Genome-wide identification of m6A-associated functional SNPs as potential functional variants for thyroid cancer

Abstract

m6A methylation has been demonstrated to be one of the most important epigenetic regulation mechanisms in cell differentiation and cancer development especially m6A derived diagnostic and prognostic biomarkers have been identified in the past several years. However, systemic investigation to the interaction between germline single-nucleotide polymorphisms (SNPs) and m6A has not been conducted yet. In this study, we collected previous identified significant thyroid cancer associated SNPs from UKB cohort (358 cases and 407,399 controls) and ICR cohort (3,001 patients and 287,550 controls) and thyroid eQTL (sample size = 574 from GTEx project) and m6A-SNP (N = 1,678,126) were applied to prioritize the candidate SNPs. Finally, five candidate genes (PLEKHA8, SMUG1, CDC123, RMI2, ACSM5) were identified to be thyroid cancer associated m6A-related genetic susceptibility. Loss and gain function studies of m6A writer proteins confirm that ACSM5 is regulated by m6A methylation of mRNA. Moreover, ACSM5 is downregulated in thyroid cancer and inversely correlated with PTC malignancy and patient survival. Together, our study highlight mRNA-seq and m6A-seq double analysis provided a novel approach to identify cancer biomarkers and understanding the heterogeneity of human cancers.

Keywords: ACSM5; Epigenetics; RNA methylation; m6A; thyroid cancer.

Figure 1

Manhattan plot to show m6A-related significant differential expression thyroid cancer genes (differential expression P<10^-6 as suggestive significance). GWAS association (P<0.0001) are derived from UKB and ICR cohort including PLEKHA8, SMUG1, CDC123, RMI2, ACSM5 while TAP2, AP1S1 and C6orf89 showed significant differential expression, but without significant GWAS signals. Candidate genes are defined as consistent genes with m6A-gain and over-expression as well as m6A-loss and down-regulation in thyroid cancers by GTEx-V8 database.

Figure 2

Pan-cancer down-regulation of ACSM5 crosses 23 types of human cancers. Cancer samples were collected from the TCGA project (N = 10,490). Gene expression level was log2 transformed before the meta-analysis. Both fixed effect model and random effect model were applied for the aggregation. 95% CI was applied to show the risk and protective effect to overall survival time. In order to show more details for different studies, any standardized mean difference (SMD) higher than 3 and lower than -3 was showed with arrow. Blue filled parallelograms represent the SMD for fixed effect model and random effect model.

Figure 3

Knockdown METTL3 inhibits ACSM5 expression and reduces its RNA m6A modification in thyroid cancer cell. A. METTL3 level was detected by western blot in METTL3 stable knock-down TPC-1 cells. B. m6A level was analyzed by ELSIA in TPC-1 cells. C. SMUG1, PLEKHA8, CDC123, RIM2 and ACSM5 mRNA in METTL3 knock-down TPC-1 cells were examined by qRT-PCR. D. Western blot of ACSM5 level in METTL3 knock-down TPC-1 cells, GADPH was as the loading control. E. m6A RIP-PCR analysis of ACSM5 mRNA in the control and METTL3 knockdown thyroid cancer cells. In all bars, values are shown as mean ± SEM. *P<0.05, **P<0.01, ***P<0.001.

Figure 4

Overexpression METTL3 and METTL14 upregulate ACSM5 expression in thyroid cancer. A. METTL3 or METTL14 level was detected by western blot in METTL3 or METTL14 overexpression KTC-1 cells. B. SMUG1, PLEKHA8, CDC123, RIM2 and ACSM5 mRNA in METTL3 or METTL14 overexpression KTC-1 cells were examined by qRT-PCR. C. ACSM5 protein level was detected in METTL3 or METTL14 overexpression KTC-1 cells, GAPDH was as the loading control. D. m6A RIP-PCR analysis of ACSM5 mRNA in the control and METTL3 overexpression thyroid cancer cells. In all bars, values are shown as mean ± SEM. *P<0.05, **P<0.01, ***P<0.001.

Figure 5

ACSM5 is downregulated in PTC specimens and associated with cancer progression. A. ACSM5 mRNA levels in PTC samples and matched normal thyroid tissues (MN) were analyzed by qRT-PCR (n = 16). B. ACSM5 expression levels in PTCs (n = 504) and normal thyroid tissues (NT) (n = 59) or PTC samples and matched normal thyroid tissues (MN) (n = 59) in TCGA dataset. C. ACSM5 expression levels of different T stage, N stage and TNM stage in PTCs, data were obtained from TCGA dataset. D. Relationship between ACSM5 expression levels and METTL3 or METTL14 expression levels in PTCs. *P<0.05, **P<0.01, ***P<0.001.

Figure 6

The prognostic value of ACSM5 expression for papillary thyroid cancer patients. Low expression of ACSM5 in PTCs was related with poor PFI (A) and OS (B), Data were obtained from TCGA database.