背景:结直肠癌(CRC)的发病率和致死率非常高,是最危险的癌症类型之一。及时诊断可有效降低结直肠癌的发病率。癌旁组织的变化可能是肿瘤发生的早期信号。比较癌旁粘膜和正常粘膜之间基因表达的差异可以帮助诊断CRC并了解其发展机制。
目的:本研究旨在从基因表达水平鉴定特定基因,在正常粘膜中表达,可以预测CRC的风险。
方法:使用机器学习方法分析了322例CRC和137例非CRC的459例正常结肠粘膜组织的转录组数据,其中每个样本包含28,706个基因表达水平。使用四种基于重要性估计的排序方法对基因进行排序(LASSO,LightGBM,MCFS,mRMR,和RF)和四种分类算法(决策树[DT],K-最近邻[KNN],随机森林[RF],和支持向量机[SVM])与增量特征选择[IFS]方法相结合,构建了性能优异的预测模型。
结果:排名靠前的基因,即,根据先前的研究,HOXD12、CDH1和S100A12与肿瘤发生相关。
结论:本研究总结了四组基于DT算法的定量分类规则,为了解CRC引起的微环境变化提供线索。根据规则,可以确定CRC对正常粘膜的影响。
BACKGROUND: Colorectal cancer (CRC) has a very high incidence and lethality rate and is one of the most dangerous cancer types. Timely diagnosis can effectively reduce the incidence of colorectal cancer. Changes in para-cancerous tissues may serve as an early signal for tumorigenesis. Comparison of the differences in gene expression between para-cancerous and normal
mucosa can help in the diagnosis of CRC and understanding the mechanisms of development.
OBJECTIVE: This study aimed to identify specific genes at the level of gene expression, which are expressed in normal
mucosa and may be predictive of CRC risk.
METHODS: A machine learning approach was used to analyze transcriptomic data in 459 samples of normal colonic mucosal tissue from 322 CRC cases and 137 non-CRC, in which each sample contained 28,706 gene expression levels. The genes were ranked using four ranking methods based on importance estimation (LASSO, LightGBM, MCFS, mRMR, and RF) and four classification algorithms (decision tree [DT], K-nearest neighbor [KNN], random forest [RF], and support vector machine [SVM]) were combined with incremental feature selection [IFS] methods to construct a prediction model with excellent performance.
RESULTS: The top-ranked genes, namely, HOXD12, CDH1, and S100A12, were associated with tumorigenesis based on previous studies.
CONCLUSIONS: This study summarized four sets of quantitative classification rules based on the DT algorithm, providing clues for understanding the microenvironmental changes caused by CRC. According to the rules, the effect of CRC on normal
mucosa can be determined.