Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, China.
Hubei Clinical Center and Key Lab of Intestinal and Colorectal Diseases, Wuhan, China.
DNA Cell Biol. 2020 Jul;39(7):1181-1193. doi: 10.1089/dna.2020.5490. Epub 2020 May 12.
We aimed to establish a novel immunoscore (IS) model based on the transcriptomes of tumor tissues to improve the relapse-free survival (RFS) prediction of colorectal cancer (CRC). CIBERSORT was used to estimate the immune cell fractions based on the Gene Expression Omnibus (GEO) database. Then, a least absolute shrinkage and selection operator regression was applied to construct the IS model based on the immune cell fractions. After screening, four GEO databases were included in the CIBERSORT transformation. A total of 13 types of immune cells were selected and constructed an IS model. In the training set ( = 613) and test set ( = 262), the patients in the high-immunoscore group showed a significant poor RFS than that in the low-immunoscore group. Stratified analysis also found similar results in patients with identical age, sex, adjunctive chemotherapy, or TNM stage I-II. Multivariate Cox regression further demonstrated that the IS model was an independent predictor of RFS in CRC. In addition, the IS was highly associated with the expression of several immune checkpoints, inflammatory mediators, cell cycle, and epithelial-mesenchymal transformation regulators in CRC. We proposed a novel IS model for estimating RFS in CRC patients.
我们旨在建立一种基于肿瘤组织转录组的新型免疫评分（IS）模型，以提高结直肠癌（CRC）的无复发生存（RFS）预测。CIBERSORT 用于根据基因表达综合数据库（GEO）数据库估计免疫细胞分数。然后，应用最小绝对收缩和选择算子回归基于免疫细胞分数构建 IS 模型。筛选后，四个 GEO 数据库被纳入 CIBERSORT 转换。共选择了 13 种免疫细胞，并构建了一个 IS 模型。在训练集（n=613）和测试集（n=262）中，高免疫评分组的患者 RFS 明显低于低免疫评分组。分层分析也在年龄、性别、辅助化疗或 TNM Ⅰ-Ⅱ期相同的患者中发现了类似的结果。多变量 Cox 回归进一步表明，IS 模型是 CRC 患者 RFS 的独立预测因子。此外，IS 与 CRC 中几种免疫检查点、炎症介质、细胞周期和上皮-间充质转化调节剂的表达高度相关。我们提出了一种新的 IS 模型，用于估计 CRC 患者的 RFS。
