Jingchao Liu1,2, Wei Zhang1,2, Jiawen Wang1,2, Zhengtong Lv1,2, Haoran Xia1,2, Zhipeng Zhang1, Yaoguang Zhang3,4, Jianye Wang5,6. 1. Department of Urology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, No. 1 DaHua Road, Dong Dan, Beijing, 100730, China. 2. Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, 9 DongDan SANTIAO, Beijing, 100730, China. 3. Department of Urology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, No. 1 DaHua Road, Dong Dan, Beijing, 100730, China. zygbjhospital@126.com. 4. Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, 9 DongDan SANTIAO, Beijing, 100730, China. zygbjhospital@126.com. 5. Department of Urology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, No. 1 DaHua Road, Dong Dan, Beijing, 100730, China. wjy08pro@126.com. 6. Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, 9 DongDan SANTIAO, Beijing, 100730, China. wjy08pro@126.com.
Abstract
PURPOSE: Early biochemical recurrence (eBCR) indicated a high risk for potential recurrence and metastasis in prostate cancer. The N6-methyladenosine (m6A) methylation modification played an important role in prostate cancer progression. This study aimed to develop a m6A lncRNA signature to accurately predict eBCR in prostate cancer. METHODS: Pearson correlation analysis was first conducted to explore m6A lncRNAs and univariate Cox regression analysis was further performed to identify m6A lncRNAs of prognostic roles for predicting eBCR in prostate cancer. The m6A lncRNA signature was constructed by least absolute shrinkage and selection operator analysis (LASSO) in training cohort and further validated in test cohort. Furthermore, half maximal inhibitory concentration (IC50) values were utilized to explore potential effective drugs for high-risk group in this study. RESULTS: Five hundred and thirty-eighth m6A lncRNAs were searched out through Pearson correlation analysis and 25 out of 538 m6A lncRNAs were identified to pose prediction roles for eBCR in prostate cancers. An m6A lncRNA signature including 5 lncRNAs was successfully built in training cohort. The high-risk group derived from m6A lncRNA signature could efficiently predict eBCR occurrence in both training (p < 0.001) and test cohort (p = 0.002). ROC analysis also confirmed that lncRNA signature in this study posed more accurate prediction roles for eBCR occurrence when compared with PSA, TNM stages and Gleason scores. Drug sensitivity analysis further discovered that various drugs could be potentially utilized to treat high-risk samples in this study. CONCLUSIONS: The m6A lncRNA signature in this study could be utilized to efficiently predict eBCR occurrence, various clinical characteristic and immune microenvironment for prostate cancer.
PURPOSE: Early biochemical recurrence (eBCR) indicated a high risk for potential recurrence and metastasis in prostate cancer. The N6-methyladenosine (m6A) methylation modification played an important role in prostate cancer progression. This study aimed to develop a m6A lncRNA signature to accurately predict eBCR in prostate cancer. METHODS: Pearson correlation analysis was first conducted to explore m6A lncRNAs and univariate Cox regression analysis was further performed to identify m6A lncRNAs of prognostic roles for predicting eBCR in prostate cancer. The m6A lncRNA signature was constructed by least absolute shrinkage and selection operator analysis (LASSO) in training cohort and further validated in test cohort. Furthermore, half maximal inhibitory concentration (IC50) values were utilized to explore potential effective drugs for high-risk group in this study. RESULTS: Five hundred and thirty-eighth m6A lncRNAs were searched out through Pearson correlation analysis and 25 out of 538 m6A lncRNAs were identified to pose prediction roles for eBCR in prostate cancers. An m6A lncRNA signature including 5 lncRNAs was successfully built in training cohort. The high-risk group derived from m6A lncRNA signature could efficiently predict eBCR occurrence in both training (p < 0.001) and test cohort (p = 0.002). ROC analysis also confirmed that lncRNA signature in this study posed more accurate prediction roles for eBCR occurrence when compared with PSA, TNM stages and Gleason scores. Drug sensitivity analysis further discovered that various drugs could be potentially utilized to treat high-risk samples in this study. CONCLUSIONS: The m6A lncRNA signature in this study could be utilized to efficiently predict eBCR occurrence, various clinical characteristic and immune microenvironment for prostate cancer.
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