Technol Health Care. 2024;32(6):4431-4444. doi: 10.3233/THC-240574.
Osteoarthritis (OA) and osteoporosis (OS) are the most common orthopedic diseases.
To identify important genes as biomarkers for the pathogenesis of OA and OS.
Microarray data for OA and OS were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between the OA and healthy control groups and between the OS and healthy control groups were identified using the Limma software package. Overlapping hub DEGs were selected using MCC, MNC, DEGREE, and EPC. Weighted gene co-expression network analysis (WGCNA) was used to mine OA- and OS-related modules. Shared hub DEGs were identified, human microRNA disease database was used to screen microRNAs associated with OA and OS, and an miRNA-target gene network was constructed. Finally, the expression of shared hub DEGs was evaluated.
A total of 104 overlapping DEGs were identified in both the OA and OS groups, which were mainly related to inflammatory biological processes, such as the Akt and TNF signaling pathways Forty-six hub DEGs were identified using MCC, MNC, DEGREE, and EPC modules using different algorithms. Seven modules with 392 genes that highly correlated with disease were identified in the WGCNA. Furthermore, 10 shared hub DEGs were identified between the OA and OS groups, including OGN, FAP, COL6A3, THBS4, IGFBP2, LRRC15, DDR2, RND3, EFNB2, and CD48. A network consisting of 8 shared hub DEGs and 55 miRNAs was constructed. Furthermore, CD48 was significantly upregulated in the OA and OS groups, whereas EFNB2, DR2, COL6A3, and RND3 were significantly downregulated in OA and OS. Other hub DEGs were significantly upregulated in OA and downregulated in OS.
The ten genes may be promising biomarkers for modulating the development of both OA and OS.
骨关节炎（OA）和骨质疏松症（OS）是最常见的骨科疾病。
确定重要的基因作为 OA 和 OS 发病机制的生物标志物。
从基因表达综合数据库中下载 OA 和 OS 的微阵列数据。使用 Limma 软件包识别 OA 与健康对照组和 OS 与健康对照组之间的差异表达基因（DEGs）。使用 MCC、MNC、DEGREE 和 EPC 选择重叠的 hub DEGs。使用加权基因共表达网络分析（WGCNA）挖掘 OA 和 OS 相关模块。鉴定共享 hub DEGs，筛选与 OA 和 OS 相关的 microRNA 数据库，构建 miRNA-靶基因网络。最后，评估共享 hub DEGs 的表达。
在 OA 和 OS 两组中鉴定出 104 个重叠的 DEGs，这些基因主要与炎症的生物过程有关，如 Akt 和 TNF 信号通路。使用 MCC、MNC、DEGREE 和 EPC 模块使用不同的算法确定了 46 个 hub DEGs。在 WGCNA 中鉴定出 7 个模块，包含 392 个与疾病高度相关的基因。此外，在 OA 和 OS 组之间鉴定出 10 个共享 hub DEGs，包括 OGN、FAP、COL6A3、THBS4、IGFBP2、LRRC15、DDR2、RND3、EFNB2 和 CD48。构建了一个包含 8 个共享 hub DEG 和 55 个 miRNAs 的网络。此外，CD48 在 OA 和 OS 组中显著上调，而 EFNB2、DR2、COL6A3 和 RND3 在 OA 和 OS 中显著下调。其他 hub DEGs 在 OA 中显著上调，在 OS 中显著下调。
这 10 个基因可能是调节 OA 和 OS 发展的有前途的生物标志物。
