Dataset Information

Clinical significance and prospective molecular mechanism of MALAT1 in pancreatic cancer exploration: a comprehensive study based on the GeneChip, GEO, Oncomine, and TCGA databases.

ABSTRACT: PURPOSE:Long noncoding RNAs (lncRNAs) are known to function as regulators in the development and occurrence of various tumors. MALAT1 is a highly conserved lncRNA and has vital functions in diverse tumors, including pancreatic cancer (PC). However, the underlying molecular regulatory mechanism involved in the occurrence and development of PC remains largely unknown. Thus, it is important to explore MALAT1 in PC and elucidate its function, which might offer a new perspective for clinical diagnosis and therapy. METHODS:First, we used the Gene Expression Omnibus, Oncomine, and The Cancer Genome Atlas databases to determine the clinical diagnostic and prognostic values of MALAT1. We next used our own GeneChip and The Cancer Genome Atlas database to collect the possible target genes of MALAT1 and further utilized a bioinformatics analysis to explore the underlying significant pathways that might be crucial in PC. Finally, we identified several key target genes of MALAT1 and hope to offer references for future research. RESULTS:We found that the expression of MALAT1 was significantly elevated in patients with PC. A receiver operating characteristics curve analysis showed a moderate diagnostic value (area under the curve =0.75, sensitivity =0.66, specificity =0.72). A total of 224 important overlapping genes were collected, and six hub genes (CCND1, MAPK8, VEGFA, FOS, CDH1, and HSP90AA1) were identified, of which CCND1, MAPK8, and VEGFA, are important genes in PC. Several pathways, including the mTOR signaling pathway, pathways in cancer, and the MAPK signaling pathway, were suggested to be the vital MALAT1 pathways in PC. CONCLUSION:MALAT1 is suggested to be a promising diagnostic biomarker in PC. Six hub genes (CCND1, MAPK8, VEGFA, FOS, CDH1, and HSP90AA1), and specifically CCND1, MAPK8, and VEGFA, might be key MALAT1 target genes in PC. Due to their possible clinical significance in PC, several pathways, such as the mTOR signaling pathway, pathways in cancer, and the MAPK signaling pathway, are worthy of further study.

SUBMITTER: Xie ZC

PROVIDER: S-EPMC5558580 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Clinical significance and prospective molecular mechanism of MALAT1 in pancreatic cancer exploration: a comprehensive study based on the GeneChip, GEO, Oncomine, and TCGA databases.

Xie Zu-Cheng ZC Dang Yi-Wu YW Wei Dan-Ming DM Chen Peng P Tang Rui-Xue RX Huang Qian Q Liu Jiang-Hua JH Luo Dian-Zhong DZ

OncoTargets and therapy 20170810

<h4>Purpose</h4>Long noncoding RNAs (lncRNAs) are known to function as regulators in the development and occurrence of various tumors. MALAT1 is a highly conserved lncRNA and has vital functions in diverse tumors, including pancreatic cancer (PC). However, the underlying molecular regulatory mechanism involved in the occurrence and development of PC remains largely unknown. Thus, it is important to explore MALAT1 in PC and elucidate its function, which might offer a new perspective for clinical ...[more]

PMID: 28860807

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Project description:BACKGROUNDS:Lung adenocarcinoma (LUAD) is one of the most common malignancies, and is a serious threat to human health. The aim of the present study was to assess potential biomarkers for the prognosis of LUAD through the analysis of gene expression microarrays. METHODS:The gene expression data for GSE118370 was downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between normal lung and LUAD samples were screened using the R language. The DAVID database was used to analyze the functions and pathways of DEGs. The STRING database was used to the map protein-protein interaction (PPI) networks, and these were visualized with the Cytoscape software. Finally, the prognostic analysis of the hub gene in the PPI network was performed using the Kaplan-Meier tool. RESULTS:A total of 406 downregulated and 203 upregulated DEGs were identified. The GO analysis results revealed that downregulated DEGs were significantly enriched in angiogenesis, calcium ion binding and cell adhesion. The upregulated DEGs were significantly enriched in the extracellular matrix disassembly, collagen catabolic process, chemokine-mediated signaling pathway and endopeptidase inhibitor activity. The KEGG pathway analysis revealed that downregulated DEGs were enriched in neuroactive ligand-receptor interaction, hematopoietic cell lineage and vascular smooth muscle contraction, while upregulated DEGs were enriched in phototransduction. In addition, the top 10 hub genes and the most closely interacting modules of the top 3 proteins in the PPI network were screened. Finally, the independent prognostic value of each hub gene in LUAD patients was analyzed through the Kaplan-Meier plotter. Seven hub genes (ADCY4, S1PR1, FPR2, PPBP, NMU, PF4, and GCG) were closely correlated to overall survival time. CONCLUSION:The discovery of these candidate genes and pathways reveals the etiology and molecular mechanisms of LUAD, providing ideas and guidance for the development of new therapeutic approaches to LUAD.

Project description:BackgroundClear cell renal cell carcinoma (ccRCC) is a commonly occurring tumor. Through a deeper understanding of the immune regulatory mechanisms in the tumor microenvironment, immunotherapy may serve as a potential treatment for cancer patients. This study aimed at identifying the survival-related immune cells and hub genes, which could be potential targets for immunotherapy in ccRCC.MethodsThe gene expression profiles and clinical data of ccRCC patients were extracted from UCSC Xena and Gene Expression Omnibus (GEO) databases. Kaplan-Meier (KM) survival and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses were utilized to select the survival-related tumor-infiltrating immune cells. Multivariate Cox regression was utilized to develop a signature based on the tumor-infiltrating immune cells (TIICs). Based on the signature, the risk score was calculated, following which the samples were divided into high-risk and low-risk groups. Differentially expressed genes (DEGs) between the two risk groups were identified. Functional enrichment analysis was performed and cytoHubba plug-in of Cytoscape was used to identify the hub genes. Multiple datasets were utilized to validate these hub genes, including the Gene Expression Profiling Interactive Analysis (GEPIA), UALCAN, and the Human Protein Atlas (HPA), and the GEO datasets. Finally, a correlation analysis was performed to evaluate the relationship between the hub genes and TIICs.ResultsFour immune survival-related cells, including T cell CD4 memory-activated, T cell regulatory (Tregs), eosinophils, and mast cell resting were identified. Nine immune-specific hub genes were identified, which included APOE, CASR, CTLA4, CXCL8, EGF, F2, KNG1, MMP9, and IL6. Furthermore, these hub genes were significantly correlated with clinical traits and closely associated with some TIICs.ConclusionA total of four survival-related immune cell types and nine hub genes were found to be closely associated with ccRCC. These findings may have implications for the development of novel potential immunotherapeutic targets for ccRCC.

Dataset Information

Clinical significance and prospective molecular mechanism of MALAT1 in pancreatic cancer exploration: a comprehensive study based on the GeneChip, GEO, Oncomine, and TCGA databases.

Publications

Clinical significance and prospective molecular mechanism of MALAT1 in pancreatic cancer exploration: a comprehensive study based on the GeneChip, GEO, Oncomine, and TCGA databases.

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