Decoding the association between mucosal-associated invariant T cells and prostate cancer via multi-omics analysis

Prostate cancer (PCa) is a highly heterogeneous malignancy characterized by significant variations in gene expression among distinct cell populations. Traditional sequencing technologies often obscure these differences, necessitating advanced approaches for a comprehensive understanding. Single-cell RNA sequencing (scRNA-seq) and Mendelian randomization (MR) analysis have emerged as powerful tools for unraveling the intricacies of PCa, enabling the identification of unique biological features at the individual cell level. In this study, droplet-based scRNA-seq analysis was performed on 4 PCa samples and 4 normal adjacent to tumor samples. The dataset, obtained from the NCBI GEO database, comprised 13,139 cells, facilitating the identification of diverse cell types and their respective gene expression patterns. Robust analysis pipelines, including pseudotemporal ordering, regional plot analysis, MR analysis, and comprehensive gene expression dynamics, were employed to investigate the tumor microenvironment, immune landscape, and potential causal associations with PCa susceptibility. Our scRNA-seq analysis revealed altered immune landscapes in PCa, underscoring the significance of T cells, particularly mucosal-associated invariant T (MAIT) cells. The study unveiled heterogeneity within the tumor microenvironment and identified key genes, such as ZFP36L2, associated with PCa susceptibility. MR analysis provided insights into the potential causal effects of MAIT cells on PCa, supported by analyses of gene expression dynamics and metabolic landscapes. ZFP36L2 emerged as a pivotal regulator, validated through various experimental approaches, including real-time quantitative polymerase chain reaction, western blot, and immunohistochemistry. Our findings underscore the significance of MAIT cells, presenting a novel diagnostic modality for advancing the diagnosis and treatment strategies of PCa. The identified genes, particularly ZFP36L2, offer potential avenues for precision medicine and targeted therapies.