Abstract
Yue Zhang1, Dong-Hui Guan2, Rong-Xiu Bi2, Jin Xie2, Chuan-Hua Yang3 and Yue-Hua Jiang4
1First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan 250355, Shandong, People’s Republic of China
2Department of Orthopedics, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan 250011, Shandong, People’s Republic of China
3Department of Cardiology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan 250011, Shandong, People’s Republic of China
4Central Laboratory, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan 250011, Shandong, People’s Republic of China
Correspondence to:
Yue-Hua Jiang, email: [email protected]
Keywords: microRNA, gastric cancer, prognosis, meta-analysis
Received: March 31, 2017 Accepted: May 08, 2017 Published: June 21, 2017
ABSTRACT
Background: Previous articles have reported that expression levels of microRNAs (miRNAs) are associated with survival time of patients with gastric cancer (GC). A systematic review and meta-analysis was performed to study the outcome of it.
Design: Meta-analysis.
Methods: English studies estimating expression levels of miRNAs with any of survival curves in GC were identified up till March 19, 2017 through performing online searches in PubMed, EMBASE, Web of Science and Cochrane Database of Systematic Reviews by two authors independently. The pooled hazard ratios (HR) with 95% confidence intervals (CI) were used to estimate the correlation between miRNA expression and overall survival (OS).
Results: Sixty-nine relevant articles about 26 miRNAs with 6148 patients were ultimately included. GC patients with high expression of miR-20b (HR=2.38, 95%CI=1.16-4.87), 21 (HR=1.77, 95%CI=1.01-3.08), 106b (HR=1.84, 95%CI=1.15-2.94), 196a (HR=2.66, 95%CI=1.94-3.63), 196b (HR=1.67, 95%CI=1.38-2.02), 214 (HR=1.84, 95%CI=1.27-2.67) or low expression of miR-125a (HR=2.06, 95%CI=1.26-3.37), 137 (HR=3.21, 95%CI=1.68-6.13), 141 (HR=2.47, 95%CI=1.34-4.56), 145 (HR=1.62, 95%CI=1.07-2.46), 146a (HR=2.60, 95%CI=1.63-4.13), 206 (HR=2.85, 95%CI=1.73-4.70), 218 (HR=2.61, 95%CI=1.74-3.92), 451 (HR=1.73, 95%CI=1.19-2.52), 486-5p (HR=2.45, 95%CI=1.65-3.65), 506 (HR=2.07, 95%CI=1.33-3.23) have significantly poor OS (P<0.05).
Conclusions: In summary, miR-20b, 21, 106b, 125a, 137, 141, 145, 146a, 196a, 196b, 206, 214, 218, 451, 486-5p and 506 demonstrate significantly prognostic value. Among them, miR-20b, 125a, 137, 141, 146a, 196a, 206, 218, 486-5p and 506 are strong biomarkers of prognosis in GC.
INTRODUCTION
Great quantities of previous articles have reported that expression levels of microRNAs (miRNAs) are associated with survival time of gastric cancer (GC) patients [1–167]. GC is still the fourth most common cancer all over the world and the second most universal cause of cancer death globally, although there has been a constant descent in morbidity and mortality in the past few decades [168, 169]. The early clinical inspection of GC was under 15%, and cases of advanced GC accounted for 85% [170]. At present, the primary treatment choices are surgical intervention, chemotherapy, immunogene therapy, and target therapy. The clinical result of GC mainly depends on the stage of tumor. Unfortunately, GC patients’ median survival time is no more than 6-9 months [171]. It is unlimited proliferation of cancer cells and ability of intense invasive and metastasis that mainly causes high malignancy degree and poorer survival time. As a result, a novel diagnostic means and improved prognosis of GC might be created through identification of molecular aberrations, which can predict cancer progression and survival rate.
During the past decade, the associations between non-coding RNAs (ncRNAs) and GC have been widely researched. Generally speaking, ncRNAs have been classified as small ncRNAs, consisting of miRNAs and long non-coding RNAs (lncRNAs).
MiRNAs, a novel class of small (20-24 nucleotides [nt]) non-coding regulatory RNAs, play a significant role in multiple biological processes, such as cell division, differentiation, senescence and apoptosis [172, 173]. An increasing number of evidence shows that various miRNAs are unconventionally expressed in diverse types of human cancers, and a few miRNAs have been shown to be related with tumor formation, development, progression, and response to treatment by miRNA expression profiling [174].
Moreover, a series of studies have already demonstrated that lncRNAs also play crucial roles in GC progression. A previous investigation reported that, compared with non-tumor tissues, H19 was one of the most elevated lncRNAs with a ~8.91-fold change in human primary GC [175]. In addition, Li et al. [176] recognized certain potential lncRNAs that abnormally expressed between GC and normal tissues by screening a cohort of 74 GC patients as well, among which, H19 was chosen as a result of a significant overexpression. Furthermore, expression levels of the lncRNAs H19, ANRIL, GHET1, HOTAIR, GAS5, LET, GAPLINC and FENDRR are also significantly associated with the 5-year survival rate of GC patients [176–183].
In GC research area, quite a number of investigations have demonstrated that miRNAs are associated with survival time of patients [1–167]. However, the number of patients during the articles mentioned above is generally not big enough. Therefore, a systematic review and meta-analysis was performed for the sake of better understanding accurate prognostic value between expression levels of numerous miRNAs and HR of GC patients.
RESULTS
Study selection
A flow diagram with details of the study selection process was presented in Figure 1.
Figure 1: Flow diagram of literature search and selection.
Study frequency
Frequency of studies estimating prognostic value of miRNAs in GC were shown in Table 1 (highlighted studies were included in the present meta-analysis), including miRNA name, number of studies estimating prognostic value, and reference.
Table 1: Frequency of studies estimating prognostic value of miRNAs in gastric cancer
miRNA | N | R | miRNA | N | R | miRNA | N | R | miRNA | N | R | miRNA | N | R | miRNA | N | R | miRNA | N | R |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
let-7g | 1 | 1 | 27b | 3 | 32-34 | 126 | 2 | 54,55 | 148a | 1 | 78 | 200a | 1 | 3 | 328 | 1 | 126 | 485-5p | 1 | 146 |
10b | 1 | 2 | 29a | 2 | 35,36 | 128 | 2 | 32,34 | 150-5p | 1 | 77 | 200b | 2 | 96,97 | 335 | 3 | 41,127,128 | 486-5p | 3 | 56,147,148 |
15a | 1 | 3 | 29b | 1 | 36 | 129-5p | 1 | 56 | 150 | 2 | 12,79 | 200c | 4 | 66,96,98,99 | 337-3p | 1 | 129 | 493 | 1 | 149 |
16 | 2 | 3,4 | 29c | 1 | 36 | 130a | 1 | 57 | 153 | 1 | 80 | 203 | 2 | 100,101 | 340 | 1 | 130 | 494 | 1 | 150 |
17-5p | 2 | 5,6 | 29 | 1 | 19 | 132 | 1 | 58 | 181a-5p | 1 | 81 | 204 | 2 | 102,103 | 342-3p | 1 | 79 | 500 | 1 | 151 |
18a | 2 | 7,8 | 31 | 1 | 37 | 133a-3p | 1 | 56 | 181b | 1 | 16 | 206 | 3 | 104-106 | 361-5p | 1 | 131 | 501-5p | 1 | 152 |
19a | 1 | 9 | 34a | 5 | 3,38-41 | 133 | 2 | 59,60 | 181c | 1 | 82 | 210 | 1 | 107 | 363 | 1 | 132 | 503 | 1 | 153 |
19b | 1 | 10 | 92a | 2 | 11,42 | 135a | 1 | 61 | 182-5p | 1 | 56 | 211 | 1 | 108 | 375 | 1 | 68 | 506 | 3 | 154-156 |
20a | 3 | 3,5,11 | 93 | 2 | 43,44 | 135b-5p | 1 | 56 | 183-5p | 1 | 56 | 212 | 1 | 109 | 377 | 1 | 133 | 508-5p | 2 | 33,157 |
20b | 3 | 3,12,13 | 100 | 1 | 34 | 135b | 1 | 61 | 183 | 2 | 83,84 | 214 | 4 | 1,34,110,111 | 378 | 1 | 134 | 520c | 1 | 158 |
21-5p | 1 | 14 | 101 | 2 | 32,34 | 137 | 3 | 62-64 | 185 | 2 | 3,85 | 215 | 1 | 87 | 381 | 1 | 135 | 520d-3p | 1 | 159 |
21 | 7 | 3,6,15-19 | 103 | 1 | 3 | 141 | 3 | 65-67 | 187 | 1 | 86 | 217 | 2 | 112,113 | 421 | 2 | 136,137 | 558 | 1 | 160 |
22 | 2 | 20,21 | 106a | 2 | 3,6 | 142-5p | 1 | 68 | 192 | 3 | 48,79,87 | 218 | 3 | 114-116 | 425 | 1 | 3 | 590-5p | 1 | 161 |
23b-3p | 1 | 22 | 106b | 3 | 3,6,45 | 143 | 3 | 3,69,70 | 193b | 1 | 88 | 221 | 2 | 117,118 | 429 | 1 | 138 | 630 | 1 | 162 |
23b | 1 | 23 | 107 | 3 | 3,46,47 | 144-5p | 1 | 56 | 194 | 1 | 89 | 222 | 2 | 118,119 | 433 | 1 | 1 | 873 | 1 | 163 |
24 | 1 | 24 | 122 | 1 | 48 | 144 | 1 | 71 | 196a | 4 | 88,90-92 | 223 | 2 | 120,121 | 448 | 1 | 139 | 939 | 1 | 164 |
25 | 2 | 25,26 | 125a-3p | 1 | 49 | 145-5p | 2 | 56,72 | 196b-5p | 1 | 56 | 224 | 2 | 79,122 | 449c | 1 | 140 | 940 | 1 | 165 |
26a | 2 | 27,28 | 125a-5p | 1 | 50 | 145 | 2 | 34,73 | 196b | 3 | 91-93 | 300 | 1 | 123 | 451 | 4 | 4,141-143 | 1207-5p | 1 | 166 |
26b | 1 | 29 | 125a | 1 | 51 | 146a | 3 | 74-76 | 198 | 1 | 94 | 301a | 1 | 124 | 452 | 1 | 144 | 1225-5p | 1 | 167 |
27a | 2 | 30,31 | 125b | 2 | 52,53 | 146b-5p | 1 | 77 | 199a | 1 | 95 | 326 | 1 | 125 | 455-5p | 1 | 145 | 1266 | 1 | 166 |
Highlighted studies were included in the present meta-analysis; N: Number of studies estimating prognostic value; R: Reference.
Study characteristics
Characteristics of articles with Kaplan-Meier survival curves in GC were comprehensively detailed in Table 2, including miRNA name, names of the first authors, publication year, reference number, country, study design, detected sample, number of patients, stage, cut-off value, main miRNA method, maximum months of follow-up, survival analysis and HR of low or high expression on the basis of relevant survival analysis with 95%CI. If the data were not provided visually and only as Kaplan-Meier survival curves, the data were extracted from the graphical survival plots, and estimations of the HR with 95%CI were then performed using a previously described method [184] with the software Engauge Digitizer version 4.1. Furthermore, if both the univariate and multivariate results were reported, then only the latter was selected, since these results were adjusted for confounding factors.
Table 2: Characteristics of articles with Kaplan-Meier survival curves in gastric cancer
miRNA | Study | Country | Study | Sample | Number | Stage | Cut-off | Method | Follow-up | Result | HR | HR | 95%CI |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
20a | Osawa S, 2011 [3] | Japan | R | FFPE | 37 | II-III | 70% | qRT-PCR | 60 | OSu | 1.93 | 0.48-7.87 | |
20a | Wang M, 2012 [5] | China | R | Plasma | 65 | I-IV | 0.26 | RT-qPCR | 36 | OSm | 1.58 | 1.10-2.25 | |
20a | Wu Q, 2013 [11] | China | R | FFPE | 97 | None | Median | qRT-PCR | 66 | OSm | 1.01 | 1.00-1.02 | |
20b | Katada T, 2009 [12] | Japan | R | Frozen | 42 | None | None | qRT-PCR | 60 | OSm | 2.01 | 0.59-6.85 | |
20b | Osawa S, 2011 [3] | Japan | R | FFPE | 34 | II-III | 70% | qRT-PCR | 60 | OSu | 1.21 | 0.20-7.23 | |
20b | Xue TM, 2015 [13] | China | R | Tissue | 102 | I-IV | Median | RT-qPCR | 75 | OSm | 3.32 | 1.20-9.14 | |
21-5p | Park SK, 2016 [14] | Korea | R | FFPE | 50 | III | ROC | qRT-PCR | 168 | RFSu | 2.05 | 1.26-3.34 | |
21 | Jiang J, 2011 [15] | China | R | FFPE | 55 | III-IV | None | qRT-PCR | 17 | OSu | 5.88 | 2.22-16.67 | |
21 | Osawa S, 2011 [3] | Japan | R | FFPE | 33 | II-III | 70% | qRT-PCR | 60 | OSu | 2.58 | 0.34-19.79 | |
21 | Xu Y, 2012 [16] | China | R | Frozen | 86 | I-IV | 5.12 | qRT-PCR | 36 | OSu | 1.15 | 0.59-2.25 | |
21 | Hirata K, 2013 [17] | Japan | P | Tissue | 61 | None | 3.58 | IHC | 42 | RFSu | 0.82 | 0.27-2.43 | |
21 | Komatsu S, 2013 [6] | Japan | R | Plasma | 69 | I-IV | 0.03 | qRT-PCR | 40 | CSSm | 13.39 | 1.72-104.42 | |
21 | Song J, 2013 [18] | China | R | Serum | 103 | I-IV | 0.64 | qRT-PCR | 54 | OSu | 0.99 | 0.48-2.07 | |
21 | Wang D, 2015 [19] | China | R | Tissue | 50 | I-IV | ROC | qRT-PCR | 12 | OSu | 1.89 | 1.17-3.07 | |
27b | Liu HT, 2015 [32] | China | R | FFPE | 103 | I-IV | None | qRT-PCR | 66 | OSu | 0.80 | 0.46-1.41 | |
27b | Shang Y, 2016 [33] | China | R | Tissue | 114 | I-IV | None | ISH | 84 | OSu | 1.61 | 0.92-2.80 | |
27b | Liu HT, 2017 [34] | China | R | FFPE | 102 | I-IV | Median | RT-qPCR | 67 | OSm | 1.33 | 0.60-2.98 | |
34a | Osawa S, 2011 [3] | Japan | R | FFPE | 37 | II-III | 70% | qRT-PCR | 60 | OSu | 0.20 | 0.06-0.68 | |
34a | Hui WT, 2015 [38] | China | R | Frozen | 76 | I-III | Mean | qRT-PCR | >60 | OSm | 2.33 | 1.10-4.93 | |
34a | Wei B, 2015 [39] | TCGA | R | Tissue | 157 | I-IV | X-tile | Downloaded | >100 | OSu | 2.31 | 0.13-40.12 | |
34a | Zhang H, 2015 [40] | China | R | Frozen | 137 | I-IV | 2.44 | qRT-PCR | 68 | OSm | 1.33 | 1.14-1.61 | |
34a | Yang B, 2016 [41] | China | R | Tissue | 50 | I-IV | Median | qRT-PCR | 60 | OSu | 3.05 | 0.60-15.50 | |
106b | Osawa S, 2011 [3] | Japan | R | FFPE | 37 | II-III | 70% | qRT-PCR | 60 | OSu | 2.70 | 0.43-17.06 | |
106b | Komatsu S, 2013 [6] | Japan | R | Plasma | 69 | I-IV | 0.05 | qRT-PCR | 40 | CSSu | 1.22 | 0.52-2.84 | |
106b | Yang TS, 2014 [45] | China | R | Tissue | 120 | None | Median | qRT-PCR | 45 | OSu | 1.79 | 1.10-2.90 | |
107 | Li X, 2011 [46] | China | R | FFPE | 50 | None | 90.95 | qRT-PCR | 48 | OSu | 0.48 | 0.28-0.82 | |
107 | Osawa S, 2011 [3] | Japan | R | FFPE | 37 | II-III | 70% | qRT-PCR | 60 | OSu | 4.09 | 1.26-13.32 | |
107 | Inoue T, 2012 [47] | Japan | R | Frozen | 161 | I-IV | 2.74 | RT-qPCR | 60 | OSm | 2.21 | 1.18-4.61 | |
125a-3p | Hashiguchi Y, 2012 [49] | Japan | R | Frozen | 70 | I-IV | 7.42 | RT-qPCR | 147.6 | OSu | 3.01 | 1.26-7.20 | |
125a-5p | Nishida N, 2011 [50] | Japan | R | Frozen | 87 | I-IV | None | RT-qPCR | 147.6 | OSu | 2.16 | 0.96-4.86 | |
125a | Dai J, 2015 [51] | China | R | FFPE | 73 | I-IV | None | qRT-PCR | 62 | OSu | 1.31 | 0.54-3.18 | |
137 | Gu Q, 2015 [62] | China Set I | R | Frozen | 67 | I-III | Median | qRT-PCR | 96 | OSm | 6.80 | 2.06-22.48 | |
137 | Zheng X, 2015 [63] | China | R | FFPE | 38 | I-IV | Median | qRT-PCR | 56 | DFSu | 2.70 | 1.18-6.17 | |
137 | Du Y, 2016 [64] | China | R | Tissue | 14 | I-IV | 0.01 | qRT-PCR | 96 | OSu | 2.49 | 0.32-19.59 | |
141 | Lu YB, 2015 [65] | China | R | Frozen | 95 | I-IV | Median | qRT-PCR | 60 | OSm | 2.97 | 1.30-10.00 | |
141 | Zhou X, 2015 [66] | China | R | Frozen | 63 | IIB-IV | Median | qRT-PCR | >30 | DFSu | 2.47 | 1.22-5.00 | |
141 | Huang M, 2016 [67] | China | R | Frozen | 30 | I-IV | None | qRT-PCR | 26.83 | OSu | 2.23 | 1.04-4.79 | |
143 | Osawa S, 2011 [3] | Japan | R | FFPE | 37 | II-III | 70% | qRT-PCR | 60 | OSu | 2.95 | 0.19-46.23 | |
143 | Naito Y, 2014 [69] | Japan | R | Frozen | 66 | I-IV | 1/3 | qRT-PCR | 50 | CSSm | 2.62 | 1.21-5.80 | |
miRNA | Study | Country | Study | Sample | Number | Stage | Cut-off | Method | Follow-up | Result | HR | HR | 95%CI |
143 | Li JH, 2016 [70] | China | R | Frozen | 44 | I-IV | 1.18 | qRT-PCR | 26 | OSu | 0.40 | 0.23-0.70 | |
145-5p | Zhang Y, 2016 [72] | China | R | Frozen | 145 | I-IV | None | RT-qPCR | 66 | OSm | 3.87 | 1.13-11.44 | |
145-5p | Li CY, 2017 [56] | TCGA | R | Tissue | 361 | I-IV | None | Downloaded | 60 | OSu | 1.37 | 1.08-1.74 | |
145 | Naito Y, 2014 [73] | Japan | R | FFPE | 71 | I-IV | Median | qRT-PCR | 66.67 | CSSm | 0.71 | 0.33-1.49 | |
145 | Liu HT, 2017 [34] | China | R | FFPE | 102 | I-IV | Median | RT-qPCR | 67 | OSu | 1.68 | 0.87-3.25 | |
146a | Kogo R, 2011 [74] | Japan | R | Frozen | 90 | I-IV | Median | qRT-PCR | 132 | OSu | 2.20 | 1.31-3.70 | |
146a | Hou Z, 2012 [75] | China | R | FFPE | 30 | I-IV | 0.34 | qRT-PCR | 36 | OSu | 2.59 | 1.24-5.39 | |
146a | Luo Z, 2017 [76] | China | R | Frozen | 93 | III-IV | ROC | RT-qPCR | 72 | OSu | 7.75 | 1.66-35.71 | |
150-5p | Yoon SO, 2016 [77] | Korea | R | FFPE | 140 | I-IV | 2.00 | RT-qPCR | 101.8 | OSm | 0.88 | 0.37-2.09 | |
150 | Katada T, 2009 [12] | Japan | R | Frozen | 42 | None | None | qRT-PCR | 60 | OSm | 6.10 | 0.76-50.00 | |
150 | Smid D, 2016 [79] | Czech | R | FFPE | 41 | None | 6.00 | qRT-PCR | >100 | OSu | 1.91 | 1.14-3.21 | |
183-5p | Li CY, 2017 [56] | TCGA | R | Tissue | 361 | I-IV | None | Downloaded | 60 | OSu | 0.64 | 0.47-0.87 | |
183 | Cao LL, 2014 [83] | China | R | Frozen | 52 | I-IV | 3.55 | qRT-PCR | 60 | OSu | 2.83 | 1.31-6.10 | |
183 | Xu L, 2014 [84] | China | R | Tissue | 65 | I-IV | Median | RT-qPCR | 102 | OSu | 1.94 | 1.11-3.39 | |
192 | Chen Q, 2014 [48] | China | R | Plasma | 61 | III-IV | 2.00 | qRT-PCR | 43 | OSm | 0.89 | 0.39-2.04 | |
192 | Xu YJ, 2015 [87] | China | R | Frozen | 38 | I-IV | None | qRT-PCR | 81 | OSu | 0.99 | 0.96-1.02 | |
192 | Smid D, 2016 [79] | Czech | R | FFPE | 41 | None | 2.30 | qRT-PCR | >100 | OSu | 7.43 | 2.71-20.41 | |
196a | Sun M, 2012 [90] | China | R | Frozen | 31 | II-IV | 40.90 | RT-qPCR | 36 | OSu | 4.19 | 1.78-9.83 | |
196a | Mu YP, 2014 [88] | China | R | Frozen | 48 | I-IV | 5.69 | qRT-PCR | 60 | OSu | 2.88 | 1.43-5.79 | |
196a | Tsai MM, 2014 [91] | China | R | Tissue | 109 | I-IV | 77.30 | qRT-PCR | 60 | OSu | 2.27 | 1.50-3.43 | |
196a | Tsai MM, 2016 [92] | China | R | Plasma | 98 | I-IV | 1.15 | qRT-PCR | 72 | OSm | 3.06 | 1.10-8.50 | |
196b-5p | Li CY, 2017 [56] | TCGA | R | Tissue | 361 | I-IV | None | Downloaded | 60 | OSu | 2.07 | 1.37-3.13 | |
196b | Lim JY, 2013 [93] | South Korea | R | Frozen | 57 | I-IV | None | qRT-PCR | 75 | OSu | 1.50 | 1.06-2.12 | |
196b | Tsai MM, 2014 [91] | China | R | Tissue | 109 | I-IV | 21.70 | qRT-PCR | 60 | OSu | 1.55 | 1.16-2.06 | |
196b | Tsai MM, 2016 [92] | China | R | Plasma | 98 | I-IV | 0.93 | qRT-PCR | 72 | OSm | 2.91 | 1.04-8.17 | |
200c | Valladares-Ayerbes M, 2012 [98] | Spain | R | Blood | 52 | I-IV | 62.4 | qRT-PCR | 54 | OSm | 0.45 | 0.22-0.92 | |
200c | Tang H, 2013 [96] | China | R | Tissue | 126 | I-IV | 2.00 | qRT-PCR | 58 | OSu | 2.29 | 1.38-3.81 | |
200c | Zhang HP, 2015 [99] | China | R | Serum | 98 | I-IV | Median | qRT-PCR | 60 | OSm | 0.25 | 0.10-0.37 | |
200c | Zhou X, 2015 [66] | China | R | Frozen | 63 | IIB-IV | Median | qRT-PCR | >30 | DFSu | 1.70 | 1.21-2.38 | |
206 | Yang Q, 2013 [104] | China | R | Tissue | 98 | I-IV | 2.40 | RT-qPCR | 139 | OSm | 2.56 | 1.13-5.82 | |
206 | Shi H, 2015 [105] | China | R | Frozen | 220 | I-IV | Median | qRT-PCR | 60 | OSm | 6.82 | 1.51-21.29 | |
206 | Hou CG, 2016 [106] | China | R | Serum | 150 | I-III | Median | RT-qPCR | 60 | OSm | 2.39 | 1.16-4.91 | |
214 | Ueda T, 2010 [1] | Japan | R | Frozen | 101 | I-IV | None | qRT-PCR | 102.33 | OSm | 2.70 | 1.30-5.61 | |
214 | Yang TS, 2013 [110] | China | R | Frozen | 120 | I-IV | None | qRT-PCR | 45 | OSu | 1.77 | 1.06-2.96 | |
214 | Wang YW, 2014 [111] | China | R | FFPE | 80 | I-IV | Median | RT-qPCR | 72 | OSu | 1.20 | 0.67-2.15 | |
214 | Liu HT, 2017 [34] | China | R | FFPE | 102 | I-IV | Median | RT-qPCR | 67 | OSm | 2.75 | 1.12-6.76 | |
218 | Tie J, 2010 [114] | China | R | Frozen | 40 | I-IV | 13.81 | qRT-PCR | 72 | OSu | 2.33 | 1.40-3.89 | |
218 | Xin SY, 2014 [115] | China | R | Serum | 68 | I-IV | None | qRT-PCR | 36 | OSm | 3.16 | 1.06-9.40 | |
218 | Wang XX, 2016 [116] | China | R | Tissue | 112 | I-IV | Median | qRT-PCR | 60 | OSm | 3.19 | 1.55-8.37 | |
335 | Yan Z, 2012 [127] | China | R | Both | 74 | I-IV | None | RT-qPCR | 108 | OSu | 0.14 | 0.04-0.49 | |
335 | Yang B, 2016 [41] | China | R | Tissue | 50 | I-IV | Median | qRT-PCR | 60 | OSu | 4.88 | 1.90-12.55 | |
335 | Zhang JK, 2017 [128] | China | R | Frozen | 221 | I-IV | Median | qRT-PCR | 60 | DFSu | 1.65 | 1.11-2.45 | |
451 | Ren C, 2016 [4] | China | R | FFPE | 180 | I-IV | None | ISH | 97.2 | OSm | 2.01 | 1.36-2.96 | |
451 | Bandres E, 2009 [141] | Spain | R | FFPE | 45 | I-III | Median | qRT-PCR | 172 | OSu | 2.02 | 0.76-5.38 | |
451 | Brenner B, 2011 [142] | Israel | R | FFPE | 45 | I-III | Median | qRT-PCR | 50 | RFSu | 0.05 | 0.01-0.29 | |
451 | Su Z, 2015 [143] | China | R | FFPE | 107 | I-IV | Mean | qRT-PCR | 72 | OSu | 1.08 | 0.53-2.19 | |
486-5p | Li CY, 2017 [56] | TCGA | R | Tissue | 361 | I-IV | None | Downloaded | 60 | OSu | 1.85 | 1.22-2.81 | |
486-5p | Chen H, 2015 [147] | China | R | FFPE | 84 | I-IV | None | ISH | 75 | OSm | 3.61 | 1.99-6.54 | |
486-5p | Ren C, 2016 [148] | China | R | FFPE | 84 | I-IV | None | ISH | 93.6 | OSm | 2.55 | 1.39-4.69 | |
506 | Deng J, 2015 [154] | China | R | Frozen | 63 | None | None | qRT-PCR | >60 | OSu | 3.05 | 1.19-7.79 | |
506 | Li Z, 2015 [155] | China | R | Frozen | 84 | I-IV | Mean | qRT-PCR | >60 | OSu | 1.76 | 0.73-4.27 | |
506 | Sakimura S, 2015 [156] | Japan | R | Tissue | 141 | I-IV | Median | qRT-PCR | >140 | OSm | 1.90 | 1.05-3.59 |
HR (L/H): hazard ratios of low expression versus high expression of miRNAs; HR (H/L): hazard ratios of high expression versus low expression of miRNAs; CI: confidence intervals; TCGA: The Cancer Genome Atlas; R: retrospective; P: prospective; FFPE: formalin-fixed paraffin-embedded; ROC: receiver operating characteristic; qRT-PCR: quantitative real-time polymerase chain reaction; RT-qPCR: reverse transcription quantitative real-time polymerase chain reaction; IHC: immunohistochemistry; ISH: in-situ hybridization; OS: overall survival; RFS: recurrence-free survival; CSS: cause-specific survival; DFS: disease-free survival; PFS: progression-free survival; uUnivariate analysis; mMultivariate analysis. In order to facilitate read and statistics, studies estimating prognostic value of different miRNAs are shown in blue and white; studies which cannot be merged are shown in yellow.
Meta-analysis
A summary of the HR evaluated from the whole combined analysis for all the miRNAs was shown in Table 3.
Table 3: Summary of the HR for miRNA expression in gastric cancer
miRNA | Survival | Number | Included | HR | 95%CI | Figure | P value | Heterogeneity | Total |
|---|---|---|---|---|---|---|---|---|---|
High miR-20a | OS | 3 | 3,5,11 | 1.25 | 0.84-1.87 | 3 | 0.27 | I2=70.7%, P=0.03 | 199 |
High miR-20b | OS | 3 | 3,12,13 | 2.38 | 1.16-4.87 | 3 | 0.02 | I2=0.0%, P=0.60 | 178 |
High miR-21 | RFS/CSS | 3 | 6,14,17 | 2.10 | 0.72-6.12 | 2A | 0.17 | I2=65.6%, P=0.06 | 180 |
High miR-21 | OS | 5 | 3,15,16,18,19 | 1.77 | 1.01-3.08 | 2A | <0.05 | I2=57.8%, P=0.05 | 327 |
Low miR-27b | OS | 3 | 32-34 | 1.18 | 0.75-1.85 | 3 | 0.47 | I2=36.1%, P=0.21 | 319 |
Low miR-34a | OS | 5 | 3,38-41 | 1.25 | 0.59-2.65 | 2D | 0.56 | I2=68.4%, P=0.13 | 457 |
Low miR-34a | OSm | 2 | 38,40 | 1.56 | 0.95-2.55 | 2D | 0.08 | I2=51.0%, P=0.15 | 213 |
High miR-106b | OS | 2 | 3,45 | 1.84 | 1.15-2.94 | 3 | 0.01 | I2=0.0%, P=0.67 | 157 |
High miR-107 | OS | 3 | 3,46,47 | 1.52 | 0.42-5.57 | 3 | 0.52 | I2=88.8%, P<0.01 | 248 |
Low miR-125a | OS | 3 | 49-51 | 2.06 | 1.26-3.37 | 4 | <0.01 | I2=0.0%, P=0.42 | 230 |
Low miR-137 | OS | 2 | 62,64 | 3.21 | 1.68-6.13 | 4 | <0.01 | I2=6.0%, P=0.35 | 168 |
Low miR-141 | OS | 2 | 65,67 | 2.47 | 1.34-4.56 | 4 | <0.01 | I2=0.0%, P=0.66 | 125 |
High miR-143 | OS | 2 | 3,70 | 0.68 | 0.12-3.81 | 4 | 0.66 | I2=48.8%, P=0.16 | 81 |
Low miR-145 | OS | 3 | 34,56,72 | 1.62 | 1.07-2.46 | 4 | 0.02 | I2=36.9%, P=0.21 | 608 |
Low miR-146a | OS | 3 | 74-76 | 2.60 | 1.63-4.13 | 5 | <0.01 | I2=14.1%, P=0.31 | 213 |
High miR-150 | OS | 3 | 12,77,79 | 1.63 | 0.77-3.45 | 5 | 0.20 | I2=47.8%, P=0.15 | 223 |
High miR-150 | RFS/PFS | 2 | 77,79 | 1.96 | 1.25-3.05 | 5 | <0.01 | I2=0.0%, P=0.79 | 158 |
Low miR-183 | OS | 3 | 56,83,84 | 1.46 | 0.55-3.83 | 5 | 0.45 | I2=90.2%, P<0.01 | 478 |
High miR-192 | OS | 3 | 48,79,87 | 1.71 | 0.60-4.85 | 5 | 0.31 | I2=87.0%, P<0.01 | 140 |
High miR-196a | OS | 4 | 88,90-92 | 2.66 | 1.94-3.63 | 6 | <0.01 | I2=0.0%, P=0.62 | 286 |
High miR-196b | OS | 4 | 56,91-93 | 1.67 | 1.38-2.02 | 6 | <0.01 | I2=0.0%, P=0.62 | 625 |
Low miR-200c | OS | 3 | 96,98,99 | 0.65 | 0.16-2.64 | 6 | 0.54 | I2=93.6%, P<0.01 | 276 |
Low miR-200c | PFS/DFS | 3 | 66,96,98 | 1.20 | 0.60-2.38 | 6 | 0.61 | I2=83.1%, P<0.01 | 241 |
Low miR-206 | OS | 3 | 104-106 | 2.85 | 1.73-4.70 | 7 | <0.01 | I2=0.0%, P=0.37 | 468 |
High miR-214 | OS | 4 | 1,34,110,111 | 1.84 | 1.27-2.67 | 7 | <0.01 | I2=23.0%, P=0.27 | 403 |
Low miR-218 | OS | 3 | 114-116 | 2.61 | 1.74-3.92 | 7 | <0.01 | I2=0.0%, P=0.77 | 220 |
Low miR-335 | OS | 2 | 41,127 | 0.85 | 0.03-27.50 | 7 | 0.93 | I2=94.9%, P<0.01 | 124 |
Low miR-451 | OS | 3 | 4,141,143 | 1.73 | 1.19-2.52 | 8 | <0.01 | I2=14.7%, P=0.31 | 332 |
Low miR-451 | DFS/RFS | 2 | 141,142 | 0.46 | 0.01-31.06 | 8 | 0.72 | I2=95.0%, P<0.01 | 90 |
Low miR-486-5p | OS | 3 | 56,147,148 | 2.45 | 1.65-3.65 | 8 | <0.01 | I2=40.0%, P=0.19 | 529 |
Low miR-506 | OS | 3 | 154-156 | 2.07 | 1.33-3.23 | 8 | <0.01 | I2=0.0%, P=0.65 | 288 |
HR: hazard ratios; CI: confidence intervals; OS: overall survival; RFS: recurrence-free survival; CSS: cause-specific survival; PFS: progression-free survival; DFS: disease-free survival; mMultivariate analysis.
High expression of miR-21 predicts poor OS
Five studies [3, 15, 16, 18, 19] analyzed associations between high expression of miR-21 and OS, indicating that GC patients with high miR-21 expression had a significantly shorter OS than those with low miR-21 expression (HR=1.77, 95%CI=1.01-3.08, P<0.05, Figure 2A).
Figure 2: (A) Forest plot of the analyses about high expression of miR-21 and RFS/CSS or OS; (B) Publication bias of the analysis about high expression of miR-21 and OS; (C) Sensitivity analysis of the study about high expression of miR-21 and OS; and (D) Forest plot of the analyses about low expression of miR-34a and OS or OS (multivariate analysis).
No significant association between high expression of miR-21 and RFS/CSS
Three researches [6, 14, 17] focused on connections between high expression of miR-21 and RFS/CSS, suggesting that there was no significant association between high expression of miR-21 and RFS/CSS (HR=2.10, 95%CI=0.72-6.12, P=0.17, Figure 2A).
Publication bias
In order to evaluate publication bias for OS of GC patients with high miR-21 expression, the Begg’s funnel plot was used by us (Figure 2B). And the P value was 0.62, indicating absence of publication bias.
Sensitivity analysis
During the study about OS of GC patients with high miR-21 expression, our sensitivity analysis did not indicate alterations in the results according to the exclusion of any individual study (Figure 2C), suggesting that no single research significantly influenced the pooled HR and the 95%CI.
No significant association between low expression of miR-34a and OS or OS (multivariate analysis)
There was no significant association between low expression of miR-34a and OS (HR=1.25, 95%CI=0.59-2.65, P=0.56, Figure 2D) or OS (multivariate analysis, HR=1.56, 95%CI=0.95-2.55, P=0.08, Figure 2D).
GC patients with high expression of miR-20b, 106b, 196a, 196b, 214 or low expression of miR-125a, 137, 141, 145, 146a, 206, 218, 451, 486-5p, 506 have a significantly poor OS
The details were shown in Table 3 and Figures 3-8.
Figure 3: Forest plot of the analyses about high expression of miR-20a, 20b, 106b, 107 or low expression of miR-27b and OS.
Figure 4: Forest plot of the analyses about high expression of miR-143 or low expression of miR-125a, 137, 141, 145 and OS.
Figure 5: Forest plot of the analyses about high expression of miR-150, 192 or low expression of miR-146a, 183 and OS or RFS/PFS.
Figure 6: Forest plot of the analyses about high expression of miR-196a, 196b or low expression of miR-200c and OS or PFS/DFS.
Figure 7: Forest plot of the analyses about high expression of miR-214 or low expression of miR-206, 218, 335 and OS.
Figure 8: Forest plot of the analyses about low expression of miR-451, 486-5p, 506 and OS or DFS/RFS.
No significant association between high expression of miR-20a, 107, 143, 150, 192 or low expression of miR-27b, 183, 200c, 335 and OS
The details were shown in Table 3 and Figures 3-7.
DISCUSSION
Present situation
Increasing evidence has shown that various miRNAs are associated with survival outcome in GC patients [1–167]. However, inconsistent results have emerged. For example, expression levels of miR-200c are up-regulated in blood [98, 99] but down-regulated [66, 96] in tissue compared with normal samples. Furthermore, expression levels of miR-214 [1, 34, 110, 111] and miR-451 [4, 141–143] are unsteadily expressed (up or down). Surprisingly, there are significant associations between aberrant expression levels of them and OS (P<0.05, Table 3, Figures 7 and 8). Therefore, it is essential to conduct a meta-analysis to better understand associations between expression levels of miRNAs and prognosis of GC patients.
Main findings
We performed the meta-analyses about 26 miRNAs and OS. As the most studied miRNA, GC patients with high miR-21 expression have a significantly poorer OS than those with low miR-21 expression (P<0.05). But there is no significant association between high miR-21 expression and RFS/CSS (P=0.17). According to our reference standard, miR-21 is still considered to be a significantly prognostic biomarker. There are some other miRNAs with significantly prognostic value in GC, including miR-20b, 106b, 125a, 137, 141, 145, 146a, 196a, 196b, 206, 214, 218, 451, 486-5p and 506 (P<0.05). Among them, miR-20b, 125a, 137, 141, 146a, 196a, 206, 218, 486-5p and 506 are strong biomarkers of prognosis in GC (HR≥2).
Molecular mechanisms for studied miRNAs
In addition to the findings mentioned above, a summary of miRNAs with altered expression, their potential targets and pathways entered this study is detailed in Table 4. It is remarkable that there is functional overlapping or connection among those miRNAs. Twenty miRNAs (miR-20a, 27b, 34a, 106b, 107, 125a, 137, 141, 143, 146a, 183, 192, 196a, 196b, 200c, 214, 218, 335, 451 and 506) are involved in cell functions, including cell apoptosis, colony formation, cycle, differentiation and so on. Zhou et al. [66] reported that miR-200c/141 likely increased E-cadherin expression indirectly through down-regulating ZEB1/2, indicating that this pathway may participate in GC migration and invasion. Additionally, Tsai et al. [91] found that GC cell migration and invasion was enhanced by overexpression of miR-196a/-196b and radixin was recognized as a target of miR-196a/-196b. In a word, these relationships may be involved in the progression of GC.
Table 4: Summary of miRNAs with altered expression, their potential targets and pathways entered this study
miRNA | Reference | Expression | Potential target | Pathway |
|---|---|---|---|---|
20a | 3,5,11 | Up | E2F1, HIPK1 | Cell differentiation, proliferation, self-renewal and Wnt/β-catenin signaling |
20b | 3,12,13 | Up | None | None |
21 | 3,6,14-19 | Up | None | None |
27b | 32-34 | Down | CCNG1, VEGF-C | Cell migration and proliferation |
34a | 3,38-41 | Down | MET, Survivin | Cell apoptosis, colony formation, invasion and proliferation |
106b | 3,6,45 | Up | PTEN | Cell invasion and migration |
107 | 3,46,47 | Up | DICER1 | Cell invasion and migration |
125a | 49-51 | Down | VEGF-A, ERBB2 | Cell proliferation |
137 | 62-64 | Down | KLF12, MYO1C, CDK6 | Cell cycle, differentiation, migration and proliferation |
141 | 65-67 | Down | ZEB1/2, E-cadherin, IGF1R | Cell colony formation, cycle, invasion, migration, viability and TGF-β/ZEB signaling |
143 | 3,69,70 | Down | BACH1 | Cell invasion, proliferation and TGF-β/Mad signaling |
145 | 34,56,72,73 | Down | α-SMA | None |
146a | 74-76 | Down | EGFR, IRAK1, LIN52 | Cell apoptosis, invasion, migration and proliferation |
150 | 12,77,79 | Up | None | None |
183 | 56,83,84 | Down | EZR, BMI1 | Cell colony formation, invasion and proliferation |
192 | 48,79,87 | Up | None | Cell invasion |
196a | 88,90-92 | Up | CDKN1B, Rdx | Cell colony formation, cycle, invasion, migration and proliferation |
196b | 56,91-93 | Up | Rdx | Cell invasion and migration |
200c | 98,99 | Up (blood) | ZEB1/2, E-cadherin | Cell invasion, migration and TGF-β/ZEB signaling |
206 | 104-106 | Down | None | None |
214 | 1,34,110,111 | Up or Down | CSF1, PTEN | Cell invasion, migration and proliferation |
218 | 114-116 | Down | ROBO1 | Cell invasion and sli/ROBO1 signaling |
335 | 41,127,128 | Down | Survivin, BIRC5, CRKL | Cell apoptosis, cycle, growth, invasion, migration and proliferaion |
451 | 4,141-143 | Up or Down | MIF | Cell invasion, migration and proliferation |
486-5p | 56,147-148 | Down | FGF9 | None |
506 | 154-156 | Down | Yap1, ETS1, SNAI2 | Cell epithelial-mesenchymal transition, growth, invasion, migration and proliferation |
E2F1: E2F transcription factor 1; HIPK1: homeodomain interacting protein kinase 1; CCNG1: cyclin G1; VEGF: vascular endothelial growth factor; PTEN: protein tyrosine phosphatase and tensin homologue; DICER1: dicer 1, ribonuclease type III; ERBB2: erb-b2 receptor tyrosine kinase 2; KLF12: krűppel-likefactor 12; MYO1C: myosin 1C; ZEB1/2: zinc finger E-boxbinding homeobox 1/2; IGF1R: insulin-like growth factor 1 receptor; BACH1: BTB domain and CNC homolog 1; α-SMA: α smooth muscle actin; EGFR: epidermal growth factor receptor; IRAK1: interleukin 1 receptor associated kinase 1; LIN52: lin-52 homolog (C. elegans); EZR: ezrin; BMI1: BMI1 proto-oncogene, polycomb ring finger; CDKN1B: cyclin dependent kinase inhibitor 1B; Rdx: radixin; CSF1: colony stimulating factor 1; Robo1: roundabout guidance receptor 1; BIRC5: baculoviral IAP repeat containing 5; CRKL: CRK like proto-ongogene, adaptor protein; MIF: macrophage migration inhibitory factor (glycosylation-inhibiting factor); FGF9: fibroblast growth factor 9; YAP1: Yes associated protein 1; ETS1: ETS proto-oncogene 1, transcription factor; SNAI2: snail family transcriptional repressor 2; TGF-β: transforming growth factor-β; Mad: mothers against dpp; AKT1: AKT serine/threonine kinase 1; sli: slit.
Strengths of the meta-analysis
This meta-analysis has several strengths which are as follows: (1) we searched almost all articles with survival outcomes in GC patients with diverse miRNAs. Moreover, the present expression profile of miRNAs was clearly listed in Table 1 in terms of names of miRNAs; (2) articles measuring at least one of survival curves about OS, CSS, DFS, RFS, PFS and MFS were finally included and articles only reporting HR or 95%CI without any of survival curves were excluded by us; (3) miRNAs investigated more than or equal to 3 times were conducted meta-analyses; (4) almost all sample sizes of included studies are more than or equal to 30 (except 1 study [64]), enhancing the power and broadening the applicability of the outcomes to GC patients.
Limitations
However, one should keep in mind the following limitations: (1) 1 miRNA considered as significant biomarker of prognosis contained a high heterogeneity (miR-21); (2) there are many variables among the present meta-analysis, such as different types of samples (tissue, plasma and serum), disease stages, cut-off values and miRNA methods; (3) our meta-analysis only included English articles, which might exclude certain relevant articles with other languages; (4) articles only reporting HR or 95%CI without survival curves were excluded by us, reducing the sample sizes of included articles; (5) as a result of substantial relevant articles and data about GC, we subjectively and selectively included some researches according to the criteria of inclusion and exclusion (Table 5), leading to ignore a few potential miRNAs with prognostic value.
Table 5: Information of search methods and criteria of inclusion and exclusion
Methods | Information |
|---|---|
Search strategy | 4 search engines, including PubMed, EMBASE, Web of |
Search deadline | March 19, 2017 |
Search term | mir and gastric cancer |
Inclusion criteria | (1) Patients with gastric cancer; |
Exclusion criteria | (1) Reviews, letters or laboratory studies without |
Implications for future clinical and scientific research
It is worth mentioning that this meta-analysis is the first systematic estimation of the relevance between miRNA expression and prognosis of GC patients. There are some implications for future clinical and scientific research in the present meta-analysis: (1) for clinical doctors and other healthcare providers, combined detection of miRNA expression can greatly enhance the estimation about survival time of GC patients and timely treatment can be offered; (2) for scientific researchers, the present study trend on associations between miRNAs and prognosis of GC patients can be conveniently seen in Table 1. As a result, selectively basic experiments can be performed by them (Table 4); (3) inconsistent outcomes of prognosis about miRNAs may be solved according to the basement of the current meta-analysis.
MATERIALS AND METHODS
Search strategy, inclusion criteria and exclusion criteria
The details were presented in Table 5. Two authors (Yue Zhang and Dong-Hui Guan) independently performed this comprehensive online search.
Quality assessment
Yue Zhang and Dong-Hui Guan confirmed all eligible investigations that analyzed the prognostic value of miRNAs in GC, and Yue-Hua Jiang reassessed uncertain data.
Statistical analysis
All analyses were conducted using Stata version 13.0 (StataCorp, College Station, Texas, USA). The relative effect sizes for HR were characterized as moderate (protective [0.51-0.75] or contributory [1.35-1.99]) and large (≤0.50 or≥2). The HR was considered significant at the P<0.05 level if the 95%CI did not include the value 1. If the P values from OS and other survival results about corresponding miRNAs were inconsistent, the HR from OS was considered to the main reference standard. Because different types of samples (tissue, plasma and serum) from GC patients at different disease stages, cut-off values and miRNA methods were used in individual studies, random-effects models (DerSimonian-Laird method) were more appropriate than fixed-models (Mantel-Haenszel method) for most of the analyses. Consequently, the random-effects models were used in the current meta-analysis. Publication bias was estimated using the Begg’s funnel plot. A two-tailed P value <0.05 was considered significant. Sensitivity analysis (influence analysis) was carried out to test how powerful the combined effect size was to removal of individual investigations. If the point assessment was out the 95%CI of the pooled effect size after it was removed from the analysis, an individual study was doubted to have excessive influence.
CONCLUSIONS
In summary, miR-20b, 21, 106b, 125a, 137, 141, 145, 146a, 196a, 196b, 206, 214, 218, 451, 486-5p and 506 demonstrate significantly prognostic value. Among them, miR-20b, 125a, 137, 141, 146a, 196a, 206, 218, 486-5p and 506 are strong biomarkers of prognosis in GC.
Author contributions
Study concept and design: Yue Zhang and Yue-Hua Jiang.
Acquisition of data: Yue Zhang and Dong-Hui Guan.
Analysis and interpretation of data: Yue Zhang, Dong-Hui Guan, Rong-Xiu Bi and Jin Xie.
Drafting of the manuscript: Yue Zhang.
Revision of manuscript: Yue Zhang, Dong-Hui Guan, Rong-Xiu Bi, Jin Xie, Chuan-Hua Yang and Yue-Hua Jiang.
Supervision of work: Rong-Xiu Bi, Jin Xie, Chuan-Hua Yang and Yue-Hua Jiang.
All authors read and approved the final manuscript.
CONFLICTS OF INTEREST
The authors declare that they have no conflicts of interest.
FUNDING
This work was supported by the National Natural Science Foundation of China (No. 81673807).
Role of funding source: The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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