Joint similarity nonnegative matrix factorization model for identification of recurrence-related association patterns in tumor

The high recurrence rate of tumor limits the growth of precision medicine, whereas the exploration of correlations in multimodal data enables mining of features linked to tumor recurrence, ultimately identifying prospective biomarkers. Nevertheless, existing multimodal approaches centered on genetic molecular data inadequately leveraged data structure and ignored the involvement of genes in the pathway or biological processes, thereby hampering interpretability of association models. In this study, a novel joint similarity nonnegative matrix factorization (JSNMF) model based on data-driven idea was proposed by adding pathway scoring data based on utilizing pathological images of tumor, gene expression data. The similarity network fusion model was applied to calculate the fusion matrices of the three-modality data with tumor recurrence as the label. Additionally, the prior information was calculated using the principal component analysis method, which was then applied to the joint nonnegative matrix factorization model with network regularization constraints. The solving efficiency of JSNMF model was enhanced by incorporating sparse orthogonality constraints on objective function. Experimental results demonstrate that incorporating prior knowledge enhances the search efficiency for joint patterns across multimodal data. The model identified recurrence-related common modules, including cellular features, genes, and pathways. Bioinformatics analysis indicated that the model can identify potential biomarkers associated with immune cell infiltration levels for recurrence diagnosis. Furthermore, the proposed method provides a new perspective for mining task-specific associations in multimodal data. This study also improves understanding of association patterns among genetic molecular features linked to tumor recurrence.