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Optimized systematic review tool: Application to candidate biomarkers for the diagnosis of hepatocellular carcinoma

U, M.R.A., Shen, E.Y-L, Cartlidge, C., Alkhatib, A., Thursz, M.R., Waked, I., Gomaa, A.I., Holmes, E., Sharma, R. and Taylor-Robinson, S.D. (2022) Optimized systematic review tool: Application to candidate biomarkers for the diagnosis of hepatocellular carcinoma. Cancer Epidemiology, Biomarkers & Prevention, 31 (7). pp. 1261-1274.

Link to Published Version: https://doi.org/10.1158/1055-9965.EPI-21-0687
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Abstract

This review aims to develop an appropriate review tool for systematically collating metabolites that are dysregulated in disease and applies the method to identify novel diagnostic biomarkers for hepatocellular carcinoma (HCC). Studies that analyzed metabolites in blood or urine samples where HCC was compared with comparison groups (healthy, precirrhotic liver disease, cirrhosis) were eligible. Tumor tissue was included to help differentiate primary and secondary biomarkers. Searches were conducted on Medline and EMBASE. A bespoke “risk of bias” tool for metabolomic studies was developed adjusting for analytic quality. Discriminant metabolites for each sample type were ranked using a weighted score accounting for the direction and extent of change and the risk of bias of the reporting publication. A total of 84 eligible studies were included in the review (54 blood, 9 urine, and 15 tissue), with six studying multiple sample types. High-ranking metabolites, based on their weighted score, comprised energy metabolites, bile acids, acylcarnitines, and lysophosphocholines. This new review tool addresses an unmet need for incorporating quality of study design and analysis to overcome the gaps in standardization of reporting of metabolomic data. Validation studies, standardized study designs, and publications meeting minimal reporting standards are crucial for advancing the field beyond exploratory studies.

Item Type: Journal Article
Murdoch Affiliation(s): Australian National Phenome Centre
Centre for Computational and Systems Medicine
Publisher: American Association for Cancer Research
Copyright: © 2022 American Association for Cancer Research
URI: http://researchrepository.murdoch.edu.au/id/eprint/65535
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