Multidimensional decoding of colorectal cancer heterogeneity: Artificial intelligence-enabled precision exploration of single-cell and spatial transcriptomics

Abstract

As a common malignant tumor, the heterogeneity of colorectal cancer plays an important role in tumor progression and treatment response. In recent years, the rapid development of single-cell transcriptomics and spatial transcriptomics technologies has provided new perspectives for resolving the heterogeneity of colorectal cancer. These techniques can reveal the complexity of cellular composition and their interactions in the tumor microenvironment, and thus facilitate a deeper understanding of tumor biology. However, in practical applic ations, researchers still face technical challenges such as data processing and result interpretation. The aim of this paper is to explore how to use artificial intelligence (AI) technology to enhance the research efficiency of single-cell and spatial transcriptomics, analyze the current research progress and its limitations, and explore how combining AI approaches can provide new ideas for decoding the heterogeneity of colorectal cancer, and ultimately provide theoretical basis and practical guidance for the clinical precision treatment.