ABSTRACT

Background

Despite recent progress in screening survival-related genes, there have been few attempts to apply methods based on cancer stem cells (CSCs) for prognosis. We aimed to identify a CSC-based model to predict survival in colorectal cancer (CRC) patients.

Material/Methods

Differentially expressed genes between CRC and normal tissues and between CD133- and CD133+ cells were obtained from The Cancer Genome Atlas and Gene Expression Omnibus, and intersections were evaluated. Gene Ontology functional and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyzes were performed. STRING was used to investigate interactions between the encoded proteins and the Kaplan-Meier method to verify mRNAs associated with survival. A prognostic model based on CSCs was established via univariate and multivariate Cox regression. Receiver operating characteristic curve analysis was conducted to test the model’s sensitivity and specificity. The KS test was applied to provide evidence for relationships between expression levels of nine mRNAs in our model and pathological stage.

Results

In total, 155 common differentially expressed mRNAs were identified, and nine (AOC1, UCN, MTUS1, CDC20, SNCB, MAT1A, TUBB2B, GABRA4 and ALPP) were screened after regression analyses to establish a predictive model for classifying patients into high- and low-risk groups with significantly different overall survival times, especially for stage II and IV patients.

Conclusions

We developed a novel model that provides additional and powerful prognostic information beyond conventional clinicopathological factors for CRC survival prediction. It also provides new insight into the molecular mechanisms underlying the transition from normal tissues to CSCs and formation of tumor tissues.

中文翻译：

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CSCs 生成的转录信息和 9-mRNA 特征的构建，以改善结直肠癌的预后预测。

摘要

背景

尽管最近在筛选生存相关基因方面取得了进展，但很少有人尝试将基于癌症干细胞 (CSC) 的方法应用于预后。我们旨在确定一种基于 CSC 的模型来预测结直肠癌 (CRC) 患者的存活率。

材料/方法

CRC 和正常组织之间以及 CD133- 和 CD133+ 细胞之间的差异表达基因从癌症基因组图谱和基因表达综合中获得，并评估了交叉点。进行了基因本体功能和京都基因和基因组百科全书途径富集分析。STRING 用于研究编码蛋白质和 Kaplan-Meier 方法之间的相互作用，以验证与存活相关的 mRNA。通过单变量和多变量 Cox 回归建立基于 CSC 的预后模型。进行接收者操作特征曲线分析以测试模型的敏感性和特异性。KS 检验用于为我们模型中 9 种 mRNA 的表达水平与病理阶段之间的关系提供证据。

结果

共鉴定出155个常见的差异表达mRNA，并通过回归分析筛选出9个（AOC1、UCN、MTUS1、CDC20、SNCB、MAT1A、TUBB2B、GABRA4和ALPP），建立预测模型将患者分为高低-具有显着不同总生存时间的风险组，特别是对于 II 期和 IV 期患者。

结论

我们开发了一种新模型，该模型为 CRC 生存预测提供了超越常规临床病理因素的额外且强大的预后信息。它还提供了对从正常组织向 CSC 转变和肿瘤组织形成的分子机制的新见解。