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Updated: May 6, 2026
Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
Published on: July 20, 2022
Analysis on differential gene expression data for prediction of new biological features in permanent atrial
Feng Ou1, Nini Rao, Xudong Jiang
1School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
Abstract:
Permanent Atrial fibrillation (pmAF) has largely remained incurable since the existing information for explaining precise mechanisms underlying pmAF is not sufficient. Microarray analysis offers a broader and unbiased approach to identify and predict new biological features of pmAF. By considering the unbalanced sample numbers in most microarray data of case - control, we designed an asymmetric principal component analysis algorithm and applied it to re - analyze differential gene expression data of pmAF patients and control samples for predicting new biological features. Finally, we identified 51 differentially expressed genes using the proposed method, in which 42 differentially expressed genes are new findings compared with two related works on the same data and the existing studies. The enrichment analysis illustrated the reliability of identified differentially expressed genes. Moreover, we predicted three new pmAF - related signaling pathways using the identified differentially expressed genes via the KO-Based Annotation System. Our analysis and the existing studies supported that the predicted signaling pathways may promote the pmAF progression. The results above are worthy to do further experimental studies. This work provides some new insights into molecular features of pmAF. It has also the potentially important implications for improved understanding of the molecular mechanisms of pmAF.
Insights
Researchers identified new molecular features and signaling pathways in permanent atrial fibrillation (pmAF) using an advanced asymmetric principal component analysis on gene expression data. This study offers novel insights into pmAF mechanisms.
Area of Science:
- Cardiovascular Biology
- Genomics
- Bioinformatics
Background:
- Permanent atrial fibrillation (pmAF) mechanisms remain poorly understood, hindering effective treatments.
- Existing research lacks sufficient molecular insights into pmAF.
- Microarray analysis provides a comprehensive approach to uncover new biological features of pmAF.
Purpose of the Study:
- To identify novel differentially expressed genes and signaling pathways associated with permanent atrial fibrillation (pmAF).
- To develop and apply an asymmetric principal component analysis algorithm for re-analyzing unbalanced microarray data in pmAF.
- To provide new molecular insights for understanding and potentially treating pmAF.
Main Methods:
- Designed an asymmetric principal component analysis algorithm to address unbalanced sample sizes in case-control microarray data.
- Re-analyzed differential gene expression data from pmAF patients and control samples.
- Utilized enrichment analysis and KO-Based Annotation System for pathway prediction.
Main Results:
- Identified 51 differentially expressed genes in pmAF, with 42 being novel findings compared to previous studies.
- Enrichment analysis confirmed the reliability of the identified differentially expressed genes.
- Predicted three novel pmAF-related signaling pathways that may contribute to disease progression.
Conclusions:
- The study identified new molecular features and signaling pathways implicated in permanent atrial fibrillation (pmAF).
- The findings provide a foundation for further experimental validation and a deeper understanding of pmAF.
- This research offers potential implications for improved therapeutic strategies targeting pmAF.
