N6-methyladenosine regulators-related immune genes enable predict graft loss and discriminate T-cell mediate rejection in kidney transplantation biopsies for cause

作者全名："Pang, Qidan; Chen, Hong; Wu, Hang; Wang, Yong; An, Changyong; Lai, Suhe; Xu, Jia; Wang, Ruiqiong; Zhou, Juan; Xiao, Hanyu"

作者地址："[Pang, Qidan; Wu, Hang; Xu, Jia; Wang, Ruiqiong; Zhou, Juan] Chongqing Med Univ, Dept Nephrol, Bishan Hosp, Chongqing, Peoples R China; [Chen, Hong; Wang, Yong; An, Changyong; Lai, Suhe; Xiao, Hanyu] Chongqing Med Univ, Dept Gen Surg Gastrointestinal Surg, Bishan Hosp, Chongqing, Peoples R China"

通信作者："Zhou, J (通讯作者)，Chongqing Med Univ, Dept Nephrol, Bishan Hosp, Chongqing, Peoples R China.; Xiao, HY (通讯作者)，Chongqing Med Univ, Dept Gen Surg Gastrointestinal Surg, Bishan Hosp, Chongqing, Peoples R China."

来源：FRONTIERS IN IMMUNOLOGY

ESI学科分类：IMMUNOLOGY

WOS号：WOS:000893879200001

JCR分区：Q1

影响因子：7.3

年份：2022

卷号：13

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：N6-methyladenosine (m6A); kidney transplantation; alloimmunity; graft loss; T-cell mediate rejection; biopsies for cause; prediction model

摘要："ObjectiveThe role of m6A modification in kidney transplant-associated immunity, especially in alloimmunity, still remains unknown. This study aims to explore the potential value of m6A-related immune genes in predicting graft loss and diagnosing T cell mediated rejection (TCMR), as well as the possible role they play in renal graft dysfunction. MethodsRenal 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. ResultsThe 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. ConclusionsTogether, our findings explore the value of m6A-related immune genes in predicting the prognosis of renal grafts and diagnosis of TCMR."

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