Identification of differentially expressed circular RNAs in human colorectal cancer

Abstract

Circular RNA, a class of non-coding RNA, is a new group of RNAs and is related to tumorigenesis. Circular RNAs are suggested to be ideal candidate biomarkers with potential diagnostic and therapeutic implications. However, little is known about their expression in human colorectal cancer. In our study, differentially expressed circular RNAs were detected using circular RNA array in paired tumor and adjacent non-tumorous tissues from six colorectal cancer patients. Expression levels of selected circular RNAs (hsa_circRNA_103809 and hsa_circRNA_104700) were measured by real-time polymerase chain reaction in 170 paired colorectal cancer samples for validation. Statistical analyses were conducted to investigate the association between hsa_circRNA_103809 and hsa_circRNA_104700 expression levels and respective patient clinicopathological features. Receiver operating characteristic curve was constructed to evaluate the diagnostic values. Our results indicated that there were 125 downregulated and 76 upregulated circular RNAs in colorectal cancer tissues compared with normal tissues. We also first demonstrated that the expression levels of hsa_circRNA_103809 (p < 0.0001) and hsa_circRNA_104700 (p = 0.0003) were significantly lower in colorectal cancer than in normal tissues. The expression level of hsa_circRNA_103809 was significantly correlated with lymph node metastasis (p = 0.021) and tumor-node-metastasis stage (p = 0.011), and the expression level of hsa_circRNA_104700 was significantly correlated with distal metastasis (p = 0.036). The area under receiver operating characteristic curves of hsa_circRNA_103809 and hsa_circRNA_104700 were 0.699 (p < 0.0001) and 0.616 (p < 0.0001), respectively. In conclusion, these results suggest that hsa_circRNA_103809 and hsa_circRNA_104700 may be potentially involved in the development of colorectal cancer and serve as potential biomarkers for the diagnosis of colorectal cancer.

Keywords

Circular RNAs colorectal cancer circular RNA array biomarker hsa_circRNA_103809 hsa_circRNA_104700

Introduction

Colorectal cancer (CRC) is frequently among the most common of cancers and the main cause of morbidity and mortality worldwide.¹ Although the available methods involving a combination of surgical resection, radiation, and chemotherapy have improved a patient’s 5-year survival, the rate of mortality is high (~50,000 annually), and almost 50% of CRC patients still experience recurrence. Likely reasons for this would be the lack of early cancer detection and reliable prognostic predictions. Statistics from well-known sources have reported that CRC made up over 9% in all incidence of malignancies^2,3 and about 8% of all new cases of malignancy in 2014.⁴ Therefore, research for determining biomarkers and effective molecular targets for CRC are extremely critical.

Circular RNA (circRNA), is a class of non-coding RNAs (ncRNAs), formed mainly by back-splicing and covalent binding that are mostly found in the eukaryotic transcriptome and exosomes.^5,6 They play major roles in gene expression regulation and biological processes.^7–10 CircRNAs, different from linear RNAs, form a closed continuous loop, and are an emerging class of ncRNAs in the latest study, although they have been around for about 20 years.¹¹ CircRNAs as a special subset of endogenous RNAs have indicated enormous capabilities for gene regulation and high tissue-specific expression in humans.¹² Compared with other RNAs, circRNAs may have potential biological functions in gene expression regulation and will become ideal biomarkers in the diagnosis of cancers. In recent circRNA studies, a mass of unknown circRNAs have been confirmed in various human cell types.^12–15 Meanwhile, a large number of circRNAs have been found in CRC, which may provide significant clues for gaining a better understanding of the roles of circRNAs in the development and progression of CRC.

In this study, we detected differentially expressed circRNAs using human circRNA array and screened the most significantly different circRNAs in CRC tissues. We examined the expression levels of hsa_circRNA_103809 and hsa_circRNA_104700 in 170 paired CRC and normal adjacent tissues using quantitative reverse transcription (RT)-polymerase chain reaction (PCR), and found that hsa_circRNA_103809 and hsa_circRNA_104700 were significantly downregulated in CRC tissues compared with normal adjacent tissues. In addition, we found that the expression level of hsa_circRNA_103809 was significantly associated with lymph node metastasis (p = 0.021) and tumor-node-metastasis (TNM) stage (p = 0.011), while the expression level of hsa_circRNA_104700 was significantly associated with distal metastasis (p = 0.036). Our results indicate that hsa_circRNA_103809 and hsa_circRNA_104700 may be excellent diagnostic markers for CRC.

Materials and methods

Patients and clinical specimens

In this study, we collected a total of 170 paired CRC tissues and matched adjacent non-cancerous tissue samples (5 cm from the edge of the cancer as assessed by a pathologist) from patients undergoing surgical resection between 2013 and 2015 at the First Affiliated Hospital of Wenzhou Medical University, China.

All tissue samples were verified by a trained pathologist and were immediately preserved at −80°C until further use. No patient had received any form of anti-cancer treatment before surgery. Six CRC tissues were selected for human circRNA array. Tumor grades were defined according to the criteria of World Health Organization (WHO; 2010).¹⁶ The pathological TNM status was assessed according to the criteria of the sixth edition of the TNM classification of the International Union Against Cancer (2002).¹⁷ Written informed consent was provided from each patient or her or his guardian. The study was approved by the Ethics Committee of the First Affiliated Hospital of Wenzhou Medical University.

Human circRNA array

Total RNA was extracted from six paired CRC and adjacent normal tissues using TRIzol (Invitrogen, Carlsbad, CA) according to the manufacturer’s instructions and was quantified using NanoDrop ND-1000. The sample preparation and microarray hybridization were achieved based on the Arraystar’s standard protocols. Total RNA from each sample was amplified and transcribed into fluorescent cRNA utilizing random primer according to Arraystar’s Super RNA Labeling protocol (Arraystar Inc., Rockville, MD, USA). The labeled cRNAs were further hybridized with the Arraystar Human circRNA Array (Arraystar). After washing the slides, the arrays were scanned using the Agilent Scanner G2505C. The acquired array images were analyzed using the Agilent Feature Extraction software (version 11.0.1.1). Quantile normalization and subsequent data processing were performed using the R software package. The volcano plot filtering was used to identify the differentially expressed circRNAs, which possess statistical significance between two groups. The Fold Change (FC) filtering was used to identify the differentially expressed circRNAs between two samples. Hierarchical Clustering was performed to show distinguishable circRNA expression pattern among samples. CircRNAs with FCs greater than 1.5 and p < 0.05 were selected as being significantly differentially expressed.

Experimental validation with quantitative RT-PCR

Hsa_circRNA_103809 and hsa_circRNA_104700 were selected for experimental validation using a quantitative RT-PCR. Total RNA was extracted from the CRC and paired adjacent normal tissues samples using TRIzol reagent (Invitrogen). Complementary DNA (cDNA) was synthesized with random primers using a Revert Aid First Strand cDNA Synthesis kit (Thermo Fisher, no. K1622, Rockford, IL, USA), and each RT reaction consisted of 1.0 µg RNA. Real-time PCR was performed with SYBR Premix Ex Taq (Takara, Japan) on ABI 7500 Real-time PCR system (Applied Biosystems, Warrington, UK). Divergent primers of hsa_circRNA_103809 and hsa_circRNA_104700 were designed. The relative amount of gene expression was normalized to that of glyceraldehyde-3-phosphate dehydrogenase (GAPDH; an endogenous housekeeping gene). The sequences of hsa_circRNA_103809 primers were 5′-ACG CAT TCT TCG AGA CCT CT-3′ (sense) and 5′-TGC CTG TAA CTC CTC TTC AGT-3′ (antisense). The sequences of hsa_circRNA_104700 primers were 5′-TCC GAC AGC AAC AGG AAA TG-3′ (sense) and 5′-TGG ACC TCG GAC TGG GAT AA-3′ (antisense). The sequences of GAPDH primers were 5′-GTC AGC CGC ATC TTC TTT TG-3′ (sense) and 5′-GCG CCC AAT ACG ACC AAA TC-3′ (antisense). The amplified PCR products of the expected length were dissected from a gel and were verified by DNA sequencing to confirm the PCR results. The relative expression level was calculated using the ΔCt method.

Statistical analysis

All experimental data were analyzed using Statistical Product and Service Solutions software 16.0 (SPSS, Chicago, IL) and GraphPad Prism 5.0 (GraphPad Software, La Jolla, CA). The area under the curve (AUC) was used to assess the predictive power and to determine the cutoff scores for the high-expression and low-expression of circRNAs (the quantitative polymerase chain reaction (qPCR) Ct value cutoff of hsa_circRNA_104700 was 10.753 and the Ct value cutoff of hsa_circRNA_103809 was 13.9). All of the enumerated data were compared using chi-square test, and comparisons of the continuous data between two groups were tested using an independent t-test. The p value <0.05 was considered significant.

Results

Profiles of differently expressed circRNAs in CRC

Six patient samples were randomly chosen from a CRC cohort. Histopathology was assessed by a pathologist, and patient characteristics are shown in Table 1. The percentage of tumor cells and stromal components stained with hematoxylin and eosin was analyzed, and the results showed that the CRC tissues harbored more than 50% tumor cells (Table 1). A total of 4,342 circRNAs were detected in all six pairs of samples, and 201 (4.6%) differentially expressed circRNAs were revealed through a comparison between normal colorectal tissues and CRC tissues (FC > 1.5, p < 0.05) (Figure 1(a)). In the differently expressed circRNAs, more than 85% of circRNAs were located in exons (Figure 1(b)). Hierarchical clustering revealed expression differences of circRNAs between six paired CRC and adjacent normal tissues (Figure 1(c)).

Table 1.

Clinical and histopathological characteristics of patients for circRNA assay in this study.

Patient ID	Age	Gender	Histology	Differentiation	TNM classification	Anatomic site	Percentage tumor/stromal cells
1	59	male	adenocarcinoma	moderate	T2 N0 M0	rectum	60/40
2	53	female	adenocarcinoma	moderate	T4 N0M0	rectum	80/20
3	73	male	adenocarcinoma	moderate	T4 N0 M0	sigmoid	50/50
4	73	male	adenocarcinoma	moderate-low	T4 N2 M0	sigmoid	80/20
5	55	male	adenocarcinoma	low	T4 N2 M0	rectum	90/10
6	71	female	adenocarcinoma	moderate	T2 N1 M0	rectum	70/30

circRNA: circular RNA; TNM: tumor-node-metastasis.

Figure 1.

Expression of circRNAs in CRC tissues. (a) The percentage of significantly differentially expressed circRNAs with different fold changes in CRC tissues when compared with normal colorectal tissues, (b) the percentage of significantly differentially expressed circRNAs arising from different genomic locus (exonic, intronic, antisense, intragenic, and intergenic), and (c) the cluster heat map of the significantly differentially expressed circRNAs in normal and malignant tissues of CRC (FC > 1.5, p < 0.05). Significantly differentially expressed circRNAs between normal and malignant tissues of CRC are shown on a scale from green (low) to red (high). Individual tissues subsets are depicted as columns.

A volcano plot was used as a tool for visualizing differential expression between normal and malignant CRC tissues. It was constructed using FC values and p values to visualize the relationship between FC (magnitude of change) and statistical significance (which takes both magnitude of change and variability into consideration). The red point in the plot represents the differentially expressed circRNAs with statistical significance (Figure 2(a)). Hierarchical clustering showed different circRNAs expression levels (FC > 3, p < 0.05) between normal and malignant CRC tissues from the six CRC patients (Figure 2(b)). Among the differently expressed circRNAs, there were 125 downregulated circRNAs in CRC tissues (FC > 1.5, p < 0.05). Among them, 43 circRNAs were downregulated compared to normal colorectal tissues (FC > 2, p < 0.05), and seven circRNAs were downregulated more than threefold (FC > 3, p < 0.05) (Figure 2(c)). Similarly, there were 76 upregulated circRNAs in CRC tissues (FC > 1.5, p < 0.05). Among them, 32 circRNAs were upregulated compared to normal colorectal tissues (FC > 2, p < 0.05), and eight circRNAs were upregulated more than threefold (FC > 3, p < 0.05) (Figure 2(d)). Detailed information of significant circRNAs was shown in Table 2 according to the extent of the changes between CRC tissues and normal colorectal tissues (FC > 3, p < 0.05).

Figure 2.

Identification of differentially expressed circRNAs in normal and CRC tissues. (a) Volcano plot shows circRNAs data. The green lines show where the fold change is 1.5-fold and the p value is 0.05. The vertical lines correspond to 1.5-fold up and down, respectively, and the horizontal line represents a p value of 0.05. The red points in the plot represent differentially expressed circRNAs with statistical significance that indicates both large fold changes (x-axis) and high statistical significance (−log10 of p value, y-axis). The gray points represent the circRNAs that have no statistical significance, (b) heat map from microarray analysis of circRNAs expressions in normal and CRC tissues (FC > 3, p < 0.05), and (c and d) differentially expressed circRNAs according to the extent of changes between CRC and normal colorectal tissues.

Table 2.

Detailed information of significant differentially expressed circRNAs between normal and malignant CRC tissues (FC > 3, p < 0.05).

CircRNA	p value	FC	Type	Chrom	Strand	Gene symbol	miRNA binding sites
Downregulated
hsa_circRNA_104700	0.0001	4.74	Exonic	8	−	PTK2	miR-141-5p,miR-500a-5p,miR-509-3p,miR-619-3p,miR-578
hsa_circRNA_103829	0.0006	3.78	Exonic	5	−	HMGCS1	miR-625-3p,miR-129-5p,miR-532-5p, miR-548c-3p,let-7c-5p
hsa_circRNA_104640	0.0000	3.74	Exonic	8	+	CSPP1	miR-193a-5p,miR-520g-3p,miR-520h,miR-508-5p, miR-519d-5p
hsa_circRNA_103809	0.0008	3.57	Exonic	5	−	ZFR	miR-511-5p,miR-130b-5p,miR-642a-5p, miR-532-3p, miR-329-5p
hsa_circRNA_001506	0.0001	3.52	Intronic	2	−	LAPTM4A	miR-517-5p,miR-550a-3p,miR-590-3p, miR-200c-5p,miR-651-5p
hsa_circRNA_104168	0.0003	3.11	Exonic	6	−	RTN4IP1	miR-450b-5p,miR-139-5p,miR-224-5p, miR-193a-3p, miR-193b-3p
hsa_circRNA_104575	0.0004	3.08	Exonic	8	+	EPHX2	miR-519e-5p, miR-519d-5p, miR-200b-3p, miR-145-5p, miR-515-5p
Upregulated
hsa_circRNA_102049	0.0001	4.96	Exonic	17	+	TADA2A	miR-103a-2-5p,miR-455-3p, miR-302c-3p,miR-520d-3p,miR-302b-3p
hsa_circRNA_104204	0.0000	3.70	Exonic	6	+	AIG1	miR-619-5p,miR-370-3p,miR-448,miR-18b-5p, miR-18a-5p
hsa_circRNA_100181	0.0001	3.35	Exonic	1	+	CTPS1	miR-223-5p,miR-148a-3p, miR-148b-3p, miR-152-3p, miR-146b-5p
hsa_circRNA_102704	0.0001	3.17	Exonic	2	+	EML4	miR-708-3p,miR-376c-3p,miR-211-3p, miR-18b-5p, miR-513a-5p
hsa_circRNA_101765	0.0003	3.17	Exonic	16	+	PRKCB	miR-181b-5p,miR-181c-5p, miR-181d-5p, miR-181a-5p, miR-329-5p
hsa_circRNA_101934	0.0001	3.15	Exonic	17	−	YWHAE	miR-153-5p,miR-15b-3p,miR-665, miR-130a-5p, miR-22-5p
hsa_circRNA_100160	0.0000	3.13	Exonic	1	+	ZMYM4	miR-193b-5p,miR-518a-5p,miR-527, miR-1264,miR-584-3p
hsa_circRNA_100988	0.0001	3.01	Exonic	11	+	FLI1	miR-889-5p,miR-766-3p, miR-30b-3p,miR-608, miR-149-3p

circRNA: circular RNA; CRC: colorectal cancer; TNM: tumor-node-metastasis; FC: fold change in CRC tissues vs normal tissues; Chrom: chromosome; miRNA: microRNA.

Identification of differentially expressed circRNAs in CRC tissues correlates with lymph node metastasis

To identify circRNAs involved in CRC lymph node metastasis, the circRNAs expression profiles in CRC with and without lymph node metastasis were compared (Table 1, Patient ID 1, 2, 3 without lymph node metastasis and Patient ID 4, 5, 6 with lymph node metastasis). There were 13 differentially expressed circRNAs (seven downregulated and six upregulated, FC > 1.5, p < 0.05). Changes in circRNAs expression and statistical significance were calculated and illustrated using volcano plots (Figure 3(a)). Hierarchical clustering analysis indicated the expression levels of different circRNAs (Figure 3(b)). Detailed information of significant circRNAs according to the extent of the changes in CRC tissues with lymph node metastasis (FC > 1.5, p < 0.05) was shown in Table 3.

Figure 3.

Identification of differentially expressed circRNAs in CRC tissues correlated with lymph node metastasis. (a) Volcano plot indicates fold changes (log2 values) and probability values (log10) for individual circRNAs in CRC tissues with lymph node metastasis compared to that without lymph node metastasis. The red points correspond with statistically significant values (SAM p values plotted as −log10) and changes in expression (fold change values plotted as log 2) (FC > 1.5, p < 0.05) and (b) heat map from microarray analysis of circRNAs expressions correlated with lymph node metastasis.

Table 3.

Detailed information of significantly differentially expressed circRNAs in CRC tissues between positive and negative lymph node metastasis groups (FC > 1.5, p < 0.05).

CircRNA	p value	FC	Type	Chrom	Strand	Gene symbol	miRNA binding sites
Downregulated
hsa_circRNA_101238	0.0490	1.68	Exonic	13	+	PAN3	miR-320b,miR-320a,miR-138-5p, miR-593-5p,miR-320c
hsa_circRNA_101853	0.0303	1.62	Exonic	16	+	SNTB2	miR-345-3p,miR-146b-3p,miR-16-5p, miR-195-5p, miR-874-3p
hsa_circRNA_102021	0.0448	1.60	Exonic	17	−	SSH2	miR-620,miR-200c-5p,miR-510-3p, miR-539-5p, miR-497-3p
hsa_circRNA_102694	0.0376	1.61	Exonic	2	−	ATL2	miR-195-5p,miR-15b-5p,miR-16-5p, miR-497-5p, miR-15a-5p
hsa_circRNA_104075	0.0301	1.64	Exonic	6	−	NUP153	miR-16-2-3p,miR-129-5p,miR-33a-5p, miR-582-3p, miR-16-1-3p
hsa_circRNA_104598	0.0276	1.68	Exonic	8	−	KAT6A	miR-374a-3p,miR-541-5p,miR-383-3p,miR-501-5p,miR-145-5p
hsa_circRNA_400090	0.0435	1.75	Intronic	6	−	C6orf132	miR-328-3p,miR-212-5p,miR-135b-5p,miR-548d-3p,miR-135a-5p
Upregulated
hsa_circRNA_001302	0.0048	1.58	Intragenic	4	+	IGFBP7-AS1	miR-22-3p
hsa_circRNA_100146	0.0280	1.64	Exonic	1	+	EIF3I	miR-744-3p,miR-616-3p,miR-361-3p, miR-615-5p,miR-135b-3p
hsa_circRNA_101555	0.0369	1.66	Exonic	15	−	CSNK1G1	miR-644a,miR-485-5p,miR-889-5p, miR-329-5p,miR-148a-5p
hsa_circRNA_102034	0.0426	1.57	Exonic	17	+	RHOT1	miR-26b-3p,miR-382-5p,miR-181a-3p, miR-330-5p,hsa-miR-125a-3p
hsa_circRNA_103809	0.0485	2.01	Exonic	5	−	ZFR	miR-511-5p,miR-130b-5p,miR-642a-5p,miR-532-3p,miR-329-5p
hsa_circRNA_104062	0.0388	1.58	Exonic	6	−	RANBP9	miR-376c-5p,miR-376b-5p,miR-150-5p,miR-891a-3p,miR-135b-5p

circRNA: circular RNA; CRC: colorectal cancer; FC: fold change in CRC tissues vs normal tissues; Chrom: chromosome; miRNA: microRNA.

Decreased expression levels of hsa_circRNA_103809 and hsa_circRNA_104700 in CRC and their related clinical implications

Based on our circRNAs profiling study, hsa_circRNA_103809 and hsa_circRNA_104700 were randomly selected for the validation in relatively large-scale samples (170 paired CRC and matched adjacent non-cancerous tissues). The expression levels of hsa_circRNA_103809 and hsa_circRNA_104700 were significantly decreased 3.57 fold and 4.17 fold, respectively, in CRC tissues compared with normal colorectal tissues based on the results from the circRNAs array. qRT-PCR results indicated that hsa_circRNA_103809 and hsa_circRNA_104700 had significantly lower expression levels in CRC vs non-tumorous tissues (Figure 4(a) and (b)). Furthermore, the receiver operating characteristic (ROC) curve analysis was performed for the expression levels of hsa_circRNA_103809 and hsa_circRNA_104700. The ROC curve is a comprehensive index reflecting the sensitivity and specificity of continuous variables. The area under the ROC curves of hsa_circRNA_103809 and hsa_circRNA_104700 were 0.699 (p < 0.0001, Figure 4(c)) and 0.616 (p < 0.0001) (Figure 4(d)), respectively. The correlations between circRNAs expression levels and clinicopathological features of patients with CRC were also analyzed. We found that the expression level of hsa_circRNA_103809 was significantly associated with lymph node metastasis (p = 0.021) and TNM stage (p = 0.011), while the expression level of hsa_circRNA_104700 was significantly correlated with distal metastasis (p = 0.036) (Table 4).

Figure 4.

The expressions of hsa_circRNA_103809 and hsa_circRNA_104700 were decreased in CRC tissues and might serve as potential novel biomarkers for CRC. (a) The relative expression level of hsa_circRNA_103809 in human CRC tissues (n = 170) was detected by qRT-PCR; they were matched with normal colorectal tissues (n = 170). GAPDH was used as the control for mRNA loading, and hsa_circRNA_103809 abundance was normalized to GAPDH mRNA (***p < 0.0001). (b) Hsa_circRNA_104700 expression level between CRC tissues (n = 170) and normal colorectal tissue samples (n = 170) was measured by qRT-PCR (p = 0.0003), (c) receiver operating characteristics (ROC) curve analysis using hsa_circRNA_103809 for discriminating CRC (p < 0.0001), and (d) ROC curve analysis using hsa_circRNA_104700 for discriminating CRC (p < 0.0001).

Table 4.

Correlation between has_circRNA_103809 and has_circRNA_104700 expression levels and clinicopathological characteristics in 170 CRC patients.

	All cases	Hsa_circRNA_103809			Hsa_circRNA_104700
	All cases	High expression	Low expression	p value	High expression	Low expression	p value
Age (years)				0.131			0.629
>60	99	82 (82.8%)	17 (17.2%)		30 (30.3%)	69 (69.7%)
⩽60	71	52 (73.2%)	19 (26.8%)		24 (33.8%)	47 (66.2%)
Gender				0.361			0.485
Male	101	82 (81.2%)	19 (18.8%)		30 (29.7%)	71 (70.3%)
Female	69	52 (75.4%)	17 (24.6%)		24 (34.8%)	45 (65.2%)
Histological grade				0.261			0.313
Low	117	95 (81.2%)	22 (18.8%)		40 (34.8%)	77 (65.8%)
High	53	39 (73.6%)	14 (26.4%)		14 (26.4%)	39 (73.6%)
pN status				0.021*			0.491
N0	104	76 (73.1%)	28 (26.9%)		31 (29.8)	73 (70.2)
N1–N2	66	58 (87.9%)	8 (12.1%)		23 (34.8)	43 (65.2)
pM status				0.197			0.036*
M0	133	102 (76.7%)	31 (23.3%)		37 (27.8)	96 (72.2)
M1	37	32 (86.5%)	5 (13.5%)		17 (45.9)	20 (54.1)
Clinical stage				0.011*			0.212
I & II	106	77 (72.6)	29 (27.1)		30 (28.3)	76 (71.7)
III & IV	64	57 (89.1)	7 (10.9)		24 (37.5)	40 (62.5)

pN: pathological node; pM: pathological metastasis.

p < 0.05.

Discussion

CircRNAs were found more than 20 years ago from a few transcribed genes.^11,18–21 The phenomenon of the RNA presenting itself as a circle in the eukaryotic cytoplasm was first reported by Hsu and Coca-Prados.²² Nevertheless, these RNA molecules had generally been thought to be of low abundance and may be splicing errors.¹³ In the last few years, a large number of circRNAs have been successfully identified in different species or tissues through high-throughput RNA sequencing and bioinformatics analysis.^23–25 It was reported that circRNAs are highly conserved sequences²⁶ largely originating from exons or introns,²⁷ and are formed by exonic transcripts through gene rearrangement or non-linear reverse splicing.²⁸ Numerous researches have also shown that a large abundance of circRNAs are highly expressed^13,28 and stable in human cells.^13,26 Compared with other RNAs, circRNAs may have the potential to become ideal candidate biomarkers of cancers.²⁹

In this study, we performed a circRNA profiling using six pairs of CRC and normal tissues, and identified 201 circRNAs that were significantly dysregulated in CRC compared with normal tissues. We also identified 13 differentially expressed circRNAs in CRC tissues correlated with lymph node metastasis. It has been shown that circRNAs can act as potential biomarkers for atherosclerosis,³⁰ degenerative diseases,³¹ central nervous system diseases,³² and cancers.^33,34 Cyclization of an INK4/ARF-relevant RNA was associated with atherosclerosis.³⁰ CDR1 was connected with Alzheimer’s disease.³² A global reduction of circRNA abundance was observed in CRC compared with normal tissues, and a negative correlation of global circRNA abundance and proliferation was found.³⁵ Hsa_circ_002059 was found to be significantly downregulated in gastric cancer tissues compared with paired normal tissues, and its expression level was significantly correlated with distal metastasis and TNM stage, suggesting that hsa_circ_002059 may be used as a potential novel biomarker for gastric cancer.³³ Hsa_circ_0001649 may become a new biomarker for hepatocellular carcinoma,³⁶ and hsa_circ_001988 has also been suggested as a potential biomarker in the diagnosis of CRC.³⁷

In this study, we investigated the expression levels of hsa_circRNA_103809 and hsa_circRNA_104700 by qPCR in 170 paired CRC and normal tissues. Our results indicated that the expressions of hsa_circRNA_103809 and hsa_circRNA_104700 were significantly decreased in CRC tissues compared with adjacent normal tissues. Furthermore, the expression level of hsa_circRNA_103809 was significantly associated with lymph node metastasis and TNM stage, while the expression level of hsa_circRNA_104700 was significantly correlated with distal metastasis. These results suggest that lower expression levels of hsa_circRNA_103809 and hsa_circRNA_104700 were associated with poor prognosis of CRC. ROC analyses also showed that hsa_circRNA_103809 and hsa_circRNA_104700 may be used as potential biomarkers in the diagnosis of CRC.

Although the mechanisms underlying the action of circRNAs in cancer are still unknown, it has been proposed that circRNAs could act as a microRNA (miRNA) sponge.²⁶ For instance, ciRS-7 has more than 70 binding sites for miR-7 and was found to bind miR-7 specifically to impair its regulatory effect. Another circRNA is the testis-specific transcript of the sex-determining region (Sry), which serves as a miR-138 sponge to control the expression level of miR-138.²⁶ Nevertheless, there are only a few detailed studies of circRNAs that include their respective miRNA binding sites. Researchers also have inferred that circRNAs possess other functions related to protein or RNA transport.³¹ In our study, the circRNA/miRNA interaction was predicted with Arraystar’s home-made miRNA target prediction software based on TargetScan³⁸ and miRanda.³⁹ We found that hsa_circRNA_103809 has putative miRNA binding sites including hsa-miR-511-5p, hsa-miR-130b-5p, hsa-miR-642a-5p, hsa-miR-532-3p, and hsa-miR-329-5p, and hsa_circRNA_104700 has putative miRNA binding sites including hsa-miR-141-5p, hsa-miR-500a-5p, hsa-miR-509-3p, hsa-miR-619-3p, and hsa-miR-578. These data may provide important clues to the “miRNA sponge” functions of hsa_circRNA_103809 and hsa_circRNA_104700.

Conclusions

In summary, we identified 125 downregulated and 76 upregulated circRNAs in CRC tissues by microarray circRNA expression profile analysis. Our study is also the first to demonstrate that the expression levels of hsa_circRNA_103809 and hsa_circRNA_104700 were significantly downregulated in 170 CRC tissues and they were associated with tumor progression. The aberrantly expressed circRNAs may be potentially involved in the development of CRC and serve as potential diagnosis biomarkers for CRC. However, further studies are needed to validate this observation in plasma sample. Moreover, it will be necessary to explore functions and mechanisms of hsa_circRNA_103809 and hsa_circRNA_104700.

Footnotes

Author contribution

P.Z., Z.Z., and W.S. carried out the study and performed statistical analysis. P.Z., Z.Z., and L.J. conceived the study design and drafted the manuscript. R.B., S.L., X.S., and J.W. participated in the design of the study and helped to draft the manuscript. All the authors participated in the discussion, provided conceptual input, and have read and approved the final manuscript.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Natural Science Foundation of China (81672385), Zhejiang Provincial Natural Science Foundation of China (LY15H160057 and LY17H160056) and Wenzhou Science and Technology Bureau Program (Y20140667). Microarray experiments were performed by KangChen Bio-tech, Shanghai, China.

References

Ferlay

Soerjomataram

Dikshit

. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer 2015; 136: E359–E386.

Boyle

Langman

ABC of colorectal cancer: epidemiology. BMJ 2000; 321: 805–808.

Wiseman

The second world cancer research fund/American institute for cancer research expert report. Food, nutrition, physical activity, and the prevention of cancer: a global perspective. Proc Nutr Soc 2008; 67: 253–256.

Siegel

DeSantis

Jemal

Colorectal cancer statistics, 2014. CA Cancer J Clin 2014; 64: 104–117.

Hentze

Preiss

Circular RNAs: splicing’s enigma variations. EMBO J 2013; 32: 923–925.

Chen

Chuang

TJ.

Biogenesis, identification, and function of exonic circular RNAs. Wiley Interdiscip Rev RNA 2015; 6: 563–579.

Guttman

Rinn

JL.

Modular regulatory principles of large non-coding RNAs. Nature 2012; 482: 339–346.

Batista

Chang

HY.

Long noncoding RNAs: cellular address codes in development and disease. Cell 2013; 152: 1298–1307.

Sabin

Delás

Hannon

GJ.

Dogma derailed: the many influences of RNA on the genome. Mol Cell 2013; 49: 783–794.

10.

Ulitsky

Bartel

DP.

lincRNAs: genomics, evolution, and mechanisms. Cell 2013; 154: 26–46.

11.

Nigro

Cho

Fearon

. Scrambled exons. Cell 1991; 64: 607–613.

12.

Memczak

Jens

Elefsinioti

. Circular RNAs are a large class of animal RNAs with regulatory potency. Nature 2013; 495: 333–338.

13.

Jeck

Sorrentino

Wang

. Circular RNAs are abundant, conserved, and associated with ALU repeats. RNA 2013; 19: 141–157.

14.

Salzman

Chen

Olsen

. Cell-type specific features of circular RNA expression. PLoS Genet 2013; 9: e1003777.

15.

Danan

Schwartz

Edelheit

. Transcriptome-wide discovery of circular RNAs in Archaea. Nucleic Acids Res 2012; 40: 3131–3142.

16.

Hamilton

Bosman

Boffetta

. World Health Organization, International Agency for Research on Cancer, editors. Carcinoma of the colon and rectum. In: Bosman

(ed) WHO classification of tumours of the digestive system. Lyon: International Agency for Research on Cancer, 2010, pp. 134–146.

17.

Sobin

Wittekind

International Union Against Cancer (UICC) TNM classification of malignant tumours. 6th ed. New York: Wiley, 2002.

18.

Cocquerelle

Mascrez

Hetuin

. Mis-splicing yields circular RNA molecules. FASEB J 1993; 7: 155–160.

19.

Zaphiropoulos

PG.

Circular RNAs from transcripts of the rat cytochrome P450 2C24 gene: correlation with exon skipping. Proc Natl Acad Sci U S A 1996; 93: 6536–6541.

20.

Wang

Cao

. Homology-independent discovery of replicating pathogenic circular RNAs by deep sequencing and a new computational algorithm. Proc Natl Acad Sci U S A 2012; 109: 3938–3943.

21.

Wilusz

Sharp

PA.

Molecular biology: a circuitous route to noncoding RNA. Science 2013; 340: 440–441.

22.

Hsu

Coca-Prados

Electron microscopic evidence for the circular form of RNA in the cytoplasm of eukaryotic cells. Nature 1979; 280: 339–340.

23.

Glazar

Papavasileiou

Rajewsky

circBase: a database for circular RNAs. RNA 2014; 20: 1666–1670.

24.

Ebbesen

Kjems

Hansen

TB.

Circular RNAs: identification, biogenesis and function. Biochim Biophys Acta 2016; 1859: 163–168.

25.

Zheng

Bao

Guo

. Circular RNA profiling reveals an abundant circHIPK3 that regulates cell growth by sponging multiple miRNAs. Nat Commun 2016; 7: 11215.

26.

Hansen

Jensen

Clausen

. Natural RNA circles function as efficient microRNA sponges. Nature 2013; 495: 384–388.

27.

Zhang

X-O

Chen

. Circular intronic long noncoding RNAs. Mol Cell 2013; 51: 792–806.

28.

Salzman

Gawad

Wang

. Circular RNAs are the predominant transcript isoform from hundreds of human genes in diverse cell types. PLoS ONE 2012; 7: e30733.

29.

Xie

Ren

Xin

. Emerging roles of circRNA_001569 targeting miR-145 in the proliferation and invasion of colorectal cancer. Oncotarget 2016; 7: 26680–26691.

30.

Burd

Jeck

Liu

. Expression of linear and novel circular forms of an INK4/ARF-associated non-coding RNA correlates with atherosclerosis risk. PLoS Genet 2010; 6: e1001233.

31.

Ashwal-Fluss

Meyer

Pamudurti

. circRNA biogenesis competes with pre-mRNA splicing. Mol Cell 2014; 56: 55–66.

32.

Lukiw

WJ.

Circular RNA (circRNA) in Alzheimer’s disease (AD). Front Genet 2013; 4: 307.

33.

Chen

. Using circular RNA as a novel type of biomarker in the screening of gastric cancer. Clin Chim Acta 2015; 444: 132–136.

34.

Sand

Bechara

Sand

. Circular RNA expression in basal cell carcinoma. Epigenomics 2016; 8: 619–632.

35.

Bachmayr-Heyda

Reiner

Auer

. Correlation of circular RNA abundance with proliferation—exemplified with colorectal and ovarian cancer, idiopathic lung fibrosis, and normal human tissues. Sci Rep 2015; 5: 8057.

36.

Qin

Liu

Huo

. Hsa_circ_0001649: a circular RNA and potential novel biomarker for hepatocellular carcinoma. Cancer Biomark 2016; 16: 161–169.

37.

Wang

Zhang

Huang

. Decreased expression of hsa_circ_001988 in colorectal cancer and its clinical significances. Int J Clin Exp Pathol 2015; 8: 16020–16025.

38.

Enright

John

Gaul

. MicroRNA targets in drosophila. Genome Biol 2003; 5: R1.

39.

Pasquinelli

AE.

MicroRNAs and their targets: recognition, regulation and an emerging reciprocal relationship. Nat Rev Genet 2012; 13: 271–282.