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基于机器学习算法的CRPC进展机制及其抑制剂发现的全面分析

Zhen Wang¹, Jing Zou², Le Zhang¹

¹College of Basic Medical Sciences, Dali University, Dali, Yunnan, China.

Frontiers in genetics

|July 21, 2023

概括

鉴定出了耐结前列腺癌 (CRPC) 生物标志物CCNA2和CKS2. 阿普雷皮坦和多卢特格拉维尔显示出作为抗CRPC药物的潜力,阿普雷皮坦在体外显示出更高的疗效.

科学领域:

在瘤学瘤学.
分子生物学分子生物学
生物信息学是一种生物信息学.

背景情况:

在几乎所有患者中,抗抗雄激素剥夺疗法 (ADT) 导致割抵抗性前列腺癌 (CRPC).
了解推动前列腺癌进展到CRPC的分子机制对于有效的治疗策略至关重要.

研究的目的:

确定潜在的生物标志物和分子机制,从前列腺癌转变为CRPC.
评估用于CRPC治疗的新型治疗剂.

主要方法:

对CRPC的三个基因表达综合 (GEO) 微阵列数据集的分析.
差异表达基因 (DEG) 的识别,其次是基因本体学 (GO),基因和基因组的京都百科全书 (KEGG) 和基因组丰富分析 (GSEA).
权重基因同表达网络分析 (WGCNA),LASSO逻辑回归和支持矢量机-递归特征消除 (SVM-RFE) 用于生物标志物发现.
虚拟药物查和体外验证使用细胞计数套件-8 (CCK-8),抓取和跨井入侵试验.

主要成果:

确定了719种DEG,它们在细胞循环和新陈代谢途径中富含.
鉴定出CCNA2和CKS2是具有强大的预测能力的CRPC有前途的诊断生物标志物.
艾普雷皮坦和多卢特格拉维尔被确定为潜在的药物,分别针对CCNA2和CKS2,在体外对CRPC细胞表现出显著的抑制作用.

结论:

增加的CCNA2和CKS2表达与前列腺癌的进展相关,可以作为CRPC的诊断生物标志物和治疗点.
阿普雷皮坦和多卢特格拉维尔显示出作为CRPC新型抗瘤药物的潜力,阿普雷皮坦显示出更大的疗效.

关键词:

这是一种阿普雷皮坦特 (Aprepitant).在CCNA2中使用CCNA2.在Cks2中使用.多卢特格拉维尔 (Dolutegravir) 是一种药物.割抵抗性前列腺癌 (CRPC) 是一种癌症.机器学习算法机器学习算法虚拟选虚拟选虚拟选权重基因同表达网络分析 (WGCNA)

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