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Gene signatures predict biochemical recurrence-free survival in primary prostate cancer patients after radical therapy

文献类型：期刊论文

作者Su, Qiang1,2,3,4; Liu, Zhenyu5,6,7; Chen, Chi1,4; Gao, Han1,4; Zhu, Yongbei1,4; Wang, Liusu1,4; Pan, Meiqing1,4; Liu, Jiangang1,4; Yang, Xin5,7; Tian, Jie1,4,5,6,8
刊名CANCER MEDICINE
出版日期2021-08-28
页码11
关键词biochemical recurrence-free survival gene signature LASSO-Cox regression primary prostate cancer radical therapy
ISSN号2045-7634
DOI10.1002/cam4.4092
通讯作者Tian, Jie(jie.tian@ia.ac.cn)
英文摘要Background This study evaluated the predictive value of gene signatures for biochemical recurrence (BCR) in primary prostate cancer (PCa) patients. Methods Clinical features and gene expression profiles of PCa patients were attained from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets, which were further classified into a training set (n = 419), a validation set (n = 403). The least absolute shrinkage and selection operator Cox (LASSO-Cox) method was used to select discriminative gene signatures in training set for biochemical recurrence-free survival (BCRFS). Selected gene signatures established a risk score system. Univariate and multivariate analyses of prognostic factors about BCRFS were performed using the Cox proportional hazards regression models. A nomogram based on multivariate analysis was plotted to facilitate clinical application. Kyoto Encyclopedia of Gene and Genomes (KEGG) and Gene Ontology (GO) analyses were then executed for differentially expressed genes (DEGs). Results Notably, the risk score could significantly identify BCRFS by time-dependent receiver operating characteristic (t-ROC) curves in the training set (3-year area under the curve (AUC) = 0.820, 5-year AUC = 0.809) and the validation set (3-year AUC = 0.723, 5-year AUC = 0.733). Conclusions Clinically, the nomogram model, which incorporates Gleason score and the risk score, could effectively predict BCRFS and potentially be utilized as a useful tool for the screening of BCRFS in PCa.
WOS关键词ESTRO-SIOG GUIDELINES ; PROGNOSTIC BIOMARKER ; REGULARIZATION PATHS ; PROGRESSION ; RISK ; OUTCOMES ; AURKA ; TPX2
资助项目Ministry of Science and Technology of China[2017YFA0205200] ; Ministry of Education of China[201902075003] ; National Natural Science Foundation of China[81922040] ; National Natural Science Foundation of China[81930053] ; National Natural Science Foundation of China[92059103] ; Beijing Natural Science Foundation[Z200027] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB32030200] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB01030200] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] ; Youth Innovation Promotion Association CAS[2019136] ; Youth Fund of Beijing Shijitan Hospital[2020-q06]
WOS研究方向Oncology
语种英语
WOS记录号WOS:000690691500001
出版者WILEY
资助机构Ministry of Science and Technology of China ; Ministry of Education of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Strategic Priority Research Program of Chinese Academy of Sciences ; Chinese Academy of Sciences ; Youth Innovation Promotion Association CAS ; Youth Fund of Beijing Shijitan Hospital
源URL[http://ir.ia.ac.cn/handle/173211/45881]  
专题自动化研究所_中国科学院分子影像重点实验室
通讯作者Tian, Jie
作者单位1.Beihang Univ, Sch Med & Engn, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing 100191, Peoples R China
2.Capital Med Univ, Beijing Shijitan Hosp, Clin Lab Med, Beijing, Peoples R China
3.Beijing Key Lab Urinary Cellular Mol Diagnost, Beijing, Peoples R China
4.Beihang Univ, Key Lab Big Data Based Precis Med, Minist Ind & Informat Technol, Beijing, Peoples R China
5.Chinese Acad Sci, Beijing Key Lab Mol Imaging, State Key Lab Management & Control Complex Syst, Inst Automat,CAS Key Lab Mol Imaging, Beijing, Peoples R China
6.Chinese Acad Sci, Inst Automat, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing, Peoples R China
7.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
8.Xidian Univ, Sch Life Sci & Technol, Engn Res Ctr Mol & Neuro Imaging, Minist Educ, Xian, Shaanxi, Peoples R China
推荐引用方式
GB/T 7714		 Su, Qiang,Liu, Zhenyu,Chen, Chi,et al. Gene signatures predict biochemical recurrence-free survival in primary prostate cancer patients after radical therapy[J]. CANCER MEDICINE,2021:11.
APA Su, Qiang.,Liu, Zhenyu.,Chen, Chi.,Gao, Han.,Zhu, Yongbei.,...&Tian, Jie.(2021).Gene signatures predict biochemical recurrence-free survival in primary prostate cancer patients after radical therapy.CANCER MEDICINE,11.
MLA Su, Qiang,et al."Gene signatures predict biochemical recurrence-free survival in primary prostate cancer patients after radical therapy".CANCER MEDICINE (2021):11.

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来源：自动化研究所

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