Integrative Omics Analysis Reveals Post-Transcriptionally Enhanced Protective Host Response in Colorectal Cancers with Microsatellite Instability

• Published in: Journal of proteome research Vol. 15; no. 3; pp. 766 - 776
• Main Authors: Liu, Qi, Zhang, Bing
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 04.03.2016
• Subjects: Cohort Studies; colorectal neoplasms; Colorectal Neoplasms - genetics; Colorectal Neoplasms - immunology; data collection; gene expression; Gene Expression Profiling; gene expression regulation; Gene Expression Regulation, Neoplastic - genetics; genes; Humans; immune response; Immunity - genetics; lymphocytes; messenger RNA; Microsatellite Instability; microsatellite repeats; phenotype; protein synthesis; proteome; Proteomics; regression analysis; transcriptome; Transcriptome - genetics; transcriptomics; microsatellite instability; colorectal cancer; integrative omics analysis; post-transcription regulation; protective host response
• ISSN: 1535-3893; 1535-3907; 1535-3907
• DOI: 10.1021/acs.jproteome.5b00847

Microsatellite instability (MSI) is a frequent and clinically relevant molecular phenotype in colorectal cancer. MSI cancers have favorable survival compared with microsatellite stable cancers (MSS), possibly due to the pronounced tumor-infiltrating lymphocytes observed in MSI cancers. Consistent with the strong immune response that MSI cancers trigger in the host, previous transcriptome expression studies have identified mRNA signatures characteristic of immune response in MSI cancers. However, proteomics features of MSI cancers and the extent to which the mRNA signatures are reflected at the protein level remain largely unknown. Here, we performed a comprehensive comparison of global proteomics profiles between MSI and MSS colorectal cancers in The Cancer Genome Atlas (TCGA) cohort. We found that protein signatures of MSI are also associated with increased immunogenicity. To reliably quantify post-transcription regulation in MSI cancers, we developed a resampling-based regression method by integrative modeling of transcriptomics and proteomics data sets. Compared with the popular simple method, which detects post-transcriptional regulation by either identifying genes differentially expressed at the mRNA level but not at the protein level or vice versa, our method provided a quantitative, more sensitive, and accurate way to identify genes subject to differential post-transcriptional regulation. With this method, we demonstrated that post-transcriptional regulation, coordinating protein expression with key players, initiates de novo and enhances protective host response in MSI cancers.