GSFM:一个基因组规模的功能模块转换来表示药物有效性,用于在体中发现药物
Saisai Tian1,2, Xuyang Liao2,3,4, Wen Cao1
1Department of Phytochemistry, School of Pharmacy, Second Military Medical University, Shanghai 200433, China.
Acta pharmaceutica Sinica. B
|March 5, 2025
概括
我们开发了一个基因组规模功能模块 (GSFM) 框架,通过分析基因表达数据来改进计算药物发现. 这种方法提高了药物疗效的评估,并确定了新的癌症治疗方法.
科学领域:
- 计算生物学是一种计算生物学.
- 基因组学就是基因组学.
- 药理学 药理学是指药理学的学科.
背景情况:
- 药物转录形状对于药物发现至关重要,但目前的方法忽视了基因网络关联和药物疗效关系.
- 现有的计算方法无法捕捉基因功能网络中的复杂相互作用,限制了它们在药物发现中的有效性.
研究的目的:
- 开发一种新的基因组规模功能模块 (GSFM) 转换框架,用于在药物发现中进行定量药物疗效评估.
- 建立一个强大的计算方法,将基因级,途径级和网络级数据集成为全面的药物活性评估.
主要方法:
- 开发了GSFM转换框架,使用四个量化器 (GSFM_Up,GSFM_Down,GSFM_ssGSEA,GSFM_TF) 来进行多维功能模块活动评估.
- 采用数据转换策略,将基因表达数据转换为可靠的功能模块主动矩阵,提高对现有方法的稳定性和准确性.
- 研究了一种新型指标RS_GSFM与药物疗效之间的相关性.
主要成果:
- 与其他计算方法相比,GSFM框架在评估药物疗效方面表现出卓越的稳定性和准确性.
- 在RS_GSFM和药物疗效之间发现了积极的相关性,确定RS_GSFM作为治疗疗效的潜在衡量标准.
- 分别确定了WYE-354, perhexiline和NTNCB作为乳腺侵入性癌症,肺腺癌和抵抗割的前列腺癌的候选药物.
结论:
- 该GSFM框架为in silico药物发现提供了一个强大的工具,在药物疗效评估中提供了更高的准确性和稳定性.
- 已识别的候选治疗剂需要进一步调查和临床试验,以确定它们对癌症的适用性.
- 实验验证证证实了已识别的化合物的强效抗瘤作用,验证了计算方法.
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