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Ubiquitin-Specific Protease As the Underlying Gene Biomarker for Aortic Stenosis Yin Yang†, Lian-Qun Wang†, Bo-Chen Yao† and Zhi-Gang Guo*

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Ubiquitin-Specific Protease As the Underlying Gene Biomarker for Aortic Stenosis Yin Yang†, Lian-Qun Wang†, Bo-Chen Yao† and Zhi-Gang Guo* Yang et al. Lipids in Health and Disease (2020) 19:115 https://doi.org/10.1186/s12944-020-01299-3 RESEARCH Open Access Ubiquitin-specific protease as the underlying gene biomarker for aortic stenosis Yin Yang†, Lian-qun Wang†, Bo-chen Yao† and Zhi-gang Guo* Abstract Background: Aortic stenosis is a common heart valvular disease whose pathological processes include an inflammatory reaction and lipid accumulation. However, its detailed pathogenesis is yet to be completely elucidated. Therefore, it is of great significance to further explore the molecular mechanisms of aortic stenosis. Methods: Four datasets were downloaded from the Gene Expression Omnibus (GEO) database. Firstly, the differently expressed genes (DEGs) were screened between control and aortic stenosis samples. Secondly, weighted gene co-expression network analysis (WGCNA) was performed to find the highly relevant gene modules. Enrichment analysis and protein-protein interaction (PPI) networking were also performed, then Cytoscape was used to identify hub genes. Finally, the six participants (3 control participants and 3 patients with aortic stenosis) were recruited at the Tianjin Chest Hospital. In order to verify the expression level of USP14, several molecular experiments were performed, including hematoxylin-eosin (HE) staining, immunohistochemistry, immunofluorescence technology, real time-quantitative polymerase chain reaction (RT-qPCR), and western blotting. Results: A total of 9636 DEGs were found between the control and aortic stenosis samples. The DEGs were mainly enriched in the autophagy-animal, cellular lipid catabolic process, apoptosis, and glycoside metabolic process categories. Eleven hub genes were identified via four different algorithms. Following verification of the patient samples, Ubiquitin-specific protease 14 (USP14) was found to be displayed at higher levels in the aortic stenosis samples. Conclusion: USP14 might be involved in the occurrence and development of aortic stenosis, so it would be a molecular target for early diagnosis and specific treatment of aortic stenosis. There is a significant association between the high expression of USP14 and aortic stenosis, indicating that this gene may be a genetic risk factor for aortic stenosis. Keywords: Aortic stenosis, Ubiquitin-specific protease 14, Gene biomarker, Bioinformatics analysis, Lipid metabolism * Correspondence: [email protected] †Yin Yang, Lian-qun Wang and Bo-chen Yao contributed equally to this work. Department of Cardiac Surgery, Tianjin Chest Hospital, No. 261 South Taierzhuang Road, Jinnan District, Tianjin 300222, China © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Yang et al. Lipids in Health and Disease (2020) 19:115 Page 2 of 16 Background can use genomic data to explore and mine disease- Aortic stenosis (AS) is a common heart valvular disease related differentially expressed genes. At present, it is an [1]. In recent years, the incidence of AS has been on the effective method to explore the pathogenesis of diseases rise. Apart from its degenerative, congenital (bicuspid [8]. To meet these goals, this paper uses microarray valve), and inflammatory causes, AS might also be technology to mine the differentially expressed genes be- caused by rheumatic heart disease and other less com- tween stenotic valves and normal aortic valves, and to mon diseases, such as Paget’s disease, Fabry disease, and identify the hub genes of aortic stenosis. Results are fur- lupus erythematosus. The diagnosis of aortic stenosis is ther verified by molecular experiments. based on an echocardiographic assessment [2]. The valve – 2 area is 3 4cm in healthy adults. In general, AS patients Methods do not exhibit clinical symptoms until the valve area is Data from the GEO database 2 less than 1.0 cm . At that point, the extra work required On October 16, 2019, the key words “(aortic valve) AND to open the valve prevents the heart from functioning calcified” were set to detect the datasets, using a filter of properly. Currently, age-related degenerative aortic sten- “expression profiling by array.” There were five inclusion osis has become the most common cause of aortic sten- criteria: a sample number of more than ten per dataset osis in adults. The prevalence of aortic stenosis increases (samples of less than ten were excluded), data from with age, averaging 0.2% in people aged 50 to 59 and Homo sapiens (data from other species were excluded), a 9.8% in people aged 80 to 89 [3]. series entry type, expression profiling by array (data The pathological process of aortic stenosis includes an in- using methylation profiling by array were excluded), and flammatory reaction, lipid accumulation, angiotensin a diagnosis of aortic stenosis caused by the aortic valve converting enzyme activation, and macrophage and T sclerosis or calcification (data from mitral valve stenosis lymphocyte infiltration. The activity of the valve is limited diagnoses were excluded). due to the deposition of calcium at the base of the valve, The study obtained the transcriptome expression pro- which leads to aortic stenosis. Aortic valve calcification is files GSE12644 (GPL570 [HG-U133_Plus_2] Affymetrix similar to coronary heart disease and has a high correlation Human Genome U133 Plus 2.0 Array), GSE51472 with coronary artery calcification. Hypertension, dyslipid- (GPL570 [HG-U133_Plus_2] Affymetrix Human emia, diabetes, and smoking are the risk factors [4, 5]. Genome U133 Plus 2.0 Array), GSE83453 (GPL10558 Aortic stenosis is a slowly progressing disease. Patients Illumina Human HT-12 V4.0 expression beadchip) and with aortic stenosis have a long asymptomatic period; GSE88803 (GPL6244 [HuGene-1_0-st] Affymetrix those without symptoms need no treatment but should Human Gene 1.0 ST Array [transcript (gene) version]) follow-up regularly. However, when symptoms appear or from the GEO database (Table 1). obstruction becomes very severe, rapid diagnosis and treatment is essential [6]. At present, the treatments for aortic stenosis include surgical aortic valve replacement Screening of DEGs (AVR) and transcatheter aortic valve implantation The four GEO series were merged and normalized by (TAVI) [7]. In fact, sometimes patients are referred to the R packages sva and limma. The DEGs were screened P AVR after the myocardium has started to decompensate, by R package limma; the cut-off criteria were value < ≥ ≤− which might cause new tissue damage and lead to ser- 0.05 and log [Fold Change (FC)] 100 or 100. ious complications. Thus, early diagnosis and prevention are needed. In addition, TAVI is also associated with WGCNA analysis paravalvular aortic regurgitation, stroke, vascular com- WGCNA is an algorithm for mining module information plications, and the need for a new pacemaker. from chip data, which can describe the patterns of genes Therefore, it is of great clinical significance to further between microarray samples and find highly relevant explore the molecular mechanisms of aortic stenosis, in gene modules. In this study, WGCNA analysis was con- order to find molecular targets for early diagnosis, pre- ducted by the R package WGCNA. The lowest thresh- vention, and specific treatment. Microarray technology olding power for the scale-free topology fit index was Table 1 A summary of aortic valve stenosis microarray datasets from different GEO datasets Series Platform Affymetrix GeneChip Samples Normal Calcification 1 GSE12644 GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array 20 10 10 2 GSE51472 GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array 15 5 10 3 GSE83453 GPL10558 Illumina HumanHT-12 V4.0 expression beadchip 27 8 10 4 GSE88803 GPL6244 [HuGene-1_0-st] Affymetrix Human Gene 1.0 ST Array [transcript (gene) version] 10 5 5 Yang et al. Lipids in Health and Disease (2020) 19:115 Page 3 of 16 0.9. The ME-Diss Thresh was set at 0.1 to merge similar Table 2 The demographic details of participants modules. Characteristics Aortic stenosis P No Yes Functional annotation of DEGs Sex Male 4 1(16.7%) 3(50.0%) 0.083 Gene Ontology (GO) (see http://geneontology.org/docs/ Female 2 2(33.3%) 0(0.0%) introduction-to-go-resource/, version https://doi.org/10. 5281/zenodo.2529950, released January 1, 2019) analysis Age 6 58.00 ± 4.58 65.33 ± 3.06 0.082 is an ontology widely used in bioinformatics analysis, Co-morbidities No 1 1(16.7%) 0(0.0%) 0.273 containing three aspects of biology: biological processes Yes 5 2(33.3%) 3(50.0%) (BP), cellular components (CC), and molecular functions Independent-samples T test was used to compare continuous data. Pearson’s (MF). The Kyoto Encyclopedia of Genes and Genomes chi-squared test was used to compare the categorical data. The co-morbidities in this research included hypertension, diabetes, and coronary heart disease. (KEGG) (https://www.genome.jp/kegg/docs/relnote.html, “No” of “Co-morbidities” presented that the participants did not suffer from version 92.0, released October 1, 2019) analysis can pro- any of the above three co-morbidities. “Yes” of “Co-morbidities” presented vide specific pathways and link genomic information that the participant suffered from all above three co-morbidities with higher-order functional information.
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