基于机器学习和实验验证的PANoptosis相关新诊断生物标志物和潜在药物的探索。

IF 3.5 4区 医学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
Zhibo Deng, Chao Song, Rongsheng Zhang, Yu Xiu, Linhai Yang, Hanhao Dai, Jun Luo, Jie Xu
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引用次数: 0

摘要

背景:骨骼肌减少症是一种无菌性慢性炎症性疾病,是一种以骨骼肌进行性变性为特征的复杂和衰弱性疾病。PANoptosis是一种新的促炎程序性细胞死亡途径,与多种疾病有关。然而,panoposis相关特征在肌肉减少症中的确切作用仍不确定。方法:根据肌少症数据集GSE167186中差异表达基因(DEGs)与PANoptosis基因集的交叉,采用共识聚类方法将患者分为PANoptosis相关亚型(PANRS)。用加权基因共表达网络分析(WGCNA)对PANRS的deg进行交叉分析。利用蛋白相互作用网络和cytoHubba算法进一步鉴定与PANoptosis相关的潜在基因。采用LASSO回归筛选最具特征的基因,进行ROC曲线分析验证,并进行相关免疫浸润分析。此外,使用Cmap进行小分子药物筛选。聚合酶链反应证实hub基因在肌少症中的相对表达量。最后,利用单细胞分析和GSEA对枢纽基因的分布和功能进行了研究。结果:通过WGCNA和PANRS鉴定出35个候选基因。机器学习和ROC曲线分析揭示了三个核心基因LTBP2、ETS2和H3.3B在肌少症患者中均上调(p结论:本研究通过生物信息学和实验验证相结合的方法,成功鉴定了肌少症PANoptosis相关的枢纽基因LTBP2、ETS2和H3.3B。这为肌少症新的候选诊断和治疗靶点奠定了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring PANoptosis Related Novel Diagnostic Biomarkers and Potential Drugs for Sarcopenia based on Machine Learning and Experimental Validation.

Background: Sarcopenia, an aseptic chronic inflammatory disease, is a complex and debilitating disease characterized by the progressive degeneration of skeletal muscle. PANoptosis, a novel proinflammatory programmed cell death pathway, has been linked to various diseases. However, the precise role of PANoptosis-related features in sarcopenia remains uncertain.

Methods: According to the intersection of differentially expressed genes (DEGs) in the sarcopenia dataset GSE167186 and the PANoptosis gene set, we classified patients into PANoptosis-related subtypes (PANRS) using consensus clustering. The DEGs of PANRS were intersected with weighted gene co-expression network analysis (WGCNA). Proteinprotein interaction network and cytoHubba algorithms were employed to further identify potential genes related to PANoptosis. The most characteristic genes were selected using LASSO regression and validated by ROC curve analysis, followed by relevant immune infiltration analysis. Additionally, small-molecule drug screening was performed using Cmap. The relative expression levels of hub genes in sarcopenia were confirmed by PCR. Finally, single-cell analysis and GSEA were used to examine the distribution and function of hub genes.

Results: Thirty-five candidate genes were identified through WGCNA and PANRS. Machine learning and ROC curve analysis revealed three core genes: LTBP2, ETS2, and H3.3B, all of which were up-regulated in patients with sarcopenia (p<0.01). Immune infiltration analysis indicated that these three diagnostic genes were linked to the activation of NK cells and macrophages. Single-cell analysis demonstrated that LTBP2 was mainly localized in fibroblasts, while ETS2 and H3.3B exhibited a uniform distribution. Enrichment analysis indicated that the three hub genes were predominantly associated with the inhibition of energy metabolism.

Conclusion: In this study, the hub genes LTBP2, ETS2, and H3.3B associated with PANoptosis in sarcopenia were successfully identified through a combination of bioinformatics and experimental verification methods. This establishes a foundation for new candidate diagnostic and therapeutic targets for sarcopenia.

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来源期刊
Current medicinal chemistry
Current medicinal chemistry 医学-生化与分子生物学
CiteScore
8.60
自引率
2.40%
发文量
468
审稿时长
3 months
期刊介绍: Aims & Scope Current Medicinal Chemistry covers all the latest and outstanding developments in medicinal chemistry and rational drug design. Each issue contains a series of timely in-depth reviews and guest edited thematic issues written by leaders in the field covering a range of the current topics in medicinal chemistry. The journal also publishes reviews on recent patents. Current Medicinal Chemistry is an essential journal for every medicinal chemist who wishes to be kept informed and up-to-date with the latest and most important developments.
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