A novel signature of cartilage aging-related immunophenotyping biomarkers in osteoarthritis

Abstract

The objective of this study was to identify aging-related immunophenotypic biomarkers associated with osteoarthritis (OA) using advanced machine learning techniques. We employed a combination of lasso regression and random forest algorithms to analyze transcriptomic data obtained from OA patients. Differential expression analysis and functional enrichment analysis were conducted to identify aging-related differentially expressed genes (ag-DEGs) and annotate their biological functions. Furthermore, correlation analysis among hub genes and immune cell infiltration analysis were performed to understand the molecular phenotypes of OA. Our analysis identified 43 ag-DEGs enriched in immune-related biological processes and pathways. Lasso regression and random forest analysis narrowed down the gene pool to three hub genes: CACNA1A, FLT1 and KCNAB3. These genes exhibited differential expression between normal and OA groups and demonstrated high accuracy in distinguishing between them. Clustering analysis revealed two distinct molecular phenotypes of OA: an "immune-activated subgroup" and an "immune-suppressed subgroup." Experimental validation confirmed the expression patterns of hub genes. This study identified biomarkers associated with the aging-related immune phenotype in OA, shedding light on potential targets for immunotherapy and personalized medical treatments. Characterized by CACNA1A, FLT1, and KCNAB3, clustering analysis suggests that OA can be divided into two molecular phenotypes: an "immune-activated subgroup" and an "immune-suppressed subgroup." The findings may contribute to the development of novel therapeutic strategies aimed at modulating immune responses in OA patients, ultimately improving treatment outcomes and prognosis.