Identification of angiogenesis‐related subtypes, the development of a prognosis model, and features of tumor microenvironment in colon cancer

Abstract

Angiogenesis is associated with tumor progression, prognosis, and treatment effect. However, the angiogenesis’ underlying mechanisms in the tumor microenvironment (TME) still remain unclear. Understanding the dynamic interactions between angiogenesis and TME in colon adenocarcinoma (COAD) is necessary. We downloaded the transcriptome data and corresponding clinical data of colon cancer patients from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, respectively. We identified two distinct angiogenesis‐related molecular subtypes (subtype A and subtype B) and assessed the clinical features, prognosis, and infiltrating immune cells of patients in the two subtypes. According to the prognostic differential genes, we defined two different gene clusters to further explore the correlation between angiogenesis and tumor heterogeneity. Then, we construct the prognostic risk scoring model angiogenesis‐related gene (ARG‐score) including seven genes (ARMCX2, latent transforming growth factor β binding protein 1, ADAM8, FABP4, CCL11, CXCL11, ITLN1) using Lasso‐multivariate cox method. We analyzed the correlation between ARG‐score and prognosis, clinicopathological features, TME, molecular feature, cancer stem cells (CSCs), and microsatellite instability (MSI) status. To assess the application value of ARG‐score in clinical treatment, immunophenotype score was used to predict patients’ immunotherapy response in colon cancer. We found the mutations of ARGs in TCGA–COAD dataset from genetic levels and discussed their expression patterns based on TCGA and GEO datasets. We observed important differences in clinicopathological features, prognosis, immune feature, molecular feature between the two molecular subgroups. Then, we established an ARG‐score for predicting OS and validated its predictive capability. A high ARG‐score characterized by higher transcription level of ARGs, suggested lower MSI‐high (MSI‐H), lower immune score, and worse clinical stage and survival outcome. Additionally, the ARG‐score was remarkably related to the CSCs index and immunotherapy sensitivity. We found two new molecular subtypes and two gene clusters based on ARGs and established an ARG‐score. Multilayered analysis revealed that ARGs were remarkably correlated to the heterogeneity of colon cancer patients and explained the process of tumorigenesis and progression better. The ARG‐score can help us better assess patients’ survival outcomes and provide guidance for individualized treatment.

Elucidating the role of angiogenesis-related genes in colorectal cancer: a multi-omics analysis

“…However, minimal differences were noticed regarding EREG-mRNAsi, TMB, and the top 10 gene mutations. These findings align with previous research ( 14 , 15 ). Utilizing three machine learning algorithms, we identified four genes significantly associated with CRC subtypes.…”

“…While previous studies have reported on the role of angiogenesis-related clusters in CRC ( 14 , 15 ), these studies lacked clinical samples and larger sample sizes of CRC datasets for gene expression verification. In contrast, our study utilized three machine learning algorithms to screen for important genes, resulting in a smaller but more accurate predictive set compared to previous studies.…”

“…These subtypes have demonstrated substantial influence over prognosis, response to therapy, and overall clinical outcomes ( 12 , 13 ). Recent studies have leveraged gene profile datasets to develop angiogenesis-related molecular subtypes, with the aim of investigating associations with CRC’s clinical characteristics, prognosis, and tumor microenvironment (TME) ( 14 , 15 ). However, there still remain numerous angiogenesis genes related to CRC development that require further elucidation.…”