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Key pathways in prostate cancer with SPOP mutation identified by bioinformatic analysis.

ABSTRACT: Prostate cancer (PCa) is a leading adult malignant tumor. Recent research has shown that speckle-type BTB/POZ protein (SPOP) mutant is the top frequently mutated gene in PCa, which makes it an important biomarker. In this paper, we aimed at identifying critical genes and pathways related to SPOP mutation in PCa. Recent The Cancer Genome Atlas data showed that 12% of patients with PCa were SPOP mutant. There were 1,570 differentially expressed genes, and online enrichment analysis showed that these genes were mainly enriched in metabolism, pathways in cancer and reactive oxygen species. INS, GNG13, IL6, HTR5A, SAA1, PPY, CXCR5, CXCL13, CD19 and CCL20 were identified as hub genes. The lower SPOP expression level was associated with poor prognosis. In all, our findings showed that various pathways and genes could play critical roles in SPOP mutation in PCa, providing potential options for individualized treatment.

SUBMITTER: Ding G

PROVIDER: S-EPMC7718645 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Key pathways in prostate cancer with SPOP mutation identified by bioinformatic analysis.

Ding Guanxiong G Sun Jianliang J Jiang Lianhua L Gao Peng P Zhou Qidong Q Wang Jianqing J Tong Shijun S

Open medicine (Warsaw, Poland) 20201009 1

Prostate cancer (PCa) is a leading adult malignant tumor. Recent research has shown that speckle-type BTB/POZ protein (SPOP) mutant is the top frequently mutated gene in PCa, which makes it an important biomarker. In this paper, we aimed at identifying critical genes and pathways related to SPOP mutation in PCa. Recent The Cancer Genome Atlas data showed that 12% of patients with PCa were SPOP mutant. There were 1,570 differentially expressed genes, and online enrichment analysis showed that the ...[more]

PMID: 33336059

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Project description:Background: The molecular mechanism of tumorigenesis remains to be fully understood in breast cancer. It is urgently required to identify genes that are associated with breast cancer development and prognosis and to elucidate the underlying molecular mechanisms. In the present study, we aimed to identify potential pathogenic and prognostic differentially expressed genes (DEGs) in breast adenocarcinoma through bioinformatic analysis of public datasets. Methods: Four datasets (GSE21422, GSE29431, GSE42568, and GSE61304) from Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) dataset were used for the bioinformatic analysis. DEGs were identified using LIMMA Package of R. The GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analyses were conducted through FunRich. The protein-protein interaction (PPI) network of the DEGs was established through STRING (Search Tool for the Retrieval of Interacting Genes database) website, visualized by Cytoscape and further analyzed by Molecular Complex Detection (MCODE). UALCAN and Kaplan-Meier (KM) plotter were employed to analyze the expression levels and prognostic values of hub genes. The expression levels of the hub genes were also validated in clinical samples from breast cancer patients. In addition, the gene-drug interaction network was constructed using Comparative Toxicogenomics Database (CTD). Results: In total, 203 up-regulated and 118 down-regulated DEGs were identified. Mitotic cell cycle and epithelial-to-mesenchymal transition pathway were the major enriched pathways for the up-regulated and down-regulated genes, respectively. The PPI network was constructed with 314 nodes and 1,810 interactions, and two significant modules are selected. The most significant enriched pathway in module 1 was the mitotic cell cycle. Moreover, six hub genes were selected and validated in clinical sample for further analysis owing to the high degree of connectivity, including CDK1, CCNA2, TOP2A, CCNB1, KIF11, and MELK, and they were all correlated to worse overall survival (OS) in breast cancer. Conclusion: These results revealed that mitotic cell cycle and epithelial-to-mesenchymal transition pathway could be potential pathways accounting for the progression in breast cancer, and CDK1, CCNA2, TOP2A, CCNB1, KIF11, and MELK may be potential crucial genes. Further, it could be utilized as new biomarkers for prognosis and potential new targets for drug synthesis of breast cancer.

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Key pathways in prostate cancer with SPOP mutation identified by bioinformatic analysis.

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Key pathways in prostate cancer with SPOP mutation identified by bioinformatic analysis.

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