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Abstract：
UNASSIGNED: To screen for long non-coding RNA (lncRNA) molecular markers characteristic of osteoarthritis (OA) by utilizing the Gene Expression Omnibus (GEO) database combined with machine learning.
UNASSIGNED: The samples of 185 OA patients and 76 healthy individuals as normal controls were included in the study. GEO datasets were screened for differentially expressed lncRNAs. Three algorithms, the least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF), were used to screen for candidate lncRNA models and receiver operating characteristic (ROC) curves were plotted to evaluate the models. We collected the peripheral blood samples of 30 clinical OA patients and 15 health controls and measured the immunoinflammatory indicators. RT-PCR was performed for quantitative analysis of the expression of lncRNA molecular markers in peripheral blood mononuclear cells (PBMC). Pearson analysis was performed to examine the correlation between lncRNA and indicators for inflammation of the immune system.
UNASSIGNED: A total of 14 key markers were identified with LASSO, 6 genes were identified with SVM-RFE, and 24 genes were identified with RF. Venn diagram was used to screen for overlapping genes identified with the three algorithms, showing HOTAIR, H19, MIR155 HG, and NKILA to be the overlapping genes. The ROC curves showed that these four lncRNAs all had an area under the curve ( AUC) greater than 0.7. The RT-PCR findings revealed relatively elevated expression of HOTAIR, H19, and MIR155HG and decreased expression of NKILA in the PBMC of OA patients compared with those of the normal group ( P<0.01). The results were consistent with the bioinformatics predictions. Pearson analysis showed that the candidate lncRNAs were correlated with clinical indicators for inflammation.
UNASSIGNED: HOTAIR, H19, MIR155 HG, and NKILA can be used as molecular markers for the clinical diagnosis of OA and are correlate with clinical indicators of inflammation of the immune system.
UNASSIGNED: The samples of 185 OA patients and 76 healthy individuals as normal controls were included in the study. GEO datasets were screened for differentially expressed lncRNAs. Three algorithms, the least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF), were used to screen for candidate lncRNA models and receiver operating characteristic (ROC) curves were plotted to evaluate the models. We collected the peripheral blood samples of 30 clinical OA patients and 15 health controls and measured the immunoinflammatory indicators. RT-PCR was performed for quantitative analysis of the expression of lncRNA molecular markers in peripheral blood mononuclear cells (PBMC). Pearson analysis was performed to examine the correlation between lncRNA and indicators for inflammation of the immune system.
UNASSIGNED: A total of 14 key markers were identified with LASSO, 6 genes were identified with SVM-RFE, and 24 genes were identified with RF. Venn diagram was used to screen for overlapping genes identified with the three algorithms, showing HOTAIR, H19, MIR155 HG, and NKILA to be the overlapping genes. The ROC curves showed that these four lncRNAs all had an area under the curve ( AUC) greater than 0.7. The RT-PCR findings revealed relatively elevated expression of HOTAIR, H19, and MIR155HG and decreased expression of NKILA in the PBMC of OA patients compared with those of the normal group ( P<0.01). The results were consistent with the bioinformatics predictions. Pearson analysis showed that the candidate lncRNAs were correlated with clinical indicators for inflammation.
UNASSIGNED: HOTAIR, H19, MIR155 HG, and NKILA can be used as molecular markers for the clinical diagnosis of OA and are correlate with clinical indicators of inflammation of the immune system.
摘要：
■通过利用基因表达综合（GEO）数据库结合机器学习，筛选骨关节炎（OA）特征性的长链非编码RNA（lncRNA）分子标记。
■185名OA患者和76名健康个体作为正常对照的样本被包括在研究中。针对差异表达的lncRNA筛选GEO数据集。三种算法，最小绝对收缩和选择运算符(LASSO)，支持向量机递归特征消除(SVM-RFE)，和随机森林(RF)，用于筛选候选lncRNA模型，并绘制受试者工作特征（ROC）曲线以评估模型。我们收集了30例临床OA患者和15例健康对照者的外周血样本，并测量了免疫炎症指标。进行RT-PCR以定量分析外周血单核细胞（PBMC）中lncRNA分子标志物的表达。进行Pearson分析以检查lncRNA与免疫系统炎症指标之间的相关性。
■用LASSO共鉴定了14个关键标记，用SVM-RFE鉴定了6个基因，用RF鉴定了24个基因。维恩图用于筛选用三种算法鉴定的重叠基因，显示HOTAIR,H19,MIR155HG,和NKILA是重叠的基因。ROC曲线显示这四种lncRNA均具有大于0.7的曲线下面积(AUC)。RT-PCR结果显示HOTAIR的表达相对升高，与正常组比较,OA患者PBMC中H19、MIR155HG和NKILA的表达降低(P<0.01)。结果与生物信息学预测一致。Pearson分析显示候选lncRNAs与炎症的临床指标相关。
■HOTAIR,H19,MIR155HG,和NKILA可用作OA临床诊断的分子标志物,并且与免疫系统炎症的临床指标相关。
■185名OA患者和76名健康个体作为正常对照的样本被包括在研究中。针对差异表达的lncRNA筛选GEO数据集。三种算法，最小绝对收缩和选择运算符(LASSO)，支持向量机递归特征消除(SVM-RFE)，和随机森林(RF)，用于筛选候选lncRNA模型，并绘制受试者工作特征（ROC）曲线以评估模型。我们收集了30例临床OA患者和15例健康对照者的外周血样本，并测量了免疫炎症指标。进行RT-PCR以定量分析外周血单核细胞（PBMC）中lncRNA分子标志物的表达。进行Pearson分析以检查lncRNA与免疫系统炎症指标之间的相关性。
■用LASSO共鉴定了14个关键标记，用SVM-RFE鉴定了6个基因，用RF鉴定了24个基因。维恩图用于筛选用三种算法鉴定的重叠基因，显示HOTAIR,H19,MIR155HG,和NKILA是重叠的基因。ROC曲线显示这四种lncRNA均具有大于0.7的曲线下面积(AUC)。RT-PCR结果显示HOTAIR的表达相对升高，与正常组比较,OA患者PBMC中H19、MIR155HG和NKILA的表达降低(P<0.01)。结果与生物信息学预测一致。Pearson分析显示候选lncRNAs与炎症的临床指标相关。
■HOTAIR,H19,MIR155HG,和NKILA可用作OA临床诊断的分子标志物,并且与免疫系统炎症的临床指标相关。
