As a highly heterogeneous and complex disease, the identification of cancer’s molecular subtypes is crucial for accurate diagnosis and personalized treatment. The integration of multi-omics data enables a comprehensive interpretation of the molecular characteristics of cancer at various biological levels. In recent years, an increasing number of multi-omics clustering algorithms for cancer molecular subtyping have been proposed. However, the absence of a definitive gold standard makes it challenging to evaluate and compare these methods effectively. In this study, we developed a general framework for the comprehensive evaluation of multi-omics clustering algorithms and introduced an innovative metric, the accuracy-weighted average index, which simultaneously considers both clustering performance and clinical relevance. Using this framework, we performed a thorough evaluation and comparison of 11 state-of-the-art multi-omics clustering algorithms, including deep learning-based methods. By integrating the accuracy-weighted average index with computational efficiency, our analysis reveals that PIntMF demonstrates the best overall performance, making it a promising tool for molecular subtyping across a wide range of cancers.

BACKGROUND: Cervical cancer (CC) remains the fourth leading cause of cancer-related deaths in women globally, with poor prognosis for metastatic and recurrent cases. Although genomic alterations have been extensively characterized, global proteogenomic landscape of the disease is largely under explored. METHODS: Here, we present the first genome-wide proteogenomic characterization of CC, analyzing 139 tumor-normal tissue pairs using whole-genome sequencing, transcriptomics, proteomics, and phosphoproteomics. RESULTS: We identified 4 distinct molecular subtypes with unique clinical outcomes: epithelial-mesenchymal transition (EMT, C1), proliferation (C2), immune response (C3), and epithelial differentiation (C4). A 4-protein classifier (CDH13, TP53BP1, NNMT, HSPB1) was developed with strong prognostic and predictive value, particularly for immunotherapy response in subtype C3. Phosphoproteomic profiling uncovered subtype-specific kinase activity, identifying actionable therapeutic targets. CONCLUSION: Our findings further revealed previously uncharacterized somatic copy number alterations, extrachromosomal DNA landscape, and human-HPV fusion peptides, with implications for genetic heterogeneity and therapeutic targets. This study enhances the understanding of cervical cancer through deeper proteogenomic insights and facilitates the development of personalized therapeutic strategies to improve patient outcomes. FUNDING: Noncommunicable Chronic Diseases-National Science and Technology Major Project (2025ZD0544102);The National Natural Science Foundation of China (82172584); Key Technology R&D Program of Hubei (2024BCB057 and 2025BCB053); National Natural Science Foundation of China (82373260); the “4+X” clinical trial programs of Women’s Hospital, School of Medicine, Zhejiang University (LY2022004); and the programs of Zhejiang Traditional Chinese Medicine Innovation Team (CZ2024009); and Guangxi Natural Science Foundation (2024GXNSFBA010045).