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RIPK2-Regulated Gene Signature Associated with Poor Prognosis in Prostate Cancer

ABSTRACT: Receptor-interacting protein kinase 2 (RIPK2) has emerged as a promising drug target in various cancers, including prostate cancer (PC). However, the absence of reliable biomarkers to assess RIPK2 activity limits patient selection for anti-RIPK2 therapies and treatment monitoring. To address this gap, we performed RNA-Seq analysis on PC cancer cell lines (22Rv1, DU145, and PC3) with CRISPR/Cas9-mediated RIPK2 knockout (RIPK2-KO) using two independent guide RNAs. This analysis identified 13 candidate RIPK2-regulated genes. Reverse transcription quantitative PCR (RT-qPCR) validated eight of these genes. Furthermore, treatment with two distinct RIPK2 inhibitors significantly reduced RIPK2 signature scores in four independent PC cell lines in a dose- and time-dependent manner. Clinical association analyses revealed that high RIPK2 signature scores correlate with metastasis and worse biochemical recurrence-free, progression-free, disease-free, and overall survival, outperforming RIPK2 mRNA levels as a prognostic biomarker. This study establishes, for the first time, a RIPK2-regulated gene signature as a potential biomarker for RIPK2 activity and PC prognosis, warranting further validation in clinical specimens to provide a much-needed tool for patient stratification and response monitoring in RIPK2-targeted therapies.

ORGANISM(S): Homo sapiens

PROVIDER: GSE292278 | GEO | 2025/03/31

REPOSITORIES: GEO

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Project description:Objective: Production of pathogenic autoantibodies by self-reactive plasma cells (PC) is a hallmark of autoimmune diseases. Investigating the prevalence of PC in autoimmune disease and their relationship with known pathogenic pathways may increase our understanding of the role of PC in disease progression and treatment response. Methods: We developed a sensitive gene expression based method to overcome the challenges of measuring PC using flow cytometry. Whole genome microarray analysis of sorted cellular fractions identified a panel of genes, IGHA, IGJ, IGKC, IGKV, and TNFRSF17, expressed predominantly in PC. The sensitivity of the PC signature score created from the combined expression levels of these genes was assessed through ex vivo experiments with sorted cells. This PC gene expression signature was used for monitoring changes in PC levels following anti-CD19 therapy; evaluating the relationship between PC and other autoimmune disease-related genes; and estimating PC levels in affected blood and tissue from multiple autoimmune diseases. Results: The PC signature was highly sensitive and capable of detecting as few as 300 PCs. The PC signature was reduced over 90% in scleroderma patients following anti-CD19 treatment and this reduction was highly correlated (r = 0.77) with inhibition of collagen gene expression. Evaluation of multiple autoimmune diseases revealed 30-35% of lupus, rheumatoid arthritis, and scleroderma patients with increased PC levels. Conclusion: This newly developed PC signature provides a robust and accurate method to measure PC levels in the clinic. Our results highlight subsets of patients across multiple autoimmune diseases that may benefit from PC depleting therapy. MI-CP200 is a Phase 1, randomized, double-blind, placebo-controlled study to evaluate the safety and tolerability of escalating single IV doses of MEDI-551 in adult subjects with scleroderma. In this study, 5 cohorts of subjects (n=25) received 1 of 5 single IV doses of MEDI-551 (0.1, 0.3, 1.0, 3.0, or 10.0 mg/kg) or placebo. Twenty-four healthy donor control samples are also included.

Project description:Objective: Production of pathogenic autoantibodies by self-reactive plasma cells (PC) is a hallmark of autoimmune diseases. Investigating the prevalence of PC in autoimmune disease and their relationship with known pathogenic pathways may increase our understanding of the role of PC in disease progression and treatment response. Methods: We developed a sensitive gene expression based method to overcome the challenges of measuring PC using flow cytometry. Whole genome microarray analysis of sorted cellular fractions identified a panel of genes, IGHA, IGJ, IGKC, IGKV, and TNFRSF17, expressed predominantly in PC. The sensitivity of the PC signature score created from the combined expression levels of these genes was assessed through ex vivo experiments with sorted cells. This PC gene expression signature was used for monitoring changes in PC levels following anti-CD19 therapy; evaluating the relationship between PC and other autoimmune disease-related genes; and estimating PC levels in affected blood and tissue from multiple autoimmune diseases. Results: The PC signature was highly sensitive and capable of detecting as few as 300 PCs. The PC signature was reduced over 90% in scleroderma patients following anti-CD19 treatment and this reduction was highly correlated (r = 0.77) with inhibition of collagen gene expression. Evaluation of multiple autoimmune diseases revealed 30-35% of lupus, rheumatoid arthritis, and scleroderma patients with increased PC levels. Conclusion: This newly developed PC signature provides a robust and accurate method to measure PC levels in the clinic. Our results highlight subsets of patients across multiple autoimmune diseases that may benefit from PC depleting therapy. To examine gene expression in purified cellular fractions, normal human blood was collected from 2 donors as per institutional policy. The granulocyte (CD15+), monocyte (CD14+), T cell (CD3+), B cell (Non-PC gated, CD19+), and PC (CD27++CD38++) fractions from peripheral blood were separated. White blood cells were washed with FACS buffer (PBS + 0.5%BSA + 2mM EDTA (Gibco)) and incubated with 20% heat-inactivated FBS for 10-15 minutes on ice. The following mAbs were added directly to the cells: CD15 (HI98); CD14 (M5E2), CD3 (UCHT1), CD27 (M-T271), CD38 (HB7), and DAPI (Molecular probes). Cells were sorted on a Becton Dickinson FACS Aria II flow cytometer. All sorted fractions were collected in FACS buffer, centrifuged, and the resulting cell pellet was suspended in RNA lysis buffer (Ambion).

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RIPK2-Regulated Gene Signature Associated with Poor Prognosis in Prostate Cancer

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