Objectives: This study aimed to investigate the differentiation state and clinical significance of colorectal cancer cells, as well as to predict the immune response and prognosis of patients based on differentiation-related genes of colorectal cancer.
Introduction: Colorectal cancer cells exhibit different differentiation states under the influence of the tumor microenvironment, which determines the cell fates.
Methods: We combined single-cell sequencing (scRNA-seq) data from The Cancer Genome Atlas source with extensive transcriptome data from the Gene Expression Omnibus database. We obtained colorectal cancer differentiation-related genes using cell trajectory analysis and developed a colorectal cancer differentiation-related gene based molecular typing and prognostic model to predict the immune response and prognosis of patients with colorectal cancer.
Results: We identified 5 distinct cell differentiation subsets and 620 colorectal cancer differentiation-related genes. Colorectal cancer differentiation-related genes were significantly associated with metabolism, angiogenesis, and immunity. We separated patients into 3 subtypes based on colorectal cancer differentiation-related gene expression in the tumor and found differences among the different subtypes in immune infiltration status, immune checkpoint gene expression, clinicopathological features, and overall survival. Immunotherapeutic interventions involving a highly expressed immune checkpoint blockade may be selectively effective in the corresponding cancer subtypes. We built a risk score prediction model (5-year AUC: .729) consisting of the 4 most important predictors of survival (TIMP1, MMP1, LGALS4, and ITLN1). Finally, we generated and validated a nomogram consisting of the risk score and clinicopathological variables.
Conclusion: This study highlights the significance of genes involved in cell differentiation for clinical prognosis and immunotherapy in patients and provides prospective therapeutic targets for colorectal cancer.
Keywords: colorectal cancer; molecular typing; single-cell transcriptome analysis; survival prediction model; tumor microenvironment.