基于集合策略的肾透明细胞癌miRNA谱差异表达分析

Enyang Zhao, Ziqi Xi, Qiong Wu
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引用次数: 0

摘要

背景:肾透明细胞癌(KIRC)是最常见的肾癌类型,约占所有肾癌的60-85%。然而,很少有早期治疗的选择。因此,鉴定KIRC的生物标志物和研究其治疗靶点是非常重要的。方法:由于对KIRC的研究很少,我们使用数据驱动的方法来识别差异基因。在这里,我们使用来自KIRC的TCGA数据库物种的miRNA基因表达谱数据,并提出了一种基于机器学习的方法来量化每个基因的重要性评分。然后,利用集合方法通过聚类找到用于KIRC预测的最优基因子集。然后使用最遗传的子集对KIRC进行分类和预测。结果:通过几种传统的差异分析方法筛选出差异基因,筛选出的基因子集表现出较好的性能。使用GEO数据库中的独立测试集来验证最优基因子集的有效性。通过交叉验证验证了该方法的有效性。结论:最后,我们发现miR-140、miR-210等重要基因参与了KIRC的生化过程,这也证明了我们方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Differential Expression Analysis Based on Ensemble Strategy on miRNA Profiles of Kidney Clear Cell Carcinoma
Background: Kidney clear cell carcinoma (KIRC) is the most common type of kidney cancer, accounting for approximately 60–85% of all the kidney cancers. However, there are few options available for early treatment. Therefore, it is extremely important to identify biomarkers and study therapeutic targets for KIRC. Methods: Since there are few studies on KIRC, we used a data-driven approach to identify differential genes. Here, we used miRNA gene expression profile data from the TCGA database species of KIRC and proposed a machine learning-based approach to quantify the importance score of each gene. Then, an ensemble method was utilized to find the optimal subset of genes used to predict KIRC by clustering. The most genetic subset was then used to classify and predict KIRC. Results: Differential genes were screened by several traditional differential analysis methods, and the selected gene subset showed a better performance. Independent testing sets from the GEO database were used to verify the effectiveness of the optimal subset of genes. Besides, cross-validation was made to verify the effectiveness of the approach. Conclusions: Finally, important genes, such as miR-140 and miR-210, were found to be involved in the biochemical processes of KIRC, which also proved the effectiveness of our approach.
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