Abstract
Breast cancer (BC) is the malignancy with the highest mortality rate among women, identification of immune-related biomarkers facilitates precise diagnosis and improvement of the survival rate in early-stage BC patients. 38 hub genes significantly positively correlated with tumor grade were identified based on weighted gene coexpression network analysis (WGCNA) by integrating the clinical traits and transcriptome analysis. Six candidate genes were screened from 38 hub genes basing on least absolute shrinkage and selection operator (LASSO)-Cox and random forest. Four upregulated genes (CDC20, CDCA5, TTK and UBE2C) were identified as biomarkers with the log-rank p < 0.05, in which high expression levels of them showed a poor overall survival (OS) and recurrence-free survival (RFS). A risk model was finally constructed using LASSO-Cox regression coefficients and it possessed superior capability to identify high risk patients and predict OS (p < 0.0001, AUC at 1-, 3- and 5-years are 0.81, 0.73 and 0.79, respectively). Decision curve analysis demonstrated risk score was the best prognostic predictor, and low risk represented a longer survival time and lower tumor grade. Importantly, multiple immune cell types and immunotherapy targets were observed increase in expression levels in high-risk group, most of which were significantly correlated with four genes. In summary, the immune-related biomarkers could accurately predict the prognosis and character the immune responses in BC patients. In addition, the risk model is conducive to the tiered diagnosis and treatment of BC patients.
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All the datasets in this study were downloaded from public databases (GEO, https://www.ncbi.nlm.nih.gov/geo/), and the current research follows the public databases’ access policies and publications guidelines. Users can download relevant data for free, our study is based on open-source data, there are no ethical issues and other conflicts of interest.
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Funding
This study was financially supported by Fundamental Research Funds for Henan University of Science and Technology (QNY, grant No. 13510001) and A-type Doctoral Talent Project of Henan University of Science and Technology (XWZ, grant No. 13480038).
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XWZ, QNY and WBH planned the research concept and designed it, made provisions for study material. QNY and XWZ collected data and analyzed them, and wrote and approved the manuscript. XWZ and HDM searched for data, wrote programming code, and collected pictures and graphs. JXW provided the help for statistical analysis. YRD, MMF, JXW, JL, MH and AMY helped correct the manuscript. XX provided the valuable comments in revised manuscript. All authors contributed to the article and approved the submitted version.
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Zheng, X., Ma, H., Dong, Y. et al. Immune-related biomarkers predict the prognosis and immune response of breast cancer based on bioinformatic analysis and machine learning. Funct Integr Genomics 23, 201 (2023). https://doi.org/10.1007/s10142-023-01124-x
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DOI: https://doi.org/10.1007/s10142-023-01124-x