Multi-omics data integration has become a powerful approach for understanding complex diseases, such as cancer, and enables researchers to uncover biological mechanisms, identify biomarkers, and improve patient stratification. In this study, two supervised multi-omics integration methods were systematically compared, DIABLO (Data Integration Analysis for Biomarker Discovery using Latent variable approaches for Omics studies) and NOLAS (Noise-reduced Latent Structure), to predict breast cancer survival status (Alive/Deceased) using matched RNA-Seq, RPPA, and miRNA data from the TCGA-BRCA cohort. DIABLO, a multivariate block-integration method, was optimized for joint feature selection and classification, while NOLAS employed matrix factorization to reduce noise and extract latent structures. Both models were evaluated using stratified 50 train-50% test split, with performance assessed via AUC, F1-score and McNemar’s test (p< 0.001). Stability analysis (50 iterations with 80% subsampling) quantified feature selection consistency, followed by KEGG pathway enrichment to assess biological relevance. Results showed that DIABLO demonstrated slightly better classification performance than NOLAS in classification (AUC: 0.632 vs. 0.549), with McNemar’s test confirming significant differences (χ² = 71.63, p < 2.2×10⁻¹⁶). However, NOLAS showed higher feature stability in miRNA/RPPA layers (stability scores: 58.8% and 53.1% vs. DIABLO’s 51.28% and 38.46%). Enrichment analysis revealed DIABLO’s selected genes were linked to PI3K-Akt signaling, while NOLAS prioritized ECM-receptor interactions, highlighting distinct biological insights. These findings highlight the trade-offs between predictive accuracy and interpretability in multi-omics integration and offer insights relevant to decision making in precision oncology. The aim of this study is to systematically compare the supervised multi-omics integration methods DIABLO and NOLAS in predicting breast cancer survival. The comparison includes performance evaluation using multiple metrics, assessment of feature selection stability across RNA-Seq, RPPA, and miRNA data, and analysis of the biological relevance of stable features through KEGG pathway enrichment.