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.
Highlights
As one of the most common malignant tumors in humans, lung cancer has experienced a gradual increase in morbidity and mortality
In this study, based on the genomic methylation data provided by The Cancer Genome Atlas (TCGA) for lung adenocarcinoma (LUAD) patients, we obtained 118 methylation-driven genes associated with LUAD using the MethylMix algorithm
Univariate and multivariate Cox regression analyses showed that the prognostic survival model constructed from five aberrant methylation-driven genes, CCDC181, PLAU, Sphingosine 1-phosphate receptor 1 (S1PR1), ELF3, and KLHDC9, was an independent predictor of disease prognosis including
Summary
As 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. Lung cancer is one of the most malignant tumors in the world, with high morbidity and mortality [1]. LUAD-associated driver genes by bioinformatics analysis and construction of risk model are necessary for the prognosis evaluation and post-treatment of patients. DNA methylation can participate in many cellular processes such as cell differentiation, genome stability, and gene imprinting [10, 11]. Biological processes, changes in DNA methylation, can provide an important basis for early diagnosis and prognosis of cancer and a new ideas for further clinical applications
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