■免疫细胞相互作用和代谢变化对于确定肿瘤微环境和影响各种临床结果至关重要。然而,在结直肠癌(CRC)中,免疫细胞的代谢进化的临床意义仍有待探讨.
■单细胞RNA测序(scRNA-seq)和大量RNA测序数据从TCGA和GEO数据集获得。对于巨噬细胞分化轨迹的分析,我们使用了R包Seurat和Monocle。一致性聚类被进一步应用于识别分子分类。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,GSEA,估计分数,预后,临床特征,突变负担,免疫细胞浸润,并比较了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预后模型和列线图。
■我们发现两组异质性的M2巨噬细胞通过进化过程具有不同的临床结果。第2组的预后较差。进一步的研究表明,簇2构成代谢活性组,而簇1则相对代谢惰性。肿瘤发展过程中M2巨噬细胞的代谢变化与肿瘤预后有关。此外,簇2显示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,特别是与ECM相关的那些。我们确定了八个代谢基因(PRELP,NOTCH3、CNOT6、ASRGL1、SRSF1、PSMD4、RPL31和CNOT7)建立在CRC数据集中验证的预测模型。然后,基于M2风险评分的列线图改善了预测性能。此外,我们的研究表明,免疫检查点抑制剂治疗可能使低危患者受益.
■我们的研究揭示了代谢表型和免疫谱之间的潜在关系,并提出了一种针对CRC的独特M2分类技术。确定的基因特征可能是连接免疫和肿瘤代谢的关键因素,保证进一步调查。
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.