Novel blood-based microRNA biomarkers for diagnosis of pancreatic cancer: a meta-analysis



Recently, several studies have shown that blood-based microRNAs in patients with pancreatic cancer (PC) could be aberrantly expressed. The purpose of this meta-analysis was to evaluate blood-based microRNAs as novel biomarkers for diagnosis of PC.


Eligible studies which had evaluated the diagnostic performance of blood-based microRNAs and had been published from February 2004 to February 2014 were retrieved. The quality of the studies was evaluated with the QUADAS-2 tool. The performance characteristics were pooled using random-effects models. Statistical analysis was performed with STATA and Meta-Disc1.4 software.


The global meta-analysis included 12 studies from 8 articles, which contained 1,060 blood-based samples of PC patients and 935 blood-based samples of non-PC patients. Summary results suggested pooled sensitivity of 0.87 (95% confidence interval [95% CI], 0.85-0.89), specificity 0.92 (95% CI, 0.90-0.94), positive likelihood ratio 11.18 (95% CI, 5.57-22.46), negative likelihood ratio 0.16 (95% CI, 0.11-0.23), diagnostic odds ratio 88.98 (95% CI, 39.85-198.69) and the area under the summary receiver operating characteristic (SROC) curve 0.96.


This meta-analysis demonstrated blood-based microRNA expression profiles with the potential to discriminate PC patients from non-PC patients, which have moderate diagnostic accuracy. However, further validation studies are needed for their clinical significance in the diagnosis of PC to be established.

Tumori 2015; 101(2): 199 - 205




Qing-Cai Meng, Wang-Wang Qiu, Zi-Qiang Wen, Hong-Cheng Wang, Ze-Zhi Shan, Zhou Yuan, Xin-Yu Huang

Article History


Financial support: This work was funded by the Science and Technology Commission of Shanghai Municipality (No. 12DZ1940808), Shanghai, China.
Conflict of interest: The authors have no conflict of interest.

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Pancreatic cancer (PC) is a common gastrointestinal malignancy and the fourth leading cause of cancer-related deaths in world, with a 1- and 5-year survival rates of about 20% and 6% (1-2-3). Nowadays, surgery is still the only effective treatment, while a majority of patients are diagnosed with locally advanced or metastatic lesions and lose the chance for an operation due to the difficulty of early diagnosis of PC. In a clinical setting, carbohydrate antigen 19-9 (CA19-9) and carcinoembryonic antigen (CEA) as conventional serum biomarkers, have been used for diagnosis and detection of PC (4, 5), but they lack sufficient sensitivity and specificity for the early diagnosis of PC. Therefore, to improve the survival rate of PC, effective diagnosis methods for early-stage tumors are urgently needed.

MicroRNAs (miRNAs) are small noncoding RNAs included 17 to 25 nucleotides, regulating the function of the target gene by inhibiting translation of mRNA or degrading mRNA. The aberrant expression of miRNAs is closely correlated with initiation and progression of tumors as well as the prognosis. Today, there are more than 2,500 human miRNAs being reported gradually, and miRNA expression profiles originating from samples of PC tissue have been described (6-7-8-9). Human miRNAs stably present in the blood by resisting degradation of Rnase and which show a difference between peripheral blood miRNA in patients with tumors and the normal population have been reported (10-11-12-13-14-15). To date, several clinical studies have identified aberrant miRNA expression panels from blood-based samples in PC patients, such as plasma, serum, whole blood, etc. (11-12-13-14-15-16-17-18), which suggest that the blood-based miRNA expression pattern = novel biomarkers for PC diagnosis.

Therefore, the present meta-analysis was performed to establish the overall diagnostic accuracy of miRNAs, to provide more reliable evidence that may help surgeons in the diagnosis of PC.

Methods and Study design

Search strategy and study selection

We carefully searched online PubMed/Medline, Web of Science and Embase from February 2004 to February 2014 to identify relevant studies, using the terms “pancreatic cancer/neoplasm” and “microRNAs,” in combination with keywords such as “diagnosis,” “plasma,” “serum” and “biomarker.” Eligible studies had to meet the following criteria: (i) study of miRNA expression profiling in PC patients; (ii) blood-based samples obtained from PC patients before treatment; (iii) each study included more than 10 patients; and (iv) data for diagnostic accuracy, such as sensitivity and specificity. Studies were excluded according to the following criteria: (i) non-English-language publication; (ii) samples from animals; and (iii) reviews, case reports and letters. Each study was assessed independently by 2 reviewers (Q.-C. M. and W.-W. Q.).

Quality assessment

According to the revised Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2), we systematically assessed the quality of all studies included. Four key domains of the tool include the following: (i) patient selection; (ii) index test; (iii) reference standard; (iv) flow and timing. Each domain is assessed by the risk of bias, and the first 3 are also assessed by applicability. Assessment results are presented as low, high or unclear risk of bias or concerns regarding applicability for each domain.

Data extraction

In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement (19), 2 reviewers (Q.-C. M. and W.-W. Q.) independently carried out the data extraction, and disagreements between reviewers were resolved by a third reviewer (Z.-Q. W.). The following data elements were collected from each study: author, year of publication, origin of population, test method, number of samples, origin of sample, sensitivity and specificity data, cutoff criteria and miRNA expression profiles.

Statistical analysis

We used the statistical methods recommended for diagnostic accuracy meta-analysis (20). Indexes of test accuracy were analyzed and calculated from the extracted data of each study, including sensitivity, specificity, true positive rate (TP), false positive rate (FP), true negative rate (TN), false negative rate (FN), positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and the area under the curve (AUC). The AUC is a comprehensive, representative test accuracy, combining sensitivity with specificity. An AUC of 1.0 (100%) demonstrates perfect ability to distinguish cases from noncases. The pooled indexes from all studies were used in random-effects models or fixed-effects (weighted with inverse variance) models. A random-effects model was chosen if heterogeneity was >50%, whereas a fixed-effects model was chosen if heterogeneity was <50% (21). Heterogeneity analysis was carried out using chi-square test and I-square test. Publication bias was evaluated using a funnel plot with the Egger’s regression test (22). All statistical analyses were performed using Meta-Disc 1.4 (XI Cochrane Colloquium, Barcelona, Spain) and Stata Version 12.0 software (Stata Corp., College Station, TX, USA). The significance level of all statistical tests was 2-sided and set at a p value <0.05.


Study characteristics

By means of the above search strategy, a total of 268 potentially relevant articles were identified. After prudential screening of the titles, abstracts and key words, 259 articles were excluded because they were non-English articles or were reviews, letters, case reports, samples of nonblood origin or studies irrelevant to the current analysis. For the remaining 10 articles, we reviewed the full text again in detail, and 2 papers were excluded because they lacked data associated with sensitivity and specificity. Finally 12 studies from 8 articles were included in this meta-analysis (11-12-13-14-15-16-17-18). The process of study selection is shown in a flow diagram (Fig. 1).

Flow chart showing the process of study selection.

The main characteristics of the eligible studies are listed in Table I. We collected data from 12 studies including a total of 1,995 blood-based samples of participants from the United States (4 studies, 33.3%), China (4 studies, 33.3%), Denmark (2 studies, 16.7%) and Japan (2 studies, 16.7%). All patients with PC were verified histologically in surgical specimens, while inoperable patients were diagnosed by computed tomography (CT) showing a solid mass in the pancreas and histology or cytology from this primary tumor or a liver metastasis by fine-needle aspiration (FNA). The sources of blood-based samples included plasma (6 studies, 50.0%), serum (4 studies, 33.3%) and whole blood (2 study, 16.7%). The method of quantitative real-time polymerase chain reaction (qRT-PCR) was widely used to analyze miRNA expression profiles of the samples involved in all studies. A total of 26 differentially expressed miRNAs were collected in this meta-analysis which compared PC patients with non-PC. Notably, the cutoff criteria of miRNA expression profiles were different in each study, with mean fold applied in 3 studies, 2-fold used in 5 studies and criteria not mentioned in other studies.

Characteristics of the included studies

Study Author (ref.) Year Origin of population Test method Sample size Sample origin TP (%) FP (%) FN (%) TN (%) Cutoff criteria MicroRNA profiles
FN = false negative rate; FP = false positive rate; TN = true negative rate; TP = true positive rate; qRT-PCR = quantitative real-time polymerase chain reaction; “-” indicates that the study does not mention cutoff criteria. *from the opposite arm of the precursor(miR126).
1 Wang et al (18) 2009 USA qRT-PCR 47 Plasma 38.3 4.3 21.3 36.1 2-fold miR-21, 155, 196a, 210
2 Morimura et al (16) 2011 Japan qRT-PCR 66 Plasma 34.8 0 19.7 45.5 Mean miR-18a
3 Liu et al (14) 2012 China qRT-PCR 245 Plasma 49.4 1.6 6.9 42.1 - miR-16, 196a
4 Liu et al (14) 2012 China qRT-PCR 206 Plasma 61.7 1.5 5.3 31.5 - miR-16, 196a
5 Liu et al (15) 2012 China qRT-PCR 202 Serum 59.4 1.5 0 39.1 2-fold miR-20a, 21, 24, 25, 99a, 185, 191
6 Liu et al (15 ) 2012 China qRT-PCR 69 Serum 46.4 5.8 7.2 40.6 2-fold miR-20a, 21, 24, 25, 99a, 185, 191
7 Li et al (13) 2013 America qRT-PCR 120 Serum 59.2 5.0 8.3 27.5 2-fold miR-1290
8 Li et al (13) 2013 America qRT-PCR 213 Serum 31.5 15.9 6.6 46.0 2-fold miR-1290
9 Kawaguchi et al (11) 2013 Japan qRT-PCR 47 plasma 61.7 8.5 2.1 27.7 Mean miR-221
10 Ganepola et al (12) 2014 America qRT-PCR 22 Plasma 45.5 4.5 4.5 45.5 - miR-22.3p, 642b.3p, 885.5p
11 Schultz et al (17) 2014 Denmark qRT-PCR 379 Whole blood 40.4 2.9 7.1 49.6 Mean miR-145, 150, 223, 636
12 Schultz et al (17) 2014 Denmark qRT-PCR 379 Whole blood 40.4 1.1 7.1 51.4 Mean miR-26b, 34a, 122, 126*, 145, 150, 223, 505, 636, 885.5p

QUADAS-2 assessment

QUADAS-2 is designed to assess the quality of primary diagnostic accuracy studies. Figure 2 shows the overall methodological quality of the 12 included studies. In the domain of patient selection, risk of bias was judged low in 8 studies, and a high risk of bias was shown in another 4 studies resulting from overoptimistic estimates of diagnostic accuracy. Similarly, risk of bias was rated as low, high or unclear in 6 studies - 2 studies and 4 studies in the domain of index test, respectively. Other items were reported as above.

Methodological quality graph: judgments about overall methodological quality across 11 included studies, using the revised Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2).

Diagnostic accuracy assessment

Forest plots of sensitivity, specificity, PLR and NLR for miRNA expression profiles are shown in Figure 3. Pooled results are listed in Table II. After pooling accuracy indicators of miRNA in diagnosis of PC, summary results showed that pooled sensitivity was 0.87 (95% confidence interval [95% CI], 0.85-0.89), specificity was 0.92 (95% CI, 0.90-0.94), PLR was 11.18 (95% CI, 5.57-22.46) and NLR was 0.16 (95% CI, 0.11-0.23). On testing the heterogeneity of these studies, chi-squared values of sensitivity, specificity, PLR and NLR were found to be 64.67 (p = 0.000), 72.16 (p = 0.000), 89.24 (p = 0.000) and 48.45 (p = 0.000), respectively. Thus, there was significant heterogeneity between these studies, so the pooled indicators were calculated using the random-effects model.

Pooled diagnostic accuracy

Sensitivity (95% CI) Specificity (95% CI) PLR (95% CI) NLR (95% CI) DOR (95% CI) AUC (SEM)
AUC = the area under the curve; CI = confidence interval; DOR = diagnostic odds ratio; NLR = negative likelihood ratio; PC = pancreatic cancer; PLR = positive likelihood ratio.
PC vs. non-PC 0.87 (0.85-0.89) 0.92 (0.90-0.94) 11.18 (5.57-22.46) 0.16 (0.11-0.23) 88.98 (39.85-198.69) 0.96 (0.01)
Heterogeneity (p value) 64.67 (0.00) 72.16 (0.00) 89.24 (0.00) 48.45 (0.00) 48.04 (0.00) -

Forest plots of sensitivity (A), specificity (B), PLR (C) and NLR (D) for miRNA assays in the diagnosis of PC. Each solid circle represents an eligible study. The size of the solid circle reflects the sample size of each eligible study. Error bars represent 95% confidence interval (CI). LR = likelihood ratio.

Clinical value

The pooled DOR for the diagnostic value showed a significant difference between PC with non-PC (DOR = 88.98; 95% CI, 39.85-198.69). The chi-squared value for the heterogeneity test was 48.04%, so the pooled DOR for the diagnostic value was calculated using the random-effects model. The summary receiver operating characteristic (SROC) curve for the miRNA expression profiles was drawn based on a series of 2 different classification methods, including TP (sensitivity) for the vertical axis and FP (1-specificity) for the horizontal axis (Fig. 4). The area under the SROC curve was 0.96, which demonstrates potential diagnostic capability to distinguish PC patients from non-PC. The Q-value is the intersection of the SROC curve with a diagonal line of the ROC diagram from the upper left corner to the lower right corner, reflecting the highest diagnostic value for both the sensitivity and specificity. Furthermore, the Q-value also can be used as an overall indicator to assess the diagnostic value of these studies. In this meta-analysis, the Q-value was 0.91, which indicates that the intersection is very close to the upper left corner of the ROC diagram, and the overall test is provided with high sensitivity and specificity. In summary, these results indicated that the miRNA expression profiles could differentiate PC from non-PC.

Summary receiver operating characteristic (SROC) curve for miRNA assays in the diagnosis of pancreatic cancer (PC). Each solid circle represents an eligible study. The size of solid circles reflects the sample size of each eligible study. The regression SROC curve summarizes the overall diagnostic accuracy. AUC = the area under the curve; SE = standard error.

Publication bias

In this meta-analysis, strict inclusion or exclusion criteria and the careful design of the data analysis were used to minimize the potential publication bias. Finally, publication bias of the included studies was assessed by funnel plots and Egger’s tests. There was no evidence for significant publication bias in the meta-analysis if the p value was not <0.05. Hence, publication bias was not found according to this funnel plot, which is almost symmetric, and Egger’s test (p = 0.324). The funnel plot is shown in Figure 5.

Funnel graph for the assessment of potential publication bias of the 12 included studies. The funnel graph plots the log of diagnostic odds ratio (DOR) against the standard error of the log of the DOR. Each solid circle represents an eligible study.


Although surgical resection shows promise as an effective treatment, PC remains a malignant disease with poor prognosis because it is generally asymptomatic, and there are no effective biomarkers for diagnosis at the early stage. Currently, many serum biomarkers have been reported for PC diagnosis, such as CA19-9, CEA-CAM1, MUC-1, TIMP-1, MMP-9, lectins, REG4 and K-ras mutations (23-24-25-26-27-28). Serum CA19-9 is the most widely available diagnostic biomarker, with a sensitivity of 70%-80% in PC patients but a specificity of less than 50% (29). Thus, the conventional diagnostic biomarkers in PC patients have certain limitations.

Aberrant miRNA expression profiles between PC and non-PC were previously reported in several studies, with miRNAs derived from tumor tissues and cell lines (8, 9, 30-31-32). A study of measurement of miRNA abundance in PC and chronic pancreatitis (CP) was conducted by Bauer et al, enunciating the suggestion that miRNAs derived from tissues or blood could have an immediate diagnostic value for the evaluation of tumor reoccurrence in patients who have undergone curative surgical resection, and proposing the hypothesis that inflammation, such as CP, could be required as 1 step toward the development of PC (8). However, although it can be used as a new tumor marker, tumor tissue miRNA is obtained by invasive surgery or biopsy, which still can not meet the needs of diagnosis. In contrast, blood-based miRNAs are stable, easily detectable and significantly disease-specific (33), and have been reported as potential biomarkers for early diagnosis of lung and liver cancer (34, 35). Similarly, this present meta-analysis showed the diagnostic value of blood-based miRNAs in PC.

The present meta-analysis included 12 studies, each of which obtained miRNA quantitative expression data using qRT-PCR. Ultimately, there are a total of 26 miRNAs (miRNA-16, 18a, 20a, 21, 22-3p, 24, 25, 26b, 34a, 99a, 122, 126*, 145, 150, 155, 185, 191, 196a, 210, 221, 223, 505, 636, 642b-3p, 885-5p, 1290) included in this analysis, some of which were also compared with CA19-9. From the summary results, the pooled sensitivity and specificity of miRNA biomarkers were 0.87 and 0.92, indicating a better diagnostic result compared with CA19-9 alone. The PLR and NLR are relatively ­independent, clinically meaningful indicators to assess diagnostic test effects. When the PLR value was >10 or the NLR value was <0.1, possibility of a disease diagnosis was markedly increased. Hence, the pooled PLR value was 11.18, which indicates PC patients can have an 11 times higher possibility of a positive miRNA test compared with non-PC. Similarly, the NLR value was 0.16, which suggests that the likelihood of a patient having PC is only 16% if the miRNA test is negative.

As an evaluation index of overall performance in diagnostic testing, the DOR has a significant advantage, which is not affected by the morbidity, and also takes into account the sensitivity and specificity. Diagnostic accuracy is better with a higher value of the DOR. For instance, a diagnostic test has almost no ability to distinguish case from noncase if the DOR of the test is 1.0. The pooled DOR in this meta-analysis was 88.98, indicating a high diagnostic accuracy.

AUC is also a very important indicator of diagnostic accuracy similar to DOR. The area under the SROC curve was 0.96, which demonstrates potential diagnostic capability. Thus, these indicators reflect the promising clinical value of blood-based miRNA as a diagnostic biomarkers.

The origins of blood-based samples include plasma, serum and whole blood. There is almost no difference in miRNA extraction methods between plasma- and serum-based samples, while miRNAs extracted from whole blood collected in PAXgene blood RNA tubes avoid the interference caused by centrifugation (17, 36, 37). Liu et al have identified a 7 miRNAs–based serum panel for accurately discriminating PC cases from cancer-free controls (15). Another study has indicated that the combination of plasma miRNA-16, miRNA-196a and CA19-9 was more effective for PC diagnosis (14). The diagnostic panels of miRNA expression in whole blood were identified by Schultz et al, with the potential to distinguish patients with PC from healthy controls, with sensitivity and specificity superior to those of CA19-9 (17). As a risk factor for PC, CP has similar clinical symptoms, making it difficult to distinguish between the 2 diseases (38, 39). Several studies have shown that blood-based miRNAs could serve as a specific biomarker to distinguish PC from CP (14, 15, 17, 40). Furthermore, to achieve an early diagnosis, Schultz et al used samples from patients with only stage I/II PC and observed that a panel of miRNA expression profiles also had significant diagnostic value (17). Although miRNAs were derived from blood-based samples, their expression profiles have varied in different studies, which means they could not form a unified diagnostic panel.

Several limitations have to be considered in the current meta-analysis. Firstly, the heterogeneity of the population was increased due to the difference in the characteristics of patients (age, race, country, etc.), the cutoff criteria and miRNA expression profiles. Secondly, significant differences in the number of samples in different studies may have had some impact on the results. Finally, to further validate a novel miRNA biomarkers in the diagnosis of PC, we need more multicenter prospective clinical studies.


In summary, the present meta-analysis, representing a quantified synthesis of published studies, has shown that blood-based miRNA expression profiles have the potential to discriminate PC patients from non-PC patients. As a novel method, blood-based miRNA biomarkers have diagnostic advantages compared with other indicators. However, more validation studies should be conducted before these can be implemented as part of the routine clinical diagnosis of PC.


We thank all clinical investigators who were involved in this meta-analysis.


Financial support: This work was funded by the Science and Technology Commission of Shanghai Municipality (No. 12DZ1940808), Shanghai, China.
Conflict of interest: The authors have no conflict of interest.
  • 1. Ferlay J.,Steliarova-Foucher E.,Lortet-Tieulent J. Cancer incidence and mortality patterns in Europe: estimates for 40 countries in 2012. Eur J Cancer 2013; 49: 1374-1403 Google Scholar
  • 2. Hidalgo M. Pancreatic cancer. N Engl J Med 2010; 362: 1605-1617 Google Scholar
  • 3. Siegel R.,Naishadham D.,Jemal A. Cancer statistics, 2013. CA Cancer J Clin 2013; 63: 11-30 Google Scholar
  • 4. Locker GY.,Hamilton S.,Harris J. ASCO. ASCO 2006 update of recommendations for the use of tumor markers in gastrointestinal cancer. J Clin Oncol 2006; 24: 5313-5327 Google Scholar
  • 5. Satake K.,Kanazawa G.,Kho I.,Chung YS.,Umeyama K. A clinical evaluation of carbohydrate antigen 19-9 and carcinoembryonic antigen in patients with pancreatic carcinoma. J Surg Oncol 1985; 29: 15-21 Google Scholar
  • 6. Lee EJ.,Gusev Y.,Jiang J. Expression profiling identifies microRNA signature in pancreatic cancer. Int J Cancer 2007; 120: 1046-1054 Google Scholar
  • 7. Szafranska AE.,Doleshal M.,Edmunds HS. Analysis of microRNAs in pancreatic fine-needle aspirates can classify benign and malignant tissues. Clin Chem 2008; 54: 1716-1724 Google Scholar
  • 8. Bauer AS.,Keller A.,Costello E. Diagnosis of pancreatic ductal adenocarcinoma and chronic pancreatitis by measurement of microRNA abundance in blood and tissue. PLoS ONE 2012; 7: e34151- Google Scholar
  • 9. LaConti JJ.,Shivapurkar N.,Preet A. Tissue and serum microRNAs in the Kras(G12D) transgenic animal model and in patients with pancreatic cancer. PLoS ONE 2011; 6: e20687- Google Scholar
  • 10. Chen X.,Ba Y.,Ma L. Characterization of microRNAs in serum: a novel class of biomarkers for diagnosis of cancer and other diseases. Cell Res 2008; 18: 997-1006 Google Scholar
  • 11. Kawaguchi T.,Komatsu S.,Ichikawa D. Clinical impact of circulating miR-221 in plasma of patients with pancreatic cancer. Br J Cancer 2013; 108: 361-369 Google Scholar
  • 12. Ganepola GA.,Rutledge JR.,Suman P.,Yiengpruksawan A.,Chang DH. Novel blood-based microRNA biomarker panel for early diagnosis of pancreatic cancer. World J Gastrointest Oncol 2014; 6: 22-33 Google Scholar
  • 13. Li A.,Yu J.,Kim H. MicroRNA array analysis finds elevated serum miR-1290 accurately distinguishes patients with low-stage pancreatic cancer from healthy and disease controls. Clin Cancer Res 2013; 19: 3600-3610 Google Scholar
  • 14. Liu J.,Gao J.,Du Y. Combination of plasma microRNAs with serum CA19-9 for early detection of pancreatic cancer. Int J Cancer 2012; 131: 683-691 Google Scholar
  • 15. Liu R.,Chen X.,Du Y. Serum microRNA expression profile as a biomarker in the diagnosis and prognosis of pancreatic cancer. Clin Chem 2012; 58: 610-618 Google Scholar
  • 16. Morimura R.,Komatsu S.,Ichikawa D. Novel diagnostic value of circulating miR-18a in plasma of patients with pancreatic cancer. Br J Cancer 2011; 105: 1733-1740 Google Scholar
  • 17. Schultz NA.,Dehlendorff C.,Jensen BV. MicroRNA biomarkers in whole blood for detection of pancreatic cancer. JAMA 2014; 311: 392-404 Google Scholar
  • 18. Wang J.,Chen J.,Chang P. MicroRNAs in plasma of pancreatic ductal adenocarcinoma patients as novel blood-based biomarkers of disease. Cancer Prev Res (Phila) 2009; 2: 807-813 Google Scholar
  • 19. Moher D.,Liberati A.,Tetzlaff J.,Altman DG. PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med 2009; 151: 264-269 Google Scholar
  • 20. Devillé WL.,Buntinx F.,Bouter LM. Conducting systematic reviews of diagnostic studies: didactic guidelines. BMC Med Res Methodol 2002; 2: 9- Google Scholar
  • 21. DerSimonian R.,Laird N. Meta-analysis in clinical trials. Control Clin Trials 1986; 7: 177-188 Google Scholar
  • 22. Copas J.,Shi JQ. Meta-analysis, funnel plots and sensitivity analysis. Biostatistics 2000; 1: 247-262 Google Scholar
  • 23. Gold DV.,Modrak DE.,Ying Z.,Cardillo TM.,Sharkey RM.,Goldenberg DM. New MUC1 serum immunoassay differentiates pancreatic cancer from pancreatitis. J Clin Oncol 2006; 24: 252-258 Google Scholar
  • 24. Joergensen MT.,Brünner N.,De Muckadell OBS. Comparison of circulating MMP-9, TIMP-1 and CA19-9 in the detection of pancreatic cancer. Anticancer Res 2010; 30: 587-592 Google Scholar
  • 25. Marchese R.,Muleti A.,Pasqualetti P. Low correspondence between K-ras mutations in pancreatic cancer tissue and detection of K-ras mutations in circulating DNA. Pancreas 2006; 32: 171-177 Google Scholar
  • 26. Patwa TH.,Zhao J.,Anderson MA.,Simeone DM.,Lubman DM. Screening of glycosylation patterns in serum using natural glycoprotein microarrays and multi-lectin fluorescence detection. Anal Chem 2006; 78: 6411-6421 Google Scholar
  • 27. Simeone DM.,Ji B.,Banerjee M. CEACAM1, a novel serum biomarker for pancreatic cancer. Pancreas 2007; 34: 436-443 Google Scholar
  • 28. Takayama R.,Nakagawa H.,Sawaki A. Serum tumor antigen REG4 as a diagnostic biomarker in pancreatic ductal adenocarcinoma. J Gastroenterol 2010; 45: 52-59 Google Scholar
  • 29. Ni XG.,Bai XF.,Mao YL. The clinical value of serum CEA, CA19-9, and CA242 in the diagnosis and prognosis of pancreatic cancer. Eur J Surg Oncol 2005; 31: 164-169 Google Scholar
  • 30. Bauer AS.,Keller A.,Costello E. Diagnosis of pancreatic ductal adenocarcinoma and chronic pancreatitis by measurement of microRNA abundance in blood and tissue. PLoS ONE 2012; 7: e34151- Google Scholar
  • 31. Bloomston M.,Frankel WL.,Petrocca F. MicroRNA expression patterns to differentiate pancreatic adenocarcinoma from normal pancreas and chronic pancreatitis. JAMA 2007; 297: 1901-1908 Google Scholar
  • 32. Schultz NA.,Werner J.,Willenbrock H. MicroRNA expression profiles associated with pancreatic adenocarcinoma and ampullary adenocarcinoma. Mod Pathol 2012; 25: 1609-1622 Google Scholar
  • 33. Mitchell PS.,Parkin RK.,Kroh EM. Circulating microRNAs as stable blood-based markers for cancer detection. Proc Natl Acad Sci USA 2008; 105: 10513-10518 Google Scholar
  • 34. Wang K.,Zhang S.,Marzolf B. Circulating microRNAs, potential biomarkers for drug-induced liver injury. Proc Natl Acad Sci USA 2009; 106: 4402-4407 Google Scholar
  • 35. Yanaihara N.,Caplen N.,Bowman E. Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell 2006; 9: 189-198 Google Scholar
  • 36. Chen X.,Liang H.,Zhang J.,Zen K.,Zhang C-Y. Secreted microRNAs: a new form of intercellular communication. Trends Cell Biol 2012; 22: 125-132 Google Scholar
  • 37. Patnaik SK.,Yendamuri S.,Kannisto E.,Kucharczuk JC.,Singhal S.,Vachani A. MicroRNA expression profiles of whole blood in lung adenocarcinoma. PLoS ONE 2012; 7: e46045- Google Scholar
  • 38. Lowenfels AB.,Maisonneuve P.,Cavallini G. International Pancreatitis Study Group. Pancreatitis and the risk of pancreatic cancer. N Engl J Med 1993; 328: 1433-1437 Google Scholar
  • 39. Maisonneuve P.,Lowenfels AB. Chronic pancreatitis and pancreatic cancer. Dig Dis 2002; 20: 32-37 Google Scholar
  • 40. Kong X.,Du Y.,Wang G. Detection of differentially expressed microRNAs in serum of pancreatic ductal adenocarcinoma patients: miR-196a could be a potential marker for poor prognosis. Dig Dis Sci 2011; 56: 602-609 Google Scholar



  • Department of General Surgery, Sixth People’s Hospital Affiliated With Shanghai Jiao Tong University, Shanghai - PR China

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