N6-methyladenosine (m6A) is the most abundant internal mRNA modification in eukaryotic cells. There is accumulating evidence that m6A methylation can play a significant role in the early diagnosis and treatment of cancers. However, the mechanism underlying the involvement of m6A in cervical cancer remains unclear.
Here, we examined the m6A modification patterns of immune cells in the tumour microenvironments (TMEs) of 306 patients with cervical cancer from The Cancer Genome Atlas dataset and analysed the relations between them according to 32 m6A regulators. Immune infiltration in the TME of cervical cancer was analysed using the CIBERSORT algorithm and single-sample gene set enrichment analysis. The m6Ascore was structured though principal component analysis.
Two different m6A modification patterns were detected in 306 patients with cervical cancer, designated as m6Acluster A and B. The immune cell infiltration characteristics and biological behaviour differed between the two patterns, with m6Acluster A showing a higher level of immune infiltration. The samples were also divided into two genomic subtypes according to 114 m6A regulatory genes shown to be closely correlated with prognosis on univariate Cox regression analysis. Survival analysis showed that gene cluster B was related to better survival than gene cluster A. Most of the m6A regulators showed higher expression in gene cluster B than in gene cluster A. Single-sample gene set enrichment analysis indicated a higher level of immune cell infiltration in gene cluster A. The m6Ascore signature was examined to determine the m6A modification patterns in cervical cancer. Patients with a high m6Ascore showed better survival, while the low m6Ascore group had a higher mutation frequency and better response to treatment.
This study showed that m6A modification patterns play important roles in cervical cancer. Analysis of m6A modification patterns will yield an improved understanding of the TME in cervical cancer, and facilitate the development of better immunotherapy strategies.