Shanghai Key Laboratory of Organ Transplantation, Shanghai 200032, China.
Department of Urology, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
Biomolecules. 2023 Feb 1;13(2):275. doi: 10.3390/biom13020275.
Ischemia-reperfusion injury (IRI) often occurs in the process of kidney transplantation, which significantly impacts the subsequent treatment and prognosis of patients. The prognosis of patients with different subtypes of IRI is quite different. Therefore, in this paper, the gene expression data of multiple IRI samples were downloaded from the GEO database, and a double Laplacian orthogonal non-negative matrix factorization (DL-ONMF) algorithm was proposed to classify them. In this algorithm, various regularization constraints are added based on the non-negative matrix factorization algorithm, and the prior information is fused into the algorithm from different perspectives. The connectivity information between different samples and features is added to the algorithm by Laplacian regularization constraints on samples and features. In addition, orthogonality constraints on the basis matrix and coefficient matrix obtained by the algorithm decomposition are added to reduce the influence of redundant samples and redundant features on the results. Based on the DL-ONMF algorithm for clustering, two PRGs-related IRI isoforms were obtained in this paper. The results of immunoassays showed that the immune microenvironment was different among PRGS-related IRI types. Based on the differentially expressed PRGs between subtypes, we used LASSO and SVM-RFE algorithms to construct a diagnostic model related to renal transplantation. ROC analysis showed that the diagnostic model could predict the outcome of renal transplant patients with high accuracy. In conclusion, this paper presents an algorithm, DL-ONMF, which can identify subtypes with different disease characteristics. Comprehensive bioinformatic analysis showed that pyroptosis might affect the outcome of kidney transplantation by participating in the immune response of IRI.
缺血再灌注损伤（IRI）在肾移植过程中经常发生，这对患者的后续治疗和预后有很大影响。不同亚型的 IRI 患者的预后差异很大。因此，本文从 GEO 数据库中下载了多个 IRI 样本的基因表达数据，并提出了一种双拉普拉斯正交非负矩阵分解（DL-ONMF）算法对其进行分类。在该算法中，在非负矩阵分解算法的基础上添加了各种正则化约束，并从不同角度将先验信息融合到算法中。通过对样本和特征的拉普拉斯正则化约束，向算法中添加了不同样本和特征之间的连通性信息。此外，还添加了算法分解得到的基矩阵和系数矩阵之间的正交性约束，以减少冗余样本和冗余特征对结果的影响。基于该算法进行聚类，本文获得了两种与 PRGS 相关的 IRI 亚型。免疫检测结果表明，PRGS 相关 IRI 类型的免疫微环境不同。基于亚型之间差异表达的 PRGS，我们使用 LASSO 和 SVM-RFE 算法构建了一个与肾移植相关的诊断模型。ROC 分析表明，该诊断模型可以高精度预测肾移植患者的预后。总之，本文提出了一种算法 DL-ONMF，它可以识别具有不同疾病特征的亚型。综合生物信息学分析表明，细胞焦亡可能通过参与 IRI 的免疫反应影响肾移植的结局。
