Integrative Multi-Omics Profiling Identifies Candidate Biomarkers in Triple-Negative Breast Cancer 2258118

Introduction Triple-negative breast cancer (TNBC) remains one of the most therapeutically resistant breast cancers and often displays pronounced immune heterogeneity. This study integrates multi-omics datasets to identify candidate biomarkers that may influence TNBC progression and modulate therapeutic response. Methods Transcriptomic, DNA methylation, microRNA (miRNA), and single nucleotide polymorphism (SNP) datasets from public repositories were analyzed using differential expression and correlation pipelines. Expression, mutation, and regulatory metrics were then integrated to prioritize key genes. Top candidates, including CCND2 and KIF1A, were preliminarily validated using qPCR and Kaplan—Meier plots. CIBERSORT was applied to examine tumor immune cell infiltration and to assess the immune-related expression of candidate genes. Results Multi-omics integration highlighted CCND2 and KIF1A as highly ranked candidates associated with TNBC proliferation and cell cycle regulation. Literature and pathway analysis suggested potential involvement of these genes in key oncogenic and immune-related signaling pathways. Preliminary qPCR results supported their differential expression relative to normal breast tissue. Conclusion These findings nominate CCND2 and KIF1A as candidate biomarkers and potential therapeutic targets in TNBC. Continued pathway and functional validation will clarify how these genes contribute to tumor progression and may inform precision treatment strategies for TNBC.