Tang Wenwu, Wang Zhixin, Yuan Xinzhu, Chen Liping, Guo Haiyang, Qi Zhirui, Zhang Ying, Xie Xisheng
Department of Nephrology, Nanchong Central Hospital Affiliated to North Sichuan Medical College, Nanchong, China.
Department of Nephrology, Guangyuan Central Hospital, Guangyuan, China.
Front Cardiovasc Med. 2025 Jan 8;11:1442238. doi: 10.3389/fcvm.2024.1442238. eCollection 2024.
Heart failure (HF) has a very high prevalence in patients with maintenance hemodialysis (MHD). However, there is still a lack of effective and reliable HF diagnostic markers and therapeutic targets for patients with MHD.
In this study, we analyzed transcriptome profiles of 30 patients with MHD by high-throughput sequencing. Firstly, the differential genes between HF group and control group of patients with MHD were screened. Secondly, HF-related genes were screened by WGCNA, and finally the genes intersecting the two were selected as candidate genes. Machine learning was used to identify hub gene and construct a , which was verified by ROC curve and RT-qPCR. In addition, we further explored potential mechanism and function of hub genes in HF of patients with MHD through GSEA, immune cell infiltration analysis, drug analysis and establishment of molecular regulatory network.
Totally 23 candidate genes were screened out by overlapping 673 differentially expressed genes (DEGs) and 147 key module genes, of which four hub genes (DEPDC1B, CDCA2, APOBEC3B and TYMS) were obtained by two machine learning algorithms. Through GSEA analysis, it was found that the four genes were closely related to ribosome, cell cycle, ubiquitin-mediated proteolysis. We constructed a ceRNA regulatory network, and found that 4 hub genes (TYMS, CDCA2 and DEPDC1B) might be regulated by 4 miRNAs (hsa-miR-1297, hsa-miR-4465, hsa-miR-27a-3p, hsa-miR-129-5p) and 21 lncRNAs (such as HCP5, CAS5, MEG3, HCG18). 24 small molecule drugs were predicted based on TYMS through DrugBank website. Finally, qRT-PCR experiments showed that the expression trend of biomarkers was consistent with the results of transcriptome sequencing.
Overall, our results reveal the molecular mechanism of HF in patients with MHD and provide insights into potential diagnostic markers and therapeutic targets.
心力衰竭（HF）在维持性血液透析（MHD）患者中患病率极高。然而，MHD患者仍缺乏有效且可靠的HF诊断标志物和治疗靶点。
在本研究中，我们通过高通量测序分析了30例MHD患者的转录组图谱。首先，筛选MHD患者HF组与对照组之间的差异基因。其次，通过加权基因共表达网络分析（WGCNA）筛选HF相关基因，最后选取两者交集的基因作为候选基因。利用机器学习识别枢纽基因并构建模型，通过ROC曲线和逆转录定量聚合酶链反应（RT-qPCR）进行验证。此外，我们通过基因集富集分析（GSEA）、免疫细胞浸润分析、药物分析以及建立分子调控网络，进一步探究枢纽基因在MHD患者HF中的潜在机制和功能。
通过重叠673个差异表达基因（DEG）和147个关键模块基因，共筛选出23个候选基因，其中通过两种机器学习算法获得了4个枢纽基因（DEPDC1B、CDCA2、载脂蛋白B编辑酶催化多肽样3B（APOBEC3B）和胸苷酸合成酶（TYMS））。通过GSEA分析发现，这4个基因与核糖体、细胞周期、泛素介导的蛋白水解密切相关。我们构建了一个竞争性内源性RNA（ceRNA）调控网络，发现4个枢纽基因（TYMS、CDCA2和DEPDC1B）可能受4个微小RNA（miRNA）（hsa-miR-1297、hsa-miR-4465、hsa-miR-27a-3p、hsa-miR-129-5p）和21个长链非编码RNA（lncRNA）（如HCP5、CAS5、母系表达基因3（MEG3）、人绒毛膜促性腺激素18（HCG18））调控。基于TYMS通过DrugBank网站预测了24种小分子药物。最后，qRT-PCR实验表明生物标志物的表达趋势与转录组测序结果一致。
总体而言，我们的研究结果揭示了MHD患者HF的分子机制，并为潜在的诊断标志物和治疗靶点提供了见解。
