标题： 利用核机器回归识别肺癌药物靶点的多组学相互作用 显示英文标题

标题： Identifying multi-omics interactions for lung cancer drug targets discovery using Kernel Machine Regression

摘要： 癌症表现出由多方面分子相互作用驱动的多样且复杂的表型。最近的生物医学研究强调了通过整合多组学数据集（基因组、蛋白质组、转录组、表观基因组）对这类疾病进行全面研究。这种方法提供了一种有效的方法来识别与癌症相关的遗传变异，并有助于更深入地理解疾病的发展和扩散机制。然而，与单一组学相比，理解多组学数据特征之间的复杂相互作用更具挑战性。在本文中，我们分析了来自癌症基因组图谱（TCGA）的肺癌多组学数据集。使用四种统计方法，LIMMA、T检验、典型相关分析（CCA）和Wilcoxon检验，我们在基因表达、DNA甲基化和miRNA表达数据中识别出了差异表达基因。然后，我们使用核机器回归（KMR）方法整合这些多组学数据。我们的研究结果揭示了肺癌中三种组学：基因表达、miRNA表达和DNA甲基化之间的重要相互作用。从我们的数据分析中，我们识别出38个与肺癌显著相关的基因。从我们的数据分析中，我们识别出38个与肺癌显著相关的基因。其中，八种排名最高的基因（PDGFRB、PDGFRA、SNAI1、ID1、FGF11、TNXB、ITGB1、ZIC1）通过严格的统计分析被突出显示。此外，在计算机研究中识别出三种排名靠前的潜在候选药物（Selinexor、Orapred和Capmatinib），它们可能在肺癌治疗中发挥关键作用。这些提出的药物也得到了其他独立研究结果的支持，这突显了它们在对抗肺癌方面的潜在疗效。 显示英文摘要

摘要： 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.