Research Paper Volume 12, Issue 10 pp 9380—9404

Prognostic value of microRNAs in pancreatic cancer: a meta-analysis

Fei Zhao1, , Chao Wei2, , Meng-Ying Cui3, , Qiang-Qiang Xia4, , Shuai-Bin Wang5, , Yue Zhang6,7, ,

  • 1 , Department of Traditional Chinese Medicine, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
  • 2 College of Integrated Traditional Chinese and Western Medicine, Jining Medical University, Jining, Shandong, China
  • 3 Department of Hepatobiliary and Pancreatic Surgery, The Second Hospital of Jilin University, Changchun, Jilin, China
  • 4 Department of Neurobiology, University of Alabama at Birmingham, Birmingham, AL 35233, USA
  • 5 Department of Urology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
  • 6 Department of Genetics, University of Alabama at Birmingham, Birmingham, AL 35233, USA
  • 7 Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL 35233, USA

Received: January 14, 2020       Accepted: April 17, 2020       Published: May 18, 2020
How to Cite

Copyright © 2020 Zhao et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Background: The prognostic impact of microRNA (miRNA) expression levels in pancreatic cancer (PC) has been estimated for years, but the outcomes are controversial and heterogeneous. Therefore, we comprehensively reviewed the evidence collected on miRNA expression in PC to determine this effect.

Results: PC patients with high miR-21 (HR=2.61, 95%CI=1.68-4.04), miR-451a (HR=2.23, 95%CI=1.23-4.04) or miR-1290 (HR=1.43, 95%CI=1.04-1.95) levels in blood had significantly poorer OS (P<0.05). Furthermore, PC patients with high miR-10b (HR=1.73, 95%CI=1.09-2.76), miR-17-5p (HR=1.91, 95%CI=1.30-2.80), miR-21 (HR=1.90, 95%CI=1.61-2.25), miR-23a (HR=2.18, 95%CI=1.52-3.13), miR-155 (HR=2.22, 95%CI=1.27-3.88), miR-203 (HR=1.65, 95%CI=1.14-2.40), miR-221 (HR=1.72, 95%CI=1.08-2.74), miR-222 levels (HR=1.72, 95%CI=1.02-2.91) or low miR-29c (HR=1.39, 95%CI=1.08-1.79), miR-126 (HR=1.55, 95%CI=1.23-1.95), miR-218 (HR=2.62, 95%CI=1.41-4.88) levels in tissues had significantly shorter OS (P<0.05).

Conclusions: In summary, blood miR-21, miR-451a, miR-1290 and tissue miR-10b, miR-17-5p, miR-21, miR-23a, miR-29c, miR-126, miR-155, miR-203, miR-218, miR-221, miR-222 had significant prognostic value.

Methods: We searched PubMed, EMBASE, Web of Science and Cochrane Database of Systematic Reviews to recognize eligible studies, and 57 studies comprising 5445 PC patients and 15 miRNAs were included to evaluate the associations between miRNA expression levels and overall survival (OS) up to June 1, 2019. Summary hazard ratios (HR) with 95% confidence intervals (CI) were calculated to assess the effect.


Much effort has been made over a long period of time to identify prognostic biomarkers in pancreatic cancer (PC) patients. Fortunately, a large body of literature has covered the survival of PC patients with abnormal microRNA (miRNA) expression [1169]. Among all kinds of human cancers, PC has one of the worst prognoses, with a 5-year overall survival (OS) rate of lower than 5% [170]. Despite advances in clinical treatments and new surgical techniques, the survival rate of PC patients has been low for more than 30 years [171]. PC is highly aggressive; therefore, distant metastasis and tissue invasion may occur at early stages [172]. Since invasion and metastasis are the biggest obstacles to effective treatment of PC, it is imperative to explore the molecular biological mechanism leading to such invasive behavior to improve the survival time of patients.

miRNAs are small noncoding RNAs involved in gene regulation [173]. In cancers, a few upregulated miRNAs can serve as oncogenes (oncomiRs) [174], and downregulated miRNAs can serve as tumor suppressors [175]. Expression profiling data analyses have revealed signatures of diagnosis and prognosis that have been employed to stratify various tumor types [174, 176]. As a consequence, miRNAs have the potential to turn into clinical biomarkers for human tumors and into molecular therapeutic targets [177].

Despite comprehensive studies focused on illustrating the molecular biological mechanisms in PC, there are still challenges confronting the identification of minimally invasive and sensitive biomarkers of prognosis. Consequently, it is of vital significance to find prognostic signatures that can be conveniently and reliably applied in the clinical setting to improve the survival time of PC patients.

Increasing evidence indicates that miRNAs have the potential to act as PC prognostic biomarkers in clinical practice [1169]. Regrettably, there has not been a meta-analysis to evaluate the relationship between dysregulated miRNA expression and survival in PC patients. In view of our previous work, meta-analyses of miRNA expression and cancer patients [178, 179], it is necessary to conduct the current work by searching the recently published literature about miRNAs as prognostic tools in PC tissue or blood.



An overview of the HR with 95%CI obtained from the overall comprehensive analysis for all included miRNAs is shown in Table 1. Based on the logical order of the miRNA names, the forest plot, Begg’s funnel plot, sensitivity analysis and funnel plot of the merged analysis adjusted with the trim and fill method are shown in Figures 17. The mean NOS score of the included studies was 7.0 (5.0-8.0), indicating that their quality was adequate (Table 2).

Table 1. Summary about results of meta-analysis for miRNA expression in pancreatic cancer.

miRNASampleSurvival analysisNumber of articlesIncluded studiesHR95%CIFigureP valueHeterogeneity (Higgins I2 statistic)Total patients
High miR-21BloodOS54-82.611.68-4.042<0.01I2=33.8%, P=0.20326
High miR-196aBloodOS216,171.610.50-5.2320.43I2=79.5%, P=0.0366
High miR-451aBloodOS37,8,232.231.23-4.042<0.01I2=2.1%, P=0.36137
High miR-1290BloodOS224,261.431.04-1.9520.03I2=0.0%, P=0.76223
High miR-10bTissueOS435-381.731.09-2.7630.02I2=61.5%, P=0.03375
High miR-17-5pTissueOS339-411.911.30-2.803<0.01I2=0.0%, P=0.96164
High miR-21TissueOS195,43-601.901.61-2.253<0.01I2=43.9%, P=0.021947
High miR-21TissueOSm85,45-48,50-522.431.89-3.134<0.01I2=0.0%, P=0.73592
High miR-21TissueOSAdjusted1.581.32-1.89<0.01I2=58.6%, P<0.01
High mIR-23aTissueOS450,53,61,622.181.52-3.138<0.01I2=0.0%, P=0.51251
Low miR-29cTissueOS433,46,69,701.391.08-1.7980.01I2=51.8%, P=0.10463
Low miR-126TissueOS327,68,821.551.23-1.958<0.01I2=0.0%, P=0.99455
High miR-155TissueOS314,50,512.221.27-3.888<0.01I2=0.0%, P=0.47211
Low mIR-200cTissueOS3109-1111.400.51-3.7980.51I2=87.2%, P<0.01258
High miR-203TissueOS459,112-1141.651.14-2.408<0.01I2=83.6%, P<0.01619
Low miR-218TissueOS3121-1232.621.41-4.888<0.01I2=57.5%, P=0.10248
High miR-221TissueOS446,50,125,1261.721.08-2.7480.02I2=4.9%, P=0.37187
High miR-222TissueOS328,126,1271.721.02-2.9180.04I2=36.8%, P=0.21322
HR: hazard ratios; CI: confidence intervals; OS: overall survival; mmultivariate analysis; AdjustedAdjusted with the trim and fill method.

Table 2. Newcastle-Ottawa scale quality assessment results.

First authorYearReferenceSelectionComparabilityOutcomeTotal
Forest plot about OS of PC patients with high miR-21, miR-196a, miR-451a or miR-1290 level in blood

Figure 1. Forest plot about OS of PC patients with high miR-21, miR-196a, miR-451a or miR-1290 level in blood

High miR-21, miR-451a and miR-1290 levels in the blood predict poor OS

Five studies [48] analyzed the connections between high blood miR-21 levels and OS, indicating that PC patients with high blood miR-21 levels had significantly poorer OS than those with low levels (HR=2.61, 95%CI=1.68-4.04, P<0.01, Figure 1).

Two studies [16, 17] reported the relationship between high blood miR-196a levels and OS, but no significant associations were found between high blood miR-196a and OS (HR=1.61, 95%CI=0.50-5.23, P=0.43, Figure 1).

Three studies [7, 8, 23] focused on the correlativity between high blood miR-451a levels and OS, indicating that PC patients with high miR-451a levels had significantly shorter OS than those with low levels (HR=2.23, 95%CI=1.23-4.04, P<0.01, Figure 1).

Two studies [24, 26] stressed the pertinence between high blood miR-1290 levels and OS, suggesting that PC patients with high miR-1290 levels had significantly worse OS than those with low levels (HR=1.43, 95%CI=1.04-1.95, P=0.03, Figure 1).

High miR-10b, miR-17-5P, miR-21, miR-23a, miR-155, miR-203, miR-221, and miR-222 levels or low miR-29c, miR-126, and miR-218 levels in tissues predict poor OS

The details are shown in Table 1 and Figures 2 and 7.

Forest plot about OS of PC patients with high miR-10b, miR-17-5P or miR-21 level in tissue.

Figure 2. Forest plot about OS of PC patients with high miR-10b, miR-17-5P or miR-21 level in tissue.

High miR-21 levels in tissues predict poor OS (multivariate analysis)

The details are shown in Table 1 and Figure 3.

Forest plot about OS of PC patients with high miR-21 level in tissue (multivariate analysis).

Figure 3. Forest plot about OS of PC patients with high miR-21 level in tissue (multivariate analysis).

Publication bias

Begg’s funnel plot was employed to estimate publication bias in the study of OS in PC patients with high tissue miR-21 levels (Figure 4). The results showed that the P value was less than 0.01, indicating the presence of publication bias.

Begg’s funnel plot about OS of PC patients with high miR-21 level in tissue.

Figure 4. Begg’s funnel plot about OS of PC patients with high miR-21 level in tissue.

Sensitivity analysis

Sensitivity analysis was used to estimate whether any single study had undue influence on the OS of PC patients with high tissue miR-21 levels (Figure 5). The outcome showed that no single investigation significantly affected the pooled HR and 95%CI.

Sensitivity analysis about OS of PC patients with high miR-21 level in tissue.

Figure 5. Sensitivity analysis about OS of PC patients with high miR-21 level in tissue.

The trim and fill method

As such (Figure 4), the trim and fill method was conducted, and the pooled HR was recalculated with assumed lost studies to assess dissymmetry in the funnel plot (Figure 6), manifesting no publication bias (P=0.80). The recalculated HR did not change significantly for OS (HR=1.58, 95%CI=1.32-1.89, P<0.01).

Funnel plot about pooled analysis adjusted with trim and fill method of OS of PC patients with high miR-21 level in tissue. Circles: included studies; diamonds: presumed missing studies.

Figure 6. Funnel plot about pooled analysis adjusted with trim and fill method of OS of PC patients with high miR-21 level in tissue. Circles: included studies; diamonds: presumed missing studies.

Forest plot about OS of PC patients with high miR-23a, miR-155, miR-203, miR-221, miR-222 or low miR-29c, miR-126, miR-200c, miR-218 level in tissue.

Figure 7. Forest plot about OS of PC patients with high miR-23a, miR-155, miR-203, miR-221, miR-222 or low miR-29c, miR-126, miR-200c, miR-218 level in tissue.


Foremost findings

The current meta-analysis included 57 English articles that incorporated 15 miRNAs and 5445 patients. As the most researched miRNA, PC patients with high blood or tissue miR-21 levels had significantly poorer OS than those with low levels. It also proved true among PC patients with high tissue miR-21 levels (multivariate analysis) and pooled analysis adjusted with the trim and fill method of OS, indicating that miR-21 is a stable and useful prognostic biomarker in PC. Moreover, a few other miRNAs had significant prognostic impact on PC, including blood miR-451a, and miR-1290 and tissue miR-10b, miR-17-5p, miR-29c, miR-126, miR-155, miR-203, miR-218, miR-221, and miR-222. Among these, blood miR-21, and miR-451a and tissue miR-23a, miR-155, and miR-218 were strong biomarkers of prognosis for PC.

Altered expression, potential targets and pathways for studied miRNAs

In addition, an overview of the 15 miRNAs with dysregulated levels, covering the validated targets and pathways, is shown in Table 3. Most of the included miRNAs showed stable expression levels, higher or lower than the control groups except miR-200c. In brief, Table 3 could support a better understanding of the molecular biological mechanisms of miRNAs in PC.

Table 3. Summary of miRNAs with altered expression, their validated targets and pathways entered this study.

miRNAReferenceExpressionPotential targetPathway
10b[3538]UpNoneCell invasion
17-5p[3941]UpPTEN,RBL2Cell cycle, invasion and proliferation
21[48,4360]UpBTG2,FASL,PDCD4,SPRY2Cell apopsotis, chemoresistance, cycle, proliferation, FASL/FAS, MAPK/ERK and PI3K/AKT signaling
23a[50, 53, 61, 62]UpESRP1,FOXP2,NEDD4LCell invasion, epithelial-mesenchymal transition, migration and proliferation
29c[33, 46, 69, 70]DownMMP2Cell invasion, migration and Wnt signaling
126[27, 68, 82]DownNoneNone
155[14, 50, 51]UpNoneNone
200c[109111]UnstableNoneCell invasion and proliferation
203[59, 112114]UpNoneNone
218[121123]DownUGT8,VOPP1Cell proliferation
221[46, 50, 125, 126]UpNoneCell migration and proliferation
222[28, 126, 127]UpNOSTRINNone
451a[7, 8, 23]UpNoneNone
1290[24, 26]UpNoneNone
PTEN: phosphatase and tensin homolog; RBL2: RB transcriptional corepressor like 2; BTG2: BTG anti-proliferation factor 2; FASL: Fas ligand; PDCD4: programmed cell death 4; SPRY2: sprouty RTK signaling antagonist 2; ESRP1: epithelial splicing regulatory protein 1; FOXP2: forkhead box P2; NEDD4L: NEDD4 like E3 ubiquitin protein ligase; UGT8: UDP glycosyltransferase 8; VOPP1: VOPP1 WW domain binding protein; NOSTRIN: nitric oxide synthase trafficking; FAS: Fas cell surface death receptor; MAPK: mitogen-activated protein kinase; ERK: extracellular regulated protein kinases; PI3K: phosphoinositide-3-kinase; AKT: AKT serine/threonine kinase 1.

Superiorities of the meta-analysis

The present work had two strengths : (1) we looked for and found out almost all studies with OS in PC patients with dysregulated miRNA levels. In addition, the recent miRNA expression pattern is shown in Tables 4 and 5 that differentiates miRNA names and the sample types. (2) The majority of included articles had large sample sizes (≥30, all but 4 studies [6, 41, 121, 125]), intensifying and widening the applicability of the prognostic outcomes for PC patients.

Table 4. Frequency of studies estimating prognostic value of blood miRNA expression in pancreatic cancer.


Highlighted studies were included in the present meta-analysis; N: Number of studies estimating prognostic value; R: References.

Table 5. Frequency of studies estimating prognostic value of tissue miRNA expression in pancreatic cancer.

Highlighted studies were included in the present meta-analysis; N: Number of studies estimating prognostic value; R: References.


The following drawbacks of the current meta-analysis should considered: (1) there were numerous variables, consisting of dissimilar sample types from PC patients at different stages, cutoffs, and miRNA detection methods, among which the differences in sample type and cutoffs were the main drawbacks; (2) we only selected English articles, perhaps excluding potential papers published in other languages about PC patients with miRNA expression levels and prognostic outcomes; (3) we only chose studies estimating OS, perhaps excluding potential investigations reporting prognosis with other survival results, such as disease-free and recurrence-free survival; (4) the prognostic impact of miRNA expression levels in pancreatic cancer should be adjusted for risk factors that have an important influence on pancreatic cancer prognosis, such as age, educational level, sex, smoking, obesity, heavy alcohol intake, underlying illnesses and family history of cancer, which indicates possible mutations. However, the searched papers may not all contain the very concerned information. Therefore, the impact of bias in predicting miRNAs involved in pancreatic cancer prognosis may occur due to the lack of adjustment for risk factors in a rigorous conclusion.

Insight for future clinical and experimental studies

Notably, this study was the first meta-analysis of the associations between abnormal miRNA levels and prognosis in PC patients. This study provides direction for further clinical and experimental study: (1) joint detection of various miRNA levels could be utilized by clinical workers and other health care providers, which might extremely expand the ability to assess the prognosis of PC patients such that immediate treatment might be supplied; (2) advances and trends regarding miRNA expression levels and the survival time of PC patients could be obviously acquired by the experimental researchers mentioned in Tables 4 and 5. In addition, miRNA molecular mechanisms could be obtained by assessing the data in Table 3; and (3) several contradictory outcomes concerning the prognostic value of miRNAs might be resolved on account of the present work.


In summary, blood miR-21, miR-451a, miR-1290 and tissue miR-10b, miR-17-5p, miR-21, miR-23a, miR-29c, miR-126, miR-155, miR-203, miR-218, miR-221, miR-222 had significant prognostic value.

Materials and Methods

Search strategy

Two independent authors (Fei Zhao and Chao Wei) performed the literature search from 4 online databases, PubMed, EMBASE, Web of Science and Cochrane Database of Systematic Reviews. Afterwards, Yue Zhang reassessed undetermined information. An extensive and comprehensive search was performed utilizing the keywords: ‘microRNA’, ‘miRNA’, ‘miR’, and ‘pancreatic cancer’, ‘pancreatic carcinoma’ and ‘pancreatic adenocarcinoma’. After duplicates were eliminated, 875 reports remained. Accordingly, 671 articles were excluded by titles and abstracts. For the residual 204 studies, 35 full-text studies were removed. The details of the literature selection are shown in Figure 8. The search deadline was June 1, 2019.

Flow diagram of literature search and selection.

Figure 8. Flow diagram of literature search and selection.

Inclusion criteria

The inclusion criteria were as follows: (1) articles on the correlation between miRNA expression level and survival time of PC patients; (2) inclusion of estimated OS outcomes; and (3) full-text in English.

Exclusion criteria

The exclusion criteria were as follows: (1) articles without original data (reviews, letters or laboratory studies); (2) nondichotomous miRNA level; and (3) frequency of studies evaluating OS of miRNA expression level equal or less than 2 in tissue. In addition, on the condition that more than one article was published on the same subjects, the most well-rounded paper was chosen for the present work. Likewise, if both univariate and multivariate analysis of OS were covered, the latter was chosen, as this type of analysis considers interferential factors.

Quality assessment

Fei Zhao and Chao Wei confirmed all qualified studies that analyzed the prognostic value of miRNAs in PC, and Yue Zhang reevaluated undetermined information. Quality assessment for each paper was performed employing the modified Newcastle–Ottawa Scale (NOS) [180]. NOS scores were calculated according to selection, comparability, and outcome. Articles with NOS scores ≥6 were considered high-quality articles [181].

Study selection

The flow chart with details of the study selection process is given in Figure 8.

Study frequency

The frequency of studies estimating the OS of PC patients with and miRNA expressions of PC patients is presented in Tables 4 (blood) and 5 (tissue), and includes the miRNA names, the frequency of included miRNAs, and the reference number.

Study characteristics

The fundamental particulars of the included literature are fully listed in Table 6. On the condition that the data were not offered in the article but just as Kaplan–Meier survival curves, the data were abstracted from the curves, and the generation of HR with 95% CI was next carried out employing the software Engauge Digitizer version 4.1.

Table 6. Characteristics of included studies about pancreatic cancer.

miRNAStudyCountrySampleNumberStageCut-offMethodFollow-up (month)ResultHR (L/H)HR (H/L)95%CI
21Liu, 2012 [4]ChinaSerum38I-IVMedianqRT-PCR24OSu3.261.47-7.23
21Wang, 2013 [5]ChinaSerum177III-IVMedianqRT-PCR30OSm1.711.15-2.54
21Abue, 2015 [6]JapanPlasma24I-IV850qRT-PCR>20OSu5.991.95-18.40
21Goto, 2018 [7]JapanSerum32I-IVMedianqRT-PCR>40OSu2.570.90-7.35
21Kawamura, 2019 [8]JapanPlasma55I-IIMeanqRT-PCR60OSm3.101.19-9.10
196aKong, 2010 [16]ChinaSerum35I-IV-5.22qRT-PCR>16OSu3.371.14-9.97
196aYu, 2017 [17]ChinaPlasma31NoneMedianqRT-PCR15OSm0.990.92-1.06
451aGoto, 2018 [7]JapanSerum32I-IVMedianqRT-PCR>40OSu1.450.63-3.31
451aTakahasi, 2018 [23]JapanPlasma50I-IIMedianqRT-PCR54OSm3.201.07-11.94
451aKawamura, 2019 [8]JapanPlasma55I-IIMeanqRT-PCR60OSm3.601.13-11.31
1290Li, 2013 [24]USASerum56I-IIIMedianqRT-PCR>80OSu1.630.66-3.98
1290Tavano, 2013 [26]ItalyPlasma167I-IVROCddPCR>40OSu1.401.00-1.96
10bNakata, 2011 [35]JapanFFPE115NoneNoneqRT-PCR101OSu2.191.37-3.50
10bPreis, 2011 [36]LebanonFFPE95I-IV5000ISH36OSu3.591.73-7.43
10bNguyen, 2016 [37]USAFrozen55I-II1.5 foldqRT-PCR34.25OSu1.120.54-2.32
10bYang, 2017 [38]Germany IFrozen69I-IVNoneqRT-PCR>60OSu1.991.07-3.73
Germany IIFrozen41I-IVNoneqRT-PCR>60OSu0.810.39-1.67
17-5pYu, 2010 [39]JapanFFPE80I-IV5.69qRT-PCR100OSu1.851.08-3.15
17-5pGu, 2016 [40]ChinaTissue58I-IVNoneqRT-PCR>50OSu1.890.98-3.64
17-5pZhu, 2018 [41]ChinaTissue26NoneNoneqRT-PCR>50OSu2.180.77-6.17
21Dillhoff, 2008 [43]USAFFPE80NoneMedianISH>60OSu4.232.17-8.25
21Giovannetti, 2010 [44]ItalyFrozen59I-IVMedianqRT-PCR60.5OSu2.311.30-4.10
21Hwang, 2010 [45]Korea and ItalyTissue97II-IVMedianqRT-PCR>60OSm3.161.67-6.02
21Jamieson, 2011 [46]UKFrozen48NoneMedianqRT-PCR>50OSm3.221.21-8.58
21Nagao, 2012 [47]JapanFFPE65NoneMeanqRT-PCR>40OSm2.121.07-4.20
21Caponi, 2013 [48]Italy and UKFFPE57NoneMedianqRT-PCR117.3OSm3.281.52-7.05
21Kadera, 2013 [49]USATissue145I-II,IVMedianISH100OSu1.060.70-1.60
21Ma, 2013 [50]ChinaFrozen78I-IV2 foldqRT-PCR>25OSm2.601.15-5.87
21Papaconstantinou, 2013 [51]GreeceFFPE88NoneMeanqRT-PCR>60OSm3.931.25-12.35
21Wang, 2013 [5]ChinaTissue65III-IVMedianqRT-PCR60OSm2.241.14-4.37
21Donahue, 2014 [52]USA IFFPE94I-IVMedianISH72OSm1.701.03-2.82
USA IIFFPE87I-IVMedianISH72OSu0.940.59-1.49
21Frampton, 2014 [53]UKFrozen91IIA,IIBMedianqRT-PCR>48OSu1.851.08-3.18
21Mitsuhashi, 2015 [54]JapanFFPE283I-IV75%qRT-PCR48OSu1.601.07-2.39
21Vychytilova-Faltejskova, 2015 [55]CzechFFPE74None27.15qRT-PCR>40OSu1.761.08-2.86
21Morinaga, 2016 [56]JapanFFPE39NoneMedianISH114.1OSu1.800.90-3.60
21Benesova, 2018 [57]CzechFFPE91II-IVMedianqRT-PCR18OSu1.601.02-2.50
21Xi, 2018 [58]TCGATissue169I-IVMedianDownloaded60OSu1.471.00-2.16
21Zhang, 2018 [59]GEOTissue174I-IVMedianDownloaded>80OSu1.891.37-2.62
21Zhao, 2018 [60]JapanTissue630-IVNoneqRT-PCR>60OSu2.991.25-7.14
23aMa, 2013 [50]ChinaFrozen78I-IV2 foldqRT-PCR>25OSu1.640.71-3.79
23aFrampton, 2014 [53]UKFrozen91IIA,IIBMedianqRT-PCR>48OSu1.871.07-3.16
23aDiao, 2018 [61]ChinaFrozen30NoneMedianqRT-PCR25OSu2.551.10-5.92
23aWu, 2018 [62]ChinaTissue52None3.5qRT-PCR>50OSu3.641.56-8.47
29cJamieson, 2011 [46]UKFrozen48NoneMedianqRT-PCR>50OSm1.890.68-5.26
29cJiang, 2015 [69]TCGAFrozen132I-IVNoneDownloaded>50OSu1.591.15-2.18
29cZou, 2015 [70]ChinaFFPE105I-IVMedianqRT-PCR30OSm1.141.00-1.29
29cWang, 2019 [33]GEOTissue178I-IVNoneDownloaded>80OSu1.671.05-2.63
126Liang, 2018 [68]TCGAFFPE175I-IVMedianDownloaded>83.3OSm1.581.04-2.39
126Liao, 2018 [27]TCGATissue112I-IINoneDownloaded>40OSu1.510.98-2.32
126Yu, 2018 [82]TCGATissue168I-IIMedianDownloaded72.4OSm1.551.07-2.24
155Ma, 2013 [50]ChinaFrozen78I-IV2 foldqRT-PCR>25OSm1.370.52-3.58
155Papaconstantinou, 2013 [51]GreeceFFPE88NoneMeanqRT-PCR>60OSm3.141.09-9.09
155Mikamori, 2017 [14]JapanTissue45I-IIMeanqRT-PCR>72OSm2.631.07-6.46
200cYu, 2010 [109]JapanFFPE99I-IV0.64qRT-PCR101OSm2.251.10-4.60
200cPaik, 2015 [110]KoreaFFPE84IB-III0.65qRT-PCR140OSm0.560.34-0.93-
200cLiu, 2016 [111]ChinaTissue75I-IVMeanqRT-PCR60OSm2.311.73-6.38
203Ikenaga, 2010 [112]JapanFFPE107I-IV0.054qRT-PCR98OSm1.210.72-2.07
203Shao, 2017 [113]TCGATissue161I-IVNoneDownloaded>80OSu2.181.31-2.49
203Shi, 2018 [114]TCGATissue177NoneMedianDownloaded>72OSu1.241.10-1.39
203Zhang, 2018 [59]GEOTissue174I-IVMedianDownloaded>80OSu2.271.57-3.27
218Li, 2013 [121]ChinaFFPE28None1.5 foldqRT-PCR>20OSu1.860.80-4.35
218Zhu, 2014 [122]ChinaFrozen113I-IVMeanqRT-PCR>50OSm2.121.51-2.50
218Li, 2015 [123]ChinaFrozen107I-IVMedianqRT-PCR60OSm7.242.01-18.28
221Jamieson, 2011 [46]UKFrozen48NoneMedianqRT-PCR>50OSm0.920.34-2.54
221Ma, 2013 [50]ChinaFrozen78I-IV2 foldqRT-PCR>25OSm2.000.87-4.62
221Sarkar, 2013 [125]USAFFPE24NoneNoneqRT-PCR>83.3OSu1.360.52-3.51
221Wang, 2016 [126]GermanyFrozen37I-II66.7%qRT-PCR>40OSu2.851.20-6.77
222Schultz, 2012 [28]DenmarkFFPE225I-IIMedianqRT-PCR24OSm1.391.06-1.84
222Lee, 2013 [127]ChinaFrozen60I-IVMedianqRT-PCR15OSm5.161.16-22.91
222Wang, 2016 [126]GermanyFrozen37I-IINoneqRT-PCR>40OSu1.860.79-4.37
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; GEO: Gene Expression Omnibus; FFPE: formalin-fixed paraffin-embedded; qRT-PCR: quantitative real-time polymerase chain reaction; ddPCR: droplet digital polymerase chain reaction; ISH: in-situ hybridization; OS: overall survival; uUnivariate analysis; mMultivariate analysis.

Statistical analysis

All analyses were carried out employing Stata version 13.0 (StataCorp, College Station, TX, USA). OS was the primary and unique guideline for the prognosis of PC patients with miRNAs. The HR was regarded as significant at the P <0.05 level in case of the 95% CI not including the value 1. Furthermore, a single miRNA was considered a strong candidate if its HR was over 2. Most analyses used random-effects models other than fixed-effects models because of the dissimilarity of sample types from PC patients at dissimilar stages, cutoffs, and miRNA methods in single studies. Begg’s funnel plot was used to estimate publication bias. A two-tailed P value less than 0.05 was regarded as significant. If publication bias occurred, the trim and fill method was conducted. The sensitivity analysis was employed to assess how sensitive the entire effect size was to remove the impact of single investigations. If the point estimation was outside of the 95% CI of the entire effect value after it was excluded from the entire analysis, a single study was deemed to have undue influence.

Author Contributions

Study concept and design: Yue Zhang; Acquisition of data: Fei Zhao and Chao Wei; Analysis and interpretation of data: Fei Zhao, Chao Wei, Meng-Ying Cui, Qiang-Qiang Xia and Shuai-Bin Wang; Drafting of the manuscript: Yue Zhang; Revision of manuscript: Fei Zhao, Chao Wei, Meng-Ying Cui, Qiang-Qiang Xia, Shuai-Bin Wang and Yue Zhang; Supervision of work: Yue Zhang; All authors read and approved the final manuscript.

Conflicts of Interest

The authors declare that they have no conflicts of interest.


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