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使用miRNA-mRNA-lncRNA相互作用网络和机器学习方法进行瘤原生组织分类

Ankita Lawarde^1,2, Masuma Khatun³, Prakash Lingasamy^1,2

¹Department of Obstetrics and Gynecology, Institute of Clinical Medicine, University of Tartu, Tartu, Estonia.

Frontiers in bioinformatics

|May 21, 2025

概括

本研究介绍了一种机器学习框架,使用微RNA (miRNA) 网络进行精确的瘤组织起源分类. 该方法实现了99%的准确性,识别了具有精确瘤学翻译潜力的关键miRNA生物标志物.

科学领域:

基因组学就是基因组学.
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

微RNAs (miRNAs) 是基因表达的关键调节者,与癌症的发展和治疗耐药性有关.
微RNA相互作用网络用于分类原始瘤组织 (TOO) 的实用性在很大程度上尚未被探索.
准确的TOO分类对于有效的癌症诊断和治疗策略至关重要.

研究的目的:

开发和验证一个机器学习 (ML) 框架,集成miRNA-mRNA长的非编码RNA (lncRNA) 网络,用于强大的瘤TOO分类.
为了确定一组最小的信息性miRNA生物标志物,以准确地确定癌症亚型.
在临床应用中评估已识别的miRNA生物标记物的翻译潜力.

主要方法:

开发一种ML框架,利用来自癌症基因组图谱 (TCGA) 的转录组数据,跨越14种癌症类型.
构建miRNA-mRNA-lncRNA联合表达网络并应用特征选择方法 (RFE,RF,Boruta,LDA).
使用分层的五倍交叉验证对整体ML算法的训练和验证.

主要成果:

实现了99%的整体分类准确度来区分14种癌症类型,证明了高强度和通用性.
通过递归特征消除 (RFE) 确定了150个miRNA的最小集合,作为对分类的最佳选择.
排名最高的miRNAs (例如miR-21-5p,miR-93-5p,miR-10b-5p) 显示了网络的中心性,与患者存活率的相关性以及药物反应.

结论:

集成的miRNA网络框架为瘤TOO分类提供了一种生物基础,可解释和高度准确的方法.
已识别的miRNA生物标志物具有显著的翻译潜力,得到临床数据和生存分析的支持.
这种方法推进了精确的瘤诊断,并支持基于液体活检的癌症分类的发展.

关键词:

组合学习组合学习功能选择功能选择机器学习是机器学习.这就是miRNAs.网络网络网络网络网络网络瘤组织起源的瘤组织.

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