School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
PLoS One. 2013 Oct 18;8(10):e76166. doi: 10.1371/journal.pone.0076166. eCollection 2013.
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.
永久性心房颤动（pmAF）在很大程度上仍然无法治愈，因为目前解释 pmAF 潜在机制的信息还不够充分。微阵列分析提供了一种更广泛和无偏的方法来识别和预测 pmAF 的新生物学特征。考虑到大多数病例对照微阵列数据中样本数量的不平衡，我们设计了一种不对称主成分分析算法，并将其应用于重新分析 pmAF 患者和对照样本的差异基因表达数据，以预测新的生物学特征。最后，我们使用提出的方法鉴定了 51 个差异表达基因，其中 42 个差异表达基因是与同一数据的两项相关工作和现有研究相比的新发现。富集分析说明了鉴定的差异表达基因的可靠性。此外，我们还使用鉴定的差异表达基因通过 KO 基注释系统预测了三个新的 pmAF 相关信号通路。我们的分析和现有研究支持预测的信号通路可能促进 pmAF 的进展。上述结果值得进一步进行实验研究。这项工作为 pmAF 的分子特征提供了一些新的见解。它也对深入了解 pmAF 的分子机制具有潜在的重要意义。
