Identification of Novel Anoikis-Related Gene Signatures to Predict the Prognosis, Immune Microenvironment, and Drug Sensitivity of Breast Cancer Patients

Sep 28, 2024Cancer control : journal of the Moffitt Cancer Center

New Cell Death-Related Gene Patterns Linked to Outlook, Immune Environment, and Drug Response in Breast Cancer Patients

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Abstract

A novel risk score model based on five -related signatures may help predict breast cancer prognosis.

The model classified breast cancer patients into high and low risk groups based on their gene expression.
High and low risk groups displayed significant differences in overall survival (OS), tumor mutation burden (TMB), and tumor microenvironment (TME).
The model also indicated variations in stemness and drug sensitivity between the risk groups.
Both the risk score model and a nomogram demonstrated potential for predicting prognosis in breast cancer.

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INTRODUCTION: Breast cancer is one of the most prevalent types of cancer and a leading cause of cancer-related death among females worldwide. , a specific type of apoptosis that is triggered by the loss of anchoring between cells and the native extracellular matrix, plays a vital role in cancer invasion and metastasis. However, studies that focus on the prognostic values of anoikis-related genes (ARGs) in breast cancer are scarce.

METHODS: Gene expression data were obtained from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) databases. Five anoikis-related signatures (ARS) were selected from ARGs through univariate Cox regression analysis, LASSO regression analysis, and multivariate Cox regression analysis. Subsequently, an ARGs risk score model was established, and breast cancer patients were divided into high and low risk groups. The correlation between risk groups and overall survival (OS), tumor mutation burden (TMB), tumor microenvironment (TME), stemness, and drug sensitivity were analyzed. Moreover, RT-qPCR was performed to verify the gene expression levels of the five ARS in breast cancer tissues. Furthermore, a nomogram model was constructed based on ARGs risk score and clinicopathological factors.

RESULTS: A novel ARGs risk score model was constructed based on five ARS (CEMIP, LAMB3, CD24, PTK6, and PLK1), and breast cancer patients were divided into high and low risk groups. Correlation analysis showed that the high and low risk groups had different OS, TMB, TME, stemness, and drug sensitivity. Both the ARGs risk score model and the nomogram showed promising prognosis predictive value in breast cancer.

CONCLUSION: ARS could be used as promising biomarkers for breast cancer prognosis predication and treatment options selection.

Key numbers

1.03001614066725

Overall Survival Difference

Cut-off used to categorize breast cancer patients into risk groups.

43%

High Risk Group Mutation Rates

Mutation frequency of TP53 in high risk group.

21%

Low Risk Group Mutation Rates

Mutation frequency of TP53 in low risk group.

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Identification of Novel Anoikis-Related Gene Signatures to Predict the Prognosis, Immune Microenvironment, and Drug Sensitivity of Breast Cancer Patients

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