Identification of a Steroid Hormone-Associated Gene Signature Predicting the Prognosis of Prostate Cancer through an Integrative Bioinformatics Analysis

Yo Liang Lai, Chia Hsin Liu, Shu Chi Wang, Shu Pin Huang, Yi Chun Cho, Bo Ying Bao, Chia Cheng Su, Hsin Chih Yeh, Cheng Hsueh Lee, Pai Chi Teng, Chih Pin Chuu, Deng Neng Chen, Chia Yang Li, Wei Chung Cheng

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article number1565
JournalCancers
Volume14
Issue number6
DOIs
StatePublished - Mar 1 2022
Externally publishedYes

Keywords

  • Machine learning
  • Prognostic signature
  • Prostate cancer
  • Steroid hormone

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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