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Construction of key signal regulatory network in metastatic colorectal cancer.

ABSTRACT: There are many stages in the development and metastasis of colorectal cancer (CRC). In this study, we compared the differential expression genes in different stages of metastatic CRC. Then, we screened the continuously up-regulated genes and the continuously down-regulated genes that were associated with the development and metastasis of CRC. After analyzing the intersection of differential expression genes in each stage, we screened the continuously up-regulated genes and deviated genes in the extracellular matrix and the continuously down-regulated genes and deviated genes in the mitochrondia of CRC. Then, we performed gene ontology enrichment analysis of the deviated genes in different phases, and we found that key molecular events occurred in the period extending from stage II to III (early stage of metastasis) of CRC. Furthermore, in this period we found that the chemotaxis of inflammatory cells had decreased in the extracellular matrix. On the other hand, the aerobic respiration had increased in the mitochondrion. Then, we constructed protein-protein interaction network of deviated genes in the extracellular matrix and mitochondrion. We used the network module and hub network to analyze the protein-protein interaction network. The network module analysis showed that the protein complex of VEGFA and CCL7-CCR3 is the key node in the extracellular matrix, while MAPK1 is the key node in the mitochondrion. The hub network analysis showed that the signal transmission chain FN1?SPARC?COL1A1?MMP2 is the key regulatory pathway for extracellular signal transmission. Furthermore, it also showed that CAV1?MAPK3?RAF1?NR3C1?MAPK1?ESR1 is the key regulatory pathway for signal transmission in mitochondrion.

SUBMITTER: Qi L

PROVIDER: S-EPMC5814197 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Construction of key signal regulatory network in metastatic colorectal cancer.

Qi Lu L Ding Yanqing Y

Oncotarget 20171227 5

There are many stages in the development and metastasis of colorectal cancer (CRC). In this study, we compared the differential expression genes in different stages of metastatic CRC. Then, we screened the continuously up-regulated genes and the continuously down-regulated genes that were associated with the development and metastasis of CRC. After analyzing the intersection of differential expression genes in each stage, we screened the continuously up-regulated genes and deviated genes in the ...[more]

PMID: 29464057

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Project description:ObjectivesThe high morbidity and mortality of colorectal cancer (CRC) have posed great threats to human health. Circular RNA (circRNA) and microRNA (miRNA), acting as competing endogenous RNAs (ceRNAs), have been found to play vital roles in carcinogenesis. This paper aims to construct a circRNA/miRNA/mRNA regulatory network so as to explore the molecular mechanism of CRC.MethodsThe sequencing data of circRNA from CRC were obtained from Gene Expression Omnibus (GEO). The differential circRNA was screened and its structure was identified by Cancer-specific CircRNA Database (CSCD); the sequencing data of miRNA and messenger RNA (mRNAs) were downloaded from The Cancer Genome Atlas (TCGA) database and the differentially expressed genes were screened; the corresponding miRNA of differential circRNAs were predicted by CircInteractome database; DIANA, Miranda, PicTar, and TargetScan databases were used to predict the target genes of different miRNAs; the target genes from Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were enriched by R language; String database combined with Cytoscape 3.7.2 software was used to construct protein-protein interaction (PPI) network and hub genes were screened; the expressions of mRNAs in the Top10 hub genes were verified in CRC. The network diagrams of circRNAs/miRNAs/mRNAs and circRNAs/miRNAs/Top10 hub mRNAs were constructed by Cytoscape3.7.2. Real-time PCR was used to examine the expression levels of hsa_circRNA_0065173, hsa-mir-450b, hsa-mir-582, adenylate cyclase 5 (ADCY5), muscarinic acetylcholine receptor M2 (CHRM2), cannabinoid receptor 1 (CNR1), and lysophosphatidic acid receptor 1 (LPAR1) in the CRC tissues and the adjacent normal tissues.ResultsA total of 14 differential circRNAs were identified, and 8 were found in CSCD; 34 miRNAs targeted by circRNAs were obtained. The PPI network was constructed, and the Top10 hub genes were identified, which were CHRM2, melanin concentrating hormone receptor 2 (MCHR2), G-protein gamma 3 subunit (GNG3), neuropeptide Y receptor Y1 (NPY1R), CNR1, LPAR1, ADCY5, adenylate cyclase 2 (ADCY2), gamma 7 (GNG7) and chemokine 12 (CXCL12), respectively. The expressions of Top 10 hub genes were also verified, and the results showed that the Top 10 hub genes were down-regulated in CRC; the constructed network diagram showed that hsa_circRNA_0065173 may regulate ADCY5, CHRM2, and Hsa-mir-450b by modulating hsa-mir-450b and hsa-mir-582. CNR1 and LPAR1 genes might serve as potentially relevant targets for the treatment of CRC. Real-time PCR results showed that the expression levels of hsa_circRNA_0065173, ADCY5, CHRM2, CNR1 and LPAR1 in the CRC tissues were significantly reduced compared with the adjacent normal tissues (all P<0.05); the expression levels of hsa-mir-450b and hsa-miR-582 were significantly increased (both P<0.05).ConclusionsIn this study, a potential circRNAs/miRNAs/mRNAs network is successfully constructed, which provides a new insight for CRC development mechanism through ceRNA mediated by circRNAs.

Project description:ObjectiveSince circRNA can be utilized as a potential diagnostic marker for cancer, to explore the regulatory mechanism of colorectal cancer (CRC) using bioinformatics, the public database of circRNA was mined.MethodsCRC differentially expressed miRNAs were screened in the Cancer Genome Atlas (TCGA) database, CRC differentially expressed circRNAs were searched in the Gene Expression Omnibus (GEO) database, the two databases were combined to identify CRC differentially expressed mRNAs, and a circRNA-miRNA‒mRNA regulatory network was constructed by combining a plurality of target prediction databases to identify key genes. The upstream circRNA and regulatory axis of the key genes were identified for gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis to explore the biological functions of circRNA in CRC using the regulatory axis.ResultsAfter the screening of the GSE21815 dataset, a total of 22 differentially expressed circRNAs were obtained, with 12 upregulated and 10 downregulated genes. Similarly, the GSE126094 dataset yielded 104 differentially expressed circRNAs, comprising 56 upregulated and 48 downregulated genes. Among the differentially expressed circRNAs, five were identified, with VDAC3 and SETD2 showing downregulated expression, while RAD23B, RPPH1, and MYBL2 exhibited upregulated expression. Following the selection process, five DEcircRNAs, eight target miRNAs, and 105 target DEmRNAs were identified. The protein-protein interaction (PPI) network revealed close relationships among the mRNAs, with E2F2, E2F3, CCND1, TNRC6A, and KAT2B identified as key genes. Notably, CCND1 emerged as a critical gene in the PPI network. Through the upregulation of has-circ-0087862, which binds to miR-892b, the translation inhibition of CCND1 by miR-892b was attenuated, leading to enhanced CCND1 expression. Functional enrichment analysis indicated that CCND1 was involved in protein binding and positive regulation of cellular processes, among other functions.ConclusionThe differentially expressed genes (DEGs) in CRC markedly affected the survival time of patients. CircRNAs could be utilized as diagnostic markers of CRC, and the key genes in CRC could be screened out by bioinformatics, which would be helpful to understand the drug targets for the treatment of human immunodeficiency virus (HIV)-related CRC patients.

Project description:Purpose:Accumulating evidence has indicated that circRNAs are closely involved in tumorigenesis and progression of human cancers. However, the molecular mechanism underlying function of circRNAs in breast cancer has not been thoroughly elucidated. Currently, we aimed to characterize the circRNA-related competing endogenous RNA (ceRNA) regulatory network in breast cancer and to construct prognostic model. Materials and Methods:First, we constructed circRNA expression profiles for paired breast cancer in a Chinese population using a human circRNA microarray. Expression profiles of circRNAs, miRNAs, and mRNAs were retrieved from our circRNA dataset, the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. We applied the limma and edgeR packages to identify differentially expressed RNAs. Weighted gene correlation network analysis (WGCNA) was used to identify critical modules of mRNAs. Next, a ceRNA network was established based on circRNA-miRNA and miRNA-mRNA intersections. Both Cox regression analysis and ROC curve analysis were performed to generate prognostic model. Additionally, we performed Gene Set Enrichment Analysis (GSEA) on prognostic signatures. Results:Total of 59 circRNAs, 98 miRNAs and 3966 mRNAs were identified as differentially expressed RNAs. We first identified 38 miRNA-mRNA pairs and 38 circRNA-miRNA pairs to construct the circRNA-miRNA-mRNA regulatory network and then generated a prognostic model based on 7 signatures (MMD, SLC29A4, CREB5, FOS, ANKRD29, MYOCD, and PIGR), and patients with high-risk scores presented poor prognosis. Several cancer-related pathways were enriched, including the TGF-? pathway, the focal adhesion pathway, and the JAK-STAT signaling pathway, and 20 prognostic ceRNA regulatory networks were subsequently identified. Conclusion:In all, we screened a series of dysregulated circRNAs, miRNAs, and mRNAs, and constructed circRNA-related ceRNA network in breast cancer. Our findings may help to deepen the understanding of circRNA-related regulatory mechanisms. Moreover, we generated a prognostic model that provided new insight into postoperative management for breast cancer.

Dataset Information

Construction of key signal regulatory network in metastatic colorectal cancer.

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Construction of key signal regulatory network in metastatic colorectal cancer.

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