Huang Wei, Li Gen, Wang Zihang, Zhou Lin, Yin Xin, Yang Tianshu, Wang Pei, Teng Xu, Feng Yajuan, Yu Hefen
Beijing Key Laboratory of Cancer Invasion and Metastasis Research, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Capital Medical University, Beijing, China.
School of Information Science and Technology, University of Science and Technology of China, Hefei, China.
Front Oncol. 2021 Feb 17;10:567931. doi: 10.3389/fonc.2020.567931. eCollection 2020.
The study aims to analyze the expression of N-methyladenosine (mA)-modified genes in rectum adenocarcinoma (READ) and identify reliable prognostic biomarkers to predict the prognosis of READ.
RNA sequence data of READ and corresponding clinical survival data were obtained from The Cancer Genome Atlas (TCGA) database. N-methyladenosine (mA)-modified genes in READ were downloaded from the "m6Avar" database. Differentially expressed mA-modified genes in READ stratified by different clinicopathological characteristics were identified using the "limma" package in R. Protein-protein interaction (PPI) network and co-expression analysis of differentially expressed genes (DEGs) were performed using "STRING" and Cytoscape, respectively. Principal component analysis (PCA) was done using R. In addition, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were used to functionally annotate the differentially expressed genes in different subgroups. Univariate Cox regression analyses were conducted to identify the powerful independent prognostic factors in READ associated with overall survival (OS). A robust likelihood-based survival model was built using the "rbsurv" package to screen for survival-associated signature genes. The Support Vector Machine (SVM) was used to predict the prognosis of READ through the risk score of survival-associated signature genes. Correlation analysis were carried out using GraphPad prism 8.
We screened 974 differentially expressed mA-modified genes among four types of READ samples. Two READ subgroups (group 1 and group 2) were identified by K means clustering according to the expression of DEGs. The two subgroups were significantly different in overall survival and pathological stages. Next, 118 differentially expressed genes between the two subgroups were screened and the expression of 112 genes was found to be related to the prognosis of READ. Next, a panel of 10 survival-associated signature genes including adamtsl1, csmd2, fam13c, fam184a, klhl4, olfml2b, pdzd4, sec14l5, setbp1, tmem132b was constructed. The signature performed very well for prognosis prediction, time-dependent receiver-operating characteristic (ROC) analysis displaying an area under the curve (AUC) of 0.863, 0.8721, and 0.8752 for 3-year survival rate, prognostic status, and pathological stage prediction, respectively. Correlation analysis showed that the expression levels of the 10 mA-modified genes were positively correlated with that of mA demethylase FTO and ALKBH5.
This study identified potential mA-modified genes that may be involved in the pathophysiology of READ and constructed a novel gene expression panel for READ risk stratification and prognosis prediction.
本研究旨在分析N-甲基腺苷（mA）修饰基因在直肠腺癌（READ）中的表达情况，并确定可靠的预后生物标志物以预测READ的预后。
从癌症基因组图谱（TCGA）数据库中获取READ的RNA序列数据及相应的临床生存数据。从“m6Avar”数据库下载READ中N-甲基腺苷（mA）修饰的基因。使用R语言中的“limma”软件包鉴定不同临床病理特征分层的READ中差异表达的mA修饰基因。分别使用“STRING”和Cytoscape软件对差异表达基因（DEG）进行蛋白质-蛋白质相互作用（PPI）网络和共表达分析。使用R语言进行主成分分析（PCA）。此外，利用基因本体论（GO）和京都基因与基因组百科全书（KEGG）通路对不同亚组中的差异表达基因进行功能注释。进行单变量Cox回归分析以确定READ中与总生存（OS）相关的强大独立预后因素。使用“rbsurv”软件包构建基于稳健似然的生存模型以筛选生存相关的特征基因。通过生存相关特征基因的风险评分，使用支持向量机（SVM）预测READ的预后。使用GraphPad prism 8进行相关性分析。
我们在四种类型的READ样本中筛选出974个差异表达的mA修饰基因。根据DEG的表达情况，通过K均值聚类鉴定出两个READ亚组（组1和组2）。这两个亚组在总生存和病理分期方面存在显著差异。接下来，筛选出两个亚组之间的118个差异表达基因，发现其中112个基因的表达与READ的预后相关。随后，构建了一个由10个生存相关特征基因组成的面板，包括adamtsl1、csmd2、fam13c、fam184a、klhl4、olfml2b、pdzd4、sec14l5、setbp1、tmem132b。该特征在预后预测方面表现良好，时间依赖性受试者工作特征（ROC）分析显示，对于3年生存率、预后状态和病理分期预测，曲线下面积（AUC）分别为0.863、0.8721和0.8752。相关性分析表明，这10个mA修饰基因的表达水平与mA去甲基化酶FTO和ALKBH5的表达水平呈正相关。
本研究鉴定出可能参与READ病理生理过程的潜在mA修饰基因，并构建了一个用于READ风险分层和预后预测的新型基因表达面板。
