Dataset Information

Copy Number Analysis Reveal Genetic Risks of Penile Cancer.

ABSTRACT:

Objectives

To evaluate copy number alterations (CNAs) in genes associated with penile cancer (PeC) and determine their correlation and prognostic ability with PeC.

Methods

Whole-exome sequencing was performed for tumor tissue and matched normal DNA of 35 patients diagnosed with penile squamous cell carcinoma from 2011 to 2016. Somatic CNAs were detected using the Genome Analysis Toolkit (GATK). Retrospective clinical data were collected and analyzed. All the data were statistically analyzed using SPSS 16.0 software. The cancer-specific survival rates were estimated by Kaplan-Meier curves and compared with the log-rank test.

Results

CNAs in the MYCN gene was detected in 19 (amplification: 54.29%) patients. Other CNAs gene targets were FAK (amplification: 45.72%, deletion: 8.57%), TP53 (amplification: 2.86%, deletion: 51.43%), TRKA (amplification: 34.29%, deletion: 2.86%), p75NTR (amplification: 5.71%, deletion: 42.86%), Miz-1 (amplification: 14.29%, deletion: 20.00%), Max (amplification: 17.14%, deletion: 2.86%), Bmi1 (amplification:14.29%, deletion: 48.57%), and MDM2 (amplification: 5.71%, deletion: 45.72%). The CNAs in MYCN and FAK correlated significantly with patient prognosis (P<0.05). The 3-year Recurrence-free survival rate was 87.10% among patients followed up. The 5-year survival rate of patients with MYCN amplification was 69.2%, compared to 94.4% in the non-amplification group. The 5-year survival rate of patients with FAK amplification was 65.6%, compared to 94.7% in the non-amplification group. The PPI network showed that TP53 and MYCN might play meaningful functional roles in PeC.

Conclusion

MYCN and FAK amplification and TP53 deletion were apparent in PeC. MYCN and TP53 were hub genes in PeC. MYCN and FAK amplification was also detected and analyzed, and the findings indicated that these two genes are predictors of poor prognosis in PeC.

SUBMITTER: Yu Y

PROVIDER: S-EPMC7768990 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Copy Number Analysis Reveal Genetic Risks of Penile Cancer.

Yu Yongbo Y Gao Chengwen C Gao Chengwen C Chen Yuanbin Y Wang Meilan M Zhang Jianfeng J Ma Xiaocheng X Liu Shuaihong S Yuan Hang H Li Zhiqiang Z Niu Haitao H

Frontiers in oncology 20201214

<h4>Objectives</h4>To evaluate copy number alterations (CNAs) in genes associated with penile cancer (PeC) and determine their correlation and prognostic ability with PeC.<h4>Methods</h4>Whole-exome sequencing was performed for tumor tissue and matched normal DNA of 35 patients diagnosed with penile squamous cell carcinoma from 2011 to 2016. Somatic CNAs were detected using the Genome Analysis Toolkit (GATK). Retrospective clinical data were collected and analyzed. All the data were statisticall ...[more]

PMID: 33381457

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Project description:BACKGROUND: Some diseases, like tumors, can be related to chromosomal aberrations, leading to changes of DNA copy number. The copy number of an aberrant genome can be represented as a piecewise constant function, since it can exhibit regions of deletions or gains. Instead, in a healthy cell the copy number is two because we inherit one copy of each chromosome from each our parents. Bayesian Piecewise Constant Regression (BPCR) is a Bayesian regression method for data that are noisy observations of a piecewise constant function. The method estimates the unknown segment number, the endpoints of the segments and the value of the segment levels of the underlying piecewise constant function. The Bayesian Regression Curve (BRC) estimates the same data with a smoothing curve. However, in the original formulation, some estimators failed to properly determine the corresponding parameters. For example, the boundary estimator did not take into account the dependency among the boundaries and succeeded in estimating more than one breakpoint at the same position, losing segments. RESULTS: We derived an improved version of the BPCR (called mBPCR) and BRC, changing the segment number estimator and the boundary estimator to enhance the fitting procedure. We also proposed an alternative estimator of the variance of the segment levels, which is useful in case of data with high noise. Using artificial data, we compared the original and the modified version of BPCR and BRC with other regression methods, showing that our improved version of BPCR generally outperformed all the others. Similar results were also observed on real data. CONCLUSION: We propose an improved method for DNA copy number estimation, mBPCR, which performed very well compared to previously published algorithms. In particular, mBPCR was more powerful in the detection of the true position of the breakpoints and of small aberrations in very noisy data. Hence, from a biological point of view, our method can be very useful, for example, to find targets of genomic aberrations in clinical cancer samples.

Dataset Information

Copy Number Analysis Reveal Genetic Risks of Penile Cancer.

Objectives

Methods

Results

Conclusion

Publications

Copy Number Analysis Reveal Genetic Risks of Penile Cancer.

Similar Datasets

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