Bioinformatic analysis of genes associated with M2 macrophages in membranous nephropathy

doi:10.3877/cma.j.issn.2095-3216.2023.03.007

Abstract

Abstract:

Objective

This study aimed to explore the immune molecular mechanisms related to M2 macrophages in membranous nephropathy (MN) through bioinformatics analysis.

Methods

Microarray datasets (GSE104948 and GSE108109, with GSE108109 as the validation dataset) were downloaded from the Gene Expression Omnibus (GEO) database. And immune infiltration analysis was performed using the Timer database. M2 macrophages were used as a phenotype for weighted gene co-expression network analysis in order to screen M2 macrophage-related hub genes. Differentially expressed genes and the hub genes were then intersected to identify potential key genes. Meanwhile, the expression levels of the potential key genes were validated in dataset GSE108109, and their diagnostic value was assessed by the receiver operating characteristics (ROC) curve analysis.

Results

M2 macrophages were significantly different between the MN patients and normal controls. Six M2 macrophages-related key genes were identified including PPARGC1A, ESRRG, KCNJ16, HLF, HGD, and SULT1C2. The ROC curve analysis showed that values of the area under the ROC curve of the key 6 genes were greater than 0.85.

Conclusions

This study identified six key genes related to M2 macrophages in MN, and further research is needed to verify their roles in MN.

Key words: Membranous nephropathy, Immune cells infiltration, M2 macrophage, Bioinformatic analysis, Key genes

Qiong Wu, Guozhen Zhu. Bioinformatic analysis of genes associated with M2 macrophages in membranous nephropathy[J]. Chinese Journal of Kidney Disease Investigation(Electronic Edition), 2023, 12(03): 156-162.

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Figures/Tables 9

References 21

[1]	Stahl RA, Reinhard L, Hoxha E. Characterization of autoantibodies in primary membranous nephropathy and their clinical significance [J]. Expert Rev Clin Immunol, 2019, 15(2): 165-175.
[2]	Liu Q, Liu J, Lin B, et al. Novel biomarkers in membranous nephropathy [J]. Front Immunol, 2022, 13: 845767.
[3]	Hu W, Li G, Lin J, et al. M2 macrophage subpopulations in glomeruli are associated with the deposition of IgG subclasses and complements in primary membranous nephropathy [J]. Front Med (Lausanne), 2021, 8: 657232.
[4]	Clough E, Barrett T. The gene expression omnibus database [J]. Methods Mol Biol, 2016, 1418: 93-110.
[5]	Leek JT, Johnson WE, Parker HS, et al. The sva package for removing batch effects and other unwanted variation in high-throughput experiments [J]. Bioinformatics, 2012, 28(6): 882-883.
[6]	Ritchie ME, Phipson B, Wu D, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies [J]. Nucleic Acids Res, 2015, 43(7): e47.
[7]	Li T, Fu J, Zeng Z, et al. TIMER2.0 for analysis of tumor-infiltrating immune cells [J]. Nucleic Acids Res, 2020, 48(W1): W509-W514.
[8]	Pei G, Chen L, Zhang W. WGCNA application to proteomic and metabolomic data analysis [J]. Methods Enzymol, 2017, 585: 135-158.
[9]	Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets [J]. Nat Commun, 2019, 10(1): 1523.
[10]	Couser WG. Primary membranous nephropathy [J]. Clin J Am Soc Nephrol, 2017, 12(6): 983-997.
[11]	Di Benedetto P, Ruscitti P, Vadasz Z, et al. Macrophages with regulatory functions, a possible new therapeutic perspective in autoimmune diseases [J]. Autoimmun Rev, 2019, 18(10): 102369.
[12]	Alikhan MA, Ricardo SD. Mononuclear phagocyte system in kidney disease and repair [J]. Nephrology (Carlton), 2013, 18(2): 81-91.
[13]	Hobson-Gutierrez SA, Carmona-Fontaine C. The metabolic axis of macrophage and immune cell polarization [J]. Dis Model Mech, 2018，11(8): dmm034462.
[14]	Mills EL, O′Neill LA. Reprogramming mitochondrial metabolism in macrophages as an anti-inflammatory signal [J]. Eur J Immunol, 2016, 46(1): 13-21.
[15]	靳鑫，倪田根，王宁，等. PGC-1α通过线粒体介导巨噬细胞极化状态的机制研究[J]. 第三军医大学学报，2019, 41(1): 56-62.
[16]	Tran M, Parikh SM. Mitochondrial biogenesis in the acutely injured kidney [J]. Nephron Clin Pract, 2014, 127(1-4): 42-45.
[17]	Noordmans GA, Huang Y, Savage H, et al. Genetic analysis of intracapillary glomerular lipoprotein deposits in aging mice [J]. PLoS One, 2014, 9(10): e111308.
[18]	刘浩，杨慧，宋宁，等. PGC-1α和ERRγ在子宫内膜癌组织中的表达及临床意义[J]. 现代肿瘤医学，2017, 25(13): 2108-2112.
[19]	孟桂林，孟欣欣，谷瑞民，等. 高钾饮食调节血压的肾脏机制[J]. 生理学报，2022, 74(1): 110-116.
[20]	Gachon F, Olela FF, Schaad O, et al. The circadian PAR-domain basic leucine zipper transcription factors DBP, TEF, and HLF modulate basal and inducible xenobiotic detoxification [J]. Cell Metab, 2006, 4(1): 25-36.
[21]	杨利，黄慧，杨玉，等. 尿黑酸尿症一家系基因诊断及分析[J]. 中华实用儿科临床杂志，2015, 30(8): 608-610.

上调/下调	核心基因
上调	HNF1B、CHI3L2、ELF3、WWC1、HBD、ITGB6、MAPK13、S100A14、C3orf52、VTCN1
下调	PPARGC1A、CLCN5、ESRRG、GLS、TMEM176B、HADH、HGD、ACADL、IMPA2、KCNJ16、GRAMD1C、RAB17、SLC15A1、SLC22A2、SULT1C2、KMO、AKR1C3、CLDN10、MTCL1、GALNT3、TOX3、HLF、MET、RAB25、TFAP2B、TPD52L1、FHOD3、SLC38A1

上调/下调	核心基因
上调	HNF1B、CHI3L2、ELF3、WWC1、HBD、ITGB6、MAPK13、S100A14、C3orf52、VTCN1
下调	PPARGC1A、CLCN5、ESRRG、GLS、TMEM176B、HADH、HGD、ACADL、IMPA2、KCNJ16、GRAMD1C、RAB17、SLC15A1、SLC22A2、SULT1C2、KMO、AKR1C3、CLDN10、MTCL1、GALNT3、TOX3、HLF、MET、RAB25、TFAP2B、TPD52L1、FHOD3、SLC38A1

指标	功能或通路	P值	基因
GO功能富集分析
0044282	小分子分解代谢过程	<0.001	ACADL、GLS、HADH、HGD、IMPA2、KMO、AKR1C3
0016491	氧化还原酶活性	<0.001	ACADL、HADH、HBD、HGD、KMO、AKR1C3
0006520	细胞氨基酸代谢过程	<0.001	GLS、HGD、KMO、SLC38A1
0003014	肾脏系统过程	<0.001	CLCN5、TFAP2B、AKR1C3
0006631	脂肪酸代谢过程	<0.001	ACADL、HADH、AKR1C3、PPARGC1A
0034599	细胞对氧化应激的反应	0.003	MAPK13、AKR1C3、PPARGC1A
0062197	细胞对化学应激的反应	0.005	MAPK13、AKR1C3、PPARGC1A
KEGG通路富集分析
hsa05208	化学致癌-活性氧	0.003	MET、MAPK13、AKR1C3

指标	功能或通路	P值	基因
GO功能富集分析
0044282	小分子分解代谢过程	<0.001	ACADL、GLS、HADH、HGD、IMPA2、KMO、AKR1C3
0016491	氧化还原酶活性	<0.001	ACADL、HADH、HBD、HGD、KMO、AKR1C3
0006520	细胞氨基酸代谢过程	<0.001	GLS、HGD、KMO、SLC38A1
0003014	肾脏系统过程	<0.001	CLCN5、TFAP2B、AKR1C3
0006631	脂肪酸代谢过程	<0.001	ACADL、HADH、AKR1C3、PPARGC1A
0034599	细胞对氧化应激的反应	0.003	MAPK13、AKR1C3、PPARGC1A
0062197	细胞对化学应激的反应	0.005	MAPK13、AKR1C3、PPARGC1A
KEGG通路富集分析
hsa05208	化学致癌-活性氧	0.003	MET、MAPK13、AKR1C3

通路	基因数目	标准化的富集分数(NES)	错误发现率(FDR)	P值
HLF
色氨酸代谢	32	1.843	0.077	0.008
脂肪酸代谢	37	1.701	0.108	0.023
细胞色素P450对外源性物质的代谢	45	1.701	0.099	0.016
精氨酸和脯氨酸代谢	45	1.691	0.099	0.042
PPARGC1A
精氨酸和脯氨酸代谢	45	2.112	0.001	0.002
甘氨酸、丝氨酸和苏氨酸代谢	26	2.077	0.002	<0.001
氧化磷酸化	94	2.053	0.002	<0.001
脂肪酸代谢	37	1.950	0.005	0.001
ESRRG
精氨酸和脯氨酸代谢	45	1.989	0.012	0.005
三羧酸循环	27	1.836	0.023	0.005
补体和凝血级联反应	66	1.656	0.058	0.030
抗原处理和呈递	71	－1.667	0.167	0.047
FCγR介导的吞噬作用	84	－1.578	0.250	0.041
HGD
精氨酸和脯氨酸代谢	45	2.146	<0.001	<0.001
甘氨酸、丝氨酸和苏氨酸代谢	26	2.061	0.002	<0.001
脂肪酸代谢	37	1.936	0.006	0.005
PPAR信号通路	57	1.872	0.009	0.003
KCNJ16
精氨酸和脯氨酸代谢	45	2.062	0.002	0.001
过氧化物酶体	68	1.977	0.005	0.003
三羧酸循环	27	1.920	0.008	0.001
补体和凝血级联反应	66	1.920	0.007	0.005
抗原处理和呈递	71	－1.664	0.233	0.047
SULT1C2
精氨酸和脯氨酸代谢	45	2.139	0.001	<0.001
过氧化物酶体	68	1.954	0.005	0.004
补体和凝血级联反应	66	1.721	0.032	0.026
自然杀伤细胞介导的细胞毒性	114	－1.692	0.234	0.035
抗原处理和呈递	71	－1.666	0.249	0.046

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