Department of Pain Management, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
Department of Anesthesiology, Qilu Hospital of Shandong University, Jinan, China.
Oxid Med Cell Longev. 2022 Oct 4;2022:9033342. doi: 10.1155/2022/9033342. eCollection 2022.
Fibromyalgia (FM) is a chronic nonarticular rheumatic disease mainly characterized by diffuse disseminated skeletal muscle pain, with varied symptoms including anxiety, sleep disturbance, and fatigue. Due to its unknown etiology and pathogenesis, FM is easily ignored in clinical practice, resulting in unclear diagnosis and difficult treatment. This study is aimed at investigating whether AKAP12 and RNF11 can be used as biomarkers for the diagnosis of FM and at determining their correlation with immune infiltration. The FM dataset in Gene Expression Omnibus (GEO) database was downloaded and was randomly divided into the training and test sets. Differentially expressed genes (DEGs) were screened, and functional correlation analysis was performed. Diagnostic markers of FM were screened and validated by random forest (RF). The least absolute shrinkage and selection operator (LASSO) logistic regression algorithm was then used to evaluate immune cell infiltration in the FM patients' peripheral blood. Finally, Spearman's rank correlation analysis was used to identify correlation between the diagnostic indexes and immune cell infiltration. A total of 69 DEGs were selected. Results indicated that AKAP12 and RNF11 can be used as diagnostic markers of FM, and CD8 + T cells might contribute in the pathogenesis of FM. In addition, AKAP12 was positively correlated with CD8 + T cells, while RNF11 was negatively correlated with CD8 + T cells. In conclusion, AKAP12 and RNF11 can be used as diagnostic indicators of FM, and CD8 + T cells may be involved in the occurrence and development of FM.
纤维肌痛症（FM）是一种慢性非关节性风湿疾病，主要表现为弥漫性骨骼肌疼痛，伴有焦虑、睡眠障碍和疲劳等多种症状。由于其病因和发病机制尚不清楚，FM 在临床实践中容易被忽视，导致诊断不明确，治疗困难。本研究旨在探讨 AKAP12 和 RNF11 是否可作为 FM 的诊断标志物，并确定其与免疫浸润的相关性。从基因表达综合数据库（GEO）下载 FM 数据集，随机分为训练集和测试集。筛选差异表达基因（DEGs），并进行功能相关分析。通过随机森林（RF）筛选和验证 FM 的诊断标志物。采用最小绝对收缩和选择算子（LASSO）逻辑回归算法评估 FM 患者外周血中的免疫细胞浸润情况。最后，采用 Spearman 秩相关分析确定诊断指标与免疫细胞浸润的相关性。共筛选出 69 个 DEGs。结果表明，AKAP12 和 RNF11 可作为 FM 的诊断标志物，CD8+T 细胞可能参与 FM 的发病机制。此外，AKAP12 与 CD8+T 细胞呈正相关，而 RNF11 与 CD8+T 细胞呈负相关。综上所述，AKAP12 和 RNF11 可作为 FM 的诊断指标，CD8+T 细胞可能参与 FM 的发生发展。
