Identification of cuproptosis-related subtypes, characterization of immune microenvironment infiltration, and development of a prognosis model for osteoarthritis - Nong, Jiao; Lu, Guanyu; Huang, Yue; Liu, Jinfu; Chen, Lihua; Pan, Haida; Xiong, Bo (Frontiers in Immunology, 2023)
Background: Osteoarthritis (OA) is a prevalent chronic joint disease with unclear underlying molecular mechanisms. Cuproptosis plays a crucial role in various biological processes. However, the association between cuproptosis-mediated immune infiltration and the progression of OA remains unexplored. Therefore, this study aimed to elucidate the pathological process and potential mechanisms underlying cuproptosis in OA. Methods: The GSE12021, GSE32317, GSE55235, and GSE55457 datasets were obtained from the Gene Expression Omnibus. Cuproptosis-related genes were extracted and subjected to differential expression analysis to construct a prediction model. Cuproptosis signature genes were screened by random forest (RF) and support vector machine (SVM). A nomogram was developed based on cuproptosis signature genes. A consensus clustering was used to distinguish OA patients into different cuproptosis patterns. To quantify the cuproptosis pattern, a principal component analysis was developed to generate the cuproptosis score for each sample. Single-sample gene set enrichment analysis (ssGSEA) was used to provide the abundance of immune cells in each sample and the relationship between these significant cuproptosis signature genes and immune cells. Results: In this study, the RF model had the smallest residuals confirming the selection of the RF for predicting the occurrence of OA. Seven cuproptosis signature genes were screened by RF (DBT, LIPT1, GLS, PDHB, FDX1, DLAT, and PDHA1) to predict the risk of OA. The clinical impact curve predicted that the nomogram had strong predictive power. Two different clusters of OA were identified by analyzing the expression of ten cuproptosis signature genes. PCA analysis showed that ten cuproptosis signature genes differed significantly between the two clusters. Furthermore, the characteristics of cuproptosis in cluster A were associated with the inflammatory factors IL-1β, IL-17, IL-21, and IL-22, suggesting that the cuproptosis signature genes play a vital role in developing OA. Conclusion: This is the first study to address the specific mechanisms underlying cuproptosis-mediated immune infiltration in OA by combining microarray data and cuproptosis-related genes. Moreover, the findings related to cuproptosis patterns may help to guide future immunotherapeutic strategies against OA.