基于机器学习的转录组分析确定脓毒症诱导凝血病中的候选基因,并探索黄芩素的免疫调节潜力。

Machine learning-based transcriptomic analysis identifies candidate genes in sepsis-induced coagulopathy and explores the immunomodulatory potential of baicalein.

作者信息

Mu Lifang, Zhang Yuxue, Yuan Tingting, Zhang Dingshun, Liu Zhifeng, Wu Ming, Zhong Li

机构信息

Guizhou University of Traditional Chinese Medicine, Guiyang, 550025, China.

Department of Traditional Chinese Medicine, The First Affiliated Hospital, Guizhou University of Traditional Chinese Medicine, Guiyang, 550001, China.

出版信息

Hum Genomics. 2025 Aug 31;19(1):102. doi: 10.1186/s40246-025-00818-6.

Abstract

BACKGROUND

Sepsis is a major contributor to high morbidity and mortality, often leading to coagulation disorders (CD) in affected individuals. Baicalein, a natural compound with well-established anti-inflammatory properties, shows promise as a potential treatment for sepsis. However, its molecular mechanisms in sepsis-associated CD remain poorly understood. This study investigated the therapeutic effects of baicalein in sepsis and identified candidate genes involved in its mechanism of action.

METHODS

Transcriptomic data, baicalein-related targets from public databases, and CD-related genes from the literature were analyzed to identify potential candidate genes. Machine learning algorithms and expression validation techniques were employed to screen initial candidate genes from the candidates. A nomogram was then constructed based on these candidate genes. Functional enrichment and immune infiltration analyses were conducted to explore the underlying mechanisms, while molecular docking was used to assess interactions between baicalein and the candidate genes. Gene expression was further validated by reverse transcription-quantitative PCR (RT-qPCR).

RESULTS

Seven initial candidate genes were identified. Machine learning and expression validation confirmed MMP9, ARG1, and FYN as the final candidate genes involved in sepsis. A highly accurate nomogram, constructed using these candidate genes, demonstrated strong predictive value for sepsis diagnosis. Functional enrichment analysis revealed their pivotal roles in sepsis pathogenesis, while immune infiltration analysis indicated immune dysregulation in sepsis. Additionally, molecular docking revealed strong binding interactions between baicalein and proteins encoded by these candidate genes, supporting further investigation of its therapeutic potential in sepsis. However, these in silico findings are preliminary and require validation through in vitro and in vivo experiments to confirm biological activity. RT-qPCR further validated differential expression of these genes in patients with sepsis compared to healthy controls, confirming the results.

CONCLUSION

This study identified MMP9, ARG1, and FYN as candidate genes in sepsis involved in immune regulation. Additionally, molecular docking revealed strong binding interactions between baicalein and the proteins encoded by these candidate genes, supporting further investigation of its therapeutic potential in sepsis.

摘要

背景

脓毒症是导致高发病率和死亡率的主要因素,常使受影响个体出现凝血障碍(CD)。黄芩素是一种具有公认抗炎特性的天然化合物,有望成为脓毒症的潜在治疗药物。然而,其在脓毒症相关凝血障碍中的分子机制仍知之甚少。本研究调查了黄芩素对脓毒症的治疗效果,并确定了其作用机制中涉及的候选基因。

方法

分析转录组数据、来自公共数据库的黄芩素相关靶点以及文献中的CD相关基因,以确定潜在的候选基因。采用机器学习算法和表达验证技术从候选基因中筛选初始候选基因。然后基于这些候选基因构建列线图。进行功能富集和免疫浸润分析以探索潜在机制,同时使用分子对接评估黄芩素与候选基因之间的相互作用。通过逆转录定量PCR(RT-qPCR)进一步验证基因表达。

结果

确定了7个初始候选基因。机器学习和表达验证证实基质金属蛋白酶9(MMP9)、精氨酸酶1(ARG1)和FYN是参与脓毒症的最终候选基因。使用这些候选基因构建的高度准确的列线图对脓毒症诊断具有很强的预测价值。功能富集分析揭示了它们在脓毒症发病机制中的关键作用,而免疫浸润分析表明脓毒症中存在免疫失调。此外,分子对接显示黄芩素与这些候选基因编码的蛋白质之间存在强烈的结合相互作用,支持进一步研究其在脓毒症中的治疗潜力。然而,这些计算机模拟结果是初步的,需要通过体外和体内实验进行验证以确认其生物学活性。RT-qPCR进一步验证了与健康对照相比脓毒症患者中这些基因的差异表达,证实了结果。

结论

本研究确定MMP9、ARG1和FYN是脓毒症中参与免疫调节的候选基因。此外,分子对接显示黄芩素与这些候选基因编码的蛋白质之间存在强烈的结合相互作用,支持进一步研究其在脓毒症中的治疗潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/06ba/12398980/53c4b6496c85/40246_2025_818_Fig1_HTML.jpg

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