METHODS: Lipophagy-related biomarkers for COVID-19 were identified using machine learning algorithms such as random forest, Support Vector Machine-Recursive Feature Elimination, Generalized Linear Model, and Extreme Gradient Boosting in three COVID-19-associated GEO datasets: scRNA-seq (GSE145926) and bulk RNA-seq (GSE183533 and GSE190496). The cMAP database was searched for potential COVID-19 medications.
RESULTS: The lipophagy pathway was downregulated, and the lipid droplet formation pathway was upregulated, resulting in impaired lipid metabolism. Seven lipophagy-related genes, including ACADVL, HYOU1, DAP, AUP1, PRXAB2, LSS, and PLIN2, were used as biomarkers and drug targets for COVID-19. Moreover, lipophagy may play a role in COVID-19 pathogenesis. As prospective drugs for treating COVID-19, seven potential downregulators (phenoxybenzamine, helveticoside, lanatoside C, geldanamycin, loperamide, pioglitazone, and trichostatin A) were discovered. These medication candidates showed remarkable binding energies against the seven biomarkers.
CONCLUSIONS: The lipophagy-related genes ACADVL, HYOU1, DAP, AUP1, PRXAB2, LSS, and PLIN2 can be used as biomarkers and drug targets for COVID-19. Seven potential downregulators of these seven biomarkers may have therapeutic effects for treating COVID-19.
方法:使用随机森林等机器学习算法鉴定了COVID-19的与脂肪吞噬相关的生物标志物,支持向量机-递归特征消除,广义线性模型,以及三个与COVID-19相关的GEO数据集的极端梯度提升:scRNA-seq(GSE145926)和批量RNA-seq(GSE183533和GSE190496)。在cMAP数据库中搜索潜在的COVID-19药物。
结果:脂质吞噬途径下调,脂滴形成途径上调,导致脂质代谢受损。七个吸脂相关基因,包括ACADVL,HYOU1,DAP,AUP1,PRXAB2,LSS,和PLIN2,被用作COVID-19的生物标志物和药物靶标。此外,吸脂性可能在COVID-19发病机制中起作用。作为治疗COVID-19的前瞻性药物,七个潜在的下调节剂(苯氧基苄胺,Helveticoside,lanatosideC,格尔德霉素,洛哌丁胺,吡格列酮,和曲古抑菌素A)被发现。这些候选药物显示出与七种生物标志物的显著结合能。
结论:脂质吞噬相关基因ACADVL,HYOU1,DAP,AUP1,PRXAB2,LSS,PLIN2可作为COVID-19的生物标志物和药物靶标。这七种生物标志物的七种潜在下调物可能对治疗COVID-19具有治疗作用。