m(6)A Methylation Regulators Are Predictive Biomarkers for Tumour Metastasis in Prostate Cancer

SIMPLE SUMMARY: Recurrence and metastatic progression always lead to dismal outcomes in prostate cancer (PCa). There is no reliable biomarker for the prediction of recurrence and metastasis other than the Prostate Cancer Antigen (PCA). N6-methyladenosine (m(6)A) is the most common post-transcriptional mRNA modification and is regulated by m(6)A regulators dynamically. Since m(6)A modification is associated with cancer development and outgrowth, we performed a consensus clustering on PCa with regard to the gene expression of all m(6)A regulators. We identified three subtypes of Pca with distinct m(6)A expression patterns and enriched biological pathways. We also established an m(6)A score for metastasis prediction based on our clustering, which is potentially a predictive biomarker for Pca metastasis. ABSTRACT: Prostate cancer (PCa) is one of the most common cancers in men. Usually, most PCas at initial diagnosis are localized and hormone-dependent, and grow slowly. Patients with localized PCas have a nearly 100% 5-year survival rate; however, the 5-year survival rate of metastatic or progressive PCa is still dismal. N6-methyladenosine (m(6)A) is the most common post-transcriptional mRNA modification and is dynamically regulated by m(6)A regulators. A few studies have shown that the abnormal expression of m(6)A regulators is significantly associated with cancer progression and immune cell infiltration, but the roles of these regulators in PCa remain unclear. Here, we examined the expression profiles and methylation levels of 21 m(6)A regulators across the Cancer Genome Atlas (TCGA), 495 PCas by consensus clustering, and correlated the expression of m(6)A regulators with PCa progression and immune cell infiltration. Consensus clustering was applied for subtyping Pca samples into clusters based on the expression profiles of m(6)A regulators. Each subtype’s signature genes were obtained by a pairwise differential expression analysis. Featured pathways of m(6)A subtypes were predicted by Gene Ontology. The m(6)A score was developed to predict m(6)A activation. The association of the m(6)A score with patients’ survival, metastasis and immune cell infiltration was also investigated. We identified three distinct clusters in PCa based on the expression profiles of 21 m(6)A regulators by consensus clustering. The differential expression and pathway analyses on the three clusters uncovered the m(6)A regulators involved in metabolic processes and immune responses in PCa. Moreover, we developed an m(6)A score to evaluate the m(6)A regulator activation for PCa. The m(6)A score is significantly associated with Gleason scores and metastasis in PCa. The predictive capacity of the m(6)A score on PCa metastasis was also validated in another independent cohort with an area under the curve of 89.5%. Hence, our study revealed the critical role of m(6)A regulators in PCa progression and the m(6)A score is a promising predictive biomarker for PCa metastasis.