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Abstract

Background

An Increasing number of studies in the literature have shown that microRNAs (miRNAs) can be used as early diagnostic markers for esophageal carcinoma (EC), but their conclusions remain controversial. Hence, we performed this meta-analysis to evaluate the diagnostic accuracy of using miRNAs in EC and to provide an experimental basis for early diagnosis of the disease.

Methods

This meta-analysis included 39 Asian studies from 18 articles, which covered 3,708 EC patients and 2,689 healthy controls. We used a bivariate random-effects model, the chi-square test and the I² test to assess sensitivity and heterogeneity.

Results

Pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and diagnostic odds ratio of miRNAs for diagnosis of EC in Asians reached 0.798, 0.785, 3.705, 0.257 and 14.391, respectively. Additionally, the area under the summary receiver operating characteristic curve was 0.86. Subgroup analysis based on research country (China vs. Japan), sample types (plasma vs. serum) and miRNAs (single vs. multiple; singly reported miRNAs vs. repeatedly reported miRNAs) showed no significant difference in accuracy of diagnosis for each subgroup.

Conclusions

MiRNAs can distinguish EC patients from healthy controls. Blood-based miRNAs have better diagnostic value in detecting EC than saliva-based miRNAs, whereas both serum and plasma are recommended for clinical specimens for miRNA detection.

Keywords

Diagnosis Esophageal carcinoma Meta-analysis MicroRNA

Introduction

Esophageal carcinoma (EC) represents the eighth most common carcinoma and the sixth leading cause of cancer death worldwide according to the International Agency for Cancer Research Globocan 2012 (1); it mainly includes esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC), based on pathological characteristics (2). EC distribution presents remarkable regional characteristics: ESCC occurs mainly in Asia, whereas EAC is mainly observed in Western countries. ESCC is the most common type of EC, accounting for about 90% of EC cases, and is one of the most aggressive carcinomas of the gastrointestinal tract (3). Given the absence of typical clinical symptoms and lack of tumor markers with sufficient sensitivity (SEN) and specificity (SPE), most EC patients are diagnosed at a relatively late stage of the disease (4). At present, although surgical techniques and chemotherapy and/or radiotherapy regimens have been improved, the overall survival rate of ESCC remains poor, ranging from 3% to 5% (5). However, the rate of 5-year survival can increase up to 90% when tumors are detected at early stages (6). Therefore, early diagnosis is important and is urgently needed for ESCC patients.

Currently, endoscopic biopsy remains the primary method for diagnosis of EC; however, its invasiveness and expense limit its widespread use (7, 8). The potential for sample errors with random endoscopic biopsy decreases the effectiveness of this procedure (9). With the developments in medical research and technology, more tumor markers have been discovered; these markers can be used to distinguish EC patients from the healthy population. Conventional serum tumor markers, which include carcinoembryonic antigen, carbohydrate antigen 19-9 and squamous cell carcinoma antigen, are used in early diagnosis and monitoring tumor dynamics of ESCC (10 -12). However, insufficient SEN and SPE seriously restrict clinical application of these markers. Therefore, further studies should center on exploring novel molecular biomarkers with accurate SEN and SPE and noninvasiveness to detect early-stage EC.

Considering the molecular biomarkers, attention has been drawn to microRNAs (miRNAs) due to their potential as diagnostic biomarkers of cancers. MiRNAs are small (19-24 nucleotides in length), well conserved and noncoding RNAs, regulating expression of more than 30% of human human genes by inducing mRNA degradation or inhibiting posttranscription (13, 14). MiRNAs play vital roles in cell growth, proliferation, apoptosis, migration and invasion (15 -17), and at present, increasing numbers of studies have indicated that abnormal miRNA expression takes part in the initiation and progression of various cancers, including EC. For example, miRNA-203 is down-regulated in human ESCC, and it can inhibit cell proliferation, migration and invasion (18, 19). MiRNAs are potential biomarkers for early diagnosis of cancers, including ESCC; miRNA-205 is expressed at a low level in ESCC and is a good indicator for early diagnosis, with 76% SEN and 86% SPE (20). MiRNA expression profiles are cell-specific or tissue-specific; these specificities may help diagnose different cancer types and predict progression of malignancies (21, 22). Furthermore, miRNAs exist in a stable form in the blood or other tissue fluids, and their expression levels are reproducible (23, 24). In conclusion, these findings provide a promising, high-efficiency, nonaggressive, early-stage diagnosis method for ESCC patients using miRNAs.

Currently, a growing number of studies have investigated that abnormal expression of miRNAs can be good diagnostic biomarkers of early-stage EC. However, published studies on miRNAs in EC still present inconsistent conclusions. For example, Hirajima et al reported good diagnostic characteristics, with 86.8% SEN and 100% SPE, for miR-18a (25) in ESCC, whereas Xie et al reported a lower SPE of 47.4% for miR-144 (26) in EC. Additionally, Komatsu et al (27) found that miR-21 has poor diagnostic accuracy, with 60% SEN in ESCC, whereas Ye et al found a significantly higher SEN of 97% for miR-21 in ESCC. Up to now, most published meta-analyses have evaluated the diagnostic accuracy of miRNAs in patients with EC. In these meta-analyses, Wan et al insisted that sample types (saliva-, serum- and plasma-based) showed no difference in diagnostic accuracy in EC (28); Wang et al suggested that blood-based miRNA assay displayed higher diagnostic accuracy than saliva-based miRNA assay in detecting ESCC (29), and Fen et al further analyzed diagnostic differences between serum-based and plasma-based assays in ESCC and found no discrepancy (30). In other carcinoma meta-analyses, investigators have shown better accuracy for plasma-based specimens than serum-based ones in breast cancer and gastric cancer (31 -33). To further explore the clinical applicability of miRNA in EC patients, this present meta-analysis was performed by expanding the sample size.

Materials and methods

Search strategy

For this meta-analysis, we searched electronic databases, including PubMed, Embase, China National Knowledge Infrastructure and other sources, for studies that had assessed the diagnostic value of miRNA in EC until October 2016. Keywords used were as follows: “(miRNA or microRNA)” and “(esophageal or esophageal carcinoma or esophageal cancer)” and “(diagnosis or sensitivity or specificity or receiver operating characteristic [ROC] curve)”. Reference lists of various articles were also manually examined to find additional eligible studies. No language restriction was imposed on the search criteria.

Inclusion and exclusion criteria

Inclusion criteria for this meta-analysis consisted of the following: (i) patients were diagnosed with EC; (ii) miRNAs were used to diagnose EC; (iii) sufficient data were presented to generate 2 × 2 tables, including true positives, false positives, true negatives and false negatives, which were used to calculate values of SEN and SPE. Exclusion criteria were the following: (i) focus on survival or prognosis of EC; (ii) articles that were reviews, case reports or meta-analyses; and (iii) insufficient data.

Data extraction

Two authors independently screened the full texts of the included articles and extracted information as follows: lead author, year of publication, country of publication, sample size, patient spectrum, miRNA profiles, SEN, SPE and data of 2 × 2 tables. When a study contained both training and validation groups, each group was regarded an independent study in this meta-analysis. Table I presents the details of the studies included.

TABLE I

Characteristics of studies included in the meta-analysis

Author (ref.)	Year	Country	Race	Case/control	Patient spectrum	miRNA	Sample	TP	FN	FP	TN	Sensitivity	Specificity
Zhang et al (49)	2010	China	Asian	149/100	ESCC	7 miRNAs	Serum	117	32	4	96	0.7850	0.9600
	2010	China	Asian	149/100	ESCC	miRNA-10a	Serum	121	28	20	80	0.8120	0.8000
	2010	China	Asian	149/100	ESCC	miRNA-22	Serum	132	17	14	86	0.8860	0.8600
	2010	China	Asian	149/100	ESCC	miRNA-100	Serum	95	54	19	81	0.6380	0.8100
	2010	China	Asian	149/100	ESCC	miRNA-148b	Serum	99	50	13	87	0.6640	0.8700
	2010	China	Asian	149/100	ESCC	miRNA-223	Serum	124	25	17	83	0.8320	0.8300
	2010	China	Asian	149/100	ESCC	miRNA-133a	Serum	97	52	17	83	0.6510	0.8300
	2010	China	Asian	149/100	ESCC	miRNA127-3p	Serum	117	32	13	87	0.7850	0.8700
Komatsu et al (27)	2011	Japan	Asian	50/20	ESCC	miRNA-21,-375	Plasma	44	6	6	14	0.8800	0.7000
Zhang et al (50)	2011	China	Asian	201/202	ESCC	miRNA-31	Serum	174	27	36	166	0.8700	0.8200
Kurashige et al (51)	2012	Japan	Asian	71/39	ESCC	miRNA-21	Serum	33	38	0	39	0.4650	1.0000
Wang et al (48)	2012	China	Asian	31/39	EC	miRNA-21	Serum	22	9	12	27	0.7100	0.6920
Hirajima et al (25)	2013	Japan	Asian	106/54	ESCC	miRNA-18a	Plasma	92	14	0	54	0.8680	1.0000
Takeshita et al (52)	2013	Japan	Asian	101/46	ESCC	miRNA-1246	Serum	72	29	12	34	0.7130	0.7390
Xie et al (26)	2013	China	Asian	39/19	EC	miRNA-10b	Saliva	35	4	8	11	0.8970	0.5790
	2013	China	Asian	39/19	EC	miRNA-144	Saliva	36	3	10	9	0.9230	0.4740
	2013	China	Asian	39/19	EC	miRNA-451	Saliva	33	6	8	11	0.8460	0.5790
Zhang et al (53)	2013	China	Asian	201/201	ESCC	miRNA-1322	Serum	166	35	37	164	0.8260	0.8160
Wu et al (54)	2014	China	Asian	63/63	ESCC	7 miRNA	Serum	51	12	12	51	0.8100	0.8100
	2014	China	Asian	63/63	ESCC	miRNA-25	Serum	50	13	20	43	0.7930	0.6820
	2014	China	Asian	63/63	ESCC	miRNA-100	Serum	48	15	22	41	0.7620	0.6500
	2014	China	Asian	63/63	ESCC	miRNA-193a-3p	Serum	57	6	24	39	0.9040	0.6190
	2014	China	Asian	63/63	ESCC	miRNA-194	Serum	55	8	28	35	0.8730	0.5560
	2014	China	Asian	63/63	ESCC	miRNA-223	Serum	47	16	20	43	0.7460	0.6820
	2014	China	Asian	63/63	ESCC	miRNA-337-5p	Serum	55	8	14	49	0.8730	0.7780
	2014	China	Asian	63/63	ESCC	miRNA-483-5p	Serum	50	13	25	38	0.7930	0.6030
Komatsu et al (55)	2014	Japan	Asian	20/50	ESCC	miRNA-25	Plasma	17	3	7	43	0.8500	0.8600
Ye et al (56)	2014	China	Asian	100/50	EC	miRNA-21	Plasma	97	3	22	28	0.9700	0.5600
	2014	China	Asian	100/50	EC	miRNA-21	Saliva	89	11	18	32	0.8900	0.6400
Sun et al (57)	2015	China	Asian	120/51	ESCC	miRNA-718	Plasma	83	37	17	34	0.6920	0.6670
Xu et al (20)	2015	China	Asian	50/50	ESCC	miRNA-10b	Serum	38	12	8	42	0.7600	0.8400
	2015	China	Asian	50/50	ESCC	miRNA-29c	Serum	34	16	16	34	0.6800	0.6800
	2015	China	Asian	50/50	ESCC	miRNA-205	Serum	35	15	18	32	0.7000	0.6400
He et al (58)	2015	China	Asian	70/40	ESCC	miRNA-20a	Plasma	45	25	10	30	0.6430	0.7500
	2015	China	Asian	70/40	ESCC	let-7a	Plasma	52	18	6	34	0.7430	0.8500
Wang et al (59)	2016	China	Asian	154/154	ESCC	miRNA-146a	Serum	129	25	36	118	0.8380	0.7660
Li et al (60)	2016	China	Asian	110/40	ESCC	miRNA-506	Plasma	89	21	5	35	0.8120	0.8730
Dong et al (61)	2016	China	Asian	120/51	ESCC	miRNA-216a	Plasma	96	24	5	46	0.8000	0.9020
	2016	China	Asian	120/51	ESCC	miRNA-216b	Plasma	67	53	5	46	0.5580	0.9020

EC = esophageal carcinoma; ESCC = esophageal squamous cell carcinoma; FN = false negative; FP = false positive; TP = true positive; TN = true negative.

Statistical analysis

All data analyses were conducted using standard methods recommended for diagnostic accuracy in Stata software (version 12.0; Stata, College Station, TX, USA). We calculated SEN, SPE, positive likelihood ratio (PLR), negative likelihood ratio (NLR) and diagnostic odds ratio (DOR) with 95% confidence interval (95% CI) using a bivariate meta-analysis model (34). SEN and SPE of articles included were pooled to plot the summary receiver operating characteristic (SROC) curve and to calculate the area under the curve (AUC) (35). We used the chi-square and I² tests to assess heterogeneity among studies. Values of p<0.1 or I²>50% indicated significant heterogeneity (36, 37). To evaluate heterogeneity between studies, we performed both subgroup and sensitivity analysis. Deeks' funnel plots were used to investigate publication bias (38).

Results

Search results and characteristics of studies

Through searching for keywords in databases, we found 231 articles, of which 50 were considered for further full-text review after checking titles and abstracts. In the end, we included 18 articles according to our above-mentioned inclusion and exclusion criteria (Supplementary Figure 1, available online at www.biological-markers.com – Flow diagram of the study).

Fig. 1

Forest plot of sensitivity of miRNA assays in the diagnosis of esophageal cancer. Pooled sensitivity was 0.80 (95% CI, 0.76-0.83).

Table I shows specific characteristics of the included literature. In 18 articles, 39 studies were included, covering 3,708 patients and 2,689 healthy controls. Among the studies included, 33 studies from 15 publications were related to miRNAs as ESCC diagnostic biomarkers, and 6 studies from 3 publications were related to miRNAs as EC diagnostic biomarkers (no clear description was observed on type of EC); 34 studies had been performed in China, 5 studies in Japan and all studies considered Asian populations. Twenty-five studies used serum samples, 10 studies depended on plasma specimens and 4 studies were based on saliva samples. This present meta-analysis included reports of 30 different miRNAs, with 5 miRNAs repeated in these reports. Two studies focused on panel miRNAs, and 37 studies discussed single miRNAs.

Diagnostic accuracy

To evaluate the diagnostic accuracy of miRNAs in detecting EC, we first constructed forest plots of SEN and SPE of miRNA assays for diagnosis of EC (Figs. 1 and 2). With the existence of significant heterogeneity between SEN and SPE data (I² = 83.16%, p<0.01; and I² = 79.77%, p<0.01, respectively), we used a random-effects model to calculate pooled estimates. Pooled results and their corresponding 95% CIs were as follows: SEN, 0.798 (95% CI, 0.763-0.829); SPE, 0.785 (95% CI, 0.739-0.824); PLR, 3.705 (95% CI, 3.078-4.458); NLR, 0.257 (95% CI, 0.221-0.300) and DOR, 14.391 (95% CI, 11.103-18.653). The AUC reached 0.86 (95% CI, 0.83-0.89), and Figure 3 shows the corresponding overall SROC curve. In summary, our findings suggested that miRNAs could be used to discriminate EC samples from healthy controls.

Fig. 2

Forest plot of specificity of miRNA assays in the diagnosis of esophageal cancer. Pooled specificity was 0.78 (95% CI, 0.74-0.82).

Fig. 3

Summary receiver operating characteristic (SROC) curve for overall studies. The AUC was 0.86 (95% CI, 0.83-0.89). AUC = area under the curve; SENS = sensitivity; SPEC = specificity.

ESCC is the most common subtype of EC. Our study included 15 articles on ESCC. We performed an independent meta-analysis on miRNA diagnostic accuracy to discriminate ESCC patients from healthy controls. Pooled SEN, SPE, PLR, NLR and DOR totaled 0.777 (95% CI, 0.742-0.809), 0.809 (95% CI, 0.766-0.846), 4.072 (95% CI, 3.312-5.005), 0.275 (95% CI, 0.237-0.319) and 14.789 (95% CI, 11.027-19.833), respectively, and the AUC reached 0.86 (95% CI, 0.83-0.89). The miRNA diagnostic values obtained for ESCC were similar to those for EC.

Subgroup analyses

For the sake of exploring potential sources of heterogeneity, subgroup analysis was performed based on sample types (blood or saliva, serum or plasma). Table II lists the pooled diagnostic parameters for each group.

TABLE II

Summary estimates of diagnostic criteria

Analysis	Sensitivity (95% CI)	Specificity (95% CI)	PLR(95%CI)	NLR (95% CI)	DOR (95% CI)	AUC (95% CI)
Combinec	0.798 (0.763-0.829)	0.785 (0.739-0.824)	3.705 (3.078-4.458)	0.257 (0.221-0.300)	14.391 (11.103-18.653)	0.86 (0.83-0.89)
Sample
Blood	0.785 (0.748-0.818)	0.801 (0.757-0.839)	3.948 (3.243-4.806)	0.268 (0.229-0.313)	14.738 (11.120-19.532)	0.86 (0.83-0.89)
Saliva	0.889 (0.840-0.925)	0.589 (0.493-0.678)	2.163 (1.716-2.726)	0.188 (0.125-0.283)	11.514 (6.493-20.420)	0.84 (0.81-0.87)
Serum	0.781 (0.742-0.815)	0.788 (0.740-0.830)	3.687 (3.010-4.516)	0.278 (0.237-0.327)	13.247 (9.924-17.682)	0.85 (0.82-0.88)
Plasma	0.804 (0.710-0.873)	0.840 (0.739-0.906)	5.014 (3.025-8.311)	0.233 (0.155-0.351)	21.485 (10.448-44.182)	0.89 (0.86-0.91)

AUC = area under the curve; CI = confidence interval (95%); DOR = diagnostic odds ratio; EC = esophageal carcinoma; ESCC = esophageal squamous cell carcinoma; NLR = negative likelihood ratio; PLR = positive likelihood ratio

For blood samples, pooled values were SEN = 0.785, SPE = 0.801, PLR = 3.948, NLR = 0.268, DOR = 14.74 and AUC = 0.86, respectively. For saliva samples, pooled values were SEN = 0.889, SPE = 0.589, PLR = 2.163, NLR = 0.188, DOR = 11.514 and AUC = 0.84. For serum samples, the pooled values were SEN = 0.781, SPE = 0.788, PLR = 3.687, NLR = 0.278, DOR = 13.247 and AUC = 0.85. For plasma samples, pooled values were SEN = 0.804, SPE = 0.840, PLR = 5.014, NLR = 0.233, DOR = 21.485 and AUC = 0.89.

The above results suggest that sample type (blood or saliva) is a potential source of heterogeneity. Blood-based miRNA assays displayed better diagnostic accuracy than saliva-based ones. However, no significant difference was observed in diagnostic accuracy between serum and plasma. We also conducted subgroup analysis for countries (China vs. Japan) and miRNA (single vs. multiple miRNAs; singly reported vs. repeatedly reported miRNAs) and found no significant differences. The results are shown in Table III.

TABLE III

Summary estimates of diagnostic criteria

Analyses	Sensitivity (95% CI)	Specificity (95% CI]	PLR (95% CI)	NLR (95% CI)	DOR (95% CI)	AUC (95% CI)
Country
China	0.802 (0.767-0.832)	0.766 (0.723-0.804)	3.428 (2.911-4.038)	0.259 (0.222-0.302)	13.242 (10.349-16.943)	0.85 (0.82-0.88)
Japan	0.777 (0.619-0.883)	0.947 (0.643-0.994)	14.700 (1.755-123.111)	0.235 (0.133-0.415)	62.575 (7.132-548.996)	0.90 (0.87-0.92)
miRNA
Single miRNA	0.800 (0.746-0.845)	0.787 (0.718-0.842)	3.748 (2.858-4.915)	0.254 (0.202-0.319)	14.749 (10.146-21.439)	0.86 (0.83-0.89)
Multiple miRNA	0.795 (0.752-0.832)	0.783 (0.722-0.835)	3.668 (2.849-4.723)	0.262 (0.216-0.317)	14.000 (9.765-20.072)	0.86 (0.82-0.88)
Single reported miRNA	0.792 (0.755-0.824)	0.798 (0.744-0.843)	3.922 (3.072-5.008)	0.261 (0.222-0.309)	15.036 (10.585-21.359)	0.86 (0.83-0.89)
Repeat reported miRNA	0.806 (0.727-0.866)	0.751 (0.670-0.817)	3.233 (2.525-4.138)	0.258 (0.191-0.350)	12.507 (8.857-17.660)	0.85 (0.81-0.87)

Sensitivity analysis and publication bias

To ensure that our findings were not significantly influenced by any individual study, we performed an SEN analysis and observed no significant heterogeneity. To further evaluate potential publication bias, Deeks' funnel plot asymmetry test (t = 1.54, p = 0.133) was conducted, and no significant publication bias was detected. These tests all confirmed the validity of our meta-analysis results.

Discussion

Our meta-analysis included 39 studies, and it suggested that miRNAs were ideal diagnostic markers of EC, with a pooled SEN of 0.789, pooled SPE of 0.785 and AUC of 0.86. Through subgroup analysis, a blood-based miRNA assay was found to display better diagnostic accuracy than a saliva-based miRNA assay in detecting EC, but no significant difference in diagnostic accuracy existed between serum and plasma. These results indicate that both serum and plasma can be recommended as clinical specimens for miRNA detection.

MiRNA was first discovered in 1993 by Lee while studying nematode development (39). In 2008, miRNA was detected in human serum for the first time by Lawrie et al (40). Then miRNAs were detected in different body fluids (including plasma, serum, urine, milk, semen and tears) (41), and they showed strong stability in these media (41 -43). With research developments, miRNA was reported to participate in the occurrence of cancer in oncogenes or tumor suppressor genes (Supplementary Table I, available online at www.biological-markers.com – Expression of miRNA in esophageal carcinoma in meta-analysis).

An increasing number of recent studies have discovered that miRNAs can be used as markers for early diagnosis of tumors. For example, Tsujiura et al found that miR-106b was overexpressed in plasma of patients with gastric cancer, while 1 month after surgery, levels of miR-106b decreased rapidly. Thus, miR-106b could be used as a biomarker for early diagnosis of gastric cancer (44). Detection of serum miR-21 can be used in the early diagnosis of colorectal cancer (45). Low serum levels of miR-361-3p and miR-625 serve as markers for early diagnosis of lung cancer (46). Urinary miR-574-3p and miR-107 are markers for early diagnosis of prostate cancer (47). Some studies have suggested that miRNAs can be used as early diagnostic biomarkers for EC, presenting high SEN and SPE. Wang and Zhang discovered higher serum miR-21 in EC patients than in healthy controls and that this miRNA could serve as a diagnostic marker for EC, with 71.0% SEN and 69.2% SPE (48). However, conclusions remain contradictory. Therefore, more studies are needed to further confirm research results.

The results of our meta-analysis confirmed that miRNAs are noninvasive biomarkers that can be used to discriminate EC patients from healthy controls; these results are similar to those of Wan et al (28). In a study by Wang (29), a blood-based miRNA assay displayed better diagnostic accuracy than saliva-based miRNA assay in ESCC. Our results in EC were similar to those of Wang, but opposite to those of Wan et al (28), who insisted that saliva-, serum- and plasma-based miRNA samples showed no difference in diagnostic accuracy for EC. Our study also indicated that no difference exists between plasma-based and serum-based specimens in terms of diagnostic value for EC detection; whereas some previous studies have shown that plasma-based specimens exhibited higher accuracy than serum-based specimens in gastric cancer and breast cancer (28, 29).

The reasons for these differences remain unclear, and the mechanism of abnormal expression of miRNA in different sample species needs further studies. In spite of this uncertainty, our study results are significant regarding early diagnosis of EC. Detecting miRNA levels in serum and plasma presents a higher accuracy than in saliva.

In addition to the above-mentioned data, we also performed subgroup analyses based on number of miRNAs. A nonsignificant higher accuracy was observed in multiple miRNAs vs. individual miRNAs; this result was contrary to those of some previous studies (31 -33), which stated that multiple miRNAs present significantly better accuracy than single miRNAs. As only 2 articles included multiple miRNAs in our analysis, these findings may be insufficient to support such a conclusion. Thus, further research should include more studies on multiple miRNAs.

In subgroup analyses between countries of patients (China vs. Japan), our results indicated that no difference exists between Chinese and Japanese populations in terms of accuracy of diagnosis with miRNA. For the first time, we conducted subgroup analyses between repeated and single reports on miRNA, and no statistical difference was observed. An independent meta-analysis of ESCC was also conducted because it is one of the most common types of EC. Pooled SEN, SPE and the AUC reached 0.777, 0.809 and 0.86, respectively. These results are similar to miRNA diagnostic values in EC, indicating that miRNAs are potential diagnostic biomarkers of EC regardless of their subtype.

To guarantee the reliability of our research, we performed SEN analysis to ensure that findings were not significantly influenced by any individual study. We also conducted Deeks' funnel plot asymmetry test to evaluate potential publication bias. In our meta-analysis, all diagnostic miRNAs were detected in body fluids (blood or saliva) and also in tumor tissues. However, detection of miRNA in tissues is not ideal for early diagnosis considering its invasiveness and high cost. A significant role of miRNAs in cancer research should not be ignored.

Despite using a relatively large sample size, and although our study showed that miRNAs are potential noninvasive early diagnostic markers of EC, several limitations to this meta-analysis must be noted. First, few studies included multiple miRNAs. Second, the type of EC was not clearly noted in some studies, hence, we could not perform a subgroup analysis on the type of EC. Finally, the research participants studied in Asia were mainly concentrated in China and Japan. These populations may all be potential sources of heterogeneity. Therefore, more high-quality research is needed to study the accuracy of using miRNAs in the diagnosis of EC.

Conclusions

Our meta-analysis provides evidence that miRNAs may serve as noninvasive biomarkers for distinguishing EC patients from healthy controls. Blood-based miRNA assays have a higher diagnostic value than saliva-based ones in detecting EC, and both plasma- and serum-based miRNA are recommended for clinical diagnosis of EC. More issues must be considered before these findings can be translated into clinical applications, and the exact roles of miRNAs in EC progression will need to be clearly elucidated in the future.

Footnotes

Financial support: This research was supported in part by the National Natural Science Foundation of China (grant nos. 81260301 and 81560399).

Conflict of interest: The authors declare that they have no competing interests.

References

Ferlay

, Soerjomataram

, Dikshit

Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012.

Int J Cancer. 2015; 136(5): E359–E386.

, Yin

, Wang

, Xia

, Chen

, Tang

MicroRNAs in esophageal cancer [review].

Mol Med Rep. 2012; 6(3): 459–465.

Hiyama

, Yoshihara

, Tanaka

, Chayama

Genetic polymorphisms and esophageal cancer risk.

Int J Cancer. 2007; 121(8): 1643–1658.

Urba

S.G.

, Orringer

M.B.

, Turrisi

, lannettoni

, Forastiere

, Strawderman

Randomized trial of preoperative chemo-radiation versus surgery alone in patients with locoregional esophageal carcinoma.

J Clin Oncol. 2001; 19(2): 305–313.

Kim

, Grobmyer

S.R.

, Smith

Esophageal cancer: the five year survivors.

J Surg Oncol. 2011; 103(2): 179–183.

Daly

J.M.

, Fry

W.A.

, Little

A.G.

Esophageal cancer: results of an American College of Surgeons patient care evaluation study.

J Am Coll Surg. 2000; 190(5): 562–572 [discussion: 572-573].

Barber

T.W.

, Duong

C.P.

, Leong

, Bressel

, Drummond

E.G.

, Hicks

R.J.

18F-FDG PET/CT has a high impact on patient management and provides powerful prognostic stratification in the primary staging of esophageal cancer: a prospective study with mature survival data.

J Nucl Med. 2012; 53(6): 864–871.

Tachimori

, Kanamori

, Uemura

, Hokamura

, Igaki

, Kato

Salvage esophagectomy after high-dose chemoradiotherapy for esophageal squamous cell carcinoma.

J Thorac Cardiovasc Surg. 2009; 137(1): 49–54.

Zhao

, Wei

W.Q.

, Zhao

D.L.

Population-based study of DNA image cytometry as screening method for esophageal cancer.

World J Gastroenterol. 2012; 18(4): 375–382.

10.

Kosugi

, Nishimaki

, Kanda

, Nakagawa

, Ohashi

, Hatakeyama

Clinical significance of serum carcinoembryonic antigen, carbohydrate antigen 199, and squamous cell carcinoma antigen levels in esophageal cancer patients.

World J Surg. 2004; 28(7): 680–685.

11.

Yilmaz

, Eroglu

, Dag

, Karaoglanoglu

, Yilmaz

Serum levels of IGF-I and IGFBP-III and their relation with carcinoembryonic antigen and carbohydrate antigen 199 in cases of esophageal cancer.

Int J Clin Pract. 2006; 60(12): 1604–1608.

12.

Parenti

, Porzionato

, Pizzi

Expression pattern of squamous cell carcinoma antigen in oesophageal dysplasia and squamous cell carcinoma.

Histol Histopathol. 2007; 22(9): 989–995.

13.

Lewis

B.P.

, Burge

C.B.

, Bartel

D.P.

Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets.

Cell. 2005; 120(1): 15–20.

14.

Bartel

D.P.

MicroRNAs: genomics, biogenesis, mechanism, and function.

Cell. 2004; 116(2): 281–297.

15.

Martinez

, Dimaio

B-Myb, cancer, senescence, and microRNAs.

Cancer Res. 2011; 71(16): 5370–5373.

16.

Subramanyam

, Blelloch

From microRNAs to targets: pathway discovery in cell fate transitions.

Curr Opin Genet Dev. 2011; 21(4): 498–503.

17.

Wilmott

J.S.

, Zhang

X.D.

, Hersey

, Scolyer

R.A.

The emerging important role of microRNAs in the pathogenesis, diagnosis and treatment of human cancers.

Pathology. 2011; 43(6): 657–671.

18.

Yuan

, Zeng

Z.Y.

, Liu

X.H.

MicroRNA-203 inhibits cell proliferation by repressing ΔNp63 expression in human esophageal squamous cell carcinoma.

BMC Cancer. 2011; 11(1): 57.

19.

Takeshita

, Mori

, Kano

miR-203 inhibits the migration and invasion of esophageal squamous cell carcinoma by regulating LASP1.

Int J Oncol. 2012; 41(5): 1653–1661.

20.

, Yao

, Meng

Predictive Value of Serum miR-10b, miR-29c, and miR-205 as promising biomarkers in esophageal squamous cell carcinoma screening.

Medicine. 2015; 94(44): e1558.

21.

Madhavan

, Cuk

, Burwinkel

, Yang

Cancer diagnosis and prognosis decoded by blood-based circulating microRNA signatures.

Front Genet. 2013; 4: 116.

22.

Calin

G.A.

, Croce

C.M.

MicroRNA signatures in human cancers.

Nat Rev Cancer. 2006; 6(11): 857–866.

23.

Mitchell

P.S.

, Parkin

R.K.

, Kroh

E.M.

Circulating microRNAs as stable blood-based markers for cancer detection.

Proc Natl Acad Sci USA. 2008; 105(30): 10513–10518.

24.

Chen

, Ba

, Ma

Characterization of microRNAs in serum: a novel class of biomarkers for diagnosis of cancer and other diseases.

Cell Res. 2008; 18(10): 997–1006.

25.

Hirajima

, Komatsu

, Ichikawa

Clinical impact of circulating miR-18a in plasma of patients with oesophageal squamous cell carcinoma.

Br J Cancer. 2013; 108(9): 1822–1829.

26.

Xie

, Chen

, Zhang

Salivary microRNAs as promising biomarkers for detection of esophageal cancer.

PLoS ONE. 2013; 8(4): e57502.

27.

Komatsu

, Ichikawa

, Takeshita

Circulating microRNAs in plasma of patients with oesophageal squamous cell carcinoma.

Br J Cancer. 2011; 105(1): 104–111.

28.

Wan

, Wu

, Che

, Kang

, Zhang

Insights into the potential use of microRNAs as a novel class of biomarkers in esophageal cancer.

Dis Esophagus. 2016; 29(5): 412–420.

29.

Wang

, Wang

, Zhang

Identification of microRNAs as novel biomarkers for detecting esophageal squamous cell carcinoma in Asians: a meta-analysis.

Tumour Biol. 2014; 35(11): 11595–11604.

30.

Liu

, Tian

, Xia

L.L.

, Ding

, Cormier

R.T.

, He

Circulating miRNAs as novel potential biomarkers for esophageal squamous cell carcinoma diagnosis: a meta-analysis update.

Dis Esophagus. 2017; 30(2): 1–9.

31.

Liu

, Wang

, Cao

, Liu

Analysis of circulating microRNA biomarkers for breast cancer detection: a meta-analysis.

Tumour Biol. 2014; 35(12): 12245–12253.

32.

Liu

, Wang

, Cao

, Liu

Diagnostic value of circulating microRNAs for gastric cancer in Asian populations: a meta-analysis.

Tumour Biol. 2014; 35(12): 11995–12004.

33.

Wang

, Wen

, Xu

Circulating microRNAs as a novel class of diagnostic biomarkers in gastrointestinal tumors detection: a meta-analysis based on 42 articles.

PLoS ONE. 2014; 9(11): e113401.

34.

Mitchell

A.J.

, Vaze

, Rao

Clinical diagnosis of depression in primary care: a meta-analysis.

Lancet. 2009; 374(9690): 609–619.

35.

Harbord

R.M.

, Deeks

J.J.

, Egger

, Whiting

, Sterne

J.A.

A unification of models for meta-analysis of diagnostic accuracy studies.

Biostatistics. 2007; 8(2): 239–251.

36.

Dinnes

, Deeks

, Kirby

, Roderick

A methodological review of how heterogeneity has been examined in systematic reviews of diagnostic test accuracy.

Health Technol Assess. 2005; 9(12): 1–113, iii.

37.

Higgins

J.P.

, Thompson

S.G.

, Deeks

J.J.

, Altman

D.G.

Measuring inconsistency in meta-analyses.

BMJ. 2003; 327(7414): 557–560.

38.

Deeks

J.J.

, Macaskill

, Irwig

The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed.

J Clin Epidemiol. 2005; 58(9): 882–893.

39.

Lee

R.C.

, Feinbaum

R.L.

, Ambros

Ambros V. The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14.

Cell. 1993; 75(5): 843–54.

40.

Lawrie

C.H.

, Gal

, Dunlop

H.M.

Detection of elevated levels of tumour-associated microRNAs in serum of patients with diffuse large B-cell lymphoma.

Br J Haematol. 2008; 141(5): 672–675.

41.

Weber

J.A.

, Baxter

D.H.

, Zhang

The microRNA spectrum in 12 body fluids.

Clin Chem. 2010; 56(11): 1733–1741.

42.

Etheridge

, Lee

, Hood

, Galas

, Wang

Extracellular microRNA: a new source of biomarkers.

Mutat Res. 2011; 717(1-2): 85–90.

43.

Cortez

M.A.

, Bueso-Ramos

, Ferdin

, Lopez-Berestein

, Sood

A.K.

, Calin

G.A.

MicroRNAs in body fluids: the mix of hormones and biomarkers.

Nat Rev Clin Oncol. 2011; 8(8): 467–477.

44.

Tsujiura

, Ichikawa

, Komatsu

Circulating microRNAs in plasma of patients with gastric cancers.

Br J Cancer. 2010; 102(7): 1174–1179.

45.

Toiyama

, Takahashi

, Hur

Serum miR-21 as a diagnostic and prognostic biomarker in colorectal cancer.

J Natl Cancer Inst. 2013; 105(12): 849–859.

46.

Roth

, Stückrath

, Pantel

, Izbicki

J.R.

, Tachezy

, Schwarzenbach

Low levels of cell-free circulating miR-3613p and miR-625* as blood-based markers for discriminating malignantfrom benign lungtumors.

PLoS ONE. 2012; 7(6): e38248.

47.

Bryant

R.J.

, Pawlowski

, Catto

J.W.

Changes in circulating microRNA levels associated with prostate cancer.

Br J Cancer. 2012; 106(4): 768–774.

48.

Wang

, Zhang

The expression and clinical significance of circulating microRNA-21 in serum of five solid tumors.

J Cancer Res Clin Oncol. 2012; 138(10): 1659–1666.

49.

Zhang

, Wang

, Chen

Expression profile of microRNAs in serum: a fingerprint for esophageal squamous cell carcinoma.

Clin Chem. 2010; 56(12): 1871–1879.

50.

Zhang

, Wang

, Zhao

The oncogenetic role of microRNA-31 as a potential biomarker in oesophageal squamous cell carcinoma.

Clin Sci (Lond). 2011; 121(10): 437–447.

51.

Kurashige

, Kamohara

, Watanabe

Serum microRNA-21 is a novel biomarker in patients with esophageal squamous cell carcinoma.

J Surg Oncol. 2012; 106(2): 188–192.

52.

Takeshita

, Hoshino

, Mori

Serum microRNA expression profile: miR-1246 as a novel diagnostic and prognostic biomarker for oesophageal squamous cell carcinoma.

Br J Cancer. 2013; 108(3): 644–652.

53.

Zhang

, Zhao

, Wang

MicroRNA-1322 regulates ECRG2 allele specifically and acts as a potential biomarker in patients with esophageal squamous cell carcinoma.

Mol Carcinog. 2013; 52(8): 581–590.

54.

, Wang

, Guan

Diagnostic and prognostic implications of a serum miRNA panel in oesophageal squamous cell carcinoma.

PLoS ONE. 2014; 9(3): e92292.

55.

Komatsu

, Ichikawa

, Hirajima

Plasma microRNA profiles: identification of miR-25 as a novel diagnostic and monitoring biomarker in oesophageal squamous cell carcinoma.

Br J Cancer. 2014; 111(8): 1614–1624.

56.

, Ye

, Zhang

[Diagnostic values of salivary versus and plasma microRNA-21 for early esophageal cancer].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University. 2014; 34(6): 885–9.

57.

Sun

, Dong

Predictive value of plasma miRNA-718 for esophageal squamous cell carcinoma.

Cancer Biomark. 2016; 16(2): 265–273.

58.

F.C.

, Meng

W.W.

, Qu

Y.H.

Expression of circulating microRNA-20a and let-7a in esophageal squamous cell carcinoma.

World J Gastroenterol. 2015; 21(15): 4660–4665.

59.

Wang

, Guan

, Liu

Prognostic and diagnostic potential of miR-146a in oesophageal squamous cell carcinoma.

Br J Cancer. 2016; 114(3): 290–297.

60.

S.P.

, Su

H.X.

, Zhao

, Guan

Q.L.

Plasma miRNA-506 as a prognostic biomarker for esophageal squamous cell carcinoma.

Med Sci Monit. 2016; 22: 2195–2201.

61.

Dong

, Yin

, Dong

Predictive value of plasma microRNA-216a/b in the diagnosis of esophageal squamous cell carcinoma.

Dis Markers. 2016; 2016: 1857067.

Meta-Analysis of microRNAs as Potential Biomarkers for Detecting Esophageal Carcinoma in Asian Populations

Abstract

Background

Methods

Results

Conclusions

Keywords

Introduction

Materials and methods

Search strategy

Inclusion and exclusion criteria

Data extraction

Statistical analysis

Results

Search results and characteristics of studies

Diagnostic accuracy

Subgroup analyses

Sensitivity analysis and publication bias

Discussion

Conclusions

Footnotes

References