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通过多种机器学习模型发现和验证质与相关的标记基因,以准确的状体诊断

Zicheng Guo^1,2, Qingli Yu¹, Wencheng Huang¹

¹Department of Orthopaedics, Huizhou First Hospital, Huizhou, People's Republic of China.

Clinical, cosmetic and investigational dermatology

|February 5, 2024

概括

这项研究确定了10个与cuproptosis相关的基因 (CRG),这些基因对于诊断 keloid 痕至关重要. 这些发现为化体发育和潜在的向治疗提供了新的见解.

科学领域:

生物医学研究的研究.
遗传学和基因组学遗传学和基因组学
细胞生物学细胞生物学

背景情况:

状体是一种异常的皮肤痕,在全球范围内影响着许多人.
化形成可能涉及减少细胞死亡,最近的发现突出显示一种新的细胞死亡模式,依赖于铜离子.
了解这些机制是开发有效的 keloid 治疗的关键.

研究的目的:

为了确定与 keloid 诊断相关的新型 cuproptosis 相关基因 (CRG).
探索 keloid 发育的分子基础.
为新基洛因疗法提供潜在的点.

主要方法:

使用了多个基因表达数据集 (GSE44270,GSE145725,GSE7890,GSE92566,GSE121618) 的数据集.
集成机器学习模型 (SVM,RF,GLM,XGB) 用于识别诊断CRG.
使用RT-qPCR,西方抹杀,IHC和独立数据集的验证结果.

主要成果:

鉴定了10个用于 keloid 诊断的 CRG,并根据 CRG 表达分类了 keloid 样本成两组.
在WGCNA的分析中,发现了110个与cuproptosis相关的候选基因.
功能性丰富分析表明,MAPK途径与化细胞生长有关;GSVA表明CRG对ECM受体相互作用的影响.

结论:

成功识别了10个CRG,这些CRG具有胆固醇的诊断价值.
这些CRG提供了洞察力,以化体的病原体.
这些发现可能为开发向化物治疗铺平了道路.

关键词:

这就是cuproptosis.凯洛伊德 (Keloid) 是一种类型的物质.机器学习是机器学习.这是一个新的生物标志物.

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