Dataset Information

Identification of novel biomarkers affecting the metastasis of colorectal cancer through bioinformatics analysis and validation through qRT-PCR.

ABSTRACT: Background:Tumor progression and distant metastasis are the main causes of deaths in colorectal cancer (CRC) patients, and the molecular mechanisms in CRC metastasis have not been completely discovered. Methods:We identified differentially expressed genes (DEGs) and lncRNAs (DELs) of CRC from The Cancer Genome Atlas (TCGA) database. Then we conducted the weighted gene co-expression network analysis (WGCNA) to investigate co-expression modules related with CRC metastasis. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, DEG-DEL co-expression network and survival analyses of significant modules were also conducted. Finally, the expressions of selected biomarkers were validated in cell lines by quantitative real-time PCR (qRT-PCR). Results:2032 DEGs and 487 DELs were involved the construction of WGCNA network, and greenyellow, turquoise and brown module were identified to have more significant correlation with CRC metastasis. GO and KEGG pathway analysis of these three modules have proven that the functions of DEGs were closely involved in many important processes in cancer pathogenesis. Through the DEG-DEL co-expression network, 12 DEGs and 2 DELs were considered as hub nodes. Besides, survival analysis showed that 30 DEGs were associated with the overall survival of CRC. Then 10 candidate biomarkers were chosen for validation and the expression of CA2, CHP2, SULT1B1, MOGAT2 and C1orf115 were significantly decreased in CRC cell lines when compared to normal human colonic epithelial cells, which were consistent with the results of differential expression analysis. Especially, low expression of SULT1B1, MOGAT2 and C1orf115 were closely correlated with poorer survival of CRC. Conclusion:This study identified 5 genes as new biomarkers affecting the metastasis of CRC. Besides, SULT1B1, MOGAT2 and C1orf115 might be implicated in the prognosis of CRC patients.

SUBMITTER: Lian W

PROVIDER: S-EPMC7106634 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Identification of novel biomarkers affecting the metastasis of colorectal cancer through bioinformatics analysis and validation through qRT-PCR.

Lian Wenping W Jin Huifang H Cao Jingjing J Zhang Xinyu X Zhu Tao T Zhao Shuai S Wu Sujun S Zou Kailu K Zhang Xinyun X Zhang Mingliang M Zheng Xiaoyong X Peng Mengle M

Cancer cell international 20200330

<h4>Background</h4>Tumor progression and distant metastasis are the main causes of deaths in colorectal cancer (CRC) patients, and the molecular mechanisms in CRC metastasis have not been completely discovered.<h4>Methods</h4>We identified differentially expressed genes (DEGs) and lncRNAs (DELs) of CRC from The Cancer Genome Atlas (TCGA) database. Then we conducted the weighted gene co-expression network analysis (WGCNA) to investigate co-expression modules related with CRC metastasis. Gene onto ...[more]

PMID: 32256214

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Project description:Purpose: The rapidly rising incidence of esophageal adenocarcinoma (EAC), which is usually diagnosed late with a poor prognosis, has become a growing problem. This study investigated the potential transcription factor (TF)-related molecular mechanisms of EAC by using bioinformatics analysis and qRT-PCR validation. Methods: Expression profile datasets for mRNAs (GSE92396, GSE13898, GSE26886 and GSE1420) and miRNAs (GSE16456) were downloaded from the GEO database. Overlapping differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) were identified through integrative analysis. Then, a TF-miRNA-mRNA network was constructed based on bioinformatics data from the TRRUST, TRED and miRTarBase database. Furthermore, overall survival analysis for the mRNAs and miRNAs in the TF-miRNA-mRNA network was performed with data from TCGA, and qRT-PCR was used to validate the results. Results: A total of 294 overlapping DEGs were identified in EAC tissues compared to normal tissues, including 181 downregulated and 113 upregulated genes. Then, 16 TFs that could target the DEGs and were related to cancer were predicted based on public databases, and 41 DEGs that could be targeted were identified as key genes. Additionally, 12 DEMs were predicted through miRTarBase to be associated with the key genes, and TP53-(miR-125b)-ID2 and JUN-(miR-30a)-IL1A from the TF-miRNA-mRNA network were identified to potentially play significant roles in EAC. Furthermore, CCL20, IL1A, ABCC3, hsa-miR-23b, and hsa-miR-191, which are involved in the TF-miRNA-mRNA network, were found to be significantly associated with patient survival in EAC. Finally, the expression of a miRNA-mRNA pair (hsa-miR-30a-5p and IL1A) was revealed to be correlated with prognosis. Conclusion: In this study, a TF-miRNA-mRNA network was constructed to analyze the potential molecular mechanisms of EAC. Key genes and miRNAs associated with patient survival were identified, which may reveal promising approaches for EAC diagnosis and therapy.

Project description:Synovial sarcoma (SS) is a highly aggressive soft tissue tumor with high risk of local recurrence and metastasis. However, the mechanisms underlying SS metastasis are still largely unclear. The purpose of this study is to screen metastasis-associated biomarkers in SS by integrated bioinformatics analysis. Two mRNA datasets (GSE40018 and GSE40021) were selected to analyze the differentially expressed genes (DEGs). Using the Database for Annotation, Visualization and Integrated Discovery (DAVID) and gene set enrichment analysis (GSEA), functional and pathway enrichment analyses were performed for DEGs. Then, the protein-protein interaction (PPI) network was constructed via the Search Tool for the Retrieval of Interacting Genes (STRING) database. The module analysis of the PPI network and hub genes validation were performed using Cytoscape software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the hub genes were performed using WEB-based GEne SeT AnaLysis Toolkit (WebGestalt). The expression levels and survival analysis of hub genes were further assessed through Gene Expression Profiling Interactive Analysis (GEPIA) and the Kaplan-Meier plotter database. In total, 213 overlapping DEGs were identified, of which 109 were upregulated and 104 were downregulated. GO analysis revealed that the DEGs were predominantly involved in mitosis and cell division. KEGG pathways analysis demonstrated that most DEGs were significantly enriched in cell cycle pathway. GSEA revealed that the DEGs were mainly enriched in oocyte meiosis, cell cycle and DNA replication pathways. A key module was identified and 10 hub genes (CENPF, KIF11, KIF23, TTK, MKI67, TOP2A, CDC45, MELK, AURKB, and BUB1) were screened out. The expression and survival analysis disclosed that the 10 hub genes were upregulated in SS patients and could result in significantly reduced survival. Our study identified a series of metastasis-associated biomarkers involved in the progression of SS, and may provide novel therapeutic targets for SS metastasis.

Project description:BackgroundOsteosarcoma (OS) metastasis is the most common cause of cancer-related mortality, however, no sufficient clinical biomarkers have been identified. In this study, we identified five genes to help predict metastasis at diagnosis.MethodsWe performed weighted gene co-expression network analysis (WGCNA) to identify the most relevant gene modules associated with OS metastasis. An important machine learning algorithm, the support vector machine (SVM), was employed to predict key genes for classifying the OS metastasis phenotype. Finally, we investigated the clinical significance of key genes and their enriched pathways.ResultsEighteen modules were identified in WGCNA, among which the pink, red, brown, blue, and turquoise modules demonstrated good preservation. In the five modules, the brown and red modules were highly correlated with OS metastasis. Genes in the two modules closely interacted in protein-protein interaction networks and were therefore chosen for further analysis. Genes in the two modules were primarily enriched in the biological processes associated with tumorigenesis and development. Furthermore, 65 differentially expressed genes were identified as common hub genes in both WGCNA and protein-protein interaction networks. SVM classifiers with the maximum area under the curve were based on 30 and 15 genes in the brown and red modules, respectively. The clinical significance of the 45 hub genes was analyzed. Of the 45 genes, 17 were found to be significantly correlated with survival time. Finally, 5/17 genes, including ADAP2 (P = 0.0094), LCP2 (P = 0.013), ARHGAP25 (P = 0.0049), CD53 (P = 0.016), and TLR7 (P = 0.04) were significantly correlated with the metastatic phenotype. In vitro verification, western blotting, wound healing analyses, transwell invasion assays, proliferation assays, and colony formation assays indicated that ARHGAP25 promoted OS cell migration, invasion, proliferation, and epithelial-mesenchymal transition.ConclusionWe identified five genes, namely ADAP2, LCP2, ARHGAP25, CD53, and TLR7, as candidate biomarkers for the prediction of OS metastasis; ARHGAP25 inhibits MG63 OS cell growth, migration, and invasion in vitro, indicating that ARHGAP25 can serve as a promising specific and prognostic biomarker for OS metastasis.

Dataset Information

Identification of novel biomarkers affecting the metastasis of colorectal cancer through bioinformatics analysis and validation through qRT-PCR.

Publications

Identification of novel biomarkers affecting the metastasis of colorectal cancer through bioinformatics analysis and validation through qRT-PCR.

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