Pancreatic cancer remains one of the leading causes of cancer-related deaths worldwide. Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic tumor. Many circular RNAs (circRNAs) have proven to play vital roles in the physiological and pathological processes of tumorigenesis; however, their biogenesis in PDAC remains unclear. In this study, the expression profiles of circRNAs from 10 PDAC tissues and their paired adjacent nontumor tissues were analyzed through RNA sequencing analysis. An enrichment analysis was employed to predict the functions of the differentially expressed circRNAs. Sequence alignment information and mRNA microarray projects were used to predict the RNA regulatory network. The knockdown of circRNAs by small interfering RNAs followed by wound healing and western blot assays was used to confirm their functions in a PDAC cell line. A total of 278 circRNAs were identified as differentially expressed in PDAC tissue. Of these, we found that hsa_circRNA_0007334 was significantly upregulated and may serve as a competing endogenous RNA to regulate matrix metallopeptidase 7 (MMP7) and collagen type I alpha 1 chain (COL1A1) by the competitive adsorption of hsa-miR-144-3p and hsa-miR-577 to enhance the expression and functions of MMP7 and COL1A1 in PDAC. In vitro experiments confirmed these results. The present study is the first to propose two regulatory pathways in PDAC: hsa_circRNA_0007334–hsa-miR-144-3p–MMP7 and hsa_circRNA_0007334–hsa-miR-577–COL1A1.
Pancreatic cancer remains one of the leading causes of cancer-related deaths worldwide. The number of global deaths caused by pancreatic cancers of all types was 411,600 in 2015 [
Noncoding RNAs, including circular RNAs (circRNAs), long noncoding RNAs (lncRNAs), and microRNAs (miRNAs), all play vital roles in physiological and pathological processes, as suggested in a growing number of studies [
In the present study, we analyzed the expression profiles of circRNAs in PDAC tissue through RNA sequencing to further investigate differentially expressed circRNAs and their biological roles in PDAC. Our findings indicate that hsa_circRNA_0007334 promotes the expression levels of matrix metallopeptidase 7 (MMP7) and collagen type I alpha 1 chain (COL1A1) by blocking the functions of miR-144-3p and miR-577 in PDAC. We propose two regulatory pathways in PDAC: hsa_circRNA_0007334–hsa-miR-144-3p–MMP7 and hsa_circRNA_0007334–hsa-miR-577–COL1A1. The circular RNA hsa_circRNA_0007334 can also be used as a potential biomarker in PDAC diagnosis and therapy.
Ten PDAC tissues and ten paired adjacent nontumor tissues were provided by the Tissue Bank of China-Japan Union Hospital, Jilin University (Changchun, China). The clinical information of the patients is shown in Table
Clinicopathological characteristics of patients with PDAC.
Clinicopathological characteristics | Patients in which RNA sequencing was performed (n=5) | Patients in which qPCR was performed (n=10) |
---|---|---|
Age (mean ± standard deviation) | 59.67±8.26 | 60.70±8.45 |
Gender | ||
Male | 2 | 6 |
Female | 4 | 4 |
TNM stage1 | ||
Stage 0 | 0 | 0 |
Stage IA | 0 | 1 |
Stage IB | 0 | 1 |
Stage II | 1 | 4 |
Stage III | 4 | 4 |
Stage IV | 0 | 0 |
1 According to the WHO classification of tumors of the digestive system.
The Ethics Committee of the China-Japan Union Hospital of Jilin University was given detailed study information and approved all experimental protocols (approval number: 2018-NSFC-006). All participants were informed of the use of their specimens, and written informed consent was obtained from each participant.
Total RNA was extracted using TRIzol reagent (Invitrogen, USA) according to the manufacturer’s instructions. The concentration and purity of the isolated RNA were determined by a NanoDrop 2000 instrument (Thermo Scientific, USA) by measuring the absorbance values at 260 nm (A260) and 280 nm (A280).
Five PDAC tissues and five paired adjacent nontumor tissues were employed for the circRNA sequencing analysis. A total of 5
Clustering of the index-coded samples was performed on a cBot Cluster Generation System using a HiSeq PE Cluster Kit v4 cBot (Illumina) according to the manufacturer’s instructions. The library preparations were sequenced on an Illumina HiSeq 2500 platform, and 125 bp paired-end reads were generated. Fold change values were calculated by the negative binomial distribution DESeq2 method using the expression level (read count data). We calculated the p value between the data of two groups by negative binomial distribution and corrected the multiple hypothesis by Benjamini-Hochberg (BH) correction to obtain the adjusted p value.
Gene Ontology (GO) enrichment analysis was performed by GOseq (version 1.18.0) [
For mRNA and circRNA, 1
For miRNA, 500 ng of total RNA was used for cDNA synthesis and for RT-qPCR analysis on an All-in-One™ miRNA RT-qPCR Detection System (GeneCopoeia, USA) according to the manufacturer’s instructions. The primers were purchased from GeneCopoeia (USA) (hsa-miR-144-3p: Cat. #HmiRQP0190; hsa-miR-577: Cat. #HmiRQP0678).
RT-qPCR analysis was performed on a 7500 Fast Dx Real-Time PCR Instrument (Applied Biosystems, USA). RT-qPCR was repeated three times for each sample. The cycle threshold value (Ct) data were analyzed using the
PANC-1 cells were purchased from the National Infrastructure of Cell Line Resource of China and cultivated in DMEM basic (Gibco, USA) containing 10% fetal bovine serum (Gibco, USA). The cells were adjusted to a density of 1 × 105 cells per well in 24-well plates and incubated at 37°C in a 5% CO2 humidified atmosphere. After 24 hours, 50 nM siRNA transfection reagent was made according to the manufacturer’s instructions and added to the cells. After an additional 24 hours, RNA and protein were extracted from the cells. The cells were divided into 4 groups: the si-circRNA_0007334 group was transfected with si-hsa_circRNA_0007334, the si-circRNA_0007334+miR-144-3p inhibitor group was transfected with both si-hsa_circRNA_0007334 and the hsa-miR-144-3p inhibitor, the si-circRNA_0007334+miR-577 inhibitor group was transfected with both si-hsa_circRNA_0007334 and the hsa-miR-577 inhibitor, and the negative control (NC) group was treated with only transfection reagents in the absence of the siRNA or miRNA inhibitors. The interfering RNA target sequence of hsa_circRNA_0007334 was 5′-GGAGAACATGCACAAGTCA-3′. The siRNA (Cat. #siG180808051115), miR-144-3p inhibitor (Cat. #miR20000436), hsa-miR-577 (Cat. #miR20003242), and transfection reagents (ribo FECT™ CP Transfection Kit Cat. # C10511-05) were purchased from RiboBio Co., Ltd. (China) and operated according to the manufacturer’s instructions.
A wound healing assay was performed to detect the invasion ability of PANC-1 cells after the knockdown of hsa_circRNA_0007334. A Culture-Insert 2 Well (ibidi, Germany) was used to prepare the scratch in 24-well plates. The Culture-Insert 2 Well was removed after 24 hours, and hsa_circRNA_0007334 knockdown was immediately performed as previously described. Microscopic cell images were collected at 0, 6, 12, and 24 hours after knockdown. The migration rate was analyzed by ImageJ software based on the scratch areas.
RIPA lysis buffer (Beyotime, China) containing protease inhibitor cocktail (Beyotime, China) was used to extract total protein according to the manufacturer’s instructions. Fifty micrograms of total protein from each sample was used for SDS-PAGE and then transferred to PVDF membranes (Millipore, China). The antibodies used were anti-MMP7 (1:1000, ab189277, Abcam, USA), anti-COL1A1 (1:1000, ab6308, Abcam, USA), and anti-GAPDH (1:5000, ab245, Abcam, USA). A luminescent and fluorescent biological image analysis system (Furi Science & Technology, China) was used to detect exposure after adding the enhanced chemiluminescent (ECL) reagent. Images were analyzed by ImageJ software to calculate relative expression levels.
The quantitative data are shown as the mean±standard deviation (SD). Statistical analyses were performed using SPSS version 21 (SPSS, Inc., USA). Student’s t-test was used to compare the expression levels of hsa_circRNA_0007334, MMP7, and COL1A1. Two-tailed Pearson’s correlation analysis was used to determine the association between the expression levels of hsa-miR-144-3p and hsa-miR-577. P<0.05 indicates a statistically significant difference. Genes with a fold change ≥ 2 and a p value < 0.05 in the sequencing or microarray data were regarded as significantly differentially expressed.
The circRNA expression profiles in PDAC tissues and adjacent nontumor tissues (5 PDAC tissues and 5 adjacent nontumor tissues) were revealed by hierarchical clustering. The variation in circRNA expression between PDAC tissues and adjacent nontumor tissues is presented in a volcano plot (Figure
circRNA expression profile in PDAC. (a) The volcano plot was constructed according to the fold change and p value. The x-axis presents the log2(fold change) value of differential expression, and the y-axis presents the -log10(padj) value of differential expression. The vertical lines correspond to 2.0-fold up- and downregulation between PDAC and adjacent nontumor tissues, and the horizontal line represents a p value of 0.05. The red circles in the plot represent the upregulated circRNAs, and the green circles in the plot represent the downregulated differentially expressed circRNAs with statistical significance. (b) Hierarchical clustering analysis of circRNAs that were differentially expressed (over a 2.5-fold change) between PDAC and adjacent nontumor tissues. The x-axis presents samples (N1-N5: adjacent nontumor tissues; T1-T5: PDAC tissues), and the y-axis presents the differentially expressed circRNAs. Expression values are represented in different colors; red indicates a high expression level, and blue indicates a low expression level.
According to the correspondence between the circRNA and its parental gene, GO and KEGG enrichment analyses were performed to determine the parental gene function of the differentially expressed circRNAs. GO classification, mainly including biological process, cellular component, and molecular function, was applied to analyze the main functions of the differentially expressed genes according to GO terms. The gene number and the proportion of all annotated genes that are related to the top 15 functions in each term are shown in Figure
GO and KEGG analyses of the parental gene function of the differentially expressed circRNAs. (a) GO analysis covers the three domains of biological process, cellular component, and molecular function. The x-axis presents the gene number and the proportion of all annotated genes, and the y-axis presents the top 15 functions in each term. (b) KEGG analysis revealed the most important biochemical metabolic pathways and signal transduction pathways in which the genes are involved. The x-axis presents the Rich factor in this pathway, the y-axis presents the related pathways, the q values are represented in different colors, and the size of the circle represents the number of genes in this pathway. The degree of KEGG enrichment is measured by the Rich factor, the q value, and the number of genes in this pathway. The Rich factor refers to the ratio of the number of differentially expressed genes located in the pathway to the total number of genes in the pathway. The larger the Rich factor, the greater the degree of enrichment. The q value is the p value after the multiplicative hypothesis test and correction, and the value range of the q value is 0-1. The closer the value to 0, the more significant the enrichment.
To predict the functions of the differentially expressed circRNAs, we constructed a ceRNA network with Cytoscape. The ten circRNAs with the largest fold change (5 upregulated and 5 downregulated) and the ten circRNAs with the smallest p value were chosen to construct the network. To enhance data reliability, the circRNAs that have not been recorded in ENCODE were removed. First, we used the Circular RNA Interactome database (
ceRNA analysis of the differentially expressed circRNAs. Ten circRNAs (red nodes), 173 miRNAs (yellow nodes), and 1,460 target genes (blue nodes) are involved in the ceRNA network. Solid lines represent the relationship between two nodes.
The expression level of hsa_circRNA_0007334 in PDAC tissues was upregulated 4.18-fold. Although hsa_circRNA_0007334 was not the most differentially expressed, the significance of its difference ranked third among all the circRNAs. To verify the sequencing results, RT-qPCR was performed. The relative expression level results showed that the expression of hsa_circRNA_0007334 was upregulated 3.92-fold in PDAC tissues compared with adjacent nontumor tissues. According to the sequencing results and the human reference genome (GRCh37/hg19) obtained from the University of California at Santa Cruz (UCSC) genome database (
To determine whether there is an EIciRNA form for hsa_circRNA_0007334, we designed primers based on the possible intron regions of hsa_circRNA_0007334 and performed reverse transcription PCR according to previous studies [
Assuming that hsa_circRNA_0007334 functions in PDAC through a ceRNA mechanism, we performed the following analysis. First, we used the Circular RNA Interactome database (
Pattern diagram of hsa_circRNA_0007334 and its miRNA binding sites. (a) A schematic model showing the putative miRNA binding sites for hsa_circRNA_0007334. The indigo circle represents the structure of hsa_circRNA_0007334, and the yellow fragments represent miRNAs. (b) Binding sites of the miRNAs hsa-miR-144-3p and hsa-miR-577 in circRNA hsa_circRNA_0007334 and binding sites of the miRNAs hsa-miR-144-3p and hsa-miR-577 in the target genes, MMP7 and COL1A1.
Venn analysis of the differentially expressed genes. The Venn diagram shows 17 genes mentioned in both the databases and 3 microarray projects. Among these 17 genes, MMP1, MMP7, MMP11, MMP14, COL1A1, COL1A2, FN1, LAMA3, NOX4, and PLAU are highly expressed in PDAC tissues (marked as orange, round rectangles); BTG2 and BNIP3 are weakly expressed in PDAC tissues (marked as green, round rectangles); BACE1, CCL20, EGF, LEF1, and MET are not expressed in pancreatic tumor according to the NCBI-UniGene database (marked as gray, round rectangles).
ceRNA analysis of hsa_circRNA_0007334. hsa_circRNA_0007334 (red octagon), 14 miRNAs (blue, round rectangles), and 13 target genes (yellow circles) are involved in the ceRNA network. Solid lines represent the relationship between two nodes.
To verify the functions and interactions of these 13 target genes, we performed a protein-protein interaction (PPI) analysis with the GeneMANIA database (
Protein-protein interaction (PPI) analysis of the predicted target genes. (a) The network shows the protein interactions between 13 predicted target genes and their related genes. (b) The network shows the protein interactions between MMP7, COL1A1, and their related genes. The gray nodes represent genes. The node size represents the correlation strength between genes. The line between two nodes represents the mode of action (color of the line) and strength (width of the line) between genes.
To validate the results of the ceRNA analysis, RT-qPCR was further performed in 10 PDAC tissues and 10 adjacent nontumor tissues. The relative expression levels of the miRNAs hsa-miR-144-3p and hsa-miR-577 and MMP7 and COL1A1 mRNAs were measured by the
Expression profiles of the miRNAs hsa-miR-144-3p and hsa-miR-577 and MMP7 and COL1A1 mRNAs in PDAC. (a) The expression levels of the circRNA hsa_circRNA_0007334, the miRNAs hsa-miR-144-3p and hsa-miR-577, and MMP7 and COL1A1 mRNAs were analyzed by the
These results suggest that hsa_circRNA_0007334 plays a role by suppressing hsa-miR-144-3p and hsa-miR-577. Correspondingly, the level of COL1A1 upregulation is greater than the level of MMP7 upregulation. Taken together, these results strongly suggest that hsa_circRNA_0007334 plays a role in PDAC by functioning via the ceRNA mechanism by the competitive adsorption of hsa-miR-144-3p and hsa-miR-577 to enhance the level of MMP7 and COL1A1 expression.
Furthermore, we used a siRNA to knockdown the expression level of circRNA_0007334 in the pancreatic cancer cell line PANC-1. Wound healing assay results showed that after siRNA knockdown, the migration ability of cells decreased significantly, especially at 6-12 hours after knockdown (Figure
Wound healing assay after hsa_circRNA_0007334 knockdown in the PANC-1 cell line. (a) Microscopic cell imaging (6.4×) at 0, 6, 12, and 24 hours after hsa_circRNA_0007334 knockdown. (b) Migration rate based on scratch areas at 0, 6, 12, and 24 hours after hsa_circRNA_0007334 knockdown.
Expression profiles of hsa_circRNA_0007334, MMP7, and COL1A1 in the PANC-1 cell line after hsa_circRNA_0007334 knockdown. (a) The expression levels of hsa_circRNA_0007334 in the negative control (NC) and hsa_circRNA_0007334 knockdown groups. (b) The mRNA expression levels of MMP7 in the NC, hsa_circRNA_0007334 knockdown, and knockdown plus miR-144-3p inhibitor groups. (c) The mRNA expression levels of COL1A1 in the NC, hsa_circRNA_0007334 knockdown, and knockdown plus miR-577 inhibitor groups. (d) Western blot results of MMP7 and COL1A1 expression in the NC, hsa_circRNA_0007334 knockdown, and knockdown plus miRNA inhibitor groups. (e) The protein expression levels of MMP7 in the NC, hsa_circRNA_0007334 knockdown, and knockdown plus miR-144-3p inhibitor groups. (f) The protein expression levels of COL1A1 in the NC, hsa_circRNA_0007334 knockdown, and knockdown plus miR-577 inhibitor groups.
To further clarify the relationship between the expression levels of hsa-miR-144-3p, hsa-miR-577, MMP7, and COL1A1 in PDAC, we conducted a survey of the survival time of PDAC patients with different expression profiles. The survival data were obtained from the Cancer Genome Atlas (TCGA) database and analyzed by the OncoLnc database (
Survival analysis of hsa-miR-144-3p, hsa-miR-577, MMP7, and COL1A1 expression level in PDAC patients. The x-axis presents the survival time, and the y-axis presents the remaining rate of surviving patients. Red curves present high expression cases of each genes, and blue curves present low expression cases of each genes.
As suggested in a considerable number of studies, the expression profiles of noncoding RNAs, including circRNAs, lncRNAs, and miRNAs, are abnormal in many types of cancer [
The ceRNA hypothesis illustrates the way in which different types of coding and noncoding members of the transcriptome interact with each other via miRNAs [
Recent studies have shown that some mature circRNA molecules may consist of exon and intron sequences or that there are residual intron sequences. This kind of circRNA is called an EIciRNA [
We have made efforts to understand the gene expression profiles between PDAC and normal tissues. First, the results of the three databases (HMDD, miRWalk 2.0, and MalaCards) were summarized to predict miRNAs, their target genes, and elements relevant to pancreatic cancer. Then, three mRNA microarray projects (GSE28735, GSE60980, and GSE62452) from GEO DataSets, with 163 PDAC tissues and 118 adjacent nontumor tissues in total, were investigated. The three gene expression profile microarray projects had several shared pathways that have been reported to be associated with the pathogenesis of PDAC. However, because of the differences in platforms and samples, the differentially expressed genes of the three projects showed differences as well. Next, a Venn analysis was conducted to identify the differentially expressed genes in both the databases and microarray projects. The results indicated that 17 genes were differentially expressed. Of all the 17 genes, MMP1, MMP7, MMP11, MMP14, COL1A1, COL1A2, FN1, LAMA3, NOX4, PLAU, CCL20, LEF1, and MET are highly expressed in PDAC tissues; BTG2, BNIP3, BACE1, and EGF are weakly expressed in adjacent nontumor tissues. As BACE1, CCL20, EGF, LEF1, and MET are not expressed in pancreatic tumors according to the NCBI-UniGene database (
On the basis of the PPI analysis of coexpression, colocalization, shared protein domains, and predicted or genetic interactions, we have learned more about the functions of and communication between these genes. MMP7 and COL1A1 are closely correlated with the occurrence and development of multiple tumors. MMP7 encodes a member of the peptidase M10 family of matrix metalloproteinases. Notably, proteins in this family play an active role in the breakdown of the extracellular matrix in normal physiological processes (such as embryonic development, reproduction, and tissue remodeling) and in disease processes (such as arthritis and metastasis). Additionally, MMP7 is highly expressed in multiple human malignant tumors [
One hsa_circRNA_0007334-miRNA-target gene ceRNA network was established to research the potential interactions between the 13 genes and hsa_circRNA_0007334 (Figure
A survey on the survival time of PDAC patients with different expression profiles was performed to further substantiate the relationship between the expression levels of hsa-miR-144-3p, hsa-miR-577, MMP7, and COL1A1 in PDAC. The survival time of the low hsa-miR-144-3p and hsa-miR-577 expression group was remarkably shorter than that of the high expression group, whereas the survival time of the high MMP7 and COL1A1 expression group was significantly shorter than that of the low expression group. However, we have to note the limitation of the current study: merely ten circRNAs with the largest fold change (five upregulated and five downregulated) and ten circRNAs with the smallest p value were chosen to construct the ceRNA network, but the roles of other circRNAs remain unclear.
In this study, we propose two regulatory pathways in PDAC: hsa_circRNA_0007334–hsa-miR-144-3p–MMP7 and hsa_circRNA_0007334–hsa-miR-577–COL1A1. The results indicate that by the competitive adsorption of hsa-miR-144-3p and hsa-miR-577, hsa_circRNA_0007334 functions as a ceRNA for MMP7 and COL1A1 to elevate their expression levels and plays an important role in PDAC.
The data used to support the findings of this study are included within the article.
The authors declare no financial and/or nonfinancial conflicts of interest in relation to the work described.
This study was supported by Changchun Kebang Technology Co., Ltd. (No. 2017YX107, 3R217C883430), the Science and Technology Department of Jilin Province of China (No. 20180520111JH), the Education Department of Jilin Province of China (No. JJKH20190058KJ), the Second Hospital of Jilin University (No. KYPY2018-02), and the Training Program of Outstanding Doctoral Student by Norman Bethune Health Science Center of Jilin University (No. 470110000646).
Supplementary Data 1. The information about reads’ mapping, quality filtering, normalization of data and analysis of differential expression. Supplementary Data 2. The information of differential expressed circRNAs. Supplementary Data 3. GO analysis on 217 parental genes of circRNAs. Supplementary Data 4. The predicted miRNAs binding to hsa_circRNA_0007334. Supplementary Figure 1. Identification of EIciRNA structure. (A) The pattern diagram of primer setting. (B) Agarose gel electrophoresis detection of reverse transcript PCR. F-R: amplification using F-primer and R-primer (expected size of product: 65 bp), F-R′: amplification using F-primer and R′-primer (expected size of product: 222 bp), F′-R: amplification using F′′-primer and R-primer (expected size of product: 218 bp), F′-R′: amplification using F′-primer and R′-primer (expected size of product: 375 bp).