A multiomics analysis-assisted deep learning model identifies a macrophage-oriented module as a potential therapeutic target in colorectal cancer.

ABSTRACT: Colorectal cancer (CRC) is a common malignancy involving multiple cellular components. The CRC tumor microenvironment (TME) has been characterized well at single-cell resolution. However, a spatial interaction map of the CRC TME is still elusive. Here, we integrate multiomics analyses and establish a spatial interaction map to improve the prognosis, prediction, and therapeutic development for CRC. We construct a CRC immune module (CCIM) that comprises FOLR2⁺ macrophages, exhausted CD8⁺ T cells, tolerant CD8⁺ T cells, exhausted CD4⁺ T cells, and regulatory T cells. Multiplex immunohistochemistry is performed to depict the CCIM. Based on this, we utilize advanced deep learning technology to establish a spatial interaction map and predict chemothera

SUBMITTER: Bao X 

PROVIDER: S-EPMC10897549 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature