Clinical Data Investigation Identifies MARK3 as an Oncogenic Driver in Castration-Resistant Prostate Cancer
Castration-resistant prostate cancer (CRPC) represents an aggressive and fatal form of prostate cancer that emerges following resistance to androgen deprivation therapy. Despite the availability of various drugs that can enhance the quality and prolong the survival of CRPC patients, resistance to these therapies is frequently observed, making the disease increasingly difficult to treat. Alterations in the expression of kinases and phosphatases are key drivers of CRPC and present potential targets for more effective treatments. In this study, we have performed a comprehensive transcriptomic analysis of ~359 normal and CRPC patient samples from the cancer genome atlas to identify the differentially expressed kinases and phosphatases. We shortlisted the candidate genes based on their differential expression profiles, associations with patient survival, Gleason scores, and their impact on the fitness of prostate cancer cell lines. Our in-silico analysis identified microtubule affinity-regulating kinase 3 (MARK3) as a novel CRPC driver that is upregulated in CRPC patients, correlates with poor survival outcomes, and affects the fitness of CRPC cells. Pharmacological inhibition of MARK3 using PCC0208017, a MARK3 inhibitor, leads to reduced cell viability and migratory potential, as well as cell cycle arrest at G1 phase. Additionally, RNA sequencing analysis of 22Rv1 cells treated with the MARK3 inhibitor revealed that MARK3 influences genes involved in androgen response, epithelial-mesenchymal transition, mTOR and Myc signalling, underscoring its pivotal role in CRPC progression. Taken together, our results establish MARK3 as a novel and promising therapeutic target in CRPC. Overall design: This experiment was performed using 22Rv1 cell line. Total 8 samples were subjected to the RNA sequencing with the Illumina NovaSeq 6000. Among them, 4 samples were treated with DMSO and 4 were treated with MARK3 inhibitor PCC0208017. Raw fastq files were processed and aligned to the Hg38 genome assembly and quantified with featureCounts. Count matrix was then utilized to perform differential gene expression analysis with DESeq2 package in R Studio.