Chen Dongfeng, Li Ying, Wang Qiang, Zhan Peng
Department of Bone and Joint Sports Medicine, Longyan First Hospital Affiliated to Fujian Medical University, Longyan, 364000, Fujian, People's Republic of China.
Calcif Tissue Int. 2023 Dec;113(6):618-629. doi: 10.1007/s00223-023-01147-3. Epub 2023 Oct 25.
Osteoporosis disproportionately affects older women, yet gender differences in human osteoblasts remain unexplored. Identifying mechanisms and biomarkers of osteoporosis will enable the development of preventative and therapeutic approaches. Transcriptome data of 187 osteoblast samples from men and women were compared. Differentially expressed genes (DEGs) were identified, and weighted gene co-expression network analysis (WGCNA) was used to discover co-expressed modules. Enrichment analysis was performed to annotate DEGs. Preservation analysis determined whether modules and pathways were similar between genders. Blood methylation, transcriptome data, mouse phenotype data, and drug treatment data were utilized to identify key osteoporosis genes. We identified 1460 DEGs enriched in immune response, neurogenesis, and GWAS osteoporosis-related genes. WGCNA uncovered 8 modules associated with immune response, development, collagen metabolism, mitochondrion, and amino acid synthesis. Preservation analysis indicated modules and pathways were generally similar between genders. Incorporating GWAS and mouse phenotype data revealed 9 key genes, including GMDS, SMOC2, SASH1, MMP2, AHCYL1, ARRDC2, IGHMBP2, ATP6V1A, and CTSK. These genes were differentially methylated in patient blood and differentiated high and low bone mineral density patients in pre- and postmenopausal women. Denosumab treatment in postmenopausal women down-regulated 6 key genes, up-regulated T cell proportions, and down-regulated fibroblast proportion. qRT-PCR was used to confirm the genes in postmenopausal women. We identified 9 key osteoporosis genes by comparing the transcriptome of osteoblasts in women and men. Our findings' clinical implications were confirmed by multi-omics data and qRT-PCR, and our study provides novel biomarkers and therapeutic targets for osteoporosis diagnosis and treatment.
骨质疏松症对老年女性的影响尤为严重，但人类成骨细胞中的性别差异仍未得到充分研究。确定骨质疏松症的发病机制和生物标志物将有助于开发预防和治疗方法。我们比较了187份男性和女性成骨细胞样本的转录组数据。识别出差异表达基因（DEG），并使用加权基因共表达网络分析（WGCNA）来发现共表达模块。进行富集分析以注释DEG。保存分析确定了性别之间的模块和途径是否相似。利用血液甲基化、转录组数据、小鼠表型数据和药物治疗数据来识别关键的骨质疏松症基因。我们鉴定出1460个DEG，这些基因富集于免疫反应、神经发生和全基因组关联研究（GWAS）骨质疏松症相关基因中。WGCNA发现了8个与免疫反应、发育、胶原代谢、线粒体和氨基酸合成相关的模块。保存分析表明，性别之间的模块和途径总体上相似。整合GWAS和小鼠表型数据后，发现了9个关键基因，包括GMDS、SMOC2、SASH1、MMP2、AHCYL1、ARRDC2、IGHMBP2、ATP6V1A和CTSK。这些基因在患者血液中存在差异甲基化，并且在绝经前和绝经后女性中区分出高骨密度和低骨密度患者。绝经后女性使用地诺单抗治疗可下调6个关键基因，上调T细胞比例，下调成纤维细胞比例。采用qRT-PCR对绝经后女性的这些基因进行了验证。通过比较男性和女性成骨细胞的转录组，我们鉴定出9个关键的骨质疏松症基因。多组学数据和qRT-PCR证实了我们研究结果的临床意义，我们的研究为骨质疏松症的诊断和治疗提供了新的生物标志物和治疗靶点。
