Identification of m7G-related hub biomarkers and m7G regulator expression pattern in immune landscape during the progression of osteoarthritis

Background: Accumulating evidence has shown that aberrant N7-methylguanosine (m7G) RNA methylation played an important role in the occurrence and development of cancer. However, knowledge of m7G modifications in inflammatory diseases is limited. Osteoarthritis (OA) is the most common arthritic disease with poor prognosis. Our research aimed to identify m7G-related hub biomarkers and investigate m7G regulator expression pattern in immune landscape of OA patients. Methods: Gene expression profiles and their clinical information were obtained from the Gene Expression Omnibus (GEO) database, and differential analysis of 14 m7G-related regulators between elective OA and normal samples was performed. M7G-related hub genes for OA were mined based on single-sample gene set enrichment analysis (ssGSEA) and the random forest (RF) algorithm, and qRT-PCR was performed to confirm the abnormal expression of hub genes. Enrichment, protein-protein interaction (PPI), transcription factor (TF)-gene interaction and microRNA (miRNA)-gene coregulatory analysis based on m7G hub genes were performed. Then we predicted several candidate drugs related to m7G hub genes using DSigDB database. Moreover, we comprehensively evaluated m7G methylation patterns in OA samples and systematically correlated these modification patterns with the characteristics of immune cell infiltration. The m7G score was generated to quantify m7G methylation patterns for individual OA patients by the application of principal component analysis (PCA) algorithm. Results: We constructed an OA predictive model based on 4 m7G hub genes (SNUPN, METTL1, EIF4E2 and CYFIP1). Two m7G methylation patterns in OA were discovered to show distinct biological characteristics, and an m7G score were generated. M7G cluster A and a higher m7G score were found to be related to an inflamed phenotype. Conclusions: Our study was the first to comprehensively investigate the m7G methylation dysregulations in immune landscape during the progression of OA. These 4 m7G gene-related signatures can be used as novel OA biomarkers to predict the occurrence of OA. Evaluating the m7G methylation patterns of OA individuals will contribute to enhancing our cognition of immune infiltration characterization and guiding more effective immunotherapy strategies.