Colon adenocarcinoma (COAD) is a malignant tumor with a high mortality rate. Angiogenesis plays a key role in the development and progression of cancer. However, in COAD, studies between angiogenesis and prognosis, immune cell infiltration, and personalized treatment guidance are currently lacking. In the present study, we comprehensively assessed 35 angiogenesis-related genes (ARG) and identified key ARGs affecting OS in COAD patients. The ARG Prognostic Index (ARGPI) was constructed based on a univariate Cox regression model and its prognostic value was evaluated in TCGA-COAD, GSE39582, GSE161158 and TRSJTUSM Cohort. We constructed ARGPI as an independent risk factor for OS in COAD patients and combined with clinical parameters to further construct an ARGPI-based nomogram, which showed a strong ability to predict overall survival in COAD patients. High ARGPI is associated with cancer-related and immune-related biological processes and signaling pathways; high TP53 mutation rate; high infiltration of MSC, pericytes, and stromal cells; and more CMS4 subtype. And low ARGPI benefited more from immune checkpoint inhibitor treatment. In addition, we also predicted the sensitivity of different ARGPI groups to common chemotherapeutic and targeted agents. In conclusion, this study constructed an ARGPI based on ARG, which robustly predicted the OS of COAD patients and provided a possible personalized treatment regime for COAD patients.

Background: Studies highlight the role of migrasomes as mediators of intercellular communication and signaling, critical in influencing tumorigenesis and progression. Yet migrasome-related genes and their potential role in colorectal cancer prognosis remain unexplored. Methods: Differentially expressed gene set A (DEG set A) was identified in the TCGA-CRC dataset, and Weighted Gene Co-expression Network Analysis (WGCNA) was performed to identify the most important modules associated with migrasome-related gene (MRG) scores. Single-cell RNA-seq dataset GSE231559 DEG set B was determined. Candidate migrasome-related genes were filtered by intersecting DGE set A, key module genes, and DEG set B. Prognostic genes were subsequently screened through regression analysis, and a risk model was developed. Patients with CRC in the TCGA cohort were stratified into high- and low-risk groups based on the optimal cutoff of the risk score. Immunotherapy response-related analyses were then performed. Finally, cell-to-cell communication analysis was carried out for key cells identified based on prognostic gene expression analysis in annotated cells. Results: The six candidate migrasome-related genes were identified through the overlap of 5158 DEG set A, 1960 key module genes, and 146 DEG set B. Further screening led to the selection of T1MP1, CXCL8, and MGP as potential prognostic biomarkers. Immune-related analysis indicated that the high-risk group exhibited a better response to immunotherapy. Notably, the prognostic genes showed elevated expression levels in monocytes and tissue stem cells, thereby designating them as key cell types. Conclusions: We conducted bioinformatic analysis of migrasome-related genes and identified significant involvement of T1MP1, CXCL8, and MGP in influencing CRC prognosis and immunotherapy response. Our research provides novel insights into the role of migrasomes in CRC biology.