用核机器回归发现肺癌多组学互作关键基因与潜在药物。
Identifying multi-omics interactions for lung cancer drug targets discovery using Kernel Machine Regression
- 通过核机器回归整合基因表达、miRNA和甲基化数据，挖掘多组学交互作用。
- 识别出38个与肺癌显著相关的基因，其中8个为高置信度候选靶点。
- 筛选出塞利奈索尔等3种潜在治疗药物，且获独立研究支持。
癌症表现出由多重分子互作驱动的复杂表型。近年来，整合基因组、蛋白质组、转录组和表观基因组等多组学数据成为深入研究疾病的新范式，有助于识别癌症相关遗传变异并揭示其发生发展机制。然而，相较于单组学分析，理解多组学特征间的复杂交互仍具挑战。本文基于泛癌基因组图谱（TCGA）的肺癌多组学数据，采用LIMMA、t检验、典型相关分析（CCA）和Wilcoxon检验四种统计方法，分别在基因表达、DNA甲基化和miRNA表达数据中识别差异表达基因。随后，利用核机器回归（KMR）方法对多组学数据进行整合分析。结果表明，基因表达、miRNA表达与DNA甲基化在肺癌中存在显著交互作用。数据分析共识别出38个与肺癌显著相关的基因，其中排名前八的基因（PDGFRB、PDGFRA、SNAI1、ID1、FGF11、TNXB、ITGB1、ZIC1）经严格统计验证具有高置信度。此外，通过体外模拟研究，发现三种高排名潜在候选药物（Selinexor、Orapred、Capmatinib），其疗效在其他独立研究中亦得到支持，提示其在肺癌治疗中的应用潜力。
原文摘要 · Abstract (English)
Cancer exhibits diverse and complex phenotypes driven by multifaceted molecular interactions. Recent biomedical research has emphasized the comprehensive study of such diseases by integrating multi-omics datasets (genome, proteome, transcriptome, epigenome). This approach provides an efficient method for identifying genetic variants associated with cancer and offers a deeper understanding of how the disease develops and spreads. However, it is challenging to comprehend complex interactions among the features of multi-omics datasets compared to single omics. In this paper, we analyze lung cancer multi-omics datasets from The Cancer Genome Atlas (TCGA). Using four statistical methods, LIMMA, the T test, Canonical Correlation Analysis (CCA), and the Wilcoxon test, we identified differentially expressed genes across gene expression, DNA methylation, and miRNA expression data. We then integrated these multi-omics data using the Kernel Machine Regression (KMR) approach. Our findings reveal significant interactions among the three omics: gene expression, miRNA expression, and DNA methylation in lung cancer. From our data analysis, we identified 38 genes significantly associated with lung cancer. From our data analysis, we identified 38 genes significantly associated with lung cancer. Among these, eight genes of highest ranking (PDGFRB, PDGFRA, SNAI1, ID1, FGF11, TNXB, ITGB1, ZIC1) were highlighted by rigorous statistical analysis. Furthermore, in silico studies identified three top-ranked potential candidate drugs (Selinexor, Orapred, and Capmatinib) that could play a crucial role in the treatment of lung cancer. These proposed drugs are also supported by the findings of other independent studies, which underscore their potential efficacy in the fight against lung cancer.
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