RNA Sequence Analysis of Differentially Expressed Genes in Left Atrial Appendage Thrombus

RNA Sequence Analysis of Differentially Expressed Genes in Left Atrial Appendage Thrombus | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article RNA Sequence Analysis of Differentially Expressed Genes in Left Atrial Appendage Thrombus Junji Maeda, Motoki Furutani, Shunsuke Miyauchi, Mika Nakashima, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6680046/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Oct, 2025 Read the published version in Journal of Thrombosis and Thrombolysis → Version 1 posted You are reading this latest preprint version Abstract Background Cardioembolic stroke is a major complication of atrial fibrillation (AF). We investigated differentially expressed genes (DEGs) in the left atrial appendage (LAA) with and without LAA thrombus (LAAT) using RNA sequencing (RNA-seq). Methods LAA tissue samples were obtained during cardiac surgery. We analyzed samples with LAAT (n = 6) and without LAAT (n = 5). Differential gene expression analysis was conducted to identify significantly altered genes. Results RNA-seq identified 27 differentially expressed genes (false discovery rate 2). Among these, four DEGs— DIRAS3 , CYP26B1 , PRG4 , and ITLN —exhibited particularly large fold changes. Protein-protein interaction network analysis revealed two hub genes, FKBP5 and TUBA3D , based on degree (≥ 30) and betweenness centrality (≥ 3000). Quantitative PCR confirmed consistent expression patterns for these genes. Furthermore, consistent results were obtained in another independent set (10 cases with LAAT and 10 cases without LAAT). Linear regression analysis, adjusted for age and gender, showed that DIRAS3 expression was significantly associated with both fibrosis ratio (β = 2.99, 95% confidence interval [CI] 0.22–5.75, p = 0.034) and NT-proBNP levels (β = 373, 95% CI 238–574, p = 5.71E-08). Additionally, CYP26B1 and TUBA3D expression levels were significantly associated with NT-proBNP (β = 349, 95% CI 23.8–674, p = 0.036; β = -140, 95% CI -272 to -8.81, p = 0.038, respectively) . Conclusions We identified candidate genes potentially involved in LAAT in AF patients through RNA-seq analysis. These findings may elucidate the molecular mechanisms underlying LAAT pathogenesis. atrial fibrillation left atrial appendage thrombus RNA sequencing Figures Figure 1 Figure 2 Title page abstract We investigated candidate genes associated with left atrial appendage thrombus (LAAT) in patients with atrial fibrillation using RNA sequencing of left atrial appendage tissue obtained during cardiac surgery. RNA sequencing analysis identified six key genes potentially associated with LAAT: DIRAS3 , CYP26B1 , PRG4 , ITLN , FKBP5 , and TUBA3D . Among them, DIRAS3 expression showed a positive correlation with both the degree of fibrosis and NT-proBNP levels. CYP26B1 expression was positively correlated with NT-proBNP, whereas TUBA3D expression was negatively correlated. This transcriptomic approach provides valuable insight into the pathogenesis of LAAT formation and the identification of potential biomarkers. Further research is needed to validate these findings and assess their clinical implications. Introduction Cardiogenic embolus is mostly derived from the left atrial appendage (LAA) [ 1 ], which influences patient prognosis, quality of life, and social cost [ 2 ]. Recently, technical advances in LAA management, such as percutaneous left atrial appendage (LAA) closure [ 3 ] and thoracoscopic left atrial appendectomy [ 4 ], have led to the development of effective therapeutic strategies. In this era of less invasive LAA management, it is essential to identify patients at high risk for forming LAA thrombus (LAAT). Previous studies have shown that decreased LAA function [ 5 ] and non-chicken wing LAA morphology [ 6 ] are associated with the formation of LAAT. However, the exact mechanisms of LAAT formation have not been well elucidated. Analysis of whole messenger RNA (mRNA) expression play a crucial role in uncovering the molecular mechanisms underlying diseases. One of the most powerful tools for detecting the biomarkers or molecular functions in human tissues is whole RNA sequencing (RNA-seq). A comprehensive transcriptome analysis using next-generation sequencing has been widely applied, including paired RNA-seq analysis of left and right appendages in human [ 7 ]. In this study, RNA-seq transcriptome analyses of LAA specimens were performed to detect key genes related to the LAAT and to uncover the underlying mechanisms of LAAT. Methods LAA Samples and Baseline Data Acquisition The LAA samples were obtained during cardiac surgery from six patients with evidence of LAAT and five with no clinical evidence of LAAT. All patients were diagnosed with AF, documented using more than one modality (e.g., 12-lead electrocardiographic testing, 24-hour Holter recording, long-term Holter recording, or mobile electrocardiographic monitoring), and subjected to LAA management during cardiac surgery. Data on the patients’ baseline clinical characteristics, including their AF status, coexisting disease, and the results of the routine peripheral blood tests and echocardiography examinations, was obtained from their medical records. The CHADS 2 score was calculated following the standard manner [ 8 ]. Moreover, paroxysmal AF was defined as recurrent AF that terminated spontaneously within seven days, whereas non-paroxysmal AF was defined as AF that persisted beyond seven days and included long-standing and permanent AF. The presence of an LAAT was confirmed via transesophageal echocardiography (TEE) that was conducted by experienced echocardiographers before surgery using an EPIQ 7 ultrasound imaging system (Philips) and X8-2t 3-dimensional TEE transducer (Philips). An LAAT was defined as a mass with a high echogenic density, distinct from the LAA wall density, and attached to the LAA wall as well as a mass that moved independently of the LAA wall. This study was conducted in accordance with the Declaration of Helsinki, and the study approval was obtained from the Hiroshima University Ethics Committee (approval number E-1931). Written informed consent was obtained from all patients. LAA Excision For patients who required LAA management alone, LAA excision was performed through thoracoscopic stand-alone left atrial appendectomy as previously reported [ 4 ]. For the other patients, LAAs were excised concomitant with other cardiac procedures. Supplemental Table 1 shows the primary cardiac disease and procedures. After resection, the LAA is thoroughly washed with saline, so thrombi and blood clots are not included in the analysis samples. The two 10 × 10 mm sections from the distal side of each LAA specimen were randomly resected and fixed in formalin and paraffin-embedded. The remaining specimens were snap-frozen in liquid nitrogen immediately and stored at a temperature of − 80°C. Quantification of LAA Fibrosis The quantification of the degree of LAA fibrosis was performed as previously reported [ 9 ]. In brief, the two formalin-fixed and paraffin-embedded LAA sections (4.5-µm thick) were deparaffinized and subjected to Azan–Mallory staining. The two microscopic fields with 200× magnification were randomly selected and imaged for each LAA section. After the manual removal of the perivascular tissue, epicardium, endocardium, and fatty tissue, the red (myocardium) and blue areas (fibrosis) in each image were measured using the BZ-X800 Analyzer software (Keyence, Osaka, Japan). Moreover, the degree of the LAA fibrosis (%) in each microscopic field was measured by dividing the fibrosis area by the fibrosis area plus the myocardium area and multiplying by 100. Furthermore, the average of the four microscopic fields was calculated. These procedures were performed by experienced pathologists who had no access to the clinical data. RNA Extraction From the frozen LAA specimens, 25 mg sections were randomly resected from the distal side of the LAA. Each section was homogenized with zirconia-silica beads using a bead beater homogenizer (µT-12, TAITEC, Saitama, Japan). After homogenization, the RNA was extracted using the easy-spin Total RNA Extraction Kit (iNtRON Biotechnology, Seongnam, Korea) in accordance with the manufacturer’s instructions. RNA-seq Data Analysis RNA-seq–based transcriptome profiling was performed by the Beijing Genomics Institute (Wuhan) using the BGISEQ platform [ 10 ]. Mapping to a human reference genome (GRCh37) with STAR (ver. 2.5.2b) was performed using clean sequenced reads from BGISEQ. The read counts for each gene were used to measure the RSEM program (version 1.3.0 [ https://deweylab.github.io/RSEM ]). Differential gene expression analysis was performed using the edgeR package (ver. 3.8.1) program ( https://bioconductor.org/packages/release/bioc/html/edgeR.html ) after the read counts from each sample were combined into a count file. Moreover, we filtered by minimum read counts using the “filterByExpr” function of the edgeR package. The “caclNormFactors” function in the edgeR package was used to obtain the TMM (trimmed mean of M-values) normalization factors to account for library sizes. We applied the “exactTest” function in the edgeR package to obtain differentially expressed genes (DEGs) between the samples from patients with and without LAAT. The DEGs were defined as genes with a false discovery rate (FDR) of 2 and a normalized transcript per million (nTPM) of ≥ 1 in the heart muscle from the human protein atlas database ( https://www.proteinatlas.org ). The FDR values were measured using the Benjamini–Hochberg method. The TPM was obtained using RSEM (version 1.3.0 [ https://deweylab.github.io/RSEM ]) after utilizing STAR to align the RNA-seq reads to the human reference genome. Gene Ontology Molecular Function Term Enrichment Analysis A Gene Ontology (GO) term enrichment analysis was performed using the significant DEGs to assess molecular functions. Statistically significant GO terms (FDR < 0.05) were investigated based on the DEGs using the Database that includes Annotation, Visualization, and Integrated Discovery (DAVID, version 2023q4; https://david.ncifcrf.gov/ ). Protein–Protein Interaction Network Analysis To identify key genes and critical gene modules, a protein–protein interaction (PPI) network analysis was conducted using NetworkAnalyst ( https://www.networkanalyst.ca ), integrating data from the DifferentialNet database ( https://netbio.bgu.ac.il/diffnet/ ). Tissue-specific interaction data corresponding to the heart atrial appendage were employed, with a filter level of 15 applied. The PPI network was visualized using Cytoscape (version 3.10.3, http://www.cytoscape.org/ ). Validation of RNA-seq Results by Quantitative PCR and in an Independent Sample Set Using the ReverTra Ace qPCR RT kit (TOYOBO, Osaka, Japan), cDNA was synthesized. The following conditions were used for reverse transcription: the first step to prepare the mixture was one cycle of 65°C for 5 min and cooling immediately, and the second step was one cycle of 37°C for 15 min, one cycle of 50°C for 5 min, and one cycle of 98°C for 5 min. A real-time PCR analysis was performed using QuantStudio5™ Real-Time PCR Systems 384-well plates (Thermo Fisher Scientific, Waltham, MA, USA) and PowerUp™ SYBR™ Green Master Mix (Thermo Fisher Scientific, Waltham, MA, USA). Supplemental Table 2 shows the target genes and their corresponding primers. The real-time PCR conditions were as follows: one cycle of 50°C for 2 min, one cycle of 95°C for 2 min, 40 cycles of 95°C for 15 s, one cycle of 60°C for 60 s, one cycle of 95°C for 15 s, one cycle of 60°C for 60 s, and one cycle of 95°C for 15 s. Each gene was assayed in duplicate. GAPDH ( Supplemental Table 2 ) was selected as a reference gene for the normalization of the target gene expression levels. Moreover, the target gene expression levels were estimated from the samples of the RNA-seq analysis. The relative gene expression levels were calculated using the 2 −ΔΔCt method. Additionally, we analyzed independent LAA samples, comprising of 10 samples with LAAT and 10 samples without LAAT and verified the expression levels of the candidate DEGs using the same method. Association between Gene Expression and Clinical Information The correlation between the candidate DEGs and CHADS 2 scores (congestive heart failure, hypertension, Age, diabetes, stroke) was assessed by Spearman's rank correlation coefficient. Additionally, the associations between the expression of candidate DEGs and clinical information (fibrosis ratio and NT-proBNP) were evaluated using linear regression analysis. Expression Patterns of Candidate DEGs in Heart Cell Atlas To investigate the cell type–specific expression patterns of the DEGs identified by our RNA-seq analysis, we utilized publicly available single-cell RNA-seq data from the Heart Cell Atlas ( https://www.heartcellatlas.org/ ) [ 11 ] to estimate the expression and localization of the candidate DEGs across various cardiac cell populations. Statistical Analysis Continuous variables are presented as mean ± Standard deviation (SD), and categorical variables are presented as counts (percentages). Statistical analysis was performed using Python version 3.8.12 and R version 4.3.1. For clinical data, continuous variables were compared using the Mann–Whitney U test when the data did not follow a normal distribution, and Welch’s t-test when the data followed a normal distribution. Categorical variables were compared using Fisher’s exact test. The correlation between the CHADS2 scores and gene expression was estimated using Spearman's rank correlation coefficient. Association between clinical information and gene expression were assessed using linear regression adjusted for age and gender. A P value of < 0.05 was considered statistically significant for clinical items, and an FDR of < 0.05 was considered significant in the RNA-seq analysis. Results Clinical Information Six patients with evidence of LAAT (33.33% female, mean age 69.97 ± 9.07 years) and five patients without clinical evidence of LAAT (40% female, mean age 70.60 ± 11.46) were enrolled in this study. Table 1 and Supplemental Table 1 show the baseline demographic data of the participants. Persistent AF was significantly more common in the LAAT participants, while NT-proBNP was significantly higher in participants with LAAT compared to those without ( P < 0.05). No statistical differences were observed between other clinical factors and the LAA fibrosis ratio ( Table 1 ). Detection of DEGs RNA-seq analysis was performed on all LAA samples using an average of >48.7 million high-quality read sequences, with >96.21% uniquely mapped to the human reference genome (GRCh37) ( Supplemental Table 3 ). A total of 27 significant DEGs were identified from the 13,722 analyzed genes, based on the criteria of an FDR 2, and a nTPM ≥ 1 in heart muscle, according to the Human Protein Atlas. Among these, 2 genes were upregulated and 25 were downregulated in the LAAT samples. Notably, four DEGs— DIRAS3 , CYP26B1 , PRG4 , and ITLN1 —exhibited particularly large fold changes and were selected for further analysis ( Figure 1a , Table 2, and Supplemental Table 4 ). GO Term Enrichment Analysis and PPI Network Analysis There was no GO molecular function term with an FDR < 0.05. Subsequently, we performed a PPI network analysis using the 27 DEGs, utilizing tissue-specific interaction data for heart atrial appendage from DifferentialNet database (https://netbio.bgu.ac.il/labwebsite/software/differentialnet/). The PPI network comprised 165 noses and 169 edges. We identified the most highly ranked hub genes in terms of network topology measures of the degree of centrality (DC) and betweenness centrality (BC). Two hub genes were detected with DC ≥ 30 and BC ≥ 3000: FKBP5 (DC = 75, BC = 9989.08), and TUBA3D (DC = 38, BC = 7993.42) ( Figure 1b and Table 3 ). Verification of the Quantitative PCR Assay Quantitative PCR analysis was performed to validate six genes identified by our RNA-seq analysis: DIRAS3, CYP26B1, ILTN1, PRG4, FKBP , and TUBA3D . The mean relative gene expression ratios (LAAT/without LAAT) were as follows: 4.65 for DIRAS3, 4.15 for CYP26B1 , 0.28 for ITLN1 , 0.57 for PRG4 , 0.33 for TUBA3D , and 0.29 for FKBP5 . These gene expressions were consistent with the RNA-seq results, supporting the reliability of the transcriptomic findings ( Figure 2 ). The expression levels of the candidate DEGs were evaluated in two independent LAA sample sets, consisting of 10 cases with LAAT and 10 cases without LAAT. The qPCR results using these samples also showed results consistent with the expression patterns observed in the RNA-seq analysis Supplementary Figure 1 . Association Study Between Clinical Information and Gene Expression No significant DEGs were correlated with CHADS2 scores. However, DIRAS3 expression showed a significant positive correlation with Heart failure (spearman’s r = 0.66, P = 0.030) ( Supplementary Table 5 ). Linear regression analysis further revealed that DIRAS3 expression was positively associated with the fibrosis ratio (β = 2.99, 95% confidence interval [CI] 0.22–5.75, p = 0.034) and NT-proBNP (β = 373, 95% CI 238–574, p = 5.71E-08). Additionally, CYP26B1 and TUBA3D expression levels were significantly associated with NT-proBNP (β = 349, 95% CI 23.8–674, p = 0.036; β = -140, 95% CI -272 to -8.81, p = 0.038, respectively). ( Supplementary Table 6 ). Expression Patterns of Candidate DEGs in Heart Cell Atlas We used the publicly available Heart Cell Atlas (https://www.heartcellatlas.org/) to investigate the cell type–specific expression patterns of the candidate DEGs ( CYP26B1 , DIRAS3 , and TUBA3D ) identified in our RNA-seq analysis [11]. CYP26B1 was predominantly expressed in fibroblasts, mesothelial cells, and adipocytes. DIRAS3 is mainly expressed in both atrial and ventricular cardiomyocytes, as well as in lymphatic endothelial and mesothelial cells. TUBA3D expression was observed in ventricular cardiomyocytes (Supplementary Figure 2) . Discussion AF is a major risk factor for stroke; intracardiac thrombi are more likely to form in patients with AF, and more than 90% of these thrombi form within the LAA. Virchow’s triad (hypercoagulability, hemodynamic changes, and endothelial injury) has been implicated in thrombus formation [12]. Recently, atrial cardiomyopathy, which is caused by atrial structural and electrophysiological remodeling has been considered an important risk factor of cardioembolic stroke [13]. Previously, we suggested that LAA fibrosis and endocardial endothelial damage are associated with LAAT and stroke using a histological approach and confirmed the relationship between LAAT and stroke within the atrial cardiomyopathy context [14]. The present study aimed to focus on not only the histological approach but also the approach for detecting the molecular mechanism via RNA-seq analysis to provide a better understanding of LAAT. We identified 27 DEGs and focused on four DEGs with large fold changes (i.e., DIRAS3 , CYP26BI , PRG4 , and ITLN1 ). In addition, we detected two hub genes from PPI analysis ( FKBP5 and TUBA3D ). The expression of these six genes could be reproduced by qPCR. Of them, DIRAS3 was significantly associated with a past history of heart failure and linear regression analysis revealed that the expression of DIRAS3 was significantly associated with fibrosis ratio and NT-pro BNP. The expression of CYP26B1 and TUBA3D were also associated with NT-proBNP. DIRAS3 (DIRAS family GTPase 3) is tumor suppressor gene which inhibit RAS function [15]. Not only tumor, Asim Ejaz et al. reported that DIRAS3 in human white adipose progenitor cells inhibits adipogenesis and activated autophagy through Akt-mTOR inhibition, then incapacitate cellular senescence and convince extension of lifespan [16]. With regard to the heart, Chuanjun Zhuo et al. reported that high glucose increased DIRAS3 expression in cardiomyocytes, and DIRAS3 induced autophagy by inhibiting mTOR signaling, which cause diabetic cardiomyopathy [17]. Autophagy plays an important role for cardiac fibrosis [18-20]. S Ghavami et al. reported that autophagy is a regulator of fibrogenesis in human atrial myofibroblasts [21]. In this study, the upregulation of DIRAS3 in the LAA with tthrombus might indicate the enhancement of autophagy in LAA, and the positive correlation between ratio of LAA fibrosis and expression of DIRAS3 was also detected. Previously, we revealed the histological evidence of association between LAA fibrosis and endocardial endothelial damage with LAAT, ischemic stroke [14]. The upregulation of DIRAS3 in LAA may play an important role for thrombus formation and stabilization through fibrosis of LAA. Autophagy also associated with cause of heart failure [18, 22] and cardiac aging which causes hypertrophy, dysfunction of mitochondria as well as fibrosis [23]. DIRAS3 was also correlated with NTproBNP. The upregulation of DIRAS3 in LAA with thrombus might contribute to heart failure such as atrial myopathy through autophagy in LAA and fibrosis. The expression of DIRAS3 was observed in the atrial and ventricular cardiomyocytes, mesothelial cell and fibroblast from the Heart Cell Atlas data (https://www.heartcellatlas.org/) [11]. These results implicated that expression of DIRAS3 in LAA could be the potential key to assessing the risk of stroke in AF patients. Previous study reported that CYP26B1 (cytochrome P450, family 26 subfamily B, polypeptide 1) related to atherosclerosis through retinoic acid catabolism [24]. Activation of retinoic acid receptors upregulates the antiatherogenic genes in macrophages [25], and reduces inflammation [26], and vascular cell proliferation, and coagulation [27, 28]. Oleysta Kruvispitskata et al. revealed that atherosclerotic arteries had higher levels of CYP26B1 [24], and they also revealed that the strongest expression of CYP26B1 in macrophage-rich inflammatory lesions [24]. From our investigation, the upregulation of CYP26B1 in the LAA may also indicate the high inflammation levels of this lesion and may contribute to the LAAT. Although we were unable to find a significant relationship between the expression of CYP26B1 and the degree of fibrosis, the Heart Cell Atlas data (https://www.heartcellatlas.org/) shows that CYP26B1 is highly expressed in fibroblasts [11], and it is possible that CYP26B1 is also related to fibrosis. TUBA3D encodes a member of the alpha-tubulin family, a key structural component of microtubules. Microtubules are dynamic cytoskeletal polymers composed of alpha- and beta-tubulin heterodimers, along with microtubule-associated proteins. They play essential roles in maintaining cellular architecture, facilitating intracellular transport, and forming the mitotic spindle during cell division [29]. A previous study, using the Gene Expression Omnibus dataset (GSE116250) obtained from human left ventricles, showed that the expression of TUBA3D in dilated cardiomyopathy-induced heart failure is significantly decreased compared to non-failing donors [30]. Although the tissue differs between the ventricle and the atrium, reduced TUBA3D in the atrial appendage suggests some myocardial damage and may be involved in thrombus formation and stabilization. Study limitations First, the number of samples available for RNA-seq analysis was limited. We identified candidate DEGs potentially related to pathogenesis of LAAT, but it was initially unclear whether these findings could be applied to other LAA samples. However, validation of the expression of the candidate DEGs using additional independent samples supported the robustness and potential generalizability of our results. Second, we could not perform a single-cell RNA-seq analysis or spatial transcriptomics analysis. If a single-cell RNA-seq analysis or spatial transcriptomics analysis had been performed, the genetic background for the LAAT could have been thoroughly assessed. In fact, a previous single-cell RNA-seq analysis using matched atrial appendage revealed the significance of the complement and coagulation cascade in LAAT formation [31], single-cell RNAseq is a powerful tool to detect the pathogenesis of LAAT. Third, persistent AF was more prevalent in the LAAT group. The previous large cohort study in Japan reported that paroxysmal AF was independently associated with a lower incidence of stroke than persistent AF [32]. This suggests that patients in the LAAT group have had a higher predisposition to thrombus. It is difficult to determine whether the candidate DEGs are directly involved in the pathogenesis of LAAT or reflect effects associated with persistent AF. Moreover, the temporal relationship of these DEGs—specifically, whether they contribute to thrombus formation or its subsequent stabilization—also remains unclear. Further investigations are warranted to elucidate these aspects. Despite these limitations, our investigation provides new possibilities for the key to the formation of LAAT. The identified genes may serve as practical and clinically relevant targets in the future. As these genes could apply to prospective data, their effectiveness in thrombus formation in LAA should be confirmed. Conclusion In this study, we identified candidate genes associated with LAAT in AF patients using RNA-seq analysis. These genes may contribute to the pathogenesis of LAAT and serve as potential biomarkers. Further validation may help to elucidate the potential clinical relevance of these genes and deepen our understanding of the mechanisms underlying LAAT formation. Declarations Acknowledgments The authors thank Nobuhiro Nakatani (Technical Center, Hiroshima University) for manufacturing the tissue slides. They also thank the clerical and medical staff members at Hiroshima University Hospital for their assistance. Source of Funding This work was supported by the Japan Society for the Promotion of Science (JSPS, Tokyo, Japan) Grant-in-Aid for Young Scientists (Research Project number 22K16140) to Dr. Miyauchi. All authors have reported that they have no relationships relevant to the contents of this paper to disclose. Disclosures None. The Graphical Abstract was designed using Servier Medical Art images (https://smart.servier.com). References Price MJ, Saw J (2020) Transcatheter Left Atrial Appendage Occlusion in the DOAC Era. J Am Coll Cardiol 75: 3136-3139 Johnsen SP, Dalby LW, Tackstrom T, Olsen J, Fraschke A (2017) Cost of illness of atrial fibrillation: a nationwide study of societal impact. 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Biochim Biophys Acta 1740: 155-161 Pino-Lagos K, Benson MJ, Noelle RJ (2008) Retinoic acid in the immune system. Ann N Y Acad Sci 1143: 170-187 Streb JW, Miano JM (2003) Retinoids: pleiotropic agents of therapy for vascular diseases? Curr Drug Targets Cardiovasc Haematol Disord 3: 31-57 Gidlof AC, Ocaya P, Krivospitskaya O, Sirsjo A (2008) Vitamin A: a drug for prevention of restenosis/reocclusion after percutaneous coronary intervention? Clin Sci (Lond) 114: 19-25 Hao XD, Chen P, Zhang YY, Li SX, Shi WY, Gao H (2017) De novo mutations of TUBA3D are associated with keratoconus. Sci Rep 7: 13570 Zhou L, Peng F, Li J, Gong H (2023) Exploring novel biomarkers in dilated cardiomyopathy‑induced heart failure by integrated analysis and in vitro experiments. Exp Ther Med 26: 325 Yang J, Tan H, Sun M, Chen R, Jian Z, Song Y, Zhang J, Bian S, Zhang B, Zhang Y, Gao X, Chen Z, Wu B, Ye X, Lv H, Liu Z, Huang L (2023) Single-cell RNA sequencing reveals a mechanism underlying the susceptibility of the left atrial appendage to intracardiac thrombogenesis during atrial fibrillation. Clin Transl Med 13: e1297 Takabayashi K, Hamatani Y, Yamashita Y, Takagi D, Unoki T, Ishii M, Iguchi M, Masunaga N, Ogawa H, Esato M, Chun YH, Tsuji H, Wada H, Hasegawa K, Abe M, Lip GY, Akao M (2015) Incidence of Stroke or Systemic Embolism in Paroxysmal Versus Sustained Atrial Fibrillation: The Fushimi Atrial Fibrillation Registry. Stroke 46: 3354-3361 Tables Tables are available in the Supplementary Files section. Graphical Abstract The Graphical Abstract file is not available with this version. Graphical Abstract Transcriptomic analysis of LAAT in patients with AF identified six genes— DIRAS3 , CYP26B1 , PRG4 , ITLN , FKBP5 , and TUBA3D —that are associated with thrombus formation. Among them, DIRAS3 expression was positively associated with both fibrosis ratio and NT-proBNP levels. CYP26B1 expression was also positively associated with NT-proBNP, whereas TUBA3D expression showed a negative association. This transcriptomic approach provides valuable insights into the pathogenesis of LAAT and highlights potential biomarkers for future investigation. Additional Declarations No competing interests reported. Supplementary Files Table1demographicdatafixed20250515.xlsx Table2mostsigDEGs.xlsx Table3hubgenes.xlsx eFigure1qPCRotherLAA.pdf eFigure2scRNAseq.pdf eTable1basicinfofixed20250515.xlsx eTable2primer.xlsx etable3Q20BGIpdfrmTH2fixed.xlsx etable4Significant27DEGsfixed.xlsx eTable5correlationDEGsCHADS2.xlsx eTable6linearreg.xlsx Cite Share Download PDF Status: Published Journal Publication published 05 Oct, 2025 Read the published version in Journal of Thrombosis and Thrombolysis → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6680046","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":457733931,"identity":"eba82b95-7119-4c3f-9ff7-4c772d65da8e","order_by":0,"name":"Junji Maeda","email":"","orcid":"","institution":"Hiroshima University Graduate School of Biomedical and Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Junji","middleName":"","lastName":"Maeda","suffix":""},{"id":457733932,"identity":"373d130f-b85c-4551-a453-4ff71afa4f6c","order_by":1,"name":"Motoki Furutani","email":"","orcid":"","institution":"Hiroshima University Graduate School of Biomedical and Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Motoki","middleName":"","lastName":"Furutani","suffix":""},{"id":457733933,"identity":"266e6034-1b23-4177-bc77-edae8aac543d","order_by":2,"name":"Shunsuke Miyauchi","email":"","orcid":"","institution":"Hiroshima University Graduate School of Biomedical and Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Shunsuke","middleName":"","lastName":"Miyauchi","suffix":""},{"id":457733934,"identity":"9f27b7ca-90bb-4a9e-aecf-f05b9bc8af95","order_by":3,"name":"Mika Nakashima","email":"","orcid":"","institution":"Hiroshima University Graduate School of Biomedical and Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mika","middleName":"","lastName":"Nakashima","suffix":""},{"id":457733935,"identity":"8557ef20-1712-4a84-8d8e-e26132c7c84a","order_by":4,"name":"Naoki Ishibashi","email":"","orcid":"","institution":"Hiroshima University Graduate School of Biomedical and Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Naoki","middleName":"","lastName":"Ishibashi","suffix":""},{"id":457733936,"identity":"a8388d23-b2fe-4210-ab57-b2aaadc5c827","order_by":5,"name":"Takumi Sakai","email":"","orcid":"","institution":"Hiroshima University Graduate School of Biomedical and Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Takumi","middleName":"","lastName":"Sakai","suffix":""},{"id":457733937,"identity":"0cb66cea-ebf2-45f9-bf6d-0a48c7fe08c9","order_by":6,"name":"Naoto Oguri","email":"","orcid":"","institution":"Hiroshima University Graduate School of Biomedical and Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Naoto","middleName":"","lastName":"Oguri","suffix":""},{"id":457733938,"identity":"ac1ec2aa-2753-4415-b6eb-a6cb02cd4d45","order_by":7,"name":"Shogo Miyamoto","email":"","orcid":"","institution":"Hiroshima University Graduate School of Biomedical and Health 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Nakano","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYFACNjApZ8AMJBkbJODizIS0GJOuJXEDA1gLEc4yZz+W+Lmg4nD6dnbehw9/7rCI5m/gMWD4UcPAbo5Di2VP2mHpGWcO5+5sZjc25j0jkTvjAI8BY88xBmZLHFYaHEhvkOZtO5y74TAbmzRjm0Ruw/03Bgy8DQzMBgdwaDn/vPk3UEu6AVCL5E+glvkgW/7i03Ij7RjIlgSQFgleoJYNQC3MeG258SzNmudMuiHQYczGIC0bD7AVHJY5JoHbL+fTjG/zVFjLG5w/xvjwZ1td7rwDzBsfvqmxScYVYlDQjMoFOkki2QC/ljpMITsCWkbBKBgFo2DkAABM0FYoSc3D8gAAAABJRU5ErkJggg==","orcid":"","institution":"Hiroshima University Graduate School of Biomedical and Health Sciences","correspondingAuthor":true,"prefix":"","firstName":"Yukiko","middleName":"","lastName":"Nakano","suffix":""}],"badges":[],"createdAt":"2025-05-16 10:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6680046/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6680046/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11239-025-03184-1","type":"published","date":"2025-10-05T15:57:17+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84201540,"identity":"cbc77011-0c28-4a1b-8ee4-99d7e685f6d6","added_by":"auto","created_at":"2025-06-09 08:31:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1145738,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResults of RNA-seq analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) DEG detection by RNA-seq. Each point represents a DEG. The blue and red dots represent the downregulated and upregulated DEGs, respectively. (b) PPI network analysis. The hub genes were defined as genes with a DC of ≥30 and BC of ≥ 3000.\u003c/p\u003e\n\u003cp\u003eDEG, differentially expressed gene; PPI, protein-protein interaction; BC, betweenness of centrality; DC, degree of centrality; FC, fold change; FDR, false discovery ratio\u003c/p\u003e","description":"","filename":"Figure1forpdf.png","url":"https://assets-eu.researchsquare.com/files/rs-6680046/v1/ee02a7ef63aa048937784590.png"},{"id":84202831,"identity":"7a5374a6-ead5-43de-af1a-91e60cda8dfb","added_by":"auto","created_at":"2025-06-09 08:39:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":382089,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQuantitative PCR verification of the RNA-seq data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe error bars in the quantitative PCR indicate standard errors.\u003c/p\u003e\n\u003cp\u003eThe mean relative gene expression ratios (LAAT/without LAAT) were as follows: 4.65 for \u003cem\u003eDIRAS3,\u003c/em\u003e 4.15 for \u003cem\u003eCYP26B1\u003c/em\u003e, 0.28 for \u003cem\u003eITLN1\u003c/em\u003e, 0.57 for \u003cem\u003ePRG4\u003c/em\u003e, 0.33 for \u003cem\u003eTUBA3D\u003c/em\u003e, and 0.29 for \u003cem\u003eFKBP5\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e*: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05\u003c/p\u003e\n\u003cp\u003eLAAT, left atrial appendage thrombus\u003c/p\u003e","description":"","filename":"Figure2qPCRboxplot.png","url":"https://assets-eu.researchsquare.com/files/rs-6680046/v1/144bd5d3c188b13f78c8d78c.png"},{"id":92883938,"identity":"16829a43-ae46-4e9b-8ef1-da460cc91d85","added_by":"auto","created_at":"2025-10-06 16:11:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2839412,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6680046/v1/cf2d26ab-0693-49a1-8a15-1df87ec9b12d.pdf"},{"id":84202830,"identity":"5c51e34e-3aa9-486b-9659-433c4a34f408","added_by":"auto","created_at":"2025-06-09 08:39:42","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10889,"visible":true,"origin":"","legend":"","description":"","filename":"Table1demographicdatafixed20250515.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6680046/v1/f767da98c520c105da728d8d.xlsx"},{"id":84201539,"identity":"9a59627c-23d4-4cbf-aed2-384a8d468bd4","added_by":"auto","created_at":"2025-06-09 08:31:42","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":9488,"visible":true,"origin":"","legend":"","description":"","filename":"Table2mostsigDEGs.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6680046/v1/89503e97f73f985b6476283b.xlsx"},{"id":84201537,"identity":"f0bcff34-5e42-4a48-8c09-53536261b00e","added_by":"auto","created_at":"2025-06-09 08:31:42","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":9399,"visible":true,"origin":"","legend":"","description":"","filename":"Table3hubgenes.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6680046/v1/5bea8734f5f33717f5415853.xlsx"},{"id":84201544,"identity":"aea21706-1843-4a7c-88aa-0ee3270b9663","added_by":"auto","created_at":"2025-06-09 08:31:42","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":204567,"visible":true,"origin":"","legend":"","description":"","filename":"eFigure1qPCRotherLAA.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6680046/v1/ef404545e5361884e9da0003.pdf"},{"id":84201547,"identity":"9522f472-0215-415f-805f-ca254d385a5d","added_by":"auto","created_at":"2025-06-09 08:31:42","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":316679,"visible":true,"origin":"","legend":"","description":"","filename":"eFigure2scRNAseq.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6680046/v1/b63f7580ee3d16efce2de7b8.pdf"},{"id":84201542,"identity":"9a68d897-66b3-4b36-ab6c-1ecb34088bb6","added_by":"auto","created_at":"2025-06-09 08:31:42","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":11315,"visible":true,"origin":"","legend":"","description":"","filename":"eTable1basicinfofixed20250515.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6680046/v1/551b0a2af2e5ae690b6eab7e.xlsx"},{"id":84201543,"identity":"f2a502f9-9064-4910-928f-c40010ea18a5","added_by":"auto","created_at":"2025-06-09 08:31:42","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":9759,"visible":true,"origin":"","legend":"","description":"","filename":"eTable2primer.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6680046/v1/40b52ba78e8a7581a296e4a4.xlsx"},{"id":84201541,"identity":"a7349754-a4df-45ca-b8d2-8332e3acf5f8","added_by":"auto","created_at":"2025-06-09 08:31:42","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":11242,"visible":true,"origin":"","legend":"","description":"","filename":"etable3Q20BGIpdfrmTH2fixed.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6680046/v1/155510882924a161af3e31d4.xlsx"},{"id":84201546,"identity":"126a9fc5-289a-44b1-ba4c-955ec222ec46","added_by":"auto","created_at":"2025-06-09 08:31:42","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":11068,"visible":true,"origin":"","legend":"","description":"","filename":"etable4Significant27DEGsfixed.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6680046/v1/9d5791e195f70f5b5189e332.xlsx"},{"id":84202832,"identity":"fedad300-c3dc-4a2d-9cb0-b803549e699b","added_by":"auto","created_at":"2025-06-09 08:39:42","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":10860,"visible":true,"origin":"","legend":"","description":"","filename":"eTable5correlationDEGsCHADS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6680046/v1/b2566c70f812d407a4b5b0c8.xlsx"},{"id":84201548,"identity":"05aa57f0-e91a-47c0-903e-39a8ee2c3cd3","added_by":"auto","created_at":"2025-06-09 08:31:42","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":10224,"visible":true,"origin":"","legend":"","description":"","filename":"eTable6linearreg.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6680046/v1/82f08b5bb3ca1b80a6a8e31e.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"RNA Sequence Analysis of Differentially Expressed Genes in Left Atrial Appendage Thrombus","fulltext":[{"header":"Title page abstract","content":"\u003cul start=\"50\"\u003e\n \u003cli\u003eWe investigated candidate genes associated with left atrial appendage thrombus (LAAT) in patients with atrial fibrillation using RNA sequencing of left atrial appendage tissue obtained during cardiac surgery.\u003c/li\u003e\n \u003cli\u003eRNA sequencing analysis identified six key genes potentially associated with LAAT:\u0026nbsp;\u003cem\u003eDIRAS3\u003c/em\u003e, \u003cem\u003eCYP26B1\u003c/em\u003e, \u003cem\u003ePRG4\u003c/em\u003e, \u003cem\u003eITLN\u003c/em\u003e, \u003cem\u003eFKBP5\u003c/em\u003e, and \u003cem\u003eTUBA3D\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eAmong them, \u003cem\u003eDIRAS3\u0026nbsp;\u003c/em\u003eexpression showed a positive correlation with both the degree of fibrosis and NT-proBNP levels. \u003cem\u003eCYP26B1\u003c/em\u003e expression was positively correlated with NT-proBNP, whereas \u003cem\u003eTUBA3D\u0026nbsp;\u003c/em\u003eexpression was negatively correlated.\u003c/li\u003e\n \u003cli\u003eThis transcriptomic approach provides valuable insight into the pathogenesis of LAAT formation and the identification of potential biomarkers. Further research is needed to validate these findings and assess their clinical implications.\u003cstrong\u003e\u003cbr\u003e \u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Introduction","content":"\u003cp\u003eCardiogenic embolus is mostly derived from the left atrial appendage (LAA) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], which influences patient prognosis, quality of life, and social cost [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Recently, technical advances in LAA management, such as percutaneous left atrial appendage (LAA) closure [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and thoracoscopic left atrial appendectomy [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], have led to the development of effective therapeutic strategies. In this era of less invasive LAA management, it is essential to identify patients at high risk for forming LAA thrombus (LAAT). Previous studies have shown that decreased LAA function [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and non-chicken wing LAA morphology [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] are associated with the formation of LAAT. However, the exact mechanisms of LAAT formation have not been well elucidated.\u003c/p\u003e \u003cp\u003eAnalysis of whole messenger RNA (mRNA) expression play a crucial role in uncovering the molecular mechanisms underlying diseases. One of the most powerful tools for detecting the biomarkers or molecular functions in human tissues is whole RNA sequencing (RNA-seq). A comprehensive transcriptome analysis using next-generation sequencing has been widely applied, including paired RNA-seq analysis of left and right appendages in human [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, RNA-seq transcriptome analyses of LAA specimens were performed to detect key genes related to the LAAT and to uncover the underlying mechanisms of LAAT.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eLAA Samples and Baseline Data Acquisition\u003c/h2\u003e \u003cp\u003eThe LAA samples were obtained during cardiac surgery from six patients with evidence of LAAT and five with no clinical evidence of LAAT. All patients were diagnosed with AF, documented using more than one modality (e.g., 12-lead electrocardiographic testing, 24-hour Holter recording, long-term Holter recording, or mobile electrocardiographic monitoring), and subjected to LAA management during cardiac surgery. Data on the patients\u0026rsquo; baseline clinical characteristics, including their AF status, coexisting disease, and the results of the routine peripheral blood tests and echocardiography examinations, was obtained from their medical records. The CHADS\u003csub\u003e2\u003c/sub\u003e score was calculated following the standard manner [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Moreover, paroxysmal AF was defined as recurrent AF that terminated spontaneously within seven days, whereas non-paroxysmal AF was defined as AF that persisted beyond seven days and included long-standing and permanent AF.\u003c/p\u003e \u003cp\u003eThe presence of an LAAT was confirmed via transesophageal echocardiography (TEE) that was conducted by experienced echocardiographers before surgery using an EPIQ 7 ultrasound imaging system (Philips) and X8-2t 3-dimensional TEE transducer (Philips). An LAAT was defined as a mass with a high echogenic density, distinct from the LAA wall density, and attached to the LAA wall as well as a mass that moved independently of the LAA wall.\u003c/p\u003e \u003cp\u003e This study was conducted in accordance with the Declaration of Helsinki, and the study approval was obtained from the Hiroshima University Ethics Committee (approval number E-1931). Written informed consent was obtained from all patients.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLAA Excision\u003c/h3\u003e\n\u003cp\u003eFor patients who required LAA management alone, LAA excision was performed through thoracoscopic stand-alone left atrial appendectomy as previously reported [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. For the other patients, LAAs were excised concomitant with other cardiac procedures. \u003cb\u003eSupplemental Table\u0026nbsp;1\u003c/b\u003e shows the primary cardiac disease and procedures. After resection, the LAA is thoroughly washed with saline, so thrombi and blood clots are not included in the analysis samples. The two 10 \u0026times; 10 mm sections from the distal side of each LAA specimen were randomly resected and fixed in formalin and paraffin-embedded. The remaining specimens were snap-frozen in liquid nitrogen immediately and stored at a temperature of \u0026minus;\u0026thinsp;80\u0026deg;C.\u003c/p\u003e\n\u003ch3\u003eQuantification of LAA Fibrosis\u003c/h3\u003e\n\u003cp\u003eThe quantification of the degree of LAA fibrosis was performed as previously reported [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In brief, the two formalin-fixed and paraffin-embedded LAA sections (4.5-\u0026micro;m thick) were deparaffinized and subjected to Azan\u0026ndash;Mallory staining. The two microscopic fields with 200\u0026times; magnification were randomly selected and imaged for each LAA section. After the manual removal of the perivascular tissue, epicardium, endocardium, and fatty tissue, the red (myocardium) and blue areas (fibrosis) in each image were measured using the BZ-X800 Analyzer software (Keyence, Osaka, Japan). Moreover, the degree of the LAA fibrosis (%) in each microscopic field was measured by dividing the fibrosis area by the fibrosis area plus the myocardium area and multiplying by 100. Furthermore, the average of the four microscopic fields was calculated. These procedures were performed by experienced pathologists who had no access to the clinical data.\u003c/p\u003e\n\u003ch3\u003eRNA Extraction\u003c/h3\u003e\n\u003cp\u003eFrom the frozen LAA specimens, 25 mg sections were randomly resected from the distal side of the LAA. Each section was homogenized with zirconia-silica beads using a bead beater homogenizer (\u0026micro;T-12, TAITEC, Saitama, Japan). After homogenization, the RNA was extracted using the easy-spin Total RNA Extraction Kit (iNtRON Biotechnology, Seongnam, Korea) in accordance with the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e\n\u003ch3\u003eRNA-seq Data Analysis\u003c/h3\u003e\n\u003cp\u003eRNA-seq\u0026ndash;based transcriptome profiling was performed by the Beijing Genomics Institute (Wuhan) using the BGISEQ platform [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Mapping to a human reference genome (GRCh37) with STAR (ver. 2.5.2b) was performed using clean sequenced reads from BGISEQ. The read counts for each gene were used to measure the RSEM program (version 1.3.0 [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://deweylab.github.io/RSEM\u003c/span\u003e\u003cspan address=\"https://deweylab.github.io/RSEM\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e]). Differential gene expression analysis was performed using the edgeR package (ver. 3.8.1) program (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioconductor.org/packages/release/bioc/html/edgeR.html\u003c/span\u003e\u003cspan address=\"https://bioconductor.org/packages/release/bioc/html/edgeR.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) after the read counts from each sample were combined into a count file. Moreover, we filtered by minimum read counts using the \u0026ldquo;filterByExpr\u0026rdquo; function of the edgeR package. The \u0026ldquo;caclNormFactors\u0026rdquo; function in the edgeR package was used to obtain the TMM (trimmed mean of M-values) normalization factors to account for library sizes. We applied the \u0026ldquo;exactTest\u0026rdquo; function in the edgeR package to obtain differentially expressed genes (DEGs) between the samples from patients with and without LAAT. The DEGs were defined as genes with a false discovery rate (FDR) of \u0026lt;\u0026thinsp;0.05, |fold change (FC)| \u0026gt; 2 and a normalized transcript per million (nTPM) of \u0026ge;\u0026thinsp;1 in the heart muscle from the human protein atlas database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.proteinatlas.org\u003c/span\u003e\u003cspan address=\"https://www.proteinatlas.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The FDR values were measured using the Benjamini\u0026ndash;Hochberg method. The TPM was obtained using RSEM (version 1.3.0 [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://deweylab.github.io/RSEM\u003c/span\u003e\u003cspan address=\"https://deweylab.github.io/RSEM\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e]) after utilizing STAR to align the RNA-seq reads to the human reference genome.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGene Ontology Molecular Function Term Enrichment Analysis\u003c/h2\u003e \u003cp\u003eA Gene Ontology (GO) term enrichment analysis was performed using the significant DEGs to assess molecular functions. Statistically significant GO terms (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were investigated based on the DEGs using the Database that includes Annotation, Visualization, and Integrated Discovery (DAVID, version 2023q4; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eProtein–Protein Interaction Network Analysis\u003c/h3\u003e\n\u003cp\u003eTo identify key genes and critical gene modules, a protein\u0026ndash;protein interaction (PPI) network analysis was conducted using NetworkAnalyst (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.networkanalyst.ca\u003c/span\u003e\u003cspan address=\"https://www.networkanalyst.ca\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), integrating data from the DifferentialNet database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://netbio.bgu.ac.il/diffnet/\u003c/span\u003e\u003cspan address=\"https://netbio.bgu.ac.il/diffnet/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Tissue-specific interaction data corresponding to the heart atrial appendage were employed, with a filter level of 15 applied. The PPI network was visualized using Cytoscape (version 3.10.3, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cytoscape.org/\u003c/span\u003e\u003cspan address=\"http://www.cytoscape.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eValidation of RNA-seq Results by Quantitative PCR and in an Independent Sample Set\u003c/h3\u003e\n\u003cp\u003eUsing the ReverTra Ace qPCR RT kit (TOYOBO, Osaka, Japan), cDNA was synthesized. The following conditions were used for reverse transcription: the first step to prepare the mixture was one cycle of 65\u0026deg;C for 5 min and cooling immediately, and the second step was one cycle of 37\u0026deg;C for 15 min, one cycle of 50\u0026deg;C for 5 min, and one cycle of 98\u0026deg;C for 5 min. A real-time PCR analysis was performed using QuantStudio5\u0026trade; Real-Time PCR Systems 384-well plates (Thermo Fisher Scientific, Waltham, MA, USA) and PowerUp\u0026trade; SYBR\u0026trade; Green Master Mix (Thermo Fisher Scientific, Waltham, MA, USA). \u003cb\u003eSupplemental Table\u0026nbsp;2\u003c/b\u003e shows the target genes and their corresponding primers. The real-time PCR conditions were as follows: one cycle of 50\u0026deg;C for 2 min, one cycle of 95\u0026deg;C for 2 min, 40 cycles of 95\u0026deg;C for 15 s, one cycle of 60\u0026deg;C for 60 s, one cycle of 95\u0026deg;C for 15 s, one cycle of 60\u0026deg;C for 60 s, and one cycle of 95\u0026deg;C for 15 s. Each gene was assayed in duplicate. \u003cem\u003eGAPDH\u003c/em\u003e (\u003cb\u003eSupplemental Table\u0026nbsp;2\u003c/b\u003e) was selected as a reference gene for the normalization of the target gene expression levels. Moreover, the target gene expression levels were estimated from the samples of the RNA-seq analysis. The relative gene expression levels were calculated using the 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method. Additionally, we analyzed independent LAA samples, comprising of 10 samples with LAAT and 10 samples without LAAT and verified the expression levels of the candidate DEGs using the same method.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between Gene Expression and Clinical Information\u003c/h2\u003e \u003cp\u003eThe correlation between the candidate DEGs and CHADS\u003csub\u003e2\u003c/sub\u003e scores (congestive heart failure, hypertension, Age, diabetes, stroke) was assessed by Spearman's rank correlation coefficient. Additionally, the associations between the expression of candidate DEGs and clinical information (fibrosis ratio and NT-proBNP) were evaluated using linear regression analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eExpression Patterns of Candidate DEGs in Heart Cell Atlas\u003c/h2\u003e \u003cp\u003eTo investigate the cell type\u0026ndash;specific expression patterns of the DEGs identified by our RNA-seq analysis, we utilized publicly available single-cell RNA-seq data from the Heart Cell Atlas (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.heartcellatlas.org/\u003c/span\u003e\u003cspan address=\"https://www.heartcellatlas.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] to estimate the expression and localization of the candidate DEGs across various cardiac cell populations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eContinuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;Standard deviation (SD), and categorical variables are presented as counts (percentages). Statistical analysis was performed using Python version 3.8.12 and R version 4.3.1. For clinical data, continuous variables were compared using the Mann\u0026ndash;Whitney U test when the data did not follow a normal distribution, and Welch\u0026rsquo;s t-test when the data followed a normal distribution. Categorical variables were compared using Fisher\u0026rsquo;s exact test. The correlation between the CHADS2 scores and gene expression was estimated using Spearman's rank correlation coefficient. Association between clinical information and gene expression were assessed using linear regression adjusted for age and gender. A \u003cem\u003eP\u003c/em\u003e value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant for clinical items, and an FDR of \u0026lt;\u0026thinsp;0.05 was considered significant in the RNA-seq analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eClinical Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSix patients with evidence of LAAT (33.33% female, mean age 69.97 \u0026plusmn; 9.07 years) and five patients without clinical evidence of LAAT (40% female, mean age 70.60 \u0026plusmn; 11.46) were enrolled in this study. \u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;Supplemental Table 1\u0026nbsp;\u003c/strong\u003eshow\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ethe baseline demographic data of the participants. Persistent AF was significantly more common in the LAAT participants, while NT-proBNP was significantly higher in participants with LAAT compared to those without (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). No statistical differences were observed between other clinical factors and the LAA fibrosis ratio (\u003cstrong\u003eTable 1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eDetection of DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRNA-seq analysis was performed on all LAA samples using an average of \u0026gt;48.7 million high-quality read sequences, with \u0026gt;96.21% uniquely mapped to the human reference genome (GRCh37) (\u003cstrong\u003eSupplemental Table 3\u003c/strong\u003e). A total of 27 significant DEGs were identified from the 13,722 analyzed genes, based on the criteria of an FDR \u0026lt; 0.05, |FC| \u0026gt; 2, and a nTPM \u0026ge; 1 in heart muscle, according to the Human Protein Atlas. Among these, 2 genes were upregulated and 25 were downregulated in the LAAT samples. Notably, four DEGs\u0026mdash;\u003cem\u003eDIRAS3\u003c/em\u003e, \u003cem\u003eCYP26B1\u003c/em\u003e, \u003cem\u003ePRG4\u003c/em\u003e, and \u003cem\u003eITLN1\u003c/em\u003e\u0026mdash;exhibited particularly large fold changes and were selected for further analysis (\u003cstrong\u003eFigure 1a\u003c/strong\u003e,\u003cstrong\u003e\u0026nbsp;Table 2,\u0026nbsp;\u003c/strong\u003eand \u003cstrong\u003eSupplemental Table 4\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eGO Term Enrichment Analysis and PPI Network Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was no GO molecular function term with an FDR \u0026lt; 0.05. Subsequently, we performed a PPI network analysis using the 27 DEGs, utilizing tissue-specific interaction data for heart atrial appendage from DifferentialNet database (https://netbio.bgu.ac.il/labwebsite/software/differentialnet/). The PPI network comprised 165 noses and 169 edges. We identified the most highly ranked hub genes in terms of network topology measures of the degree of centrality (DC) and betweenness centrality (BC). Two hub genes were detected with DC \u0026ge; 30 and BC \u0026ge; 3000:\u003cem\u003e\u0026nbsp;FKBP5\u003c/em\u003e (DC = 75, BC = 9989.08), and \u003cem\u003eTUBA3D\u0026nbsp;\u003c/em\u003e(DC = 38, BC = 7993.42) (\u003cstrong\u003eFigure 1b\u0026nbsp;\u003c/strong\u003eand \u003cstrong\u003eTable 3\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eVerification of the Quantitative PCR Assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQuantitative PCR analysis was performed to validate six genes identified by our RNA-seq analysis: \u003cem\u003eDIRAS3, CYP26B1, ILTN1, PRG4,\u003c/em\u003e \u003cem\u003eFKBP\u003c/em\u003e, and \u003cem\u003eTUBA3D\u003c/em\u003e. The mean relative gene expression ratios (LAAT/without LAAT) were as follows: 4.65 for \u003cem\u003eDIRAS3,\u003c/em\u003e 4.15 for \u003cem\u003eCYP26B1\u003c/em\u003e, 0.28 for \u003cem\u003eITLN1\u003c/em\u003e, 0.57 for \u003cem\u003ePRG4\u003c/em\u003e, 0.33 for \u003cem\u003eTUBA3D\u003c/em\u003e, and 0.29 for \u003cem\u003eFKBP5\u003c/em\u003e.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThese gene expressions were consistent with the RNA-seq results,\u0026nbsp;supporting the reliability of the transcriptomic findings (\u003cstrong\u003eFigure 2\u003c/strong\u003e). The expression levels of the candidate DEGs were evaluated in two independent LAA sample sets, consisting of 10 cases with LAAT and 10 cases without LAAT. The qPCR results using these samples also showed results consistent with the expression patterns observed in the RNA-seq analysis \u003cstrong\u003eSupplementary Figure 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAssociation Study Between Clinical Information and Gene Expression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo significant DEGs were correlated with CHADS2\u003csub\u003e\u0026nbsp;\u003c/sub\u003escores. However, \u003cem\u003eDIRAS3\u003c/em\u003e expression showed a significant positive correlation with Heart failure (spearman\u0026rsquo;s \u003cem\u003er\u0026nbsp;\u003c/em\u003e= 0.66, \u003cem\u003eP\u003c/em\u003e = 0.030) (\u003cstrong\u003eSupplementary Table 5\u003c/strong\u003e). Linear regression analysis further revealed that \u003cem\u003eDIRAS3\u003c/em\u003e expression was positively associated with the fibrosis ratio (\u0026beta; = 2.99, 95% confidence interval [CI] 0.22\u0026ndash;5.75, \u003cem\u003ep\u003c/em\u003e = 0.034) and NT-proBNP (\u0026beta; = 373, 95% CI 238\u0026ndash;574, \u003cem\u003ep\u003c/em\u003e = 5.71E-08). Additionally, \u003cem\u003eCYP26B1\u003c/em\u003e and \u003cem\u003eTUBA3D\u003c/em\u003e expression levels were significantly associated with NT-proBNP (\u0026beta; = 349, 95% CI 23.8\u0026ndash;674, p = 0.036; \u0026beta; = -140, 95% CI -272 to -8.81, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.038, respectively). (\u003cstrong\u003eSupplementary Table 6\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eExpression Patterns of Candidate DEGs in Heart Cell Atlas\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used the publicly available Heart Cell Atlas (https://www.heartcellatlas.org/) to investigate the cell type\u0026ndash;specific expression patterns of the candidate DEGs (\u003cem\u003eCYP26B1\u003c/em\u003e, \u003cem\u003eDIRAS3\u003c/em\u003e, and \u003cem\u003eTUBA3D\u003c/em\u003e) identified in our RNA-seq analysis [11].\u003cem\u003e\u0026nbsp;CYP26B1\u0026nbsp;\u003c/em\u003ewas predominantly expressed in fibroblasts, mesothelial cells, and adipocytes. \u003cem\u003eDIRAS3\u0026nbsp;\u003c/em\u003eis mainly expressed in both atrial and ventricular cardiomyocytes, as well as in lymphatic endothelial and mesothelial cells. \u003cem\u003eTUBA3D\u003c/em\u003e expression was observed in ventricular cardiomyocytes\u0026nbsp;\u003cstrong\u003e(Supplementary Figure 2)\u003c/strong\u003e.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAF is a major risk factor for stroke; intracardiac thrombi are more likely to form in patients with AF, and more than 90% of these thrombi form within the LAA. Virchow\u0026rsquo;s triad (hypercoagulability, hemodynamic changes, and endothelial injury) has been implicated in thrombus formation\u0026nbsp;[12]. Recently, atrial cardiomyopathy, which is caused by atrial structural and electrophysiological remodeling has been considered an important risk factor of cardioembolic stroke [13]. Previously, we suggested that LAA fibrosis and endocardial endothelial damage are associated with LAAT and stroke using a histological approach and confirmed the relationship between LAAT and stroke within the atrial cardiomyopathy context [14]. The present study aimed to focus on not only the histological approach but also the approach for detecting the molecular mechanism via RNA-seq analysis to provide a better understanding of LAAT.\u003c/p\u003e\n\u003cp\u003eWe identified 27 DEGs and focused on four DEGs with large fold changes (i.e., \u003cem\u003eDIRAS3\u003c/em\u003e, \u003cem\u003eCYP26BI\u003c/em\u003e, \u003cem\u003ePRG4\u003c/em\u003e, and \u003cem\u003eITLN1\u003c/em\u003e). In addition, we detected two hub genes from PPI analysis (\u003cem\u003eFKBP5\u003c/em\u003e and \u003cem\u003eTUBA3D\u003c/em\u003e). The expression of these six genes could be reproduced by qPCR. Of them, \u003cem\u003eDIRAS3\u0026nbsp;\u003c/em\u003ewas significantly associated with a past history of heart failure and linear regression analysis revealed that the expression of \u003cem\u003eDIRAS3\u0026nbsp;\u003c/em\u003ewas significantly associated with fibrosis ratio and NT-pro BNP. The expression of \u003cem\u003eCYP26B1\u003c/em\u003e and \u003cem\u003eTUBA3D\u003c/em\u003e were also associated with NT-proBNP.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDIRAS3\u003c/em\u003e (DIRAS family GTPase 3) is tumor suppressor gene which inhibit RAS function [15]. Not only tumor, Asim Ejaz et al. reported that DIRAS3 in human white adipose progenitor cells inhibits adipogenesis and activated autophagy through Akt-mTOR inhibition, then incapacitate cellular senescence and convince extension of lifespan [16]. With regard to the heart, Chuanjun Zhuo et al. reported that high glucose increased DIRAS3 expression in cardiomyocytes, and DIRAS3 induced autophagy by inhibiting mTOR signaling, which cause diabetic cardiomyopathy [17]. Autophagy plays an important role for cardiac fibrosis [18-20]. S Ghavami et al. reported that autophagy is a regulator of fibrogenesis in human atrial myofibroblasts [21]. \u0026nbsp;In this study, the upregulation of\u003cem\u003e\u0026nbsp;DIRAS3\u003c/em\u003e in the LAA with tthrombus might indicate the enhancement of autophagy in LAA, and the positive correlation between ratio of LAA fibrosis and expression of \u003cem\u003eDIRAS3\u003c/em\u003e was also detected. Previously, we revealed the histological evidence of association between LAA fibrosis and endocardial endothelial damage with LAAT, ischemic stroke [14]. The upregulation of \u003cem\u003eDIRAS3\u0026nbsp;\u003c/em\u003ein LAA may play an important role for thrombus formation and stabilization through fibrosis of LAA. Autophagy also associated with cause of heart failure [18, 22] and cardiac aging which causes hypertrophy, dysfunction of mitochondria as well as fibrosis [23]. \u003cem\u003eDIRAS3\u003c/em\u003e was also correlated with NTproBNP. The upregulation of \u003cem\u003eDIRAS3\u003c/em\u003e in LAA with thrombus might contribute to heart failure such as atrial myopathy through autophagy in LAA and fibrosis. The expression of \u003cem\u003eDIRAS3\u003c/em\u003e was observed in the atrial and ventricular cardiomyocytes, mesothelial cell and fibroblast from the Heart Cell Atlas data (https://www.heartcellatlas.org/) [11]. These results implicated that expression of\u003cem\u003e\u0026nbsp;DIRAS3\u003c/em\u003e in LAA could be the potential key to assessing the risk of stroke in AF patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrevious study reported that \u003cem\u003eCYP26B1\u003c/em\u003e (cytochrome P450, family 26 subfamily B, polypeptide 1) related to atherosclerosis through retinoic acid catabolism [24]. Activation of retinoic acid receptors upregulates the antiatherogenic genes in macrophages [25], and reduces inflammation [26], and vascular cell proliferation, and coagulation [27, 28]. Oleysta Kruvispitskata et al. revealed that atherosclerotic arteries had higher levels of CYP26B1 [24], and they also revealed that the strongest expression of CYP26B1 in macrophage-rich inflammatory lesions [24]. From our investigation, the upregulation of \u003cem\u003eCYP26B1\u003c/em\u003e in the LAA may also indicate the high inflammation levels of this lesion and may contribute to the LAAT. Although we were unable to find a significant relationship between the expression of \u003cem\u003eCYP26B1\u003c/em\u003e and the degree of fibrosis, the Heart Cell Atlas data (https://www.heartcellatlas.org/) shows that \u003cem\u003eCYP26B1\u003c/em\u003e is highly expressed in fibroblasts [11], and it is possible that \u003cem\u003eCYP26B1\u003c/em\u003e is also related to fibrosis.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTUBA3D\u003c/em\u003e encodes a member of the alpha-tubulin family, a key structural component of microtubules. Microtubules are dynamic cytoskeletal polymers composed of alpha- and beta-tubulin heterodimers, along with microtubule-associated proteins. They play essential roles in maintaining cellular architecture, facilitating intracellular transport, and forming the mitotic spindle during cell division [29]. A previous study, using the Gene Expression Omnibus dataset (GSE116250) obtained from human left ventricles, showed that the expression of\u003cem\u003e\u0026nbsp;TUBA3D\u0026nbsp;\u003c/em\u003ein dilated cardiomyopathy-induced heart failure is significantly decreased compared to non-failing donors [30]. Although the tissue differs between the ventricle and the atrium, reduced\u003cem\u003e\u0026nbsp;TUBA3D\u0026nbsp;\u003c/em\u003ein the atrial appendage suggests some myocardial damage and may be involved in thrombus formation and stabilization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eStudy limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst, the number of samples available for RNA-seq analysis was limited. We identified candidate DEGs potentially related to pathogenesis of LAAT, but it was initially unclear whether these findings could be applied to other LAA samples. However, validation of the expression of the candidate DEGs using additional independent samples supported the robustness and potential generalizability of our results. Second, we could not perform a single-cell RNA-seq analysis or spatial transcriptomics analysis. If a single-cell RNA-seq analysis or spatial transcriptomics analysis had been performed, the genetic background for the LAAT could have been thoroughly assessed. In fact, a previous single-cell RNA-seq analysis using matched atrial appendage\u0026nbsp;revealed the significance of the complement and coagulation cascade in LAAT formation [31], single-cell RNAseq is a powerful tool to detect the pathogenesis of LAAT. Third, persistent AF was more prevalent in the LAAT group. The previous large cohort study in Japan reported that paroxysmal AF was independently associated with a lower incidence of stroke than persistent AF [32]. This suggests that patients in the LAAT group have had a higher predisposition to thrombus. It is difficult to determine whether the candidate DEGs are directly involved in the pathogenesis of LAAT or reflect effects associated with persistent AF. Moreover, the temporal relationship of these DEGs\u0026mdash;specifically, whether they contribute to thrombus formation or its subsequent stabilization\u0026mdash;also remains unclear. Further investigations are warranted to elucidate these aspects.\u003c/p\u003e\n\u003cp\u003eDespite these limitations, our investigation provides new possibilities for the key to the formation of LAAT. The identified genes may serve as practical and clinically relevant targets in the future. As these genes could apply to prospective data, their effectiveness in thrombus formation in LAA should be confirmed.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we identified candidate genes associated with LAAT in AF patients using RNA-seq analysis. These genes may contribute to the pathogenesis of LAAT and serve as potential biomarkers. Further validation may help to elucidate the potential clinical relevance of these genes and deepen our understanding of the mechanisms underlying LAAT formation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Nobuhiro Nakatani (Technical Center, Hiroshima University) for manufacturing the tissue slides. They also thank the clerical and medical staff members at Hiroshima University Hospital for their assistance.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eSource of Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Japan Society for the Promotion of Science (JSPS, Tokyo, Japan) Grant-in-Aid for Young Scientists (Research Project number 22K16140) to Dr. Miyauchi. All authors have reported that they have no relationships relevant to the contents of this paper to disclose.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eDisclosures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone. The Graphical Abstract was designed using Servier Medical Art images (https://smart.servier.com).\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePrice MJ, Saw J (2020) Transcatheter Left Atrial Appendage Occlusion in the DOAC Era. J Am Coll Cardiol 75: 3136-3139\u003c/li\u003e\n\u003cli\u003eJohnsen SP, Dalby LW, Tackstrom T, Olsen J, Fraschke A (2017) Cost of illness of atrial fibrillation: a nationwide study of societal impact. BMC Health Serv Res 17: 714\u003c/li\u003e\n\u003cli\u003eTuragam MK, Velagapudi P, Kar S, Holmes D, Reddy VY, Refaat MM, Di Biase L, Al-Ahmed A, Chung MK, Lewalter T, Edgerton J, Cox J, Fisher J, Natale A, Lakkireddy DR (2018) Cardiovascular Therapies Targeting Left Atrial Appendage. 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Clin Transl Med 13: e1297\u003c/li\u003e\n\u003cli\u003eTakabayashi K, Hamatani Y, Yamashita Y, Takagi D, Unoki T, Ishii M, Iguchi M, Masunaga N, Ogawa H, Esato M, Chun YH, Tsuji H, Wada H, Hasegawa K, Abe M, Lip GY, Akao M (2015) Incidence of Stroke or Systemic Embolism in Paroxysmal Versus Sustained Atrial Fibrillation: The Fushimi Atrial Fibrillation Registry. Stroke 46: 3354-3361\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables are available in the Supplementary Files section.\u003c/p\u003e"},{"header":"Graphical Abstract","content":"\u003cp\u003eThe Graphical Abstract file is not available with this version.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eGraphical Abstract\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTranscriptomic analysis of LAAT in patients with AF identified six genes\u0026mdash;\u003cem\u003eDIRAS3\u003c/em\u003e, \u003cem\u003eCYP26B1\u003c/em\u003e, \u003cem\u003ePRG4\u003c/em\u003e, \u003cem\u003eITLN\u003c/em\u003e, \u003cem\u003eFKBP5\u003c/em\u003e, and \u003cem\u003eTUBA3D\u003c/em\u003e\u0026mdash;that are associated with thrombus formation. Among them, \u003cem\u003eDIRAS3\u003c/em\u003e expression was positively associated with both fibrosis ratio and NT-proBNP levels. \u003cem\u003eCYP26B1\u003c/em\u003e expression was also positively associated with NT-proBNP, whereas \u003cem\u003eTUBA3D\u003c/em\u003e expression showed a negative association. This transcriptomic approach provides valuable insights into the pathogenesis of LAAT and highlights potential biomarkers for future investigation.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"atrial fibrillation, left atrial appendage thrombus, RNA sequencing","lastPublishedDoi":"10.21203/rs.3.rs-6680046/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6680046/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCardioembolic stroke is a major complication of atrial fibrillation (AF). We investigated differentially expressed genes (DEGs) in the left atrial appendage (LAA) with and without LAA thrombus (LAAT) using RNA sequencing (RNA-seq).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eLAA tissue samples were obtained during cardiac surgery. We analyzed samples with LAAT (n\u0026thinsp;=\u0026thinsp;6) and without LAAT (n\u0026thinsp;=\u0026thinsp;5). Differential gene expression analysis was conducted to identify significantly altered genes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eRNA-seq identified 27 differentially expressed genes (false discovery rate\u0026thinsp;\u0026lt;\u0026thinsp;0.05, |fold change| \u0026gt; 2). Among these, four DEGs\u0026mdash;\u003cem\u003eDIRAS3\u003c/em\u003e, \u003cem\u003eCYP26B1\u003c/em\u003e, \u003cem\u003ePRG4\u003c/em\u003e, and \u003cem\u003eITLN\u003c/em\u003e\u0026mdash;exhibited particularly large fold changes. Protein-protein interaction network analysis revealed two hub genes, \u003cem\u003eFKBP5\u003c/em\u003e and \u003cem\u003eTUBA3D\u003c/em\u003e, based on degree (\u0026ge;\u0026thinsp;30) and betweenness centrality (\u0026ge;\u0026thinsp;3000). Quantitative PCR confirmed consistent expression patterns for these genes. Furthermore, consistent results were obtained in another independent set (10 cases with LAAT and 10 cases without LAAT). Linear regression analysis, adjusted for age and gender, showed that \u003cem\u003eDIRAS3\u003c/em\u003e expression was significantly associated with both fibrosis ratio (β\u0026thinsp;=\u0026thinsp;2.99, 95% confidence interval [CI] 0.22\u0026ndash;5.75, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034) and NT-proBNP levels (β\u0026thinsp;=\u0026thinsp;373, 95% CI 238\u0026ndash;574, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.71E-08). Additionally, \u003cem\u003eCYP26B1\u003c/em\u003e and \u003cem\u003eTUBA3D\u003c/em\u003e expression levels were significantly associated with NT-proBNP (β\u0026thinsp;=\u0026thinsp;349, 95% CI 23.8\u0026ndash;674, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036; β = -140, 95% CI -272 to -8.81, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038, respectively) .\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eWe identified candidate genes potentially involved in LAAT in AF patients through RNA-seq analysis. These findings may elucidate the molecular mechanisms underlying LAAT pathogenesis.\u003c/p\u003e","manuscriptTitle":"RNA Sequence Analysis of Differentially Expressed Genes in Left Atrial Appendage Thrombus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-09 08:31:37","doi":"10.21203/rs.3.rs-6680046/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3d5517ce-82b4-40e3-9d34-1c4cf63414f9","owner":[],"postedDate":"June 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-06T16:05:26+00:00","versionOfRecord":{"articleIdentity":"rs-6680046","link":"https://doi.org/10.1007/s11239-025-03184-1","journal":{"identity":"journal-of-thrombosis-and-thrombolysis","isVorOnly":false,"title":"Journal of Thrombosis and Thrombolysis"},"publishedOn":"2025-10-05 15:57:17","publishedOnDateReadable":"October 5th, 2025"},"versionCreatedAt":"2025-06-09 08:31:37","video":"","vorDoi":"10.1007/s11239-025-03184-1","vorDoiUrl":"https://doi.org/10.1007/s11239-025-03184-1","workflowStages":[]},"version":"v1","identity":"rs-6680046","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6680046","identity":"rs-6680046","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}