Phosphorylation-related genes in lupus nephritis:Single-cell and machine learning insights  

作　　者：Lisha Mou Zhihao Chen Xinran Tian Yupeng Lai Zuhui Pu Meiying Wang 

机构地区：[1]Department of Rheumatology and Immunology,Institute of Translational Medicine,Health Science Center,The First Affiliated Hospital of Shenzhen University,Shenzhen Second People’s Hospital,Shenzhen,Guangdong 518035,China [2]MetaLife Center,Shenzhen Institute of Translational Medicine,Shenzhen,Guangdong 518035,China [3]Imaging Department,The First Affiliated Hospital of Shenzhen University,Shenzhen Second People’s Hospital,Shenzhen,Guangdong 518035,China

出　　处：《Genes & Diseases》2025年第3期31-34,共4页基因与疾病(英文)

基　　金：supported by the Science and Technology Program for Basic Research in Shenzhen,Guangdong,China(No.JCYJ20200109140412476,JCYJ20190809095811254,GCZX2015043017281705);the Clinical Research Project in Shenzhen,Guangdong,China(No.20213357002,20213357028);the Team-based Medical Science Research Program in Shenzhen,Guangdong,China(No.2024YZZ06);Shenzhen High-level Hospital Construction Fund in Shenzhen,Guangdong,China(No.2024).

摘　　要：This study investigates key genes contributing to lupus nephritis(LN).While extensive research has elucidated various aspects of LN pathogenesis,the specific involvement of phosphorylation-related genes(PRGs)in this context remains an area of growing interest.We employ single-cell RNA sequencing analysis on renal tissues from 24 LN patients and 10 healthy controls.Leveraging the nonnegative matrix factorization(NMF)algorithm,we identified critical gene patterns and constructed 61 predictive models using a comprehensive suite of 12 machine learning algorithms.We developed a predictive model using 6 PRGs,enhanced by a LASSO plus Naive Bayes approach.

关 键 词：nonnegative matrix factorization nmf algorithmwe machine learning renal tissues lupus nephritis critical gene patterns single cell RNA sequencing lupus nephritis ln phosphorylation related genes 

分 类 号：R69[医药卫生—泌尿科学]