在多omics数据中结合多种数据类型是否会改善或阻碍生存预测的性能? 来自一项大型基准研究的见解
Yingxia Li1, Tobias Herold2, Ulrich Mansmann3
1Institute for Medical Information Processing, Biometry and Epidemiology, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany. liyingxia1991@hotmail.com.
BMC medical informatics and decision making
|September 2, 2024
概括
结合多个omics数据类型来预测癌症存活率并不总是提高准确性. 通常,仅使用mRNA或mRNA和miRNA数据就足够了,挑战了更多数据总是更好的假设.
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.
- 癌症研究 癌症研究
背景情况:
- 多omics数据集成显示了改善预测建模的承诺,而不是单个omics方法.
- 当前的研究通常假设更多的omics数据类型导致更好的预测,忽视了冗余或信息不足的数据可能导致的性能下降.
- 识别最佳的omics数据组合对于具有成本效益和准确的预测建模至关重要.
研究的目的:
- 系统地评估所有五种基因组数据类型 (mRNA,miRNA,甲基化,DNAseq,副本数变异) 的31种可能组合,用于癌症生存预测.
- 确定omics数据类型的最有效组合,以提高预测性能.
- 挑战一种普遍的假设,即数据类型的增加总是会提高多omics预测准确度.
主要方法:
- 从TCGA数据库中利用了14个癌症数据集,与TCGA数据库中的正确审查的生存结果.
- 评估了五种omics数据类型的所有31种组合,在所有模型中加权临床数据.
- 采用哈雷尔的C指数和集成的Brier分数进行绩效评估,并对强度进行引导分析.
主要成果:
- 对于大多数癌症类型,单独的mRNA数据或与miRNA数据相结合的数据提供了足够的预测性能.
- 包括甲基化数据改善了对某些癌症类型的预测.
- 添加更多的数据类型经常导致性能下降,使用的两个指标之间存在差异.
结论:
- 这些发现挑战了普遍的信念,即整合众多omics数据类型可以提高多omics生存预测.
- 该研究建议重新考虑将最大数据类型纳入战略,以避免低于最佳的预测并降低成本.
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