Renal transplant-related cohorts and transcript expression data were obtained from the GEO database. First, we conducted correlation analysis in the discovery cohort to identify the m6A-related immune genes. Then, lasso regression and random forest were used respectively to build prediction models in the prognosis and diagnosis cohort, to predict graft loss and discriminate TCMR in dysfunctional renal grafts. Connectivity map (CMap) analysis was applied to identify potential therapeutic compounds for TCMR.
The prognostic prediction model effectively predicts the prognosis and survival of renal grafts with clinical indications (P< 0.001) and applies to both rejection and non-rejection situations. The diagnostic prediction model discriminates TCMR in dysfunctional renal grafts with high accuracy (area under curve = 0.891). Meanwhile, the classifier score of the diagnostic model, as a continuity index, is positively correlated with the severity of main pathological injuries of TCMR. Furthermore, it is found that METTL3, FTO, WATP, and RBM15 are likely to play a pivotal part in the regulation of immune response in TCMR. By CMap analysis, several small molecular compounds are found to be able to reverse TCMR including fenoldopam, dextromethorphan, and so on.
Together, our findings explore the value of m6A-related immune genes in predicting the prognosis of renal grafts and diagnosis of TCMR.
UNASSIGNED:肾移植相关的队列和转录物表达数据来自GEO数据库。首先,我们在发现队列中进行了相关性分析,以鉴定m6A相关免疫基因.然后,分别采用Lasso回归和随机森林建立预后和诊断队列的预测模型,预测功能失调的移植肾的移植物丢失和鉴别TCMR。应用连接性图(CMap)分析来鉴定用于TCMR的潜在治疗化合物。
UNASSIGNED:预后预测模型可有效预测具有临床指征的肾移植物的预后和生存率(P<0.001),适用于排斥和非排斥情况。诊断预测模型可以高精度(曲线下面积=0.891)区分功能失调的肾移植物中的TCMR。同时,诊断模型的分类器得分,作为连续性指数,与主要病理损伤的严重程度呈正相关。此外,发现METTL3,FTO,WATP,和RBM15可能在TCMR的免疫应答调节中起关键作用。通过CMap分析,发现几种小分子化合物能够逆转TCMR,包括非诺多泮,右美沙芬,等等。
未经批准:一起,我们的发现探讨了m6A相关免疫基因在预测肾移植物预后和TCMR诊断中的价值。