Zhang H G, Yao W R, Zhou Z Y, Liu L
Department of Anesthesiology, First Affiliated Hospital of Nanchang University, Nanchang, 330052, China.
Department of Oncology, Jiangxi Provincial Hospital, The First Affiliated Hospital to Nanchang Medicine College), Nanchang, 330000, Jiangxi, China.
Discov Oncol. 2025 May 3;16(1):664. doi: 10.1007/s12672-025-02411-8.
The tumor microenvironment in colorectal cancer (CRC) significantly influences disease progression and immune responses, particularly the role of macrophages in regulating immune evasion requires further investigation.
This study integrated data from the TCGA-COAD dataset with the GEO database, along with single-cell RNA sequencing data, to systematically analyze key genes in colorectal cancer. R software was utilized for data normalization and differential analysis, with criteria set at ∣log2FoldChange ∣ > 1 and adjusted p-value < 0.05 for gene selection. The Seurat package was employed for clustering single-cell data, while the "Monocle2" algorithm was used to perform pseudo-time analysis on the differentiation trajectory of macrophages. Additionally, non-negative matrix factorization (NMF) was applied for subtype classification of CRC patients, and various machine learning algorithms (such as LASSO and random forest models) were utilized to identify key pathogenic genes. Finally, PCR was employed to validate the expression of these key genes, and immune analysis software was used to assess their impact on immune cells, alongside pathway enrichment analysis.
Through the integration of multi-omics data, we identified significant differential expression of VSIG4, CYBBC3AR1, and FCGR1A in CRC patients. LASSO and random forest models selected these three genes as critical pathogenic factors for CRC, with AUC values exceeding 0.8 across multiple machine learning models, demonstrating their high diagnostic efficacy. PCR validation further supported the differential expression of VSIG4 and other genes in CRC. Single-cell transcriptomic analysis revealed that VSIG4 was highly enriched in specific macrophage subpopulations and significantly influenced the tumor microenvironment by regulating CD8 + T cell immune exhaustion. Pseudo-time analysis indicated that the differentiation trajectory of macrophages during tumor progression was closely associated with VSIG4 expression. Additionally, cell communication analysis. highlighted the important role of VSIG4 in the interactions between macrophages and endothelial cells. Pathway enrichment analysis demonstrated that VSIG4 expression was closely linked to the regulation of the JAK-STAT pathway and metabolic pathways such as the TCA cycle.
This study provides the first evidence that VSIG4, CYBBC3AR1, and FCGR1A play critical roles in the immune microenvironment of colorectal cancer, particularly emphasizing the immunoregulatory function of VSIG4 in macrophage activity and CD8 + T cell immune exhaustion. PCR validation further confirmed the differential expression of these genes. These findings offer new insights into the molecular mechanisms of CRC and provide a potential theoretical basis for targeting VSIG4 in immunotherapy.
结直肠癌(CRC)中的肿瘤微环境显著影响疾病进展和免疫反应,尤其是巨噬细胞在调节免疫逃逸中的作用需要进一步研究。
本研究整合了来自TCGA-COAD数据集和GEO数据库的数据以及单细胞RNA测序数据,以系统分析结直肠癌中的关键基因。使用R软件进行数据归一化和差异分析,基因选择标准设定为∣log2倍变化∣>1且调整后的p值<0.05。使用Seurat软件包对单细胞数据进行聚类,同时使用“Monocle2”算法对巨噬细胞的分化轨迹进行伪时间分析。此外,应用非负矩阵分解(NMF)对CRC患者进行亚型分类,并利用各种机器学习算法(如LASSO和随机森林模型)来识别关键致病基因。最后,采用PCR验证这些关键基因的表达,并使用免疫分析软件评估它们对免疫细胞的影响,同时进行通路富集分析。
通过整合多组学数据,我们在CRC患者中发现了VSIG4、CYBBC3AR1和FCGR1A的显著差异表达。LASSO和随机森林模型将这三个基因选为CRC的关键致病因素,在多个机器学习模型中AUC值均超过0.8,表明它们具有较高的诊断效能。PCR验证进一步支持了VSIG4和其他基因在CRC中的差异表达。单细胞转录组分析显示,VSIG4在特定巨噬细胞亚群中高度富集,并通过调节CD8+T细胞免疫耗竭显著影响肿瘤微环境。伪时间分析表明,肿瘤进展过程中巨噬细胞的分化轨迹与VSIG4表达密切相关。此外,细胞间通讯分析突出了VSIG4在巨噬细胞与内皮细胞相互作用中的重要作用。通路富集分析表明,VSIG4表达与JAK-STAT通路以及TCA循环等代谢通路的调节密切相关。
本研究首次证明VSIG4、CYBBC3AR1和FCGR1A在结直肠癌免疫微环境中起关键作用,尤其强调了VSIG4在巨噬细胞活性和CD8+T细胞免疫耗竭中的免疫调节功能。PCR验证进一步证实了这些基因的差异表达。这些发现为CRC的分子机制提供了新的见解,并为免疫治疗中靶向VSIG4提供了潜在的理论基础。