Dataset Information

Novel methylation-driven genes identified as prognostic indicators for lung squamous cell carcinoma.

ABSTRACT: Lung cancer remains the leading cause of cancer death. DNA methylation plays an essential role in carcinogenesis through regulating gene expression and gene alternative splicing. However, the role of methylation in the tumorigenesis of lung squamous cell carcinoma (SCC) and its association with prognosis remains unclear. Here, we used an integrative approach to evaluate the prognostic value of epigenetic processes in lung SCC by examining the data provided by The Cancer Genome Atlas (TCGA). We found that the mean methylation level was significantly decreased in lung SCC. We also identified methylation-driven genes which were associated with cancer-related pathways. The multivariate Cox regression analysis showed four methylation-driven genes, GCSAM, GPR75, NHLRC1, and TRIM58, could be served as prognostic indicators for lung SCC. Validation on two external GEO datasets showed consistent methylation alterations of the four genes. These findings may have important implications in the understanding of the potential therapeutic method for lung SCC.

SUBMITTER: Li Y

PROVIDER: S-EPMC6511754 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Novel methylation-driven genes identified as prognostic indicators for lung squamous cell carcinoma.

Li Yin Y Gu Jie J Xu Fengkai F Zhu Qiaoliang Q Ge Di D Lu Chunlai C

American journal of translational research 20190415 4

Lung cancer remains the leading cause of cancer death. DNA methylation plays an essential role in carcinogenesis through regulating gene expression and gene alternative splicing. However, the role of methylation in the tumorigenesis of lung squamous cell carcinoma (SCC) and its association with prognosis remains unclear. Here, we used an integrative approach to evaluate the prognostic value of epigenetic processes in lung SCC by examining the data provided by The Cancer Genome Atlas (TCGA). We f ...[more]

PMID: 31105813

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Project description:BackgroundAberrant DNA methylation plays an crucial role in tumorigenesis through regulating gene expression. Nevertheless, the exact role of methylation in the carcinogenesis of thyroid cancer and its association with prognosis remains unclear. The purpose of this study is to explore the DNA methylation-driven genes in thyroid cancer by integrative bioinformatics analysis.MethodsThe transcriptome profiling data and DNA methylation data of thyroid cancer were downloaded from The Cancer Genome Atlas (TCGA) database. The methylmix R package was used to screen DNA methylation-driven genes in thyroid cancer. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were conducted to annotate the function of methylation-driven genes. Univariate Cox regression analyses was performed to distinguish prognosis-related methylation-driven genes. Multivariate Cox regression analyses was utilized to build a prognostic multi-gene signature. A survival analysis was carried out to determine the individual prognostic significance of this multi-gene signature.ResultsA total of 51 methylation-driven genes were identified. The functional analysis indicated that these genes were significantly enriched in diverse biological processes (BP) and pathways related to the malignancy processes. Four of these genes (RDH5, TREM1, BIRC7, and SLC26A7) were selected to construct the risk evaluation model. Patients in the low-risk group had an conspicuously better overall survival (OS) than those in high-risk group (p < 0.001). The area under the receiver operating characteristic (ROC) curve for this model was 0.836, suggesting a good specificity and sensitivity. Subsequent survival analysis revealed that this four-gene signature served as an independent indicator for the prognosis of thyroid cancer. Moreover, the prognostic signature was well validated in a external thyroid cancer cohort.ConclusionWe identified methylation-driven genes in thyroid cancer with independent prognostic value, which may offer new insight into molecular mechanisms of thyroid cancer and provide new possibility for individualized treatment of thyroid cancer patients.

Project description:BackgroundLung squamous cell carcinoma (LUSC), as the second frequent subtype of lung cancer, causes lots of mortalities primarily due to a lack of precise prognostic markers and timely treatment intervention. Previous studies have constructed several risk prognostic models based on DNA methylation sites in multiple tumors, whereas, DNA methylation signature of LUSC remains to be built, and its predictive value need to be evaluated.MethodsThe genome-wide DNA methylation data of LUSC samples was obtained from The Cancer Genome Atlas dataset. Univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) were implemented to identify DNA methylation sites related to overall survival of LUSC patients. Thus, we performed multivariate Cox regression to establish a DNA methylation signature. The Kaplan-Meier (K-M) survival curves and time-dependent receiver operating characteristic (ROC) curves were plotted to estimate the prognostic power of the signature. Comparison with other known prognostic biomarkers, our DNA methylation signature showed higher predictive specificity and sensitivity. In addition, multivariate Cox regression screened out independent prognostic factors and constructed a nomogram.ResultsSeveral statistical methods were performed to construct an 11-DNA methylation signature. LUSC patients were divided into low- and high-risk group based on risk score, and high-risk group had a shorter survival time. According to the results of K-M and ROC analyses, the 11-DNA methylation signature showed significant sensitivity and specificity in predicting the LUSC patients’ overall survival. Finally, we integrated some independent prognostic factors (risk score, metastasis stage, and tobacco smoking history) to construct a nomogram, which has excellent prognostic power and may provide guidance for the therapeutic strategies.ConclusionsWe constructed the first risk prognosis model based on DNA methylation site in LUSC, which showed better predictive ability. In addition, a nomogram integrating the DNA methylation signature, metastasis stage, and tobacco smoking history was developed.

Project description:Background: Abnormal DNA methylation of is one of the important mechanisms leading to tumor pathogenesis. The purpose of this study was to explore differentially methylated genes that may drive the development of renal clear cell carcinoma through a comprehensive analysis of the TCGA database. Materials and methods: Methylation data and RNA-seq data for clear cell renal cell carcinoma were downloaded from The Cancer Genome Atlas (TCGA). Differentially methylated genes and the differential genes associated with survival were then screened by MethylMix R package and univariate Cox proportional-hazards model, respectively. Their common genes were then intersected and obtained for further analysis. Correlation of gene expression and methylation levels, gene set enrichment analysis (GSEA) enrichments, survival curve, and ROC curve plotting for DNA methylation-driven genes were finally performed. The methylation alterations of the three genes were validated via two GEO datasets (GSE70303 and GSE113501), and the genes expression level was verified through two GEO datasets (GSE6344 and GSE53757). Results: Three novel DNA methylation-driven genes LAT, HOXD3 and NFE2L3 were identified in clear cell renal cell carcinoma. Expression analysis further revealed that hypomethylation levels of LAT and NFE2L3 showed higher gene expression levels, while HOXD3 exhibited opposite methylation-expression pattern. The CpG sites of LAT (cg16462073), HOXD3 (cg24000528) and NFE2L3 (cg16882373) that may affect respective gene expressions were also identified. For the survival analysis, we found that hypomethylation and over-expression of LAT and NFE2L3 were correlated with poor survival, while hypermethylation and low-expression HOXD3 was correlated with poor survival of clear cell renal cell carcinoma patients. In addition, GSEA KEGG analysis and biological processes of these genes were also enriched for functional analysis. Kaplan-Meier survival and ROC analyses of these genes showed an average risk score of 0.9140593, AUC = 0.692, which suggested a good clinical application value. Finally, the opposite methylation-expression pattern of these three genes were verified in GEO datasets. Conclusions: In this study, we successfully exhibited the potential DNA methylation-driven genes LAT, HOXD3, and NFE2L3 involved in clear cell renal cell carcinoma. Moreover, gene functions and prognostic risk models were also elucidated, which facilitated the expansion of the current study on the role of methylation in the pathology process of clear cell renal cell carcinoma.

Dataset Information

Novel methylation-driven genes identified as prognostic indicators for lung squamous cell carcinoma.

Publications

Novel methylation-driven genes identified as prognostic indicators for lung squamous cell carcinoma.

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