Dataset Information

Genome-Wide Analysis of Methylation-Driven Genes and Identification of an Eight-Gene Panel for Prognosis Prediction in Breast Cancer.

ABSTRACT: Background:Aberrant DNA methylation is a crucial epigenetic regulator that is closely related to the occurrence and development of various cancers, including breast cancer (BC). The present study aimed to identify a novel methylation-based prognosis biomarker panel by integrally analyzing gene expression and methylation patterns in BC patients. Methods:DNA methylation and gene expression data of breast cancer (BRCA) were downloaded from The Cancer Genome Atlas (TCGA). R packages, including ChAMP, SVA, and MethylMix, were applied to identify the unique methylation-driven genes. Subsequently, these genes were subjected to Metascape for GO analysis. Univariant Cox regression was used to identify survival-related genes among the methylation-driven genes. Robust likelihood-based survival modeling was applied to define the prognosis markers. An independent data set (GSE72308) was used for further validation of our risk score system. Results:A total of 879 DNA methylation-driven genes were identified from 765 BC patients. In the discovery cohort, we identified 50 survival-related methylation-driven genes. Finally, we built an eight-methylation-driven gene panel that serves as prognostic predictors. Conclusions:Our analysis of transcriptome and methylome variations associated with the survival status of BC patients provides a further understanding of basic biological processes and a basis for the genetic etiology in BC.

SUBMITTER: Kuang Y

PROVIDER: S-EPMC7186397 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Genome-Wide Analysis of Methylation-Driven Genes and Identification of an Eight-Gene Panel for Prognosis Prediction in Breast Cancer.

Kuang Yanshen Y Wang Ying Y Zhai Wanli W Wang Xuning X Zhang Bingdong B Xu Maolin M Guo Shaohua S Ke Mu M Jia Baoqing B Liu Hongyi H

Frontiers in genetics 20200421

<h4>Background</h4>Aberrant DNA methylation is a crucial epigenetic regulator that is closely related to the occurrence and development of various cancers, including breast cancer (BC). The present study aimed to identify a novel methylation-based prognosis biomarker panel by integrally analyzing gene expression and methylation patterns in BC patients.<h4>Methods</h4>DNA methylation and gene expression data of breast cancer (BRCA) were downloaded from The Cancer Genome Atlas (TCGA). R packages, ...[more]

PMID: 32373154

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Project description:BackgroundLung cancer has become the most common cancer type and caused the most cancer deaths. Lung adenocarcinoma (LUAD) is one of the major types of lung cancer. Accumulating evidence suggests the tumor microenvironment is correlated with the tumor progress and the patient's outcome. This study aimed to establish a gene signature based on tumor microenvironment that can predict patients' outcomes for LUAD.MethodsDataset TCGA-LUAD, downloaded from the TCGA portal, were taken as training cohort, and dataset GSE72094, obtained from the GEO database, was set as validation cohort. In the training cohort, ESTIMATE algorithm was applied to find intersection differentially expressed genes (DEGs) among tumor microenvironment. Kaplan-Meier analysis and univariate Cox regression model were performed on intersection DEGs to preliminarily screen prognostic genes. Besides, the LASSO Cox regression model was implemented to build a multi-gene signature, which was then validated in the validation cohorts through Kaplan-Meier, Cox, and receiver operating characteristic curve (ROC) analyses. In addition, the correlation between tumor mutational burden (TMB) and risk score was evaluated by Spearman test. GSEA and immune infiltrating analyses were conducted for understanding function annotation and the role of the signature in the tumor microenvironment.ResultsAn eight-gene signature was built, and it was examined by Kaplan-Meier analysis, revealing that a significant overall survival difference was seen. The eight-gene signature was further proven to be independent of other clinico-pathologic parameters via the Cox regression analyses. Moreover, the ROC analysis demonstrated that this signature owned a better predictive power of LUAD prognosis. The eight-gene signature was correlated with TMB. Furthermore, GSEA and immune infiltrating analyses showed that the exact pathways related to the characteristics of eight-genes signature, and identified the vital roles of Mast cells resting and B cells naive in the prognosis of the eight-gene signature.ConclusionIdentifying the eight-gene signature (INSL4, SCN7A, STAP1, P2RX1, IKZF3, MS4A1, KLRB1, and ACSM5) could accurately identify patients' prognosis and had close interactions with Mast cells resting and B cells naive, which may provide insight into personalized prognosis prediction and new therapies for LUAD patients.

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Dataset Information

Genome-Wide Analysis of Methylation-Driven Genes and Identification of an Eight-Gene Panel for Prognosis Prediction in Breast Cancer.

Publications

Genome-Wide Analysis of Methylation-Driven Genes and Identification of an Eight-Gene Panel for Prognosis Prediction in Breast Cancer.

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