Ding Shoupeng, Gao Jinghua, Huang Chunxiao, Zhou Yuyang, Yang Yimei, Cai Zihan
Department of Laboratory Medicine, Gutian County Hospital, Gutian, 352200, China.
Chuxiong Yi Autonomous Prefecture People's Hospital, Chuxiong, 675000, China.
Sci Rep. 2024 Dec 2;14(1):29982. doi: 10.1038/s41598-024-81790-4.
Tuberculosis (TB), ranking just below COVID-19 in global mortality, is a highly complex infectious disease involving intricate immunological molecules, diverse signaling pathways, and multifaceted immune processes. N6-methyladenosine (m6A), a critical epigenetic modification, regulates various immune-metabolic and pathological pathways, though its precise role in TB pathogenesis remains largely unexplored. This study aims to identify m6A-associated genes implicated in TB, elucidate their mechanistic contributions, and evaluate their potential as diagnostic biomarkers and tools for molecular subtyping. Using TB-related datasets from the GEO database, this study identified differentially expressed genes associated with m6A modification. We applied four machine learning algorithms-Random Forest, Support Vector Machine, Extreme Gradient Boosting, and Generalized Linear Model-to construct diagnostic models focusing on m6A regulatory genes. The Random Forest algorithm was selected as the optimal model based on performance metrics (area under the curve [AUC] = 1.0, p < 0.01), and a clinical predictive model was developed based on these critical genes. Patients were stratified into distinct subtypes according to m6A gene expression profiles, followed by immune infiltration analysis across subtypes. Additionally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses elucidated the biological functions and pathways associated with the identified genes. Quantitative real-time PCR (RT-qPCR) was used to validate the expression of key m6A regulatory genes. Analysis of the GSE83456 dataset revealed four differentially expressed m6A-related genes-YTHDF1, HNRNPC, LRPPRC, and ELAVL1-identified as critical m6A regulators in TB through the Random Forest model. The diagnostic significance of these genes was further supported by a nomogram, achieving a high predictive accuracy (95% confidence interval [CI]: 0.87-0.94). Consensus clustering classified patients into two m6A subtypes with distinct immune profiles, as principal component analysis (PCA) showed significantly higher m6A scores in Group A than in Group B (p < 0.05). Immune infiltration analysis highlighted significant correlations between key m6A genes and specific immune cell infiltration patterns across subtypes. This study highlights the potential of key m6A regulatory genes as diagnostic biomarkers and immunotherapy targets for TB, supporting their role in TB pathogenesis. Future research should aim to further validate these findings across diverse cohorts to enhance their clinical applicability.
结核病（TB）在全球死亡率方面仅次于新型冠状病毒肺炎（COVID-19），是一种高度复杂的传染病，涉及复杂的免疫分子、多样的信号通路和多方面的免疫过程。N6-甲基腺苷（m6A）是一种关键的表观遗传修饰，可调节各种免疫代谢和病理通路，但其在结核病发病机制中的精确作用在很大程度上仍未得到探索。本研究旨在确定与结核病相关的m6A相关基因，阐明其机制作用，并评估其作为诊断生物标志物和分子分型工具的潜力。利用来自基因表达综合数据库（GEO数据库）的结核病相关数据集，本研究确定了与m6A修饰相关的差异表达基因。我们应用了四种机器学习算法——随机森林、支持向量机、极端梯度提升和广义线性模型——来构建聚焦于m6A调控基因的诊断模型。基于性能指标（曲线下面积[AUC]=1.0，p<0.01），随机森林算法被选为最优模型，并基于这些关键基因开发了临床预测模型。根据m6A基因表达谱将患者分层为不同亚型，随后对各亚型进行免疫浸润分析。此外，基因本体论（GO）和京都基因与基因组百科全书（KEGG）通路富集分析阐明了与所鉴定基因相关的生物学功能和通路。采用定量实时聚合酶链反应（RT-qPCR）验证关键m6A调控基因的表达。对GSE83456数据集的分析揭示了四个差异表达的m6A相关基因——YTHDF1、HNRNPC、LRPPRC和ELAVL1——通过随机森林模型被确定为结核病中的关键m6A调节因子。列线图进一步支持了这些基因的诊断意义，实现了较高的预测准确性（95%置信区间[CI]：0.87-0.94）。共识聚类将患者分为两种具有不同免疫特征的m6A亚型，主成分分析（PCA）显示A组的m6A评分显著高于B组（p<0.05）。免疫浸润分析突出了关键m6A基因与各亚型特定免疫细胞浸润模式之间的显著相关性。本研究强调了关键m6A调控基因作为结核病诊断生物标志物和免疫治疗靶点的潜力，支持了它们在结核病发病机制中的作用。未来的研究应旨在跨不同队列进一步验证这些发现，以提高其临床适用性。
