5-Methylcytosine RNA Methyltransferases-Related Long Non-coding RNA to Develop and Validate Biochemical Recurrence Signature in Prostate Cancer
| Autor: | Ke Wang, Weibo Zhong, Zining Long, Yufei Guo, Chuanfan Zhong, Taowei Yang, Shuo Wang, Houhua Lai, Jianming Lu, Pengxiang Zheng, Xiangming Mao |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Biochemical recurrence
5-methylcytosine in RNA (m5C) Methyltransferase Receiver operating characteristic QH301-705.5 Competing endogenous RNA Proportional hazards model RNA Computational biology Biology prostate cancer medicine.disease Biochemistry Genetics and Molecular Biology (miscellaneous) Biochemistry Long non-coding RNA Prostate cancer lncRNA biochemical recurrence medicine prognostic model Molecular Biosciences Biology (General) Molecular Biology Original Research |
| Zdroj: | Frontiers in Molecular Biosciences Frontiers in Molecular Biosciences, Vol 8 (2021) |
| ISSN: | 2296-889X |
| Popis: | The effects of 5-methylcytosine in RNA (m5C) in various human cancers have been increasingly studied recently; however, the m5C regulator signature in prostate cancer (PCa) has not been well established yet. In this study, we identified and characterized a series of m5C-related long non-coding RNAs (lncRNAs) in PCa. Univariate Cox regression analysis and least absolute shrinkage and selector operation (LASSO) regression analysis were implemented to construct a m5C-related lncRNA prognostic signature. Consequently, a prognostic m5C-lnc model was established, including 17 lncRNAs: MAFG-AS1, AC012510.1, AC012065.3, AL117332.1, AC132192.2, AP001160.2, AC129510.1, AC084018.2, UBXN10-AS1, AC138956.2, ZNF32-AS2, AC017100.1, AC004943.2, SP2-AS1, Z93930.2, AP001486.2, and LINC01135. The high m5C-lnc score calculated by the model significantly relates to poor biochemical recurrence (BCR)-free survival (p MAFG-AS1 was selected for experimental validation; it was upregulated in PCa and probably promoted PCa proliferation and invasion in vitro. These results offer some insights into the m5C's effects on PCa and reveal a predictive model with the potential clinical value to improve the prognosis of patients with PCa. |
| Databáze: | OpenAIRE |
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