Abstract / Summary
Accurately analyzing cervical cancer prognosis holds significant importance for enhancing clinical outcomes within the framework of precision medicine. Since single-omics data only capture a limited perspective of cervical cancer patients, integrating multiple types of omics data is essential to provide more comprehensive insights. However, capturing the key information in multi-omics data fusion remains challenging owing to the presence of a large number of redundant variables and a relatively small sample size. In this study, we propose a deep neural network called AWMP to better integrate multi-omics data for cervical cancer survival analysis. The experiment demonstrated that AWMP enhances cancer prognosis prediction by 4.74% relative to the mean performance of the four comparator methods, benefiting from its adaptive weighted mechanism that effectively addresses the greedy nature of learning in multi-omics integration. In this study, based on the risk subgroups stratified by AWMP, four key genes ( CDC42BPB , KLRG1 , MAP7 , LIMD2 ) are identified as been highly associated with cervical cancer prognosis and immune cell infiltration, highlighting their role in tumor heterogeneity. Additionally, single-cell tumor heterogeneity analysis revealed that the B-cell C2 subset correlates with both prognosis and immune response in cervical cancer, suggesting its potential as a therapeutic target. These findings underscore the accuracy and biological relevance of our deep learning framework for multi-omics integration in cervical cancer prognosis. Collectively, the identification of B-cell C2 subset advances the understanding of the precise role of B cells in cancer immune evasion and paves the way for more effective immunotherapy for cervical cancer.