Exploration of methylation-driven genes for monitoring and prognosis of patients with lung adenocarcinoma

Abstract

BackgroundAs one of the most common malignant tumors in humans, lung cancer has experienced a gradual increase in morbidity and mortality. This study examined prognosis-related methylation-driven genes specific to lung adenocarcinoma (LUAD) to provide a basis for prognosis prediction and personalized targeted therapy for LUAD patients.MethodsThe methylation and survival time data from LUAD patients in the TCGA database were downloaded. The MethylMix algorithm was used to identify the differential methylation status of LUAD and adjacent tissues based on the β-mixture model to obtain disease-related methylation-driven genes. A COX regression model was then used to screen for LUAD prognosis-related methylation-driven genes, and a linear risk model based on five methylation-driven gene expression profiles was constructed. A methylation and gene expression combined survival analysis was performed to further explore the prognostic value of 5 genes independently.ResultsThere were 118 differentially expressed methylation-driven genes in the LUAD tissues and adjacent tissues. Five of the genes, CCDC181, PLAU, S1PR1, ELF3, and KLHDC9, were used to construct a prognostic risk model. Overall, the survival time was significantly lower in the high-risk group compared with that in the low-risk group (P < 0.05). In addition, the methylation and gene expression combined survival analysis found that the combined expression levels of the genes CCDC181, PLAU, and S1PR1 as well as KLHDC9 alone can be used as independent prognostic markers or drug targets.ConclusionOur findings provide an important bioinformatic basis and relevant theoretical basis for guiding subsequent LUAD early diagnosis and prognosis assessments.Electronic supplementary materialThe online version of this article (10.1186/s12935-018-0691-z) contains supplementary material, which is available to authorized users.

Integrating genetic mutations and expression profiles for survival prediction of lung adenocarcinoma

“…Zhao et al developed a TCGA‐based model using 20 filtered genes that showed lower accuracy (61.5% vs. 67%) for predicting OS compared to ours . Gao et al established a TCGA‐based survival model using methylation‐driven genes that was also inferior to ours (66% vs. 67%) …”

“…43 Gao et al established a TCGA-based survival model using methylation-driven genes that was also inferior to ours (66% vs. 67%). 44 There were several limitations to our study. First, the investigated cohort was retrieved from TCGA and no external validation cohort was available.…”

Integrated analysis of DNA methylation and mRNA expression profiles to identify key genes in head and neck squamous cell carcinoma

“…Another study conducted integrated analysis on TCGA data to get a better understanding of molecular mechanisms involved in esophageal squamous cell carcinoma, contributing to a more precise prognosis detection [23]. What is more, a study concentrated on lung adenocarcinoma found out several hub genes that could be employed to facilitate early diagnosis and accurate prognosis assessments of patients [24]. In the present study, methylation microarray dataset (GSE87053) and gene expression microarray dataset (GSE23558) were analyzed through R language and a total of 255 hypermethylated-down-regulated genes and 114 hypomethylated-upregulated genes were identified.…”

Integrative analysis of DNA methylation-driven genes for the prognosis of lung squamous cell carcinoma using MethylMix

“…Next, we used the MethylMix R package with the screening criteria (|logFC|>0, P<0.05, Cor<-0.3) to extract the methylation-driven genes. Analysis of average methylation differences at 3000 bp (base pair) sites upstream of genes to identify differential methylation levels in gene promoters [23][24][25]. The differential level of methylation in the promoter of genes was performed by using the limma R package [26].…”

“…Currently, few studies based on using MethylMix R package to identify specific methylation-driven genes have been reported [20][21][22]. Recently, a study based on MethylMix reveals potential prognostic methylationdriven genes for predicting the prognosis in lung adenocarcinoma (LUAD) has been reported [23]. In the present study, we extracted the DNA methylation and RNA-Seq data using bioinformatics methods from The Cancer Genome Atlas (TCGA) database, and then the MethylMix R package was performed to obtain methylation-driven genes.…”