第一部分：肥厚型心肌病中血浆炎症因子与心肌纤维化的关系

 

摘要

 

研究背景及目的

肥厚型心肌病 (Hypertrophic cardiomyopathy，HCM) 是一种常见的遗传性心肌疾病，在正常人群中发病率为1:200 至 1:500。其主要特征是左室心肌肥厚、左心室流出道梗阻（Left ventricular outflow tract，LVOT）、心肌细胞紊乱和心肌纤维化（myocardial fibrosis，MF）。肥厚型心肌病是一个慢性进展性的疾病，常常伴随的慢性低度炎症会促进不良心室重塑如心肌纤维化，但是目前心肌纤维化与炎症指标的关系还研究较少。因此本研究旨在探讨肥厚型心肌病患者炎症标志物与心肌纤维化包括组织病理评估的间质性纤维化和磁共振评估的替代性纤维化即钆延迟强化（late gadolinium enhancement，LGE）之间的关系。

 

研究方法

本研究连续纳入2017年12月至2020年7月在阜外医院行室间隔切除术并且完善术前血清炎症因子检测的肥厚型心肌病患者102例。所有患者拥有完整临床基线。通过心肌组织病理和心脏磁共振（Cardiac magnetic resonance，CMR）明确患者的心肌纤维化情况。使用标准实验室程序测量炎症标记物的血浆水平。采用单变量和多变量 Logistic 回归分析来探讨炎症标志物与心肌纤维化之间的关系。

 

研究结果

在参与分析的102名肥厚型心肌病患者中，平均年龄为48.9岁，其中69（67.6%）名为男性。总体病理性心肌纤维化范围为2.5%至40.7%，平均值为15.2±8.1%，中位数为13.0%，四分位距（Interquartile Range，IQR）为9.9%-18.4%。我们根据病理性心肌纤维化的中位数13%分为两组，每组为51人。结果显示，高病理性心肌纤维化组的左心房直径和左心室射血分数较大。与低病理性心肌纤维化患者相比，高心肌纤维化患者的白细胞介素2（Interleukin -2，IL-2）、肿瘤坏死因子α（tumour necrosis factor-α，TNF-α）和干扰素α（Interferon-α，IFN-α）水平显著升高，分别为4.0 vs. 2.3pg/mL，3.9 vs. 3.1 pg/mL和4.7 vs. 4.2 pg/mL；所有P < 0.05。在进行年龄、性别和其他临床特征进行调整的多变量模型中，IL-2、IL-5 和 TNF-α 与病理性心肌纤维化增加相关[比值比 (Odds ratio，OR)：1.54，95% 置信区间 (Confidence interval， CI)：1.10- 2.14； OR：1.42，95% CI：1.02-1.98； OR：1.33，95% CI：1.04-1.70]。在对影像学指标进行额外调整后，IL-2和TNF-α仍然显著（OR：1.49，95％CI：1.06-2.09，P = 0.021；OR：1.35，95％CI：1.01-1.80，P = 0.044）。一共有97名患者进行心脏核磁共振检查，通过测量LGE来进行替代性纤维化的评估，72名患者（74.2%）表现出LGE阳性，平均值为6.1%。相关性分析显示，血浆炎症指标与LGE阳性或程度之间没有发现显著相关性。

 

研究结论

肥厚型心肌病患者中较高水平的IL-2和TNF-α与组织间质性心肌纤维化增加相关，但炎症标志物与替代性纤维化无关。鉴于肥厚型心肌病中的心肌纤维化是逐渐进展的，早期开始抗炎治疗可能会延缓进展。

 

第二部分：基于蛋白质组学的肥厚型心肌病机制研究

 

摘要

研究背景和目的

肥厚型心肌病（hypertrophic cardiomyopathy，HCM）是最常见的遗传性心血管疾病，具有显著的遗传背景和临床表型异质性，可能包含由不同分子机制驱动的多种亚型。不同基因型对应的临床特征可能存在特异性差异，但深层的分子机制仍不明确。此外，肥厚型心肌病患者在病程中常出现进展性的心肌纤维化和经历重大不良心血管事件（major adverse cardiovascular event, MACE），但当前基于分子水平对疾病进展和不良预后发生的研究仍不足，明确相关机制对于开发新的治疗策略尤为重要。本研究利用梗阻性肥厚型心肌病（Obstructive Hypertrophic Cardiomyopathy, HOCM）患者手术切除的福尔马林固定石蜡包埋（Formalin-Fixed, Paraffin-Embedded, FFPE）室间隔心肌组织样本，进行蛋白组学分析来识别与基因型及纤维化相关的分子信号通路。同时，通过分子分型评估其与疾病严重程度及预后的关联性，为疾病分子机制研究及靶向治疗开发提供新依据。

 

研究方法

我们对105例HOCM患者术后获得的室间隔组织FFPE标本进行蛋白质组学分析，其中包括MYPBC3基因突变组、MYH7基因突变组以及无基因突变组各35例。通过液相色谱-质谱串联技术（LC-MS/MS），共鉴定出4332种蛋白质。疾病严重程度是通过影像学和病理评估的心肌纤维化、影像学评估的心肌结构及功能等指标。主要结局事件是MACE，定义为心血管死亡、新发心力衰竭、新发房颤、新发急性冠脉综合征及新发卒中组成的复合事件。对患者进行分子分型，探索不同分子亚型与临床表型及预后的关联性。通过加权基因共表达网络分析（Weighted Gene Co-expression Network Analysis，WGCNA），对鉴定的蛋白质进行模块划分，挖掘与关键临床特征密切相关的模块。采用单样本基因集富集分析（Single Sample Gene Set Enrichment Analysis，ssGSEA）对单个样本进行分析，揭示疾病相关的潜在分子机制，为疾病进展及分子亚型的机制研究提供依据。最终通过蛋白印迹试验（Western blot，WB）和免疫组化（Immunohistochemistry，IHC）行关键通路及蛋白的验证。

 

研究结果

在纳入的105个患者中，患者的平均年龄为41.7岁，其中69（65.9%）名患者为男性。在随访期间（中位随访时间为6.8年），一共有30（28.6%）名患者出现MACE事件。对105个FFPE心肌组织样本进行了全面的蛋白质组分析，共分成4种最佳的分子亚型，其中S-I亚型39人，S-II亚型38人，S-III亚型27人，S-IV亚型1人。S-II亚型患者以炎症和纤维化相关的通路显著上调为主，而心肌收缩和多种代谢相关通路显著下调，其临床特征为舒张功能最差、纤维化最严重，预后最差。S-I亚型患者表现出代谢相关通路，而纤维化相关通路则下调。该亚型主要由携带MYPBC3基因突变的患者组成，占比84.6%。S-III亚型则以上述的纤维化等相关通路的下调为主，而代谢相关通路的变化不明显，其疾病严重程度和预后相对最好。WGNCA分析发现：与MYH7基因突变正相关最强的模块也与心肌纤维化和MACE事件的发生正相关，主要富集到的通路是炎症和纤维化及代谢相关通路。与MYPBC3基因突变的正相关强的模块，反而与MYH7基因突变、心肌纤维化等为负相关关系，主要富集到氧化磷酸化、代谢以及MAPK信号通路等。

 

研究结论

本研究通过蛋白质组学分析，揭示了HOCM患者基因型、心肌纤维化及预后相关的分子通路和潜在机制，识别出与心肌纤维化和不良预后密切相关的分子亚型及其核心机制，或可为未来治疗靶点的开发提供新方向。

 

第三部分：肥厚型心肌病的生物标志物探索

 

摘要

 

研究背景及目的

肥厚型心肌病（Hypertrophic Cardiomyopathy, HCM）是一种高度异质性的疾病，涉及多种基因突变类型。在部分患者中，疾病可迅速进展，表现为广泛纤维化、舒张功能障碍以及心房颤动（atrial fibrillation, AF）、心源性猝死（sudden cardiac death, SCD）等不良预后。目前亟需开发新的生物标志物和更精确的预测模型来评估这些风险。蛋白质组学技术的出现，为揭示HCM不良特征的发生机制提供了前所未有的机会。因此，本研究旨在利用心肌组织蛋白质组学分析，鉴定与HCM基因型、严重纤维化及不良预后相关的生物标志物，解析其差异调控的信号通路，并筛选出最具潜力的蛋白组合，以构建预测不良特征的模型。

 

研究方法

本研究在对105例梗阻性肥厚型心肌病（obstructive hypertrophic cardiomyopathy，HOCM）患者的术后室间隔石蜡样本组织进行蛋白质检测及分析，评估了组织蛋白水平与基因类型、心肌纤维化程度及主要不良预后事件（major adverse cardiovascular events，MACE）的关系。此外，通过机器学习对蛋白预测重要性进行排序，筛选关键蛋白构建模型用于预测不良特征及结局。

 

研究结果

本研究纳入105例HOCM患者，其中检出MYPBC3基因突变35例（33.3%）、MYH7基因突变35例（33.3%），合并广泛性晚期钆增强（late gadolinium enhancement，LGE）25例（23.8%）、重度心肌纤维化53例（50.5%）。经过中位6.8年的随访，共记录30例（28.6%）MACE事件。基于4036个心肌组织蛋白组学生物标志物的系统性分析，并校正性别和年龄的混杂因素后，研究发现与MYBPC3突变、MYH7突变、重度心肌纤维化、广泛LGE及MACE显著相关的蛋白分别有634、2425、29、519和148个。通过蛋白质重要性排序，最终筛选出用于预测上述指标的关键蛋白，数量分别为5、9、8、9和6个。进一步应用机器学习算法构建预测模型，各模型在测试集中均展现出良好的预测效能，受试者工作特征曲线下面积（Area Under the Receiver Operating Characteristic Curve，AUC）均在0.75以上，灵敏度为80-100%，特异度为40-100%。

 

研究结论

本研究利用蛋白质组学方法，确定了与基因型、严重纤维化及不良预后相关的潜在组织生物标志物，揭示了这些不良特征可能涉及的潜在机制，并通过构建蛋白模型，能够预测和区分具有不良特征的患者，提高对疾病进展及预后的预测准确性。本研究强调了组织蛋白分析在识别风险生物标志物及预测不良特征方面的价值。

Part I: The association between plasma inflammatory factors and myocardial fibrosis in patients with obstructive hypertrophic cardiomyopathy

 

Abstract

 

Background and objective

Hypertrophic cardiomyopathy (HCM) is a prevalent hereditary cardiac muscle disorder, affecting 1:200 to 1:500 in the general population. It is mainly characterized by left ventricular hypertrophy, left ventricular outflow tract (LVOT) obstruction, myocyte disarray, and myocardial fibrosis (MF). HCM is a chronic progressive disease often accompanied by chronic low-grade inflammation, which promotes adverse ventricular remodeling such as myocardial fibrosis. However, the relationship between MF and inflammatory markers remains scarce. Therefore, this study aimed to investigate the relationship between inflammatory markers and MF in patients with HCM, including interstitial fibrosis assessed by histopathology and replacement fibrosis evaluated by cardiac magnetic resonance, represented by late gadolinium enhancement (LGE).

 

Methods

This study consecutively enrolled 102 patients with HCM who underwent septal myectomy at Fuwai Hospital between December 2017 and July 2020. All patients had preoperative serum inflammatory factor measurements and complete clinical baseline data. Myocardial fibrosis was assessed using myocardial histopathology and cardiac magnetic resonance (CMR). Plasma levels of inflammatory markers were measured using standard laboratory procedures. Univariate and multivariate logistic regression analyses were performed to investigate the relationship between inflammatory markers and MF.

 

Results

Among the 102 participants with HCM included in the analysis, the mean age was 48.9 years, with 69 (67.6%) being men. The overall MF ranged from 2.5% to 40.7% [mean = 15.2 ± 8.1%, median = 13.0%, Interquartile Range (IQR) = 9.9%-18.4%]. Participants were divided into two groups based on a median MF of 13%. The high MF group had a larger left atrial diameter and left ventricular ejection fraction. Levels of interleukin (IL)-2, tumour necrosis factor (TNF)-α and interferon (IFN)-α were significantly higher in patients with high MF compared to those with low MF (2.3 vs. 4.0 pg/mL, 3.1 vs. 3.9 pg/mL, 4.2 vs.4.7 pg/mL, respectively; all P < 0.05). In multivariate models adjusted for age, sex and other clinical features, IL-2, IL-5 and TNF-α, were correlated with increased interstitial MF [odds ratio (OR): 1.54, 95% confidence interval (CI): 1.10-2.14; OR: 1.42, 95% CI: 1.02-1.98; OR: 1.33, 95% CI: 1.04-1.70]. After additional adjustment for imaging indicators, IL-2 and TNF-α remained significant (OR: 1.49, 95% CI: 1.06-2.09, P = 0.021; OR:1.35, 95% CI: 1.01-1.80, P = 0.044). A total of 97 patients underwent CMR to assess replacement fibrosis via LGE. Among them, 72 patients (74.2%) exhibited LGE, with an average LGE extent of 6.1%. Correlation analysis revealed no significant associations between plasma inflammatory markers and the presence or extent of LGE.

 

Conclusion

Higher levels of IL-2 and TNF-α were associated with increased histopathological interstitial MF in patients with HCM, whereas inflammatory markers are not related to replacement fibrosis. Given the gradual progression of MF in HCM, initiating anti-inflammatory treatment in the early stages may delay its progression.

 

 

Part II: Mechanistic Insights into Hypertrophic Cardiomyopathy through Proteomics

 

Abstract

Background and objective

Hypertrophic cardiomyopathy (HCM) is the most common hereditary cardiovascular disease, characterized by a significant genetic background and clinical phenotypic heterogeneity, potentially including multiple subtypes driven by different molecular mechanisms. Although clinical features associated with different genotypes may show specific differences, the underlying molecular mechanisms remain unclear. Additionally, HCM patients often experience progressive myocardial fibrosis and major adverse cardiovascular events (MACE) during the course of the disease. However, there is insufficient research at the molecular level on disease progression and the occurrence of adverse prognosis. Understanding the related mechanisms is crucial for developing new therapeutic strategies. This study utilizes formalin-fixed, paraffin-embedded (FFPE) interventricular myocardial tissue samples from patients with obstructive hypertrophic cardiomyopathy (HOCM) to conduct proteomics analysis to identify molecular signaling pathways related to genotype, fibrosis and adverse prognosis. Additionally, molecular subtyping is used to assess its association with disease severity and prognosis, providing new insights for disease mechanism research and the development of targeted therapies.

 

Methods

We performed proteomics analysis on FFPE samples obtained from 105 patients with HOCM, including 35 patients with MYPBC3 gene mutations, 35 with MYH7 gene mutations, and 35 with no gene mutations. Using LC-MS/MS, a total of 4,332 proteins were identified. Disease severity was assessed through imaging and pathological evaluation of myocardial fibrosis, cardiac structure, and function. The primary outcome event was MACE, defined as a composite of cardiovascular death, new-onset heart failure, atrial fibrillation, acute coronary syndrome, and stroke. Molecular subtyping of the patients was performed using unsupervised clustering methods to explore the relationship between different molecular subtypes, clinical phenotypes, and prognosis. Weighted gene co-expression network analysis (WGCNA) was employed to categorize the identified proteins into modules, uncovering those closely related to key clinical features. Single sample gene set enrichment analysis (ssGSEA) was applied to conduct analysis on individual samples to reveal potential molecular mechanisms associated with the disease. Finally, key pathways and proteins were validated using Western blot (WB) and Immunohistochemistry (IHC).

 

Results

Among the 105 HOCM patients included, the average age was 41.7 years, with 69 (65.9%) being male. During a median follow-up of 6.8 years, MACE occurred in 30 patients (28.6%). Comprehensive proteomics analysis was performed on the 105 FFPE samples, which were classified into 4 molecular subtypes: S-I (39 patients), S-II (38 patients), S-III (27 patients), and S-IV (1 patient). The S-II subtype was predominantly characterized by significant upregulation of pathways related to inflammation and fibrosis, along with downregulation of myocardial contraction and metabolic pathways. These patients had the worst diastolic function, most severe fibrosis, and poorest clinical prognosis. In addition, S-I subtype patients exhibited metabolism-related pathways, while fibrosis-related pathways were downregulated. This subtype was predominantly composed of patients with MYPBC3 gene mutations (84.6%). The S-III subtype was marked by downregulation of fibrosis-related pathways, with no significant changes in metabolic pathways, and had the best disease severity and prognosis. WGCNA analysis revealed that the strongest modules positively correlated with MYH7 gene mutations were also strongly correlated with myocardial fibrosis and the occurrence of MACE events, and these modules were primarily enriched in inflammation, fibrosis, and metabolism-related pathways. In contrast, modules strongly associated with MYPBC3 gene mutations were negatively correlated with MYH7 gene mutations and myocardial fibrosis, and were mainly enriched in oxidative phosphorylation, metabolism, and MAPK signaling pathways.

 

Conclusion

This study uncovered the molecular pathways and potential mechanisms associated with genotype, myocardial fibrosis, and prognosis in HOCM patients by proteomics analysis. It identified molecular subtypes closely related to myocardial fibrosis and poor prognosis, along with their core mechanisms, which may offer new directions for the development of future therapeutic targets.

 

 

Part III: Exploration of Biomarkers in Hypertrophic Cardiomyopathy

Abstract

 

Background and Objectives

Hypertrophic cardiomyopathy (HCM) is a highly heterogeneous disease characterized by a variety of genetic mutations. In some patients, the disease can progress rapidly, manifesting as extensive fibrosis, diastolic dysfunction, and adverse outcomes such as atrial fibrillation (AF) and sudden cardiac death (SCD). There is an urgent need to develop novel biomarkers and more precise predictive models to assess these risks. The advent of proteomics offers an unprecedented opportunity to uncover the mechanisms underlying adverse features in HCM. Therefore, this study aims to employ myocardial tissue proteomic analysis to identify biomarkers associated with HCM genotypes, severe fibrosis, and adverse outcomes, to elucidate the differentially regulated signaling pathways, and to screen for the most promising protein combinations for constructing predictive models of adverse features.

Methods

Protein detection and analysis were performed on paraffin-embedded interventricular septum tissue samples from 105 patients with obstructive hypertrophic cardiomyopathy (HOCM) following surgery. The relationships between tissue protein levels and genotype, the extent of myocardial fibrosis, and major adverse cardiovascular events (MACE) were evaluated. Additionally, machine learning was utilized to rank the predictive importance of proteins and to select key proteins for constructing models to predict adverse features and outcomes.

Results

Among the 105 HOCM patients included in the study, 35 cases (33.3%) had MYBPC3 mutations and another 35 cases (33.3%) had MYH7 mutations. Extensive late gadolinium enhancement (LGE) was observed in 25 cases (23.8%), and severe myocardial fibrosis was detected in 53 cases (50.5%). Over a median follow-up of 6.8 years, 30 MACE events (28.6%) were recorded. After adjusting for gender and age as confounding factors, a systematic analysis of 4,036 myocardial tissue proteomic biomarkers identified 634, 2,425, 29, 519, and 148 proteins significantly associated with MYBPC3 mutation, MYH7 mutation, severe myocardial fibrosis, extensive LGE, and MACE, respectively. Subsequent protein importance ranking led to the selection of key proteins for predicting these endpoints, with 5, 9, 8, 9, and 6 proteins chosen, respectively. Further application of machine learning algorithms to construct predictive models demonstrated robust performance in the test cohort, with the area under the receiver operating characteristic curve (AUC) exceeding 0.75 for all models, sensitivity ranging from 80% to 100%, and specificity between 40% and 100%.

Conclusions

This study employed proteomic methods to identify potential tissue biomarkers associated with genotype, severe fibrosis, and adverse outcomes, thereby revealing the underlying mechanisms that may contribute to these adverse features. By constructing protein-based models, the study was able to predict and distinguish patients with unfavorable characteristics, thereby improving the accuracy of predictions regarding disease progression and prognosis. Overall, the findings underscore the value of tissue proteomic analysis in identifying risk biomarkers and forecasting adverse features.