Zhang Peng, Geng Lou, Zhang Kandi, Liu Dongsheng, Wei Meng, Jiang Zheyi, Lu Yihua, Zhang Tiantian, Chen Jie, Zhang Junfeng
Department of Cardiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Department of Hematology, Institute of Hematology, Changhai Hospital, Naval Medical University, Shanghai, China.
Heliyon. 2024 Apr 20;10(8):e30086. doi: 10.1016/j.heliyon.2024.e30086. eCollection 2024 Apr 30.
Heart failure (HF) and idiopathic pulmonary fibrosis (IPF) are global public health concerns. The relationship between HF and IPF is widely acknowledged. However, the interaction mechanisms between these two diseases remain unclear, and early diagnosis is particularly difficult. Through the integration of bioinformatics and machine learning, our work aims to investigate common gene features, putative molecular causes, and prospective diagnostic indicators of IPF and HF.
The Gene Expression Omnibus (GEO) database provided the RNA-seq datasets for HF and IPF. Utilizing a weighted gene co-expression network analysis (WGCNA), possible genes linked to HF and IPF were found. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) were then employed to analyze the genes that were shared by HF and IPF. Using the cytoHubba and iRegulon algorithms, a competitive endogenous RNA (ceRNA) network was built based on seven basic diagnostic indicators. Additionally, hub genes were identified using machine learning approaches. External datasets were used to validate the findings. Lastly, the association between the number of immune cells in tissues and the discovered genes was estimated using the CIBERSORT method.
In total, 63 shared genes were identified between HF- and IPF-related modules using WGCNA. Extracellular matrix (ECM)/structure organization, ECM-receptor interactions, focal, and protein digestion and absorption, were shown to be the most enrichment categories in GO and KEGG enrichment analysis of common genes. Furthermore, a total of seven fundamental genes, including , , , , , , and , were recognized as pivotal genes implicated in the shared pathophysiological pathways of HF and IPF, and TCF12 may be the most important regulatory transcription factor. Two characteristic molecules, and , were selected as potential diagnostic markers for HF and IPF, respectively, using a support vector machine-recursive feature elimination (SVM-RFE) model. Furthermore, the development of diseases and diagnostic markers may be associated with immune cells at varying degrees.
This study demonstrated that ECM/structure organisation, ECM-receptor interaction, focal adhesion, and protein digestion and absorption, are common pathogeneses of IPF and HF. Additionally, and were identified as potential diagnostic biomarkers for both HF and IPF. The results of our study contribute to the comprehension of the co-pathogenesis of HF and IPF at the genetic level and offer potential biological indicators for the early detection of both conditions.
心力衰竭（HF）和特发性肺纤维化（IPF）是全球公共卫生关注的问题。HF与IPF之间的关系已得到广泛认可。然而，这两种疾病之间的相互作用机制仍不清楚，早期诊断尤其困难。通过整合生物信息学和机器学习，我们的工作旨在研究IPF和HF的共同基因特征、推定的分子病因和前瞻性诊断指标。
基因表达综合数据库（GEO）提供了HF和IPF的RNA测序数据集。利用加权基因共表达网络分析（WGCNA），发现了与HF和IPF相关的可能基因。然后利用京都基因与基因组百科全书（KEGG）和基因本体论（GO）对HF和IPF共有的基因进行分析。使用cytoHubba和iRegulon算法，基于七个基本诊断指标构建了竞争性内源性RNA（ceRNA）网络。此外，使用机器学习方法鉴定了枢纽基因。使用外部数据集验证研究结果。最后，使用CIBERSORT方法估计组织中免疫细胞数量与发现的基因之间的关联。
使用WGCNA在HF和IPF相关模块之间总共鉴定出63个共享基因。在共同基因的GO和KEGG富集分析中，细胞外基质（ECM）/结构组织、ECM-受体相互作用、粘着斑以及蛋白质消化和吸收是最富集的类别。此外，总共七个基本基因，包括……，被认为是与HF和IPF共同病理生理途径相关的关键基因，而TCF12可能是最重要的调节转录因子。使用支持向量机递归特征消除（SVM-RFE）模型，分别选择了两个特征分子……和……作为HF和IPF的潜在诊断标志物。此外，疾病的发展和诊断标志物可能在不同程度上与免疫细胞有关。
本研究表明，ECM/结构组织、ECM-受体相互作用、粘着斑以及蛋白质消化和吸收是IPF和HF的共同发病机制。此外，……和……被鉴定为HF和IPF的潜在诊断生物标志物。我们的研究结果有助于在基因水平上理解HF和IPF的共同发病机制，并为这两种疾病的早期检测提供潜在的生物学指标。
