A bioinformatic methodology to investigating possible genes linked to the recurrence of atrial fibrillation

doi:10.21203/rs.3.rs-2200443/v1

Among arrhythmias, atrial fibrillation is one of the most prevalent. The most popular procedure for treating atrial fibrillation is now surgery. The prognosis is significantly impacted by postoperative atrial fibrillation recurrence, regardless of whether it is treated with maze or radiofrequency ablation. Genes are linked to the onset, progression of AF, as well as to individual variability in recurrence. Through bioinformatics analysis of open data sets, we sought to uncover probable important genes associated with AF recurrence in the current study. Differentially expressed genes (DEGs) were found using the GSE176166 microarray data set that was downloaded from the Gene Expression Omnibus (GEO) database. Based on the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Disease Ontology (DO) resources, functional enrichment studies were conducted. Using the STRING database, a protein-protein interaction (PPI) network was created. Possible essential genes were then chosen using the important bioinformatics data given before. The association of possible key genes with AF recurrence (AFR) was investigated using the comparative toxicogenomics database (CTD). In comparison to controls with sinus rhythm, we discovered 27 DEGs with |log2 FC|≥1 and 7 with |log2 FC|≥3.5 fold changes in gene expression in AFR patients. The probable bringing great were TNNC1, GABARAPL1, GNAS, PHLPP1, ELL2, SNORD108 and miR-548v. With AF, CTD revealed that TNNC1, GABARAPL1, GNAS,PHLPP1, and ELL2 had increased scores. The 4 possible risk factors for AFR, TNNC1, GABARAPL1, GNAS and PHLPP1 may be connected. Our research revealed novel genetic, molecular etiology, and treatment targets of AFR.

One of the most prevalent arrhythmias is atrial fibrillation (AF), and both its incidence and prevalence are rising quickly around the globe. Additionally, they can lengthen patients' hospital stays and length of hospitalization, which has a negative impact on their quality of life. Stroke, heart failure, thromboembolism, and other consequences of atrial fibrillation substantially impair people's quality of life and prognosis, and they add to the social and financial burden[1]. The number of patients with AF is anticipated to increase quickly due to the acceleration of global aging, which emphasizes the significance of optimizing illness care[2]. The aging population and unhealthful lifestyle choices are contributing to an increase in the burden of stroke. The significant increase in atrial fibrillation (AF) is a result of increased awareness of risk factors and screening initiatives. Strong evidence exists that links ischemic stroke and AF. Cardioembolic stroke, which is very severe and has the highest rates of mortality and permanent disability, is the subtype most frequently linked to AF. The increased incidence of AF and the management of atherosclerotic risk factors in high-income nations are likely to be to blame for the rising prevalence of cardioembolic stroke[3].

The success rate of medicinal or surgical therapy for AF varies widely around the globe, and there is a tiny possibility of recurrence, which may be connected to variations in surgical techniques, demographic traits, and assessment indicators. Recurrence of atrial fibrillation remains a major medical issue that has an impact on patients' quality of life and prognosis[4]. In the recent years, several studies have examined numerous parameters that may be linked to AF recurrence, such as age, left atrial size, organic heart disease, type of AF, and length of AF, in order to more accurately assess the results of AF therapy[5, 6]. A recent systematic review and meta-analysis revealed that variations on chromosome 4Q25 were connected to the recurrence of atrial fibrillation following catheter ablation[7]. In preclinical AF models during the past few years, genetic therapy methods have been investigated, and they hold promise as a therapeutic approach with tissue-specificity, targeted delivery, and therapy designed to address the arrhythmia's underlying processes. Studies revealed contentious findings on the impact of common genetic variants on the recurrence of atrial fibrillation (AF)[8–13]. A recent meta-analysis found that rs2200733 and rs10033464 on chromosome 4Q25 (near PITX2) were associated with the risk of AF recurrence[14]. Zhang et al. investigated the possibility that the aldosterone synthetase (CYP11B2) gene − 344T/C polymorphism and the angiotensin converting enzyme (ACE) gene insertion/deletion (I/D) could affect the risk and recurrence of isolated atrial fibrillation (AF). The left atrial diameter was revealed to be partially responsible for the association between the DD genotype of the ACE gene and an increased incidence of solitary AF and its recurrence following ablation[15].

The exploration of differentially expressed genes for AF recurrence has received less attention than current research' focus on common genetic variations for AF recurrence. With the goal of identifying potential biomarkers that may be crucial in the recurrence of atrial fibrillation and illuminating the molecular basis of postoperative atrial fibrillation, the bioinformatics method was used in this study to compare and analyze the differential gene data between atrial fibrillation recurrence and sinus rhythm. Additionally, we can only examine patients prior to surgery and refine surgical approaches to reduce recurrence by looking for shared differential genes.

Gene information acquisition

Data about the left atrial appendage were obtained from a public database and compared between the SR maintenance group and the AF recurrence group (GEO). Only human left atrial appendage (LAA) samples from atrial fibrillation (AF) and sinus rhythm (SR) participants were selected; there were 7 samples in GSE176166, including 3 samples from AF recurrence (AFR) and 4 samples from SR subjects. In the initial investigation, FFPE samples of the left atrial appendage were used in the microarray analysis to compare the differences between the SR maintenance group and the AF recurrence group following Maze surgery. For additional examination, the raw data were obtained.

Data process

Various R packages (version: 3.6.3) running under RStudio were used for data processing. The NCBI GEO database was accessed to download GSE176166 series matrix.txt and its accompanying annotation platform GPL23126. To lower the false detection rate (FDR) in the initial investigation, quality control of each data set was carried out. The normalizeBetweenArrays function found in the limma package was then used to normalize the expression matrix. To identify differentially expressed genes (DEGs) between AF recurrence and sinus rhythm, the normalized data were further processed using the limma program. DEGs were defined as genes having p-values less than 0.05 and fold-changes greater than 1.0.

Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Disease Ontology (DO) enrichment

A bioinformatics tool called the Gene Ontology (GO) Resource (http://geneontology.org/) offers a conceptual framework and a collection of ideas for characterizing the roles played by the gene products in all species. After performing GO enrichment, the DEGs' projected biological processes (BP), cell composition (CC), and molecular functions (MF) were examined. The Kyoto Encyclopedia of Genes and Genomes (KEGG) is a database resource that incorporates data on genomes, biological pathways, diseases, and chemicals (https://www.kegg.jp/), the pathways in which the DEGs participated were predicted using the KEGG database. A thorough knowledge base covering 8043 hereditary, developmental, and acquired human diseases is represented by the Disease Ontology (DO) (http://disease-ontology.org). The human genome annotation software "org.Hs.eg.db" was used to translate gene symbol codes to Entrez IDs prior to enrichment analysis. Using the "clusterProfiler" package for Go and KEGG enrichment studies as well as the "DOSE" package for DO enrichment analyses, R software was used to conduct enrichment analysis in order to better understand the biological function and features. The plots were displayed using the R software packages "ggplot2" ,"pathview" and "enrichplot". GO terms and KEGG maps of biological functions were deemed to be significantly enriched if they had an adjusted P value of 0.05 and a Q value of 0.05. For the most significant pathways connected to AFR, the cutoff of the significance level nominal P-value, false discovery rate (FDR) Q-value, and family wise-error rate (FWER) P-value were all adjusted at 0.05.

Protein-protein interaction (PPI) network analysis

STRING established the PPI network. In a nutshell, protein-protein interactions were examined using the default condition after the up-regulated DEGs were submitted to the STRING database.

Identifying potentially important genes related to AF

To create ideas about disease mechanisms, the comparative toxicogenomics database (CTD; http://ctdbase.org/) incorporated data on chemical-gene/protein connections, chemical-disease linkages, and gene-disease relationships. Using the information from CTD, the relationship between possible critical genes and AF was examined.

DEG identification and connection analysis between AFR and SR

According to examination of the gene expression of samples and the data matrix of GSE176166, we screened 1186 differentially expressed genes in this study, 639 of which were upregulated and 547 of which were downregulated, using a 0.05 p-value cutoff and a minimum 1.0-fold change. Figures 1 and 2 displayed a heatmap plot and a volcano plot of the 1186 DEGs included in the subsequent studies. Seven genes were found using the screening criterion |log2 FC|≥3.5, with one gene up-regulated and six down-regulated (Table 1). Figure 3 displays boxplots for the 7 genes (TNNC1, GABARAPL1, GNAS,PHLPP1,ELL2,SNORD108 and miR-548v) that were chosen.

Functional enrichment analyses were carried out to investigate the biological roles of the DEGs in more detail (Table 2).

Gene TNNC1 was found to be enriched for regulation of muscle filament sliding speed, diaphragm contraction, cardiac Troponin complex and involuntary skeletal muscle contraction, according to GO enrichment analysis. In the processes of post-embryonic body morphogenesis,regulation of parathyroid hormone secretionandparathyroid hormone secretion, GNAS genes were particularly enriched. utilizing the screening parameters of an adjusted P value and Q value of 0.05. Adrenergic signaling in cardiomyocytes, dilated cardiomyopathy, and calcium signaling pathway were all enriched (P values = 0.00133, 0.00323, and 0.00811, respectively). Genes enriched in these signaling pathways included TNNC1 and GNAS. DEGs were enriched in 20 biological processes, according to a DO enrichment analysis of the data (Figure 4 and 5).

PPI analysis

The 7 DEGs' core genes were then eliminated, and a PPI network was created using STRING. The PPI network was then used to produce 15 nodes and 33 edges. Due to their high degree of connectedness (degree 5, Figure 6), select GABARAPL1, GNAS,PHLPP1, and TNNC1 were chosen as hub genes. All these genes were implicated in performing crucial regulatory functions in the PPI network.

The comparative toxicogenomics database results

CTD was used to investigate how possible key genes and AF interacted. The correlation between a chemical, a disease, and a gene was reflected in CTD inference scores. ELL2, GABARAPL1, GNAS, PHLPP1 and TNNC1 had higher scores with AF, according to the interaction results(Figure 7).

In the current study, we combined gene expression profiles from GEO datasets from 3 AFR samples and 4 SR samples, and then we used bioinformatics tools to analyze the data. In total, 1186 genes in AFR compared to SR samples had |log2 FC| 1 and 7 had |log2 FC| 3.5. In addition, numerous significant pathways and 4 putative critical genes (TNNC1, GABARAPL1, GNAS and PHLPP1) that were linked to AF recurrence were discovered. This suggests that these genes may be crucial in the process of AFR.

Maintaining cellular homeostasis and orchestrating proper responses to external stimuli like hormones, cytokines, and pathogenic microorganisms are both accomplished through the finely regulated process of gene expression[16, 17].

Slow skeletal and cardiac tissue express the TNNC1 gene (3p21.1) (we will refer to it as cardiac troponin C, cTnC). The cTnC gene is expressed in embryonic skeletal muscle during development, but it is subsequently silenced as the muscle type changes to fast twitch[18–20]. Numerous investigations have demonstrated that the cardiomyopathies dilated cardiomyopathy, hypertrophic cardiomyopathy, and heart failure all strongly correlate with TNNC1[21, 22]. The TNNC1 gene regulates tolerance induction, skeletal muscle adaptation, and the switch between fast and slow fibers, according to GO enrichment analysis. Our research revealed that TNNC1 expression was downregulated in the AFR group as compared to the SR group, which may suggest that TNNC1 affects the recurrence of AF.

The GABARAP-LIKE1 (GABARAPL1) gene was initially identified as an early estrogen-regulated gene that belongs to the GABARAP family due to its strong sequence homology with GABARAP. Like GABARAP, GABARAPL1 interacts with tubulin and the GABAA receptor to encourage tubulin polymerization. The GABARAP family members—GABARAP, GABARAPPL1 and GABARAPPL2—as well as their closely related homologs—LC3 and Atg8—are not only engaged in the transport of proteins or vesicles but also in several other processes, including autophagy, cell death, cell proliferation, and tumor progression. Despite these similarities, GABARAP family member GABARAPL1 has a complicated regulatory system that is distinct from the other members of the family. Additionally, it exhibits controlled tissue expression and is the family's most highly expressed gene in the central nervous system[23, 24]. In our study, PPI network analysis revealed that the GABARAPL1 gene was strongly connected with the ATG family. The most closely related cysteine protease, ATG4B, regulates the beginning of macroautophagy and autophagy in response to stress by reversibly altering atg8 family proteins to encourage the development of autophagosomes[25]. Inhibiting miR-490-3p has been shown to increase autophagy and lessen cardiac ischemia-reperfusion injury by upregulating ATG4B, which offers a novel perspective on the protective role of autophagy in ischemia-reperfusion injury[26]. Like all this, enrichment analysis revealed that the GABARAPL1 gene was connected to the NOD-like receptor signaling pathway, and it has been established that NOD-like receptors (NLRs) are essential for controlling tumorigenesis linked to inflammation, angiogenesis, cancer cell stem, and chemotherapy resistance[27]. It is crucial to comprehend the function of the GABARAPL1 gene in the emergence and recurrence of atrial fibrillation.

Similar to TNNC1, enrichment analysis revealed a correlation between the GANS gene and dilated cardiomyopathy, adrenergic signaling in cardiomyocytes, and the calcium signaling pathway. According to certain research, the GNAS gene's polymorphism in exon 5 has been associated to both essential hypertension and sensitivity to beta-blocking medications[28]. A recent study revealed that GNAS has 2 SNPs, of which 1 was effectively replicated in a community-based cohort of SCD cases and was linked to an elevated risk for VT in ICD patients[29].

One of the newest members of the phosphatome is PH domain Leucine-rich repeat Protein Phosphatase 1 (PHLPP1)[30, 31]. Genuine tumor suppressor PHLPP1[32, 33]. PHLPP1 also affects growth factor signaling, particularly that mediated by the epidermal growth factor (EGF) receptor, by suppressing receptor tyrosine kinase gene expression through a mechanism different from its effects on Akt[34, 35]. According to Katsenelson et al., mice are shielded against deadly lipopolysaccharide (LPS) challenge and live Escherichia coli infection by the deletion of the gene encoding PH domain Leucine-rich repeat Protein Phosphatase 1 (PHLPP1)[36]. Numerous earlier investigations have demonstrated how strongly connected inflammation is to the recurrence of AF[37–40]. In order to reduce the risk of AF recurrence, it may be possible to target the PHLPP1 gene.

Further functional, pathway, or bioinformatics investigations may be performed using the differentially expressed miRNA or mRNA sequences. In AFR, it was discovered that miR-548v was an upregulated micro-RNA. The short arm (region 22) of chromosome 8 contains miR-548v, a member of the miR-548 family that has received little research to yet. Previous research has demonstrated a downregulation trend for miR-548v, which is primarily linked to malignancies. The term "triple-negative breast cancer" (TNBC) refers to a subtype of breast cancer in which the human epidermal growth factor 2 receptor is not expressed by the tumor cells. mRNA sequences and microRNA expression profiles (miRNA/miR) have been extensively used in the diagnosis of TNBC thus far. This work performed a thorough investigation of the miRNA-mRNA test arrays. It was discovered that miR-548v was downregulated in TNBC[41]. Hsa-mir-142 and Hsa-mir-548v were identified as independent predictive variables for lung cancer patients by multivariate Cox regression[42]. Additional research is required to determine how miR-548v is related to cardiovascular conditions, particularly AF and recurrent AF. In the future, miR-548v might be used as a therapeutic target for AF.

In the present employment, we have examined the involvement of 4 potentially important genes and one upregulated micro-RNA in the recurrence of AF, indicating that these genes may serve as prospective markers and targets for AFR. However, it is important to consider this study's shortcomings. First, it is challenging to consider several crucial variables including areas, races, and ages. Given that many environmental and genetic factors contribute to the development of AFR, additional research should consider unmeasured factors such geography, family history, and AFR risk factors. Additionally, additional RT-qPCR testing in clinical samples is required to confirm the likely essential genes. The processes by which these genes function is also not entirely understood. To determine the biological basis, more data is needed.

Our study combined information from GEO datasets and, using bioinformatic analysis, identified four putative critical genes (TNNC1, GABARAPL1, GNAS, and PHLPP1) and pathways. The investigation of putative key genes for AF recurrence may help to identify new biomarkers for the vulnerability of AF recurrence and practical therapy targets.

DEGs：differentially expressed genes

GO：Gene Ontology enrichment

KEGG：Kyoto Encyclopedia of Genes and Genomes

BP：biological process

CC：cell composition

MF：molecular function

PPI：Protein-protein interaction

CTD: comparative toxicogenomics database

Acknowledgments: None.

Conflicts： Dan Qi, no conflicts of interest. Haiyan Sun, no conflicts of interest. Xiaolu Nie, no conflicts of interest. Jianjun Zhang, no conflicts of interest.

Funding: None.

Author’s contributions: Dan Qi and Haiyan Sun collected the data, Dan Qi and Xiaolu Nie analyzed the data, Dan Qi and Jianjun Zhang wrote the article.

Availability of data and materials: the datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.

Ethical approval: not applicable.

Consent for publication: not applicable.

Hindricks, G.T. PotparaN. DagresE. ArbeloJ. J. BaxC. Blomström-Lundqvist, et al. (2021). "2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS): The Task Force for the diagnosis and management of atrial fibrillation of the European Society of Cardiology (ESC) Developed with the special contribution of the European Heart Rhythm Association (EHRA) of the ESC." Eur Heart J 42(5): 373-498.
Rahman, F., G. F. Kwan and E. J. Benjamin (2016). "Global epidemiology of atrial fibrillation." Nat Rev Cardiol 13(8): 501.
Pistoia, F., S. Sacco, C. Tiseo, D. Degan, R. Ornello and A. Carolei (2016). "The Epidemiology of Atrial Fibrillation and Stroke." Cardiol Clin 34(2): 255-268.
Pranata, R.J. HenrinaE. YonasI. C. S. PutraI. CahyadiM. A. Lim, et al. (2021). "BMI and atrial fibrillation recurrence post catheter ablation: A dose-response meta-analysis." Eur J Clin Invest 51(6): e13499.
Nedios, S.M. TangM. RoserN. SolowjowaJ. H. Gerds-LiE. Fleck, et al. (2011). "Characteristic changes of volume and three-dimensional structure of the left atrium in different forms of atrial fibrillation: predictive value after ablative treatment." J Interv Card Electrophysiol 32(2): 87-94.
Berruezo, A.D. TamboreroL. MontB. BenitoJ. M. TolosanaM. Sitges, et al. (2007). "Pre-procedural predictors of atrial fibrillation recurrence after circumferential pulmonary vein ablation." Eur Heart J 28(7): 836-841.
Rattanawong, P., J. Chenbhanich, W. Vutthikraivit and P. Chongsathidkiet (2018). "A Chromosome 4q25 Variant is Associated with Atrial Fibrillation Recurrence After Catheter Ablation: A Systematic Review and Meta-Analysis." J Atr Fibrillation 10(6): 1666.
Husser, D.P. BüttnerD. StübnerL. UeberhamP. G. PlatonovB. Dinov, et al. (2017). "PR Interval Associated Genes, Atrial Remodeling and Rhythm Outcome of Catheter Ablation of Atrial Fibrillation-A Gene-Based Analysis of GWAS Data." Front Genet 8: 224.
Nielsen, J. B.L. G. FritscheW. ZhouT. M. TeslovichO. L. HolmenS. Gustafsson, et al. (2018). "Genome-wide Study of Atrial Fibrillation Identifies Seven Risk Loci and Highlights Biological Pathways and Regulatory Elements Involved in Cardiac Development." Am J Hum Genet 102(1): 103-115.
Weng, L. C.K. L. LunettaM. Müller-NurasyidA. V. SmithS. ThériaultP. E. Weeke, et al. (2017). "Genetic Interactions with Age, Sex, Body Mass Index, and Hypertension in Relation to Atrial Fibrillation: The AFGen Consortium." Sci Rep 7(1): 11303.
Lee, J. Y.T. H. KimP. S. YangH. E. LimE. K. ChoiJ. Shim, et al. (2017). "Korean atrial fibrillation network genome-wide association study for early-onset atrial fibrillation identifies novel susceptibility loci." Eur Heart J 38(34): 2586-2594.
Low, S. K.A. TakahashiY. EbanaK. OzakiI. E. ChristophersenP. T. Ellinor, et al. (2017). "Identification of six new genetic loci associated with atrial fibrillation in the Japanese population." Nat Genet 49(6): 953-958.
Roselli, C., M. D. Chaffin, L. C. Weng, S. Aeschbacher, G. Ahlberg, C. M. Albert, et al. (2018). "Multi-ethnic genome-wide association study for atrial fibrillation." Nat Genet 50(9): 1225-1233.
Jiang, T., Y. N. Wang, Q. Qu, T. T. Qi, Y. D. Chen and J. Qu (2019). "Association between gene variants and the recurrence of atrial fibrillation: An updated meta-analysis." Medicine (Baltimore) 98(23): e15953.
Zhang, X. L.L. Q. WuX. LiuY. Q. YangH. W. TanX. H. Wang, et al. (2012). "Association of angiotensin-converting enzyme gene I/D and CYP11B2 gene -344T/C polymorphisms with lone atrial fibrillation and its recurrence after catheter ablation." Exp Ther Med 4(4): 741-747.
Dawson, M. A. and T. Kouzarides (2012). "Cancer epigenetics: from mechanism to therapy." Cell 150(1): 12-27.
Flavahan, W. A., E. Gaskell and B. E. Bernstein (2017). "Epigenetic plasticity and the hallmarks of cancer." Science 357(6348).
Gahlmann, R., R. Wade, P. Gunning and L. Kedes (1988). "Differential expression of slow and fast skeletal muscle troponin C. Slow skeletal muscle troponin C is expressed in human fibroblasts." J Mol Biol 201(2): 379-391.
Schreier, T., L. Kedes and R. Gahlmann (1990). "Cloning, structural analysis, and expression of the human slow twitch skeletal muscle/cardiac troponin C gene." J Biol Chem 265(34): 21247-21253.
Townsend, P. J., M. H. Yacoub and P. J. Barton (1997). "Assignment of the human cardiac/slow skeletal muscle troponin C gene (TNNC1) between D3S3118 and GCT4B10 on the short arm of chromosome 3 by somatic cell hybrid analysis." Ann Hum Genet 61(Pt 4): 375-377.
Li, M. X. and P. M. Hwang (2015). "Structure and function of cardiac troponin C (TNNC1): Implications for heart failure, cardiomyopathies, and troponin modulating drugs." Gene 571(2): 153-166.
Landim-Vieira, M.J. R. JohnstonW. JiE. K. MisJ. TijerinoM. Spencer-Manzon, et al. (2019). "Familial Dilated Cardiomyopathy Associated With a Novel Combination of Compound Heterozygous TNNC1 Variants." Front Physiol 10: 1612.
Xin, Y.L. YuZ. ChenL. ZhengQ. FuJ. Jiang, et al. (2001). "Cloning, expression patterns, and chromosome localization of three human and two mouse homologues of GABA(A) receptor-associated protein." Genomics 74(3): 408-413.
Lee, Y. K. and J. A. Lee (2016). "Role of the mammalian ATG8/LC3 family in autophagy: differential and compensatory roles in the spatiotemporal regulation of autophagy." BMB Rep 49(8): 424-430.
Xiong, H., L. Sun, J. Lian and F. He (2022). "Involvement of acetylation of ATG4B in controlling autophagy induction." Autophagy: 1-3.
Wu, Y., Q. Mao and X. Liang (2021). "Targeting the MicroRNA-490-3p-ATG4B-Autophagy Axis Relieves Myocardial Injury in Ischemia Reperfusion." J Cardiovasc Transl Res 14(1): 173-183.
Liu, P.Z. LuL. LiuR. LiZ. LiangM. Shen, et al. (2019). "NOD-like receptor signaling in inflammation-associated cancers: From functions to targeted therapies." Phytomedicine 64: 152925.
Jia, H.A. D. HingoraniP. SharmaR. HopperC. DickersonD. Trutwein, et al. (1999). "Association of the G(s)alpha gene with essential hypertension and response to beta-blockade." Hypertension 34(1): 8-14.
Wieneke, H.J. H. SvendsenJ. LandeS. SpenckerJ. G. MartinezB. Strohmer, et al. (2016). "Polymorphisms in the GNAS Gene as Predictors of Ventricular Tachyarrhythmias and Sudden Cardiac Death: Results From the DISCOVERY Trial and Oregon Sudden Unexpected Death Study." J Am Heart Assoc 5(12).
Chen, M. J., J. E. Dixon and G. Manning (2017). "Genomics and evolution of protein phosphatases." Sci Signal 10(474).
Gao, T., F. Furnari and A. C. Newton (2005). "PHLPP: a phosphatase that directly dephosphorylates Akt, promotes apoptosis, and suppresses tumor growth." Mol Cell 18(1): 13-24.
Chen, M.C. P. PrattM. E. ZeemanN. SchultzB. S. TaylorA. O'Neill, et al. (2011). "Identification of PHLPP1 as a tumor suppressor reveals the role of feedback activation in PTEN-mutant prostate cancer progression." Cancer Cell 20(2): 173-186.
Liu, J., H. L. Weiss, P. Rychahou, L. N. Jackson, B. M. Evers and T. Gao (2009). "Loss of PHLPP expression in colon cancer: role in proliferation and tumorigenesis." Oncogene 28(7): 994-1004.
Patterson, S. J.J. M. HanR. GarciaK. AssiT. GaoA. O'Neill, et al. (2011). "Cutting edge: PHLPP regulates the development, function, and molecular signaling pathways of regulatory T cells." J Immunol 186(10): 5533-5537.
Reyes, G.M. NiederstK. Cohen-KatsenelsonJ. D. StenderM. T. KunkelM. Chen, et al. (2014). "Pleckstrin homology domain leucine-rich repeat protein phosphatases set the amplitude of receptor tyrosine kinase output." Proc Natl Acad Sci U S A 111(38): E3957-3965.
Cohen Katsenelson, K.J. D. StenderA. T. KawashimaG. LordénS. UchiyamaV. Nizet, et al. (2019). "PHLPP1 counter-regulates STAT1-mediated inflammatory signaling." Elife 8.
Wu, N.B. XuY. XiangL. WuY. ZhangX. Ma, et al. (2013). "Association of inflammatory factors with occurrence and recurrence of atrial fibrillation: a meta-analysis." Int J Cardiol 169(1): 62-72.
Letsas, K. P.R. WeberG. BürkleC. C. MihasJ. MinnersD. Kalusche, et al. (2009). "Pre-ablative predictors of atrial fibrillation recurrence following pulmonary vein isolation: the potential role of inflammation." Europace 11(2): 158-163.
Okumura, Y.I. WatanabeT. NakaiK. OhkuboT. KofuneM. Kofune, et al. (2011). "Impact of biomarkers of inflammation and extracellular matrix turnover on the outcome of atrial fibrillation ablation: importance of matrix metalloproteinase-2 as a predictor of atrial fibrillation recurrence." J Cardiovasc Electrophysiol 22(9): 987-993.
Henningsen, K. M., B. Nilsson, H. Bruunsgaard, X. Chen, B. K. Pedersen and J. H. Svendsen (2009). "Prognostic impact of hs-CRP and IL-6 in patients undergoing radiofrequency catheter ablation for atrial fibrillation." Scand Cardiovasc J 43(5): 285-291.
Zhu, H., M. Dai, X. Chen, X. Chen, S. Qin and S. Dai (2017). "Integrated analysis of the potential roles of miRNA‑mRNA networks in triple negative breast cancer." Mol Med Rep 16(2): 1139-1146.
Chen, G.Y. XingX. PengL. MiaoM. ZhaoH. Zhou, et al. (2020). "Microarray expression profile of microRNAs in lung adenocarcinoma patients with the Cancer Genome Atlas and its clinical validation." J buon 25(2): 821-827.

Table 1: The DEGs of merged data set with the use of criteria of P value < 0.05 and |log2FC| ≥ 3.5

Gene	Log2FC	AveExpr	t	P value	B
TNNC1	-5.776365	13.804891	-3.558014	0.009258835	-4.308167
GABARAPL1	-4.386457	11.789759	-3.287913	0.013359237	-4.329622
GNAS	-3.970002	9.468463	-2.729057	0.029406277	-4.382230
miR-548V	3.807799	8.657413	2.604915	0.035202472	-4.395506
PHLPP1	-4.095475	8.342211	-2.544321	0.038451768	-4.402197
SNORD108	-4.543703	7.200748	-2.479854	0.042251262	-4.409467
ELL2	-4.835343	8.239130	-2.466956	0.043056784	-4.410940

Log2FC: log2 Fold Change; AveExpr: Average Expression

Table 2: Significant enriched GO terms and pathways of DEGs

	Term	Count	Gene	P value	Adj.p.Val	Q value
GO: 0032972	regulation of muscle filament sliding speed	1	TNNC1	0.0003	0.0213	0.0092
GO: 0040032	Post-embryonic body morphogenesis	1	GNAS	0.0003	0.0213	0.0092
GO: 0002086	diaphragm contraction	1	TNNC1	0.0006	0.0213	0.0092
GO: 1990584	cardiac Troponin complex	1	TNNC1	0.0009	0.0365	0.0153
GO: 0002249	lymphocyte anergy	1	PHLPP1	0.0012	0.0213	0.0092
KEGG	Dilated cardiomyopathy	2	TNNC1/GNAS	0.0013	0.0802	0.0591
KEGG	Adrenergic signaling in cardiomyocyte	2	TNNC1/GNAS	0.0032	0.0969	0.0714
KEGG	Calcium signaling pathway	2	TNNC1/GNAS	0.0081	0.1035	0.0762

GO：Gene Ontology enrichment

KEGG：Kyoto Encyclopedia of Genes and Genomes

DEGs：differentially expressed genes

No competing interests reported.

A bioinformatic methodology to investigating possible genes linked to the recurrence of atrial fibrillation

Status:

Version 1

Abstract

Figures

Introduction

Materials And Methods

Gene information acquisition

Data process

Protein-protein interaction (PPI) network analysis

Identifying potentially important genes related to AF

Results

DEG identification and connection analysis between AFR and SR

PPI analysis

The comparative toxicogenomics database results

Discussion

Conclusion

Abbreviations

Declarations

References

Tables

Additional Declarations

Status:

Version 1