Circular RNAs expression profiles in human gastric cancer

Circular RNAs (circRNAs) are implicated in a variety of cancers. However, the roles of circRNAs in gastric cancer (GC) remain largely unknown. In the current study, circRNAs expression profiles were screened in GC, using 5 pairs of GC and matched non-GC tissues with circRNA chip. Preliminary results were verified with quantitative PCR (qRT-PCR). Briefly, total of 713 circRNAs were differentially expressed in GC tissues vs. non-GC tissues (fold change ≥ 2.0, p < 0.05): 191 were upregulated, whereas 522 were downregulated in GC tissues. qRT-PCR analysis of randomly selected 7 circRNAs from the 713 circRNAs in 50 paired of GC vs. non-GC control tissues confirmed the microarray data. Gene ontology (GO) and KEGG pathway analyses showed that many circRNAs are implicated in carcinogenesis. Among differentially expressed circRNAs, hsa_circ_0076304, hsa_circ_0035431, and hsa_circ_0076305 had the highest magnitude of change. These results provided a preliminary landscape of circRNAs expression profile in GC.

Bioinformatics analysis. circRNA targets identified with profiling data were subjected to gene ontology (GO) and KEGG pathway analyses based on their correlated mRNAs using Gene Ontology (http://www.geneongoloty.org/) and KOBAS software (KEGG Orthology-Based Annotation System). The differentially expressed circRNAs-targeted miRNAs were sought and predicted by miRanda software coupled with statistical analysis. In order to understand the association between circRNAs and their related miRNAs, 3 most significantly altered circRNAs were used to draw the circRNA-miRNA network using miRanda combined with patterning software. The circRNAs expression profile microarray chip assay and data and bioinformatics analysis were carried out by Capitalbio Corporation (Beijing, China).
qRT-PCR assay. Total RNA was extracted by TRIzol reagent as described previously 10 . The expression levels of 7 randomly selected differentially expressing circRNAs (Fold changes ≥ 2, p < 0.05) were measured by qRT-PCR; among them, 2 were upregulated and 5 were downregulated in the GC tissues: (upregulated:  Table 1. The information of patients with gastric cancer subjected to circRNA expression profile chip assay. Differentially expressed circRNAs were displayed by volcano plots. The green and red parts indicated >2 folddecreased and -increased expression of the dysregulated circRNAs in GC tissues, respectively (p < 0.05). (C) Differentially expressed circRNAs were displayed by scatter plots. The green and red parts indicated >2 folddecreased and -increased expression of the dysregulated circRNAs in GC tissues (p < 0.05).
Statistical analysis. For

CircRNAs expression profiles in GC.
The microarray screening detected a total of 62,998 circRNAs, in GC, non-GC or both tissues (such information could be accessed with GSE100170 at https://www.ncbi.nlm. nih.gov/geo/query/acc.cgi?acc = GSE100170). As illustrated in Fig. 1, 713 of these exhibited differential expressions between GC and non-GC tissues (FC ≥ 2.0, p < 0.05) (Table S2), among which 191 were upregulated and   Continued the remaining 522 were downregulated in cancer tissues. A total of 207 circRNAs were differentially expressed between GC and non-GC tissues by both long and short probes (the two kinds of probe were named CBC1 and CBC2, respectively), among which 57 were upregulated and 150 were downregulated. The magnitude of fold change was highest for hsa_circ_0044516 in upregulated circRNAs (fold changes = 6.28, p = 0.036), and hsa_ circ_0076305 for downregulated circRNAs (fold changes = −125.95, p = 0.030). Hierarchical clustering (Fig. 1A), volcano plots (Fig. 1B), and scatter plots (Fig. 1C) revealed that the expression profiles of circRNAs between GC and non-GC tissues were diverse. The top up-and down-regulated circRNAs are displayed in Table 2.
The results of qRT-PCR verification of the differentially expressed circRNAs. Seven differentially expressed circRNAs were randomly selected for qRT-PCR verification by using 50 paired of samples. The results confirmed the upregulation of hsa_circ_0081146 and hsa_circ_0084720 in GC, and downregulation of hsa_ circ_0060108, hsa_circ_0057104, hsa_circ_0054971, hsa_circ_0063561, and hsa_circ_0058766 in GC (Fig. 2).
The results of bioinformatics analysis. Differentially expressed circRNAs could be mapped to all chromosomes, except for chromosome 21 and Y. A lot of miRNAs were predicted to be their targets (Table 3). 1026 miRNAs were predicted to be the targets of hsa_circ_0001210, which is an intragenic circRNA, located on chromosome 22 with a length of 25285 nt and downregulated in GC. 116 mRNAs were shown to be the potential  corresponding linear transcripts of these dysregulated circRNAs (Table S3). GO, KEGG, and enrichment (Table S4) analyses suggest that these differentially expressed circRNAs are relevant to several vital physiological processes, cellular components, molecular functions, and critical signaling pathways such as growth factor binding, cell adhesion molecule binding, and response to transforming growth factor beta (TGF-β). Many of the known pathways associated with carcinogenesis, such as focal adhesion pathway, PI3K-Akt signaling pathway, and degradation of the extracellular matrix pathway were also implicated. Figure 3A-C illustrated the top 30 significantly enriched GO terms, pathway terms, and disease terms.
CircRNA-miRNA network. The 3 circRNAs with most robust differential expression were used to construct a represent circRNA-miRNA network. The CBC1 and CBC2 probes identified a total of 207 differentially expressed circRNAs; among these circRNAs, hsa_circ_0076304, hsa_circ_0035431, and hsa_circ_0076305 had the highest magnitude of difference. Figure 4 illustrates the interaction of the 3 circRNAs with miRNA.

Discussion
CircRNAs are recently identified as disease-related and ubiquitously expressed noncoding RNAs, that can act as sponges of miRNAs and affect the expression of parent gene [11][12][13][14] . During the past several years, increasing evidence suggested that circRNAs play a vital role in cancer development and may be used as novel biomarkers [15][16][17][18] . By comparing circRNAs expression profiles in parental cell line and established cell line with radioresistant effects, Su et al. found that dysregulated circRNAs are related to the progression of radiation resistance in esophageal cancer cells 19 . Huang et al. 20 reported that dysregulated lncRNAs and circRNAs are linked to the development of bladder cancer. They identified that several hundreds of circRNAs showed altered expression in bladder cancer tissues as analyzed by the expression profiles of 4 paired cancer and para-carcinoma tissues. They postulated that several of the dysregulated circRNAs are functional molecules and contribute to bladder cancer tumorigenesis.
In the present study, 207 circRNAs were found to be differentially expressed between GC and non-cancerous tissues by both CBC1 and CBC2 probes in the microarray chip. Hsa_circ_0044516 had the highest magnitude of upregulation, whereas hsa_circ_0076305 had the highest magnitude of downregulation. The randomly selected 7 circRNAs that were significantly altered were further verified by qRT-PCR. These results conformed the validity of the microarray findings. Some of the previously identified circRNAs are implicated to be associated with tumorigenesis and malignant behavior of cancer cells, such as uncontrolled growth, proliferation, migration, and invasion. For example, Hsa_ circ_0067934 has been shown to be upregulated in esophageal squamous cell carcinoma (ESCC) 21 , and associated with poor tumor differentiation. In their findings, hsa_circ_0067934 was able to increase ESCC cell proliferation, migration, and cell cycle progression 21 . Xu et al. 22 showed that patients with hepatocellular carcinoma (HCC) with higher expression level of circular RNA ciRS-7 (Cdr1as) in cancerous tissues had shorter median recurrent time than those with lower circRNA expression. Additionally, Cdr1as was related to the high hepatic microvascular invasion (MVI) in HCC, and the mechanism may be associated with its potential activity as the sponge of miR-7. Therefore, the study concluded that Cdr1as might be a novel biomarker and treatment target for MVI.
CircRNAs can regulate the transcription of parent genes. In the present study, we identified 116 corresponding linear mRNAs. GO and pathway enrichment analysis showed that these mRNAs are involved in critical pathways associated with cancer, including the PI3K-AKT pathway. Previously studies have shown that activation of the PI3K-AKT pathway promote cancer cell growth and proliferation 23,24 . One of the potential targets of hsa_circ_0039090, hsa-let-7c-5p is associated with stage I endometrioid endometrial carcinoma progression potentially through regulation of cell cycle pathway 25 . Hsa-miR-107, one of the targets of several dysregulated circRNAs identified in the present study, is widely confirmed to be associated with cancers [26][27][28][29][30] , which is the downstream target of circTCF25, and the interaction between this circRNA with miR-107 and miR-103a-3p leads to increased proliferation and migration of bladder cancer cells 31 .
CircRNA-miRNA network is a widely accepted approach for exploring the function of dysregulated circRNAs and the interaction between these two types of non-coding RNAs. Hence, this network was constructed based on the microarray data. Among altered circRNAs, hsa_circ_0076304, hsa_circ_0035431, and hsa_circ_0076305 had the highest magnitude of difference. Concurrently, the potential links between them and the most important targeted miRNAs were established.
In summary, this study provided a preliminary landscape of circRNA differential expression in GC vs. non-GC. Further studies are required to explore their potential as biomarkers for GC as well as their pathologic role.