Abstract Tryptophan metabolism is intricately associated with the progression of colon cancer. This research endeavored to meticulously analyze tryptophan metabolic characteristics in colon cancer and forecast immunotherapy responses. This study analyzed colon cancer samples from a training cohort of 473 tumors and 41 normal tissues from TCGA, with validation in 902 cancer patients across multiple GEO datasets. Patients were stratified into subtypes through consistent clustering, and a tryptophan metabolic risk score model was constructed using the random forest algorithm. Based on these risk scores, patients were delineated into high and low-risk groups, and their clinicopathologic characteristics, immune cell infiltration, immune checkpoint expression, and signaling pathway disparities were examined. The Oncopredict algorithm facilitated the identification of sensitive chemotherapeutic agents, while the immune escape score was employed to evaluate the immunotherapy response across risk groups. Transcriptomic sequencing findings were corroborated by single-cell sequencing from Shanghai Ruijin Hospital. Two distinct subtypes of colon cancer patients emerged, exhibiting significant prognostic and immune cell infiltration differences. The high-risk group demonstrated a poorer prognosis (p