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Spatial omics-based machine learning algorithms for the early detection of hepatocellular carcinoma.
- Source :
-
Communications medicine [Commun Med (Lond)] 2024 Dec 03; Vol. 4 (1), pp. 258. Date of Electronic Publication: 2024 Dec 03. - Publication Year :
- 2024
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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.<br />Methods: To find new biomarkers of HCC, we utilized a spatial omics N-glycan imaging method to identify altered glycosylation in cancer tissue (nā=ā53) and in paired serum of individuals with HCC (nā=ā23). 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 were used to create machine learning algorithms, which were tested in a case-control sample set of 100 patients with cirrhosis and HCC and 101 matched patients with cirrhosis alone.<br />Results: Spatial glycan imaging identifies thirteen branched, fucosylated, and high mannose glycans as altered in HCC tissue and in matched patient serum. Glycoproteomics identifies over 50 proteins containing these changes, of which sixteen glycoproteins were selected for further testing in an independent patient set. Algorithms using a combination of glycan and glycoproteins accurately differentiate early-stage and all HCC from cirrhosis with AUROC values of 0.88-0.97.<br />Conclusions: In conclusion, we present the development and application of a new biomarker platform, which can identify effective biomarkers for the early detection of HCC. This platform may also apply to other diseases, in which changes in N-linked glycosylation are known to occur.<br />Competing Interests: Competing interests: The authors declare the following competing interests: A.S.M., P.M.A., and R.R.D. are listed as inventors on a patent filed by MUSC on the technology described in this manuscript. Authors M.W., S.G., M.F.B., J.D., A.D., C.B., H.L., D.S., G.H., A.B., S.-O.R., D.L., L.B., J.B., S.C., Y.K., N.K., Y.H., L.Q., A.Y., P.G., and A.S. declare no competing interest.<br /> (© 2024. The Author(s).)
Details
- Language :
- English
- ISSN :
- 2730-664X
- Volume :
- 4
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- Communications medicine
- Publication Type :
- Academic Journal
- Accession number :
- 39627514
- Full Text :
- https://doi.org/10.1038/s43856-024-00677-7