Integrating Bioinformatics and Machine Learning Algorithms to Screen Inflammatory Biomarkers for Atrial Fibrillation and Experimental Validation

BU Qi, ZHANG Wei, HUANG Ying, LI Zheng, WANG Yang, ZHAO Xuan, JIA Yunli, FAN Xiaoyan, YANG Yuan, WANG Zhimi

Abstract

Objective

To identify potential biomarkers of atrial fibrillation (AF) using bioinformatics, machine learning (ML) and experimental methods.

Methods

AF transcriptomic data were obtained from the GEO and MSigDB databases to screen for differentially expressed genes (DEGs) and inflammation-related gene sets (IRGs). The overlap between DEGs and IRGs was used to identify the DE-IRGs set. Two machine learning algorithms were used to filter AF-related DE-IRGs. Twelve male SD rats were randomly divided into a control group and an AF group. Rats received acetylcholine (66 μg/mL) and calcium chloride (10 mg/kg) via tail vein injection for five consecutive weeks to establish the AF model. Morphological characteristics of atrial myocytes were assessed with HE staining, while RT-qPCR and immunohistochemistry (IHC) were used to measure changes in mRNA and protein levels.

Results

In the training set, 119 DEGs were identified, with IRGs showing the highest correlation with two co-expression modules (r = 0.60 and 0.56, P < 0.0001). The intersection of DEGs and IRGs yielded nine DE-IRGs. The SVM-RFE and RF algorithms identified 5-hydroxytryptamine receptor 2B gene (HTR2B), latent-transforming growth factor beta-binding protein 2 gene (LTBP2), matrix remodeling associated protein 5 gene ( MXRA5), and transforming growth factor β-induced protein gene (TGFBI) as highly expressed in both the training and test sets in AF groups with high diagnostic efficacy (AUC > 0.77). The electrocardiogram limb leads of AF rats showed numerous f-waves. HE staining revealed disorganized atrial myocyte arrangement and inflammatory cell infiltration in the AF group. The TUNEL fluorescence assay showed an apoptosis rate of (55.34 ± 4.29)% in the AF group, compared to (8.69 ± 3.12)% in the control group (P = 0.0001). There were statistically significant differences in the relative expression levels of LTBP2 and TGFBI between the AF group (4.97 ± 4.20, 2.62 ± 1.85) and the control group (1.12 ± 0.21, 1.18 ± 0.77) (P = 0.0137, P = 0.0444). IHC revealed positive rates of LTBP2 and TGFBI proteins in the AF group of (36.50 ± 1.31)% and (27.39 ± 4.57)%, respectively, compared to (22.95 ± 2.62)% and (18.26 ± 3.70)% in the control group (P = 0.0008, P = 0.0485).

Conclusion

The characteristic genes LTBP2 and TGFBI are highly expressed in AF and serve as valuable diagnostic biomarkers.

 

Keywords: Atrial fibrillation, Machine learning algorithms, Biomarkers,  Inflammatory response

 

Full Text:

PDF


References


BRUNDEL B, AI X, HILLS M T, et al. Atrial fibrillation. Nat Rev Dis Primers, 2022, 8(1): 21. doi: 10.1038/s41572-022-00347-9.

BORIANI G, LAROCHE C, DIEMBERGER I, et al. Asymptomatic atrial fibrillation: clinical correlates, management, and outcomes in the EORP-AF Pilot General Registry. Am J Med, 2015, 128(5): 509-518.e2. doi: 10. 1016/j.amjmed.2014.11.026.

WITKOWSKI M, BISSINGER A, GRYCEWICZ T, et al. Asymptomatic atrial fibrillation in patients with atrial fibrillation and implanted pacemaker. Int J Cardiol, 2017, 227: 583-588. doi: 10.1016/j.ijcard.2016.10.097.

WEGNER F K, KOCHHÄUSER S, ELLERMANN C, et al. Prospective blinded evaluation of the smartphone-based AliveCor Kardia ECG monitor for atrial fibrillation detection: The PEAK-AF study. Eur J Intern Med, 2020, 73: 72-75. doi: 10.1016/j.ejim.2019.11.018.

Van Den BERG M P, MULDER B A, KLAASSEN S H C, et al. Heart failure with preserved ejection fraction, atrial fibrillation, and the role of senile amyloidosis. Eur Heart J, 2019, 40(16): 1287-1293. doi: 10.1093/eurheartj/ehz057.

FUHER A N, BORNE R, CUNNINGHAM J. Diagnosing atypical flutter in the post-atrial fibrillation ablation patient: a case report. Clin Pract Cases Emerg Med, 2023, 7(2): 106-109. doi: 10.5811/cpcem.1413.

WU L D, LI F, CHEN J Y, et al. Analysis of potential genetic biomarkers using machine learning methods and immune infiltration regulatory mechanisms underlying atrial fibrillation. BMC Med Genomics, 2022, 15(1): 64. doi: 10.1186/s12920-022-01212-0.

DEMERS J, TON A T, HUYNH F, et al. Atrial electrical remodeling in mice with cardiac-specific overexpression of angiotensin Ⅱ type 1 receptor. J Am Heart Assoc, 2022, 11(8): e023974. doi: 10.1161/jaha.121. 023974.

ZHENG L, JIA X, ZHANG C, et al. Angiotensin Ⅱ in atrial structural remodeling: the role of Ang Ⅱ/JAK/STAT3 signaling pathway. Am J Transl Res, 2015, 7(6): 1021-1031.

MURAKATA Y, YAMAGAMI F, MURAKOSHI N, et al. Electrical, structural, and autonomic atrial remodeling underlies atrial fibrillation in inflammatory atrial cardiomyopathy. Front Cardiovasc Med, 2022, 9: 1075358. doi: 10.3389/fcvm.2022.1075358.

SMORODINOVA N, LANTOVÁ L, BLÁHA M, et al. Bioptic study of left and right atrial interstitium in cardiac patients with and without atrial fibrillation: interatrial but not rhythm-based differences. PLoS One, 2015, 10(6): e0129124. doi: 10.1371/journal.pone.0129124.

ALTIERI D I, ETZION Y, ANDERSON H D. Cannabinoid receptor agonist attenuates angiotensin Ⅱ-induced enlargement and mitochondrial dysfunction in rat atrial cardiomyocytes. Front Pharmacol, 2023, 14: 1142583. doi: 10.3389/fphar.2023.1142583.

LANGFELDER P, HORVATH S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics, 2008, 9(1): 559. doi: 10.1186/1471-2105-9-559.

HÄNZELMANN S, CASTELO R, GUINNEY J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics, 2013, 14: 7. doi: 10.1186/1471-2105-14-7.

WU T, HU E, XU S, et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation (Camb), 2021, 2(3): 100141. doi: 10.1016/j.xinn.2021.100141.

LIU P, SUN H, ZHOU X, et al. CXCL12/CXCR4 axis as a key mediator in atrial fibrillation via bioinformatics analysis and functional identification. Cell Death Dis, 2021, 12(9): 813. doi: 10.1038/s41419-021-04109-5.

SZILÁGYI J, SÁGHY L. Atrial remodeling in atrial fibrillation. Comorbidities and markers of disease progression predict catheter ablation outcome. Curr Cardiol Rev, 2021, 17(2): 217-229. doi: 10.2174/ 1573403x16666200721153620.

LI C Y, ZHANG J R, HU W N, et al. Atrial fibrosis underlying atrial fibrillation (review). Int J Mol Med, 2021, 47(3): 9. doi: 10.3892/ijmm. 2020.4842.

MOLINA C E, ABU-TAHA I H, WANG Q, et al. Profibrotic, electrical, and calcium-handling remodeling of the atria in heart failure patients with and without atrial fibrillation. Front Physiol, 2018, 9: 1383. doi: 10. 3389/fphys.2018.01383.

WANG W W, ZHANG F L, CHEN J H, et al. Telmisartan reduces atrial arrhythmia susceptibility through the regulation of RAS-ERK and PI3K-Akt-eNOS pathways in spontaneously hypertensive rats. Can J Physiol Pharmacol, 2015, 93(8): 657-665. doi: 10.1139/cjpp-2014-0416.

LIU Z, FINET J E, WOLFRAM J A, et al. Calcium/calmodulin-dependent protein kinase Ⅱ causes atrial structural remodeling associated with atrial fibrillation and heart failure. Heart Rhythm, 2019, 16(7): 1080-1088. doi: 10.1016/j.hrthm.2019.01.013.

YU J F, DONG Q, DU Y M. Interleukin-6: molecular mechanisms and therapeutic perspectives in atrial fibrillation. Curr Med Sci, 2025, 45(2): 157-168. doi: 10.1007/s11596-025-00021-7.

LARSEN B C, Al-BADRI K H A, MELLEMKJÆR S, et al. Inflammatory-mediated atrial cardiomyopathy diagnosed using multimodality imaging and successfully treated with prednisolone: a case report. Eur Heart J Case Rep, 2024, 8(4): ytae167. doi: 10.1093/ehjcr/ytae167.

LAKIN R, POLIDOVITCH N, YANG S, et al. Cardiomyocyte and endothelial cells play distinct roles in the tumour necrosis factor (TNF)-dependent atrial responses and increased atrial fibrillation vulnerability induced by endurance exercise training in mice. Cardiovasc Res, 2023, 119(16): 2607-2622. doi: 10.1093/cvr/cvad144.

LI C L, PEI L J, HOU P. Biomarkers related to atrial fibrillation. Chiese Journal of Cardiovascular Medicine, 2020, 25(4): 397-400. doi: 10.3969/j. issn.1007-5410.2020.04.020.

WU X, LI Y, PAN J, et al. Pathogenesis and potential diagnostic biomarkers of atrial fibrillation in Chinese population: a study based on bioinfor-matics. Journal of Zhejiang University (Medical Sciences), 2024, 53(5): 593-603. doi: 10.3724/zdxbyxb-2024-0027.

ROBERTSON I B, HORIGUCHI M, ZILBERBERG L, et al. Latent TGF-β-binding proteins. Matrix Biol, 2015, 47: 44-53. doi: 10.1016/j.matbio. 2015.05.005.

PANG X F, LIN X, DU J J, et al. LTBP2 knockdown by siRNA reverses myocardial oxidative stress injury, fibrosis and remodelling during dilated cardiomyopathy. Acta Physiol (Oxf), 2020, 228(3): e13377. doi: 10.1111/apha.13377.


Refbacks

  • There are currently no refbacks.