A Ten-N6-Methyladenosine (m6A)-Modified Gene Signature Based on a Risk Score System Predicts Patient Prognosis in Rectum Adenocarcinoma

Frontiers in Oncology

Pei Wang¹  Hefen Yu¹  Tianshu Yang¹  Gen Li¹  Xin Yin¹  Wei Huang¹  Xu Teng¹  Zihang Wang²  Lin Zhou²  Yajuan Feng² 

[1] 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;

关键词: m6A;    risk score;    prognostic prediction;    READ;    gene signature;   

DOI  :  10.3389/fonc.2020.567931

来源: DOAJ

【 摘 要 】

ObjectivesThe study aims to analyze the expression of N6-methyladenosine (m6A)-modified genes in rectum adenocarcinoma (READ) and identify reliable prognostic biomarkers to predict the prognosis of READ.Materials and MethodsRNA sequence data of READ and corresponding clinical survival data were obtained from The Cancer Genome Atlas (TCGA) database. N6-methyladenosine (m6A)-modified genes in READ were downloaded from the “m6Avar” database. Differentially expressed m6A-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.ResultsWe screened 974 differentially expressed m6A-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 m6A-modified genes were positively correlated with that of m6A demethylase FTO and ALKBH5.ConclusionThis study identified potential m6A-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.

【 授权许可】

Unknown