Xie Yun-Hui, Jiang Hui-Zhong
Center of Gastrointestinal and Minimally Invasive Surgery, Department of General Surgery, The Third People's Hospital of Chengdu, Affiliated Hospital of Southwest Jiaotong University, Chengdu, China.
College of Graduate, Guizhou University of Traditional Chinese Medicine, Guiyang, China.
Cancer Rep (Hoboken). 2024 Jan;7(1):e1914. doi: 10.1002/cnr2.1914. Epub 2023 Oct 30.
Cancer cell survival, proliferation, and metabolism are all intertwined with mitochondria. However, a complete description of how the features of mitochondria relate to the tumor microenvironment (TME) and immunological landscape of colorectal cancer (CRC) has yet to be made. We performed subgroup analysis on CRC patient data obtained from the databases using non-negative matrix factorization (NMF) clustering. Construct a prognostic model using the mitochondrial-related gene (MRG) risk score, and then compare it to other models for accuracy. Comprehensive analyses of the risk score, in conjunction with the TME and immune landscape, were performed, and the relationship between the model and different types of cell death, radiation and chemotherapy, and drug resistance was investigated. Results from immunohistochemistry and single-cell sequencing were utilized to verify the model genes, and a drug sensitivity analysis was conducted to evaluate possible therapeutic medicines. The pan-cancer analysis is utilized to further investigate the role of genes in a wider range of malignancies.
We found that CRC patients based on MRG were divided into two groups with significant differences in survival outcomes and TME between groups. The predictive power of the risk score was further shown by building a prognostic model and testing it extensively in both internal and external cohorts. Multiple immune therapeutic responses and the expression of immunological checkpoints demonstrate that the risk score is connected to immunotherapy success. The correlation analysis of the risk score provide more ideas and guidance for prognostic models in clinical treatment.
The TME, immune cell infiltration, and responsiveness to immunotherapy in CRC were all thoroughly evaluated on the basis of MRG features. The comparative validation of multiple queues and models combined with clinical data ensures the effectiveness and clinical practicality of MRG features. Our studies help clinicians create individualized treatment programs for individuals with cancer.
癌细胞的存活、增殖和代谢都与线粒体密切相关。然而，线粒体的特征如何与结直肠癌（CRC）的肿瘤微环境（TME）和免疫格局相关联，目前尚未有完整的描述。我们使用非负矩阵分解（NMF）聚类对从数据库中获取的CRC患者数据进行亚组分析。利用线粒体相关基因（MRG）风险评分构建预后模型，然后将其与其他模型进行准确性比较。对风险评分与TME和免疫格局进行综合分析，研究该模型与不同类型细胞死亡、放疗和化疗以及耐药性之间的关系。利用免疫组织化学和单细胞测序结果验证模型基因，并进行药物敏感性分析以评估可能的治疗药物。利用泛癌分析进一步研究这些基因在更广泛恶性肿瘤中的作用。
我们发现，基于MRG的CRC患者被分为两组，两组之间的生存结果和TME存在显著差异。通过构建预后模型并在内部和外部队列中进行广泛测试，进一步显示了风险评分的预测能力。多种免疫治疗反应和免疫检查点的表达表明，风险评分与免疫治疗的成功相关。风险评分的相关性分析为临床治疗中的预后模型提供了更多思路和指导。
基于MRG特征对CRC中的TME、免疫细胞浸润和对免疫治疗的反应进行了全面评估。多个队列和模型与临床数据相结合的比较验证确保了MRG特征的有效性和临床实用性。我们的研究有助于临床医生为癌症患者制定个性化的治疗方案。
