30 October 2017 : Laboratory Research
C10orf116 Gene Copy Number Loss in Prostate Cancer: Clinicopathological Correlations and Prognostic SignificanceJie Meng12BE, Li-Hui Wang1C, Chun-Lin Zou1C, Sheng-Ming Dai2A, Jian Zhang34G*, Yi Lu13AG
Med Sci Monit 2017; 23:5176-5183
BACKGROUND: Prostate cancer (PCa) is the second most commonly diagnosed cancer in males worldwide. This study aimed to identify differentially expressed genes and to investigate the potential correlation between gene abnormalities and clinical features in PCa to evaluate disease progression and prognosis.
MATERIAL AND METHODS: A total of 4 independent microarrays of PCa patients from the Oncomine database were used to identify differences in expression of genes contributing to cancer progression. Quantitative real-time polymerase chain reaction (RT-qPCR) analysis was used to evaluate the mRNA expression of the target in human prostate cancer cells. To explore the relationship between the DNA copy number alteration and mRNA expression changes, dataset containing copy number alteration, DNA methylation, and gene expression in PCa were obtained from the cBioPortal online platform (n=273).
RESULTS: We identified 40 genes that were significantly dysregulated in PCa from 4 independent microarrays. Among these, 3 genes showed a consistent change of over 2-fold in the 4 microarrays. The mRNA expression of C10orf116 showed consistent expression in prostate cancer cells compared with that in prostate gland cells as assessed by RT-qPCR. Moreover, C10orf116 loss was associated with poor distant relapse-free survival (DFS) by analyzing data of 273 PCa patients, but it was not identified as an independent prognostic risk factor for DFS. In addition, we found that C10orf116 loss was associated with higher pathological stage, higher clinical stage, and lymph node metastasis in PCa, and that C10orf116 copy number was highly correlated with PTEN copy number and mRNA expression.
CONCLUSIONS: As a predictive indicator, C10orf116 loss contributes to our understating of the biology of aggressive changes in PCa and also helps evaluate the prognosis of patients.
Keywords: Computational Biology, Disease Progression, Prognosis, Prostatic Neoplasms
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