Literature DB >> 29887958

Analysis of differentially expressed genes, clinical value and biological pathways in prostate cancer.

Zhaohui He1, Fucai Tang1, Zechao Lu2, Yucong Huang3, Hanqi Lei1, Zhibiao Li3, Guohua Zeng1.   

Abstract

The present study aimed to investigate the gene expression changes in prostate cancer (PC) and screen the hub genes and associated pathways of PC progression. The authors employed integrated analysis of GSE46602 downloaded from the Gene Expression Omnibus and The Cancer Genome Atlas databases to identify 484 consensual differentially expressed genes (DEGs) in PC, when compared with adjacent normal tissue samples. Functional annotation and pathway analysis were performed. The protein-protein interaction (PPI) networks and module were constructed. RT-qPCR was used to validate the results in clinical PC samples. Survival analysis of hub genes was performed to explore their clinical value. GO analysis results revealed that DEGs were significantly enriched in negative regulation of nitrobenzene metabolic process, extracellular space and protein homodimerization activity. KEGG pathway analysis results revealed that DEGs were most significantly enriched in focal adhesion. The top 10 hub genes were identified to be hub genes from the PPI network, and the model revealed that these genes were enriched in various pathways, including neuroactive ligand-receptor interaction, p53 and glutathione metabolism signaling pathways. RT-qPCR results validated that expression levels of eight genes (PIK3R1, BIRC5, ITGB4, RRM2, TOP2A, ANXA1, LPAR1 and ITGB8) were consistent with the bioinformatics analysis. ITGB4 and RRM2 with genetic alterations exhibited association with a poorer survival rate, compared with those without alterations. These results revealed that PC-related genes and pathways have an important role in tumor expansion, metastasis and prognosis. In summary, these hub genes and related pathways may act as biomarkers or therapeutic targets for PC.

Entities:  

Keywords:  GEO; Prostate cancer; TCGA; bioinformatic analysis; differentially expressed genes

Year:  2018        PMID: 29887958      PMCID: PMC5992552     

Source DB:  PubMed          Journal:  Am J Transl Res            Impact factor:   4.060


  65 in total

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