Pancreatic cancer is a deadly disease with a 5-year survival rate of less than 5%.1 Difficulty in the early detection of pancreatic cancer remains the major obstacle to improving overall survival. Only 8% to 9% of patients with pancreatic cancer are diagnosed at an early stage.1 Early diagnosis is the most effective strategy for improving the long-term outcomes of pancreatic cancer. More than 2000 studies focusing on biomarkers of pancreatic cancer have been reported. Only serum cancer antigen 19-9 (CA19-9) has been proposed for the routine management of pancreatic cancer by the Food and Drug Administration; however, CA19-9 is not a high-performance marker for the diagnosis of pancreatic cancer. The median sensitivity and specificity for the diagnosis of pancreatic cancer using this marker are 79% and 82%, respectively.2 Ten percent of the white population is Lewis genotype-negative and is thus unable to express CA 19-9, leading to misdiagnoses.3 In addition, diseases such as benign biliary obstruction and other gastrointestinal tumors can cause an increase in CA19-9, leading to misdiagnoses.4 More accurate circulating biomarkers should be identified to reduce the misdiagnosis rate.
MicroRNAs (miRNAs) are dysregulated in multiple tumors and are involved in the regulation of tumorigenesis and development5 and may present novel biomarkers for the diagnosis of cancer. Specific miRNA profiles in pancreatic cancer tissues have been described and are different from those of chronic pancreatitis tissues, normal tissues, or ampullary adenocarcinoma tissues.6–8 In addition, abnormal expression of miRNA is observed during the multistep progression of pancreatic cancer; for example, miR-155 is abnormally expressed in pancreatic intraepithelial neoplasia 2 (PanIN-2) stage lesions, and miR-21 abnormalities are observed in PanIN-3 lesions.9–11 These studies indicate that miRNAs might be useful markers for the early diagnosis of pancreatic cancer. An ideal diagnostic method is noninvasive or is only minorly invasive. MiRNAs are stable in the serum and plasma and can exhibit diagnostically significant values.12 Thus, detection of circulating miRNAs might be a promising strategy for cancer diagnosis. Circulating miRNA profiles and their diagnostic values in pancreatic cancer has been described previously. Li et al13 demonstrated that numerous miRNAs were significantly elevated in pancreatic cancer serum compared with the sera from healthy volunteers and patients with chronic pancreatitis. Of these miRNAs, miR-1290 exhibited perfect diagnostic performance with an area under the curve (AUC) value of 0.96. However, Li et al collected only the samples from a single center. Schultz et al14 showed that 38 miRNAs in whole blood were significantly aberrant in patients with pancreatic cancer compared with controls, and 2 miRNA diagnostic panels were identified. However, patients with pancreatic neuroendocrine tumors (PNET) and other pancreatic tumors (OPT) were not included in their study. Furthermore, the results from these 2 studies were not consistent. Thus, further studies are required to identify the profiles of circulating miRNAs in pancreatic cancer and to determine valid miRNA markers for the disease.
This study aimed to screen the plasma miRNAs profiles of patients with pancreatic cancer, healthy volunteers, and patients with chronic pancreatitis using miRNA arrays. Validation was performed via a multicenter study, and the diagnostic values of the selected miRNAs were evaluated.
MATERIALS AND METHODS
This study was approved by the Institutional Review Board of Peking Union Medical College Hospital. Written informed consent was obtained from all of the patients.
Study Design and Subjects
A 3-phase, multicenter study was designed to identify plasma miRNAs, using Taqman low-density arrays (TLDA, Applied Biosystems, Life Technologies, Shanghai, China), for the early diagnosis of pancreatic cancer. During the discovery phase, miRNA profiles were generated using 3 pooled samples from 7 patients with pancreatic cancer, 6 patients with chronic pancreatitis, and 5 healthy volunteers. During the preliminary validation phase, the differentially expressed miRNAs screened via TLDA were confirmed using quantitative real-time polymerase chain reaction (qRT-PCR) in a small sample that included patients with pancreatic cancer (n = 29) or chronic pancreatitis (n = 16) and healthy volunteers (n = 31). During the large sample validation phase, 363 subjects comprising 156 patients with pancreatic cancer (see Supplemental Digital Content Table 1, available at http://links.lww.com/SLA/A801), 65 healthy volunteers, 57 patients with chronic pancreatitis, 27 patients with PNET, and 58 patients with OPT were recruited from the following medical centers: (1) the Department of General Surgery, Peking Union Medical College Hospital, Beijing, China; (2) the Pancreatic Disease Institute, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; (3) the Pancreatic Team of the Department of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, China; (4) the Department of Pancreatic and Biliary Surgery, First Affiliated Hospital of Harbin Medical University, Harbin, China; (5) the Department of General Surgery, First Affiliated Hospital with Nanjing Medical University, Nanjing, China; and (6) the Department of Pancreatic Surgery, Sichuan Provincial Pancreatitis Centre, West China Hospital, Sichuan University, Chengdu, China.
Pancreatic cancer was diagnosed on the basis of cytological or histological examinations. Chronic pancreatitis was diagnosed on the basis of clinical diagnostic criteria or histological examinations. PNET and OPT were diagnosed on the basis of histological examinations. OPT included serous or mucinous cystadenomas, solid pseudopapillary tumors, intraductal papillary mucinous neoplasms, or epithelial cysts.
Sample Collection and Plasma RNA Isolation
Peripheral venous blood (3 mL) was collected in sterile ethylene diamine tetraacetic acid–treated anticoagulant tubes before clinical intervention or surgery. The blood samples were centrifuged at 3000 revolutions per minute (rpm) for 10 minutes. Plasma was collected and stored at −80°C for long-term storage. Total RNA was extracted from 625 μL plasma using a mirVana PARIS kit (Ambion, Austin, TX) according to the manufacturer's protocol. The concentrations of RNA were determined on the basis of the absorbance at 260 nm, and the purity was evaluated on the basis of the absorbance ratio at 260/280 nm using a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Rockland, DE). The RNA was stored at −80°C until use in further experiments.
Screening the Profiles by miRNA Microarray
Plasma miRNA expression profiles were screened using TLDA, which contain 671 human miRNAs, according to the manufacturer's protocol (Applied Biosystems, Life Technologies, Shanghai, China). MiRNAs with cycle thresholds (CTs) greater than 35 were excluded from further analysis. The expression levels of individual miRNAs were determined using the ΔCt method relative to the Ct of internal controls (U6 or miR-16). The expression changes in the samples were determined using the ΔΔCt method.
Validation of miRNA Expression by Real-time Quantitative PCR
First, selected miRNAs were reverse-transcribed to complementary DNA (cDNA) using a TaqMan MicroRNA Reverse Transcription Kit (Applied Biosystems, Life Technologies, Shanghai, China) according to the manufacturer's protocol. Following reverse transcription, amplification of cDNA was performed in a total volume of 20 μL, which included 1.33 μL cDNA, 10 μL TaqMan 2× Universal PCR Master Mix with no AmpErase UNG (Applied Biosystems, Life Technologies, Shanghai, China), 1 μL miRNA probe, and 7.67 μL nuclease-free water. The Stepone Plus Real-time PCR system (Applied Biosystems, Life Technologies, Shanghai, China) was used to perform real-time quantitative PCR. The reaction mixtures were incubated at 95°C for 10 minutes, followed by 40 cycles of 95°C for 15 seconds and 60°C for 1 minute. All of the reactions were performed in triplicate. SDS software (Applied Biosystems, Life Technologies, Shanghai, China) was used to calculate the CTs. U6 served as the internal control. The ΔCT values were generated by subtracting the miRNA CT values from the U6 CT values.
Detection of Serum CA19-9 Levels
Serum samples from 271 subjects including 144 patients with pancreatic cancer, 31 patients with chronic pancreatitis, 14 patients with PNET, 44 patients with OPT, and 38 healthy volunteers were collected for detecting CA19-9 levels. The expression levels of CA19-9 in serum were detected using a human carbohydrate antigen 19-9 (CA19-9) enzyme-linked immunosorbent assay kit (CSB-E04773 h, CUSABIO, Wuhan, China) according to the protocol supplied by the manufacturer.
Statistical analyses were performed using Statistic Package for Social Science (SPSS) V.13.0 (SPSS, Inc., Chicago, IL). Continuous data were analyzed using Student t tests or analysis of variance. Receiver operating characteristic curves were generated, and the AUC, sensitivity, and specificity were calculated to evaluate the diagnostic values of candidate miRNAs using MedCalc Statistical Software version 13.1.2 (MedCalc Software bvba, Ostend, Belgium; http://www.medcalc.org; 2014). AUCs of miRNAs (AUC1) and CA19-9 (AUC1) were compared using Z tests. Z value = |AUC1-AUC2|/sqrt(SE1^2+SE^2), P = [1-NORMSDIST(Z value)]*2. P value of less than 0.05 (2 sided) was considered statistically significant.
Plasma miRNA Profiles of Pancreatic Cancer
Plasma was collected from patients with pancreatic cancer (n = 7), chronic pancreatitis (n = 6), and healthy volunteers (n = 5). The profiles of plasma miRNAs were screened using TLDA, which comprised cards A and B with a total of 671 miRNAs. U6 and miR-16 were used as internal controls. The selection criteria included fold change greater than 3 or less than 0.125 and P value of less than 0.05 (Student t tests). Compared with healthy volunteers, 41 and 24 miRNAs were significantly dysregulated in patients with pancreatic cancer when normalized to U6 and miR-16, respectively. Fifteen miRNAs were significantly dysregulated in patients with pancreatic cancer normalized to both U6 and miR-16 including miR-106b-3p, miR-1233, miR-1271-5p, miR-1285-3p, miR-15b-3p, miR-181c-5p, miR-26b-3p, miR-30d-3p, miR-335-3p, miR-454-5p, miR-589-3p, miR-616-3p, miR-663b, miR-664-3p, and miR-744-3p (see Supplemental Digital Content Table 2, available at http://links.lww.com/SLA/A801). Compared with patients with chronic pancreatitis, 94 and 26 miRNAs were significantly dysregulated in patients with pancreatic cancer when normalized to U6 or miR-16, respectively. Nineteen miRNAs were significantly dysregulated in patients with pancreatic cancer normalized to both U6 and miR-16 including miR-1233, miR-127-3p, miR-15b-3p, miR-19a-3p, miR-26a-1-3p, miR-296-5p, miR-335-3p, miR-339-5p, miR-361-3p, miR-378a-5p, miR-454–5p, miR-545-3p, miR-579, miR-584-5p, miR-589-3p, miR-629-5p, miR-645, miR-7-5p, and miR-938 (see Supplemental Table 3, available at http://links.lww.com/SLA/A801).
Twenty-nine miRNAs (miR-106b-3p, miR-1233, miR-1271-5p, miR-1285-3p, miR-15b-3p, miR-181c-5p, miR-26b-3p, miR-30d-3p, miR-335-3p, miR-454-5p, miR-589-3p, miR-616-3p, miR-663b, miR-664-3p, miR-744-3p, miR-127-3p, miR-19a-3p, miR-26a-1-3p, miR-296-5p, miR-339-5p, miR-361-3p, miR-378a-5p, miR-545-3p, miR-579, miR-584-5p, miR-629-5p, miR-645, miR-7-5p, and miR-938) were selected for further validation. An additional four miRNAs (miR-126–3p, miR-19b-3p, miR-486–5p, and miR-942) were selected on the basis of their potential diagnostic values for cancers.15–18
Preliminary Validation in a Small Sample
We validated the expression levels of 33 selected miRNAs by qRT-PCR in a small sample, which included patients with pancreatic cancers (n = 29), chronic pancreatitis (n = 16), and healthy volunteers (n = 31). When U6 was used as an internal control, 9 miRNAs were significantly upregulated, and 3 miRNAs were downregulated in patients with pancreatic cancer compared with healthy volunteers (Table 1). When miR-16 was used as the internal control, 1 and 13 miRNAs were significantly upregulated and downregulated, respectively, in patients with pancreatic cancer compared with healthy volunteers (Table 1). Only 1 miRNA was significantly upregulated in patients with pancreatic cancer compared with patients with chronic pancreatitis, whether normalized using U6 or miR-16 (Table 1). Thirteen miRNAs were dysregulated when normalized using both U6 and miR-16, including miR-106b-3p, miR-126-3p, miR-1271, miR-1285, miR-19b-3p, miR-26b-3p, miR-296-5p, miR-486-5p, miR-663B, miR-7–5p, miR-938, miR-942, and miR-181c-5p, which were selected for further validation in a large sample.
Further Validation in a Large Sample
Three hundred sixty-three subjects were collected for the phase of further validation, including 156 patients with pancreatic cancer, 65 healthy volunteers, patients with chronic pancreatitis (n = 57), patients with PNET (n = 27), and patients with OPT (n = 58). MiR-486-5p, miR-181c-5p, miR-126-3p, miR-26b-3p, and miR-938 were significantly elevated in the plasma of patients with pancreatic cancer compared with healthy volunteers. MiR-486-5p, miR-181c-5p, miR-126-3p, miR-26b-3p, miR-938, miR-19b-3p, miR-942, and miR-1285 were significantly upregulated, whereas miR-663b was significantly downregulated in the plasma of patients with pancreatic cancer compared with patients with chronic pancreatitis. MiR-126-3p, miR-26b-3p, miR-938, and miR-19b-3p were significantly upregulated in the plasma of patients with pancreatic cancer compared with patients with PNET. MiR-181c-5p, miR-19b-3p, miR-26b-3p, and miR-938 were significantly upregulated in the plasma of patients with pancreatic cancer compared with patients with OPT (Fig. 1).
Diagnostic Value of the Differentially Expressed miRNAs for Pancreatic Cancer
We assessed the diagnostic values of the miRNAs that were expressed differentially in pancreatic cancer. MiR-486-5p, miR-126-3p, and miR-938 exhibited diagnostic value in discriminating patients with pancreatic cancer from healthy volunteers, with AUC values of 0.861 [95% confidence interval (CI), 0.808–0.904, P < 0.0001], 0.618 (95% CI, 0.550–0.682, P = 0.0044), and 0.693 (95% CI, 0.628–0.753, P < 0.0001), respectively (Table 2 and Fig. 2A).
MiR-486-5p, miR-126-3p, miR-938, miR-663b, and miR-19b-3p exhibited diagnostic value in discriminating patients with pancreatic cancer from patients with chronic pancreatitis, with AUC values of 0.707 (95% CI, 0.639–0.766, P < 0.0001), 0.612 (95% CI, 0.543–0.678, P = 0.0083), 0.754 (95% CI, 0.691–0.811, P < 0.0001), 0.607 (95% CI, 0.538–0.673, P = 0.0174), and 0.585 (95% CI, 0.515–0.652, P = 0.0385), respectively (Table 2 and Fig. 2B).
MiR-126-3p, miR-26b-3p, miR-938, and miR-19b-3p displayed diagnostic value in differentiating patients with pancreatic cancer from patients with PNET, with AUC values of 0.641 (95% CI, 0.567–0.710, P = 0.0083), 0.639 (95% CI, 0.565–0.709, P = 0.0116), 0.660 (95% CI, 0.586–0.728, P = 0.0023), and 0.638 (95% CI, 0.564–0.708, P = 0.0062), respectively (Table 2 and Fig. 2C).
Only miR-938 exhibited diagnostic value in differentiating patients with pancreatic cancer from patients with OPT; the AUC of miR-938 was 0.618 (95% CI, 0.549–0.683, P = 0.0063; Table 2 and Fig. 2D).
Comparison of the Diagnostic Values of the miRNAs With CA19-9
We also examined CA19-9 levels (see Supplemental Digital Content Fig. 1, available at http://links.lww.com/SLA/A801) and compared the diagnostic values of the miRNAs with the CA19-9 levels. We demonstrated that there were no significant differences between the AUC values of miR-486-5p and CA19-9 when discriminating patients with pancreatic cancer from healthy volunteers (P = 0.602). The AUC values of miR-486-5p and miR-938 when discriminating patients with pancreatic cancer from patients with chronic pancreatitis were comparable with those of CA19-9 (P = 0.230 and P = 0.739, respectively; see Supplemental Digital Content Table 4, available at http://links.lww.com/SLA/A801).
Despite extensive research in the field, the early diagnosis of pancreatic cancer remains difficult. In this current 3-phase, multicenter study, we demonstrated that plasma miR-486-5p was equivalent to CA19-9 in differentiating patients with pancreatic cancer from healthy volunteers. The diagnostic values of miR-486-5p and miR-938 were comparable with CA19-9 when discriminating patients with pancreatic cancer from patients with chronic pancreatitis.
Circulating miRNAs might be used as stable blood-based markers for cancer detection, including for the detection of pancreatic cancer.12–14 However, there is currently no generally accepted internal control for RT-PCR analysis of circulating miRNA levels. U6 is commonly used as a normalizing gene for miRNA quantitation studies. However, the use of U6 when normalizing circulating miRNA levels remains controversial because it was noted to be not suitable for normalizing circulating miRNAs in some studies and was considered a stable internal control in other studies.19–22 MiR-16 has also been used as a stable reference when normalizing circulating miRNAs.23,24 However, some studies demonstrated that circulating miR-16 was dysregulated in cancers, which might impair its value as a reference.25,26 To minimize the risk of false-positive results, both U6 and miR-16 were used to normalize plasma miRNA levels during the discovery and preliminary validation phases in the current study, and only miRNAs displaying significant dysregulation when normalized with both U6 and miR-16 were selected for our further multicenter study.
Schultz et al14 identified 2 diagnostic panels on the basis of miRNA expression in whole blood that exhibited potential for differentiating patients with pancreatic cancer from healthy participants in a multicenter study. However, patients with PNET and OPT were not included in their study. Li et al13 demonstrated that serum miR-1290 exhibited potential when discriminating patients with pancreatic cancer from healthy controls and patients with chronic pancreatitis, PNET, or intraductal papillary mucinous neoplasm. However, all of the samples included in that study were collected from a single center. To the best of our knowledge, the current study is the first multicenter trial to describe differences in plasma miRNA expression between patients with pancreatic cancer and healthy volunteers and patients with chronic pancreatitis, PNET, or OPT. Several of the miRNAs observed to be dysregulated in the current study are associated with cancer cell biology including miR-126-3p, miR-938, miR-663b, miR-19b-3p, and miR-486-5p.27–32 MiR-126-3p downregulation is associated with poor prognosis in colorectal cancer.27 MiR-938 is involved in tumorigenesis of nonfunctioning pituitary adenomas by negatively regulating Smad3.28 MiR-663b is involved in cell proliferation, migration, and apoptosis, and broadly affects regulation circuits centered on MAPK/ERK (mitogen-activated protein kinase/ extracellular signal-regulated kinase) signaling.29 As a member of the miR-17-92 cluster, miR-19b-3p is involved in cell proliferation, tumor progression, and drug resistance.30,31 The role(s) of miR-486-5p in cancers remains controversial. In pancreatic cancer, miR-486-5p, which is considered an oncomiR, is upregulated in cancer tissues relative to normal pancreatic tissues18 and is associated with invasion and metastasis.33 However, miR-486-5p functions as a tumor suppressor miRNA in some other tumors; reduced growth has been observed when miR-486-5p is re-expressed in breast cancer cells,34 and its loss in lung cancer contributes to malignancy by targeting components of the insulin growth factor signaling cascade.32 Further investigation is required to identify the role of miR-486-5p in pancreatic cancer.
In addition, we analyzed the diagnostic values of differentially expressed miRNAs. We demonstrated that miR-486-5p displayed a comparable value to CA19-9 when differentiating patients with pancreatic cancer from healthy controls and patients with chronic pancreatitis. A diagnostic value of miR-486-5p has also been described for gastric cancer. Zhu et al35 demonstrated that miR-486-5p was elevated in the plasma of gastric cancer patients compared with controls. A combination of miR-16, miR-25, miR-92a, miR-451, and miR-486-5p exhibited high diagnostic accuracy for early-stage gastric noncardiac adenocarcinoma, with an AUC value of 0.812.35 Therefore, a more rational study including other digestive tumors, such as gastric cancer, should be designed to further evaluate miR-486-5p for the diagnosis of pancreatic cancer. We also demonstrated that the AUC value of miR-938 in discriminating patients with pancreatic cancer from patients with chronic pancreatitis was comparable with that of CA19-9. Eight miRNAs including miR-938 exhibited decreased expression in the stool of patients with colon cancer, which were also more pronounced from early to later tumor, node, metastasis stages and exhibited diagnostic value.36 miR-486-5p and miR-938 might be helpful when combined with CA19-9 or in cases in which CA19-9 levels are normal.
Other miRNAs in this study, including miR-126–3p, miR-26b-3p, miR-938, and miR-19b-3p, showed a potential for differentiating patients with pancreatic cancer from patients with PNET or OPT. The sensitivity and specificity of these miRNAs were not perfect and were inferior to those of CA19–9.
One limitation of our study is that the diagnostic value of the combination of the miRNAs with CA19-9 was not evaluated because the plasma samples used to examine miRNAs and CA19-9 were not precisely matched. Another limitation is that the study did not examine the miRNA levels in the plasma of patients with other digestive tumors, which will be addressed in future studies.
This multicenter study identified several miRNAs that show a potential for differentiating patients with pancreatic cancer from healthy volunteers and patients with chronic pancreatitis, PNET, and OPT. Detection of these miRNAs might facilitate the diagnosis of a greater number of patients with pancreatic cancer including those at early stages, which would increase the percentage of patients who could be treated with radical resections.
Jianwei Xu, MD, Zhe Cao, MD, and Wenjing Liu, MD, contributed equally to this article and are considered to be joint first authors.
All of the authors have contributed to the intellectual content of this article and have met the following 3 requirements: (1) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of the data; (2) drafting or revising the article for intellectual content; and (3) final approval of the published article.
Jianwei, Xu, MD, Zhe Cao, MD, Taiping Zhang, MD, and Yupei Zhao, MD, conceived and designed the experiments. Jianwei Xu, MD, Zhe Cao, MD, and Wenjing Liu, MD, performed the experiments. Wenjing Liu, MD, Lei You, MD, Li Zhou, MD, analyzed the data. Chunyou Wang, MD, Wenhui Lou, MD, Bei Sun, MD, Yi Miao, MD, and Xubao Liu, MD, contributed reagents/materials/analysis tools. Lei You, MD, Li Zhou, MD, Chunyou Wang, MD, Wenhui Lou, MD, Bei Sun, MD, Yi Miao, MD, and Xubao Liu, MD, contributed to the writing of the manuscript.
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