Background

Colorectal cancer (CRC) is a common malignant tumor of digestive tract which has high incidence and mortality rates. Accurate prognosis prediction of CRC patients is pivotal to reduce the mortality and disease burden.

Methods

In this study, we comprehensively analyzed the gene expression and methylation data of CRC samples from The Cancer Genome Atlas (TCGA). Differential expression genes (DEGs) and methylation CpGs (DMCs) in tumor tissues compared with adjacent normal tissues of CRC were first identified. Functional enrichment analysis of DEGs and DMCs was performed by Database for Annotation, Visualization and Integrated Discovery (DAVID). Spearman correlation analysis was used to screen DMCs that negatively correlated with gene expressions which were subsequently applied to sure independence screening (SIS) along with stepwise regression for screening optimal CpGs for CRC prognosis prediction model construction by Cox regression analysis.

Results

We identified a total of 1774 DEGs (663 upregulated and 1111 downregulated) and 11,975 DMCs (7385 hypermethylated and 4590 hypomethylated) in CRC tumor samples compared with adjacent normal samples. The hypermethylated loci were mainly located on CpG island, while the hypomethylated loci were mainly located on N-shore. Spearman correlation analysis screened 321 DMCs that negatively correlated with expressions of their annotated genes. Cox regression model consist of 10 CpGs was finally established which could effectively stratified CRC patients that exhibited significantly different overall survival probability independent of age, gender, and pathological staging.

Conclusion

We established a prognosis prediction model based on 10 methylation sites, which could evaluate the prognosis of CRC patients.

中文翻译：

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基于10基因甲基化的签名可预测大肠癌的预后

背景

大肠癌（CRC）是常见的消化道恶性肿瘤，具有较高的发病率和死亡率。CRC患者的准确预后预测对于降低死亡率和疾病负担至关重要。

方法

在这项研究中，我们全面分析了癌症基因组图谱（TCGA）的CRC样本的基因表达和甲基化数据。首先确定与邻近的CRC正常组织相比，肿瘤组织中的差异表达基因（DEGs）和甲基化CpGs（DMCs）。DEG和DMC的功能富集分析是通过用于注释，可视化和集成发现的数据库（DAVID）进行的。Spearman相关分析用于筛选与基因表达负相关的DMC，随后将其应用于确定独立性筛选（SIS）以及逐步回归，以通过Cox回归分析筛选用于CRC预后预测模型的最佳CpG。

结果

与邻近的正常样品相比，我们在CRC肿瘤样品中总共鉴定了1774个DEG（663个上调和1111个下调）和11,975个DMC（7385个高甲基化和4590个低甲基化）。高甲基化的基因座主要位于CpG岛上，而低甲基化的基因座主要位于N岸上。Spearman相关性分析筛选了321个DMC，这些DMC与其注释基因的表达呈负相关。最终建立了由10个CpG组成的Cox回归模型，该模型可以有效地对CRC患者进行分层，这些患者表现出与年龄，性别和病理分期无关的总体生存概率。

结论

我们建立了基于10个甲基化位点的预后预测模型，可以评估CRC患者的预后。