基于 GEO 数据库鉴定与心力衰竭相关的关键基因。
Identification of crucial genes related to heart failure based on GEO database.
机构信息
Department of Cardiac Surgery, Affiliated Hospital of Chengde Medical University, 36 Nanyingzi Street, Chengde, Hebei, 067000, China.
Experimental Center of Morphology, College of Basic Medicine, Chengde Medical University, Chengde, Hebei, China.
出版信息
BMC Cardiovasc Disord. 2023 Jul 28;23(1):376. doi: 10.1186/s12872-023-03400-x.
BACKGROUND
The molecular biological mechanisms underlying heart failure (HF) remain poorly understood. Therefore, it is imperative to use innovative approaches, such as high-throughput sequencing and artificial intelligence, to investigate the pathogenesis, diagnosis, and potential treatment of HF.
METHODS
First, we initially screened Two data sets (GSE3586 and GSE5406) from the GEO database containing HF and control samples from the GEO database to establish the Train group, and selected another dataset (GSE57345) to construct the Test group for verification. Next, we identified the genes with significantly different expression levels in patients with or without HF and performed functional and pathway enrichment analyses. HF-specific genes were identified, and an artificial neural network was constructed by Random Forest. The ROC curve was used to evaluate the accuracy and reliability of the constructed model in the Train and Test groups. Finally, immune cell infiltration was analyzed to determine the role of the inflammatory response and the immunological microenvironment in the pathogenesis of HF.
RESULTS
In the Train group, 153 significant differentially expressed genes (DEGs) associated with HF were found to be abnormal, including 81 down-regulated genes and 72 up-regulated genes. GO and KEGG enrichment analyses revealed that the down-regulated genes were primarily enriched in organic anion transport, neutrophil activation, and the PI3K-Akt signaling pathway. The upregulated genes were mainly enriched in neutrophil activation and the calcium signaling. DEGs were identified using Random Forest, and finally, 16 HF-specific genes were obtained. In the ROC validation and evaluation, the area under the curve (AUC) of the Train and Test groups were 0.996 and 0.863, respectively.
CONCLUSIONS
Our research revealed the potential functions and pathways implicated in the progression of HF, and designed an RNA diagnostic model for HF tissues using machine learning and artificial neural networks. Sensitivity, specificity, and stability were confirmed by ROC curves in the two different cohorts.
背景
心力衰竭（HF）的分子生物学机制仍知之甚少。因此，必须采用高通量测序和人工智能等创新方法来研究 HF 的发病机制、诊断和潜在治疗方法。
方法
首先，我们从 GEO 数据库中初步筛选出包含 HF 和对照样本的两个数据集（GSE3586 和 GSE5406），建立 Train 组，并选择另一个数据集（GSE57345）构建 Test 组进行验证。接下来，我们鉴定了 HF 患者和无 HF 患者之间表达水平差异显著的基因，并进行了功能和通路富集分析。鉴定出 HF 特异性基因，并通过随机森林构建人工神经网络。ROC 曲线用于评估构建模型在 Train 和 Test 组中的准确性和可靠性。最后，分析免疫细胞浸润，以确定炎症反应和免疫微环境在 HF 发病机制中的作用。
结果
在 Train 组中，发现与 HF 相关的 153 个显著差异表达基因（DEGs）异常，包括 81 个下调基因和 72 个上调基因。GO 和 KEGG 富集分析表明，下调基因主要富集在有机阴离子转运、中性粒细胞激活和 PI3K-Akt 信号通路。上调基因主要富集在中性粒细胞激活和钙信号。使用随机森林鉴定 DEGs，最终获得 16 个 HF 特异性基因。在 ROC 验证和评估中，Train 和 Test 组的曲线下面积（AUC）分别为 0.996 和 0.863。
结论
我们的研究揭示了 HF 进展中涉及的潜在功能和通路，并使用机器学习和人工神经网络设计了用于 HF 组织的 RNA 诊断模型。ROC 曲线在两个不同队列中确认了敏感性、特异性和稳定性。
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