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. 2021 May 17:11:670490.
doi: 10.3389/fonc.2021.670490. eCollection 2021.

Potential Impact of ALKBH5 and YTHDF1 on Tumor Immunity in Colon Adenocarcinoma

Guanyu Yan  1 , Yue An  1 , Boyang Xu  1 , Ningning Wang  1 , Xuren Sun  1 , Mingjun Sun  1
Affiliations

Potential Impact of ALKBH5 and YTHDF1 on Tumor Immunity in Colon Adenocarcinoma

Guanyu Yan et al. Front Oncol. .

Abstract

Background: ALKBH5 and YTHDF1 are regarded as the eraser and reader, respectively, in N6-methyladenosine (m6A) modification. Recently, immune contexture has been drawing increasing attention in terms of the progression and treatment of cancers. This study aimed to determine the relationship between ALKBH5/YTHDF1 and immunological characteristics of colon adenocarcinoma (COAD).

Methods: Expression of ALKBH5 and YTHDF1 was investigated across TCGA and GEO validated in our study. Patients with COAD were divided into two clusters using consensus clustering based on the expression of ALKBH5 and YTHDF1. We then compared their clinical characteristics and performed gene set enrichment analysis (GSEA) to identify the functional differences. Immune infiltration analyses were conducted using ESTIMATE, CIBERSORT, and ssGSEA. In addition, we evaluated the expression of the targets of immune checkpoint inhibitors (ICIs) and calculated the tumor mutation burden (TMB) of the tumor samples. Weighted gene co-expression network analysis (WGCNA) was used to identify the genes related to both ALKBH5/YTHDF1 expression and immunity. GSE39582 was utilized for external validation of immunological features between the two clusters.

Results: Cluster 2 had high expression of ALKBH5 and lesser so of YTHDF1, whereas Cluster 1 had just the reverse. Cluster 1 had a higher N stage and pathological stage than Cluster 2. The latter had stronger immune infiltration, higher expression of targets of ICIs, more TMB, and a larger proportion of deficiency in mismatch repair-microsatellite instability-high (dMMR-MSI-H) status than Cluster 1. Moreover, WGCNA revealed 14 genes, including PD1 and LAG3, related to both the expression of ALKBH5/YTHDF1 and immune scores.

Conclusions: ALKBH5 and YTHDF1 influence immune contexture and can potentially transform cold tumors into hot tumors in patients with COAD.

Keywords: ALKBH5; YTHDF1; colon adenocarcinoma; immune contexture; m6A modification.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Identification of m6A regulators related to immune score and clustering of TCGA-COAD patients based on ALKBH5 and YTHDF1. (A) Association between m6A regulators and results of ESTIMATE. (B) Comparison of ALKBH5 expression between tumor and normal tissues. (C) Comparison of YTHDF1 expression between tumor and normal tissues. (D) TCGA-COAD patients are divided into two clusters according to ALKBH5 and YTHDF1. (E) Association between ALKBH5 and YTHDF1 expression. The P values are labeled using asterisks (***P < 0.001).
Figure 2
Figure 2
Comparison of immune characteristics between two clusters. Comparison of functional enrichment (A), stromal score (B), immune score (C), ESTIMATE score (D), tumor purity (E), proportion of immune cells (F) and expression of immune cells (G) between two clusters. The P values are labeled using asterisks (ns, no significance, *P < 0.05, **P < 0.01, ***P < 0.001).
Figure 3
Figure 3
Comparison of immunomodulatory drugs’ targets in clinical trials for metastatic colorectal cancer between two clusters. The P values are labeled using asterisks (ns, no significance, **P < 0.01, ***P < 0.001).
Figure 4
Figure 4
Comparison of mutational landscapes between two clusters. Mutational landscape of Cluster 1 (A) and Cluster 2 (B). (C) Comparison of tumor mutation burden (TMB) between two clusters. (D) Comparison of gene mutation related to mismatch repair and POLE proofreading domain between two clusters. The P values are labeled using asterisks (***P < 0.001).
Figure 5
Figure 5
Identification of module genes associated with both clustering and immunity in the WGCNA. (A) Volcano plot of differential analysis. (B) Analysis of network topology for soft powers. (C) Gene dendrogram and module colors. (D) Heatmap between module eigengenes and cluster, ESTIMATE results. (E) Scatter plot of module eigengenes in the blue module.
Figure 6
Figure 6
Analysis of 14 hub genes. (A) The GO analysis of hub genes. (B) PPI network of hub genes. (C) Correlation between hub genes. (D) Correlation between hub genes and results of ESTIMATE. (E) Correlation between hub genes and expression of immune cells (ssGSEA).
Figure 7
Figure 7
GSE39582 validation of immune contexture between two clusters. (A) GSE39582 patients are divided into two clusters according to ALKBH5 and YTHDF1. (B) Association between ALKBH5 and YTHDF1 expression in GSE39582. (C–K) Comparison of stromal score (C), immune score (D), ESTIMATE score (E), tumor purity (F), targets of immunomodulatory drugs (G–J), proportion of immune cells (K) and expression of immune cells (L) between two clusters. The P values are labeled using asterisks (ns, no significance, *P < 0.05, **P < 0.01, ***P < 0.001).
Figure 8
Figure 8
Verification of ALKBH5 and YTHDF1 expression in CRC tissues using RT-qPCR. The P values are labeled using asterisks (ns, no significance, *P < 0.05, ***P < 0.001).
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