Eligible studies and study characteristics

Based on the search criteria, the initial search returned a total of 144 publications, 120 of which were excluded due to ineligibility criteria (4 had multiple publication data, 40 were case-only studies or had incomplete data, 18 were controls from tumor or cell lines and 58 had unclear data or were reviews). The 24 selected articles (Table S1) included 36 studies with 1460 patients from the United States, Germany, Ireland, Switzerland, China, Japan, Singapore, Spain, Greece, Italy and Chinese Taiwan. 25 of the studies were subjected to the meta-analysis with the vote-counting strategy6,9,11,13,14,15,16,17,18,19,20,21,22,23,24,25,28,29. Thirteen of the studies were estimated using odds ratio and diagnostic accuracy6,7,9,11,12,26,27,29. Two of the studies were subjected to both methods6,29. A flow diagram of the study selection process is shown in Figure 1. The publication dates of the included articles ranged from 2005 to 2014 and they were all retrospective in design. The types of cancer in these studies included colon, lung, breast, oral, glioma, esophageal, colorectal, HCC, vestibular schwannoma, pituitary adenoma, prostate, renal, EEC and melanoma. Thirty studies used quantitative real-time polymerase chain reaction (qRT-PCR) assays to measure the expression level of miR-10b and 9 studies used microarray assays. Two articles investigated 5 independent studies as a sample/control set9,16 and 4 articles investigated 2 independent studies as a sample/control set10,14,17,28 (Table 1).

Table 1 Thirty-six human cancer microRNA-10b expression studies (cancer cases versus noncancerous controls) Full size table

Figure 1 Flow chart depicting the study selection process. Full size image

Differentially expressed miR-10b in human cancers

In a panel of miR-10b expression analyses, miR-10b expression was reported to be consistently up-regulated among ten types of cancer. Vestibular schwannomas were reported to rank first in one study with an average fold-change (FC) of 269.19. Esophageal cancer, HCC, prostate cancer, oral cancer and breast cancer were also reported in one study, with average FCs of 58.70, 17.10, 13.88, 4 and 1.17, respectively. In two studies, glioma and pituitary adenomas were reported to have average FCs of 33.86 and 27.24. In five studies, lung cancer was reported to have an average FC of 3.64. Ten studies showed that miR-10b was down-regulated among seven cancers. Melanoma and HCC were reported to rank first in one study with an average FC of -3.13. Renal cancer, EEC and breast cancer were also mentioned in one study to have an average FC of -1.53, -1.27 and -1.53, respectively. CcRCC was reported in two studies to have an average FC of -2.08. Colon cancer, reported in three studies, had an average FC of -1.94. As mentioned above, miR-10b was inconsistently expressed in breast cancer or HCC cases when compared to noncancerous/normal controls (Table 2).

Table 2 Vote-counting strategy of abnormal miR-10b expression based on tumor type Full size table

Correlation between miR-10b expression and ORs

The primary results of this meta-analysis are shown in Table 3. We performed an overall analysis of the data from studies containing high-expression of miR-10b and ORs from a variety of cancers. The studies were found to have moderate heterogeneity (I2 = 44.0%, P = 0.045), so a random effects model was applied to calculate a pooled OR and its 95% confidence interval (CI) (32.80, 95% CI: 11.90–90.37, P<0.0001), which was statistically significant (Figure 2). Then, subgroup analysis by cancer type showed significant association between the high-expression of miR-10b and various types of cancer. Low heterogeneities were found among nor-digestive system cancer (I2 = 18.7%, P = 0.276); thus, a fixed effects model was applied to calculate OR (33.97, 95% CI: 11.98–96.32, P = 0.000). High heterogeneity was observed in digestive system cancer (I2 = 72.3%, P = 0.013) and so a random effects model was applied to calculate the OR (26.37, 95% CI: 3.21–216.41, P = 0.002). No significant heterogeneity existed among the studies evaluating OR for miR-10b. The result of subgroup analysis by sample source was also significant. Low and moderate heterogeneities were found from circulating based (I2 = 0%, P = 0.633) and tissue based (I2 = 37.6%, P = 0.108) miRNAs, respectively. Thus, a fixed effects model was applied to calculate ORs (circulating based: OR = 10.711, 95% CI: 3.484–32.929, P<0.0001; tissue based: OR = 45.263, 95% CI: 17.490–117.136, P<0.0001). The pooled OR being greater than 1 indicates that high-expression of miR-10b may be significantly associated with the risk of cancer (Table 3).

Table 3 Pooled diagnostic accuracy Full size table

Figure 2 Meta-analysis of the miR-10b high-expression odds ratio (OR) between cancer and noncancerous groups using the random-effects model. Bars are the 95% CI of OR in patients versus controls. The areas of the squares are proportional to the weights used for combining the data. The center of the lozenge gives the combined OR. The OR was considered statistically significant if the 95% CI for the overall OR did not cross the value 1. Full size image

Meta-analysis was not performed on the cancer cases and noncancerous controls that had low-expression of miR-10b due to insufficient data from the searched studies.

Diagnostic accuracy

A graph shows a forest plot for the sensitivity and specificity of miR-10b assays in the diagnosis of cancer for 13 studies (Figure 3). Pooled results for the diagnostic accuracy are listed in Table 3. The sensitivity (SEN) was 0.988 (95% CI: 0.899–0.999) and the specificity (SPE) was 0.624 (95% CI: 0.386–0.815). The pooled positive likelihood ratio (PLR) was 2.630 (95% CI: 1.436–4.815), the negative likelihood ratio (NLR) was 0.020 (95% CI: 0.002–0.172), the diagnostic odds ratio (DOR) was 133.145 (95% CI: 13.211–1341.874) and the area under the SROC curve (AUC) value was 0.98 (95% CI: 0.96–0.99). These results indicate that the miR-10b assay could differentiate affected individuals from those without cancer. Chi-squared values of SEN 33.16 (p = 0.00), SPE 145.83 (p = 0.00), PLR 240.04 (p = 0.00), NLR 32.93 (p = 0.00) and DOR 14.537 (p = 0.000) all indicate that significant heterogeneity exists between studies. Our data also showed that the SROC curve is positioned near the desirable upper left corner of the graph; the red point shows the maximum joint sensitivity and specificity. The area under the curve was 0.98, indicating a high level of overall accuracy (Figure 4).

Figure 3 Forest plot estimating the sensitivity and specificity of miR-10b assays in the diagnosis of cancer from 13 studies. The estimates of sensitivity and specificity from each study are shown as solid points. Error bars are 95% confidence intervals. Numbers indicate the referenced study listed in Table S. Full size image

Figure 4 Summary receiver operating characteristic (SROC) curve for miR-10b assays in the diagnosis of different types of cancer from the 13 included studies. Solid circles represent each study included in the meta-analysis. The size of each study is indicated by the size of the solid circle. The regression SROC curve summarizes the overall diagnostic accuracy. Full size image

Subgroup analyses were also performed. Table 3 shows the pooled results for diagnostic accuracy in the different subgroups. The results indicate that the different sources of control subgroups and the different types of cancer subgroups were significantly divergent. Comparing the source of the control, circulating-based miRNA assays had a DOR of 8.86 (95% CI: 2.93–26.88) and an AUC of 0.50 (95% CI: 0.09–0.91), indicating lower accuracy, whereas tissue-based miRNA assays had a SEN of 0.99 (95% CI: 0.76–1.00), a SPE of 0.66 (95% CI: 0.36–0.87), a PLR of 2.89 (95% CI: 1.29–6.49), an NLR of 0.003 (95% CI: 0.01–0.43), a DOR of 1035.07 (95% CI: 7.52–1.4E+05) and an AUC of 0.99 (95% CI: 0.98–1.00), which demonstrates a higher level of accuracy. Comparing the assays studying different types of cancer, the digestive system cancer group had a higher level of accuracy: the SEN was 0.93 (95% CI: 0.84–0.97), the SPE was 0.67 (95% CI: 0.30–0.90), the PLR was 2.77 (95% CI: 1.00–7.70), the NLR was 0.11 (95% CI: 0.04–0.29), the DOR was 25.54 (95% CI: 4.15–157.01) and the AUC was 0.93 (95% CI: 0.90–0.95), indicating that miRNA-10b was more accurate at distinguishing patients with digestive system cancer from healthy people than patients with nor-digestive system cancer, which had an AUC of 0.50 (95% CI: 0.09–0.91). For meta-analysis of digestive system cancer, the chi-squared value of SEN was 4.15 (p = 0.26), SPE was 33.5 (p = 0.00), PLR was 34.61 (p = 0.00), NLR was 4.61 (p = 0.20) and DOR was 6.24 (p = 0.02), p>0.05 indicating that low significant heterogeneity exists between the studies.

To identify the accuracy of miR-10b for nor-digestive system cancer samples, we ran the algorithms using 447 digestive system cancer samples from the Sasayama, Ma, Zhao, Teplyuk, Guessous data set and obtained different source of samples, tissues or serum. The results suggested that the signatures had a high reproducibility. By detection of miR-10b up-regulate, we were able to stratify the digestive system cancer samples into low-and high-risk groups. We tested the predictive performance of miR-10b up-regulate in the five testing cohorts, which showed that 100% accuracy for high-risk groups in the testing sets containing 447 samples (Table 4). To identify the accuracy of miR-10b for digestive system cancer samples, we ran the same algorithms using the 228 digestive system cancer samples from the Xie, Li, Lu, Tian data set and obtained three source of samples, tissues, serum and saliva. The results suggested that the signatures still had a high reproducibility. By detection of miR-10b up-regulation, the digestive system cancer samples were stratified into low-and high-risk groups. The predictive performance of miR-10b up-regulation was evaluated in the four testing cohorts. Similar to nor-digestive system cancer samples, miR-10b also well performed in digestive system cancer samples, that is, 85.71–100% accuracy for high-risk groups in the testing sets containing 228 samples (Table 5).

Table 4 Accuracy of miR-10b up-regulated detection for nor-digestive system cancer samples Full size table

Table 5 Accuracy of miR-10b up-regulated detection for digestive system cancer samples Full size table

Assessment of publication bias and sensitivity analysis

To assess publication bias in this study, the included studies were evaluated using Begg's funnel plots and the Egger's test. As shown in Figure 5, the Begg's funnel plots were almost symmetric and the Egger's regression intercept was 0.205. Thus, there was no evidence for significant publication bias in this meta-analysis.

Figure 5 Begg's funnel plots of publication bias for studies evaluating OR of miR-10b expression in cancer. Each point represents a separate study for the indicated association. Log [OR], natural logarithm of OR. The horizontal line represents the magnitude of the effect. Full size image

Sensitivity analysis was performed by omitting one study at a time to measure its individual effect on the pooled OR. As presented in Figure 6, no individual study dominantly influenced the overall OR.