Huang Fengxing, Wang Youwei, Shao Yu, Zhang Runan, Li Mengting, Liu Lan, Zhao Qiu
Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, People's Republic of China.
Hubei Clinical Center and Key Laboratory of Intestinal and Colorectal Diseases, Wuhan, 430071, People's Republic of China.
Pharmgenomics Pers Med. 2024 Jul 11;17:383-399. doi: 10.2147/PGPM.S458798. eCollection 2024.
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.
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.
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.
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.
免疫细胞相互作用和代谢变化在决定肿瘤微环境及影响各种临床结局方面至关重要。然而，结直肠癌（CRC）中免疫细胞演变的代谢演变的临床意义仍未得到探索。
从TCGA和GEO数据集中获取单细胞RNA测序（scRNA-seq）和批量RNA测序数据。为了分析巨噬细胞分化轨迹，我们使用了R包Seurat和Monocle。进一步应用共识聚类来识别分子分类。来自AOM和AOM/DSS模型的免疫组织化学结果用于验证巨噬细胞表达。随后，比较了基因集富集分析（GSEA）、ESTIMATE评分、预后、临床特征、突变负担、免疫细胞浸润以及不同簇之间基因表达的差异。我们基于通过MEGENA框架鉴定的代谢基因特征构建了一个预后模型和列线图。
通过进化过程，我们发现了两组具有不同临床结局的异质性M2巨噬细胞。第2组的预后较差。进一步研究表明，第2组构成一个代谢活跃组，而第1组相对代谢惰性。肿瘤发展过程中M2巨噬细胞的代谢变化与肿瘤预后相关。此外，第2组表现出最明显的基因组不稳定性，并且具有高度升高的代谢途径，特别是那些与细胞外基质（ECM）相关的途径。我们鉴定了八个代谢基因（PRELP、NOTCH3、CNOT6、ASRGL1、SRSF1、PSMD4、RPL31和CNOT7）来构建一个在CRC数据集中得到验证的预测模型。然后，基于M2风险评分的列线图提高了预测性能。此外，我们的研究表明免疫检查点抑制剂治疗可能使低风险患者受益。
我们的研究揭示了代谢表型与免疫谱之间的潜在关系，并提出了一种用于CRC的独特M2分类技术。所鉴定的基因特征可能是连接免疫和肿瘤代谢的关键因素，值得进一步研究。
