Yang Yongju, Zhang Yuqian, Min Dongyu, Yu Heshan, Guan Xuefeng
The Ministry of National Education Key Lab for Traditional Chinese Medicine Visceral Manifestations Theory and Application, Liaoning University of Traditional Chinese Medicine, Shenyang, China.
Department of Orthopaedic Rehabilitation, Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, Shenyang, China.
Ann Transl Med. 2022 Sep;10(18):978. doi: 10.21037/atm-22-3863.
At present, the progression mechanism of knee osteoarthritis (KOA) has not been fully elucidated, and there is a clinical need for late KOA-specific diagnostic markers to provide reference for preventive treatment. This study aimed to analyze the sequencing results of early- and late-stage KOA synovial tissue based on the key genes of late-stage KOA in combination with a machine learning algorithm.
The whole transcriptome sequencing results of synovial tissue from KOA patients (GSE176223 and GSE32317) were downloaded from the gene expression omnibus (GEO) database. Thirty-nine early KOA synovial tissue samples and 31 late KOA synovial tissue samples were included in this study. The diagnostic criteria and baseline data balance of early and late KOA were referred to the data source literature, and the two groups of data had good baseline data balance. R software (V3.5.1) and R packages were used for screening and enrichment analysis of differentially expressed genes (DEGs). The key genes were screened by weighted correlation network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) regression analysis. A receiver operating characteristic curve (ROC) curve was used to evaluate the diagnostic efficacy of key genes for advanced KOA.
A total of 211 DEGs related to knee arthritis were screened out. Compared with synovial tissue of early knee arthritis, 111 genes were upregulated and 100 genes were downregulated in the synovial tissue of late knee arthritis. Sixty-six key genes were screened out through WGCNA and 34 key genes were screened out in the LASSO analysis. The genes obtained by the two algorithms combined with three overlapping genes, namely interleukin- 6 (IL-6), C-X-C chemokine ligand 12 (CXCL12), and macrophage migration inhibitor factor (MIF). The areas under the ROC curves of , , and were 0.96, 0.944, and 0.961, respectively (P<0.001).
, , and are the key pathogenic genes of KOA, which have good diagnostic efficacy for advanced KOA.
目前，膝骨关节炎（KOA）的进展机制尚未完全阐明，临床上需要KOA晚期特异性诊断标志物，为预防性治疗提供参考。本研究旨在结合机器学习算法，基于KOA晚期关键基因分析早、晚期KOA滑膜组织的测序结果。
从基因表达综合数据库（GEO）下载KOA患者滑膜组织的全转录组测序结果（GSE176223和GSE32317）。本研究纳入39例早期KOA滑膜组织样本和31例晚期KOA滑膜组织样本。早、晚期KOA的诊断标准和基线数据平衡参考数据源文献，两组数据具有良好的基线数据平衡。使用R软件（V3.5.1）和R包对差异表达基因（DEG）进行筛选和富集分析。通过加权基因共表达网络分析（WGCNA）和最小绝对收缩和选择算子（LASSO）回归分析筛选关键基因。采用受试者工作特征曲线（ROC）评估关键基因对晚期KOA的诊断效能。
共筛选出211个与膝关节炎相关的DEG。与早期膝关节炎滑膜组织相比，晚期膝关节炎滑膜组织中111个基因上调，100个基因下调。通过WGCNA筛选出66个关键基因，在LASSO分析中筛选出34个关键基因。两种算法得到的基因与三个重叠基因，即白细胞介素-6（IL-6）、C-X-C趋化因子配体12（CXCL12）和巨噬细胞迁移抑制因子（MIF）相结合。IL-6、CXCL12和MIF的ROC曲线下面积分别为0.96、0.944和0.961（P<0.001）。
IL-6、CXCL12和MIF是KOA的关键致病基因，对晚期KOA具有良好的诊断效能。
