ArticleFrontiers in immunology2022
N6-methyladenosine regulators-related immune genes enable predict graft loss and discriminate T-cell mediate rejection in kidney transplantation biopsies for cause.
Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- Functional Role of ALKBH5 in Kidney Injury: Insights Into Mechanisms and Therapeutic Potential.Journal of cellular biochemistry · 2026Review
- Epitranscriptomics in kidney disease: insights and therapeutic potential.Kidney research and clinical practice · 2026Article
- Identification of M2 macrophage-related biomarkers for a predictive model of interstitial fibrosis and tubular atrophy after kidney transplantation by machine learning algorithms.Translational andrology and urology · 2025Article
- Metabolomic Insight into Donation After Circulatory-Death Kidney Grafts in Porcine Autotransplant Model: Normothermic Ex Vivo Kidney Perfusion Compared with Hypothermic Machine Perfusion and Static Cold Storage.International journal of molecular sciences · 2025Article
- Identification of RNA-binding protein genes associated with renal rejection and graft survival.Renal failure · 2024Article
- Key genes and immune pathways in T-cell mediated rejection post-liver transplantation identified via integrated RNA-seq and machine learning.Scientific reports · 2024Article
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10 authors at 1 institution in 1 country.
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Abstract
Objective: The 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. Methods: 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. Results: The prognostic prediction model effectively predicts the prognosis and survival of renal grafts with clinical indications ( Conclusions: Together, 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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