Integrated analysis of potential gene crosstalk between non-alcoholic fatty liver disease and diabetic nephropathy

被引:3
作者
Yan, Qianqian [1 ,2 ]
Zhao, Zihao [1 ,2 ]
Liu, Dongwei [1 ,2 ,3 ,4 ]
Li, Jia [1 ,2 ,3 ,4 ]
Pan, Shaokang [1 ,2 ,3 ,4 ]
Duan, Jiayu [1 ,2 ,3 ,4 ]
Dong, Jiancheng [2 ]
Liu, Zhangsuo [1 ,2 ,3 ,4 ]
机构
[1] Zhengzhou Univ, Dept Integrated Tradit & Western Nephrol, Affiliated Hosp 1, Zhengzhou, Peoples R China
[2] Zhengzhou Univ, Res Inst Nephrol, Zhengzhou, Peoples R China
[3] Henan Prov Res Ctr Kidney Dis, Zhengzhou, Peoples R China
[4] Key Lab Precis Diag & Treatment Chron Kidney Dis H, Zhengzhou, Peoples R China
来源
FRONTIERS IN ENDOCRINOLOGY | 2022年 / 13卷
基金
美国国家科学基金会; 中国国家自然科学基金;
关键词
non-alcoholic fatty liver disease; diabetic nephropathy; crosstalk; LPL; SPP1; bioinformatics; CHRONIC KIDNEY-DISEASE; RENAL LIPID-METABOLISM; MESENCHYMAL TRANSITION; INSULIN-RESISTANCE; ACCUMULATION; OSTEOPONTIN; RISK; DYSLIPIDEMIA; ACTIVATION; PROTEINS;
D O I
10.3389/fendo.2022.1032814
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
BackgroundGrowing evidence indicates that non-alcoholic fatty liver disease (NAFLD) is related to the occurrence and development of diabetic nephropathy (DN). This bioinformatics study aimed to explore optimal crosstalk genes and related pathways between NAFLD and DN. MethodsGene expression profiles were downloaded from Gene Expression Omnibus. CIBERSORT algorithm was employed to analyze the similarity of infiltrating immunocytes between the two diseases. Protein-protein interaction (PPI) co-expression network and functional enrichment analysis were conducted based on the identification of common differentially expressed genes (DEGs). Least absolute shrinkage and selection operator (LASSO) regression and Boruta algorithm were implemented to initially screen crosstalk genes. Machine learning models, including support vector machine, random forest model, and generalized linear model, were utilized to further identify the optimal crosstalk genes between DN and NAFLD. An integrated network containing crosstalk genes, transcription factors, and associated pathways was developed. ResultsFour gene expression datasets, including GSE66676 and GSE48452 for NAFLD and GSE30122 and GSE1009 for DN, were involved in this study. There were 80 common DEGs between the two diseases in total. The PPI network built with the 80 common genes included 77 nodes and 83 edges. Ten optimal crosstalk genes were selected by LASSO regression and Boruta algorithm, including CD36, WIPI1, CBX7, FCN1, SLC35D2, CP, ZDHHC3, PTPN3, LPL, and SPP1. Among these genes, LPL and SPP1 were the most significant according to NAFLD-transcription factor network. Five hundred twenty-nine nodes and 1,113 edges comprised the PPI network of activated pathway-gene. In addition, 14 common pathways of these two diseases were recognized using Gene Ontology (GO) analysis; among them, regulation of the lipid metabolic process is closely related to both two diseases. ConclusionsThis study offers hints that NAFLD and DN have a common pathogenesis, and LPL and SPP1 are the most relevant crosstalk genes. Based on the common pathways and optimal crosstalk genes, our proposal carried out further research to disclose the etiology and pathology between the two diseases.
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页数:14
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