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Identification of a 5-gene-risk score model for predicting luminal A-invasive lobular breast cancer survival

Publication Date May 10, 2022

Abstract

Breast cancer is a devastating malignancy, among which the luminal A (LumA) breast cancer is the most common subtype. In the present study, we used a comprehensive bioinformatics approach in the hope of identifying novel prognostic biomarkers for LumA breast cancer patients. Transcriptomic profiling of 611 LumA breast cancer patients was downloaded from TCGA database. Differentially expressed genes (DEGs) between tumor samples and controls were first identified by differential expression analysis, before being used for the weighted gene co-expression network analysis. The subsequent univariate Cox regression and LASSO algorithm were used to uncover key prognostic genes for constructing multivariate Cox regression model. Patients were stratified into high-risk and low-risk groups according to the risk score, and subjected to multiple downstream analyses including survival analysis, gene set enrichment analysis (GSEA), inference on immune cell infiltration and analysis of mutation burden. Receiving operator curve analysis was also performed. A total of 7071 DEGs were first identified by edgeR package, pink module was found significantly associated with invasive lobular carcinoma (ILC). 105 prognostic genes and 9 predictors were identified, allowing the identification of a 5-key prognostic genes (LRRC77P, CA3, BAMBI, CABP1, ATP8A2) after intersection. These 5 genes, and the resulting Cox model, displayed good prognostic performance. Furthermore, distinct differences existed between two risk-score stratified groups at various le...

Concepts
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Differentially Expressed Genes
Weighted Gene Co-expression Network Analysis
Analysis Of Mutation Burden
Prognostic Genes
Invasive Lobular Carcinoma
Gene Set Enrichment Analysis
Pink Module
Receiving Operator Curve Analysis
Breast Cancer
LASSO Algorithm

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