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Background: Long non-coding RNAs (lncRNAs) are often aberrantly expressed in hepatocellular carcinoma (HCC). The role of lncRNAs in the diagnosis of HCC has attracted increasing attention. Hence, we performed a meta-analysis based on current studies to assess the diagnostic value of lncRNAs for HCC., Methods: A systematic search was performed using PubMed, Web of Science, and Embase databases for relevant studies. The quality of the studies was assessed with the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). A fixed-effect model was used if the value of I 2 statistics < 50%; otherwise, a bivariate random effects model was applied (I 2 ≥ 50%). In addition, subgroup analysis and meta-regression analysis were conducted to explore the sources of heterogeneity. Statistical analyses were based on Meta-Disc statistical software (Version 1.4) and STATA software (Version 15.1)., Results: A total of 52 studies in 20 related articles were selected for this meta-analysis, including 4930 patients and 4614 controls. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) were 0.85 [95% confidence interval (CI) 0.82-0.88], 0.76 (95% CI 0.73-0.80), 3.6 (95% CI 3.1-4.2), 0.19 (95% CI 0.16-0.24), 19 (95% CI 14-26), and 0.88 (95% CI 0.85-0.91), respectively. The publication bias was evaluated by the Deek's funnel plot in our meta-analysis., Conclusions: LncRNAs can serve as feasible HCC diagnostic biomarkers. However, further studies are necessary to confirm its diagnostic and clinical value., (Copyright © 2021 Elsevier GmbH. All rights reserved.)

Background: Numerous studies have reported that microRNAs (miRNAs) hold great potential as the biomarkers for colorectal cancer (CRC). However, inconsistent results have made it challenging to evaluate their diagnostic performance. Thus, the aim of this meta-analysis was to systematically assess the pooled efficacy of miRNAs for CRC diagnosis., Methods: A search for eligible studies up to October 30, 2019 was conducted using PubMed, Web of Science and EMBASE databases. A random-effects model was used to evaluate the pooled sensitivity and specificity. The summary receiver operator characteristic (SROC) curve and area under the curve (AUC) were calculated to assess the overall diagnostic efficacy., Results: A total of 3258 CRC patients and 2683 healthy controls were identified in 35 included studies. The overall diagnostic accuracy was as follows: sensitivity, 0.80 [95 % confidence interval (CI) 0.75-0.83]; specificity, 0.80 (95 % CI, 0.75-0.84); positive likelihood ratio (PLR), 4.0 (95 % CI, 3.2-5.0); negative likelihood ratio (NLR), 0.26 (95 % CI, 0.21-0.31); diagnostic odds ratio (DOR), 16 (95 % CI, 11-23); and AUC, 0.87 (95 % CI, 0.83-0.89)., Conclusion: The results indicated that miRNAs, particularly serum-derived miRNAs, can serve as the powerful and promising biomarkers for early CRC screening., (Copyright © 2020 Elsevier GmbH. All rights reserved.)

Cholangiocarcinoma (CCA) is a fatal disease that is typically diagnosed late and treated ineffectively. As the morbidity and mortality rates for CCA rise markedly, patietns with CCA currently have a poor prognosis. However, if it were possible to diagnose CCA early while effective treat methods are available, CCA patients would achieve a better quality of life. Therefore, preventing the process of CCA in the early stages is an urgent problem to solve. An accurate, quick and safe method to diagnose early-stage CCA is required. The present review discusses the risk factors, status of research and certain serum markers of CCA. The sensitivity and specificity of these markers differ from each other. To explore the more accurate serum markers may be a novel direction and method for the diagnosis of CCA in laboratory medicine in the future.

Background/aims: There are no satisfactory biomarkers for hepatocellular carcinoma (HCC). The surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) technique has been used to identify biomarkers for cancer., Methodology: Four hundred thirty five serum samples were tested by SELDI-TOF-MS matching on a gold chip. Samples were assigned to a training set and a testing set according to collection order. The training set was used to identify statistically significant peaks and to develop the artificial neural network (ANN) model for diagnosing HCC. The testing set was used in a blind test to validate the diagnostic efficiency of the ANN model., Results: A total of 75 proteins that differed between patients and controls were identified (p<0.05). Seven of these proteins (p<0.01; m/z at 4207Da, 6604Da, 7734Da, 8106Da, 8545Da, 8599Da, 8894Da) were chosen to develop the ANN model. The model was subjected to a blind test using the testing set for HCC diagnosis. Sensitivity and specificity were 84.00% and 81.25%, respectively, and the accuracy was 81.90%., Conclusions: These results suggest that patients with HCC may have serum proteins that differ from healthy controls. The ANN is a new method for diagnosing and identifying HCC.