Atrial fibrillation (AF) is the most common cardiac arrhythmia. This study aimed to identify potential novel biomarkers for persistent AF (pAF) using integrated analyses and explore the immune cell infiltration in this pathological process. Three pAF datasets (GSE31821, GSE41177, and GSE79768) from the Gene Expression Omnibus (GEO) database were integrated with the elimination of batch effects. 264 differentially expressed genes (DEGs) were identified using Linear models for microarray data (LIMMA), 12 modules were screened out by weighted gene co-expression network analysis (WGCNA) in pAF compared with normal controls. Subsequently, common genes (CGs) were identified as the intersection of DEGs and genes in the most significant module. Functional enrichment analysis showed that CGs were mainly enriched in the "Calcineurin-NFAT (nuclear factor of activated T-cells)" signaling pathway, particularly regulator of calcineurin 1 (RCAN1), and protein phosphatase 3 regulatory subunit B, alpha (PPP3R1). Ulteriorly, the microRNA-transcription factor-mRNA network revealed that microRNA-34a-5p could target both RCAN1 and PPP3R1 in the pAF pathogenesis. Finally, immune infiltration analysis by CIBERSORT, a versatile computational method, displayed a higher level of monocytes, dendritic cells and neutrophils, as well as a lower level of CD8+ T cells and T cells regulatory (Tregs) in pAF compared with the control group. In conclusion, our present study revealed several novel pAF-associated genes, miRNAs, and pathways, including microRNA-34a-5p, which might target RCAN1 and PPP3R1 to regulate pAF through the calcineurin-NFAT signaling pathway. In addition, there was a difference in immune infiltration between patients with pAF and normal groups and immune cells might interact with specific genes in pAF.
Citation: Shengjue Xiao, Yufei Zhou, Ailin Liu, Qi Wu, Yue Hu, Jie Liu, Hong Zhu, Ting Yin, Defeng Pan. Uncovering potential novel biomarkers and immune infiltration characteristics in persistent atrial fibrillation using integrated bioinformatics analysis[J]. Mathematical Biosciences and Engineering, 2021, 18(4): 4696-4712. doi: 10.3934/mbe.2021238
Atrial fibrillation (AF) is the most common cardiac arrhythmia. This study aimed to identify potential novel biomarkers for persistent AF (pAF) using integrated analyses and explore the immune cell infiltration in this pathological process. Three pAF datasets (GSE31821, GSE41177, and GSE79768) from the Gene Expression Omnibus (GEO) database were integrated with the elimination of batch effects. 264 differentially expressed genes (DEGs) were identified using Linear models for microarray data (LIMMA), 12 modules were screened out by weighted gene co-expression network analysis (WGCNA) in pAF compared with normal controls. Subsequently, common genes (CGs) were identified as the intersection of DEGs and genes in the most significant module. Functional enrichment analysis showed that CGs were mainly enriched in the "Calcineurin-NFAT (nuclear factor of activated T-cells)" signaling pathway, particularly regulator of calcineurin 1 (
| [1] |
S. S. Chugh, R. Havmoeller, K. Narayanan, D. Singh, M. Rienstra, E. J. Benjamin, et al., Worldwide epidemiology of atrial fibrillation: a global burden of disease 2010 study, Circulation, 129 (2014), 837-847. doi: 10.1161/CIRCULATIONAHA.113.005119
|
| [2] |
R. S. Wijesurendra, B. Casadei, Mechanisms of atrial fibrillation, Heart, 105 (2019), 1860-1867. doi: 10.1136/heartjnl-2018-314267
|
| [3] |
E. Zacharia, N. Papageorgiou, A. Ioannou, G. Siasos, S. Papaioannou, M. Vavuranakis, et al., Inflammatory biomarkers in atrial fibrillation, Curr. Med. Chem., 26 (2019), 837-854. doi: 10.2174/0929867324666170727103357
|
| [4] |
C. Tsioufis, D. Konstantinidis, I. Nikolakopoulos, E. Vemmou, T. Kalos, G. Georgiopoulos, et al., Biomarkers of atrial fibrillation in hypertension, Curr. Med. Chem., 26 (2019), 888-897. doi: 10.2174/0929867324666171006155516
|
| [5] |
E. M. Small, E. N. Olson, Pervasive roles of microRNAs in cardiovascular biology, Nature, 469 (2011), 336-342. doi: 10.1038/nature09783
|
| [6] |
Y. Lu, Y. Zhang, N. Wang, Z. Pan, X. Gao, F. Zhang, et al., MicroRNA-328 contributes to adverse electrical remodeling in atrial fibrillation, Circulation, 122 (2010), 2378-2387. doi: 10.1161/CIRCULATIONAHA.110.958967
|
| [7] |
J. Andrade, P. Khairy, D. Dobrev, S. Nattel, The clinical profile and pathophysiology of atrial fibrillation: relationships among clinical features, epidemiology, and mechanisms, Circ. Res., 114 (2014), 1453-1468. doi: 10.1161/CIRCRESAHA.114.303211
|
| [8] |
Y. F. Hu, Y. J. Chen, Y. J. Lin, S. A. Chen, Inflammation and the pathogenesis of atrial fibrillation, Nat. Rev. Cardiol., 12 (2015), 230-243. doi: 10.1038/nrcardio.2015.2
|
| [9] |
T. Yamashita, A. Sekiguchi, S. Suzuki, T. Ohtsuka, K. Sagara, H. Tanabe, et al., Enlargement of the left atrium is associated with increased infiltration of immune cells in patients with atrial fibrillation who had undergone surgery, J. Arrhythm., 31 (2015), 78-82. doi: 10.1016/j.joa.2014.07.003
|
| [10] | T. Yan, S. Zhu, M. Zhu, C. Wang, C. Guo, Integrative identification of hub genes associated with immune cells in atrial fibrillation using weighted gene correlation network analysis, Front. Cardiovasc. Med., 7 (2020), 631775. |
| [11] |
E. Zhao, C. Zhou, S. Chen, A signature of 14 immune-related gene pairs predicts overall survival in gastric cancer, Clin. Transl. Oncol., 23 (2021), 265-274. doi: 10.1007/s12094-020-02414-7
|
| [12] |
E. Zhao, H. Xie, Y. Zhang, Identification of differentially expressed genes associated with idiopathic pulmonary arterial hypertension by integrated bioinformatics approaches, J. Comput. Biol., 28 (2021), 79-88. doi: 10.1089/cmb.2019.0433
|
| [13] |
P. Langfelder, S. Horvath, WGCNA: an R package for weighted correlation network analysis, BMC Bioinf., 9 (2008), 559. doi: 10.1186/1471-2105-9-559
|
| [14] |
E. Clough, T. Barrett, The gene expression omnibus database, Methods Mol. Biol., 1418 (2016), 93-110. doi: 10.1007/978-1-4939-3578-9_5
|
| [15] | T. Barrett, S. E. Wilhite, P. Ledoux, C. Evangelista, I. F. Kim, M. Tomashevsky, et al., NCBI GEO: archive for functional genomics data sets-update, Nucleic Acids Res., 41 (2013), D991-995. |
| [16] |
R. A. Irizarry, B. Hobbs, F. Collin, Y. D. Beazer-Barclay, K. J. Antonellis, U. Scherf, et al., Exploration, normalization, and summaries of high density oligonucleotide array probe level data, Biostatistics (Oxford, England), 4 (2003), 249-264. doi: 10.1093/biostatistics/4.2.249
|
| [17] |
J. T. Leek, W. E. Johnson, H. S. Parker, A. E. Jaffe, J. D. Storey. The sva package for removing batch effects and other unwanted variation in high-throughput experiments, Bioinformatics, 28 (2012), 882-883. doi: 10.1093/bioinformatics/bts034
|
| [18] |
M. E. Ritchie, B. Phipson, D. Wu, Y. Hu, C. W. Law, W. Shi, et al., Limma powers differential expression analyses for RNA-sequencing and microarray studies, Nucleic Acids Res., 43 (2015), e47-e47. doi: 10.1093/nar/gkv007
|
| [19] |
E. Ravasz, A. L. Somera, D. A. Mongru, Z. N. Oltvai, A. L. Barabási, Hierarchical organization of modularity in metabolic networks, Science, 297 (2002), 1551-1555. doi: 10.1126/science.1073374
|
| [20] |
W. H. Da, B. T. Sherman, R. A. Lempicki, Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists, Nucleic Acids Res., 37 (2009), 1-13. doi: 10.1093/nar/gkn923
|
| [21] |
W. H. Da, B. T. Sherman, R. A. Lempicki, Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources, Nat. Protoc., 4 (2009), 44-57. doi: 10.1038/nprot.2008.211
|
| [22] |
J. Wu, X. Mao, T. Cai, J. Luo, L. Wei, KOBAS server: a web-based platform for automated annotation and pathway identification, Nucleic Acids Res., 34 (2006), W720-724. doi: 10.1093/nar/gkl167
|
| [23] |
D. Szklarczyk, A. Franceschini, S. Wyder, K. Forslund, D. Heller, J. Huerta-Cepas, et al., STRING v10: protein-protein interaction networks, integrated over the tree of life, Nucleic Acids Res., 43 (2015), D447-452. doi: 10.1093/nar/gku1003
|
| [24] |
P. Shannon, A. Markiel, O. Ozier, N. S. Baliga, J. T. Wang, D. Ramage, et al., Cytoscape: a software environment for integrated models of biomolecular interaction networks, Genome Res., 13 (2003), 2498-2504. doi: 10.1101/gr.1239303
|
| [25] |
C. H. Chou, S. Shrestha, C. D. Yang, N. W. Chang, Y. L. Lin, K. W. Liao, et al., miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions, Nucleic Acids Res., 46 (2018), D296-d302. doi: 10.1093/nar/gkx1067
|
| [26] |
J. H. Yang, J. H. Li, P. Shao, H. Zhou, Y. Q. Chen, L. H. Qu. starBase: a database for exploring microRNA-mRNA interaction maps from Argonaute CLIP-Seq and Degradome-Seq data, Nucleic Acids Res., 39 (2011), D202-D209. doi: 10.1093/nar/gkq1056
|
| [27] | V. Agarwal, G. W. Bell, J. W. Nam, D. P. Bartel, Predicting effective microRNA target sites in mammalian mRNAs, Elife, 4 (2015). |
| [28] |
A. M. Newman, C. L. Liu, M. R. Green, A. J. Gentles, W. Feng, Y. Xu, et al., Robust enumeration of cell subsets from tissue expression profiles, Nat. Methods, 12 (2015), 453-457. doi: 10.1038/nmeth.3337
|
| [29] |
A. L. Dailey, Metabolomic bioinformatic analysis, Methods Mol. Biol., 1606 (2017), 341-352. doi: 10.1007/978-1-4939-6990-6_22
|
| [30] | K. Hu. Become competent within one day in generating boxplots and violin plots for a novice without prior R experience, Methods Protoc., 3 (2020). |
| [31] | S. Xiao, Y. Zhou, Q. Liu, T. Zhang, D. Pan, Identification of pivotal microRNAs and target genes associated with persistent atrial fibrillation based on bioinformatics analysis, Comput. Math. Methods Med., 2021 (2021), 6680211. |
| [32] |
Y. L. Zhang, H. J. Cao, X. Han, F. Teng, C. Chen, J. Yang, et al., Chemokine receptor CXCR-2 initiates atrial fibrillation by triggering monocyte mobilization in mice, Hypertension, 76 (2020), 381-392. doi: 10.1161/HYPERTENSIONAHA.120.14698
|
| [33] |
C. Herder, J. M. Kannenberg, M. Carstensen-Kirberg, A. Strom, G. J. Bönhof, W. Rathmann, et al., A systemic inflammatory signature reflecting cross talk between innate and adaptive immunity is associated with incident polyneuropathy: KORA F4/FF4 Study, Diabetes, 67 (2018), 2434-2442. doi: 10.2337/db18-0060
|
| [34] |
R. L. He, Z. J. Wu, X. R. Liu, L. X. Gui, R. X. Wang, M. J. Lin, Calcineurin/NFAT signaling modulates pulmonary artery smooth muscle cell proliferation, migration and apoptosis in monocrotaline-induced pulmonary arterial hypertension rats, Cell Physiol. Biochem., 49 (2018), 172-189. doi: 10.1159/000492852
|
| [35] |
X. Zhou, Q. Zhang, T. Zhao, X. Bai, W. Yuan, Y. Wu, et al., Cisapride protects against cardiac hypertrophy via inhibiting the up-regulation of calcineurin and NFATc-3, Eur. J. Pharmacol., 735 (2014), 202-210. doi: 10.1016/j.ejphar.2014.04.012
|
| [36] |
L. Y. Li, Q. Lou, G. Z. Liu, J. C. Lv, F. X. Yun, T. K. Li, et al., Sacubitril/valsartan attenuates atrial electrical and structural remodelling in a rabbit model of atrial fibrillation, Eur. J. Pharmacol., 881 (2020), 173120. doi: 10.1016/j.ejphar.2020.173120
|
| [37] | S. K. Lee, J. Ahnn, Regulator of calcineurin (RCAN): beyond down syndrome critical region, Mol. Cells, 43 (2020), 671-685. |
| [38] |
V. Parra, F. Altamirano, C. P. Hernández-Fuentes, D. Tong, V. Kyrychenko, D. Rotter, et al., Down syndrome critical region 1 gene, Rcan1, helps maintain a more fused mitochondrial network, Circ. Res., 122 (2018), e20-e33. doi: 10.1161/CIRCRESAHA.117.312466
|
| [39] |
H. Peiris, D. J. Keating, The neuronal and endocrine roles of RCAN1 in health and disease, Clin. Exp. Pharmacol. Physiol., 45 (2018), 377-383. doi: 10.1111/1440-1681.12884
|
| [40] |
J. Chen, H. Zhang, D. Niu, H. Li, K. Wei, L. Zhang, et al., The risk factors related to the severity of pain in patients with chronic prostatitis/chronic pelvic pain syndrome, BMC Urol., 20 (2020), 154. doi: 10.1186/s12894-020-00729-9
|
| [41] |
L. Bä;r, C. Großmann, M. Gekle, M. Föller, Calcineurin inhibitors regulate fibroblast growth factor 23 (FGF23) synthesis, Naunyn Schmiedebergs Arch. Pharmacol., 390 (2017), 1117-1123. doi: 10.1007/s00210-017-1411-2
|
| [42] |
M. S. Dzeshka, G. Y. Lip, V. Snezhitskiy, E. Shantsila, Cardiac fibrosis in patients with atrial fibrillation: mechanisms and clinical implications, J. Am. Coll. Cardiol., 66 (2015), 943-959. doi: 10.1016/j.jacc.2015.06.1313
|
| [43] | X. Lv, P. Lu, Y. Hu, T. Xu. Overexpression of miR-29b-3p inhibits atrial remodeling in rats by targeting PDGF-B signaling pathway, Oxid. Med. Cell Longev., 2021 (2021), 3763529. |
| [44] |
B. C. Bernardo, X. M. Gao, Y. K. Tham, H. Kiriazis, C. E. Winbanks, J. Y. Ooi, et al., Silencing of miR-34a attenuates cardiac dysfunction in a setting of moderate, but not severe, hypertrophic cardiomyopathy, PLoS One, 9 (2014), e90337. doi: 10.1371/journal.pone.0090337
|
| [45] | Y. Zhu, Z. Feng, W. Cheng, Y. Xiao. MicroRNA-34a mediates atrial fibrillation through regulation of Ankyrin-B expression, Mol. Med. Rep., 17 (2018), 8457-8465. |
| [46] |
C. Diener, M. Hart, D. Alansary, V. Poth, B. Walch-Rückheim, J. Menegatti, et al., Modulation of intracellular calcium signaling by microRNA-34a-5p, Cell Death Dis., 9 (2018), 1008. doi: 10.1038/s41419-018-1050-7
|
| [47] |
H. Y. Yuan, C. B. Zhou, J. M. Chen, X. B. Liu, S. S. Wen, G. Xu, et al., MicroRNA-34a targets regulator of calcineurin 1 to modulate endothelial inflammation after fetal cardiac bypass in goat placenta, Placenta, 51 (2017), 49-56. doi: 10.1016/j.placenta.2017.01.128
|
| [48] |
Y. Guo, G. Y. Lip, S. Apostolakis. Inflammation in atrial fibrillation, J. Am. Coll. Cardiol., 60 (2012), 2263-2270. doi: 10.1016/j.jacc.2012.04.063
|
| [49] | H. Suehiro, K. Kiuchi, K. Fukuzawa, N. Yoshida, M. Takami, Y. Watanabe, et al., Circulating intermediate monocytes and atrial structural remodeling associated with atrial fibrillation recurrence after catheter ablation, J. Cardiovasc. Electrophysiol., 2021. |
| [50] |
M. Shiba, Y. Sugano, Y. Ikeda, H. Okada, T. Nagai, H. Ishibashi-Ueda, et al., Presence of increased inflammatory infiltrates accompanied by activated dendritic cells in the left atrium in rheumatic heart disease, PLoS One, 13 (2018), e0203756. doi: 10.1371/journal.pone.0203756
|
| [51] |
A. J. Perros, A. Esguerra-Lallen, K. Rooks, F. Chong, S. Engkilde-Pedersen, H. M. Faddy, et al., Coronary artery bypass grafting is associated with immunoparalysis of monocytes and dendritic cells, J. Cell Mol. Med., 24 (2020), 4791-4803. doi: 10.1111/jcmm.15154
|
| [52] |
N. Kazem, P. Sulzgruber, B. Thaler, J. Baumgartner, L. Koller, G. Laufer, et al., CD8+CD28null T lymphocytes are associated with the development of atrial fibrillation after elective cardiac surgery, Thromb Haemost., 120 (2020), 1182-1187. doi: 10.1055/s-0040-1713096
|
| [53] |
S. Li, Z. Jiang, X. Chao, C. Jiang, G. Zhong, Identification of key immune-related genes and immune infiltration in atrial fibrillation with valvular heart disease based on bioinformatics analysis, J. Thorac. Dis., 13 (2021), 1785-1798. doi: 10.21037/jtd-21-168
|
| [54] | Y. Chen, G. Chang, X. Chen, Y. Li, H. Li, D. Cheng, et al., IL-6-miR-210 suppresses regulatory T cell function and promotes atrial fibrosis by targeting Foxp3, Mol. Cells, 43 (2020), 438-447. |
| [55] |
Y. L. Zhang, F. Teng, X. Han, P. B. Li, X. Yan, S. B. Guo, et al., Selective blocking of CXCR2 prevents and reverses atrial fibrillation in spontaneously hypertensive rats, J. Cell Mol. Med., 24 (2020), 11272-11282. doi: 10.1111/jcmm.15694
|
Figures(8)
Shengjue Xiao, Yufei Zhou, Ailin Liu, Qi Wu, Yue Hu, Jie Liu, Hong Zhu, Ting Yin, Defeng Pan. Uncovering potential novel biomarkers and immune infiltration characteristics in persistent atrial fibrillation using integrated bioinformatics analysis[J]. Mathematical Biosciences and Engineering, 2021, 18(4): 4696-4712. doi: 10.3934/mbe.2021238
DownLoad: