m6A related exosome gene siganture in colon cancer identifies distinct microenvironment characterization and immunotherapeutical responses

Abstract Background Gradually, more researches pay attention to the potential relationship between N6-methyladenosine (m6A) modification of RNA and immunity in tumor microenvironment (TME). Therefore, they developed unique scoring modes through detecting level of m6A in various tumors to learn immune phenotypes. Nevertheless, direct detection always is harmful even fatal to patients. Recent studies demonstrated that exosomes in body fluid would deliver RNA with m6A modification from tumors. Methods Through analyzing datasets from 1066 colon cancer samples, m6A-related exosome genes modification patterns which based on 59 m6A-related exosome genes was classified. Then we used MREGS to calculate modification patterns of each tumor with PCA algorithm. Results In accordance with 59 m6A-related exosome gene, we indicated that there were distinguishing characteristics of TME cell infiltration in three different m6A-related exosome gene modification patterns. Three immune cell infiltration profiles (immune-inflamed, immune-desert and immune-excluded) were pronouncedly relevant with the identified patterns in aspects of immune activation, EMT- and TGFβ-related pathways and activity of stroma in TME. We also discovered that tumor molecular subtypes, genetic aberrant expression and patients’ outcomes could be evaluated by identified modification patterns. Furthermore, patients in lower MREGS group, featured as prolonged lifespan and advantageous immune activation, was associated with high tumor mutation burden (TMB) and good prognosis with anti-PDL1 immune therapy. Conclusion Detecting the level of m6A methylation of exosomes in patients’ serum, MREGS could be utilized for assessing m6A-related exosome gene modification patterns of each patient and their own matching characteristics of TME cell infiltration, and next ascertain immunological classifications of tumors and assist clinicians to tailor optimal treatment for patients.