Circulating microRNAs as biomarkers in hepatocellular carcinoma screening: a validation set from China

Abstract

Hepatocellular carcinoma (HCC) is a global public health concern. Current diagnostic methods show poor performance in early-stage HCC detection. Accumulating evidences revealed the great potential of microRNAs (miRNAs) as noninvasive biomarkers in HCC detection. In this study, we examined the diagnostic performance of serum miR-10b, miR-106b, and miR-181a for HCC screening in China. Furthermore, a systematic review of previous related studies was conducted to confirm our results. One hundred eight participants including 27 HCC patients, 31 chronic liver disease (CLD) patients, and 50 healthy people were recruited in this study. Blood specimen was drawn from each participant to extract serum miRNAs. Statistical analyses were performed to assess the 3 miRNAs levels in HCC, CLD patients, and normal controls. A meta-analysis was conducted to further assess the diagnostic value of miRNAs in HCC detection based on previous studies. All these miRNAs (miR-10b, miR-181a, miR-106b) could well discriminate HCC patients from normal controls, with area under the receiver-operating characteristic curve (AUC) values of 0.85 (95% confidence interval [CI]: 0.76-0.94), 0.82 (95% CI: 0.72-0.91), and 0.89 (95% CI: 0.81-0.97), respectively. In addition, these miRNAs could distinguish HCC cases from CLD controls with a medium accuracy. However, the ability of these miRNAs in differentiating CLD patients from normal controls was not satisfactory. Panel of these miRNAs displayed a better performance compared with single miRNA assay, with AUC values of 0.94 (95% CI: 0.89-0.99) in discriminating HCC patients from normal controls and 0.91 (95% CI: 0.80-0.97) in discriminating HCC patients from CLD controls. Results of meta-analysis of previous studies combined with the current study suggested that circulating miRNAs could well differentiate HCC from normal controls, with AUC values of 0.86 (95% CI: 0.82-0.89) for single miRNA assay and 0.94 (95% CI: 0.91-0.96) for miRNA panel assay. Serum miR-10b, miR-106b, and miR-181a have great potential to serve as accurate and noninvasive biomarkers for HCC preliminary screening. Meta-analysis of previous studies combined with current study further confirmed that circulating miRNAs could play an important role in HCC detection. Further large-scale studies are needed to confirm the clinical significance of circulating miRNAs in HCC screening.

Conflict of interest statement

The authors report no conflicts of interest.

Figures

FIGURE 1

Diagnostic performance of miR-10b. (A) Relative expression levels of miR-10b in normal controls, CLD, and HCC patients. Relative expression levels of miR-10b have standardized. ∗P < 0.05; ∗∗P < 0.01; ∗∗∗P < 0.001. (B) Receiver-operating characteristic (ROC) curve of miR-10b in 3 groups (HCC vs normal; CLD vs normal; HCC vs CLD). AUC values are presented by the estimate with 95% confidence interval. AUC = the area under the summary ROC curve, CLD = chronic liver diseases, HCC = hepatocellular carcinoma, ns = nonsignificant.

FIGURE 2

Diagnostic performance of miR-181a. (A) Relative expression levels of miR-181a in normal controls, CLD, and HCC patients. Relative expression levels of miR-10b have standardized. ∗P < 0.05; ∗∗P < 0.01; ∗∗∗P < 0.001. (B) Receiver-operating characteristic (ROC) curve of miR-181a in 3 groups (HCC vs normal; CLD vs normal; HCC vs CLD). AUC values are presented by the estimate with 95% confidence interval. AUC = the area under the summary ROC curve, CLD = chronic liver diseases, HCC = hepatocellular carcinoma, ns = nonsignificant.

FIGURE 3

Diagnostic performance of miR-106b. (A) Relative expression levels of miR-106b in normal controls, CLD, and HCC patients. Relative expression levels of miR-10b have standardized. ∗P < 0.05; ∗∗P < 0.01; ∗∗∗P < 0.001. (B) Receiver-operating characteristic (ROC) curve of miR-106b in 3 groups (HCC vs normal; CLD vs normal; HCC vs CLD). AUC values are presented by the estimate with 95% confidence interval. AUC = the area under the summary ROC curve, CLD = chronic liver diseases, HCC = hepatocellular carcinoma, ns = nonsignificant.

FIGURE 4

Diagnostic performance of miRNA panel comprised by miR-10b, miR-181a, and miR-106b. (A) Receiver-operating characteristic (ROC) curve of miRNA panel in differentiating HCC from normal controls. (B) ROC curve of miRNA panel in differentiating HCC from CLD controls. AUC values are presented by the estimate with 95% confidence interval. AUC = the area under the summary ROC curve, CLD = chronic liver diseases, HCC = hepatocellular carcinoma, ns = nonsignificant.

FIGURE 5

Meta-analysis of diagnostic studies in differentiating HCC from normal controls. (A) The forest plots of sensitivity in differentiating HCC from normal controls with the corresponding heterogeneity. (B) The forest plots of specificity in differentiating HCC from normal controls with the corresponding heterogeneity. The sensitivity and specificity from each study are represented by square, and the CI is indicated by error bars. (C) The SROC curves based on single miRNA assay. (D) The SROC curves based on miRNA panel assay. (○) observed data; (♦) summary operating point, (-) SROC curve, (-) 95% confidence contour. CI = confidence interval, SROC = summary receiver operator characteristic.

FIGURE 6

Meta-analysis of diagnostic studies in differentiating HCC from CLD controls. (A) The forest plots of sensitivity in differentiating HCC from CLD controls with the corresponding heterogeneity. (B) The forest plots of specificity in differentiating HCC from normal controls with the corresponding heterogeneity. The sensitivity and specificity from each study are represented by square, and the CI is indicated by error bars. (C) The SROC curves based on single miRNA assay. (D) The SROC curves based on miRNA panel assay. (○) observed data; (♦) summary operating point, (-) SROC curve, (-) 95% confidence contour. CI = confidence interval, CLD = chronic liver diseases, HCC = hepatocellular carcinoma, SROC = summary receiver operator characteristic.