Dataset Information

DNA methylation-driven genes for constructing diagnostic, prognostic, and recurrence models for hepatocellular carcinoma.

ABSTRACT: In this study, we performed a comprehensively analysis of gene expression and DNA methylation data to establish diagnostic, prognostic, and recurrence models for hepatocellular carcinoma (HCC). Methods: We collected gene expression and DNA methylation datasets for over 1,200 clinical samples. Integrated analyses of RNA-sequencing and DNA methylation data were performed to identify DNA methylation-driven genes. These genes were utilized in univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses to build a prognostic model. Recurrence and diagnostic models for HCC were also constructed using the same genes. Results: A total of 123 DNA methylation-driven genes were identified. Two of these genes (SPP1 and LCAT) were chosen to construct the prognostic model. The high-risk group showed a markedly unfavorable prognosis compared to the low-risk group in both training (HR = 2.81; P < 0.001) and validation (HR = 3.06; P < 0.001) datasets. Multivariate Cox regression analysis indicated the prognostic model to be an independent predictor of prognosis (P < 0.05). Also, the recurrence model successfully distinguished the HCC recurrence rate between the high-risk and low-risk groups in both training (HR = 2.22; P < 0.001) and validation (HR = 2; P < 0.01) datasets. The two diagnostic models provided high accuracy for distinguishing HCC from normal samples and dysplastic nodules in the training and validation datasets, respectively. Conclusions: We identified and validated prognostic, recurrence, and diagnostic models that were constructed using two DNA methylation-driven genes in HCC. The results obtained by integrating multidimensional genomic data offer novel research directions for HCC biomarkers and new possibilities for individualized treatment of patients with HCC.

SUBMITTER: Long J

PROVIDER: S-EPMC6831284 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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DNA methylation-driven genes for constructing diagnostic, prognostic, and recurrence models for hepatocellular carcinoma.

Long Junyu J Chen Peipei P Lin Jianzhen J Bai Yi Y Yang Xu X Bian Jin J Lin Yu Y Wang Dongxu D Yang Xiaobo X Zheng Yongchang Y Sang Xinting X Zhao Haitao H

Theranostics 20190925 24

In this study, we performed a comprehensively analysis of gene expression and DNA methylation data to establish diagnostic, prognostic, and recurrence models for hepatocellular carcinoma (HCC). <b>Methods</b>: We collected gene expression and DNA methylation datasets for over 1,200 clinical samples. Integrated analyses of RNA-sequencing and DNA methylation data were performed to identify DNA methylation-driven genes. These genes were utilized in univariate, least absolute shrinkage and selection ...[more]

PMID: 31695766

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Project description: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.

Project description:Background:Recurrence remains a major obstacle to long-term survival of laryngeal squamous cell carcinoma (LSCC). We conducted a genome-wide integrated analysis of methylation and the transcriptome to establish methylation-driven genes prognostic signature (MDGPS) to precisely predict recurrence probability and optimize therapeutic strategies for LSCC. Methods:LSCC DNA methylation datasets and RNA sequencing (RNA-seq) dataset were acquired from the Cancer Genome Atlas (TCGA). MethylMix was applied to detect DNA methylation-driven genes (MDGs). By univariate and multivariate Cox regression analyses, five genes of DNA MDGs was developed a recurrence-free survival (RFS)-related MDGPS. The predictive accuracy and clinical value of the MDGPS were evaluated by receiver operating characteristic (ROC) and decision curve analysis (DCA), and compared with TNM stage system. Additionally, prognostic value of MDGPS was validated by external Gene Expression Omnibus (GEO) database. According to 5 MDGs, the candidate small molecules for LSCC were screen out by the CMap database. To strengthen the bioinformatics analysis results, 30 pairs of clinical samples were evaluated by digoxigenin-labeled chromogenic in situ hybridization (CISH). Results:A total of 88 DNA MDGs were identified, and five RFS-related MDGs (LINC01354, CCDC8, PHYHD1, MAGEB2 and ZNF732) were chosen to construct a MDGPS. The MDGPS can effectively divide patients into high-risk and low-risk group, with the area under curve (AUC) of 0.738 (5-year RFS) and AUC of 0.74 (3-year RFS). Stratification analysis affirmed that the MDGPS was still a significant statistical prognostic model in subsets of patients with different clinical variables. Multivariate Cox regression analysis indicated the efficacy of MDGPS appears independent of other clinicopathological characteristics. In terms of predictive capacity and clinical usefulness, the MDGPS was superior to traditional TNM stage. Additionally, the MDGPS was confirmed in external LSCC cohorts from GEO. CMap matched the 9 most significant small molecules as promising therapeutic drugs to reverse the LSCC gene expression. Finally, CISH analysis in 30 LSCC tissues and paired adjacent normal tissues revealed that MAGEB2 has significantly higher expression of LSCC compared to adjacent non-neoplastic tissues; LINC01354, CCDC8, PHYHD1, and ZNF732 have significantly lower expression of LSCC compared to adjacent non-neoplastic tissues, which were in line with bioinformatics analysis results. Conclusion:A MDGPS, with five DNA MDGs, was identified and validated in LSCC patients by combining transcriptome and methylation datasets analysis. Compared TNM stage alone, it generates more accurate estimations of the recurrence prediction and maybe offer novel research directions and prospects for individualized treatment of patients with LSCC.

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Project description:Hepatocellular carcinoma (HCC) is one of the most prevalent life-threatening human cancers and the leading cause of cancer-related mortality, with increased global incidence within the last decade. Identification of effective diagnostic and prognostic biomarkers would enable reliable risk stratification and efficient screening of high-risk patients, thereby facilitating clinical decision-making. Herein, we performed a comprehensive, robust DNA methylation analysis based on genome-wide DNA methylation profiling. We constructed a diagnostic signature with five DNA methylation markers, which precisely distinguished HCC patients from normal controls. Cox regression and LASSO analysis were applied to construct a prognostic signature with four DNA methylation markers. A one-to-one correlation analysis was carried out between genes of the whole genome and our prognostic signature. Exploration of the biological function and the role of the underlying significantly correlated genes was conducted. A mixed dataset of 463 HCC patients and 253 normal controls, derived from six independent datasets, was used to valid the diagnostic signature. Results showed a specificity of 96.84% and sensitivity of 96.77%. Class scores for the diagnostic signature were significantly different between normal controls, individuals with liver diseases, and HCC patients. The present signature has the potential to serve as a biomarker to monitor health in normal controls. Additionally, HCC patients were successfully separated into low-risk and high-risk groups by the prognostic signature, with a better prognosis for patients in the low-risk group. Kaplan-Meier and ROC analysis confirmed that the prognostic signature performed well. We found eight of the top ten genes to positively correlate with risk scores of the prognostic signature, and to be involved in cell cycle regulation. This eight-gene panel also served as a prognostic signature. The robust evidence presented in this study therefore demonstrates the effectiveness of the prognostic signature. In summary, we constructed diagnostic and prognostic signatures, which have potential for use in diagnosis, surveillance, and prognostic prediction for HCC patients. Eight genes that were significantly and positively correlated with the prognostic signature were strongly associated with cell cycle processes. Therefore, the prognostic signature can be used as a guide by which to measure responsiveness to cell-cycle-targeting agents.

Dataset Information

DNA methylation-driven genes for constructing diagnostic, prognostic, and recurrence models for hepatocellular carcinoma.

Publications

DNA methylation-driven genes for constructing diagnostic, prognostic, and recurrence models for hepatocellular carcinoma.

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