Liu Jingwen, Yu Fei, Liu Zhao, Wang Xiaojing, Li Jianming
Department of Pathology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, P.R. China.
Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, P.R. China.
Cancer Biother Radiopharm. 2022 Dec;37(10):963-975. doi: 10.1089/cbr.2021.0171. Epub 2021 Sep 22.
Colorectal cancer (CRC) has been a major public health problem. Tumor microenvironment (TME) greatly contributes to the heterogeneity of CRC and is crucial for the regulation of CRC progression. The authors' study aimed to develop a robust prognostic signature for CRC patients based on TME-related genes. Gene expression data and clinicopathologic information of CRC patients were collected from Gene Expression Omnibus and The Cancer Genome Atlas databases. TME-related genes with prognostic value were identified by Cox regression and bootstrap method. The authors used the prognostic genes to construct a robust prognostic model using the least absolute shrinkage and selection operator (LASSO) regression method. The immune and stromal cell abundance of CRC samples were estimated by a microenvironment cell populations-counter method. Based on a training set that comprised 893 CRC samples and 4775 TME-related genes, they established a prognostic model consisting of 25 TME-related genes. With specific risk score formulae, the prognostic model divided CRC patients into high-risk and low-risk subgroups with significantly different survival, which were further confirmed in validation cohorts consisting of other 473 CRC cases or subpopulation of specific stages. The result of time-dependent receiver operating characteristic analysis demonstrated strong predictive accuracy of the prognostic model both in training and validation cohorts. Multivariate Cox regression analysis showed that the 25-gene signature was an independent prognostic factor for overall survival, which was validated through clinical subgroups analysis. Further analysis revealed that CRC samples of high-risk group was abundant of stromal-relevant processes and had a significantly higher proportion of fibroblasts and endothelial cells infiltration. The authors established a robust prognostic signature of 25 TME-related genes which may be an effective tool for prognostic prediction and CRC patient stratification to assist in making treatment decisions.
结直肠癌（CRC）一直是一个重大的公共卫生问题。肿瘤微环境（TME）极大地促成了结直肠癌的异质性，并且对于结直肠癌进展的调控至关重要。作者的研究旨在基于TME相关基因开发一种强大的结直肠癌患者预后特征。从基因表达综合数据库（Gene Expression Omnibus）和癌症基因组图谱（The Cancer Genome Atlas）数据库收集了结直肠癌患者的基因表达数据和临床病理信息。通过Cox回归和自助法确定具有预后价值的TME相关基因。作者使用这些预后基因，采用最小绝对收缩和选择算子（LASSO）回归方法构建了一个强大的预后模型。通过微环境细胞群体计数法估计结直肠癌样本中的免疫细胞和基质细胞丰度。基于一个包含893个结直肠癌样本和4775个TME相关基因的训练集，他们建立了一个由25个TME相关基因组成的预后模型。利用特定的风险评分公式，该预后模型将结直肠癌患者分为高风险和低风险亚组，其生存率有显著差异，这在由其他473例结直肠癌病例或特定阶段亚群组成的验证队列中得到了进一步证实。时间依赖性受试者工作特征分析结果表明，该预后模型在训练队列和验证队列中均具有很强的预测准确性。多变量Cox回归分析表明，25基因特征是总生存的独立预后因素，这通过临床亚组分析得到了验证。进一步分析显示，高风险组的结直肠癌样本富含与基质相关的过程，并且成纤维细胞和内皮细胞浸润比例显著更高。作者建立了一个由25个TME相关基因组成的强大预后特征，这可能是用于预后预测和结直肠癌患者分层以协助做出治疗决策的有效工具。
