Dataset Information

A systematic analysis of a potential metabolism-related prognostic signature for breast cancer patients.

ABSTRACT:

Background

Metabolic pathways play an essential role in breast cancer. However, the role of metabolism-related genes in the early diagnosis of breast cancer remains unknown.

Methods

In our study, RNA sequencing (RNA-seq) expression data and clinicopathological information from The Cancer Genome Atlas (TCGA) and GSE20685 were obtained. Univariate cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed on the differentially expressed metabolism-related genes. Then, the formula of the metabolism-related risk model was composed, and the risk score of each patient was calculated. The breast cancer patients were divided into high-risk and low-risk groups with a cutoff of the median expression value of the risk score, and the prognostic analysis was also used to analyze the survival time between these two groups. In the end, we also analyzed the expression, interaction, and correlation among genes in the metabolism-related gene risk model.

Results

The results from the prognostic analysis indicated that the survival was significantly poorer in the high-risk group than in the low-risk group in both TCGA and GSE20685 datasets. In addition, after adjusting for different clinicopathological features in multivariate analysis, the metabolism-related risk model remained an independent prognostic indicator in TCGA dataset.

Conclusions

In summary, we systematically developed a potential metabolism-related gene risk model for predicting prognosis in breast cancer patients.

SUBMITTER: Yu S

PROVIDER: S-EPMC7944328 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Publications

A systematic analysis of a potential metabolism-related prognostic signature for breast cancer patients.

Yu Shibo S Wang Xiaowen X Zhu Lizhe L Xie Peiling P Zhou Yudong Y Jiang Siyuan S Chen Heyan H Liao Xiaoqin X Pu Shengyu S Lei Zhenzhen Z Wang Bin B Ren Yu Y

Annals of translational medicine 20210201 4

<h4>Background</h4>Metabolic pathways play an essential role in breast cancer. However, the role of metabolism-related genes in the early diagnosis of breast cancer remains unknown.<h4>Methods</h4>In our study, RNA sequencing (RNA-seq) expression data and clinicopathological information from The Cancer Genome Atlas (TCGA) and GSE20685 were obtained. Univariate cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed on the differentially expre ...[more]

PMID: 33708957

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Dataset Information

A systematic analysis of a potential metabolism-related prognostic signature for breast cancer patients.

Background

Methods

Results

Conclusions

Publications

A systematic analysis of a potential metabolism-related prognostic signature for breast cancer patients.

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