Identification of G4 regulated immune-related drug targets for prostate cancer based on G4 screen and machine learning

Methods

We performed BG4 ChIP_x001E_seq to map genome_x001E_wide G4 structures in the prostate cancer cell line C4_x001E_2. Bioinformatics analyses integrated G4_x001E_associated genes with immune pathway enrichment and machine learning algorithms (LASSO, SVM_x001E_RFE, GBM, Naïve Bayes, and GLM) to identify hub genes in prostate cancer progression. Clinical data from GTEx, TCGA, and HPA were analyzed for expression and survival.

Functional validation included qPCR, CCK_x001E_8, colony formation, and wound_x001E_healing assays. Druggability was assessed using DrugnomeAI, and AI_x001E_assisted peptide design was performed with RFdiffusion and ProteinMPNN. Results: We identified 1289 prostate cancer_x001E_specific G4 structures, predominantly in promoter regions.

Machine learning and immune enrichment analysis pinpointed IKBKB as a key hub gene in prostate cancer progression. IKBKB was overexpressed in prostate cancer tissues, correlated with advanced stage and poor prognosis, and was regulated by promoter G4 structures via transcription factors AR and ERG. IKBKB promoted genome instability, tumor stemness, and immune microenvironment remodeling.

G4 stabilization increased IKBKB expression and activated the NF_x001E_κB pathway, enhancing cancer cell viability, proliferation, and migration. Computational screening confirmed IKBKB’s druggability and identified potential inhibitors (e.g., Auranofin). AI_x001E_assisted design generated peptide inhibitors targeting IKBKB and a CRISPR_x001E_dCas9 strategy for G4 disruption.