Data Sheet 2_Integration of multi-omics and machine learning strategies identifies immune related candidate biomarkers in inflammation-associated hypertrophic cardiomyopathy.csv

BackgroundHypertrophic cardiomyopathy (HCM) is a common inherited heart disease frequently leading to heart failure. Although sarcomeric gene mutations are known, they only account for a subset of cases. The role of immune dysregulation in HCM progression has gained increasing attention, necessitating the exploration of immune-related biomarkers and therapeutic targets. This study integrates Mendelian randomization (MR), transcriptomics, machine learning, and experimental validation to investigate the immune mechanisms underlying HCM.
MethodsWe analyzed three transcriptomic datasets from the GEO database (210 healthy controls, 152 HCM patients) and identified differentially expressed genes (DEGs) using the R package limma. MR analysis was performed on 19,942 expression quantitative trait loci (eQTLs) and HCM cases using the TwoSampleMR package. Machine learning (10 algorithms) was employed to construct diagnostic models, and SHAP analysis was applied to assess key gene contributions. Functional enrichment was performed with clusterProfiler, diagnostic performance was evaluated via ROC curves, and immune cell infiltration was analyzed using CIBERSORT. A competing endogenous RNA (ceRNA) network was constructed, and drug targets were predicted via the DGIdb database. Key gene expression was validated by qPCR.
ResultsWe identified 472 DEGs and 205 HCM-associated loci, narrowing down to seven key genes: RNF165, SNCA, SRGN, MARCO, STEAP4, SIGLEC9, and TKT. These genes were enriched in immune-related pathways (e.g., cytokine activity, leukocyte migration, JAK-STAT signaling). The Random Forest model exhibited the highest diagnostic performance (AUC: 0.939), with SHAP analysis revealing MARCO as the top contributor. Gene expression was associated with immune cell infiltration: HCM samples showed increased CD4+ T cells and M0 macrophages but decreased M2 macrophages and neutrophils. The ceRNA network comprised 5 mRNAs, 40 miRNAs, and 152 lncRNAs. SRGN and SNCA were identified as potential targets for heparin and 33 other drugs, respectively. qRT-PCR performed on a small number of myocardial samples supported expression trends of the identified genes, in line with transcriptomic analysis.
ConclusionThis study reveals immune-related mechanistic biomarkers and potential therapeutic targets in HCM, highlighting the role of immune dysregulation in disease progression. Machine learning and SHAP analysis improved diagnostic model interpretability, providing a basis for future development of non-invasive diagnostic tools.

Background
肥厚型心肌病（Hypertrophic cardiomyopathy, HCM）是一种常见的遗传性心脏疾病，常可进展为心力衰竭。尽管目前已明确肌节基因突变与该病相关，但此类突变仅能解释部分病例。免疫失调在肥厚型心肌病进展中的作用日益受到关注，因此亟需探索免疫相关生物标志物与治疗靶点。本研究整合孟德尔随机化（Mendelian randomization, MR）、转录组学、机器学习与实验验证手段，探究肥厚型心肌病潜在的免疫机制。

Methods
我们从基因表达综合数据库（Gene Expression Omnibus, GEO）中获取3组转录组数据集，包含210名健康对照者与152名肥厚型心肌病患者，并通过R软件包limma筛选出差异表达基因（differentially expressed genes, DEGs）。采用TwoSampleMR软件包，针对19942个表达数量性状基因座（expression quantitative trait loci, eQTLs）与肥厚型心肌病患者队列开展孟德尔随机化分析。采用10种机器学习算法构建诊断模型，并通过SHAP分析评估关键基因的贡献度。通过clusterProfiler进行功能富集分析，采用受试者工作特征（Receiver Operating Characteristic, ROC）曲线评估诊断模型性能，并借助CIBERSORT分析免疫细胞浸润情况。构建内源竞争RNA（competing endogenous RNA, ceRNA）调控网络，并通过DGIdb数据库预测药物靶点。采用实时定量聚合酶链反应（quantitative real-time polymerase chain reaction, qPCR）验证关键基因的表达水平。

Results
本研究共筛选得到472个差异表达基因与205个肥厚型心肌病相关基因位点，最终锁定7个关键基因：RNF165、SNCA、SRGN、MARCO、STEAP4、SIGLEC9及TKT。上述关键基因显著富集于免疫相关通路，例如细胞因子活性、白细胞迁移及Janus激酶-信号转导与转录激活因子（Janus kinase-signal transducer and activator of transcription, JAK-STAT）信号通路。随机森林模型展现出最优的诊断性能（曲线下面积（Area Under Curve, AUC）=0.939），SHAP分析显示MARCO为贡献度最高的基因。关键基因的表达水平与免疫细胞浸润状态显著相关：肥厚型心肌病样本中CD4+T细胞与M0型巨噬细胞比例升高，而M2型巨噬细胞及中性粒细胞比例降低。构建的ceRNA调控网络包含5个mRNA、40个miRNA及152个lncRNA。其中SRGN与SNCA分别被预测为肝素及其他33种药物的潜在靶点。本研究通过少量心肌样本开展定量实时逆转录聚合酶链反应（quantitative real-time reverse transcription polymerase chain reaction, qRT-PCR）验证，结果显示关键基因的表达趋势与转录组分析结果一致。

Conclusion
本研究揭示了肥厚型心肌病中与免疫相关的机制性生物标志物与潜在治疗靶点，强调了免疫失调在疾病进展中的关键作用。机器学习与SHAP分析提升了诊断模型的可解释性，为未来无创诊断工具的开发提供了理论依据。