Dataset Information

Regulatory network of circRNA-miRNA-mRNA contributes to the histological classification and disease progression in gastric cancer.

ABSTRACT:

Background

Little has been known about the role of non-coding RNA regulatory network in the patterns of growth and invasiveness of gastric cancer (GC) development.

Methods

MicroRNAs (miRNAs) microarray was used to screen differential miRNA expression profiles in Ming's classification. The significant differential expressions of representative miRNAs and their interacting circular RNA (circRNA) were confirmed in GC cell line and 63 pairs of GC samples. Then, a circRNA/miRNA network was constructed by bioinformatics approaches to identify molecular pathways. Finally, we explored the clinical value of the common targets in the pathway by using receiver operating characteristic curve and survival analysis.

Results

Significantly differential expressed miRNAs were found in two pathological types of GC. Both of miR-124 and miR-29b were consistently down-regulated in GC. CircHIPK3 could play a negative regulatory role on miR-124/miR-29b expression and associated with T stage and Ming's classification in GC. The bioinformatics analyses showed that targets expression of circHIPK3-miR-124/miR-29b axes in cancer-related pathways was able to predict the status of GC and associated with individual survival time.

Conclusions

The targets of circHIPK3-miR-124/miR-29b axes involved in the progression of GC. CircHIPK3 could take part in the proliferation process of GC cell and may be potential biomarker in histological classification of GC.

SUBMITTER: Cheng J

PROVIDER: S-EPMC6071397 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Publications

Regulatory network of circRNA-miRNA-mRNA contributes to the histological classification and disease progression in gastric cancer.

Cheng Jia J Zhuo Huiqin H Xu Mao M Wang Linpei L Xu Hao H Peng Jigui J Hou Jingjing J Lin Lingyun L Cai Jianchun J

Journal of translational medicine 20180802 1

<h4>Background</h4>Little has been known about the role of non-coding RNA regulatory network in the patterns of growth and invasiveness of gastric cancer (GC) development.<h4>Methods</h4>MicroRNAs (miRNAs) microarray was used to screen differential miRNA expression profiles in Ming's classification. The significant differential expressions of representative miRNAs and their interacting circular RNA (circRNA) were confirmed in GC cell line and 63 pairs of GC samples. Then, a circRNA/miRNA network ...[more]

PMID: 30068360

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Project description:The pathogenesis of gastric cancer (GC) is still not fully understood. We aimed to find the potential regulatory network for ceRNA (circRNA-miRNA-immune-related mRNA) to uncover the pathological molecular mechanisms of GC. The expression profiles of circRNA, miRNA, and mRNA in gastric tissue from GC patients were downloaded from the Gene Expression Omnibus (GEO) datasets. Differentially expressed circRNAs, miRNAs, and immune-related mRNAs were filtered, followed by the construction of the ceRNA (circRNA-miRNA-immune-related mRNA) network. Functional annotation and protein-protein interaction (PPI) analysis of immune-related mRNAs in the network were performed. Expression validation of circRNAs and immune-related mRNAs was performed in the new GEO and TCGA datasets and in-vitro experiment. A total of 144 differentially expressed circRNAs, 216 differentially expressed miRNAs, and 2,392 differentially expressed mRNAs were identified in GC. Some regulatory pairs of circRNA-miRNA-immune-related mRNA were obtained, including hsa_circ_0050102-hsa-miR-4537-NRAS-Tgd cells, hsa_circ_0001013-hsa-miR-485-3p-MAP2K1-Tgd cells, hsa_circ_0003763-hsa-miR-145-5p-FGF10-StromaScore, hsa_circ_0001789-hsa-miR-1269b-MET-adipocytes, hsa_circ_0040573-hsa-miR-3686-RAC1-Tgd cells, and hsa_circ_0006089-hsa-miR-5584-3p-LYN-neurons. Interestingly, FGF10, MET, NRAS, RAC1, MAP2K1, and LYN had potential diagnostic value for GC patients. In the KEGG analysis, some signaling pathways were identified, such as Rap1 and Ras signaling pathways (involved NRAS and FGF10), Fc gamma R-mediated phagocytosis and cAMP signaling pathway (involved RAC1), proteoglycans in cancer (involved MET), T-cell receptor signaling pathway (involved MAP2K1), and chemokine signaling pathway (involved LYN). The expression validation of hsa_circ_0003763, hsa_circ_0004928, hsa_circ_0040573, FGF10, MET, NRAS, RAC1, MAP2K1, and LYN was consistent with the integrated analysis. In conclusion, the identified ceRNA (circRNA-miRNA-immune-related mRNA) regulatory network may be associated with the development of GC.

Project description:Gastric cancer (GC) is one of the most common malignancies in the world, with morbidity and mortality ranking second among all cancers. Accumulating evidences indicate that circular RNAs (circRNAs) are closely correlated with tumorigenesis. However, the mechanisms of circRNAs still remain unclear. This study is aimed at determining hub genes and circRNAs and analyzing their potential biological functions in GC. Expression profiles of mRNAs and circRNAs were downloaded from the Gene Expression Omnibus (GEO) data sets of GC and paracancer tissues. Differentially expressed genes (DEGs) and differentially expressed circRNAs (DE-circRNAs) were identified. The target miRNAs of DE-circRNAs and the bidirectional interaction between target miRNAs and DEGs were predicted. Functional analysis was performed, and the protein-protein interaction (PPI) network and the circRNA-miRNA-mRNA network were established. A total of 456 DEGs and 2 DE-circRNAs were identified with 3 mRNA expression profiles and 2 circRNA expression profiles. GO analysis indicated that DEGs were mainly enriched in extracellular matrix and cell adhesion, and KEGG confirmed that DEGs were mainly associated with focal adhesion, the PI3K-Akt signaling pathway, extracellular matrix- (ECM)- receptor interaction, and gastric acid secretion. 15 hub DEGs (BGN, COL1A1, COL1A2, FBN1, FN1, SPARC, SPP1, TIMP1, UBE2C, CCNB1, CD44, CXCL8, COL3A1, COL5A2, and THBS1) were identified from the PPI network. Furthermore, the survival analysis indicate that GC patients with a high expression of the following 9 hub DEGs, namely, BGN, COL1A1, COL1A2, FBN1, FN1, SPARC, SPP1, TIMP1, and UBE2C, had significantly worse overall survival. The circRNA-miRNA-mRNA network was constructed based on 1 circRNA, 15 miRNAs, and 45 DEGs. In addition, the 45 DEGs included 5 hub DEGs. These results suggested that hub DEGs and circRNAs could be implicated in the pathogenesis and development of GC. Our findings provide novel evidence on the circRNA-miRNA-mRNA network and lay the foundation for future research of circRNAs in GC.

Project description:BACKGROUND As all we know, gastric cancer (GC) is a highly aggressive disease. Recently, circular RNA (circRNA) was found to play a vital role in regulation of GC. Some circRNAs could regulate messenger RNA (mRNA) expression by functioning as a microRNA (miRNA) sponge. Nevertheless, the circRNA-miRNA-mRNA regulatory network involved GC rarely has been explored and researched. MATERIAL AND METHODS All the differentially expressed circRNAs, miRNAs, and mRNA were derived from Gene Expression Omnibus (GEO) microarray data (GSE78092, GSE89143, GSE93415, and GSE54129). GC level 3 miRNA-sequencing data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Furthermore, a circRNA-miRNA-mRNA regulatory network was constructed by Cytoscape (version 3.6.1). Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway revealed the functions and signaling pathways associated with these target genes. Hub genes of protein-protein interaction (PPI) network were identified by STRING database and cytoHubba. RESULTS The regulatory network consists of 3 circRNAs, 22 miRNAs, and 128 mRNAs. Only 3 miRNAs of the network were consistent with the expression of TCGA and were associated with some clinical features. The results of the functional analysis of 128 mRNAs showed that GO analysis and KEGG pathways of inclusion criteria were 49 and 24, respectively. PPI network and Cytoscape showed that the top 10 hub genes were MYC, CTGF, TGFBR2, TGFBR1, SERPINE1, KRAS, ZEB1, THBS1, CDK6, and TNS1; 4 of which were verified by GEPIA based on TCGA. Highly expressed SERPINE1 had a poor OS (over survival) and DFS (disease-free survival), and TGFBR1 expression increased along with the increase of clinical stages. CONCLUSIONS This study looked at a circRNA-miRNA-mRNA regulatory network associated with GC and explored the potential functions of mRNA in the network, then identified a new molecular marker for prediction, prognosis, and therapeutic targets for clinical patients.

Dataset Information

Regulatory network of circRNA-miRNA-mRNA contributes to the histological classification and disease progression in gastric cancer.

Background

Methods

Results

Conclusions

Publications

Regulatory network of circRNA-miRNA-mRNA contributes to the histological classification and disease progression in gastric cancer.

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