Bones are constantly exposed to mechanical forces from both muscles and Earth’s gravity to maintain bone homeostasis by stimulating bone formation. Mechanotransduction transforms external mechanical signals such as force, fluid flow shear, and gravity into intracellular responses to achieve force adaptation. However, the underlying molecular mechanisms on the conversion from mechanical signals into bone formation has not been completely defined yet. In the present review, we provide a comprehensive and systematic description of the mechanotransduction signaling pathways induced by mechanical stimuli during osteogenesis and address the different layers of interconnections between different signaling pathways. Further exploration of mechanotransduction would benefit patients with osteoporosis, including the aging population and postmenopausal women.

With the development of sequencing technology and the dramatic drop in sequencing cost, the functions of noncoding genes are being characterized in a wide variety of fields (e.g. biomedicine). Enhancers are noncoding DNA elements with vital transcription regulation functions. Tens of thousands of enhancers have been identified in the human genome; however, the location, function, target genes and regulatory mechanisms of most enhancers have not been elucidated thus far. As high-throughput sequencing techniques have leapt forwards, omics approaches have been extensively employed in enhancer research. Multidimensional genomic data integration enables the full exploration of the data and provides novel perspectives for screening, identification and characterization of the function and regulatory mechanisms of unknown enhancers. However, multidimensional genomic data are still difficult to integrate genome wide due to complex varieties, massive amounts, high rarity, etc. To facilitate the appropriate methods for studying enhancers with high efficacy, we delineate the principles, data processing modes and progress of various omics approaches to study enhancers and summarize the applications of traditional machine learning and deep learning in multi-omics integration in the enhancer field. In addition, the challenges encountered during the integration of multiple omics data are addressed. Overall, this review provides a comprehensive foundation for enhancer analysis.