Interplay of <scp>PRMTs</scp> and Identification of Biomarkers Through Machine Learning Algorithms in Pan‐Cancer, Highlighting <scp>PRMT3</scp> as a Biomarker in Pancreatic Cancer

Protein arginine methylation was a common post-translational modification, playing a key role in many biological processes and disease. But the regulatory mechanisms of protein arginine methyltransferases (PRMTs) in cancer were not well understood. This study aimed to examine the influence of PRMTs across various cancers and identify potential PRMTs-related prognostic biomarkers. Three machine learning algorithms were employed to identify potential PRMTs biomarkers across cancers. Additionally, drug prediction and molecular docking studies were carried out to discover new therapeutic targets. Functional studies, including Transwell and wound-healing assays, were performed to investigate the role of PRMT3 in tumor regulation. Through three machine learning algorithms, significant PRMTs prognosis-related biomarkers were identified across most cancers. The immune regulatory functions of PRMTs in pan-cancer were further examined. Daporinad, dinaciclib, and sepantronium bromide were predicted as potential drugs for the majority of cancer types. Functionally, the absence of PRMT3 notably inhibited metastasis in pancreatic cancer. Ten distinct cell types were identified from single-cell RNA sequencing data of pancreatic cancer. PRMT3 might serve as a promising prognostic biomarker for pancreatic cancer. These findings contributed to a deeper understanding of the regulatory mechanisms of PRMTs in cancer progression. Potential biomarkers had been identified that may predict responses to immunotherapy and improve survival outcomes for cancer patients. This study provided a detailed overview of the functional roles, genetic and epigenetic alterations, and prognostic significance of PRMTs in pan-cancer.