Department of Orthopedic Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, Shandong, China.
J Healthc Eng. 2022 Jan 7;2022:8684137. doi: 10.1155/2022/8684137. eCollection 2022.
Osteonecrosis is one of the most refractory orthopedic diseases, which seriously threatens the health of old patients. High-throughput sequencing (HTS) and microarray analysis have confirmed as an effective way for investigating the pathological mechanism of disease. In this study, GSE7716, GSE74089, and GSE123568 were obtained from Gene Expression Omnibus (GEO) database and used to identify differentially expressed genes (DEGs) by R language. Subsequently, the DEGs were analyzed with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. Moreover, the protein-protein interaction (PPI) network of DEGs was analyzed by STRING database and Cytoscape. The results showed that 318 downregulated genes and 58 upregulated genes were observed in GSE7116; 690 downregulated genes and 1148 upregulated genes were screened from 34183 genes in GSE74089. The DEGs involved in progression of osteonecrosis involved inflammation, immunological rejection, and bacterial infection-related pathways. The GO enrichment showed that osteonecrosis was related with extracellular matrix, external encapsulating structure organization, skeletal system development, immune response activity, cell apoptosis, mononuclear cell differentiation, and serine/threonine kinase activity. Moreover, PPI network showed that the progression of osteonecrosis of the femoral head was related with CCND1, CDH1, ESR1, SPP1, LOX, JUN, ITGA, ABL1, and VEGF, and osteonecrosis of the jaw is related with ACTB, CXCR4, PTPRC, IL1B, CXCL8, TNF, JUN, PTGS2, FOS, and RHOA. In conclusion, this study identified the hub factors and pathways which might play important roles in progression of osteonecrosis and could be used as potential biomarkers for diagnosis and treatment of osteonecrosis.
骨坏死是最顽固的骨科疾病之一，严重威胁老年患者的健康。高通量测序（HTS）和微阵列分析已被证实是研究疾病病理机制的有效方法。本研究从基因表达综合数据库（GEO）中获取 GSE7716、GSE74089 和 GSE123568，使用 R 语言识别差异表达基因（DEGs）。随后，通过基因本体论（GO）和京都基因与基因组百科全书（KEGG）富集分析 DEGs。此外，通过 STRING 数据库和 Cytoscape 分析 DEGs 的蛋白质-蛋白质相互作用（PPI）网络。结果表明，GSE7116 中观察到 318 个下调基因和 58 个上调基因；从 GSE74089 中 34183 个基因中筛选出 690 个下调基因和 1148 个上调基因。涉及骨坏死进展的 DEGs 涉及炎症、免疫排斥和细菌感染相关途径。GO 富集表明骨坏死与细胞外基质、外部封装结构组织、骨骼系统发育、免疫反应活性、细胞凋亡、单核细胞分化和丝氨酸/苏氨酸激酶活性有关。此外，PPI 网络显示，股骨头坏死的进展与 CCND1、CDH1、ESR1、SPP1、LOX、JUN、ITGA、ABL1 和 VEGF 有关，颌骨坏死与 ACTB、CXCR4、PTPRC、IL1B、CXCL8、TNF、JUN、PTGS2、FOS 和 RHOA 有关。总之，本研究确定了可能在骨坏死进展中发挥重要作用的枢纽因素和途径，可作为骨坏死诊断和治疗的潜在生物标志物。
