M2 Macrophage Classification of Colorectal Cancer Reveals Intrinsic Connections with Metabolism Reprogramming and Clinical Characteristics

Fengxing Huang,1,2,* Youwei Wang,1,2,* Yu Shao,1,2,* Runan Zhang,1,2 Mengting Li,1,2 Lan Liu,1,2,* Qiu Zhao1,2,* 1Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, People’s Republic of China; 2Hubei Clinical Center and Key Laboratory of Intestinal and Colorectal Diseases, Wuhan, 430071, People’s Republic of China*These authors contributed equally to this workCorrespondence: Lan Liu; Qiu Zhao, Email lliugi@whu.edu.cn; qiuzhao@whu.edu.cnIntroduction: Immune cell interactions and metabolic changes are crucial in determining the tumor microenvironment and affecting various clinical outcomes. However, the clinical significance of metabolism evolution of immune cell evolution in colorectal cancer (CRC) remains unexplored.Methods: Single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing data were acquired from TCGA and GEO datasets. For the analysis of macrophage differentiation trajectories, we employed the R packages Seurat and Monocle. Consensus clustering was further applied to identify the molecular classification. Immunohistochemical results from AOM and AOM/DSS models were used to validate macrophage expression. Subsequently, GSEA, ESTIMATE scores, prognosis, clinical characteristics, mutational burden, immune cell infiltration, and the variance in gene expression among different clusters were compared. We constructed a prognostic model and nomograms based on metabolic gene signatures identified through the MEGENA framework.Results: We found two heterogeneous groups of M2 macrophages with various clinical outcomes through the evolutionary process. The prognosis of Cluster 2 was poorer. Further investigation showed that Cluster 2 constituted a metabolically active group while Cluster 1 was comparatively metabolically inert. Metabolic variations in M2 macrophages during tumor development are related to tumor prognosis. Additionally, Cluster 2 showed the most pronounced genomic instability and had highly elevated metabolic pathways, notably those associated with the ECM. We identified eight metabolic genes (PRELP, NOTCH3, CNOT6, ASRGL1, SRSF1, PSMD4, RPL31, and CNOT7) to build a predictive model validated in CRC datasets. Then, a nomogram based on the M2 risk score improved predictive performance. Furthermore, our study demonstrated that immune checkpoint inhibitor therapy may benefit patients with low-risk.Discussion: Our research reveals underlying relationships between metabolic phenotypes and immunological profiles and suggests a unique M2 classification technique for CRC. The identified gene signatures may be key factors linking immunity and tumor metabolism, warranting further investigations. Keywords: CRC, macrophages, metabolic classification, tumor immunity, prognosis model