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

doi:10.1002/cam4.4092

	Gene signatures predict biochemical recurrence-free survival in primary prostate cancer patients after radical therapy
	Su, Qiang 1,2,3,4; Liu, Zhenyu5,6,7 ; Chen, Chi 1,4; Gao, Han 1,4; Zhu, Yongbei 1,4; Wang, Liusu 1,4; Pan, Meiqing 1,4; Liu, Jiangang 1,4; Yang, Xin5,7 ; Tian, Jie 1,4,5,6,8
发表期刊	CANCER MEDICINE
ISSN	2045-7634
	2021-08-28
页码	11
通讯作者	Tian, Jie(jie.tian@ia.ac.cn)
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.
关键词	biochemical recurrence-free survival gene signature LASSO-Cox regression primary prostate cancer radical therapy
DOI	10.1002/cam4.4092
关键词[WOS]	ESTRO-SIOG GUIDELINES ; PROGNOSTIC BIOMARKER ; REGULARIZATION PATHS ; PROGRESSION ; RISK ; OUTCOMES ; AURKA ; TPX2
收录类别	SCI
语种	英语
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]
项目资助者	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
WOS研究方向	Oncology
WOS类目	Oncology
WOS记录号	WOS:000690691500001
出版者	WILEY
七大方向——子方向分类	医学影像处理与分析
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/45882
专题	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
通讯作者单位	中国科学院分子影像重点实验室
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.