Dataset Information

Identification of Key Biomarkers in Bladder Cancer: Evidence from a Bioinformatics Analysis.

ABSTRACT: Bladder cancer (BCa) is one of the most common malignancies and has a relatively poor outcome worldwide. However, the molecular mechanisms and processes of BCa development and progression remain poorly understood. Therefore, the present study aimed to identify candidate genes in the carcinogenesis and progression of BCa. Five GEO datasets and TCGA-BLCA datasets were analyzed by statistical software R, FUNRICH, Cytoscape, and online instruments to identify differentially expressed genes (DEGs), to construct protein?protein interaction networks (PPIs) and perform functional enrichment analysis and survival analyses. In total, we found 418 DEGs. We found 14 hub genes, and gene ontology (GO) analysis revealed DEG enrichment in networks and pathways related to cell cycle and proliferation, but also in cell movement, receptor signaling, and viral carcinogenesis. Compared with noncancerous tissues, TPM1, CRYAB, and CASQ2 were significantly downregulated in BCa, and the other hub genes were significant upregulated. Furthermore, MAD2L1 and CASQ2 potentially play a pivotal role in lymph nodal metastasis. CRYAB and CASQ2 were both significantly correlated with overall survival (OS) and disease-free survival (DFS). The present study highlights an up to now unrecognized possible role of CASQ2 in cancer (BCa). Furthermore, CRYAB has never been described in BCa, but our study suggests that it may also be a candidate biomarker in BCa.

SUBMITTER: Zhang C

PROVIDER: S-EPMC7168923 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Identification of Key Biomarkers in Bladder Cancer: Evidence from a Bioinformatics Analysis.

Zhang Chuan C Berndt-Paetz Mandy M Neuhaus Jochen J

Diagnostics (Basel, Switzerland) 20200124 2

Bladder cancer (BCa) is one of the most common malignancies and has a relatively poor outcome worldwide. However, the molecular mechanisms and processes of BCa development and progression remain poorly understood. Therefore, the present study aimed to identify candidate genes in the carcinogenesis and progression of BCa. Five GEO datasets and TCGA-BLCA datasets were analyzed by statistical software R, FUNRICH, Cytoscape, and online instruments to identify differentially expressed genes (DEGs), t ...[more]

PMID: 31991631

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Project description:Lung cancer is one of the most widespread neoplasms worldwide. To identify the key biomarkers in its carcinogenesis and development, the mRNA microarray datasets GSE102287, GSE89047, GSE67061 and GSE74706 were obtained from the Gene Expression Omnibus database. GEO2R was used to identify the differentially expressed genes (DEGs) in lung cancer. The Database for Annotation, Visualization and Integrated Discovery was used to analyze the functions and pathways of the DEGs, while the Search Tool for the Retrieval of Interacting Genes/Proteins and Cytoscape were used to obtain the protein-protein interaction (PPI) network. Kaplan Meier curves were used to analyze the effect of the hub genes on overall survival (OS). Module analysis was completed using Molecular Complex Detection in Cytoscape, and one co-expression network of these significant genes was obtained with cBioPortal. A total of 552 DEGs were identified among the four microarray datasets, which were mainly enriched in 'cell proliferation', 'cell growth', 'cell division', 'angiogenesis' and 'mitotic nuclear division'. A PPI network, composed of 44 nodes and 886 edges, was constructed, and its significant module had 16 hub genes in the whole network: Opa interacting protein 5, exonuclease 1, PCNA clamp-associated factor, checkpoint kinase 1, hyaluronan-mediated motility receptor, maternal embryonic leucine zipper kinase, non-SMC condensin I complex subunit G, centromere protein F, BUB1 mitotic checkpoint serine/threonine kinase, cyclin A2, thyroid hormone receptor interactor 13, TPX2 microtubule nucleation factor, nucleolar and spindle associated protein 1, kinesin family member 20A, aurora kinase A and centrosomal protein 55. Survival analysis of these hub genes revealed that they were markedly associated with poor OS in patients with lung cancer. In summary, the hub genes and DEGs delineated in the research may aid the identification of potential targets for diagnostic and therapeutic strategies in lung cancer.

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Dataset Information

Identification of Key Biomarkers in Bladder Cancer: Evidence from a Bioinformatics Analysis.

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Identification of Key Biomarkers in Bladder Cancer: Evidence from a Bioinformatics Analysis.

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