ArticlePloS one2024
Deciphering the impact of senescence in kidney transplant rejection: An integrative machine learning and multi-omics analysis via bulk and single-cell RNA sequencing.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Predicting kidney graft survival with a machine learning model based on for-cause biopsy transcriptomics.Scientific reports · 2026Article
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6 authors.
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Abstract
backgroundThe demographic shift towards an older population presents significant challenges for kidney transplantation (KTx), particularly due to the vulnerability of aged donor kidneys to ischemic damage, delayed graft function, and reduced graft survival. KTx rejection poses a significant threat to allograft function and longevity of the kidney graft. The relationship between senescence and rejection remains elusive and controversial.
methodsGene Expression Omnibus (GEO) provided microarray and single-cell RNA sequencing datasets. After integrating Senescence-Related Genes (SRGs) from multiple established databases, differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms were applied to identify predictive SRGs (pSRGs). A cluster analysis of rejection samples was conducted using the consensus clustering algorithm. Subsequently, we utilized multiple machine learning methods (RF, SVM, XGB, GLM and LASSO) based on pSRGs to develop the optimal Acute Rejection (AR) diagnostic model and long-term graft survival predictive signatures. Finally, we validated the role of pSRGs and senescence in kidney rejection through the single-cell landscape.
resultsThirteen pSRGs were identified, correlating with rejection. Two rejection clusters were divided (Cluster C1 and C2). GSVA analysis of two clusters underscored a positive correlation between senescence, KTx rejection occurrence and worse graft survival. A non-invasive diagnostic model (AUC = 0.975) and a prognostic model (1- Year AUC = 0.881; 2- Year AUC = 0.880; 3- Year AUC = 0.883) for graft survival were developed, demonstrating significant predictive capabilities to early detect acute rejection and long-term graft outcomes. Single-cell sequencing analysis provided a detailed cellular-level landscape of rejection, supporting the conclusions drawn from above.
conclusionOur comprehensive analysis underscores the pivotal role of senescence in KTx rejection, highlighting the potential of SRGs as biomarkers for diagnosing rejection and predicting graft survival, which may enhance personalized treatment strategies and improve transplant outcomes.
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