Comprehensive analysis of the progression mechanisms of CRPC and its inhibitor discovery based on machine learning algorithms

Zhen Wang; Jing Zou; Le Zhang; Hongru Liu; Bei Jiang; Yi Liang; Yuzhe Zhang

doi:10.3389/fgene.2023.1184704

Comprehensive analysis of the progression mechanisms of CRPC and its inhibitor discovery based on machine learning algorithms

Front Genet. 2023 Jul 5:14:1184704. doi: 10.3389/fgene.2023.1184704. eCollection 2023.

Authors

Zhen Wang¹, Jing Zou², Le Zhang¹, Hongru Liu¹, Bei Jiang³, Yi Liang⁴, Yuzhe Zhang¹

Affiliations

¹ College of Basic Medical Sciences, Dali University, Dali, Yunnan, China.
² The First Affiliated Hospital of Dali University, Dali, Yunnan, China.
³ Yunnan Key Laboratory of Screening and Research on Anti-pathogenic Plant Resources from West Yunnan (Cultivation), Dali, Yunnan, China.
⁴ Princess Margaret Cancer Centre, TMDT-MaRS Centre, University Health Network, Toronto, ON, Canada.

Abstract

Background: Almost all patients treated with androgen deprivation therapy (ADT) eventually develop castration-resistant prostate cancer (CRPC). Our research aims to elucidate the potential biomarkers and molecular mechanisms that underlie the transformation of primary prostate cancer into CRPC. Methods: We collected three microarray datasets (GSE32269, GSE74367, and GSE66187) from the Gene Expression Omnibus (GEO) database for CRPC. Differentially expressed genes (DEGs) in CRPC were identified for further analyses, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA). Weighted gene coexpression network analysis (WGCNA) and two machine learning algorithms were employed to identify potential biomarkers for CRPC. The diagnostic efficiency of the selected biomarkers was evaluated based on gene expression level and receiver operating characteristic (ROC) curve analyses. We conducted virtual screening of drugs using AutoDock Vina. In vitro experiments were performed using the Cell Counting Kit-8 (CCK-8) assay to evaluate the inhibitory effects of the drugs on CRPC cell viability. Scratch and transwell invasion assays were employed to assess the effects of the drugs on the migration and invasion abilities of prostate cancer cells. Results: Overall, a total of 719 DEGs, consisting of 513 upregulated and 206 downregulated genes, were identified. The biological functional enrichment analysis indicated that DEGs were mainly enriched in pathways related to the cell cycle and metabolism. CCNA2 and CKS2 were identified as promising biomarkers using a combination of WGCNA, LASSO logistic regression, SVM-RFE, and Venn diagram analyses. These potential biomarkers were further validated and exhibited a strong predictive ability. The results of the virtual screening revealed Aprepitant and Dolutegravir as the optimal targeted drugs for CCNA2 and CKS2, respectively. In vitro experiments demonstrated that both Aprepitant and Dolutegravir exerted significant inhibitory effects on CRPC cells (p < 0.05), with Aprepitant displaying a superior inhibitory effect compared to Dolutegravir. Discussion: The expression of CCNA2 and CKS2 increases with the progression of prostate cancer, which may be one of the driving factors for the progression of prostate cancer and can serve as diagnostic biomarkers and therapeutic targets for CRPC. Additionally, Aprepitant and Dolutegravir show potential as anti-tumor drugs for CRPC.

Keywords: Aprepitant; CCNA2; Cks2; Dolutegravir; castration-resistant prostate cancer (CRPC); machine learning algorithm; virtual screening; weighted gene coexpression network analysis (WGCNA).

Grants and funding

This research was supported by the Yunnan Provincial Department of Science and Technology Project [2019FH001(-032)], Open Project of Key Laboratory of Screening and Research of Resistant Plant Resources in West Yunnan, Yunnan Province (APKL2101), Dali University Doctoral Research Start-up Fund Project (KYBS2018012), General Project of the Joint Special Project of Local Universities in Yunnan Province (202001BA070001-064 and 202101BA070001-102) and Yunnan Provincial Department of Education Scientific Research Fund Project (2023Y0950 and 2022Y80).