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 (n = 613) and test set (n = 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.

中文翻译：

---

大肠癌无复发生存预测的预后免疫分数。

我们旨在基于肿瘤组织的转录组建立一种新颖的免疫评分（IS）模型，以改善结直肠癌（CRC）的无复发生存期（RFS）预测。基于基因表达综合数据库（GEO），使用CIBERSORT评估免疫细胞组分。然后，基于免疫细胞分数，应用最小绝对收缩和选择算子回归来构建IS模型。筛选后，CIBERSORT转换中包含了四个GEO数据库。总共选择了13种免疫细胞并构建了IS模型。在训练集中（n  = 613）和测试集中（n = 262），高免疫评分组患者的RFS明显低于低免疫评分组。年龄，性别，辅助化疗或TNM I–II期患者的分层分析也发现了相似的结果。多元Cox回归进一步证明IS模型是CRC中RFS的独立预测因子。此外，IS与CRC中多个免疫检查点，炎症介质，细胞周期以及上皮-间质转化调节因子的表达高度相关。我们提出了一种新颖的IS模型来估计CRC患者的RFS。