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. 2020 Sep;245(15):1344-1354.
doi: 10.1177/1535370220936145. Epub 2020 Jun 30.

Gene signature and prognostic merit of M6a regulators in colorectal cancer

Jinfeng Zhang  1 , Xuedi Cheng  2 , Junzheng Wang  3 , Yongjie Huang  4 , Junhui Yuan  5 , Dawen Guo  1
Affiliations

Gene signature and prognostic merit of M6a regulators in colorectal cancer

Jinfeng Zhang et al. Exp Biol Med (Maywood). 2020 Sep.

Abstract

Although new diagnostic techniques and treatments are increasingly updated for CRC, the clinical outcomes of CRC patients are still not encouraging with a low survival rate. N6-methyladenosine (m6A) as a popular modification on mRNA is associated with multiple types of cancers. Our purpose is to identify gene signature and prognostic ability of m6A modulators in CRC. For the first time, we identified genetic changes of m6A modulators and built prognostic gene signature in CRC, which may provide effective targets for the diagnosis and management of CRC.

Keywords: Colorectal cancer; N6-methyladenosine RNA methylation; TCGA; bioinformatics; prognostic signature.

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Figures

Figure 1.
Figure 1.
The mutation frequency statistics of different functional m6A regulatory genes in COAD and READ cases. (a) The distribution of SNV in COAD. (b) The distribution of SNV in READ. (A color version of this figure is available in the online journal.)
Figure 2.
Figure 2.
Connection between CNV and expression level of m6A modulatory genes in COAD. t test was used to examine the difference between the two groups.
Figure 3.
Figure 3.
Connection between CNV and expression level of m6A regulatory genes in READ. t test was conducted to examine the difference between the two groups.
Figure 4.
Figure 4.
Relationship between gene expression level of m6A regulatory genes and tumour stage. (a) m6A regulatory genes and tumour stage in COAD. (b) m6A regulatory genes and tumour stage in READ. t test was used to assay the difference between the two groups.
Figure 5.
Figure 5.
The m6A regulatory genes associated with clinical features and survival. (a) Correlation between levels of m6A regulatory genes in patients with COAD. (b) Correlation between levels of m6A regulatory genes in patients with READ. (c) The Kaplan–Meier curves in COAD and the ROC curves illustrated the predictive ability of the m6A regulatory genes. (d) The Kaplan–Meier curves in READ and the ROC curves illustrated the predictive ability of the m6A regulatory genes. (A color version of this figure is available in the online journal.)
Figure 6.
Figure 6.
Establishment of a prognostic signature based on m6A regulatory genes. (a) The heat map of m6A regulatory genes and different clinical features in COAD. (b) The heat map of m6A regulatory genes and different clinical features in READ. (c) The Kaplan–Meier curves in COAD and the ROC curves assessed the predictive ability of the m6A regulatory genes by LASSO analysis. (d) The Kaplan–Meier curves in READ and the ROC curves assessed the predictive ability of the m6A regulatory genes by LASSO analysis. (e) The Kaplan–Meier curves in GSE33113 and the ROC curves assessed the predictive ability of the m6A regulatory genes by LASSO analysis. (A color version of this figure is available in the online journal.)
Figure 7.
Figure 7.
GSEA enrichment analysis of YTHDC2. (a) GSEA analysis in COAD. (b) GSEA analysis in READ. (A color version of this figure is available in the online journal.)
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