Journal
ONCOTARGET
Volume 8, Issue 65, Pages 108810-108824Publisher
IMPACT JOURNALS LLC
DOI: 10.18632/oncotarget.22224
Keywords
liver neoplasms; diagnostic test; meta-analysis; microRNA; metabolomics
Categories
Funding
- National Nature Science Foundation of China [81000968, 81101540, 81101637, 81172273, 81272388, 81301820, 81472673]
- Doctoral Fund of Ministry of Education of China [20120071110058]
- National Clinical Key Special Subject of China
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Background: Many new diagnostic biomarkers have been developed for hepatocellular carcinoma (HCC). We selected two methods with high diagnostic value, the detection of serum microRNAs and metabolomics based on gas chromatography/mass spectrometry (GC/MS), and attempted to establish appropriate models. Methods: We reviewed the diagnostic efficiencies of all microRNAs identified by previous diagnostic tests. Then we chose appropriate microRNAs to validate the diagnostic efficiencies, and determined the optimal combination. We included 66 patients with HCC and 82 healthy controls (HCs) and detected the expression of the microRNAs. GC/MS analysis was performed, and we used three multivariate statistical methods to establish diagnostic models. The concentration of alpha feto-protein (AFP) was determined for comparison with the novel models. Results: 82 published studies and 92 microRNAs were ultimately included in this systematic review. Seven microRNAs were selected for further validation of their diagnostic efficiencies. Among which, miR-21, miR-106b, miR-125b, miR-182 and miR-224 had a significantly different expression in HCC patients. The combination of miR-21, miR-106b and miR-224 had the highest area under the curve (AUC) at 0.950 with a sensitivity of 80.3% and a specificity of 92.7%. The GC/MS analysis exhibited an excellent diagnostic value and the AUC reached 1.0. In comparison, the AUC of the traditional biomarker, AFP, was 0.755. Conclusion: MicroRNAs and metabolomics shows promising potential as new diagnostic methods due to their high diagnostic value compared with traditional biomarkers.
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