BioMed Research International
Volume 2018 (2018), Article ID 5930951, 14 pages
https://doi.org/10.1155/2018/5930951

Review Article

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

Min Jiang,1,2 Xuelian Li,² Xiaowei Quan,² Xiaoying Li,² and Baosen Zhou1,2

¹Department of Clinical Epidemiology and Center of Evidence-Based Medicine, The First Affiliated Hospital, China Medical University, Shenyang, Liaoning 110001, China
²Department of Epidemiology, School of Public Health, China Medical University, Shenyang 110122, China

Correspondence should be addressed to Baosen Zhou; bszhou@cmu.edu.cn

Received 5 February 2018; Revised 30 April 2018; Accepted 7 June 2018; Published 28 June 2018

Academic Editor: Maria L. Tornesello

Copyright © 2018 Min Jiang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.

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]. The P 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 the P value less than 0.01 considered significant [17].

3. Results

3.1. Literature Search and the Studies’ Characteristics

As shown in Figure 1(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 miRNAs
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 miRNAs
100
97.8
4miRNAs
qRT-PCR
3

Bediaga 2013
Caucasian
tissue
47
67.8
47
67.8
HC
I-IV
8 miRNAs
97.5
96.3
4miRNAs
qRT-PCR
3

Bediaga 2013
Caucasian
tissue
22
68.4
22
68.4
HC
I-IV
8 miRNAs
100
95.0
4miRNAs
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
24miRNAs
92.5
98.1
NA
qRT-PCR
4

miR-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; miR-96, miR-450a, miR-183, miR-9, miR-577, Let-7i, miR-27b and miR-34a; miR-26a, miR-140-5p, miR-195, miR-30b;miR-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.

(a)

(b)

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).

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 Figure 1(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)).

(a)

(b)

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.

Figure 3: Fagan plot of PLR and NLR to evaluate the clinical utility of miRNAs for diagnosis of NSCLC.

(a)

(b)

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).

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 (Figure 4(b)) and P 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 [95CI]
SPE [95CI]
PLR[95CI]
NLR [95CI]
DOR[95CI]
AUC [95CI]

MiR profiling

    single
26
0.770.71-0.82]
0.800.77-0.84]
3.93.3-4.7]
0.280.22-0.36]
1410-20]
0.860.82-0.88]

    multiple
45
0.880.85-0.91]
0.910.88-0.93]
10.07.5-13.3]
0.130.10-0.17]
7950-126]
0.960.93-0.97]

Smoking status

    only smokers
4
0.800.70-0.87]
0.860.77-0.91]
5.63.2-9.9]
0.230.14-0.38]
249-66]
0.900.87-0.92]

    S+NS (imbalanced)
6
0.880.74-0.95]
0.900.73-0.97]
9.23.0-28.2]
0.130.05-0.31]
7114-360]
0.950.93-0.97]

    S+NS (balanced)  
18
0.830.74-0.90]
0.860.80-0.90]
5.93.9-8.8]
0.190.12-0.32]
3013-69]
0.910.88-0.93]

    unknown status
43
0.860.82-0.89]
0.880.85-0.91]
7.35.7-9.4]
0.160.12-0.21]
4630-70]
0.930.91-0.95]

Specimen

    plasma
22
0.820.76-0.87]
0.87[0.83-0.90]
6.34.6-8.5]
0.200.15-0.28]
3118-52]
0.920.89-0.94]

    serum
19
0.910.86-0.95]
0.850.79-0.89]
6.14.3-8.5]
0.100.06-0.17]
6028-128]
0.940.91-0.95]

    Whole blood/blood cell
9
0.840.78-0.89]
0.920.80-0.97]
10.93.9-30.3]
0.170.11-0.26]
6417-234]
0.920.89-0.94]

    not blood
21
0.800.72-0.86]
0.890.85-0.93]
7.54.9-11.7]
0.220.16-0.32]
3416-71]
0.920.89-0.94]

Ethnicity

    Asian
41
0.820.77-0.85]
0.860.82-0.88]
5.74.5-7.2]
0.210.17-0.27]
2718-40]
0.910.88-0.93]

    Caucasian
18
0.910.86-0.95]
0.920.87-0.96]
127.0-20.4]
0.090.06-0.15]
12754-302]
0.970.95-0.98]

    Caucasian/African
12
0.850.72-0.93]
0.870.81-0.91]
6.64.6-9.4]
0.170.09-0.33]
3917-88]
0.920.89-0.94]

Control-type

    BPD
13
0.840.77-0.89]
0.840.80-0.88]
5.34.1-6.8]
0.190.13-0.28]
2716-46]
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]
4730-74]
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]
429-187]
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]
4827-87]
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]
4227-66]
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]
7438-143]
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]
3120-46]
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]
4422-87]
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]
3119-50]
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.

Abbreviations

miRNA: | MicroRNA
NSCLC: | Non-small cell lung cancer
SEN: | Sensitivity
SPE: | Specificity
SROC: | Summary receiver operating characteristic
AUC: | The area under the SROC curve
mRNA: | Messenger RNA
3′-UTR: | 3′-untranslated region
BPD: | Benign pulmonary disease
HC: | Healthy Control
CNKI: | Chinese national knowledge infrastructure
PLR: | Positive likelihood ratio
NLR: | Negative likelihood ratio
DOR: | Diagnostic odds ratio
TP: | True positive
FP: | False positive
FN: | False negative
TN: | True negative
qRT-PCR: | Quantitative real-time polymerase chain reaction
PBMC: | Peripheral blood mononuclear cells
PRISMA: | Preferred Reporting Items for Systematic Reviews and Meta-Analyses
BAL: | Bronchoalveolar lavage.

Conflicts of Interest

The authors declare that there are no potential conflicts of interest.

Authors’ Contributions

Baosen Zhou, Xuelian Li, and Min Jiang conceived and designed this study. Min Jiang and Xiaoying Li searched the literature and analyzed the data. Xiaowei Quan contributed to the analysis tools and the statistical analysis. Min Jiang and Baosen Zhou wrote and revised the paper.

Acknowledgments

This study was supported by the National Natural Science Foundation of China (no. 81502878) and the Doctoral Research Project of Liaoning Province (no. 201601117).

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