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. 2022 Oct 17:13:1043297.
doi: 10.3389/fgene.2022.1043297. eCollection 2022.

Identification of genes modified by N6-methyladenosine in patients with colorectal cancer recurrence

Qianru Zhu  1   2   3   4 , Xingxing Huang  1   2   3   4 , Shuxian Yu  2   3   4 , Lan Shou  2   3   4 , Ruonan Zhang  1   2   3   4 , Han Xie  1   2   3 , Zimao Liang  1   2   3   4 , Xueni Sun  2   3   4 , Jiao Feng  2   3   4 , Ting Duan  2   3   4 , Mingming Zhang  2   3   4 , Yu Xiang  2   3   4 , Xinbing Sui  1   2   3   4   5   6 , Weiwei Jin  6   7 , Lili Yu  1 , Qibiao Wu  1   2   3   5
Affiliations

Identification of genes modified by N6-methyladenosine in patients with colorectal cancer recurrence

Qianru Zhu et al. Front Genet. .

Abstract

Background: Recent studies demonstrate that N6-methyladenosine (m6A) methylation plays a crucial role in colorectal cancer (CRC). Therefore, we conducted a comprehensive analysis to assess the m6A modification patterns and identify m6A-modified genes in patients with CRC recurrence. Methods: The m6A modification patterns were comprehensively evaluated by the NMF algorithm based on the levels of 27 m6A regulators, and tumor microenvironment (TME) cell-infiltrating characteristics of these modification patterns were systematically assessed by ssGSEA and CIBERSORT algorithms. The principal component analysis algorithm based on the m6A scoring scheme was used to explore the m6A modification patterns of individual tumors with immune responses. The weighted correlation network analysis and univariable and multivariable Cox regression analyses were applied to identify m6A-modified gene signatures. The single-cell expression dataset of CRC samples was used to explore the tumor microenvironment affected by these signatures. Results: Three distinct m6A modification patterns with significant recurrence-free survival (RFS) were identified in 804 CRC patients. The TME characterization revealed that the m6A modification pattern with longer RFS exhibited robust immune responses. CRC patients were divided into high- and low-score subgroups according to the m6A score individually, which was obtained from the m6A-related signature genes. The patients with low m6A scores had both longer RFS and overall survival (OS) with altered immune cell infiltration. Notably, m6A-modified genes showed significant differences related to the prognosis of CRC patients in the meta-GEO cohort and TCGA cohort. Single-cell expression indicated that ALVRL1 was centrally distributed in endothelial tip cells and stromal cells. Conclusion: The m6A modification plays an indispensable role in the formation of TME diversity and complexity. Importantly, the signatures (TOP2A, LRRC58, HAUS6, SMC4, ACVRL1, and KPNB1) were identified as m6A-modified genes associated with CRC recurrence, thereby serving as a promising predictive biomarker or therapeutic target for patients with CRC recurrence.

Keywords: colorectal cancer; m6A methylation modification; overall survival; recurrence; tumor immune microenvironment.

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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
Landscape of genetic and expression variation of m6A regulators in colorectal cancer recurrence population. (A) Regulation of m6A regulation and its biological functions in RNA metabolism. (B) Expression change of m6A regulators in colorectal cancer with recurrence compared with no recurrence. (C) Expression changes of m6A regulators in colorectal cancer with high stage compared with low stage. *p < 0.05, **p < 0.01, and ***p < 0.001. (D) CNV variation frequency of m6A regulators in the TCGA cohort. The height of the column represented the alteration frequency. The deletion frequency is represented by a blue dot; The amplification frequency is represented by a red dot. (E) Location of CNV alteration of m6A regulators on 23 chromosomes using the TCGA cohort.
FIGURE 2
FIGURE 2
Relationship between the m6A methylation modification pattern and prognostic characteristics. (A) Interaction of expression on 27 m6A regulators in colorectal cancer. The m6A regulators in three RNA modification clusters were depicted by circles in different colors. Readers, orange; writers, gray; erasers, red. The lines connecting m6A regulators represented their interaction with each other. The size of each circle represented the recurrence effect of each regulator and was scaled by the p-value. Inhibitory factors for patients’ recurrence were indicated by a green right semicircle and motivating factors indicated by a purple right semicircle. (B) NMF rank survey result. (C) NMF analysis identification of the three m6A modification clusters. (D,E) Kaplan–Meier curves of recurrence-free survival (D) and overall survival (E) for 804 CRC patients in the meta-GEO cohort with different m6A cluster patterns. The numbers of patients in m6A-cluster 1, m6A-cluster 2, and m6A-cluster 3 three phenotypes are 105, 313, and 386, respectively.
FIGURE 3
FIGURE 3
Immune profiles among the different m6A methylation modification patterns. (A–C) GSVA enrichment analysis shows the activation states of biological pathways in the three clusters. The biological processes are visualized with the bar plot: orange represents activated pathways; blue represents inhibited pathways. (D) Fraction of tumor-infiltrating lymphocyte cells in three m6A clusters using the ssGSEA algorithm. Within each group, the scattered dots represented TME cell expression values. The thick line represented the median value. The bottom and top of the boxes were the 25th and 75th percentiles, respectively (interquartile range). The statistical difference between the three gene clusters was compared through the Kruskal–Wallis H test. *p < 0.05; **p < 0.01; ***p < 0.001.
FIGURE 4
FIGURE 4
Construction of m6A signatures and TME cell infiltration analysis. (A,B) Kaplan–Meier curves of recurrence-free survival (A) and overall survival (B) for 804 CRC patients in the meta-GEO cohort with different m6A scores. (C) Bar plot visualizes the relative percent of 22 immune cells in each sample. (D) Boxplot of all 22 immune cells differentially infiltrated fraction. *p < 0.05; **p < 0.01; ***p < 0.001.
FIGURE 5
FIGURE 5
Weighted gene correlation network analysis of m6A methylation regulators. (A,B) Analysis of network topology to determine soft-thresholding power. (C) Eigengene dendrogram identified groups of correlated modules. (D) Gene dendrogram was obtained by clustering the dissimilarity based on consensus topological overlap with the corresponding module colors indicated by the color row. Each colored row represents a color-coded module that contains a group of highly connected genes. (E) Heatmap of the correlation between the module eigengenes and clinical traits of colorectal cancer. We selected the module red, cyan, gray60, dark turquoise, and dark gray blocks for subsequent analysis. (F) Gene Ontology analysis of genes in module red, cyan, gray60, dark turquoise, and dark gray.
FIGURE 6
FIGURE 6
Identification of key genes modified by m6A. (A) Multivariable Cox regression analyses in the meta-GEO cohort by using the RFS model. (B) Survival plot of the significant genes obtained by multivariable Cox regression, including ACVRL1 and HAUS6. (C) Overall survival of the signature was obtained by multivariable Cox regression from the meta-GEO cohort in the TCGA cohort. (D,E) Gene expression of ACVRL1 and HAUS6 in the TCGA cohort. (F) Thirty Q21 clusters of the single-cell RNA-seq analysis. (G,H) Distribution of ACVRL1 and HAUS6 in colorectal cancer patients.
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