期刊检索

  • 2024年第22卷
  • 2023年第21卷
  • 2022年第20卷
  • 2021年第19卷
  • 2020年第18卷
  • 2019年第17卷
  • 2018年第16卷
  • 2017年第15卷
  • 2016年第14卷
  • 2015年第13卷
  • 2014年第12卷
  • 2013年第11卷
  • 第1期
  • 第2期

主管单位 工业和信息化部 主办单位 哈尔滨工业大学 主编 任南琪 国际刊号ISSN 1672-5565 国内刊号CN 23-1513/Q

期刊网站二维码
微信公众号二维码
引用本文:陆雨菲,张健,王栋,丁小强,宋娜娜.肾移植术后肾功能延迟恢复的发病分子机制与诊断标志物研究[J].生物信息学,2023,21(2):96-105.
LU Yufei,ZHANG Jian,WANG Dong,DING Xiaoqiang,SONG Nana.Molecular pathogenesis and diagnostic markers of delayed graft function after kidney transplantation[J].Chinese Journal of Bioinformatics,2023,21(2):96-105.
【打印本页】   【HTML】   【下载PDF全文】   查看/发表评论  下载PDF阅读器  关闭
←前一篇|后一篇→ 过刊浏览    高级检索
本文已被:浏览 540次   下载 555 本文二维码信息
码上扫一扫!
分享到: 微信 更多
肾移植术后肾功能延迟恢复的发病分子机制与诊断标志物研究
陆雨菲,张健,王栋,丁小强,宋娜娜
(复旦大学附属中山医院 肾脏内科,上海 200032)
摘要:
移植肾功能延迟恢复(DGF)为肾移植患者常见并发症之一,越来越多的研究开始关注肾移植患者术后DGF发生的新病理生理学机制以及潜在的诊断标志物。本研究对GEO数据库中肾脏移植手术患者的基因表达谱数据进行分析,通过差异表达基因分析发现了多个表达异常的转录因子和免疫相关基因,通过基因编码蛋白之间的相互作用网络分析进一步挖掘了疾病进展过程中的核心调控基因。通过结合加权基因共表达网络分析(WGCNA)和机器学习构建了肾移植术后DGF的预测模型。模型XGBoost的准确率能够达到82.4%,其受试者工作特征曲线下面积(AUC)为0.86,马修斯相关系数(MCC)为0.652,灵敏度(Sensitivity)及特异度(Specificity)则分别为0.789和0.867。对这些获得最优预测效能的特征基因进行检索发现,这些高区分度基因与肾功能密切相关。最后通过比对CMap数据库发现了多个潜在可用于疾病治疗的小分子化合物。本研究对肾移植术后DGF的病理生理学机制进行了多角度探索,为相关疾病的诊断和治疗提供了可靠的理论和实验依据。
关键词:  肾移植  急性肾损伤  生物信息学  关键基因  CMap
DOI:10.12113/202202005
分类号:R699.2
文献标识码:A
基金项目:
Molecular pathogenesis and diagnostic markers of delayed graft function after kidney transplantation
LU Yufei, ZHANG Jian, WANG Dong, DING Xiaoqiang, SONG Nana
(Department of Nephrology, Zhongshan Hospital of Fudan University, Shanghai 200032, China)
Abstract:
Delayed graft function (DGF) is one of the common complications of kidney transplanta patients. More and more studies have begun to focus on new pathophysiological mechanisms and potential diagnostic markers of DGF after kidney transplantation. In this study, the gene expression profile dataset of kidney transplant patients in the GEO database was analyzed. Through differentially expressed genes (DEGs) screening, the dysregulated expression of multiple transcription factors and immune genes was found, and the core regulatory genes in the process of disease progression were further explored through the interaction network analysis between gene-encoded proteins. The prediction model of DGF kidney renal transplantation was constructed by combining weighted gene co-expression network analysis (WGCNA) and machine learning. The accuracy of model XGBoost reached 82.4%, its area under the receiver operating characteristic curve (AUC) was 0.86, Matthews correlation coefficient (MCC) was 0.652, and sensitivity and specificity were 0.789 and 0.867 respectively. Retrieval of these characteristic genes with optimal predictive power found that these genes were closely related to renal function. Finally, several small molecular compounds that could be used to treat DGF were found by comparing the CMap database. This study explored the pathophysiological mechanism of DGF from multiple perspectives, providing a reliable theoretical and experimental basis for the diagnosis and treatment of related diseases.
Key words:  Kidney transplantation  Acute kidney injury  Bioinformatics  Key genes  CMap

友情链接LINKS

关闭