开发一种免疫相关基因预后风险模型，并鉴定结肠癌肿瘤微环境中的免疫浸润特征。
Development of an immune-related gene prognostic risk model and identification of an immune infiltration signature in the tumor microenvironment of colon cancer.
机构信息
Department of Oncology, Beijing Shijitan Hospital, Capital Medical University, No. 10, Tieyi Road, Haidian District, Beijing, 100038, China.
Department of Oncology, Ninth School of Clinical Medicine, Peking University, Beijing, 100038, China.
出版信息
BMC Gastroenterol. 2023 Mar 8;23(1):58. doi: 10.1186/s12876-023-02679-6.
BACKGROUND
Colon cancer is a common and highly malignant tumor. Its incidence is increasing rapidly with poor prognosis. At present, immunotherapy is a rapidly developing treatment for colon cancer. The aim of this study was to construct a prognostic risk model based on immune genes for early diagnosis and accurate prognostic prediction of colon cancer.
METHODS
Transcriptome data and clinical data were downloaded from the cancer Genome Atlas database. Immunity genes were obtained from ImmPort database. The differentially expressed transcription factors (TFs) were obtained from Cistrome database. Differentially expressed (DE) immune genes were identified in 473 cases of colon cancer and 41 cases of normal adjacent tissues. An immune-related prognostic model of colon cancer was established and its clinical applicability was verified. Among 318 tumor-related transcription factors, differentially expressed transcription factors were finally obtained, and a regulatory network was constructed according to the up-down regulatory relationship.
RESULTS
A total of 477 DE immune genes (180 up-regulated and 297 down-regulated) were detected. We developed and validated twelve immune gene models for colon cancer, including SLC10A2, FABP4, FGF2, CCL28, IGKV1-6, IGLV6-57, ESM1, UCN, UTS2, VIP, IL1RL2, NGFR. The model was proved to be an independent prognostic variable with good prognostic ability. A total of 68 DE TFs (40 up-regulated and 23 down-regulated) were obtained. The regulation network between TF and immune genes was plotted by using TF as source node and immune genes as target node. In addition, Macrophage, Myeloid Dendritic cell and CD4 T cell increased with the increase of risk score.
CONCLUSION
We developed and validated twelve immune gene models for colon cancer, including SLC10A2, FABP4, FGF2, CCL28, IGKV1-6, IGLV6-57, ESM1, UCN, UTS2, VIP, IL1RL2, NGFR. This model can be used as a tool variable to predict the prognosis of colon cancer.
背景
结肠癌是一种常见且高度恶性的肿瘤，其发病率随着预后不良而迅速上升。目前，免疫疗法是治疗结肠癌的一种快速发展的治疗方法。本研究旨在构建基于免疫基因的结肠癌预后风险模型，以实现结肠癌的早期诊断和准确预后预测。
方法
从癌症基因组图谱数据库中下载转录组数据和临床数据。从 ImmPort 数据库中获取免疫基因。从 Cistrome 数据库中获取差异表达转录因子（TFs）。在 473 例结肠癌和 41 例正常相邻组织中鉴定差异表达的转录因子。建立结肠癌免疫相关预后模型，并验证其临床适用性。在 318 个肿瘤相关转录因子中，最终获得差异表达的转录因子，并根据上调-下调调控关系构建调控网络。
结果
共检测到 477 个差异表达的免疫基因（180 个上调和 297 个下调）。我们开发并验证了 12 种结肠癌免疫基因模型，包括 SLC10A2、FABP4、FGF2、CCL28、IGKV1-6、IGLV6-57、ESM1、UCN、UTS2、VIP、IL1RL2、NGFR。该模型被证明是一个具有良好预后能力的独立预后变量。共获得 68 个差异表达的 TF（40 个上调和 23 个下调）。以 TF 为源节点，免疫基因为目标节点，绘制 TF 与免疫基因的调控网络。此外，随着风险评分的增加，巨噬细胞、髓样树突状细胞和 CD4 T 细胞增加。
结论
我们开发并验证了 12 种结肠癌免疫基因模型，包括 SLC10A2、FABP4、FGF2、CCL28、IGKV1-6、IGLV6-57、ESM1、UCN、UTS2、VIP、IL1RL2、NGFR。该模型可用作预测结肠癌预后的工具变量。
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