Background
Intrahepatic cholangiocarcinoma (ICC) is an aggressive subtype of bile duct cancer, which arises in the cholangiocytes of the biliary ducts that extend into the upper hepatoduodenal ligament. While ICC is rare in developed countries such the United States (0.5 per 100,000), ICC is a significant public health problem in low and middle-income countries (LMICs) of Southeast Asia (incidence of 96 per 100,000), particularly the Mekong River Basin countries of Thailand, Laos, Cambodia, and Vietnam [
1-
3]. This variation in incidence reflects the different underlying etiologies of ICC. In the Mekong River Basin, ICC is strongly associated with chronic infection by the food-borne liver fluke
Opisthorchis viverrini (Ov) [
4]: one of only three eukaryote pathogens considered Group 1 carcinogens [
4]. Ov is a ribbon-like, two-centimeter long parasite that is acquired by eating under-cooked cyprinoid fish that harbor the metacercarial stage of this parasite [
2]. Upon ingestion, the metacercariae excyst in the host duodenum and migrate up the biliary tree, inhabiting the host bile ducts for years (even decades), feeding on epithelial cells of the biliary tract. This prolonged injury to the bile duct epithelia creates a persistent “smouldering inflammatory milieu” [
5], that eventually results in several hepatobiliary abnormalities, principal among them ICC [
5].
The location of ICC tumors in the upper hepatoduodenal ligament makes this tumor asymptomatic and hence difficult to detect in early stages. Moreover, its location in the upper hepatoduodenal ligament increases the opportunities for distant metastasis due to the proximity to the lymphatic and vascular systems of the liver [
6]. As such, these slow-growing tumors are usually diagnosed at an advanced stage, when the primary cancer is no longer amenable to surgical extirpation and has metastasized to other organs [
5]. The median survival rate of Ov-induced ICC is less than 24 months [
7]. This poor prognosis highlights the need for diagnostic biomarkers of Ov-induced ICC, especially in resource poor areas, where the incidence is highest and access to health care is difficult.
Over the last five years, microRNAs (miRNAs) have become key biomarker candidates for carcinogenesis as they play a role in numerous physiological and pathological processes, including cellular transformation, tumor differentiation, neoplastic proliferation, and apoptosis [
8]. In cholangiocarcinoma, a growing number of miRNAs have been associated with the disease and a functional role has been defined for many of these (for examples see [
9] and [
10]; also reviewed in [
11] and summarized in Table
1). MicroRNAs are very stable small non-coding RNA species and hence well preserved in formalin fixed paraffin embedded (FFPE) tumor blocks, an ample sample source, considered unsuitable for transcriptome studies. Recently, we reported the first comprehensive microarray-based profiling of miRNA expression using FFPE from the three most common subtypes of Ov-induced ICC tumors [
12]: moderately differentiated ICC, papillary type ICC, and well-differentiated ICC. Each Ov-induced ICC subtype exhibited a distinct miRNA profile, which suggested the involvement of specific sets of miRNAs in the progression of this tumor.
Table 1
Comparison of dysregulated miRNAs associated with ICC to those reported in the literature
Up-regulated in the literature
|
Let-7a | Cell survival | NF2 | - | Cell lines | |
miR-21 | Apoptosis, proliferation, | MBD2, 15-PGDH/HPGD, | Up | Cell lines, Tissue | |
| invasion, metastasis | PTEN,PDCD4, TIMP3 | | | |
miR-25 | Apoptosis | DR4 | Up | Cell lines, Tissue | |
| | | (CCT v. N-NT) | | |
miR-26a | Proliferation, colony formation, | GSK-3 | Down | Cell lines, Tissue | |
| tumor growth | | (CCT v. N-NT) | | |
miR-29b | - | - | Up | Tissue | |
| | | (Pap. v. N-NT) | | |
miR-31 | Proliferation, apoptosis | RASA1 | Up | Cell lines, Tissue | |
miR-34b | - | - | Up | Tissue | |
miR-135 | - | - | Up | Tissue | |
miR-141 | Proliferation, circadian rhythm | CLOCK | Up | Cell lines | |
miR-146a | - | - | Up | Tissue | |
| | | (Pap. v. N-NT) | | |
miR-192 | - | - | Down | Tissue | |
| | | (CCT v. N-NT) | | |
miR-194 | - | - | - | Tissue | |
miR-200a | Chemoresistance | PTPN12 | Up | Cell lines | |
miR-200b | Chemoresistance | PTPN12 | Up | Cell lines | |
miR-200c | Chemoresistance | PTPN12 | Up | Cell lines | |
miR-203 | - | - | Up | Tissue | |
miR-210 | Proliferation | Mnt | Up | Mouse tissue | |
| | | (CCT v. N-NT) | | |
miR-215 | - | - | - | Tissue | |
miR-221 | - | - | Up | Tissue | |
| | | (CCT v. N-NT) | | |
miR-361 | - | - | Up | Tissue | |
| | | (CCT v. N-NT) | | |
miR-375 | - | - | Up | Tissue | |
| | | (CCT v. N-NT) | | |
miR-421 | Proliferation, migration, | FXR | Up | Cell lines, Tissue | |
| colony formation | | (CCT v. N-NT) | | |
miR-429 | - | - | Up | Tissue | |
miR-582 | - | - | - | Tissue | |
miR-892b | - | - | Up | Tissue | |
Down-regulated in the literature
| | | |
miR-29b | Gemcitabine sensitivity, apoptosis | PIK3R1, MMP-2, Mcl1 | - | Cell lines | |
miR-34a | Cell cycle, proliferation | c-Myc | Up | Mouse tissue | |
miR-124 | Migration, invasion | SMYD3 | - | Cell lines | |
miR-138 | Proliferation, cell cycle, | RhoC | Up | Tissue | |
| migration, invasion | | | |
miR-144 | Proliferation, invasion | Pafah1b2 | Down | Tissue | |
miR-148a | Proliferation | DNMT-1 | Down | Cell lines | |
miR-200b/c | Migration, invasion | Rho-kinase2, SUZ12 | Up | Tissue | |
miR-204 | EMT, migration, | Slug, Bcl-2 | Down | Cell lines, Tissue | |
| invasion, apoptosis | | (Pap. v. N-NT) | | |
miR-214 | EMT, metastasis | Twist | Up | Tissue | |
| | | (CCT v. N-NT) | | |
miR-320 | Apoptosis | Mcl-1 | Down | Cell lines, Tissue | |
| | | (CCT v. N-NT) | | |
miR-370 | Proliferation | MAP3K8 | Down* | Cell lines | |
miR-373 | Epigenetics | MBD2 | - | Tissue | |
miR-376c | Migration | GRB2 | Down | Cell lines | |
| | | (CCT v. N-NT) | | |
miR-451 | - | - | Down | Tissue | |
miR-486 | - | - | Down | Tissue | |
miR-494 | Proliferation, cell cycle | CDK6 | - | Cell lines | |
miR-495 | - | - | Down | Tissue | |
| | | (Pap. v. N-NT) | | |
miR-513 | - | - | - | Tissue | |
miR-625 | - | - | Up | Tissue | |
| | | (CCT v. N-NT) | | |
miR-1926 | - | - | - | Tissue | |
In the current manuscript, we confirm and extend these findings using small-RNA Next Generation Sequencing (NGS). In addition we verified if tissue-based miRNA profiles were also detectable as circulating miRNA (c-miRNA) in matched plasma samples, a more accessible biomarker source than tissue. MicroRNAs in the blood circulate as signaling molecules during carcinogenesis [
13-
17], are “stable, reproducible, and consistent among individuals with the same cancer” [
18] and hence have already been used as circulating biomarkers for breast [
19], colorectal [
20] and ovarian cancers [
21]. While most studies of miRNA expression in cancer have focused on biomarker discovery in either tumor tissue or blood (i.e., serum or plasma), our study is among the first to compare different sample matrices (tissue and blood) for biomarker discovery by using paired samples (i.e., tissue and plasma from the same case), using two different discovery methods (microarray and small RNA-Seq). Hence, not only does the current manuscript inform our current basic understanding of miRNA in Ov-induced ICC, it also provides a methodological advance by following a biomarker discovery pipeline that starts with tissue-based biomarker discovery and then verifies candidate biomarkers in the blood.
Methods
Study Samples: tissue and matched plasma
FFPE liver sections and matched plasma samples from histologically confirmed Ov-induced ICC patients archived at the Liver Fluke and Cholangiocarcinoma Research Center, Faculty of Medicine, Khon Kaen University, Thailand were studied. The 14 tumor samples were derived from liver resections performed in the course of palliative treatment for confirmed cases of Ov-associated ICC at the Khon Kaen University’s Srinagarind Hospital, Khon Kaen, Thailand and are referred to as cholangiocarcinoma tissue (CTT). In addition, non-tumor tissue, microdissected distal from any observed dysplasia or frank carcinoma from the same CTT tumor block as noted above, were also examined and are referred to as Distal Non-Tumor (D-NT) tissue. Finally, non-tumor FFPE controls derived from liver biopsies of nine individuals suspected of severe steatosis or steatohepatitis prior to gastric bypass surgery were used to assess baseline liver histology of individual from non Ov endemic areas (USA) and are referred to as Normal Non-Tumor tissue (N-NT). The nine control individuals (N-NT) were female with an average age of 45 years (95% Confidence Interval of 38 to 54 years of age). Detailed clinico-pathological information and representative images of the tissues used in the current study are presented in detail in the previous manuscript, in which tissue-based miRNAs were assessed by microarray [
12].
The ICC plasma samples included the following samples matched from the tissue based studies described above: four plasma matched to the well differentiated ICC tumor tissue, two plasma matched to the moderately differentiated ICC tissue, and six plasma matched to papillary graded tumors (Table
2). All but two plasma samples, B091 and Y070 (Table
2), were matched to tissue samples used in RNA-Seq analysis. Nine control plasma from individuals not resident in an Ov endemic area (USA) were utilized in quantitative PCR (qPCR) analysis alone as non-endemic controls.
Table 2
Histological gradings of samples used for RNA-Seq and qPCR analysis of miRNA expression profiles
B070 | M | 61 | WD | Mass-forming | X | X | |
B079 | M | 61 | WD | Periductal infiltrating, invasive intraductal and mixed | X | X | X |
B083 | F | 53 | WD | Mass-forming | X | X | X |
B090 | M | 58 | WD | Mass-forming | X | X | X |
B099 | M | 48 | WD | Mass-forming | X | X | X |
Y042 | M | 61 | WD | Mass-forming | X | X | X |
B091 | M | 63 | MD | Periductal infiltrating, invasive intraductal and mixed | X | | X |
Y070 | F | 63 | MD | Mass forming | X | | X |
Y056 | F | 56 | PC | Periductal infiltrating, invasive intraductal and mixed | X | X | X |
Y062 | M | 57 | PC | Periductal infiltrating, invasive intraductal and mixed | X | X | |
B040 | M | 64 | PC | Mass forming | X | X | X |
Y083 | F | 51 | PC | Mass forming | X | X | X |
Y088 | F | 58 | PC | Periductal infiltrating, invasive intraductal and mixed | X | X | X |
Y089 | F | 60 | PC | Mass forming | X | X | |
Y093 | M | 63 | PC | Periductal infiltrating, invasive intraductal and mixed | X | X | X |
Y096 | F | 64 | PC | Mass forming | X | X | X |
The Human Research Ethics Committee, Khon Kaen University, approved the study protocols for obtaining the human liver samples (HE571294) and both the Khon Kaen University and George Washington University IRBs determined that the samples used in this study did not meet the definition of human subjects research; i.e., a living individual about whom an investigator conducting research obtains: a) data through intervention or interaction with the individual or b) private identifiable information. This determination was made since the samples were limited to preexisting, de-identified specimen analysis labeled with a random code.
Histological grading
Histological grading was done as described by the International Agency for Research on Cancer (IARC) [
22]. In brief, assignment of the histological grade of well-differentiated adenocarcinoma to a tumor sample required that 95% of the tumor contain glands. For moderately differentiated ICC, tissue was required to have between 40 to 94% of the tumor composed of glands [
22]. Though neither poorly differentiated nor undifferentiated carcinomas were used in this study, they would have had to display between 5 to 39% of the tumor containing glands or less than 5% of glandular structures, respectively [
22]. In the case of papillary ICC, we again followed the IARC classification for tumors of the gallbladder and extrahepatic bile ducts [
22], with the lesions having to consist predominantly of papillary structures lined by cells with a biliary phenotype, with good demarcation and consisting of papillary structures lined by tall columnar cells [
22].
RNA isolation from FFPE
RNA used was previously isolated from the dissected FFPE sections using the miRNeasy FFPE kit (Qiagen) [
12] according to the manufacturer’s protocol and as previously described [
23]. Total RNA was eluted in a volume of 30 μL RNase-free water. Concentration, purity and integrity for the RNA were determined by spectrophotometry (Nanodrop 1000) and Agilent 2100 Bioanalyzer/Agilent RNA 6000 Nano Kit and Agilent Small RNA kit. Purified RNA was stored at < −50°C.
RNA isolation from matched plasma
RNA was isolated from plasma using the miRNeasy Serum/Plasma kit (Qiagen) according to manufacturer’s protocol. Briefly, 1 mL QIAzol lysis reagent was added to 200 μL thawed plasma, mixed and incubated at room temperature for 5 minutes. As a miRNA mimic, 3.5 μL of Spike-In Control (at 1.6 × 108 copies/μL of cel-miR-39-3p was added in addition to 200 μL chloroform (Fisher). Following shaking, incubation and centrifugation, the upper aqueous phase was transferred and 900 μL ethanol (Acros Chemical) was added and transferred to the RNeasy MinElute column. The column was washed with RWT, RPE, and 80% Ethanol (Acros Chemical), followed by drying and eluted in 14 μL RNase-free water. The concentration, purity and integrity were analyzed and stored as described above.
Microarray analysis
Microarray analysis using the Agilent human miRNA microarray (miRBase Release 16.0) of the FFPE cases is extensively described in our previous manuscript [
12] and the data was used here to compare the results of the two discovery platform microarray and small RNA-Seq data comparison.
Small RNA sequencing
RNA purified from FFPE samples were depleted of rRNA by treatment with the Ribo-Zero rRNA Removal Kit (Cat. No. RZH1086, Epicentre), as described by the manufacturer. Briefly, biotinylated capture probes directed against rRNA sequences were added to total RNA samples and allowed to hybridize. Biotinylated complexes were removed using streptavidin-conjugated microbeads and non-ribosomal RNAs precipitated in ethanol. Libraries for small RNA sequencing were prepared using the TruSeq Small RNA Sample Prep Kit (Illumina). Illumina libraries were constructed from 1,000 ng of total RNA. Briefly, indexed oligonucleotide adapters were ligated to both the 3’-hydroxyl end and the 5’-phosphate end of the miRNAs using T4 RNA Ligase (New England Biolabs). RNA was reverse-transcribed and amplified using 14 cycles of PCR with primers targeting the 5’- and 3’- adapters, a specific index sequence, and Illumina sequencing adapters. The resulting products were analyzed and quantified using Agilent 2100 BioAnalyzer and the mole amount of mature miRNA present in the library was estimated by integrating the area under the curve in the 145–160 bp range. Individual libraries were mixed to create multiplexed pools, the mixture was gel purified, and the 145–160 bp range of RNA excised from the gel, crushed using a Gel Breaker tube (IST Engineering), eluted with nuclease-free water, and precipitated in ethanol. The concentration of the final library pool was determined using the PicoGreen system (Invitrogen) and the size distribution of the pool by the Agilent 2100 Bioanalyzer. Library pools were normalized to 2 nM for sequencing. Sequencing was performed using an Illumina Genome Analyzer IIx. Library preparation and small RNA sequencing was performed by Expression Analysis, A Quintiles Company (Durham, NC).
MicroRNA alignment, mapping and annotation
Adapter sequences were clipped from deep sequencing reads using FastqMcf (
http://code.google.com/p/ea-utils/wiki/FastqMcf and initial quality assessment performed using FastQC (
http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). To analyze miRNA expression profiles both miRDeep 2.0.0.5 [
24] and miRExpress 2.0 [
25] were used. Briefly, short reads were mapped to the human (UCSC hg19) genome allowing a minimum read length of 18, zero mismatches in the seed region and a maximum of five genomic loci. Known human miRNAs were identified and quantified based on miRBase Release 19 [
26] entries. Using miRExpress known human miRNAs were identified from miRBase Release 19 with an alignment identity of 1% a tolerance range of four and a similarity threshold of 0.8 in the analysis. Differential expression analysis was performed separately for miRDeep and miRExpress using a negative binomial distribution in EdgeR [
27]. Only miRNAs with at least one count per million in at least half of the samples analyzed were used in expression analysis and counts were normalized using the trimmed mean of M-values normalization method [
27]. For comparisons of matched samples (i.e. ICC tumor versus distal histologically normal tissue from the same patient) a generalized linear model was employed, using the Cox-Reid profile-adjusted likelihood method for estimating dispersion [
27]. For comparisons of tumor tissue to non-CCA normal tissue the quantile-adjusted conditional maximum likelihood method was employed using moderated tagwise dispersion [
27]. Differentially expressed miRNAs were defined as having a Benjamini and Hochberg corrected p value of < 0.05.
Quantitative real time PCR
cDNA was generated from 250 ng of purified plasma RNA using the miScript RT II kit (Qiagen) with heparinase co-treatment during the RT reaction as described [
23]. qPCR analysis was performed using the miScript SYBR Green PCR Kit (Qiagen) on custom printed 96 well miScript miRNA arrays (SABiosciences). Selected miRNAs and normalization controls are shown in Additional file
1: Table S2. qPCR was performed on a BioRad iCycler iQ5 with an initial activation step of 95°C for 15 minutes followed by 40 cycles of 3-step cycling (Denaturation, 15 seconds at 94°C; Annealing, 30 seconds at 55°C; and Extension, 30 seconds at 70°C) followed by melt curve analysis for 81 cycles at 55°C and 20 second dwell time. Quantitation was performed using the ΔΔCt method [
28]. Ct values were exported and analyzed using SABiosciences data analysis tools (
http://pcrdataanalysis.sabiosciences.com/mirna). Samples were normalized using miR-103a, −15b, −16, −191, −22 as well as cel-miR-39-3p (
C. elegans mimic spike-in control).
Database accession
Microarray data was previously prepared according to MIAME standards and deposited in the GEO (Gene Expression Omnibus Database, National Center for Biotechnology Information, U.S. National Library of Medicine, Bethesda, MD) under accession number GSE53992. RNA sequence data have been submitted to the Sequence Read Archive (National Center for Biotechnology Information, U.S. National Library of Medicine, Bethesda, MD) under accession number PRJNA275105 (Sample submission pending).
Discussion
MicroRNAs have great potential as predictive, diagnostic and prognostic biomarkers for Ov-induced ICC, making an understanding of the ways in which miRNA expression levels vary during ICC tumor progression essential. This manuscript expands on our previous tissue-based miRNA discovery efforts by microarray (miRBase 16.0) by employing Next Generation Sequencing (small RNA-Seq) on the same sample set [
12]. Here, we again observed that increasing histological differentiation of Ov-induced ICC tumors is reflected in an increasing number and magnitude of dysregulated miRNAs, suggesting that miRNA regulation is a key process in tumor differentiation. The use of small RNA-Seq also confirmed that adjacent non-tumor tissue (D-NT), which has with no dysplasia or frank carcinoma, shares similar miRNA dysregulation profiles with adjacent tumor tissue (CTT). Finally, our analysis of matched plasma samples by quantitative PCR showed than an eight-miRNA expression profile strongly associated with ICC.
Due to the location of ICC tumors in the upper hepatoduodenal ligament and the proximity of these tumors to the lymphatic and vascular systems of the liver [
2], we expected ICC tumors to shed miRNAs into the blood stream, as observed with other solid tumors (e.g., metastatic breast, colon, and prostate cancers as reviewed in [
19]). As Ov-induced ICC poses unique diagnostic and prognostic challenges, an accessible early diagnostic marker in blood is greatly needed. Towards this end, we generated a custom made qPCR plate containing miRNAs found to be dysregulated in ICC tumor tissue by small RNA-Seq to target these miRNAs in plasma matched samples. Eight of these dysregulated miRNAs in plasma emerged as strongly associated with ICC: i.e., eight dysregulated miRNAs were identified in all Ov-induced ICC plasma samples and not in control plasma (Figure
5). Interestingly, a negative correlation was observed between the expression levels of these eight miRNAs in tissue and in their matched plasma samples (Figure NA), with seven displaying opposite expression changes in plasma to that in tissue (miR-1275, miR-193a-5p, miR199b-5p, miR-320a, miR-483-5p, miR-505-3p, miR-874) (Figure NB). A similar inverse relationship between tissue and blood based miRNA dysregulation has been reported for several other cancers and pathologies, including for another infection-related cancer (nasopharyngeal carcinoma) by our own group [
23], as well as breast cancer [
34], endometrioid endometrial carcinoma [
35], leukemia [
36], neointimal hyperplasia [
37] and also in atherosclerotic abdominal aortic aneurysm [
38]. An additional 13 significantly dysregulated miRNAs were observed only when matched plasma was compared to control plasma, indicative of miRNA solely found circulating in the plasma of NPC cases not found in their tumor tissue. These results reflect on the possible different functions of miRNAs in tissue and circulating in peripheral blood. Moreover, the recent finding of circulating exosomes (or microvesicles) “laden” with miRNAs secreted from the bile duct of individuals with ICC offers intriguing possibilities for miRNA trafficking. As exosomes are actively exported from cells and incorporated into cells from the blood, they offer an explanation that cancer cells are able to selectively export or import particular miRNAs via these microvesicles, which would explain the inverse expression levels in tissue and plasma [
36,
39].
In this regard, the absence of a linear association between miRNA expression levels in tumor tissue and blood suggests that the primary focus of plasma biomarker discovery should be the plasma itself and not the primary tumor tissue, as we have previously assumed for our biomarker discovery pipeline [
12,
23]. The finding of divergent expression profiles in tumor tissue and matched plasma samples is especially intriguing for Ov-induced ICC, given the proximity of Ov-induced ICC tumors to the lymphatic and vascular systems of the liver [
2]. In addition to the validation in a large sample set of potential miRNA biomarkers identified here, we plan to investigate the trafficking of miRNAs by exosomes in future studies. Moreover, multiple novel miRNAs (not in miRBase) were detected in the tissue samples examined by RNA-seq and we plan to validate the association of these miRNAs with ICC in plasma and tissue and determine whether they are human miRNAs and not contributed by Ov during infection.
Ov-induced ICC tumor tissue showed few differences from adjacent “non-tumor tissue” (D-NT) in miRNA expression profile (see the MDS plot in Figure
2A). However, when histological grade was taken into account, papillary ICC tumor did show significant differences compared to its adjacent non-tumor tissue D-NT, while well-differentiated tissue exhibited no differentiation with paired distal tissue (Figure
3). This suggests a regulation of different subsets of miRNAs during tumor progression, an observation consistent with findings in hepatocarcinoma [
40,
41], where differences in the composition, numbers and relative expression levels of miRNA increase with increasing histological differentiation. Functional studies also suggest that associations between the miRNA expression profile and histological grade are derived from miRNA regulation of key processes in tumor differentiation [
40,
41]. In this context, the differences observed in miRNA dysregulation between papillary and well differentiated tumor tissue in this study, likely reflect the fact that, by definition, well differentiated tumor tissue has the most resemblance to the bile duct tissue from which the tumor arose. Similarly, in comparisons of differently graded tumor with N-NT, well differentiated tissue exhibited fewer differences with the control tissue than papillary tissue. Indeed, in all comparisons using either D-NT or N-NT control tissue similar expression profiles were observed (as reflected in the PC of 0.60 between fold-change values generated using the two controls) but with greater magnitude of dysregulation in comparisons using N-NT. Tumors and their surrounding microenvironment are in constant interaction and the greater similarity between D-NT and CTT could reflect the influence of the tumor on surrounding tissue. Although, in this study, the difficultly in obtaining control tissue (discussed below) makes it difficult to ascertain the extent of such an effect.
We also tested different discovery platforms to identify signatures of miRNAs in Ov-induced ICC tumor FFPE tissue. Our previous approach [
12] used a “targeted platform”, where known miRNAs were surveyed in ICC FFPE samples by a microarray built using miRBase 16 [
26]. Here, we used an “untargeted” discovery approach, with a high throughput analysis of all small RNA species in case and control FFPE and found that the expression profiles determined by Illumina sequencing were similar to those determined in our microarray studies of Ov-induced ICC [
12]. Robust correlations were observed between miRNA expression ratios obtained by microarray [
12] with those obtained by Illumina miRNA sequencing (PCs of 0.97 and 0.63 for papillary and well differentiated tumors respectively as shown in Additional file
3: Figure S1). When comparing significantly dysregulated miRNAs, however, differences were observed between the two methods (Additional file
3: Figure S1A). In previous work comparing microarray and NGS analysis of miRNA expression levels, we [
23], and others [
33], have reported variations in the statistical assessment of significantly dysregulated miRNAs despite the overall similarity in fold change values. This may be due to cross-hybridization of closely related miRNA species on the microarray [
23,
33] or differences in the statistical methods employed by the two platforms, for example t-tests in microarray analysis and empirical Bayes estimation and exact tests based on the negative binomial distribution in NGS [
27]. However, in the current manuscript the strong similar profiles obtained by these two discovery platforms suggests that the miRNA profiles reported here are an accurate representation of those for tissue-based miRNAs for Ov-induced ICC, despite differences in significance calling between the two platforms.
An obvious limitation of the current study was the lack of a predominantly cholangiocytic control tissue. As this type of sample is extremely rare, normal liver tissue (N-NT) obtained from liver biopsies of patients undergoing gastric bypass surgery was the best available control to represent liver tissue from non-Ov-induced ICC individuals. Nonetheless, the results reported here are, for the most part, in accord with literature (Table
1) suggesting not only that these results are an accurate reflection of the miRNA expression profile of Ov-induced ICC but that miRNAs reported here could have some utility in non-Ov-induced cholangiocarcinoma. Despite the limitations imposed by this control sample, it is clear that the miRNA profiles of D-NT tissue were more similar to ICC tumor tissue than to normal liver tissue N-NT. When ICC tumor tissue was compared to D-NT tissue, MDS plots showed differences in miRNA profiles when comparing tumor expression profiles to N-NT but not when compared to D-NT. Apart from intrinsic differences between the tumor tissue and N-NT, it is well documented that the tumor microenvironment is a major contributor to metastatic potential and the similarities between tumor tissue and their nearby non tumor tissue (D-NT) reflect this. Metastasis is closely associated with changes such as epithelial-mesenchymal transition, angiogenesis, matrix degradation, and stroma remodeling that occurs in the microenvironment [
42]. A large number of miRNAs have been associated with metastasis (reviewed in [
43]), at least one, miR-1 (also found to be dysregulated in this study), has been shown to directly influence the microenvironment of glioblastomas [
44]. Another miRNA identified in our analysis, miR210, has been repeatedly implicated in the establishment of hypoxia [
45-
47]. Interestingly, in the work described here, miR-210 was significantly up-regulated in tumor tissue when compared to N-NT but not when compared to D-NT, suggesting a role for this miRNA in the establishment of a hypoxic microenvironment in Ov-induced ICC. Extracellular exosomal transport of miR-210 and possible uptake by endothelial cells has been shown in leukemic and metastatic cancer cells [
48,
49] and the results reported are consistent with the potential trafficking of miRNAs to areas adjacent to the tumor.
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Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
JPl carried out molecular studies, contributed to drafting the manuscript and helped conceive the project. GR carried out molecular studies and contributed to drafting the manuscript. YF carried out molecular studies. JP, SE CP, VB, and BS participated in the design of the study and helped draft the study. XJ and JPo conducted bioinformatics analysis and contributed to drafting the manuscript. JB and JM conceived the project, participated in the design of the study and drafted the manuscript. All authors read and approved the final manuscript.