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Tissue MicroRNA profiles as diagnostic and prognostic biomarkers in patients with resectable pancreatic ductal adenocarcinoma and periampullary cancers

Biomarker Research volume 5, Article number: 8 (2017) Cite this article

Abstract

Background

The aim of this study was to validate previously described diagnostic and prognostic microRNA expression profiles in tissue samples from patients with pancreatic cancer and other periampullary cancers.

Methods

Expression of 46 selected microRNAs was studied in formalin-fixed paraffin-embedded tissue from patients with resected pancreatic ductal adenocarcinoma (n = 165), ampullary cancer (n=59), duodenal cancer (n = 6), distal common bile duct cancer (n = 21), and gastric cancer (n = 20); chronic pancreatitis (n = 39); and normal pancreas (n = 35). The microRNAs were analyzed by PCR using the Fluidigm platform.

Results

Twenty-two microRNAs were significantly differently expressed in patients with pancreatic cancer when compared to healthy controls and chronic pancreatitis patients; 17 miRNAs were upregulated (miR-21-5p, −23a-3p, −31-5p, −34c-5p, −93-3p, −135b-3p, −155-5p, −186-5p, −196b-5p, −203, −205-5p, −210, −222-3p, −451, −492, −614, and miR-622) and 5 were downregulated (miR-122-5p, −130b-3p, −216b, −217, and miR-375). MicroRNAs were grouped into diagnostic indices of varying complexity. Ten microRNAs associated with prognosis were identified (let-7 g, miR-29a-5p, −34a-5p, −125a-3p, −146a-5p, −187, −205-5p, −212-3p, −222-5p, and miR-450b-5p). Prognostic indices based on differences in expression of 2 different microRNAs were constructed for pancreatic and ampullary cancer combined and separately (30, 5, and 21 indices).

Conclusion

The study confirms that pancreatic cancer tissue has a microRNA expression profile that is different from that of other periampullary cancers, chronic pancreatitis, and normal pancreas. We identified prognostic microRNAs and microRNA indices that were associated with shorter overall survival in patients with radically resected pancreatic cancer.

Background

Pancreatic cancer (PC) is the fourth most common cause of cancer-related death in the Western world, although it only represents 3% of all new cancer cases [1, 2]. Most cases are pancreatic ductal adenocarcinomas (PDAC). Due to locally advanced or metastatic disease, only 20% of all patients diagnosed with PC are accessible to radical surgical treatment, and thereby have the potential for long-term survival [3, 4]. However, even in this group, the 5-year survival is only 20% due to the high recurrence rate [5, 6].

PC located in the head of the pancreas constitutes the majority (60–70%) of the group of cancers in the region, which also includes of ampullary adenocarcinomas (A-AC), accounting for 15–25%; and duodenal cancers (DC); and distal common bile duct (CBD) cancers, each accounting for approximately 10%[6]. The distribution of the different types of the periampullary cancers is variously reported, probably due to the complexity of the periampullary anatomy and histopathology. The 5-year survival rate after surgery is 45–55% for A-AC and DC [7, 8] and approximately 25% for distal CBD cancers [6].

Cancer antigen 19–9 (CA 19–9, also named carbohydrate antigen 19–9 and sialylated Lewis antigen) is the most widely used biomarker for patients with PC. Serum CA19-9 alone is insufficient as a diagnostic biomarker, although it may have prognostic value in the absence of cholestasis [9]. There is an obvious need for better biomarkers in PC, and microRNAs (miRNAs, miRs) could be interesting in this regard.

MiRNAs are small (18–24 nucleotides) non-coding RNAs that regulate gene expression post-transcriptionally by binding to messenger RNA molecules through nucleotide complementarity [10, 11]. MiRNAs regulate critical cellular processes such as differentiation, proliferation, apoptosis, and metastasis [12–16]. MiRNAs are stable and analyzable in formalin-fixed paraffin-embedded (FFPE) tissue, which is suitable for analysis [17, 18]. So far, 2603human miRNA sequences have been discovered and the number is increasing [19].

The expression patterns of miRNAs can be combined into profiles that are specific for a given type of tissue or disease. Several specific miRNA expression profiles in PC tissue have been described, with a promising consistency between studies and different array or PCR platforms. The expressions of miR-15b, −21, −95, −103, −107, −122, −135b, −148a, −155, −190, −196a, −200, −203, −210, −216b, −217, −221, −222, and miR-375 differ between PC and normal pancreas or chronic pancreatitis [20–28]. Furthermore, miRNA expression profiling indicates a close relationship between PDAC and A-AC [27]. Specific miRNAs have also been suggested as prognostic biomarkers in several cancers, including PC [23, 29–32].

The aim of the present study was to validate previously described diagnostic and prognostic miRNA expression profiles for PDAC and A-AC in FFPE specimens.

Methods

Patients

Diagnostic miRNA study

FFPE tumor specimens (n = 359 including an internal control) were obtained from patients who underwent resection with radical intent for the following diagnoses: PDAC (n = 165), A-AC (n = 59), DC (n = 6), distal CBD cancer (n = 21), chronic pancreatitis (CP) (n = 39), gastric cancer (GC) (n = 20), serous cyst adenoma (n = 2), and no cancer (n = 4; cysts or fibrosis that could not be classified as normal pancreas or pancreatitis and did not have any malignant foci) and healthy subjects (HS) (n = 35). The pancreatic and periampullary specimens came from patients who had undergone pancreaticoduodenectomy, distal pancreatectomy, or total pancreatectomy between 2004 and 2011 in Denmark (Herlev Hospital n = 9; Rigshospitalet n = 198), Germany (Heidelberg n = 69), and Norway (Bergen n = 55). The chronic pancreatitis specimens came from Copenhagen (n = 5) and Heidelberg (n = 34). All normal pancreas tissue was obtained from Heidelberg from organ donors or patients with traumatic pancreatic lesions leading to resection of healthy pancreatic tissue. The Danish patients were included in the BIOPAC Study (BIOmarkers in patients with Pancreatic Cancer). The gastric cancers came from patients who had undergone surgery at Gentofte Hospital. An experienced pathologist reassessed all samples to select the most representative part of the specimen, and tumors were classified and graded according to the World Health Organization criteria [33].

Prognostic miRNA study

One hundred fifty-seven FFPE tumor specimens were analyzed from patients who underwent surgery with radical intent for PDAC (n = 103) and A-AC (n = 54). The patients were included in the BIOPAC Study at Rigshospitalet in Denmark. Inclusion criteria were age ≥ 18 years and histologically verified PC in a resected specimen. After surgery, the majority of the patients (87%) were treated with adjuvant gemcitabine for 6 months or until disease recurrence.

Patient characteristics are shown in Table 1.

Table 1 Characteristics of the Danish patients
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MiRNA purification from FFPE tissues

One FFPE block was selected from each patient for miRNA analysis. From each of these blocks, 3 10-μm sections were cut for miRNA extraction without micro-dissection. As method control, 9×3 sections were cut from a specimen from 1 of the PDAC patients. MiRNAs were extracted using Qiagen miRNeasy FFPE kit, Cat No./ID: 217504. Briefly, the sections were deparaffinized in xylene and ethanol and then treated with proteinase K, and RNA was isolated using the one-column spin column protocol for total RNA. The concentration of small RNAs was assessed by absorbance spectrometry on a DTX 880 (Beckman Coulter).

MiRNA analysis

The following 46 miRNAs were selected for analysis: miR-21-5p, −23a-3p, −29a-5p, −31-5p, −34a-5p, −34c-5p, −93-3p, −122-5p, −125a-3p, −130b-3p, −135b-3p, −136-3p, −146a-5p, −148a-3p, −148a-5p, −155-5p, −186-5p, −187-3p, −194-3p, −196b-5p, −198, −203, −205-5p, −210, −212-3p, −216b, −217, −222-3p, −222-5p, −375, −411-5p, −431-5p, −450b-5p, −451a, −490-3p, −492, −509-5p, −571, −614, −622, −625-5p, −675-5p, −769-5p, −939, −944, and let-7 g. The selection was based on the previously described relationship of the miRNAs to PC in particular and to cancer biology in general (Detailed information on each specific miRNA is available in “Additional file 1”).

The miRNAs were analyzed in triplicate using the Fluidigm BioMark System™. This system can perform multiple simultaneous real-time PCR measurements running gold-standard Taqman® assays in nanolitre quantities. The instructions from Fluidigm were followed in all details (https://www.fluidigm.com). The analyses were performed at AROS Applied Biotechnology A/S (www.arosab.com, Aarhus, Denmark).

Statistical analysis

Differences in miRNA expression according to diagnosis were tested by univariate logistic regression including the raw miRNA expression level as continuous variables on the cycle threshold scale. Odds ratios (OR) per inter-quartile increase and 95% confidence intervals were computed for both PC vs. HS and PC vs. HS and CP.

Diagnostic indices were identified in 3 different ways among the significant miRNAs: (1) As a manually defined index by including 2 miRNA with OR > 1 and 2 with OR < 1 (indices I and IV);(2) As a computer generated index found by backwards elimination of a model with miRNAs chosen from 18 miRNAs described in an previous index (the so-called LASSO-classifier: miR-23a, 34c-5p, −122, −135b-3p, −136-3p, −186, −196b, −198, −203, −222-3p, −451, −490, −492, −509-5p, −571, −614, −622, and miR-93 [27]) which were significant at a 1% significance level, to account for multiple testing and with less than 10% missing values (indices II and V) and (3) as a computer generated index like (2) but based on all significant miRNAs (indices III and VI). A total of 6 indices were identified: I, II, and III developed for the PC vs. HS comparison and IV, V, and VI developed for the PC vs. HS + CP comparison. The indices were evaluated by means of boxplots, and their performance was evaluated by computing sensitivity, specificity, accuracy, area under curve (AUC), true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN). The indices were also tested on other cancer types. For each index, we first found a suitable cut-off by requiring a sensitivity of 85% in the PC vs. HS or vs. HS + CP comparison. Subsequently, this cut-off point was applied in all other comparisons.

It was not possible to stratify our patients according to TNM due to the very uneven distribution of cancer stages and resulting small subgroups.

For the prognostic study, the association between overall survival (OS) and miRNA expression was illustrated by Kaplan–Meier curves by dichotomizing the miRNA expression into below and above the median expression for each miRNA. The association was tested by means of univariate Cox proportional hazards regression both on the continuous variables and on the dichotomized variables, and presented as hazard ratios (HR) and corresponding 95% confidence intervals (CIs). In addition, analyses adjusted for age, sex, tumor stage, ASA score, and tumor differentiation were performed. Finally, we considered differences between 2 miRNAs at a time as a continuous variable in the Cox models (unadjusted and adjusted) for OS. Analyses were made for the diagnoses PDAC and A-AC together and separately.

In all analysis, the software package R version 3.1.1 (R Core Team 2014; R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. www.R-project.org) was used, and P-values below 5% were considered statistically significant.

Results

Diagnosis – Pancreatic cancer vs. healthy subjects

The following 14 miRNAs were upregulated in PC compared to HS: miR-21-5p, −23a-3p, −31-5p, −34c-5p, −93-3p, −135b-3p, −155-5p, −196b-5p, −203, −205-5p, −210, −222-3p, −451, and miR-622. The following 5 miRNAs were downregulated in PC: miR-122-5p, −130b-3p, −216b, − 217, and miR-375 (Table 2).

Table 2 Significantly deregulated microRNAs
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Three indices of miRNA expression, index I, II, and III, were identified to separate PC from HS (i.e., normal pancreas tissue):

(I) A manually defined index: miR-375 + miR-130b-3p – miR-451 – miR34c-5p.

(II) A computer-generated index based on univariate significant miRNAs chosen from 18 miRNAs describes in a previous index with less than 10% missing: 292.6458–3.0539×miR-34c-5p + 4.007×miR-203–10.4×miR-222-3p–3.6057×miR-451–4.3015×miR-622.

The potential miRNAs for index II were miR-34c-5p, −135-3p, −203, −222-3p, −451,and miR-622.

(III) A computer-generated index based on all univariate significant miRNAs with less than 10% missing values: 118.7249 + 77.2459×miR-130b-3p–23.7911×miR-34c-5p–49.923×miR-451.

The potential miRNAs for index III were miR-31-5p, −34c-5p,-93-3p, −130b-3p, −135b-3p, −155-5p, −203, −205-5p, −210, −216b, −217, −222-3p, −375, −451,and miR-622.

The performances of these indices are illustrated in box plots in Fig. 1 and Table 3 (upper part). The manually calculated index I was able to separate PC from HS with a sensitivity of 84.9 (CI 78.5–90.0), but could also differentiate the other malignant diagnoses from HS, with a sensitivity varying from 66.7 (distal CBD cancer) to 100.0 (DC and GC). The computer-generated index II performed in the same way with regard to PC vs. HS, but was inferior for separating the other malignancies from HS except for distal CBD cancer, where it performed better than index I. The computer-generated index III performed slightly better than index II with regard to separating A-AC and DC cancer from HS, but was inferior for separating distal CBD cancer and GC.

Fig. 1
figure 1

Performance of diagnostic indices for PC vs. HS and for PC vs. HS + CP

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Table 3 Performance of diagnostic indices
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Diagnosis - Pancreatic cancer vs. healthy subjects + chronic pancreatitis

The following 17 miRNAs were upregulated in PDAC compared with benign specimens (HS and CP combined): miR-21-5p, −23a-3p, −31-5p, −34c-5p, −93-3p, −135b-3p, −155-5p, −186-5p, −196b-5p, −203, −205-5p, −210, −222-3p, −451, −492, −614, and miR-622. The following 5 miRNAs were downregulated in PDAC compared to benign specimens (HS and CP combined): miR-122-5p, −130b-3p, −216b, −217, and miR-375 (Table 2).

Three indices, IV, V, and VI, of miRNA expression to separate PC from benign tissue (i.e., HS and CP combined) were identified.

(IV) A manually defined index: miR-375 + miR-130b-3p – miR-451 – miR-34c-5p.

(V) A computer-generated index based on significant miRNAs chosen from 18 miRNAs described in a previous index with less than 10% missing values: 20.5487–1.5899×miR-222-3p–0.4006×miR-451–0.3864×miR-203–0.5056×miR-622+ 1.203×miR-186-5p.

The potential miRNAs for index V weremiR-34c-5p, −135b-3p, −186-5p, −203, −222-3p, −451, and miR-622.

(VI) A computer-generated index based on all significant miRNAs with less than 10% missing values: 7.1834–0.5175×miR-210 + 1.3893×miR-93-3p – 0.7423×miR-375–2.6184×miR-222-3p – 0.3414×miR-451–0.3852×miR-203–0.5316×miR-622 + 1.822×miR-186-5p.

The potential miRNAs for index VI were miR-31-5p, −34c-5p, −93-3p, −130b-3p, −135b-3p, −155-5p, −186-5p, −203, −210, −216b, −217, −222-3p, −375, −451, and miR-622.

The performances of these indices are illustrated in box plots in Fig. 1 and in Table 3 (lower part). Index IV could separate HS from the other diagnoses. Indices V and VI were able to separate CP from the malignant diagnoses.

Diagnostic miRNA indices previously identified for pancreatic cancer

We have previously described the following 4 different diagnostic miRNA indices in FFPE cancer tissues consisting of 2 different miRNAs [27]: (1) miR-196b-5p – miR-217; (2) miR-411 – miR-198; (3) miR-614 – miR-122-5p; and (4) miR-614 – miR-93-3p. The performance of the 4 indices in the present cohort was tested using the Fluidigm method. Since many samples had non-detectable miRNAs, we only used observations that were non-missing, i.e., not imputed by a large Ct-value. Index 1 had 97 samples with at least 1miRNA missing, index 2 had 122 samples with at least 1 miRNA missing, index 3 had 213 samples with at least 1 miRNA missing, and index 4 had 115 samples with at least 1miRNA missing. For indices 2 and 3, it was not possible to consider HS alone. The performances of these indices are shown in box plots in Fig. 2. Index 1 could separate HS from PC patients but could not separate CP from A-AC. Index 1 could separate GC from all other diagnoses with high accuracy. Indices 2, 3, and 4 could not separate samples with benign from malignant diagnoses. Further information is given in the “Additional file 2”.

Fig. 2
figure 2

Performance of two miRNA diagnostic indices

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Prognostic miRNAs – PDAC and A-AC patients combined

In all, 157 patients with either PDAC or A-AC were available for the survival analysis, and 112died during the follow-up period. Table 4 illustrates that low expression of 6 miRNAs (miR-29a-5p, miR-34a-5p, miR-125a-3p, miR-146a-5p, miR-205-5p, and miR-212-3p) was associated with short OS, both with and without adjustment for age, sex, tumor stage/differentiation, and ASA-score. When patients were divided into 2 groups for each miRNA (defined as expression under or above the median level), low miR-34a-5p, miR-205-5p, miR-212-3p, and miR-222-5plevels were significantly associated with short OS. After adjusting for age, sex, tumor stage/differentiation, and ASA-score, let-7 g, miR-29a-5p, miR-34a-5p, miR-205-5p, and miR-212-3p were associated with short OS. Figure 3 illustrates Kaplan–Meier curves for the6 miRNAs reaching a significance level below 0.01.

Table 4 Prognostic miRNAs in patients with PC + A-AC, PC and A-AC
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Fig. 3
figure 3

Kaplan–Meier curves for miRNAs significantly associated to survival in patients with PC + A-AC

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Table 5 shows 30 and 27 combinations of 2 miRNAs significantly associated with short OS in an unadjusted and an adjusted analysis in PDAC and A-AC in combination.

Table 5 Differences of miRNA
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Prognostic miRNAs - PDAC

One hundred three patients with PDAC were available for the survival analysis, and 83 died during the follow-up period. In both the unadjusted and the adjusted (age, sex, tumor stage/differentiation, ASA-score) analyses, low expression of 2 miRNAs was associated with short OS prognosis:miR-34a-5p: HR = 0.72(CI: 0.56–0.93) (unadjusted) and HR = 0.70(CI: 0.52–0.93) (adjusted); and miR-212-3p HR = 0.83(CI: 0.71–0.99) (unadjusted) and HR = 0.82(CI: 0.68–0.99) (adjusted). Dividing the patients into 2 groups for each miRNA (defined as expression under or above the median level), low miR-34a-5p and miR-212-3p levels were associated with short OS. Figure 4 shows Kaplan–Meier curves for the miRNAs reaching a significance level below 0.01.

Fig. 4
figure 4

Kaplan–Meier curves for miRNAs significantly associated to survival in patients with PC and patients with A-AC

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Table 5 shows 5 and 12 combinations of 2 miRNAs significantly associated with short OS in an unadjusted and an adjusted analysis in PDAC.

Prognostic miRNAs – A-AC

Fifty-four patients with A-AC were available for the survival analysis, and 29 died during the follow-up period. In the unadjusted analysis, 4 miRNAs were significantly associated with prognosis: let-7 g: HR = 0.74(CI: 0.58–0.93), miR-34a-5p: HR = 0.66(CI: 0.46–0.94), miR-187: HR = 1.51(CI: 1.01–2.24), and miR-205-5p: HR = 0.74(CI: 0.63–0.86). In the adjusted analysis (age, sex, tumor stage/differentiation, ASA-score), low expression of miR-34a-5p: HR = 0.58(CI: 0.38–0.89) and miR-450b-5p: HR = 0.48(CI: 0.23–0.99) and high expression of miR-187: HR = 2.34(CI: 1.22–4.48) were associated with short OS. When patients were divided into 2 groups for each miRNA (defined as expression under or above the median level), low expression of miR-34a-5p was associated with short OS. Figure 4 shows Kaplan–Meier curves for the miRNAs reaching a significance level below 0.01.

Table 5 shows 21 and 16 combinations of 2 miRNAs in A-AC FFPE tissue significantly associated with short OS in both an unadjusted and an adjusted analysis.

Discussion

In the present study, our aim was to validate previously described tissue miRNA expression profiles as diagnostic and prognostic biomarkers of PC and other periampullary cancers [20–32]. We used non-microdissected FFPE tissue from 165 patients who had undergone surgery for PDAC and from 86 patients who had undergone resection for other periampullary cancers.

Many of the diagnostic miRNAs described in the literature [20, 21, 34] could be validated. We found the following miRNAs either upregulated or downregulated in PC tissue compared to tissue from CP and/or normal pancreas, upregulated miRNAs: miR-21-5p, −23a-3p, −31-5p, −34c-5p, −93-3p, −135b-3p, −155-5p, −186-5p, −196b-5p, −203, −205-5p, −210, −222-3p, −451, −492, −614, and miR-622; and downregulated miRNAs: miR-122-5p, −130b-3p, −216b, −217, and miR-375. Furthermore, we validated the two-miRNA index “miR-196b – miR-217” [27], and suggested new diagnostic indices for separating patients with PC vs. HS and PC vs. HS and CP combined. We found that these indices were useful in discriminating other upper gastrointestinal cancers (duodenal cancer, common bile duct cancer and gastric cancer) from normal pancreas and CP.

In addition to the diagnostic miRNAs, we demonstrated the association of 10 miRNAs with prognosis and constructed several indices based on differences of 2 miRNA associated with poor prognosis.

A major limitation of the study was the high number of non-detectable miRNAs using the Fluidigm BioMark System™. Even though we purified the miRNAs from FFPE by the same method as in our previous studies [27, 31] and repeated the analysis several times, we still experienced a high number of undetectable miRNAs. At present, we have no explanation for this problem apart from possible platform sensitivity limitations.

We consider it a strength of the study that non-microdissected samples were used, since this will also be the case in a clinical setting. The tumor microenvironment is a highly dynamic component of PC, often constitutes the bulk of the tumor, and should therefore be taken into account. The extracellular stroma participates in paracrine signaling that promotes PDAC cell survival and metastasis, and the dense extracellular matrix characteristic of PDAC acts as a physical barrier to infiltrating immune cells and the diffusion of chemotherapy [35–37]. MicroRNAs are involved in the regulation of the extracellular components in different tissues [38, 39]. Since many studies regarding miRNAs in PC are performed on microdissected tissue or cell lines the miRNAs originating from the extracellular stroma are less elucidated. The following miRNAs significantly deregulated in the present study are known to be related to the extracellular compartment of PC: miR-21, −29, −130b, −210, and-451 [40–43].

Among the validated miRNAs, high expression of miR-21, miR-31, and miR-155 and low expression of miR-217 and miR-375are the most consistently described dysregulated miRNAs in PC. Several studies have found miR-155to be upregulated in PC [20–22, 28, 32, 44, 45]. miR-155 functions as an onco-miRNA in different types of cancer,e.g., breast, cervix, colon, and lung cancer, and high miR-155 expression in cancer tissue is associated with poor prognosis in PC and lung cancer [30, 46–49]. The oncogenic effect of miR-155 maybe caused by the targeting of anti-inflammatory signal pathways such as Sh2 domain-containing inositol phosphatase-1 (Ship1) or from suppression of cytokine signaling 1 (Socs1) [50, 51].

miR-21 is also an onco-miR involved in PC tumorigenesis, invasion, metastasis, and chemoresistance [20, 21, 23, 27, 32, 44, 45, 52–57]. miR-21 is primarily upregulated in the extracellular stroma, which is considered a dynamic component of PC, and high expression is associated with poor prognosis [40]. Our study was conducted on non-microdissected tissue and thus also detects miRNAs in the extracellular stroma.miR-21 targets tumor suppressors like PTEN, PDCD4, and TIMP3, components of the p53 pathway, and modulates TGF-b signaling, thus promoting cell proliferation, survival, and migration/invasion [45, 58–60].

miR-31 is upregulated in PC [21, 27, 28, 45, 61]. miR-31 targets human mutL homolog 1 (a mismatch repair protein) [62] and activates the RAS pathway by inhibiting RAS p21 GTPase activating protein 1 (RASA1) in colorectal cancer [63].

miR-217 is downregulated in PC and in pancreatic intraepithelial neoplasm (PanIN) [21, 27, 28, 32, 45, 64]. This finding has also been replicated in studies using fine needle aspirates from PC [24, 65].miR-217 acts as a tumor suppressor in PC by targeting KRAS [66] and is involved in epithelial-mesenchymal-transition (EMT) in PC and CP via the miR-217-SIRT1 pathway, which can be triggered by TGF-β1 in inflammatory processes [67].

miR-375 is downregulated in PC compared to normal pancreas, is associated with prognosis, and can differentiate between pancreatobiliary and intestinal subtypes in ampullary adenocarcinoma [20, 21, 27, 28, 32, 68]. miR-375 is also downregulated in esophageal, gastric, breast, lung, colorectal, and cervical cancers [69–74]. miR-375 plays a role in the development and maintenance of the α- and β-cell mass in the normal pancreas and is upregulated in patients with type 2 diabetes [75, 76].miR-375 targets 3-phosphoinositide-dependent protein kinase-1 (PDK1) in PC and inhibits PC cell proliferation in vitro [77, 78].

In the literature, the following miRNAs are described as prognostic after PC resection:Let-7 g, miR-21, miR-29a-5p, miR-34a-5p, miR-146a, miR-155, miR-196a, miR-203, miR-205, miR-210, miR-212, miR-222, miR-450b-5p, and miR-675 [23, 29–32]. We have previously described prognostic indices using combinations of high expression of miR-212 and miR-675 and low expression of miR-148a-5p (previous ID: miR-148a*), miR-187 and let-7 g-3p (previous ID: let-7 g*) in FFPE tissue from patients operated for PC [31]. Only a few of these patients received adjuvant chemotherapy after surgery. In the present study, patients with PDAC and A-AC were all treated with adjuvant gemcitabine for 6 months or until disease recurrence. In this population, we could validate let-7 g, miR-29a-5p, miR-34a-5p, miR-146a-5p,miR-205-5p, and miR-212-3pas prognostic biomarkers after radical resection for PC.

The let-7 family of miRNAs includes tumor suppressor miRNAs, the expression of which is prognostic in HCC, gastric, and ovarian cancers [79–81]. Let-7 g is involved in pathways essential for the development of cancer. It targets Fas and is involved in Fas-mediated apoptosis [82]. Silencing of let-7b/g activates AKT signaling and promotes carcinogenesis in gastric cancer [83]. Let-7 inhibits cell motility in breast cancer by regulating genes in the cytoskeleton pathway and silencing of let-7 promotes metastases [84]. Let-7 inhibits proliferation in HCC by downregulation of c-Myc and upregulation of p16(INK4A) [85].

In PC, miR-29a-5p induces EMT, stimulates pancreatic stellate cells to accumulate protein in the extracellular matrix, and increases resistance to gemcitabine through the Wnt/beta-catenin pathway [41, 86, 87]. miR-34a is upregulated in cervical and colorectal cancers and downregulated in breast, prostate, renal and lung cancer [49, 88].

The miR-34 family miRNAs are described as tumor suppressor miRNAs, and miR-34a/c suppresses breast cancer invasion and metastasis by targeting Fos-related antigen-1 [89]. PC mouse models show that miR-146a acts through EGFR signaling [90]. miR-205 is involved in EMT and acts through the anti-apoptotic protein Bcl-2 (in prostate cancer) and HER3 (in breast cancer) [91–93]. We found that low expression of miR-125a-3p was associated with short OS in patients with PC, and this is a novel observation.miR-125a-3p has been described as a tumor suppressor miRNA in several cancers [94, 95].

In the present study, miR-130b was found to be downregulated in PDAC compared to benign specimens. Interestingly, this miRNA is upregulated in the stroma compared to carcinoma cells [42].

Further information about the 46 miRNAs analyzed in the present study is given in “Additional file 1”.

Conclusions

In conclusion, we could validate miRNAs selected from the literature as diagnostic and/or prognostic biomarkers in patients radically resected for PC. No microdissection of the tumors was done, and some of the miRNAs most likely originated from the stroma and not the cancer cells. The diagnostic ability of these miRNAs was also tested on duodenal cancer, common bile duct cancer, and gastric cancer – diagnoses that represent a considerable diagnostic challenge in separating from PC in a clinical setting. Hopefully, this study can contribute to the understanding of pancreatic and periampullary cancers and improve the diagnosis, prognosis, and ultimately treatment of patients with these conditions. For example, this could be achieved by allocating young patients with a miRNA expression profile suggestive of poor prognosis to a more aggressive chemotherapy regimen, or elderly patients with a more promising prognostic profile could be spared from adjuvant therapy.

Abbreviations

A-AC:

Ampullary adenocarcinoma

CBD:

Common bile duct

CP:

Chronic pancreatitis

DC:

Duodenal cancer

FFPE:

Formalin-fixed paraffin-embedded

GC:

Gastric cancer

HS:

Healthy subjects

miR:

microRNA

miRNA:

microRNA

PC:

Pancreatic cancer

PDAC:

Pancreatic ductal adenocarcinoma

References

  1. Ryan DP, Hong TS, Bardeesy N. Pancreatic adenocarcinoma. N Engl J Med. 2014;371(11):1039–49.

    CAS  Article  PubMed  Google Scholar 

  2. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2016. CA Cancer J Clin. 2016;66(1):7–30.

    Article  PubMed  Google Scholar 

  3. Yeo CJ, Cameron JL, Lillemoe KD, Sitzmann JV, Hruban RH, Goodman SN, Dooley WC, Coleman J, Pitt HA. Pancreaticoduodenectomy for cancer of the head of the pancreas. 201 patients. Ann Surg. 1995;221(6):721–31. discussion 731–723.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  4. Cancer Research UK. Cancer mortality: UK Statistics. In.; 2009. http://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/pancreatic-cancer/mortality. (Accessed 15th Mar 2016).

  5. Hartwig W, Hackert T, Hinz U, Gluth A, Bergmann F, Strobel O, Buchler MW, Werner J. Pancreatic cancer surgery in the new millennium: better prediction of outcome. Ann Surg. 2011;254(2):311–9.

    Article  PubMed  Google Scholar 

  6. He J, Ahuja N, Makary MA, Cameron JL, Eckhauser FE, Choti MA, Hruban RH, Pawlik TM, Wolfgang CL. 2564 resected periampullary adenocarcinomas at a single institution: trends over three decades. HPB (Oxford). 2014;16(1):83–90.

    Article  Google Scholar 

  7. Bettschart V, Rahman MQ, Engelken FJ, Madhavan KK, Parks RW, Garden OJ. Presentation, treatment and outcome in patients with ampullary tumours. Br J Surg. 2004;91(12):1600–7.

    CAS  Article  PubMed  Google Scholar 

  8. Struck A, Howard T, Chiorean EG, Clarke JM, Riffenburgh R, Cardenes HR. Non-ampullary duodenal adenocarcinoma: factors important for relapse and survival. J Surg Oncol. 2009;100(2):144–8.

    Article  PubMed  Google Scholar 

  9. Seufferlein T, Bachet JB, Van Cutsem E, Rougier P. Pancreatic adenocarcinoma: ESMO-ESDO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2012;23 Suppl 7:vii33–40.

    Article  PubMed  Google Scholar 

  10. Waldman SA, Terzic A. Translating MicroRNA discovery into clinical biomarkers in cancer. JAMA. 2007;297(17):1923–5.

    CAS  Article  PubMed  Google Scholar 

  11. Nelson KM, Weiss GJ. MicroRNAs and cancer: past, present, and potential future. Mol Cancer Ther. 2008;7(12):3655–60.

    CAS  Article  PubMed  Google Scholar 

  12. Ambros V. The functions of animal microRNAs. Nature. 2004;431(7006):350–5.

    CAS  Article  PubMed  Google Scholar 

  13. Hwang HW, Mendell JT. MicroRNAs in cell proliferation, cell death, and tumorigenesis. Br J Cancer. 2006;94(6):776–80.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. Li M, Marin-Muller C, Bharadwaj U, Chow KH, Yao Q, Chen C. MicroRNAs: control and loss of control in human physiology and disease. World J Surg. 2009;33(4):667–84.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Bartel DP. MicroRNAs: target recognition and regulatory functions. Cell. 2009;136(2):215–33.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  16. Almeida MI, Reis RM, Calin GA. MicroRNA history: discovery, recent applications, and next frontiers. Mutat Res. 2011;717(1–2):1–8.

    CAS  Article  PubMed  Google Scholar 

  17. Li J, Smyth P, Flavin R, Cahill S, Denning K, Aherne S, Guenther SM, O’Leary JJ, Sheils O. Comparison of miRNA expression patterns using total RNA extracted from matched samples of formalin-fixed paraffin-embedded (FFPE) cells and snap frozen cells. BMC Biotechnol. 2007;7:36.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Zhang X, Chen J, Radcliffe T, Lebrun DP, Tron VA, Feilotter H. An array-based analysis of microRNA expression comparing matched frozen and formalin-fixed paraffin-embedded human tissue samples. J Mol Diagn. 2008;10(6):513–9.

    Article  PubMed  PubMed Central  Google Scholar 

  19. MirBase [http://www.mirbase.org]. Accessed 15th Mar 2016.

  20. Bloomston M, Frankel WL, Petrocca F, Volinia S, Alder H, Hagan JP, Liu CG, Bhatt D, Taccioli C, Croce CM. MicroRNA expression patterns to differentiate pancreatic adenocarcinoma from normal pancreas and chronic pancreatitis. JAMA. 2007;297(17):1901–8.

    CAS  Article  PubMed  Google Scholar 

  21. Szafranska AE, Davison TS, John J, Cannon T, Sipos B, Maghnouj A, Labourier E, Hahn SA. MicroRNA expression alterations are linked to tumorigenesis and non-neoplastic processes in pancreatic ductal adenocarcinoma. Oncogene. 2007;26(30):4442–52.

    CAS  Article  PubMed  Google Scholar 

  22. Lee EJ, Gusev Y, Jiang J, Nuovo GJ, Lerner MR, Frankel WL, Morgan DL, Postier RG, Brackett DJ, Schmittgen TD. Expression profiling identifies microRNA signature in pancreatic cancer. Int J Cancer. 2007;120(5):1046–54.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. Dillhoff M, Liu J, Frankel W, Croce C, Bloomston M. MicroRNA-21 is overexpressed in pancreatic cancer and a potential predictor of survival. J Gastrointest Surg. 2008;12(12):2171–6.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Szafranska AE, Doleshal M, Edmunds HS, Gordon S, Luttges J, Munding JB, Barth Jr RJ, Gutmann EJ, Suriawinata AA, Marc Pipas J, et al. Analysis of microRNAs in pancreatic fine-needle aspirates can classify benign and malignant tissues. Clin Chem. 2008;54(10):1716–24.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  25. Habbe N, Koorstra JB, Mendell JT, Offerhaus GJ, Ryu JK, Feldmann G, Mullendore ME, Goggins MG, Hong SM, Maitra A. MicroRNA miR-155 is a biomarker of early pancreatic neoplasia. Cancer Biol Ther. 2009;8(4):340–6.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  26. Zhang Y, Li M, Wang H, Fisher WE, Lin PH, Yao Q, Chen C. Profiling of 95 microRNAs in pancreatic cancer cell lines and surgical specimens by real-time PCR analysis. World J Surg. 2009;33(4):698–709.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Schultz NA, Werner J, Willenbrock H, Roslind A, Giese N, Horn T, Wojdemann M, Johansen JS. MicroRNA expression profiles associated with pancreatic adenocarcinoma and ampullary adenocarcinoma. Mod Pathol. 2012;25(12):1609–22.

    CAS  Article  PubMed  Google Scholar 

  28. Bauer AS, Keller A, Costello E, Greenhalf W, Bier M, Borries A, Beier M, Neoptolemos J, Buchler M, Werner J, et al. Diagnosis of pancreatic ductal adenocarcinoma and chronic pancreatitis by measurement of microRNA abundance in blood and tissue. PLoS One. 2012;7(4), e34151.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  29. Ikenaga N, Ohuchida K, Mizumoto K, Yu J, Kayashima T, Sakai H, Fujita H, Nakata K, Tanaka M. MicroRNA-203 expression as a new prognostic marker of pancreatic adenocarcinoma. Ann Surg Oncol. 2010;17(12):3120–8.

    Article  PubMed  Google Scholar 

  30. Greither T, Grochola LF, Udelnow A, Lautenschlager C, Wurl P, Taubert H. Elevated expression of microRNAs 155, 203, 210 and 222 in pancreatic tumors is associated with poorer survival. Int J Cancer. 2010;126(1):73–80.

    CAS  Article  PubMed  Google Scholar 

  31. Schultz NA, Andersen KK, Roslind A, Willenbrock H, Wojdemann M, Johansen JS. Prognostic microRNAs in cancer tissue from patients operated for pancreatic cancer--five microRNAs in a prognostic index. World J Surg. 2012;36(11):2699–707.

    Article  PubMed  Google Scholar 

  32. Jamieson NB, Morran DC, Morton JP, Ali A, Dickson EJ, Carter CR, Sansom OJ, Evans TR, McKay CJ, Oien KA. MicroRNA molecular profiles associated with diagnosis, clinicopathologic criteria, and overall survival in patients with resectable pancreatic ductal adenocarcinoma. Clin Cancer Res. 2012;18(2):534–45.

    CAS  Article  PubMed  Google Scholar 

  33. Aaltonen LA, Hamilton SR, World Health Organization, International Agency for Research on Cancer. Pathology and genetics of tumours of the digestive system. Lyon, Oxford: IARC Press; Oxford University Press (distributor); 2000.

    Google Scholar 

  34. Frampton AE, Giovannetti E, Jamieson NB, Krell J, Gall TM, Stebbing J, Jiao LR, Castellano L. A microRNA meta-signature for pancreatic ductal adenocarcinoma. Expert Rev Mol Diagn. 2014;14(3):267–71.

    CAS  Article  PubMed  Google Scholar 

  35. Neesse A, Algul H, Tuveson DA, Gress TM. Stromal biology and therapy in pancreatic cancer: a changing paradigm. Gut. 2015;64(9):1476–84.

    CAS  Article  PubMed  Google Scholar 

  36. Hwang RF, Moore T, Arumugam T, Ramachandran V, Amos KD, Rivera A, Ji B, Evans DB, Logsdon CD. Cancer-associated stromal fibroblasts promote pancreatic tumor progression. Cancer Res. 2008;68(3):918–26.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  37. Xu Z, Vonlaufen A, Phillips PA, Fiala-Beer E, Zhang X, Yang L, Biankin AV, Goldstein D, Pirola RC, Wilson JS, et al. Role of pancreatic stellate cells in pancreatic cancer metastasis. Am J Pathol. 2010;177(5):2585–96.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Chou J, Shahi P, Werb Z. microRNA-mediated regulation of the tumor microenvironment. Cell Cycle. 2013;12(20):3262–71.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  39. Zhang Y, Yang P, Wang XF. Microenvironmental regulation of cancer metastasis by miRNAs. Trends Cell Biol. 2014;24(3):153–60.

    CAS  Article  PubMed  Google Scholar 

  40. Kadera BE, Li L, Toste PA, Wu N, Adams C, Dawson DW, Donahue TR. MicroRNA-21 in pancreatic ductal adenocarcinoma tumor-associated fibroblasts promotes metastasis. PLoS One. 2013;8(8), e71978.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  41. Kwon JJ, Nabinger SC, Vega Z, Sahu SS, Alluri RK, Abdul-Sater Z, Yu Z, Gore J, Nalepa G, Saxena R, et al. Pathophysiological role of microRNA-29 in pancreatic cancer stroma. Sci Rep. 2015;5:11450.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Sandhu V, Bowitz Lothe IM, Labori KJ, Skrede ML, Hamfjord J, Dalsgaard AM, Buanes T, Dube G, Kale MM, Sawant S, et al. Differential expression of miRNAs in pancreatobiliary type of periampullary adenocarcinoma and its associated stroma. Mol Oncol. 2016;10(2):303–16.

    CAS  Article  PubMed  Google Scholar 

  43. Takikawa T, Masamune A, Hamada S, Nakano E, Yoshida N, Shimosegawa T. miR-210 regulates the interaction between pancreatic cancer cells and stellate cells. Biochem Biophys Res Commun. 2013;437(3):433–9.

    CAS  Article  PubMed  Google Scholar 

  44. Panarelli NC, Chen YT, Zhou XK, Kitabayashi N, Yantiss RK. MicroRNA expression aids the preoperative diagnosis of pancreatic ductal adenocarcinoma. Pancreas. 2012;41(5):685–90.

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Nagao Y, Hisaoka M, Matsuyama A, Kanemitsu S, Hamada T, Fukuyama T, Nakano R, Uchiyama A, Kawamoto M, Yamaguchi K, et al. Association of microRNA-21 expression with its targets, PDCD4 and TIMP3, in pancreatic ductal adenocarcinoma. Mod Pathol. 2012;25(1):112–21.

    CAS  Article  PubMed  Google Scholar 

  46. Volinia S, Calin GA, Liu CG, Ambs S, Cimmino A, Petrocca F, Visone R, Iorio M, Roldo C, Ferracin M, et al. A microRNA expression signature of human solid tumors defines cancer gene targets. Proc Natl Acad Sci U S A. 2006;103(7):2257–61.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  47. Yanaihara N, Caplen N, Bowman E, Seike M, Kumamoto K, Yi M, Stephens RM, Okamoto A, Yokota J, Tanaka T, et al. Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell. 2006;9(3):189–98.

    CAS  Article  PubMed  Google Scholar 

  48. Saito M, Schetter AJ, Mollerup S, Kohno T, Skaug V, Bowman ED, Mathe EA, Takenoshita S, Yokota J, Haugen A, et al. The association of microRNA expression with prognosis and progression in early-stage, non-small cell lung adenocarcinoma: a retrospective analysis of three cohorts. Clin Cancer Res. 2011;17(7):1875–82.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  49. Gocze K, Gombos K, Juhasz K, Kovacs K, Kajtar B, Benczik M, Gocze P, Patczai B, Arany I, Ember I. Unique MicroRNA expression profiles in cervical cancer. Anticancer Res. 2013;33(6):2561–7.

    CAS  PubMed  Google Scholar 

  50. Jiang S, Zhang HW, Lu MH, He XH, Li Y, Gu H, Liu MF, Wang ED. MicroRNA-155 functions as an OncomiR in breast cancer by targeting the suppressor of cytokine signaling 1 gene. Cancer Res. 2010;70(8):3119–27.

    CAS  Article  PubMed  Google Scholar 

  51. Ji Y, Wrzesinski C, Yu Z, Hu J, Gautam S, Hawk NV, Telford WG, Palmer DC, Franco Z, Sukumar M, et al. miR-155 augments CD8+ T-cell antitumor activity in lymphoreplete hosts by enhancing responsiveness to homeostatic gammac cytokines. Proc Natl Acad Sci U S A. 2015;112(2):476–81.

    CAS  Article  PubMed  Google Scholar 

  52. Giovannetti E, Funel N, Peters GJ, Del Chiaro M, Erozenci LA, Vasile E, Leon LG, Pollina LE, Groen A, Falcone A, et al. MicroRNA-21 in pancreatic cancer: correlation with clinical outcome and pharmacologic aspects underlying its role in the modulation of gemcitabine activity. Cancer Res. 2010;70(11):4528–38.

    CAS  Article  PubMed  Google Scholar 

  53. Frampton AE, Castellano L, Colombo T, Giovannetti E, Krell J, Jacob J, Pellegrino L, Roca-Alonso L, Funel N, Gall TM, et al. MicroRNAs cooperatively inhibit a network of tumor suppressor genes to promote pancreatic tumor growth and progression. Gastroenterology. 2014;146(1):268–77. e218.

    CAS  Article  PubMed  Google Scholar 

  54. Hwang JH, Voortman J, Giovannetti E, Steinberg SM, Leon LG, Kim YT, Funel N, Park JK, Kim MA, Kang GH, et al. Identification of microRNA-21 as a biomarker for chemoresistance and clinical outcome following adjuvant therapy in resectable pancreatic cancer. PLoS One. 2010;5(5):e10630.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Moriyama T, Ohuchida K, Mizumoto K, Yu J, Sato N, Nabae T, Takahata S, Toma H, Nagai E, Tanaka M. MicroRNA-21 modulates biological functions of pancreatic cancer cells including their proliferation, invasion, and chemoresistance. Mol Cancer Ther. 2009;8(5):1067–74.

    CAS  Article  PubMed  Google Scholar 

  56. Zhang S, Hao J, Xie F, Hu X, Liu C, Tong J, Zhou J, Wu J, Shao C. Downregulation of miR-132 by promoter methylation contributes to pancreatic cancer development. Carcinogenesis. 2011;32(8):1183–9.

    CAS  Article  PubMed  Google Scholar 

  57. Tavano F, di Mola FF, Piepoli A, Panza A, Copetti M, Burbaci FP, Latiano T, Pellegrini F, Maiello E, Andriulli A, et al. Changes in miR-143 and miR-21 expression and clinicopathological correlations in pancreatic cancers. Pancreas. 2012;41(8):1280–4.

    CAS  Article  PubMed  Google Scholar 

  58. Niwa R, Slack FJ. The evolution of animal microRNA function. Curr Opin Genet Dev. 2007;17(2):145–50.

    CAS  Article  PubMed  Google Scholar 

  59. Papagiannakopoulos T, Shapiro A, Kosik KS. MicroRNA-21 targets a network of key tumor-suppressive pathways in glioblastoma cells. Cancer Res. 2008;68(19):8164–72.

    CAS  Article  PubMed  Google Scholar 

  60. Tili E, Michaille JJ, Croce CM. MicroRNAs play a central role in molecular dysfunctions linking inflammation with cancer. Immunol Rev. 2013;253(1):167–84.

    Article  PubMed  Google Scholar 

  61. Piepoli A, Tavano F, Copetti M, Mazza T, Palumbo O, Panza A, di Mola FF, Pazienza V, Mazzoccoli G, Biscaglia G, et al. Mirna expression profiles identify drivers in colorectal and pancreatic cancers. PLoS One. 2012;7(3), e33663.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  62. Zhong Z, Dong Z, Yang L, Chen X, Gong Z. MicroRNA-31-5p modulates cell cycle by targeting human mutL homolog 1 in human cancer cells. Tumour Biol. 2013;34(3):1959–65.

    CAS  Article  PubMed  Google Scholar 

  63. Sun D, Yu F, Ma Y, Zhao R, Chen X, Zhu J, Zhang CY, Chen J, Zhang J. MicroRNA-31 activates the RAS pathway and functions as an oncogenic MicroRNA in human colorectal cancer by repressing RAS p21 GTPase activating protein 1 (RASA1). J Biol Chem. 2013;288(13):9508–18.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  64. Xue Y, Abou Tayoun AN, Abo KM, Pipas JM, Gordon SR, Gardner TB, Barth Jr RJ, Suriawinata AA, Tsongalis GJ. MicroRNAs as diagnostic markers for pancreatic ductal adenocarcinoma and its precursor, pancreatic intraepithelial neoplasm. Cancer Genet. 2013;206(6):217–21.

    CAS  Article  PubMed  Google Scholar 

  65. Hong TH, Park IY. MicroRNA expression profiling of diagnostic needle aspirates from surgical pancreatic cancer specimens. Ann Surg Treat Res. 2014;87(6):290–7.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Zhao WG, Yu SN, Lu ZH, Ma YH, Gu YM, Chen J. The miR-217 microRNA functions as a potential tumor suppressor in pancreatic ductal adenocarcinoma by targeting KRAS. Carcinogenesis. 2010;31(10):1726–33.

    CAS  Article  PubMed  Google Scholar 

  67. Deng S, Zhu S, Wang B, Li X, Liu Y, Qin Q, Gong Q, Niu Y, Xiang C, Chen J, et al. Chronic pancreatitis and pancreatic cancer demonstrate active epithelial-mesenchymal transition profile, regulated by miR-217-SIRT1 pathway. Cancer Lett. 2014;355(2):184–91.

    CAS  Article  PubMed  Google Scholar 

  68. Kalluri Sai Shiva UM, Kuruva MM, Mitnala S, Rupjyoti T, Guduru Venkat R, Botlagunta S, Kandagaddala R, Siddapuram SP, Sekaran A, Chemalakonda R, et al. MicroRNA profiling in periampullary carcinoma. Pancreatology. 2014;14(1):36–47.

    CAS  Article  PubMed  Google Scholar 

  69. Gu J, Wang Y, Wu X. MicroRNA in the pathogenesis and prognosis of esophageal cancer. Curr Pharm Des. 2013;19(7):1292–300.

    CAS  PubMed  Google Scholar 

  70. Ding L, Xu Y, Zhang W, Deng Y, Si M, Du Y, Yao H, Liu X, Ke Y, Si J, et al. MiR-375 frequently downregulated in gastric cancer inhibits cell proliferation by targeting JAK2. Cell Res. 2010;20(7):784–93.

    CAS  Article  PubMed  Google Scholar 

  71. Luo D, Wilson JM, Harvel N, Liu J, Pei L, Huang S, Hawthorn L, Shi H. A systematic evaluation of miRNA:mRNA interactions involved in the migration and invasion of breast cancer cells. J Transl Med. 2013;11:57.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  72. Li Y, Jiang Q, Xia N, Yang H, Hu C. Decreased expression of microRNA-375 in nonsmall cell lung cancer and its clinical significance. J Int Med Res. 2012;40(5):1662–9.

    CAS  Article  PubMed  Google Scholar 

  73. Dai X, Chiang Y, Wang Z, Song Y, Lu C, Gao P, Xu H. Expression levels of microRNA-375 in colorectal carcinoma. Mol Med Rep. 2012;5(5):1299–304.

    CAS  PubMed  Google Scholar 

  74. Bierkens M, Krijgsman O, Wilting SM, Bosch L, Jaspers A, Meijer GA, Meijer CJ, Snijders PJ, Ylstra B, Steenbergen RD. Focal aberrations indicate EYA2 and hsa-miR-375 as oncogene and tumor suppressor in cervical carcinogenesis. Genes Chromosomes Cancer. 2013;52(1):56–68.

    CAS  Article  PubMed  Google Scholar 

  75. Poy MN, Hausser J, Trajkovski M, Braun M, Collins S, Rorsman P, Zavolan M, Stoffel M. miR-375 maintains normal pancreatic alpha- and beta-cell mass. Proc Natl Acad Sci U S A. 2009;106(14):5813–8.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  76. Zhao H, Guan J, Lee HM, Sui Y, He L, Siu JJ, Tse PP, Tong PC, Lai FM, Chan JC. Up-regulated pancreatic tissue microRNA-375 associates with human type 2 diabetes through beta-cell deficit and islet amyloid deposition. Pancreas. 2010;39(6):843–6.

    Article  PubMed  Google Scholar 

  77. Zhou J, Song S, Cen J, Zhu D, Li D, Zhang Z. MicroRNA-375 is downregulated in pancreatic cancer and inhibits cell proliferation in vitro. Oncol Res. 2012;20(5–6):197–203.

    CAS  Article  PubMed  Google Scholar 

  78. Song SD, Zhou J, Zhou J, Zhao H, Cen JN, Li DC. MicroRNA-375 targets the 3-phosphoinositide-dependent protein kinase-1 gene in pancreatic carcinoma. Oncol Lett. 2013;6(4):953–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  79. Ueda T, Volinia S, Okumura H, Shimizu M, Taccioli C, Rossi S, Alder H, Liu CG, Oue N, Yasui W, et al. Relation between microRNA expression and progression and prognosis of gastric cancer: a microRNA expression analysis. Lancet Oncol. 2010;11(2):136–46.

    CAS  Article  PubMed  Google Scholar 

  80. Chen KJ, Hou Y, Wang K, Li J, Xia Y, Yang XY, Lv G, Xing XL, Shen F. Reexpression of Let-7 g microRNA inhibits the proliferation and migration via K-Ras/HMGA2/snail axis in hepatocellular carcinoma. Biomed Res Int. 2014;2014:742417.

    PubMed  PubMed Central  Google Scholar 

  81. Lu L, Katsaros D, Shaverdashvili K, Qian B, Wu Y, de la Longrais IA, Preti M, Menato G, Yu H. Pluripotent factor lin-28 and its homologue lin-28b in epithelial ovarian cancer and their associations with disease outcomes and expression of let-7a and IGF-II. Eur J Cancer. 2009;45(12):2212–8.

    CAS  Article  PubMed  Google Scholar 

  82. Wang S, Tang Y, Cui H, Zhao X, Luo X, Pan W, Huang X, Shen N. Let-7/miR-98 regulate Fas and Fas-mediated apoptosis. Genes Immun. 2011;12(2):149–54.

    Article  PubMed  Google Scholar 

  83. Kang W, Tong JH, Lung RW, Dong Y, Yang W, Pan Y, Lau KM, Yu J, Cheng AS, To KF. let-7b/g silencing activates AKT signaling to promote gastric carcinogenesis. J Transl Med. 2014;12:281.

    Article  PubMed  PubMed Central  Google Scholar 

  84. Hu X, Guo J, Zheng L, Li C, Zheng TM, Tanyi JL, Liang S, Benedetto C, Mitidieri M, Katsaros D, et al. The heterochronic microRNA let-7 inhibits cell motility by regulating the genes in the actin cytoskeleton pathway in breast cancer. Mol Cancer Res. 2013;11(3):240–50.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  85. Lan FF, Wang H, Chen YC, Chan CY, Ng SS, Li K, Xie D, He ML, Lin MC, Kung HF. Hsa-let-7 g inhibits proliferation of hepatocellular carcinoma cells by downregulation of c-Myc and upregulation of p16(INK4A). Int J Cancer. 2011;128(2):319–31.

    CAS  Article  PubMed  Google Scholar 

  86. Nagano H, Tomimaru Y, Eguchi H, Hama N, Wada H, Kawamoto K, Kobayashi S, Mori M, Doki Y. MicroRNA-29a induces resistance to gemcitabine through the Wnt/beta-catenin signaling pathway in pancreatic cancer cells. Int J Oncol. 2013;43(4):1066–72.

    CAS  PubMed  Google Scholar 

  87. Chen J, Li Q, An Y, Lv N, Xue X, Wei J, Jiang K, Wu J, Gao W, Qian Z, et al. CEACAM6 induces epithelial-mesenchymal transition and mediates invasion and metastasis in pancreatic cancer. Int J Oncol. 2013;43(3):877–85.

    CAS  PubMed  Google Scholar 

  88. Di Leva G, Garofalo M, Croce CM. MicroRNAs in cancer. Annu Rev Pathol. 2014;9:287–314.

    Article  PubMed  Google Scholar 

  89. Yang S, Li Y, Gao J, Zhang T, Li S, Luo A, Chen H, Ding F, Wang X, Liu Z. MicroRNA-34 suppresses breast cancer invasion and metastasis by directly targeting Fra-1. Oncogene. 2012.

  90. Ali S, Ahmad A, Aboukameel A, Ahmed A, Bao B, Banerjee S, Philip PA, Sarkar FH. Deregulation of miR-146a expression in a mouse model of pancreatic cancer affecting EGFR signaling. Cancer Lett. 2014;351(1):134–42.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  91. Gregory PA, Bracken CP, Bert AG, Goodall GJ. MicroRNAs as regulators of epithelial-mesenchymal transition. Cell Cycle. 2008;7(20):3112–8.

    CAS  Article  PubMed  Google Scholar 

  92. Verdoodt B, Neid M, Vogt M, Kuhn V, Liffers ST, Palisaar RJ, Noldus J, Tannapfel A, Mirmohammadsadegh A. MicroRNA-205, a novel regulator of the anti-apoptotic protein Bcl2, is downregulated in prostate cancer. Int J Oncol. 2013;43(1):307–14.

    CAS  PubMed  Google Scholar 

  93. Iorio MV, Casalini P, Piovan C, Di Leva G, Merlo A, Triulzi T, Menard S, Croce CM, Tagliabue E. microRNA-205 regulates HER3 in human breast cancer. Cancer Res. 2009;69(6):2195–200.

    CAS  Article  PubMed  Google Scholar 

  94. Jiang L, Huang Q, Chang J, Wang E, Qiu X. MicroRNA HSA-miR-125a-5p induces apoptosis by activating p53 in lung cancer cells. Exp Lung Res. 2011;37(7):387–98.

    CAS  Article  PubMed  Google Scholar 

  95. Ma Y, Zhang P, Yang J, Liu Z, Yang Z, Qin H. Candidate microRNA biomarkers in human colorectal cancer: systematic review profiling studies and experimental validation. Int J Cancer. 2012;130(9):2077–87.

    CAS  Article  PubMed  Google Scholar 

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Acknowledgements

We thank Dr. Nathalia A. Giese, MD, PhD, Heidelberg, Germany, for providing tissue samples for this study.

Funding

Professor Molven received a grant from Western Norway Regional Health Authority (Helse Vest).

Availability of data and materials

The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request. All calculations are included in the manuscript or submitted in “Additional file 2”.

Authors’ contributions

DC designed the study, collected the specimens from Denmark and the corresponding clinical data, interpreted the calculations wrote the manuscript; CD performed all calculations and contributed to the manuscript; MKB contributed to the manuscript; JPH re-assessed all the specimens from Denmark; NAS contributed to data interpretation and to the manuscript; JW contributed with the German specimens; HI contributed with the Norwegian specimens; AM contributed with the Norwegian specimens and contributed to the manuscript; CPH contributed with clinical data and contributed to the manuscript. JSJ designed the study, contributed with collection of specimens, clinical data, interpretation of the calculations, preparation of the manuscript and funding. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

The patients included in the BIOPAC Study provided written informed consent. The study was approved by the Regional Ethics Committee (VEK ref. KA-20060113) and the Danish Data Protection Agency (j.nr. 2006-41-6848, jr. nr. 2012-58-004, and HGH-2015-027, I-suite 03960).

The collection of archived FFPE tissues from CBD cancer and GC was approved by the local ethics committee. The samples from Heidelberg and Bergen were obtained from patients included in studies approved by their local ethics committees.

Author information

Authors and Affiliations

  1. Department of Surgical Gastroenterology and Transplantation, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark

    Dan Calatayud, Nicolai Aagaard Schultz & Carsten Palnæs Hansen

  2. Danish Cancer Society Research Center, Danish Cancer Society, Copenhagen, Denmark

    Christian Dehlendorff

  3. Department of Oncology, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark

    Mogens K. Boisen & Julia S. Johansen

  4. Department of Pathology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark

    Jane Preuss Hasselby

  5. Department of General, Visceral, and Transplant Surgery, LMU, University of Munich, Munich, Germany

    Jens Werner

  6. Gade Laboratory for Pathology, Department of Clinical Medicine, University of Bergen, Bergen, Norway

    Heike Immervoll & Anders Molven

  7. Department of Pathology, Ålesund Hospital, Ålesund, Norway

    Heike Immervoll

  8. Department of Pathology, Haukeland University Hospital, Bergen, Norway

    Anders Molven

  9. Department of Medicine, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark

    Julia S. Johansen

  10. Institute of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

    Julia S. Johansen

  11. Department of Oncology, Herlev University Hospital, Herlev Ringvej 75, DK-2730, Herlev, Denmark

    Dan Calatayud

Authors
  1. Dan Calatayud
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  2. Christian Dehlendorff
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  3. Mogens K. Boisen
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  4. Jane Preuss Hasselby
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  5. Nicolai Aagaard Schultz
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  6. Jens Werner
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  7. Heike Immervoll
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  8. Anders Molven
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  9. Carsten Palnæs Hansen
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  10. Julia S. Johansen
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Corresponding author

Correspondence to Dan Calatayud.

Additional files

Additional file 1:

Background on all measured microRNA. (DOCX 1012 kb)

Additional file 2:

All statistical calculations including insignificant results not presented in the manuscript. (DOC 1481 kb)

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Calatayud, D., Dehlendorff, C., Boisen, M.K. et al. Tissue MicroRNA profiles as diagnostic and prognostic biomarkers in patients with resectable pancreatic ductal adenocarcinoma and periampullary cancers. Biomark Res 5, 8 (2017). https://doi.org/10.1186/s40364-017-0087-6

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  • DOI: https://doi.org/10.1186/s40364-017-0087-6

Keywords

  • Ampullary cancer
  • Biomarkers
  • microRNA
  • Pancreatic ductal adenocarcinoma
  • Pancreatic cancer

Biomarker Research

ISSN: 2050-7771

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