Department of Colorectal Surgery, The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510655, People's Republic of China.
Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510655, People's Republic of China.
Clin Transl Oncol. 2021 Sep;23(9):1769-1781. doi: 10.1007/s12094-021-02578-w. Epub 2021 Mar 10.
The development and progression of colon cancer are significantly affected by the tumor microenvironment, which has attracted much attention. The goal of our study was primarily to find out all possible tumor microenvironment-related genes in colon cancer.
This study quantified the immune and stromal landscape using the ESTIMATION algorithm using the gene expression matrix obtained from the UCSC Xena database. Dysregulated genes were harvested using the limma R package, and relevant pathways and biofunctions were identified using enrichment analysis. A least absolute shrinkage and selection operator (LASSO) regression was used to select the pivotal genes from the DEGs. Then, survival analysis was performed to determine the hub genes and a prognostic model was constructed by these hub genes with (or) TNM stage. Besides, associations between hub gene expressions and immune cell infiltration were assessed.
A total of 725 DEGs were identified. Most of the results of the enrichment analysis were immune-related items. 13 genes were selected as the hub genes and a moderate-to-strong positive correlation between most hub genes and several immune cells were observed. Besides, the prognostic value of the hub genes were comparable to TNM staging.
Our study provides a better understanding of how interactions between the 13 immune-prognostic hub genes and immune cells in the tumor microenvironment affect biological processes in colon cancer. These genes exhibit an equivalent ability to TNM staging in prognosis prediction. They are particularly expected to become novel prognostic biomarkers and targets of immunotherapies for colon cancer.
结肠癌的发生和发展受肿瘤微环境影响显著，这引起了广泛关注。本研究的主要目的是找出结肠癌中所有可能与肿瘤微环境相关的基因。
本研究使用 UCSC Xena 数据库中的基因表达矩阵，通过 ESTIMATION 算法量化免疫和基质景观。使用 limma R 包获取失调基因，并通过富集分析鉴定相关通路和生物功能。使用最小绝对收缩和选择算子（LASSO）回归从 DEGs 中选择关键基因。然后，通过这些关键基因进行生存分析，构建预后模型，并与（或）TNM 分期相结合。此外，还评估了关键基因表达与免疫细胞浸润之间的关联。
共鉴定出 725 个差异表达基因。富集分析的结果主要与免疫相关项目相关。选择了 13 个基因作为关键基因，并观察到大多数关键基因与几种免疫细胞之间存在中度至强的正相关关系。此外，关键基因的预后价值与 TNM 分期相当。
本研究深入了解了肿瘤微环境中 13 个免疫预后关键基因与免疫细胞之间的相互作用如何影响结肠癌中的生物学过程。这些基因在预后预测方面具有与 TNM 分期相当的能力。它们有望成为结肠癌新的预后生物标志物和免疫治疗靶点。
