整合 scRNA-seq 以探索新型巨噬细胞浸润相关生物标志物用于心力衰竭的诊断。
Integrating scRNA-seq to explore novel macrophage infiltration-associated biomarkers for diagnosis of heart failure.
机构信息
Department of Cardiology, Zhongda Hospital of Southeast University, Nanjing, 210009, Jiangsu, China.
School of Medicine, Southeast University, Nanjing, 210009, China.
出版信息
BMC Cardiovasc Disord. 2023 Nov 16;23(1):560. doi: 10.1186/s12872-023-03593-1.
OBJECTIVE
Inflammation and immune cells are closely intertwined mechanisms that contribute to the progression of heart failure (HF). Nonetheless, there is a paucity of information regarding the distinct features of dysregulated immune cells and efficient diagnostic biomarkers linked with HF. This study aims to explore diagnostic biomarkers related to immune cells in HF to gain new insights into the underlying molecular mechanisms of HF and to provide novel perspectives for the detection and treatment of HF.
METHOD
The CIBERSORT method was employed to quantify 22 types of immune cells in HF and normal subjects from publicly available GEO databases (GSE3586, GSE42955, GSE57338, and GSE79962). Machine learning methods were utilized to screen for important cell types. Single-cell RNA sequencing (GSE145154) was further utilized to identify important cell types and hub genes. WGCNA was employed to screen for immune cell-related genes and ultimately diagnostic models were constructed and evaluated. To validate these predictive results, blood samples were collected from 40 normal controls and 40 HF patients for RT-qPCR analysis. Lastly, key cell clusters were divided into high and low biomarker expression groups to identify transcription factors that may affect biomarkers.
RESULTS
The study found a noticeable difference in immune environment between HF and normal subjects. Macrophages were identified as key immune cells by machine learning. Single-cell analysis further showed that macrophages differed dramatically between HF and normal subjects. This study revealed the existence of five subsets of macrophages that have different differentiation states. Based on module genes most relevant to macrophages, macrophage differentiation-related genes (MDRGs), and DEGs in HF and normal subjects from GEO datasets, four genes (CD163, RNASE2, LYVE1, and VSIG4) were identified as valid diagnostic markers for HF. Ultimately, a diagnostic model containing two hub genes was constructed and then validated with a validation dataset and clinical samples. In addition, key transcription factors driving or maintaining the biomarkers expression programs were identified.
CONCLUSION
The analytical results and diagnostic model of this study can assist clinicians in identifying high-risk individuals, thereby aiding in guiding treatment decisions for patients with HF.
目的
炎症和免疫细胞是密切相关的机制，它们共同促进心力衰竭（HF）的进展。尽管如此，关于失调的免疫细胞的独特特征以及与 HF 相关的有效诊断生物标志物的信息仍然很少。本研究旨在探讨与 HF 相关的免疫细胞的诊断生物标志物，以深入了解 HF 的潜在分子机制，并为 HF 的检测和治疗提供新的视角。
方法
使用 CIBERSORT 方法从公开的 GEO 数据库（GSE3586、GSE42955、GSE57338 和 GSE79962）中量化 HF 和正常受试者的 22 种免疫细胞类型。使用机器学习方法筛选重要的细胞类型。进一步利用单细胞 RNA 测序（GSE145154）鉴定重要的细胞类型和枢纽基因。使用 WGCNA 筛选与免疫细胞相关的基因，最终构建并评估诊断模型。为了验证这些预测结果，从 40 名正常对照和 40 名 HF 患者中采集血液样本进行 RT-qPCR 分析。最后，将关键细胞簇分为高和低生物标志物表达组，以鉴定可能影响生物标志物的转录因子。
结果
该研究发现 HF 患者和正常受试者的免疫环境存在显著差异。机器学习鉴定巨噬细胞为关键免疫细胞。单细胞分析进一步表明，HF 患者和正常受试者的巨噬细胞存在显著差异。本研究揭示了存在五种不同分化状态的巨噬细胞亚群。基于与巨噬细胞最相关的模块基因、巨噬细胞分化相关基因（MDRGs）和 GEO 数据集 HF 和正常受试者中的差异表达基因，鉴定出 4 个（CD163、RNASE2、LYVE1 和 VSIG4）作为 HF 的有效诊断标志物。最终，构建了一个包含两个枢纽基因的诊断模型，并使用验证数据集和临床样本进行验证。此外，还确定了驱动或维持生物标志物表达程序的关键转录因子。
结论
本研究的分析结果和诊断模型可以帮助临床医生识别高危个体，从而为 HF 患者的治疗决策提供指导。
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