Department of General Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Front Immunol. 2024 Aug 7;15:1400722. doi: 10.3389/fimmu.2024.1400722. eCollection 2024.
Although neoadjuvant therapy has brought numerous benefits to patients, not all patients can benefit from it. Chemokines play a crucial role in the tumor microenvironment and are closely associated with the prognosis and treatment of colorectal cancer. Therefore, constructing a prognostic model based on chemokines will help risk stratification and providing a reference for the personalized treatment.
Employing LASSO-Cox predictive modeling, a chemokine-based prognostic model was formulated, harnessing the data from TCGA and GEO databases. Then, our exploration focused on the correlation between the chemokine signature and elements such as the immune landscape, somatic mutations, copy number variations, and drug sensitivity. CXCL10+M1 macrophages identified via scRNA-seq. Monocle2 showed cell pseudotime trajectories, CellChat characterized intercellular communication. CytoTRACE analyzed neoadjuvant therapy stemness, SCENIC detected cell type-specific regulation. Lastly, validation was performed through multiplex immunofluorescence experiments.
A model based on 15 chemokines was constructed and validated. High-risk scores correlated with poorer prognosis and advanced TNM and clinical stages. Individuals presenting elevated risk scores demonstrated an increased propensity towards the development of chemotherapy resistance. Subsequent scRNA-seq data analysis indicated that patients with higher presence of CXCL10+ M1 macrophages in tumor tissues are more likely to benefit from neoadjuvant therapy.
We developed a chemokine-based prognostic model by integrating both single-cell and bulk RNA-seq data. Furthermore, we revealed epithelial cell heterogeneity in neoadjuvant outcomes and identified CXCL10+ M1 macrophages as potential therapy response predictors. These findings could significantly contribute to risk stratification and serve as a key guide for the advancement of personalized therapeutic approaches.
虽然新辅助治疗给患者带来了很多好处，但并非所有患者都能从中获益。趋化因子在肿瘤微环境中发挥着重要作用，与结直肠癌的预后和治疗密切相关。因此，构建基于趋化因子的预后模型有助于进行风险分层，并为个性化治疗提供参考。
采用 LASSO-Cox 预测模型，利用 TCGA 和 GEO 数据库的数据构建了一个基于趋化因子的预后模型。然后，我们重点研究了趋化因子特征与免疫景观、体细胞突变、拷贝数变异和药物敏感性等因素之间的相关性。通过 scRNA-seq 鉴定 CXCL10+M1 巨噬细胞。Monocle2 显示细胞拟时间轨迹，CellChat 描述细胞间通讯。CytoTRACE 分析新辅助治疗干性，SCENIC 检测细胞类型特异性调控。最后，通过多重免疫荧光实验进行验证。
构建并验证了一个基于 15 个趋化因子的模型。高风险评分与较差的预后以及更高级别的 TNM 和临床分期相关。具有较高风险评分的个体更容易产生化疗耐药性。随后的 scRNA-seq 数据分析表明，肿瘤组织中 CXCL10+M1 巨噬细胞含量较高的患者更有可能从新辅助治疗中获益。
我们通过整合单细胞和批量 RNA-seq 数据构建了一个基于趋化因子的预后模型。此外，我们揭示了新辅助治疗结果中上皮细胞的异质性，并确定了 CXCL10+M1 巨噬细胞作为潜在的治疗反应预测因子。这些发现可以为风险分层提供重要依据，并为个性化治疗方法的发展提供关键指导。
