Identification of circRNA–miRNA–mRNA networks contributes to explore underlying pathogenesis and therapy strategy of gastric cancer

Abstract

Background Circular RNAs (circRNAs) are a new class of noncoding RNAs that have gained increased attention in human tumor research. However, the identification and function of circRNAs are largely unknown in the context of gastric cancer (GC). This study aims to identify novel circRNAs and determine their action networks in GC. Methods A comprehensive strategy of data mining, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and computational biology were conducted to discover novel circRNAs and to explore their potential mechanisms in GC. Promising therapeutic drugs for GC were determined by connectivity map (CMap) analysis. Results Six overlapped differentially expressed circRNAs (DECs) were screened from selected microarray and RNA-Seq datasets of GC, and the six DECs were then validated by sanger sequencing and RNase R treatment. Subsequent RT-qPCR analysis of GC samples confirmed decreased expressions of the six DECs (hsa_circ_0000390, hsa_circ_0000615, hsa_circ_0001438, hsa_circ_0002190, hsa_circ_0002449 and hsa_circ_0003120), all of which accumulated preferentially in the cytoplasm. MiRNA binding sites and AGO2 occupation of the six circRNAs were predicted using online databases, and circRNA–miRNA interactions including the six circRNAs and 33 miRNAs were determined. Then, 5320 target genes of the above 33 miRNAs and 1492 differently expressed genes (DEGs) from The Cancer Genome Atlas (TCGA) database were identified. After intersecting the miRNA target genes and the 889 downregulated DEGs, 320 overlapped target genes were acquired. The Kyoto Encyclopedia of Genes and Genomes enrichment analysis indicated that these target genes were related to two critical tumor-associated signaling pathways. A protein–protein interaction network with the 320 target genes was constructed using STRING, and fifteen hubgenes (ATF3, BTG2, DUSP1, EGR1, FGF2, FOSB, GNAO1, GNAI1, GNAZ, GNG7, ITPR1, ITPKB, JUND, NR4A3, PRKCB) in the network were identified. Finally, bioactive chemicals (including vorinostat, trichostatin A and astemizole) based on the fifteen hubgenes were identifed as therapeutic agents for GC through the CMap analysis. Conclusions This study provides a novel insight for further exploration of the pathogenesis and therapy of GC from the circRNA-miRNA-mRNA network perspective.

Study of the pathology and the underlying molecular mechanism of tissue injury around hematoma following intracerebral hemorrhage

“…By querying the protein interaction database and the associated literature, a protein interaction network was constructed ( Fig. 7 ) and 37 proteins with interactive associations were selected for further analysis ( Table V ) ( 24 ).…”

Hsa_circ_0000285 contributes to gastric cancer progression by upregulating FN1 through the inhibition of miR‐1278

“…This regulatory network helps classify histology, regulate disease progression, and explore strategies in GC. 13 , 14 , 15 For example, the hsa_circ_0004771 /miR‐149‐5p/AKT1/mTOR 16 and CircPDSS1 ( hsa_circ_0093398 )/miR‐186‐5p/NEK2 17 pathways have been shown to promote GC progression.…”

Construction of a circular RNA-based competing endogenous RNA network to screen biomarkers related to intervertebral disc degeneration

“…Xiong et al found that hsa_circRNA_100291 and hsa_circRNA_104515 may function as ceRNAs to exert critical roles in HCC [12]. Dong et al uncovered six circRNAs (hsa_ circ_0000390, hsa_circ_0000615, hsa_circ_0001438, hsa_ circ_0002190, hsa_circ_0002449 and hsa_circ_0003120) that might function as ceRNA to play important roles in gastric cancer by establishing circRNA-miRNA-mRNA regulatory networks [13]. Bai et al predicted that 3 cir-cRNAs (hsa_circ_0029340, hsa_circ_0025135, hsa_ circ_0039238) were ascertained as ceRNA to regulate the expression of 6 miRNAs (miR-1205, miR-657, miR-587, miR-637, miR-1278, miR-548p) in clear cell renal cell carcinoma [14].…”