Identification of senescence-related biomarkers for osteoporosis based on microarray analysis, Mendelian randomization, and experimental validation.

Abstract

Osteoporosis, characterized by decreased bone mineral density, is a common skeletal disorder in the aging population. Cellular senescence is a key factor in the pathophysiology of osteoporosis. This study aimed to identify senescence-related biomarkers and evaluate the functional role in osteoporosis by integrating microarray analysis, Mendelian randomization (MR), and experimental validation. Osteoporosis-related microarray dataset was downloaded from the Gene Expression Omnibus database for differential expression analysis. We integrated summary-level data from genome-wide association studies on osteoporosis with protein quantitative trait loci data to identify genes with causal relationships to osteoporosis. The senescence-related biomarker gene was identified using the SenMayo gene set and evaluated for the predictive performance through receiver operating characteristic (ROC) curve analysis. Functional enrichment analysis was conducted to explore the underlying mechanisms. Validation of gene expression was performed using quantitative real-time PCR in 50 clinical samples from patients with osteoporosis and controls. A total of 33 differentially expressed genes were identified between osteoporosis and control samples. MR analysis revealed 90 genes with causal effects on osteoporosis. Subsequently, CXCL1 was identified as the key senescence-related biomarker gene. ROC curve analysis demonstrated good predictive performance with an area under the curve value of 0.708. Functional enrichment analysis showed a significant association between CXCL1 and immune-related pathways in osteoporosis. The expression of the gene was successfully validated in clinical samples. This study identified and validated CXCL1 as a senescence-related biomarker with causal effects on osteoporosis through a combination of microarray analysis, MR, and experimental validation. These findings offer insights into the molecular mechanisms of osteoporosis and could inform the development of treatment strategies.