Dataset Information

Methylation-Driven Genes Identified as Novel Prognostic Indicators for Thyroid Carcinoma.

ABSTRACT: Background:Aberrant 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. Methods:The 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. Results:A 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. Conclusion:We 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.

SUBMITTER: Lv L

PROVIDER: S-EPMC7136565 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Methylation-Driven Genes Identified as Novel Prognostic Indicators for Thyroid Carcinoma.

Lv Liting L Cao Liyu L Hu Guinv G Shen Qinyan Q Wu Jinzhong J

Frontiers in genetics 20200331

<h4>Background</h4>Aberrant 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.<h4>Methods</h4>The transcriptome profiling data and DNA methylation data of thyroid cancer were downloaded from Th ...[more]

PMID: 32296463

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Project description:Purpose: Aberrant DNA methylation plays a crucial role in the tumorigenesis of differentiated thyroid cancer (DTC); nevertheless, the factors leading to the local and regional recurrence of DTC are not well understood. This study aimed to establish the connection between DNA methylation-driven genes and the recurrence of DTC. Methods: RNA sequencing profiles and DNA methylation profiles of DTC were downloaded from The Cancer Genome Atlas (TCGA) database. Combined application of the methylmix R package and univariate Cox regression analyses were used to screen and distinguish prognosis-related methylation-driven genes. Multivariate Cox regression analyses were utilized to identify the target genes that were closely associated with the recurrence of DTC. Then, correlations between the expression levels of the target genes and the clinicopathological features were verified, as well as their potential biological functions. Results: A total of 168 Methylation-driven genes were differentially expressed in thyroid cancer, among which 10 genes (GSTO2, GSTM5, GSTM1, GPX7, FGF2, LIF, PLAU, BCL10, SHARPIN and TNFRSF1A) were identified as Hub genes. We selected PLAU for further analysis because PLAU was most strongly correlated with DTC recurrence and the DNA methylation levels of PLAU were closely associated with multiple clinicopathological features of DTC. PLAU was significantly upregulated in DTC, and patients with a high expression level of PLAU had a higher risk of recurrence (p < 0.05). Functional predictions suggested that PLAU-related genes were mainly involved in the regulation of immune-related signaling pathways. Moreover, the mRNA level of PLAU was found to be positively correlated with the cell markers of neutrophils and dendritic cells. In addition, we found that two DNA methylation sites (cg06829584, cg19399285) were associated with abnormal expression of PLAU in DTC. Conclusion: The methylation-driven gene PLAU is an independent risk factor for the recurrence of DTC and it functions as an oncogene through the regulation of immune-related signaling pathways, which offers new insight into the molecular mechanisms of thyroid cancer and provides new possibilities for individualized treatment of thyroid cancer patients.

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.

Project description:BackgroundThe thyroid cancer subtype that occurs more frequently is papillary thyroid carcinoma (PTC). Despite a good surgical outcome, treatment with traditional antitumor therapy does not offer ideal results for patients with radioiodine resistance, recurrence, and metastasis. The evidence for the connection between iron metabolism imbalance and cancer development and oncogenesis is growing. Nevertheless, the iron metabolism impact on PTC prognosis is still indefinite.MethodsHerein, we acquired the medical data and gene expression of individuals with PTC from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. Typically, three predictive iron metabolism-related genes (IMRGs) were examined and employed to build a risk score (RS) model via the least absolute shrinkage and selection operator (LASSO) regression, univariate Cox, and differential gene expression analyses. Then we analyzed somatic mutation and immune cell infiltration among RS groups. We also validated the prognostic value of two IMRGs (SFXN3 and TFR2) by verifying their biological function through in vitro experiments.ResultsBased on RS, all patients with PTC were stratified into low- and high-risk groups, where Kaplan-Meier analysis indicated that disease-free survival (DFS) in the high-risk group was much lower than in the low-risk group (P < 0.0001). According to ROC analysis, the RS model successfully predicted the 1-, 3-, and 5-year DFS of individuals with PTC. Additionally, in the TCGA cohort, a nomogram model with RS was developed and exhibited a strong capability to anticipate PTC patients' DFS. In the high-risk group, the enriched pathological processes and signaling mechanisms were detected utilizing the gene set enrichment analysis (GSEA). Moreover, the high-risk group had a significantly higher level of BRAF mutation frequency, tumor mutation burden, and immune cell infiltration than the low-risk group. In vitro experiments found that silencing SFXN3 or TFR2 significantly reduced cell viability.ConclusionCollectively, our predictive model depended on IMRGs in PTC, which could be potentially utilized to predict the PTC patients' prognosis, schedule follow-up plans, and provide potential targets against PTC.

Project description:Recurrence is the major death cause of differentiated thyroid carcinoma (DTC), and a better understanding of recurrence risk at early stage may lead to make the optimal medical decision to improve patients' prognosis. The 2015 American Thyroid Association (ATA) risk stratification system primary based on clinic-pathologic features is the most commonly used to describe the initial risk of persistent/recurrent disease. Besides, multiple prognostics models based on multigenes expression profiles have been developed to predict the recurrence risk of DTC patients. Recent evidences indicated that aberrant DNA methylation is involved in the initiation and progression of DTC and can be useful biomarkers for clinical diagnosis and prognosis prediction of DTC. Therefore, there is a need for integrating gene methylation feature to assess the recurrence risk of DTC. Gene methylation profile from The Cancer Genome Atlas (TCGA) was used to construct a recurrence risk model of DTC by successively performed univariate Cox regression, LASSO regression, and multivariate Cox regression. Two Gene Expression Omnibus (GEO) methylation cohorts of DTC were utilized to validate the predictive value of the methylation profiles model as external cohort by receiver operating characteristic (ROC) curve and survival analysis. Besides, CCK-8, colony-formation assay, transwell, and scratch-wound assay were used to investigate the biological significance of critical gene in the model. In our study, we constructed and validated a prognostic signature based on methylation profiles of SPTA1, APCS, and DAB2 and constructed a nomogram based on the methylation-related model, age, and AJCC_T stage that could provide evidence for the long-term treatment and management of DTC patients. Besides, in vitro experiments showed that DAB2 inhibited proliferation, colony-formation, and migration of BCPAP cells and the gene set enrichment analysis and immune infiltration analysis showed that DAB2 may promote antitumor immunity in DTC. In conclusion, promoter hypermethylation and loss expression of DAB2 in DTC may be a biomarker of unfavorable prognosis and poor response to immune therapy.

Dataset Information

Methylation-Driven Genes Identified as Novel Prognostic Indicators for Thyroid Carcinoma.

Publications

Methylation-Driven Genes Identified as Novel Prognostic Indicators for Thyroid Carcinoma.

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