Background: To better understand the process of osteoarthritic degenerative meniscal lesions (DMLs) formation, this study analyzed the dataset GSE52042 using bioinformatics methods to identify the pivotal genes and pathways related to osteoarthritic DMLs. Material/Methods: The GSE52042 dataset, comprising diseased meniscus samples and healthier meniscus samples, was downloaded and the differentially-expressed genes (DEGs) were extracted. The reactome pathways assessment and functional analysis were performed using the "ClusterProfiler" package and "ReactomePA" package of Bioconductor. The protein-protein interaction network was constructed, followed by the extraction of hub genes and modules. Results: A set of 154 common DEGs, including 64 upregulated DEGs and 90 downregulated DEGs, were obtained. GO analysis suggested that the DEGs primarily participated in positive regulation of the mitotic cell cycle and extracellular matrix organization. Reactome pathway analysis showed that the DEGs were predominantly enriched in TP53, which regulates transcription of genes involved in G2 cell cycle arrest and extracellular matrix organization. The top 10 hub genes were TYMS, AURKA, CENPN, NUSAP1, CENPM, TPX2, CDK1, UBE2C, BIRC5, and CCNB1. The genes in the 2 modules were primarily associated with M Phase and keratan sulfate degradation. Conclusions: A series of pivotal genes and reactome pathways were identified elucidate the molecular mechanisms involved in the formation of osteoarthritic DMLs and to discover potential therapeutic targets.

Purpose The present study aimed to explore potential diagnostic biomarkers for fatty infiltration (FI) of the rotator cuff muscles after rotator cuff tear (RCT) and investigate the influence of stromal and immune cell infiltration on this pathology. Methods The GSE130447 and GSE103266 datasets were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified, and gene set enrichment analyses were performed by R software. Two machine learning algorithms, random forest and multiple support vector machine recursive feature elimination (mSVM-RFE), were used to screen candidate biomarkers. The diagnostic value of the screened biomarkers was further validated by the area under the ROC curve (AUC) in the GSE103266 dataset. Murine microenvironment cell population counter (mMCP-counter) method was employed to estimate stromal and immune cell infiltration of FI. The correlation between biomarkers and infiltrated immune and stromal cell subsets was further analyzed. Results A total of 2123 DEGs were identified. The identified DEGs were predominantly linked to immune system process, extracellular matrix organization and PPAR signalling pathway. FABP5 (AUC = 0.958) and MGP (AUC = 1) were screened as diagnostic biomarkers of FI. Stromal and immune cell infiltration analysis showed that monocytes, mast cells, vessels, endothelial cells and fibroblasts may be related to the process of FI. FABP5 and MGP were positively correlated with vessels whereas negatively correlated with monocytes and mast cells. Conclusion FABP5 and MGP can serve as diagnostic biomarkers of FI after RCT, and stromal and immune cell infiltration may play a crucial role in this pathology.