Patterns of DNA methylation are significantly altered in cancers. Interpreting the functional consequences of DNA methylation requires the integration of multiple forms of data. The recent advancement in the next-generation sequencing can help to decode this relationship and in biomarker discovery. In this study, we investigated the methylation patterns of papillary renal cell carcinoma (PRCC) and its relationship with the gene expression using The Cancer Genome Atlas (TCGA) multi-omics data. We found that the promoter and body of tumor suppressor genes, microRNAs and gene clusters and families, including cadherins, protocadherins, claudins and collagens, are hypermethylated in PRCC. Hypomethylated genes in PRCC are associated with the immune function. The gene expression of several novel candidate genes, including interleukin receptor IL17RE and immune checkpoint genes HHLA2, SIRPA and HAVCR2, shows a significant correlation with DNA methylation. We also developed machine learning models using features extracted from single and multi-omics data to distinguish early and late stages of PRCC. A comparative study of different feature selection algorithms, predictive models, data integration techniques and representations of methylation data was performed. Integration of both gene expression and DNA methylation features improved the performance of models in distinguishing tumor stages. In summary, our study identifies PRCC driver genes and proposes predictive models based on both DNA methylation and gene expression. These results on PRCC will aid in targeted experiments and provide a strategy to improve the classification accuracy of tumor stages.

中文翻译：

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乳头状肾细胞癌中DNA甲基化和基因表达的综合分析。

DNA甲基化的模式在癌症中显着改变。解释DNA甲基化的功能后果需要整合多种形式的数据。下一代测序的最新进展可以帮助解码这种关系和生物标记物发现。在这项研究中，我们使用癌症基因组图谱（TCGA）多组学数据研究了乳头状肾细胞癌（PRCC）的甲基化模式及其与基因表达的关系。我们发现，抑癌基因，microRNA和基因簇及家族（包括钙粘着蛋白，原钙粘着蛋白，claudins和胶原蛋白）的启动子和主体在PRCC中被高度甲基化。PRCC中的低甲基化基因与免疫功能有关。几种新的候选基因的基因表达，包括白介素受体IL17RE和免疫检查点基因HHLA2，SIRPA和HAVCR2，都与DNA甲基化密切相关。我们还使用从单组学和多组学数据中提取的特征开发了机器学习模型，以区分PRCC的早期和晚期。对不同的特征选择算法，预测模型，数据集成技术和甲基化数据表示进行了比较研究。基因表达和DNA甲基化特征的整合改善了模型在区分肿瘤阶段中的性能。总而言之，我们的研究确定了PRCC驱动基因，并基于DNA甲基化和基因表达提出了预测模型。这些关于PRCC的结果将有助于有针对性的实验，并提供改善肿瘤分期分类准确性的策略。