Data_Sheet_2_Stromal Score-Based Gene Signature: A Prognostic Prediction Model for Colon Cancer.PDF

BackgroundGrowing evidence has revealed the crucial roles of stromal cells in the microenvironment of various malignant tumors. However, efficient prognostic signatures based on stromal characteristics in colon cancer have not been well-established yet. The present study aimed to construct a stromal score-based multigene prognostic prediction model for colon cancer.
MethodsStromal scores were calculated based on the expression profiles of a colon cancer cohort from TCGA database applying the ESTIMATE algorithm. Linear models were used to identify differentially expressed genes between low-score and high-score groups by limma R package. Univariate, LASSO, and multivariate Cox regression models were used successively to select the prognostic gene signature. Two independent datasets from GEO were used as external validation cohorts.
ResultsLow stromal score was demonstrated to be a favorable factor to the overall survival of colon cancer patients in TCGA cohort (p = 0.0046). Three hundred and seven stromal score-related differentially expressed genes were identified. Through univariate, LASSO, and multivariate Cox regression analyses, a gene signature consisting of LEP, NOG, and SYT3 was recognized to build a prognostic prediction model. Based on the predictive values estimated by the established integrated model, patients were divided into two groups with significantly different overall survival outcomes (p < 0.0001). Time-dependent Receiver operating characteristic curve analyses suggested the satisfactory predictive efficacy for the 5-year overall survival of the model (AUC value = 0.733). A nomogram with great predictive performance combining the multigene prediction model and clinicopathological factors was developed. The established model was validated in an external cohort (AUC value = 0.728). In another independent cohort, the model was verified to be of significant prognostic value for different subgroups, which was demonstrated to be especially accurate for young patients (AUC value = 0.763).
ConclusionThe well-established model based on stromal score-related gene signature might serve as a promising tool for the prognostic prediction of colon cancer.

背景：越来越多的研究证据表明，基质细胞（stromal cells）在多种恶性肿瘤的微环境中发挥着至关重要的作用。然而，针对结肠癌的、基于基质特征的高效预后特征仍未得到充分确立。本研究旨在构建一种基于基质评分的结肠癌多基因预后预测模型。
方法：采用ESTIMATE算法，基于TCGA数据库（The Cancer Genome Atlas）中结肠癌队列的基因表达谱计算基质评分。借助limma R包构建线性模型，以识别高低评分组间的差异表达基因。依次采用单变量回归、LASSO回归及多变量Cox回归模型筛选预后基因特征。使用来自GEO数据库（Gene Expression Omnibus）的两个独立数据集作为外部验证队列。
结果：在TCGA队列中，低基质评分被证实为结肠癌患者总生存期的有利因素（p=0.0046）。共鉴定出307个与基质评分相关的差异表达基因。通过单变量、LASSO及多变量Cox回归分析，最终筛选出由LEP、NOG及SYT3组成的基因特征以构建预后预测模型。基于该整合模型的预测值，将患者分为两组，两组患者的总生存期存在显著差异（p<0.0001）。时间依赖性受试者工作特征曲线（Receiver Operating Characteristic curve, ROC）分析显示，该模型对5年总生存期具有良好的预测效能（AUC值=0.733）。构建了结合多基因预测模型与临床病理因素的列线图（nomogram），其预测性能优异。该模型在外部验证队列中得到验证（AUC值=0.728）。在另一独立队列中，该模型被证实对不同亚组均具有显著的预后价值，尤其在年轻患者中预测准确性尤为突出（AUC值=0.763）。
结论：本研究构建的基于基质评分相关基因特征的模型有望成为结肠癌预后预测的极具应用前景的工具。