ArticleCancer cell international2018
Exploration of methylation-driven genes for monitoring and prognosis of patients with lung adenocarcinoma.
Article in Cancer cell international, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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32 citing papers in PubMed, 44 citations in OpenAlex.
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- Tissue-Specific Methylation Biosignatures for Monitoring Diseases: An In Silico Approach.International journal of molecular sciences · 2022Article
- Is Cadmium Toxicity Tissue-Specific? Toxicogenomics Studies Reveal Common and Specific Pathways in Pulmonary, Hepatic, and Neuronal Cell Models.International journal of molecular sciences · 2022Article
- Identification of DNA methylation-driven genes and construction of a nomogram to predict overall survival in pancreatic cancer.BMC genomics · 2021Article
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- A methylation-based nomogram for predicting survival in patients with lung adenocarcinoma.BMC cancer · 2021Article
- Screening and identification of Theileria annulata subtelomere-encoded variable secreted protein-950454 (SVSP454) interacting proteins from bovine B cells.Parasites & vectors · 2021Article
- Multi-omics analysis of genomics, epigenomics and transcriptomics for molecular subtypes and core genes for lung adenocarcinoma.BMC cancer · 2021Article
- Comprehensive Profiling of Genomic and Transcriptomic Differences between Risk Groups of Lung Adenocarcinoma and Lung Squamous Cell Carcinoma.Journal of personalized medicine · 2021Article
- Analysis of Methylation-driven Genes in Pancreatic Ductal Adenocarcinoma for Predicting Prognosis.Journal of Cancer · 2021Article
- Molecular subtypes based on DNA promoter methylation predict prognosis in lung adenocarcinoma patients.Aging · 2020Article
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- Methylation-driven genes and their prognostic value in cervical squamous cell carcinoma.Annals of translational medicine · 2020Article
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- Methylation‑driven genes PMPCAP1, SOWAHC and ZNF454 as potential prognostic biomarkers in lung squamous cell carcinoma.Molecular medicine reports · 2020Article
- Integrated analysis of DNA methylation and mRNA expression profiles to identify key genes in head and neck squamous cell carcinoma.Bioscience reports · 2020Article
- Screening and Identification of Four Prognostic Genes Related to Immune Infiltration and G-Protein Coupled Receptors Pathway in Lung Adenocarcinoma.Frontiers in oncology · 2020Article
- Identification of methylation-driven genes related to the prognosis of papillary renal cell carcinoma: a study based on The Cancer Genome Atlas.Cancer cell international · 2020Article
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7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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