[Integrating Bioinformatics and Machine Learning Algorithms to Screen Inflammatory Biomarkers for Atrial Fibrillation and Experimental Validation].
Q, B., W, Z., Y, H., Z, L., Y, W., X, Z., Y, J., X, F., Y, Y., & Z, W. (2026). [Integrating Bioinformatics and Machine Learning Algorithms to Screen Inflammatory Biomarkers for Atrial Fibrillation and Experimental Validation].. Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition. https://doi.org/10.12182/20260560503
Q B, W Z, Y H, Z L, Y W, X Z, et al. [Integrating Bioinformatics and Machine Learning Algorithms to Screen Inflammatory Biomarkers for Atrial Fibrillation and Experimental Validation].. Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition. 2026; doi: 10.12182/20260560503
Q B, W Z, Y H, et al. [Integrating Bioinformatics and Machine Learning Algorithms to Screen Inflammatory Biomarkers for Atrial Fibrillation and Experimental Validation].[J]. Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition. 2026. DOI: 10.12182/20260560503.
@article{q2026,
author = {Bu Q and Zhang W and Huang Y and Li Z and Wang Y and Zhao X and Jia Y and Fan X and Yang Y and Wang Z},
title = {[Integrating Bioinformatics and Machine Learning Algorithms to Screen Inflammatory Biomarkers for Atrial Fibrillation and Experimental Validation].},
journal = {Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition},
year = {2026},
doi = {10.12182/20260560503},
note = {PMID: 42369706},
}
TY - JOUR AU - Bu Q AU - Zhang W AU - Huang Y AU - Li Z AU - Wang Y AU - Zhao X AU - Jia Y AU - Fan X AU - Yang Y AU - Wang Z TI - [Integrating Bioinformatics and Machine Learning Algorithms to Screen Inflammatory Biomarkers for Atrial Fibrillation and Experimental Validation]. T2 - Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition PY - 2026 DO - 10.12182/20260560503 AN - PMID:42369706 ER -
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.