Integrated multi-omics analysis and machine learning based on O_linked_glycosylation genes refine molecular subtypes and prognosis for hepatocellular carcinoma

Abstract

Abstract O-glycosylation exerts significant influence on cellular physiological processes and disease regulation by modulating the structure, function, and stability of proteins. However, there is still a lack of research focusing on O-glycosylation in relation to the prognosis of HCC patients. Here we explored expression and function of O-glycosylation gene in HCC from both bulk and single-cell perspectives. The multi-omics data associated with O-glycosylation, identified through the Weighted Gene Co-expression Network Analysis (WGCNA), combined with ten distinct clustering algorithms to define the molecular subgroups of HCC. CS1 was characterized by significant genomic variation, moderate immune cell infiltration and immune function enrichment. CS2 performed a better prognosis, and was featured by stable genomic structure, an immune-hot phenotype with rich immune cell infiltration and sensitive to immunotherapy. CS3 was characterized by a poor prognosis, outstanding genomic instability, an immune-cold phenotype, but can benefit more from treatment with drugs such as sorafenib, cisplatin, paclitaxel, and gemcitabine. Ultimately, we re-emphasized O-glycosylation genes in individual HCC patients, deploying 59 types of machine learning to construct and evaluate the prognostic signature. The microarray results indicated a pronounced upregulation of Oglycosylation hub genes involved in HCC stratification and modeling within HCC tumorous tissues. In conclusion, we have highlighted the significant impacts of O-glycosylation on HCC by redefining the subtypes of HCC as well as constructing the CMLS. This research has established an optimized decision-making platform that enables precise stratification of HCC patients, refines tumor treatment plans, and predicts patient survivability holding broad clinical implications.