Spatial omics-based machine learning algorithms for the early detection of hepatocellular carcinoma.

ABSTRACT:

Background

Worldwide, hepatocellular carcinoma (HCC) is the second most lethal cancer, although early-stage HCC is amenable to curative treatment and can facilitate long-term survival. Early detection has proved difficult, as proteomics, transcriptomics, and genomics have been unable to discover suitable biomarkers.

Methods

To find new biomarkers of HCC, we utilized a spatial omics N-glycan imaging method to identify altered glycosylation in cancer tissue (n = 53) and in paired serum of individuals with HCC (n = 23). Glycoproteomics identified the glycoproteins carrying these N-glycan structures, and we utilized an antibody array-based glycan imaging method to examine all the N-glycans associated with the identified glycoproteins. N-glycans from the examined glycoproteins we

SUBMITTER: Wang M 

PROVIDER: S-EPMC11614901 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature