Cuproptosis-associated NcRNAs Predict Breast Cancer Subtypes

Overview

Journal PLoS One

Specialties General Medicine
Science

Date 2024 Feb 26

PMID 38408075

Authors

Qing Xia

Jinze Shen

Qurui Wang

Ruixiu Chen

Xinying Zheng

Qibin Yan

Lihua Du

Hanbing Li

Shiwei Duan

Affiliations

Soon will be listed here.

Abstract

Background: Cuproptosis is a novel copper-dependent mode of cell death that has recently been discovered. The relationship between Cuproptosis-related ncRNAs and breast cancer subtypes, however, remains to be studied.

Methods: The aim of this study was to construct a breast cancer subtype prediction model associated with Cuproptosis. This model could be used to determine the subtype of breast cancer patients. To achieve this aim, 21 Cuproptosis-related genes were obtained from published articles and correlation analysis was performed with ncRNAs differentially expressed in breast cancer. Random forest algorithms were subsequently utilized to select important ncRNAs and build breast cancer subtype prediction models.

Results: A total of 94 ncRNAs significantly associated with Cuproptosis were obtained and the top five essential features were chosen to build a predictive model. These five biomarkers were differentially expressed in the five breast cancer subtypes and were closely associated with immune infiltration, RNA modification, and angiogenesis.

Conclusion: The random forest model constructed based on Cuproptosis-related ncRNAs was able to accurately predict breast cancer subtypes, providing a new direction for the study of clinical therapeutic targets.

Citing Articles

Identification and characterization of cuproptosis related gene subtypes through multi-omics bioinformatics analysis in breast cancer.

Fang D, Zhou Y, Liao F, Lu B, Li Y, Lv M Discov Oncol. 2025; 16(1):171.

PMID: 39945992 PMC: 11825418. DOI: 10.1007/s12672-025-01952-2.

Elucidating the evolving role of cuproptosis in breast cancer progression.

Zhu Z, Zhu K, Zhang J, Zhou Y, Zhang Q Int J Biol Sci. 2024; 20(12):4872-4887.

PMID: 39309446 PMC: 11414396. DOI: 10.7150/ijbs.98806.

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