國家衛生研究院 NHRI:Item 3990099045/14127
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    题名: Identification of a steroid hormone-associated gene signature predicting the prognosis of prostate cancer through an Integrative bioinformatics analysis
    作者: Lai, YL;Liu, CH;Wang, SC;Huang, SP;Cho, YC;Bao, BY;Su, CC;Yeh, HC;Lee, CH;Teng, PC;Chuu, CP;Chen, DN;Li, CY;Cheng, WC
    贡献者: Institute of Cellular and Systems Medicine
    摘要: The importance of anti-androgen therapy for prostate cancer (PC) has been well recognized. However, the mechanisms underlying prostate cancer resistance to anti-androgens are not completely understood. Therefore, identifying pharmacological targets in driving the development of castrationresistant PC is necessary. In the present study, we sought to identify core genes in regulating steroid hormone pathways and associating them with the disease progression of PC. The selection of steroid hormone-associated genes was identified from functional databases, including gene ontology, KEGG, and Reactome. The gene expression profiles and relevant clinical information of patients with PC were obtained from TCGA and used to examine the genes associated with steroid hormone. The machine-learning algorithm was performed for key feature selection and signature construction. With the integrative bioinformatics analysis, an eight-gene signature, including CA2, CYP2E1, HSD17B, SSTR3, SULT1E1, TUBB3, UCN, and UGT2B7 was established. Patients with higher expression of this gene signature had worse progression-free interval in both univariate and multivariate cox models adjusted for clinical variables. The expression of the gene signatures also showed the aggressiveness consistently in two external cohorts, PCS and PAM50. Our findings demonstrated a validated eightgene signature could successfully predict PC prognosis and regulate the steroid hormone pathway.
    日期: 2022-03-12
    關聯: Cancers. 2022 Mar 12;14(6):Article number 1565.
    Link to: http://dx.doi.org/10.3390/cancers14061565
    JIF/Ranking 2023: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=2072-6694&DestApp=IC2JCR
    Cited Times(WOS): https://www.webofscience.com/wos/woscc/full-record/WOS:000775889500001
    Cited Times(Scopus): https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85126538591
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