在癌症和转移中对整合素表达模式的基于机器学习的调查
Hossain Shadman1, Saghar Gomrok1, Christopher Litle1
1Department of Chemistry, The University of Memphis, Memphis, TN, 38152, USA.
Scientific reports
|February 12, 2025
概括
机器学习模型使用整合素表达数据准确地识别了癌症组织. 像ITGA7这样的特定整合素可以区分健康与癌症乳腺组织,揭示了癌症进展和转移中的关键作用.
科学领域:
- 在瘤学瘤学.
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- 整合素是癌症发展和转移至关重要的跨膜受体.
- 综合素在癌症中的作用的全面理解受到与癌症类型和进展阶段的复杂相互作用的限制.
研究的目的:
- 使用机器学习探索健康和癌症组织中的整合素表达模式.
- 确定参与区分组织类型和疾病状况的关键整合素.
- 为了研究整合素联合表达网络的变化和转移性乳腺癌中的表达.
主要方法:
- 利用基因型-组织表达 (GTEx) 和癌症基因组图谱 (TCGA) 项目的RNA-Seq数据.
- 训练有素的机器学习模型来分类健康组织,固体瘤和正常与瘤样本.
- 在健康和癌症乳腺组织中分析了整合蛋白共同表达网络.
- 在转移性乳腺癌中使用AURORA项目数据检查整合素表达.
主要成果:
- 机器学习模型在根据组织来源和疾病状态对样品进行分类时取得了高准确性.
- 确定了特定的整体蛋白,如ITGA7,能够以>0.9的精度区分健康的乳腺组织和癌症乳腺组织.
- 观察到健康和癌症乳腺组织之间的整合素联合表达网络的显著变化.
- 在转移性乳腺癌中发现ITGAD,ITGA4,ITGAL和ITGA11的表达显著降低,与初级瘤相比.
结论:
- 集成蛋白表达模式可以作为癌症检测和分类的准确生物标志物.
- 特定的整合素在癌症的进展和转移中起着关键的作用,瘤中改变了网络动态.
- 在原发性和转移性瘤之间,整蛋白表达特征有所不同,为转移性过程提供了洞察力.
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