Identification of novel biomarkers correlated with prostate cancer progression by an integrated bioinformatic analysis

Prostate cancer (PCa) is a highly aggressive malignant tumor and the biological mechanisms underlying its progression remain unclear.We performed weighted gene co-expression network analysis in PCa dataset from the Cancer Genome Atlas database to identify the key module and key genes related to the...

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Bibliographic Details
Published in:Medicine (Baltimore) 2020-07, Vol.99 (28), p.e21158-e21158
Main Authors: Ma, Zhifang, Wang, Jianming, Ding, Lingyan, Chen, Yujun
Format: Article
Language:English
Subjects:
Biomarkers, Tumor - biosynthesis
Biomarkers, Tumor - genetics
Computational Biology - methods
Disease Progression
Gene Expression Profiling
Gene Expression Regulation, Neoplastic
Humans
Male
Neoplasm Grading
Observational Study
Prognosis
Prostatic Neoplasms - diagnosis
Prostatic Neoplasms - genetics
Prostatic Neoplasms - metabolism
Transcriptome - genetics
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Summary:Prostate cancer (PCa) is a highly aggressive malignant tumor and the biological mechanisms underlying its progression remain unclear.We performed weighted gene co-expression network analysis in PCa dataset from the Cancer Genome Atlas database to identify the key module and key genes related to the progression of PCa. Furthermore, another independent datasets were used to validate our findings.A total of 744 differentially expressed genes were screened out and 5 modules were identified for PCa samples from the Cancer Genome Atlas database. We found the brown module was the key module and related to tumor grade (R2 = 0.52) and tumor invasion depth (R2 = 0.39). Besides, 24 candidate hub genes were screened out and 2 genes (BIRC5 and DEPDC1B) were identified and validated as real hub genes that associated with the progression and prognosis of PCa. Moreover, the biological roles of BIRC5 were related to G-protein coupled receptor signal pathway, and the functions of DEPDC1B were related to the G-protein coupled receptor signal pathway and retinol metabolism in PCa.Taken together, we identified 1 module, 24 candidate hub genes and 2 real hub genes, which were prominently associated with PCa progression. With more experiments and clinical trials, these genes may provide a promising future for PCa treatment.
ISSN:0025-7974
1536-5964
DOI:10.1097/MD.0000000000021158