehog signaling, adipogenesis, DNA repair, glycolysis, heme metabolism, and mTORC1 signaling was activated in the high-risk group. Moreover, Tumor Immune Dysfunction and Exclusion and Genomics of Drug Sensitivity in Cancer analysis showed that low-risk patients might be more sensitive to PD-1 therapy, cisplatin, gemcitabine, paclitaxel, and sunitinib. Finally, four genes, XCL1, XCL2, TNFRSF17, and IRF4, were identified for risk group classification. In summary, our signature is a useful tool for the prognosis and prediction of the drug sensitivity of BC.

Background: RAC2 is critical in regulating the homeostasis of hematopoietic stem cells. Nonetheless, its role in breast cancer (BC) remains unclear, necessitating further investigation. Methods: The expression of RAC2 in BC and healthy tissues was acquired from The Cancer Genome Atlas. Its validity was further assessed using datasets from the gene expression omnibus database. The Tumor Immune Single-cell Hub database was used to collect and analyze the single-cell RNA sequencing datasets of BC. The diagnostic relevance of RAC2 was evaluated using receiver operating characteristic curves. Further assessment was carried out via enrichment analyses; Gene Set Analysis, immune scoring, single-cell sequencing, and immunohistochemical analysis were conducted to confirm the relationship between RAC2 expression and immune infiltration. Results: RAC2 expression was notably heightened in BC ( p < 0.001). It was observed that a better prognosis was linked to heightened expression of RAC2 ( p < 0.01), with the diagnostic efficacy of the marker noted to be good (area under the curve = 0.858). We found a lower percentage of protumor immune cells and a greater proportion of antitumor immune cells in the high RAC2. Our analysis revealed alterations in gene expression and an enriched network of immune pathways influenced by RAC2. Notably, cytotoxic genes, chemokines, chemokine receptors, immunostimulators, and immunosuppressive molecules positively correlated with RAC2 expression. RAC2 expression reliably predicted how patients would respond to two different therapeutic approaches in BC. Conclusions: The RAC2 was found to be a key biomarker in BC in the current study, demonstrating considerable potential as a prognostic and diagnostic marker. These results highlight the RAC2 potential to improve precision medicine strategies and treatment outcomes for patients with BC.