Salivary Metabolites are Promising Non-Invasive Biomarkers of Hepatocellular Carcinoma and Chronic Liver Disease.
Metabolomics
cirrhosis
liver cancer
machine learnings
risk factor
Journal
Liver cancer international
ISSN: 2642-3561
Titre abrégé: Liver Cancer Int
Pays: United States
ID NLM: 101779089
Informations de publication
Date de publication:
Aug 2021
Aug 2021
Historique:
entrez:
20
9
2021
pubmed:
21
9
2021
medline:
21
9
2021
Statut:
ppublish
Résumé
Hepatocellular carcinoma (HCC) is a leading causes of cancer mortality worldwide. Improved tools are needed for detecting HCC so that treatment can begin as early as possible. Current diagnostic approaches and existing biomarkers, such as alpha-fetoprotein (AFP) lack sensitivity, resulting in too many false negative diagnoses. Machine-learning may be able to identify combinations of biomarkers that provide more robust predictions and improve sensitivity for detecting HCC. We sought to evaluate whether metabolites in patient saliva could distinguish those with HCC, cirrhosis, and those with no documented liver disease. We tested 125 salivary metabolites from 110 individuals (43 healthy, 37 HCC, 30 cirrhosis) and identified 4 metabolites that displayed significantly different abundance between groups (FDR Metabolites detectable in saliva may represent products of disease pathology or a breakdown in liver function. Notably, combinations of salivary metabolites derived from machine-learning may serve as promising non-invasive biomarkers for the detection of HCC.
Sections du résumé
BACKGROUND
BACKGROUND
Hepatocellular carcinoma (HCC) is a leading causes of cancer mortality worldwide. Improved tools are needed for detecting HCC so that treatment can begin as early as possible. Current diagnostic approaches and existing biomarkers, such as alpha-fetoprotein (AFP) lack sensitivity, resulting in too many false negative diagnoses. Machine-learning may be able to identify combinations of biomarkers that provide more robust predictions and improve sensitivity for detecting HCC. We sought to evaluate whether metabolites in patient saliva could distinguish those with HCC, cirrhosis, and those with no documented liver disease.
METHODS AND RESULTS
RESULTS
We tested 125 salivary metabolites from 110 individuals (43 healthy, 37 HCC, 30 cirrhosis) and identified 4 metabolites that displayed significantly different abundance between groups (FDR
CONCLUSIONS AND IMPACT
CONCLUSIONS
Metabolites detectable in saliva may represent products of disease pathology or a breakdown in liver function. Notably, combinations of salivary metabolites derived from machine-learning may serve as promising non-invasive biomarkers for the detection of HCC.
Identifiants
pubmed: 34541549
doi: 10.1002/lci2.25
pmc: PMC8447405
mid: NIHMS1701112
doi:
Types de publication
Journal Article
Langues
eng
Pagination
33-44Subventions
Organisme : NHLBI NIH HHS
ID : P01 HL147823
Pays : United States
Organisme : NIAAA NIH HHS
ID : P50 AA024333
Pays : United States
Organisme : NIDDK NIH HHS
ID : R01 DK120679
Pays : United States
Déclaration de conflit d'intérêts
Competing Interests D.M.R. has an equity stake in Interpares Biomedicine, LLC. D.M.R., F.A., D.S.A hold intellectual property related to the detection of hepatocellular carcinoma.
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