PDF(Pubmed)
Abstract：
Rejection is one of the major factors affecting the long-term prognosis of kidney transplantation, and timely recognition and aggressive treatment of rejection is essential to prevent disease progression. RBPs are proteins that bind to RNA to form ribonucleoprotein complexes, thereby affecting RNA stability, processing, splicing, localization, transport, and translation, which play a key role in post-transcriptional gene regulation. However, their role in renal transplant rejection and long-term graft survival is unclear. The aim of this study was to comprehensively analyze the expression of RPBs in renal rejection and use it to construct a robust prediction strategy for long-term graft survival. The microarray expression profiles used in this study were obtained from GEO database. In this study, a total of eight hub RBPs were identified, all of which were upregulated in renal rejection samples. Based on these RBPs, the renal rejection samples could be categorized into two different clusters (cluster A and cluster B). Inflammatory activation in cluster B and functional enrichment analysis showed a strong association with rejection-related pathways. The diagnostic prediction model had a high diagnostic accuracy for T cell mediated rejection (TCMR) in renal grafts (area under the curve = 0.86). The prognostic prediction model effectively predicts the prognosis and survival of renal grafts (p < .001) and applies to both rejection and non-rejection situations. Finally, we validated the expression of hub genes, and patient prognosis in clinical samples, respectively, and the results were consistent with the above analysis.
摘要：
排斥反应是影响肾移植远期预后的主要因素之一,及时识别和积极治疗排斥反应对于预防疾病进展至关重要。RBPs是与RNA结合形成核糖核蛋白复合物的蛋白质，从而影响RNA的稳定性，processing,拼接,本地化,运输,翻译,在转录后基因调控中起关键作用。然而,它们在肾移植排斥反应和移植物长期存活中的作用尚不清楚.这项研究的目的是全面分析肾排斥反应中RPBs的表达，并将其用于构建长期移植物存活的可靠预测策略。本研究中使用的微阵列表达谱从GEO数据库获得。在这项研究中,总共确定了8个中心RBP，所有这些在肾排斥反应样本中均上调。基于这些RBP，肾排斥反应样本可以分为两个不同的簇(簇A和簇B).B簇中的炎症激活和功能富集分析显示与排斥相关途径密切相关。诊断预测模型对肾移植物中T细胞介导的排斥反应（TCMR）具有较高的诊断准确性（曲线下面积=0.86）。预后预测模型可有效预测肾移植物的预后和生存率（p<.001），适用于排斥和非排斥情况。最后,我们验证了hub基因的表达，和临床样本中的患者预后，分别,结果与上述分析结果一致。
