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Objective  To develop a comprehensive differential expression profile for osteoporosis based on two independent data sources.    Methods  Using a hindlimb unloading (HLU) rat model to mimic osteoporosis syndrome in humans (animal experiments), the significant differentially expressed mRNAs in osteoporosis were analyzed using RNA‐seq. The enriched GO terms as well as KEGG signaling pathways were also deeply investigated. Using clinical specimens to verify the functions of potential hub genes (biomarkers) for osteoporosis (clinical experiments), 128 suspected cases for osteoporosis from January 2019 to December 2020 were randomly selected and analyzed by quantitative computed tomography (QCT) as well as modified Dixon quantification (mDixon‐Quant) techniques in the Tianjin hospital. Among these, 80 patients out of 128 suspected cases were finally diagnosed as the osteoporosis group. Meanwhile, 48 patients were selected for osteopenia group. There was no significant age and gender difference across participant subgroups. The protein levels of potential hub genes ( FST ,  CCL3 , and  RAPGEF4 ) were determined by ELISA double antibody sandwich method for osteopenia and osteoporosis groups from peripheral blood.    Result  In the RNA‐seq analysis, compared with control group, a total of 803 differentially expressed mRNAs were identified, including 288 up‐regulated and 515 down‐regulated mRNAs. Of these,  FST, CCL3, CPE, RAPGEF4, IL6, MDFI, PDZD2 , and  GATM  were primary hub genes (biomarkers) for osteoporosis. These differentially expressed genes were significantly enriched in GO terms related to extracellular matrix process and KEGG signaling pathways including osteoclast differentiation. In the functional experiments, the protein expression level of  FST ,  CCL3 , and  RAPGEF4  displayed a specific expression pattern between osteoporosis patients and control group. The protein concentration of  FST  was 23.63 ± 6.39 ng/mL in osteoporosis patients compared as 48.36 ± 9.12 ng/mL in osteopenia group ( P  < 0.01). Meanwhile, CCL3 was 1.03 ± 0.64 ng/mL in osteoporosis patients  vs  0.56 ± 0.24 in osteopenia group ( P  < 0.01) and  RAPGEF4  was 53.58 ± 11.42 ng/mL in osteoporosis patients  vs  66.47 ± 13.28 ng/mL in osteopenia group ( P  < 0.05), respectively.    Conclusion  This study has identified potential gene biomarkers (the genes with most significantly differential expression and useful for distinguishing osteoporosis from other bone disorders) and established a differential expression profile for osteoporosis, which is a valuable reference for future clinical research.

Investigation of Diagnostic Biomarkers for Osteoporosis Based on Differentially Expressed Gene Profile with QCT and mDixon‐Quant Techniques