LAUSR

mRNA vaccines have become a promising alternative to conventional cancer immunotherapy approaches. However, its application on colorectal cancer (CRC) remains poorly understood. We herein identified potential antigens for designing an effective mRNA vaccine, further to build an immune landscape for the accurate selection of patients for mRNA vaccine therapy. Raw transcriptome data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were retrieved. Consensus clustering algorithm was applied to divide the CRC samples into four immune subtypes. Immunogenomics analysis was further integrated to characterize the immune microenvironment of each immune subtype. Six tumor antigens were found to be associated with poor prognosis and infiltration of antigen-presenting cells (APCs) in CRC patients. Furthermore, each of the immune subtypes showed differential cellular and molecular features. The IS2 and IS4 exhibited significantly improved survival and higher immune cell infiltration compared with IS1 and IS3. Immune checkpoint molecules and human leukocyte antigen also showed significant differential expression in four immune subtypes. Moreover, we performed graph structure learning-based dimensionality reduction to visualize the immune landscape of CRC. Our results revealed a complex immune landscape that may provide directions for mRNA vaccine treatment of CRC and define appropriate vaccination patients.