Uncovering Molecular Mechanisms of Mechanical Force Stimulation on Bone Tissue: A Bioinformatics Approach

Abstract

This study employed multiple bioinformatics approaches to uncover the potential molecular mechanisms underlying the effects of mechanical force stimulation on bone tissue. RNA sequencing data from 132 cortical bone samples were initially retrieved from the SRA database and subjected to quality control, sequence alignment, and expression matrix generation as preprocessing steps. Subsequently, WGCNA was applied for gene co-expression network analysis, followed by the selection of approximately 3,500 genes for further investigation and the exclusion of outliers. Clustering and correlation analyses revealed sets of genes highly associated with mechanical force stimulation. To gain insight into the functional characteristics of these genes, PPI network construction and GO and KEGG enrichment analyses were performed. Results indicated distinct functional properties of different modules under various conditions. For instance, genes within the MEsalmon module were mainly associated with actin cytoskeleton and calmodulin in response to mechanical force stimulation; whereas genes within the MEbrown module, when considering growth plates and other skeletal regions, primarily involved ribosomal proteins and energy metabolism. With prolonged treatment time, genes within the MEblack module exhibited increased energy metabolic activity. These findings suggest that mechanical force stimulation can alter the expression levels of specific genes in bone tissue, thereby affecting their biological functions. This study provides a new perspective on how mechanical force stimulation regulates bone tissue and may have positive implications for therapeutic strategies in fracture healing and bone diseases.