Functional Analysis of Molecular Subtypes with Deep Similarity Learning Model Based on Multi-omics Data

The molecular subtypes are crucial for developing personalized treatments. With many biological sequencing data available, integrating multi-Omics data for subtyping cancers has become an attractive research route to explore pathogenesis at the molecular level. Although the biological meaning of molecular subtypes is crucial for explaining the biological mechanism of cancer pathogenesis and designing effective treatment, it is less known. This paper aims to reveal the molecular function of cancer subtypes that are discovered by a deep similarity learning Model based on Muti-omics data. In this paper, we first established molecular subtypes by deep similarity learning Model. Then we detected the differential levels of molecules between subtypes. According to the mapping relationship between the molecule and the gene, the function of the molecule is enriched and analyzed by the corresponding gene. The enriched Gene Ontology (GO) terms and biological pathways reveal the functions of the cancer subtypes. Finally, we estimated our designed workframe on real cancer datasets. Compared to the traditional methods, the deep similarity learning Model achieved a better performance in identifying cancer subtypes. The functional analyses of molecular subtypes provide insights into promoting the development of cancer treatments in the era of precision medicine.