# Clinically Correlated MicroRNAs in the Diagnosis of Non-Small Cell Lung Cancer: A Systematic Review and Meta-Analysis

Authors: Min Jiang; Xuelian Li; Xiaowei Quan; Xiaoying Li; Baosen Zhou
Journal: BioMed Research International
 (2018)

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## Abstract

(1) Background. Non-small cell lung cancer (NSCLC) has a high mortality rate. MiRNAs have been found to be diagnostic biomarkers for NSCLC. However, controversial results exist. We conducted this meta-analysis to evaluate the diagnostic value of miRNAs for NSCLC.(2) Methods. Databases and reference lists were searched. Pooled sensitivity (SEN), specificity (SPE), and area under the curve (AUC) were applied to examine the general diagnostic efficacy, and subgroup analysis was also performed.(3) Results. Pooled SEN, SPE, and AUC were 85%, 88%, and 0.93, respectively, for 71 studies. Multiple miRNAs (AUC: 0.96) obtained higher diagnostic value than single miRNA (AUC: 0.86), and the same result was found for Caucasian population (AUC: 0.97) when compared with Asian (AUC: 0.91) and Caucasian/African population (AUC: 0.92). MiRNA had higher diagnostic efficacy when participants contained both smokers and nonsmokers (AUC is 0.95 for imbalanced group and 0.91 for balanced group) than when containing only smokers (AUC: 0.90). Meanwhile, AUC was 0.91 for both miR-21 and miR-210.(4) Conclusions. Multiple miRNAs such as miR-21 and miR-210 could be used as diagnostic tools for NSCLC, especially for the Caucasian and nonsmoking NSCLC.


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## Body

## 1. Introduction

Lung cancer is the principal cause of cancer-associated deaths among males both in developed and in developing countries, and it has exceeded the breast cancer becoming the major cause of cancer-related deaths in females in the developed countries [1]. Non-small cell lung cancer (NSCLC) is a major type of lung cancer that is responsible for 85% lung cancer-associated deaths. Smoking has been recognized as a primary environmental risk factor of lung cancer. However, only a small number of smokers will develop into lung cancer patients.MicroRNA is a group of 19–22 nucleotide, small, single-stranded, and conserved noncoding RNA that acts as a regulator of gene expression at both the posttranscriptional and the translational levels through acting on the 3′-untranslated region (UTR) of messenger RNA (mRNA) [2]. MiRNAs play important roles in various biological processes associated with the tumorigenesis such as the cellular proliferation, differentiation, metabolism, and apoptosis [3, 4]. It is available to isolate the miRNAs from the clinical specimens including the plasma, serum, sputum, and tissue. Meanwhile, it has a high stability. Due to these advantages, the miRNAs are increasingly becoming an ideal tool for the detection of NSCLC.Recently, a series of articles have shown that different miRNAs might be applied to detect the NSCLC [5–7]. For example, miR-21, an oncogenic miRNA, has been shown to be overexpressed in lung cancer as well as other various human tumors [8]. Upregulation of miR-21 could promote the tumorigenesis of lung cancer through inhibiting the apoptosis process and negatively regulating the Ras/MEK/ERK signal pathway [9]. High miR-210 expression was correlated with the increased lymph node metastasis and a poor prognosis in patients with NSCLC [10]. Both these two, miR-21 and miR-210, have been explored to be used as diagnostic tools for NSCLC, no matter whether they are applied in combination with other miRNAs or alone [11–14]. However, as a result of the small sample sizes, the different miRNAs profiling, and the differences of the specimen and ethnicity, inconsistencies existed among studies that had examined the diagnostic value of miR-21, miR-210, and other miRNAs for NSCLC. Therefore, a meta-analysis was performed to assess the performance of miRNAs in the detection for NSCLC.


## 2. Materials and Methods

### 2.1. Search Strategy

Our meta-analysis was based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). We searched PubMed, Google Scholar, Chinese National Knowledge Infrastructure (CNKI), Embase, and Medline to find all associated articles in order to investigate the potential utility of miRNAs as diagnostic tools for NSCLC. The combination of the Medical Subject Headings (MeSH) and the keywords (“lung neoplasm” OR “lung malignancy” OR “lung cancer”) AND (“miRNA” OR “microRNAs”) AND (“ROC curve” OR “sensitivity” OR “specificity” OR “diagnosis”) was used (updated to April 5, 2017). The reference lists of the reviews were also searched to obtain all the acceptable articles.


### 2.2. Study Selection

A series of criteria were applied for study inclusion and exclusion. For inclusion, the criteria were as follows: (1) patients with NSCLC; (2) the type of the controls being healthy controls (HC) or patients with benign pulmonary diseases (BPD); (3) assessing the diagnostic value of the miRNAs; (4) the possibility of extracting or calculating TP, FP, FN, and TN from the articles. For exclusion, the criteria were as follows: (1) studies that were duplicate publications, reviews, or unrelated; (2) studies without complete data.


### 2.3. Data Collection and Quality Assessment

Two authors collected the data independently as follows: the first author, publication year, and participant demographic characteristics (ethnicity, sample size, mean or median age, smoking status, the types of the controls, and the testing method of controls and cancer); types of the specimen; miRNA profiling and the data used for this meta-analysis (SEN, SPE, TP, FP, FN, and TN). The quality of these articles were assessed with the QUADAS-2 guidelines [15].


### 2.4. Statistical Analysis

All the statistical analyses were conducted by RevMan 5.3 (version 1.4) software and STATA 11.0 (STATA-Corp, College Station, TX, version 11.0) software. The heterogeneity among the selected studies was assessed through the Q test and the I2 value [16]. TheP value for the Q test being less than 0.05 or the I2 ≥ 50 % demonstrated that there was heterogeneity among the included studies. The pooled SPE [TN/(FP+TN)], SEN [TP/(FN+TP)], diagnostic odds ratio (DOR) [PLR/NLR], the negative likelihood ratio (NLR) [(1-SPE)/SPE)], the positive likelihood ratio (PLR) [(SEN/(1-SEN)], and their 95% confidence intervals (95% CIs) were evaluated by a bivariate random-effect-regression model. The SROC curve was constructed and the AUC value was calculated too. A Fagan nomogram was also constructed to evaluate the clinical utility of miRNAs in the diagnosis of NSCLC. Subgroup analyses (grouped by miRNA profiling: single and multiple; smoking status: only smokers, smokers, and nonsmokers (imbalanced between groups), smokers and nonsmokers (balanced between groups), and unknown smoking status; specimen: serum, plasma, whole blood/blood cell, and not blood; ethnicity: Asian, Caucasian, and Caucasian/African; control-type: BPD, HC, and BPD/HC; stage: early stage and no early stage; and case number: large (≥ 50) and small (< 50)) and meta-regression analysis were used to identify the potential sources of the heterogeneity. The Deeks’ funnel plot asymmetry test was also applied to explore the publication bias, with thePvalue less than 0.01 considered significant [17].


## 2.1. Search Strategy

Our meta-analysis was based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). We searched PubMed, Google Scholar, Chinese National Knowledge Infrastructure (CNKI), Embase, and Medline to find all associated articles in order to investigate the potential utility of miRNAs as diagnostic tools for NSCLC. The combination of the Medical Subject Headings (MeSH) and the keywords (“lung neoplasm” OR “lung malignancy” OR “lung cancer”) AND (“miRNA” OR “microRNAs”) AND (“ROC curve” OR “sensitivity” OR “specificity” OR “diagnosis”) was used (updated to April 5, 2017). The reference lists of the reviews were also searched to obtain all the acceptable articles.


## 2.2. Study Selection

A series of criteria were applied for study inclusion and exclusion. For inclusion, the criteria were as follows: (1) patients with NSCLC; (2) the type of the controls being healthy controls (HC) or patients with benign pulmonary diseases (BPD); (3) assessing the diagnostic value of the miRNAs; (4) the possibility of extracting or calculating TP, FP, FN, and TN from the articles. For exclusion, the criteria were as follows: (1) studies that were duplicate publications, reviews, or unrelated; (2) studies without complete data.


## 2.3. Data Collection and Quality Assessment

Two authors collected the data independently as follows: the first author, publication year, and participant demographic characteristics (ethnicity, sample size, mean or median age, smoking status, the types of the controls, and the testing method of controls and cancer); types of the specimen; miRNA profiling and the data used for this meta-analysis (SEN, SPE, TP, FP, FN, and TN). The quality of these articles were assessed with the QUADAS-2 guidelines [15].


## 2.4. Statistical Analysis

All the statistical analyses were conducted by RevMan 5.3 (version 1.4) software and STATA 11.0 (STATA-Corp, College Station, TX, version 11.0) software. The heterogeneity among the selected studies was assessed through the Q test and the I2 value [16]. TheP value for the Q test being less than 0.05 or the I2 ≥ 50 % demonstrated that there was heterogeneity among the included studies. The pooled SPE [TN/(FP+TN)], SEN [TP/(FN+TP)], diagnostic odds ratio (DOR) [PLR/NLR], the negative likelihood ratio (NLR) [(1-SPE)/SPE)], the positive likelihood ratio (PLR) [(SEN/(1-SEN)], and their 95% confidence intervals (95% CIs) were evaluated by a bivariate random-effect-regression model. The SROC curve was constructed and the AUC value was calculated too. A Fagan nomogram was also constructed to evaluate the clinical utility of miRNAs in the diagnosis of NSCLC. Subgroup analyses (grouped by miRNA profiling: single and multiple; smoking status: only smokers, smokers, and nonsmokers (imbalanced between groups), smokers and nonsmokers (balanced between groups), and unknown smoking status; specimen: serum, plasma, whole blood/blood cell, and not blood; ethnicity: Asian, Caucasian, and Caucasian/African; control-type: BPD, HC, and BPD/HC; stage: early stage and no early stage; and case number: large (≥ 50) and small (< 50)) and meta-regression analysis were used to identify the potential sources of the heterogeneity. The Deeks’ funnel plot asymmetry test was also applied to explore the publication bias, with thePvalue less than 0.01 considered significant [17].


## 3. Results

### 3.1. Literature Search and the Studies’ Characteristics

As shown in Figure1(a), 2594 eligible articles were included, of which 2145 articles were removed as unrelated and duplicate articles. And then 370 reviews were also excluded, leaving 79 articles with full texts, and another 21 articles were then removed through carefully reading: 14 articles met the exclusion criteria and 7 articles did not have the complete data. Ultimately, 58 articles [5–7, 11–14, 18–68] with 71 studies published from 2009 to 2017 including 9,099 participants (5111 cases with NSCLC and 3988 controls from the healthy individuals and the patients with the benign pulmonary disease (BPD)) were included. The main characteristics of these 71 studies were shown in Table 1. Wang Y’s article [7], Fan LH’s article [52], Nadal E’s article [45], Tang DF’s article [32], Razzak R’s article [14], Wang W’s article [68], Yu L’s article [19], and Xing LX’s article [18] included 2 studies. Bediaga’s article [28] included 3 studies, Wang C’s article [46] included 4 studies, and the remaining articles [5, 6, 11–13, 20–27, 29–31, 33–44, 47–51, 53–67] included 1 study, respectively. Meanwhile, there were 18 studies [13, 14, 28, 31, 33, 45, 46, 48, 54, 56, 63, 65, 66] performed in Caucasian, 11 studies [18, 19, 21, 23, 27, 29, 30, 38, 44] performed in Caucasian/African, and 1 study [26] performed in African populations; the remaining studies were performed in Asian populations. A total of 50 studies detected the miRNAs in blood such as the whole blood, plasma, serum, and peripheral blood mononuclear cells (PBMC) [6, 7, 11, 20–24, 26, 29–35, 37, 39–42, 44–47, 49–54, 56–64, 66–68], while the remaining studies were detected in nonblood samples (7 tissue [5, 25, 28, 55, 68], 1 pleural effusion [43], 12 sputum [12, 14, 18, 19, 27, 36, 38, 48, 65], and 1 BAL [13]). We evaluated 45 studies for assessing the diagnostic value of multiple miRNAs and 26 studies [5, 6, 11, 20, 22, 24–26, 33–37, 39–41, 43, 47, 50, 51, 55, 57, 58, 60, 67, 68] of single miRNA.Table 1
The main features of 71 included studies in this meta-analysis.
Study ID Ethnicity Specimen Case Age Control Age Type of control Stage MiRNA profiling SEN ( % ) SPE ( % ) Reference miRNA microRNA assay Smoking status ∗ N N Zhang H 2017 Asian plasma 129 59.6 83 60.0 HC I-II miR-145, miR-20a, miR-21, miR-223 81.8 90.1 miR-16 qRT-PCR 3 Halvorsen A 2016 Caucasian serum 100 62.6 58 57.6 HC I-IV miR-429, miR-205, miR-200b, miR-203, miR-12, miR-34b 88.0 71.0 miR-220, miR-19b, U6 qRT-PCR 3 TaiMei C2016 Asian blood 110 65.0 52 65.7 HC I-III 20 miRNAsa 89.1 100 miR-159a, U6 qRT-PCR 4 Su KL 2016 Asian plasma 100 NA 100 NA HC I-III miR-195 78.0 86.0 miR-39 qRT-PCR 3 Zhu WY 2016 Asian plasma 112 58.5 40 57.9 HC I-III miR-182, miR-183, miR-210, miR-126 81.3 100 U6 qRT-PCR 2 Jiang LP 2016 Asian tissue 154 54.9 63 57.8 BPD I-IV miR-26b 79.9 79.4 U6 qRT-PCR 3 Wang Y 2016 Asian plasma 82 NA 91 NA HC I-II miR-532, miR-628, miR-425 91.5 97.8 miR-39 qRT-PCR 4 Wang Y 2016 Asian plasma 36 NA 43 NA HC I-II miR-532, miR-628, miR-425 97.2 95.3 miR-39 qRT-PCR 4 Fan LH 2016 Asian serum 94 60.5 58 58.1 HC I-III miR-15b, miR-16, miR-20a 86.2 91.4 NA qRT-PCR 4 Fan LH 2016 Asian serum 70 59.7 54 58.0 HC I-III miR-15b, miR-16, miR-20a 94.3 94.2 NA FQDs 4 Sun L 2016 Asian plasma 87 60.7 96 53.8 HC,BPD I-IV miR-30a 61.0 84.3 U6 qRT-PCR 4 Su Y 2016 Asian sputum 144 66.3 171 65.2 BPD I miR-21, miR-31, miR-210 81.5 85.9 U6 qRT-PCR 1 Gao X 2016 Asian plasma 30 61.1 30 60.2 HC I miR-324, miR-1285 93.3 90.0 miR-39 qRT-PCR 4 Wang X 2016 Asian plasma 59 55.9 59 57.6 BPD I-III miR-486 83.1 78.0 miR-16 qRT-PCR 4 Wei J 2016 Asian plasma 63 61.0 30 57.0 HC I-IV miR-21 76.2 70.0 miR-16 qRT-PCR 3 Razzak 2016 Caucasian sputum 22 68 10 58 HC,BPD III-IV miR-21, miR-210, miR-372 64 100 U6 qRT-PCR 4 Razzak 2016 Caucasian sputum 21 70 10 58 HC,BPD I-II miR-21, miR-210, miR-372 67 90 U6 qRT-PCR 4 Leidinger P 2016 Caucasian blood 74 NA 20 NA HC I-III miR-720, miR-29c, miR-199a, miR-378a,let-7f 91.0 98.0 U24,U48 qRT-PCR 4 Wang WZ 2016 Asian tissue 15 57 16 58 HC I-IV miR-182, miR-10a, miR-301b, miR-1244, miR-301a, miR-135b, miR-224, miR-21 93.3 93.8 miR-16 qRT-PCR 4 Wang WZ 2016 Asian serum 54 NA 15 NA HC I-IV miR-1244 81.5 80 miR-39 qRT-PCR 4 Kim JL O 2015 Caucasian BAL 21 70 10 59 HC,BPD I-II miR-21, miR-143, miR-155, miR-210, miR-373 85.7 100 U6 qRT-PCR 4 Wang C 2015 Asian serum 19 61.8 19 62.1 HC I-IV miR-483, miR-193a, miR-25, miR-214, miR-7 100 84 let-7d/g/i qRT-PCR 4 Li WS 2015 Asian plasma 11 59 11 55 HC I-III mir-486 90.9 81.8 miR-39, U44 qRT-PCR 4 Wang C 2015 Asian serum 63 61.9 63 59.7 HC I-IV miR-483, miR-193a, miR-25, miR-214, miR-7 89.0 68.0 let-7d/g/i qRT-PCR 4 Wang C 2015 Caucasian serum 108 67.2 56 63.7 BPD I-IV miR-483, miR-193a, miR-25, miR-214, miR-7 95.0 95.0 let-7d/g/i qRT-PCR 4 Wang C 2015 Caucasian serum 108 67.2 48 58.5 HC I-IV miR-483, miR-193a, miR-25, miR-214, miR-7 95.0 84.0 let-7d/g/i qRT-PCR 4 Nadal E 2015 Caucasian serum 70 67.5 22 67.0 HC,BPD I-III miR-141, miR-200b, miR-193b 96.0 95.0 U6 qRT-PCR 2 Nadal E 2015 Caucasian serum 84 65.5 23 60.0 HC,BPD I-III miR-141, miR-200b, miR-193b 97.0 96.0 U6 qRT-PCR 2 Guo WG 2015 Asian plasma 126 NA 50 NA HC I-IV mir-204 76.0 82.0 U6 qRT-PCR 4 Ma J 2015 Caucasian, African PBMC 84 64.1 69 62.4 BPD I-IV miR-19b, miR-29b 72.6 82.6 miR-423-3p qRT-PCR 2 Li L 2015 Asian serum 36 56.0 30 58.0 HC,BPD I-IV miR-148a, miR-148b, miR-152 72.2 90.0 U6 qRT-PCR 4 Zhang XL 2015 Asian tissue 125 61.0 125 61.0 HC I-IV miR-141 64.8 64.8 miR-191, miR-103 qRT-PCR 3 Zhao W 2015 Asian serum 80 57.6 60 55.4 HC NA miR-21 73.8 71.7 U6 qRT-PCR 4 Wang RJ 2015 Asian serum 70 64.4 70 63.7 HC NA miR-145 92.8 61.4 miR-39 qRT-PCR 3 Yang JS 2015 Asian serum 152 NA 300 NA HC I-IV miR-152, miR-148a, miR-148b, miR-21 96.0 91.0 U6 qRT-PCR 3 Xing LX 2015 Caucasian sputum 67 66.4 69 64.9 BPD I-II miR-21, miR-31, miR-210 82.1 88.4 U6, miR-16 qRT-PCR 4 Liu CM 2015 Asian Pleural effusion 61 53.8 70 54.4 BPD NA miR-192 61.3 79.5 U6 qRT-PCR 2 Dou HL 2015 Asian plasma 120 63.2 360 NA HC I-IV miR-152 86.0 81.3 U6 digital PCR 4 Yang YL 2015 Asian PBMC 74 62.5 52 61.8 HC I-IV miR-10b 86.5 76.9 miR-16 qRT-PCR 3 Li N 2014 Caucasian, African sputum 35 68.9 40 65.7 HC I miR-31, miR-210 65.7 85.0 NA qRT-PCR 4 Zhu W 2014 Asian serum 70 59.0 48 NA HC I-IV miR-429 54.3 81.2 U6,U48 qRT-PCR 4 LI M 2014 Asian serum 514 NA 54 NA HC I-IV miR-499 73.7 92.7 miR-39 qRT-PCR 3 Ulivi P 2013 Caucasian blood 86 68.0 24 65.0 HC I-II miR-328 70.0 83.0 U38B,U58A qRT-PCR 4 Bediaga 2013 Caucasian tissue 45 66.4 45 66.4 HC I-IV 8 miRNAsb 100 97.8 4miRNAsc qRT-PCR 3 Bediaga 2013 Caucasian tissue 47 67.8 47 67.8 HC I-IV 8 miRNAsb 97.5 96.3 4miRNAsc qRT-PCR 3 Bediaga 2013 Caucasian tissue 22 68.4 22 68.4 HC I-IV 8 miRNAsb 100 95.0 4miRNAsc qRT-PCR 3 Anjuman 2013 Caucasian, African sputum 39 65.6 42 62.3 BPD I miR-210, miR-31 61.5 90.5 U6 qRT-PCR 4 Tang DF 2013 Asian plasma 62 64.8 60 66.0 HC I-III miR-21, miR-145, miR-155 69.4 78.3 U6 qRT-PCR 1 Tang DF 2013 Asian plasma 34 65.2 32 66.4 HC I-III miR-21, miR-145, miR-155 76.5 81.3 U6 qRT-PCR 1 Mozzoni 2013 Caucasian plasma 54 69.1 46 64.1 BPD I-III miR-21, miR-486 87.0 86.5 miR-16 qRT-PCR 4 ZENG XL 2013 Asian PBMC 64 58.9 26 54.4 HC I-IV miR-143 75.0 92.3 U6 qRT-PCR 4 Yang XQ 2013 Asian sputum 24 60.5 24 57.8 BPD I-IV let-7a 87.5 83.3 U6 digital PCR 4 Ma J 2013 Caucasian, African plasma 36 66.7 38 64.6 HC I miR-21, miR-335 71.8 80.6 NA qRT-PCR 4 Cazzoli R 2013 Caucasian, African plasma 50 66.1 30 64.8 BPD I miR-151a, miR-30a, miR-200b, miR-629, miR-100, miR-154 96.0 60.0 let7a qRT-PCR 2 Abd-E 2013 African Serum 65 54.1 37 50.1 HC I-II miR-182 100 86.5 SNORD68 qRT-PCR 4 Sanfiorenzo C 2013 Caucasian plasma 52 65.1 10 68.9 BPD I-III miR-152, miR-145, miR-199a, miR-24, miR-20a, miR-25 90.9 83.3 miR-192, miR-16 qRT-PCR 4 Roa Wilson H 2012 Caucasian sputum 24 68.8 6 44.7 HC I-II miR-21, miR-143, miR-155, miR-210, miR-372 83.3 100 U6 qRT-PCR 4 Li GJ 2012 Asian plasma 16 NA 14 NA BPD I miR-494, miR-22, miR-200b 85.3 94.5 18S qRT-PCR 4 Ma YX 2012 Asian serum 193 NA 110 NA HC I-IV miR-125b 78.2 66.4 NA qRT-PCR 4 Hennessey P 2012 Caucasian, African serum 55 68.2 75 65.7 HC I-IV miR-15b, miR-27b 100 84.0 miR-16 qRT-PCR 4 ZengXL 2012 Asian PBMC 34 NA 26 54.4 HC I-IV miR-150 87.5 69.2 U6 qRT-PCR 4 Zhao M 2012 Asian tissue 55 NA 55 NA HC I-IV miR-29a 49.1 85.5 U6 qRT-PCR 3 Shen J 2011 Caucasian, African plasma 34 68.0 29 66.0 HC I-IV miR-21, miR-126, miR-210, miR-486 91.7 96.6 miR-16 qRT-PCR 1 Jeong H 2011 Asian blood 35 67.0 30 60.0 HC I-IV let-7a 90.3 90.3 U6 qRT-PCR 4 Wei J 2011 Asian plasma 77 59.6 36 56.4 HC I-IV miR-21 61.0 83.3 miR-16 qRT-PCR 3 Liu S 2011 Asian plasma 130 53.1 170 57.5 HC I-III miR-126 46.4 90 NA qRT-PCR 3 Yu L 2010 Caucasian, African sputum 36 68.2 36 66.7 HC I miR-486, miR-21, miR-200b, miR-375 80.6 91.7 U6 qRT-PCR 4 Yu L 2010 Caucasian, African sputum 64 67.0 58 65.0 HC I-IV miR-486, miR-21, miR-200b, miR-375 70.3 80.0 U6 qRT-PCR 3 Xing LX 2010 Caucasian, African sputum 48 67.5 48 65.9 HC I miR-205, miR-210, miR-708 73.0 96.0 U6 qRT-PCR 4 Xing LX 2010 Caucasian, African sputum 67 68.0 55 65.0 HC I-IV miR-205, miR-210, miR-708 72.0 95.0 U6 qRT-PCR 3 Keller Andreas 2009 Caucasian blood 17 64.2 19 37.9 HC I-III 24miRNAsd 92.5 98.1 NA qRT-PCR 4 amiR-451, miR-1290, miR-636, miR-30c, miR-22-3p, miR-19b, miR-486-5p, miR-20b, miR-93, miR-34b, miR-185, miR-126-5p, miR-93-3p, miR-1274a, miR-142-5p, miR-628-5p, miR-486-3p, miR-425, miR-645, miR-24; bmiR-96, miR-450a, miR-183, miR-9, miR-577, Let-7i, miR-27b and miR-34a; cmiR-26a, miR-140-5p, miR-195, miR-30b;dmiR-126, miR-423, miR-15a, let-7d, let-7i, miR-22, miR-98, miR-19a, miR-20b, miR-324, miR-574, miR-195, miR-25, let-7e, let-7c, let-7f, let-7a, let-7g, miR-140, miR-339, miR-361, miR-1283, miR-18a, miR-26b; ∗1: only smokers; 2: smokers and nonsmokers (smoking status was imbalanced between groups); 3: smokers and nonsmokers (smoking status was balanced between groups); 4: unknown smoking status. N: number; HC: healthy control; BPD: benign pulmonary disease; miR: microRNA; SEN: sensitivity; SPE: specificity; FQDs: fluorescence quantum dots; BAL: bronchoalveolar lavage.Figure 1
Flow chart of this meta-analysis of miRNAs in NSCLC detection (a) and the quality of these included articles according to the QUADAS-2 guidelines: proportion of articles with risk of bias (left) and proportion of articles with concerns regarding applicability (right) (b).
(a) (b)The quantitative real-time polymerase chain reaction (qRT-PCR) and digital polymerase chain reaction (digital PCR) were used in these studies to test the expression levels of different miRNAs, and the most common reference miRNAs were RNU6B, miR-39, and miR-16. Quality of the enrolled studies summarized in Figure1(b) was generally good.


### 3.2. Pooled Diagnostic Performance

Significant heterogeneity was obtained since I2 values for SEN and SPE were 89.05% (95% CI: 87.07-91.03%) and 79.59% (95% CI: 75.18-84.01%), respectively. Therefore, a random-effect model was conducted for this study. Results indicated the pooled SEN and SPE for these 71 studies were 85% (95% CI: 82-88%) and 88% (95% CI: 85-90%), respectively (Figure 2). The PLR and NLR were 6.9 (95% CI: 5.6-8.4) and 0.17 (95% CI: 0.14-0.21), respectively (Figure 3), the DOR was 40 (95% CI: 28-58), and the AUC was 0.93 (95% CI: 0.90-0.95) (Figure 4(a)).Figure 2
Forest plots of SEN and SPE for the NSCLC diagnosis. Both the SEN and SPE of each study were shown by squares with the 95% confidence interval shown by the error bars.
(a) (b)Figure 3
Fagan plot of PLR and NLR to evaluate the clinical utility of miRNAs for diagnosis of NSCLC.Figure 4
SROC curve of the miRNAs as diagnostic tools for NSCLC (a) and the Deeks’ test for assessing the publication bias for miRNAs in the detection of NSCLC (b).
(a) (b)


### 3.3. Publication Bias

Results of the Deeks’ funnel plot asymmetry test showed that the publication bias did not exist in these studies as the funnel plot was symmetry (Figure4(b)) andP value equaled 0.12.


### 3.4. Subgroup Analyses and Meta-Regression Analysis

Results of the meta-regression analysis demonstrated that the heterogeneity might be explained by miRNA profiling (P< 0.001) and case number (P< 0.05) for SPE and by miRNA profiling (P< 0.01) for SEN as described in Figure 5. The subgroup analyses were also conducted and the results were presented in Table 2. For the subgroups of smoking status, compared with the subgroup of only smokers (SEN: 80% (95% CI: 70-87%), SPE: 86% (95% CI: 77-91%), and AUC: 0.90 (95% CI: 0.87-0.92)), miRNAs had a higher diagnostic efficacy in the subgroups of smokers and nonsmokers (SEN: 88% (95% CI: 74-95%), SPE: 90% (95% CI: 73-97%), and AUC: 0.95 (95% CI: 0.93-0.97) for imbalanced groups and SEN: 83% (95% CI: 74-90%), SPE: 86% (95% CI: 80-90%), and AUC: 0.91 (95% CI: 0.88-0.93) for balanced groups). Subgroup analysis by specimen showed that studies with serum samples exhibited higher diagnostic accuracy with SEN: 91% (95% CI: 86-95%), SPE: 85% (95% CI: 79-89%), and AUC: 0.94 (95% CI: 0.91-0.95) than studies with plasma samples with the SEN: 82% (95% CI: 76-87%), SPE: 87% (95% CI: 83-90%), and AUC: 0.92 (95% CI: 0.89-0.94) and not blooding samples with the SEN: 80% (95% CI: 72-86%), SPE: 89% (95% CI: 85-93%), and AUC: 0.92 (95% CI: 0.89-0.94), respectively. When compared with the large sample size, miRNA might be a better diagnostic tool for small sample size with SEN: 88% (95% CI: 82-92%), SPE: 91% (95% CI: 88-94%), and AUC: 0.95 (95% CI: 0.93-0.97). In the subgroups for the ethnicity, the miRNAs obtained a better diagnostic value in the Caucasian populations with the SEN: 91% (95% CI: 86-95%), SPE: 92% (95% CI: 87-96%), and AUC: 0.97 (95% CI: 0.95-0.98), respectively, when compared with the Asian populations with the SEN: 82% (95% CI: 77-85%), SPE: 86% (95% CI: 82-88%), and AUC: 0.91 (95% CI: 0.88-0.93), respectively, and the Caucasian/African populations with SEN: 85% (95% CI: 72-93%), SPE: 87% (95% CI: 81-91%), and AUC: 0.92 (95% CI: 0.89-0.94), respectively. In the subgroups of the miRNAs profiling, the multiple miRNAs had a higher accuracy for diagnosing the NSCLC with SEN: 88% (95% CI: 85-91%), SPE: 91% (95% CI: 88-93%), and AUC: 0.96 (95% CI: 0.93-0.97), respectively, when compared with the single miRNA with the SEN: 77% (95% CI: 71-82%), SPE: 80% (95% CI: 77-84%), and AUC: 0.86 (95% CI: 0.82-0.88), respectively. miRNAs had a higher value to distinguish the NSCLC patients from healthy individuals with the SEN: 86% (95% CI: 82-89%), SPE: 88% (95% CI: 85-91%), and AUC: 0.94 (95% CI: 0.91-0.95) than controls with benign pulmonary disease with SEN: 84% (95% CI: 77-89%), SPE: 84% (95% CI: 80-88%), and AUC: 0.90 (95% CI: 0.87-0.92). Compared with other miRNAs, miR-210 and miR-21 were more often used as diagnostic tools. However, they were usually associated with other miRNAs. The sensitivity, specificity, and AUC were, respectively, 77% (95% CI: 72-81%), 93% (95% CI: 88-96%), and 0.91(95% CI: 0.88-0.93) for miR-210 with other miRNAs. The sensitivity, specificity, and AUC of miR-21 with other miRNAs were, respectively, 82% (95% CI: 77-86%), 87% (95% CI: 84-89%), and 0.91 (95% CI: 0.88-0.93).Table 2
Subgroup analyses for the selected studies.
Subgroups No SEN [95 % CI] SPE [95 % CI] PLR[95 % CI] NLR [95 % CI] DOR[95 % CI] AUC [95 % CI] MiR profiling single 26 0.77[0.71-0.82] 0.80[0.77-0.84] 3.9[3.3-4.7] 0.28[0.22-0.36] 14[10-20] 0.86[0.82-0.88] multiple 45 0.88[0.85-0.91] 0.91[0.88-0.93] 10.0[7.5-13.3] 0.13[0.10-0.17] 79[50-126] 0.96[0.93-0.97] Smoking status only smokers 4 0.80[0.70-0.87] 0.86[0.77-0.91] 5.6[3.2-9.9] 0.23[0.14-0.38] 24[9-66] 0.90[0.87-0.92] S+NS (imbalanced)∗ 6 0.88[0.74-0.95] 0.90[0.73-0.97] 9.2[3.0-28.2] 0.13[0.05-0.31] 71[14-360] 0.95[0.93-0.97] S+NS (balanced)∗ 18 0.83[0.74-0.90] 0.86[0.80-0.90] 5.9[3.9-8.8] 0.19[0.12-0.32] 30[13-69] 0.91[0.88-0.93] unknown status 43 0.86[0.82-0.89] 0.88[0.85-0.91] 7.3[5.7-9.4] 0.16[0.12-0.21] 46[30-70] 0.93[0.91-0.95] Specimen plasma 22 0.82[0.76-0.87] 0.87[0.83-0.90] 6.3[4.6-8.5] 0.20[0.15-0.28] 31[18-52] 0.92[0.89-0.94] serum 19 0.91[0.86-0.95] 0.85[0.79-0.89] 6.1[4.3-8.5] 0.10[0.06-0.17] 60[28-128] 0.94[0.91-0.95] Whole blood/blood cell 9 0.84[0.78-0.89] 0.92[0.80-0.97] 10.9[3.9-30.3] 0.17[0.11-0.26] 64[17-234] 0.92[0.89-0.94] not blood 21 0.80[0.72-0.86] 0.89[0.85-0.93] 7.5[4.9-11.7] 0.22[0.16-0.32] 34[16-71] 0.92[0.89-0.94] Ethnicity Asian 41 0.82[0.77-0.85] 0.86[0.82-0.88] 5.7[4.5-7.2] 0.21[0.17-0.27] 27[18-40] 0.91[0.88-0.93] Caucasian 18 0.91[0.86-0.95] 0.92[0.87-0.96] 12[7.0-20.4] 0.09[0.06-0.15] 127[54-302] 0.97[0.95-0.98] Caucasian/African 12 0.85[0.72-0.93] 0.87[0.81-0.91] 6.6[4.6-9.4] 0.17[0.09-0.33] 39[17-88] 0.92[0.89-0.94] Control-type BPD 13 0.84[0.77-0.89] 0.84[0.80-0.88] 5.3[4.1-6.8] 0.19[0.13-0.28] 27[16-46] 0.90[0.87-0.92] HC 50 0.86[0.82-0.89] 0.88[0.85-0.91] 7.4[5.7-9.5] 0.16[0.12-0.21] 47[30-74] 0.94[0.91-0.95] BPD, HC 8 0.81[0.67-0.90] 0.91[0.79-0.96] 8.8[3.4-22.9] 0.21[0.11-0.40] 42[9-187] 0.93[0.90-0.95] Stage I-II 18 0.84[0.78-0.89] 0.90[0.86-0.93] 8.3[5.8-11.9] 0.17[0.12-0.25] 48[27-87] 0.94[0.91-0.96] I-IV 50 0.86[0.82-0.89] 0.88[0.84-0.90] 6.5[5.4-8.7] 0.16[0.13-0.22] 42[27-66] 0.93[0.90-0.95] No. of cases small 25 0.88[0.82-0.92] 0.91[0.88-0.94] 10.0[7.1-14.2] 0.14[0.09-0.21] 74[38-143] 0.95[0.93-0.97] large 46 0.84[0.79-0.87] 0.86[0.82-0.88] 5.8[4.6-7.2] 0.19[0.15-0.24] 31[20-46] 0.91[0.89-0.94] MiR-210 12 0.77[0.72-0.81] 0.93[0.88-0.96] 11.0[6.2-19.4] 0.25[0.20-0.31] 44[22-87] 0.91[0.88-0.93] MiR-21 16 0.82[0.77-0.86] 0.87[0.84-0.89] 6.3[5.0-8.1] 0.21[0.15-0.28] 31[19-50] 0.91[0.88-0.93] No: the number of the studies; HC: healthy control; BPD: benign pulmonary disease; SEN: sensitivity; SPE: specificity; PLR: positive likelihood ratio; NLR:negative likelihood ratio; DOR: diagnostic odds ratio; AUC:area under the curve; no. of case: small (<50) and large (≥50). ∗ S: smokers; NS: nonsmokers; imbalanced: the smoking status was imbalanced between groups; balanced: the smoking status was balanced between groups.Figure 5
Forest plots for the meta-regression analysis: SEN and SPE. The factors included miRNA profiling, smoking status, specimen, ethnicity, type of control, case number, and stage.


## 3.1. Literature Search and the Studies’ Characteristics

As shown in Figure1(a), 2594 eligible articles were included, of which 2145 articles were removed as unrelated and duplicate articles. And then 370 reviews were also excluded, leaving 79 articles with full texts, and another 21 articles were then removed through carefully reading: 14 articles met the exclusion criteria and 7 articles did not have the complete data. Ultimately, 58 articles [5–7, 11–14, 18–68] with 71 studies published from 2009 to 2017 including 9,099 participants (5111 cases with NSCLC and 3988 controls from the healthy individuals and the patients with the benign pulmonary disease (BPD)) were included. The main characteristics of these 71 studies were shown in Table 1. Wang Y’s article [7], Fan LH’s article [52], Nadal E’s article [45], Tang DF’s article [32], Razzak R’s article [14], Wang W’s article [68], Yu L’s article [19], and Xing LX’s article [18] included 2 studies. Bediaga’s article [28] included 3 studies, Wang C’s article [46] included 4 studies, and the remaining articles [5, 6, 11–13, 20–27, 29–31, 33–44, 47–51, 53–67] included 1 study, respectively. Meanwhile, there were 18 studies [13, 14, 28, 31, 33, 45, 46, 48, 54, 56, 63, 65, 66] performed in Caucasian, 11 studies [18, 19, 21, 23, 27, 29, 30, 38, 44] performed in Caucasian/African, and 1 study [26] performed in African populations; the remaining studies were performed in Asian populations. A total of 50 studies detected the miRNAs in blood such as the whole blood, plasma, serum, and peripheral blood mononuclear cells (PBMC) [6, 7, 11, 20–24, 26, 29–35, 37, 39–42, 44–47, 49–54, 56–64, 66–68], while the remaining studies were detected in nonblood samples (7 tissue [5, 25, 28, 55, 68], 1 pleural effusion [43], 12 sputum [12, 14, 18, 19, 27, 36, 38, 48, 65], and 1 BAL [13]). We evaluated 45 studies for assessing the diagnostic value of multiple miRNAs and 26 studies [5, 6, 11, 20, 22, 24–26, 33–37, 39–41, 43, 47, 50, 51, 55, 57, 58, 60, 67, 68] of single miRNA.Table 1
The main features of 71 included studies in this meta-analysis.
Study ID Ethnicity Specimen Case Age Control Age Type of control Stage MiRNA profiling SEN ( % ) SPE ( % ) Reference miRNA microRNA assay Smoking status ∗ N N Zhang H 2017 Asian plasma 129 59.6 83 60.0 HC I-II miR-145, miR-20a, miR-21, miR-223 81.8 90.1 miR-16 qRT-PCR 3 Halvorsen A 2016 Caucasian serum 100 62.6 58 57.6 HC I-IV miR-429, miR-205, miR-200b, miR-203, miR-12, miR-34b 88.0 71.0 miR-220, miR-19b, U6 qRT-PCR 3 TaiMei C2016 Asian blood 110 65.0 52 65.7 HC I-III 20 miRNAsa 89.1 100 miR-159a, U6 qRT-PCR 4 Su KL 2016 Asian plasma 100 NA 100 NA HC I-III miR-195 78.0 86.0 miR-39 qRT-PCR 3 Zhu WY 2016 Asian plasma 112 58.5 40 57.9 HC I-III miR-182, miR-183, miR-210, miR-126 81.3 100 U6 qRT-PCR 2 Jiang LP 2016 Asian tissue 154 54.9 63 57.8 BPD I-IV miR-26b 79.9 79.4 U6 qRT-PCR 3 Wang Y 2016 Asian plasma 82 NA 91 NA HC I-II miR-532, miR-628, miR-425 91.5 97.8 miR-39 qRT-PCR 4 Wang Y 2016 Asian plasma 36 NA 43 NA HC I-II miR-532, miR-628, miR-425 97.2 95.3 miR-39 qRT-PCR 4 Fan LH 2016 Asian serum 94 60.5 58 58.1 HC I-III miR-15b, miR-16, miR-20a 86.2 91.4 NA qRT-PCR 4 Fan LH 2016 Asian serum 70 59.7 54 58.0 HC I-III miR-15b, miR-16, miR-20a 94.3 94.2 NA FQDs 4 Sun L 2016 Asian plasma 87 60.7 96 53.8 HC,BPD I-IV miR-30a 61.0 84.3 U6 qRT-PCR 4 Su Y 2016 Asian sputum 144 66.3 171 65.2 BPD I miR-21, miR-31, miR-210 81.5 85.9 U6 qRT-PCR 1 Gao X 2016 Asian plasma 30 61.1 30 60.2 HC I miR-324, miR-1285 93.3 90.0 miR-39 qRT-PCR 4 Wang X 2016 Asian plasma 59 55.9 59 57.6 BPD I-III miR-486 83.1 78.0 miR-16 qRT-PCR 4 Wei J 2016 Asian plasma 63 61.0 30 57.0 HC I-IV miR-21 76.2 70.0 miR-16 qRT-PCR 3 Razzak 2016 Caucasian sputum 22 68 10 58 HC,BPD III-IV miR-21, miR-210, miR-372 64 100 U6 qRT-PCR 4 Razzak 2016 Caucasian sputum 21 70 10 58 HC,BPD I-II miR-21, miR-210, miR-372 67 90 U6 qRT-PCR 4 Leidinger P 2016 Caucasian blood 74 NA 20 NA HC I-III miR-720, miR-29c, miR-199a, miR-378a,let-7f 91.0 98.0 U24,U48 qRT-PCR 4 Wang WZ 2016 Asian tissue 15 57 16 58 HC I-IV miR-182, miR-10a, miR-301b, miR-1244, miR-301a, miR-135b, miR-224, miR-21 93.3 93.8 miR-16 qRT-PCR 4 Wang WZ 2016 Asian serum 54 NA 15 NA HC I-IV miR-1244 81.5 80 miR-39 qRT-PCR 4 Kim JL O 2015 Caucasian BAL 21 70 10 59 HC,BPD I-II miR-21, miR-143, miR-155, miR-210, miR-373 85.7 100 U6 qRT-PCR 4 Wang C 2015 Asian serum 19 61.8 19 62.1 HC I-IV miR-483, miR-193a, miR-25, miR-214, miR-7 100 84 let-7d/g/i qRT-PCR 4 Li WS 2015 Asian plasma 11 59 11 55 HC I-III mir-486 90.9 81.8 miR-39, U44 qRT-PCR 4 Wang C 2015 Asian serum 63 61.9 63 59.7 HC I-IV miR-483, miR-193a, miR-25, miR-214, miR-7 89.0 68.0 let-7d/g/i qRT-PCR 4 Wang C 2015 Caucasian serum 108 67.2 56 63.7 BPD I-IV miR-483, miR-193a, miR-25, miR-214, miR-7 95.0 95.0 let-7d/g/i qRT-PCR 4 Wang C 2015 Caucasian serum 108 67.2 48 58.5 HC I-IV miR-483, miR-193a, miR-25, miR-214, miR-7 95.0 84.0 let-7d/g/i qRT-PCR 4 Nadal E 2015 Caucasian serum 70 67.5 22 67.0 HC,BPD I-III miR-141, miR-200b, miR-193b 96.0 95.0 U6 qRT-PCR 2 Nadal E 2015 Caucasian serum 84 65.5 23 60.0 HC,BPD I-III miR-141, miR-200b, miR-193b 97.0 96.0 U6 qRT-PCR 2 Guo WG 2015 Asian plasma 126 NA 50 NA HC I-IV mir-204 76.0 82.0 U6 qRT-PCR 4 Ma J 2015 Caucasian, African PBMC 84 64.1 69 62.4 BPD I-IV miR-19b, miR-29b 72.6 82.6 miR-423-3p qRT-PCR 2 Li L 2015 Asian serum 36 56.0 30 58.0 HC,BPD I-IV miR-148a, miR-148b, miR-152 72.2 90.0 U6 qRT-PCR 4 Zhang XL 2015 Asian tissue 125 61.0 125 61.0 HC I-IV miR-141 64.8 64.8 miR-191, miR-103 qRT-PCR 3 Zhao W 2015 Asian serum 80 57.6 60 55.4 HC NA miR-21 73.8 71.7 U6 qRT-PCR 4 Wang RJ 2015 Asian serum 70 64.4 70 63.7 HC NA miR-145 92.8 61.4 miR-39 qRT-PCR 3 Yang JS 2015 Asian serum 152 NA 300 NA HC I-IV miR-152, miR-148a, miR-148b, miR-21 96.0 91.0 U6 qRT-PCR 3 Xing LX 2015 Caucasian sputum 67 66.4 69 64.9 BPD I-II miR-21, miR-31, miR-210 82.1 88.4 U6, miR-16 qRT-PCR 4 Liu CM 2015 Asian Pleural effusion 61 53.8 70 54.4 BPD NA miR-192 61.3 79.5 U6 qRT-PCR 2 Dou HL 2015 Asian plasma 120 63.2 360 NA HC I-IV miR-152 86.0 81.3 U6 digital PCR 4 Yang YL 2015 Asian PBMC 74 62.5 52 61.8 HC I-IV miR-10b 86.5 76.9 miR-16 qRT-PCR 3 Li N 2014 Caucasian, African sputum 35 68.9 40 65.7 HC I miR-31, miR-210 65.7 85.0 NA qRT-PCR 4 Zhu W 2014 Asian serum 70 59.0 48 NA HC I-IV miR-429 54.3 81.2 U6,U48 qRT-PCR 4 LI M 2014 Asian serum 514 NA 54 NA HC I-IV miR-499 73.7 92.7 miR-39 qRT-PCR 3 Ulivi P 2013 Caucasian blood 86 68.0 24 65.0 HC I-II miR-328 70.0 83.0 U38B,U58A qRT-PCR 4 Bediaga 2013 Caucasian tissue 45 66.4 45 66.4 HC I-IV 8 miRNAsb 100 97.8 4miRNAsc qRT-PCR 3 Bediaga 2013 Caucasian tissue 47 67.8 47 67.8 HC I-IV 8 miRNAsb 97.5 96.3 4miRNAsc qRT-PCR 3 Bediaga 2013 Caucasian tissue 22 68.4 22 68.4 HC I-IV 8 miRNAsb 100 95.0 4miRNAsc qRT-PCR 3 Anjuman 2013 Caucasian, African sputum 39 65.6 42 62.3 BPD I miR-210, miR-31 61.5 90.5 U6 qRT-PCR 4 Tang DF 2013 Asian plasma 62 64.8 60 66.0 HC I-III miR-21, miR-145, miR-155 69.4 78.3 U6 qRT-PCR 1 Tang DF 2013 Asian plasma 34 65.2 32 66.4 HC I-III miR-21, miR-145, miR-155 76.5 81.3 U6 qRT-PCR 1 Mozzoni 2013 Caucasian plasma 54 69.1 46 64.1 BPD I-III miR-21, miR-486 87.0 86.5 miR-16 qRT-PCR 4 ZENG XL 2013 Asian PBMC 64 58.9 26 54.4 HC I-IV miR-143 75.0 92.3 U6 qRT-PCR 4 Yang XQ 2013 Asian sputum 24 60.5 24 57.8 BPD I-IV let-7a 87.5 83.3 U6 digital PCR 4 Ma J 2013 Caucasian, African plasma 36 66.7 38 64.6 HC I miR-21, miR-335 71.8 80.6 NA qRT-PCR 4 Cazzoli R 2013 Caucasian, African plasma 50 66.1 30 64.8 BPD I miR-151a, miR-30a, miR-200b, miR-629, miR-100, miR-154 96.0 60.0 let7a qRT-PCR 2 Abd-E 2013 African Serum 65 54.1 37 50.1 HC I-II miR-182 100 86.5 SNORD68 qRT-PCR 4 Sanfiorenzo C 2013 Caucasian plasma 52 65.1 10 68.9 BPD I-III miR-152, miR-145, miR-199a, miR-24, miR-20a, miR-25 90.9 83.3 miR-192, miR-16 qRT-PCR 4 Roa Wilson H 2012 Caucasian sputum 24 68.8 6 44.7 HC I-II miR-21, miR-143, miR-155, miR-210, miR-372 83.3 100 U6 qRT-PCR 4 Li GJ 2012 Asian plasma 16 NA 14 NA BPD I miR-494, miR-22, miR-200b 85.3 94.5 18S qRT-PCR 4 Ma YX 2012 Asian serum 193 NA 110 NA HC I-IV miR-125b 78.2 66.4 NA qRT-PCR 4 Hennessey P 2012 Caucasian, African serum 55 68.2 75 65.7 HC I-IV miR-15b, miR-27b 100 84.0 miR-16 qRT-PCR 4 ZengXL 2012 Asian PBMC 34 NA 26 54.4 HC I-IV miR-150 87.5 69.2 U6 qRT-PCR 4 Zhao M 2012 Asian tissue 55 NA 55 NA HC I-IV miR-29a 49.1 85.5 U6 qRT-PCR 3 Shen J 2011 Caucasian, African plasma 34 68.0 29 66.0 HC I-IV miR-21, miR-126, miR-210, miR-486 91.7 96.6 miR-16 qRT-PCR 1 Jeong H 2011 Asian blood 35 67.0 30 60.0 HC I-IV let-7a 90.3 90.3 U6 qRT-PCR 4 Wei J 2011 Asian plasma 77 59.6 36 56.4 HC I-IV miR-21 61.0 83.3 miR-16 qRT-PCR 3 Liu S 2011 Asian plasma 130 53.1 170 57.5 HC I-III miR-126 46.4 90 NA qRT-PCR 3 Yu L 2010 Caucasian, African sputum 36 68.2 36 66.7 HC I miR-486, miR-21, miR-200b, miR-375 80.6 91.7 U6 qRT-PCR 4 Yu L 2010 Caucasian, African sputum 64 67.0 58 65.0 HC I-IV miR-486, miR-21, miR-200b, miR-375 70.3 80.0 U6 qRT-PCR 3 Xing LX 2010 Caucasian, African sputum 48 67.5 48 65.9 HC I miR-205, miR-210, miR-708 73.0 96.0 U6 qRT-PCR 4 Xing LX 2010 Caucasian, African sputum 67 68.0 55 65.0 HC I-IV miR-205, miR-210, miR-708 72.0 95.0 U6 qRT-PCR 3 Keller Andreas 2009 Caucasian blood 17 64.2 19 37.9 HC I-III 24miRNAsd 92.5 98.1 NA qRT-PCR 4 amiR-451, miR-1290, miR-636, miR-30c, miR-22-3p, miR-19b, miR-486-5p, miR-20b, miR-93, miR-34b, miR-185, miR-126-5p, miR-93-3p, miR-1274a, miR-142-5p, miR-628-5p, miR-486-3p, miR-425, miR-645, miR-24; bmiR-96, miR-450a, miR-183, miR-9, miR-577, Let-7i, miR-27b and miR-34a; cmiR-26a, miR-140-5p, miR-195, miR-30b;dmiR-126, miR-423, miR-15a, let-7d, let-7i, miR-22, miR-98, miR-19a, miR-20b, miR-324, miR-574, miR-195, miR-25, let-7e, let-7c, let-7f, let-7a, let-7g, miR-140, miR-339, miR-361, miR-1283, miR-18a, miR-26b; ∗1: only smokers; 2: smokers and nonsmokers (smoking status was imbalanced between groups); 3: smokers and nonsmokers (smoking status was balanced between groups); 4: unknown smoking status. N: number; HC: healthy control; BPD: benign pulmonary disease; miR: microRNA; SEN: sensitivity; SPE: specificity; FQDs: fluorescence quantum dots; BAL: bronchoalveolar lavage.Figure 1
Flow chart of this meta-analysis of miRNAs in NSCLC detection (a) and the quality of these included articles according to the QUADAS-2 guidelines: proportion of articles with risk of bias (left) and proportion of articles with concerns regarding applicability (right) (b).
(a) (b)The quantitative real-time polymerase chain reaction (qRT-PCR) and digital polymerase chain reaction (digital PCR) were used in these studies to test the expression levels of different miRNAs, and the most common reference miRNAs were RNU6B, miR-39, and miR-16. Quality of the enrolled studies summarized in Figure1(b) was generally good.


## 3.2. Pooled Diagnostic Performance

Significant heterogeneity was obtained since I2 values for SEN and SPE were 89.05% (95% CI: 87.07-91.03%) and 79.59% (95% CI: 75.18-84.01%), respectively. Therefore, a random-effect model was conducted for this study. Results indicated the pooled SEN and SPE for these 71 studies were 85% (95% CI: 82-88%) and 88% (95% CI: 85-90%), respectively (Figure 2). The PLR and NLR were 6.9 (95% CI: 5.6-8.4) and 0.17 (95% CI: 0.14-0.21), respectively (Figure 3), the DOR was 40 (95% CI: 28-58), and the AUC was 0.93 (95% CI: 0.90-0.95) (Figure 4(a)).Figure 2
Forest plots of SEN and SPE for the NSCLC diagnosis. Both the SEN and SPE of each study were shown by squares with the 95% confidence interval shown by the error bars.
(a) (b)Figure 3
Fagan plot of PLR and NLR to evaluate the clinical utility of miRNAs for diagnosis of NSCLC.Figure 4
SROC curve of the miRNAs as diagnostic tools for NSCLC (a) and the Deeks’ test for assessing the publication bias for miRNAs in the detection of NSCLC (b).
(a) (b)


## 3.3. Publication Bias

Results of the Deeks’ funnel plot asymmetry test showed that the publication bias did not exist in these studies as the funnel plot was symmetry (Figure4(b)) andP value equaled 0.12.


## 3.4. Subgroup Analyses and Meta-Regression Analysis

Results of the meta-regression analysis demonstrated that the heterogeneity might be explained by miRNA profiling (P< 0.001) and case number (P< 0.05) for SPE and by miRNA profiling (P< 0.01) for SEN as described in Figure 5. The subgroup analyses were also conducted and the results were presented in Table 2. For the subgroups of smoking status, compared with the subgroup of only smokers (SEN: 80% (95% CI: 70-87%), SPE: 86% (95% CI: 77-91%), and AUC: 0.90 (95% CI: 0.87-0.92)), miRNAs had a higher diagnostic efficacy in the subgroups of smokers and nonsmokers (SEN: 88% (95% CI: 74-95%), SPE: 90% (95% CI: 73-97%), and AUC: 0.95 (95% CI: 0.93-0.97) for imbalanced groups and SEN: 83% (95% CI: 74-90%), SPE: 86% (95% CI: 80-90%), and AUC: 0.91 (95% CI: 0.88-0.93) for balanced groups). Subgroup analysis by specimen showed that studies with serum samples exhibited higher diagnostic accuracy with SEN: 91% (95% CI: 86-95%), SPE: 85% (95% CI: 79-89%), and AUC: 0.94 (95% CI: 0.91-0.95) than studies with plasma samples with the SEN: 82% (95% CI: 76-87%), SPE: 87% (95% CI: 83-90%), and AUC: 0.92 (95% CI: 0.89-0.94) and not blooding samples with the SEN: 80% (95% CI: 72-86%), SPE: 89% (95% CI: 85-93%), and AUC: 0.92 (95% CI: 0.89-0.94), respectively. When compared with the large sample size, miRNA might be a better diagnostic tool for small sample size with SEN: 88% (95% CI: 82-92%), SPE: 91% (95% CI: 88-94%), and AUC: 0.95 (95% CI: 0.93-0.97). In the subgroups for the ethnicity, the miRNAs obtained a better diagnostic value in the Caucasian populations with the SEN: 91% (95% CI: 86-95%), SPE: 92% (95% CI: 87-96%), and AUC: 0.97 (95% CI: 0.95-0.98), respectively, when compared with the Asian populations with the SEN: 82% (95% CI: 77-85%), SPE: 86% (95% CI: 82-88%), and AUC: 0.91 (95% CI: 0.88-0.93), respectively, and the Caucasian/African populations with SEN: 85% (95% CI: 72-93%), SPE: 87% (95% CI: 81-91%), and AUC: 0.92 (95% CI: 0.89-0.94), respectively. In the subgroups of the miRNAs profiling, the multiple miRNAs had a higher accuracy for diagnosing the NSCLC with SEN: 88% (95% CI: 85-91%), SPE: 91% (95% CI: 88-93%), and AUC: 0.96 (95% CI: 0.93-0.97), respectively, when compared with the single miRNA with the SEN: 77% (95% CI: 71-82%), SPE: 80% (95% CI: 77-84%), and AUC: 0.86 (95% CI: 0.82-0.88), respectively. miRNAs had a higher value to distinguish the NSCLC patients from healthy individuals with the SEN: 86% (95% CI: 82-89%), SPE: 88% (95% CI: 85-91%), and AUC: 0.94 (95% CI: 0.91-0.95) than controls with benign pulmonary disease with SEN: 84% (95% CI: 77-89%), SPE: 84% (95% CI: 80-88%), and AUC: 0.90 (95% CI: 0.87-0.92). Compared with other miRNAs, miR-210 and miR-21 were more often used as diagnostic tools. However, they were usually associated with other miRNAs. The sensitivity, specificity, and AUC were, respectively, 77% (95% CI: 72-81%), 93% (95% CI: 88-96%), and 0.91(95% CI: 0.88-0.93) for miR-210 with other miRNAs. The sensitivity, specificity, and AUC of miR-21 with other miRNAs were, respectively, 82% (95% CI: 77-86%), 87% (95% CI: 84-89%), and 0.91 (95% CI: 0.88-0.93).Table 2
Subgroup analyses for the selected studies.
Subgroups No SEN [95 % CI] SPE [95 % CI] PLR[95 % CI] NLR [95 % CI] DOR[95 % CI] AUC [95 % CI] MiR profiling single 26 0.77[0.71-0.82] 0.80[0.77-0.84] 3.9[3.3-4.7] 0.28[0.22-0.36] 14[10-20] 0.86[0.82-0.88] multiple 45 0.88[0.85-0.91] 0.91[0.88-0.93] 10.0[7.5-13.3] 0.13[0.10-0.17] 79[50-126] 0.96[0.93-0.97] Smoking status only smokers 4 0.80[0.70-0.87] 0.86[0.77-0.91] 5.6[3.2-9.9] 0.23[0.14-0.38] 24[9-66] 0.90[0.87-0.92] S+NS (imbalanced)∗ 6 0.88[0.74-0.95] 0.90[0.73-0.97] 9.2[3.0-28.2] 0.13[0.05-0.31] 71[14-360] 0.95[0.93-0.97] S+NS (balanced)∗ 18 0.83[0.74-0.90] 0.86[0.80-0.90] 5.9[3.9-8.8] 0.19[0.12-0.32] 30[13-69] 0.91[0.88-0.93] unknown status 43 0.86[0.82-0.89] 0.88[0.85-0.91] 7.3[5.7-9.4] 0.16[0.12-0.21] 46[30-70] 0.93[0.91-0.95] Specimen plasma 22 0.82[0.76-0.87] 0.87[0.83-0.90] 6.3[4.6-8.5] 0.20[0.15-0.28] 31[18-52] 0.92[0.89-0.94] serum 19 0.91[0.86-0.95] 0.85[0.79-0.89] 6.1[4.3-8.5] 0.10[0.06-0.17] 60[28-128] 0.94[0.91-0.95] Whole blood/blood cell 9 0.84[0.78-0.89] 0.92[0.80-0.97] 10.9[3.9-30.3] 0.17[0.11-0.26] 64[17-234] 0.92[0.89-0.94] not blood 21 0.80[0.72-0.86] 0.89[0.85-0.93] 7.5[4.9-11.7] 0.22[0.16-0.32] 34[16-71] 0.92[0.89-0.94] Ethnicity Asian 41 0.82[0.77-0.85] 0.86[0.82-0.88] 5.7[4.5-7.2] 0.21[0.17-0.27] 27[18-40] 0.91[0.88-0.93] Caucasian 18 0.91[0.86-0.95] 0.92[0.87-0.96] 12[7.0-20.4] 0.09[0.06-0.15] 127[54-302] 0.97[0.95-0.98] Caucasian/African 12 0.85[0.72-0.93] 0.87[0.81-0.91] 6.6[4.6-9.4] 0.17[0.09-0.33] 39[17-88] 0.92[0.89-0.94] Control-type BPD 13 0.84[0.77-0.89] 0.84[0.80-0.88] 5.3[4.1-6.8] 0.19[0.13-0.28] 27[16-46] 0.90[0.87-0.92] HC 50 0.86[0.82-0.89] 0.88[0.85-0.91] 7.4[5.7-9.5] 0.16[0.12-0.21] 47[30-74] 0.94[0.91-0.95] BPD, HC 8 0.81[0.67-0.90] 0.91[0.79-0.96] 8.8[3.4-22.9] 0.21[0.11-0.40] 42[9-187] 0.93[0.90-0.95] Stage I-II 18 0.84[0.78-0.89] 0.90[0.86-0.93] 8.3[5.8-11.9] 0.17[0.12-0.25] 48[27-87] 0.94[0.91-0.96] I-IV 50 0.86[0.82-0.89] 0.88[0.84-0.90] 6.5[5.4-8.7] 0.16[0.13-0.22] 42[27-66] 0.93[0.90-0.95] No. of cases small 25 0.88[0.82-0.92] 0.91[0.88-0.94] 10.0[7.1-14.2] 0.14[0.09-0.21] 74[38-143] 0.95[0.93-0.97] large 46 0.84[0.79-0.87] 0.86[0.82-0.88] 5.8[4.6-7.2] 0.19[0.15-0.24] 31[20-46] 0.91[0.89-0.94] MiR-210 12 0.77[0.72-0.81] 0.93[0.88-0.96] 11.0[6.2-19.4] 0.25[0.20-0.31] 44[22-87] 0.91[0.88-0.93] MiR-21 16 0.82[0.77-0.86] 0.87[0.84-0.89] 6.3[5.0-8.1] 0.21[0.15-0.28] 31[19-50] 0.91[0.88-0.93] No: the number of the studies; HC: healthy control; BPD: benign pulmonary disease; SEN: sensitivity; SPE: specificity; PLR: positive likelihood ratio; NLR:negative likelihood ratio; DOR: diagnostic odds ratio; AUC:area under the curve; no. of case: small (<50) and large (≥50). ∗ S: smokers; NS: nonsmokers; imbalanced: the smoking status was imbalanced between groups; balanced: the smoking status was balanced between groups.Figure 5
Forest plots for the meta-regression analysis: SEN and SPE. The factors included miRNA profiling, smoking status, specimen, ethnicity, type of control, case number, and stage.


## 4. Discussion

Due to the high mortality rate and low survival rate of NSCLC, there is an urgent need for the accurate detection method for the early detection of NSCLC especially for the nonsmoking NSCLC patients. Although miRNAs may have a high diagnostic accuracy according to the previous articles, the clinical utility of the miRNA for diagnosing NSCLC remains controversial. Compared with the previous meta-analyses [69–71], there were more studies and participants included in this meta-analysis. Our analysis showed the pooled SEN was 85% (95% CI: 82-88%), the pooled SPE was 88% (95% CI: 85-90%), and the AUC was 0.93 (95% CI: 0.90-0.95), suggesting that miRNAs had pretty high diagnostic value for NSCLC. Our results also showed that the pooled DOR was 40 (95% CI: 28-58), indicating that for an individual proved positive by miRNAs the chance of having NSCLC is 40 times higher than the negative ones. For the subgroup analyses, higher accuracy was observed in the multiple miRNA profiling when compared with the single miRNA, which was consistent with the previous conclusions [69–71]. MiRNAs might have a higher diagnostic efficacy for the nonsmoking NSCLC patients compared with the smoking ones. Meanwhile, differences were also observed among the Caucasian, Asian, and Caucasian/African populations. This result could be supported by the Wang H’s article [71]. Furthermore, miRNAs from serum samples exhibited higher diagnostic value than miRNAs from other specimen. These results meant that combinations of various miRNAs may be better diagnostic tools than the single miRNA, and miRNA isolated from serum could have a higher diagnostic value for the Caucasian populations when compared with the Asian and Caucasian/African populations. Among the different multiple miRNAs, miR-210 and miR-21 associated with other miRNAs could be used for the detection of NSCLC. However, there were still some limitations that could not be neglected in this meta-analysis such as the heterogeneity among these 71 studies, the different methods in miRNA profiling, the possibility that some articles are missed or not published online.


## 5. Conclusions

Our meta-analysis showed the practicability of miRNAs for diagnosing NSCLC and demonstrated that the multiple miRNAs might have a relatively high diagnostic value for NSCLC compared with the single miRNA diagnosis. miR-210 and miR-21 could be used as effective tools through combining with other miRNAs. In addition, miRNAs, especially isolated from serum, had a better diagnostic accuracy in Caucasian populations than the Asian populations as well as the Caucasian/African populations. When compared with the smoking NSCLC patients, miRNAs might have a higher diagnostic efficacy for the nonsmoking ones. However, studies on the large samples are still demanded to verify our results.



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*Source: 5930951-2018-06-28.xml*