Metastasis-related gene identification by compound constrained NMF and a semisupervised cluster approach using pancancer multiomics features

In recent years, with the gradual increase in pancancer-related research, more attention has been given to the field of pancancer metastasis. However, the molecular mechanism of pancancer metastasis is very unclear, and identification methods for pancancer metastasis-related genes are still lacking. In view of this research status, we developed a novel pipeline to identify pancancer metastasis-related genes based on compound constrained nonnegative matrix factorization (CCNMF). To solve the above problems, the following modules were designed. A correntropy operator and feature similarity fusion (FSF) were first adopted to process the multiomics features of genes; thus, the influences caused by irrelevant biomolecular patterns, manifested as non-Gaussian noise, were minimized. CCNMF was then adopted to handle the above features with compound constraints consisting of a gene relation network and a metastasis-relatedgene set, which maximizes the biological interpretability of the metafeatures generated by NMF. Since a negative set of pancancer metastasis-relatedgenes could hardly be obtained, semisupervised analyses were performed on gene features acquired by each step in our pipeline to examine our method's effect. 83% of the 236 candidates identified by the above method were associated with the metastasis of one or more cancers, 71.9% candidates were identified immune-related in pancancer in addition to the hallmark genes. Our study provides an effective and interpretable method for identifying metastasis-related as well as immune-related genes, and the method is successfully applied to TCGA pancancer data.

Automated summary

With the increasing research on pancancer metastasis, a novel pipeline based on compound constrained nonnegative matrix factorization (CCNMF) has been developed to identify metastasis-related and immune-related genes in pancancer. The method is successfully applied to TCGA pancancer data.