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Comprehensive Analysis of Key Proteins Involved in Radioresistance of Prostate Cancer by Integrating Protein-protein Interaction Networks

Authors :
Li Dujian
Németh Balázs
Gyöngyi Zoltán
Karádi Kázmér
Wang Xiaoxia
Memarpour Mahtab
Rasool Nouman
Hegyi Péter
Marta Katalin
Révész Péter
Tóth Barbara
Luan Yun
Taghvamanesh Sara
Tulchinsky Mark
Slonimsky Einat
Sang-Ngoen Thanyaporn
Qian Duocheng
Varga Gábor
Shao Jia
Liu Dongsen
Naseer Sheraz
M. Czumbel László
Shahbaz Umar
Li Quan
H. Morowvat Mohammad
Liu Xiaoli
Mátrai Péter
Sui Shujian
Gerber Gábor
Li Yinuo
Sun Yongchao
Gao Qi
Zhang Shanshan
Karami Forough
Su Hao
Zhu Yansong
Xu Chaoyue
Hussain Waqar
Karami Somayeh
Sadaeng Wuttapon
Szanyi István
Rafiee Azade
Zhang Zhaohua
Daanial Khan Yaser
Source :
Current Bioinformatics. 16:139-145
Publication Year :
2021
Publisher :
Bentham Science Publishers Ltd., 2021.

Abstract

Background: Radioresistance remains a significant obstacle in the treatment of prostate cancer (PCa). The mechanisms underlying the radioresistance in PCa remained to be further investigated. Methods: GSE53902 dataset was used in this study to identify radioresistance-related mRNAs. Protein-protein interaction (PPI) network was constructed based on STRING analysis. DAVID system was used to predict the potential roles of radioresistance-related mRNAs. Results: We screened and re-annotated the GSE53902 dataset to identify radioresistance-related mRNAs. A total of 445 up-regulated and 1036 down-regulated mRNAs were identified in radioresistance PCa cells. Three key PPI networks consisting of 81 proteins were further constructed in PCa. Bioinformatics analysis revealed that these genes were involved in regulating MAP kinase activity, response to hypoxia, regulation of the apoptotic process, mitotic nuclear division, and regulation of mRNA stability. Moreover, we observed that radioresistance-related mRNAs, such as PRC1, RAD54L, PIK3R3, ASB2, FBXO32, LPAR1, RNF14, and UBA7, were dysregulated and correlated to the survival time in PCa. Conclusion: We thought this study would be useful to understand the mechanisms underlying radioresistance of PCa and identify novel prognostic markers for PCa.

Details

ISSN :
15748936
Volume :
16
Database :
OpenAIRE
Journal :
Current Bioinformatics
Accession number :
edsair.doi...........79741735a5e166c8af7c5104769d5bbb