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<p>Oxidative stress influences the tumor microenvironment, driving breast cancer progression and drug resistance. This study aimed to develop a prognostic gene signature based on oxidative stress-related genes (OSRGs) to assess patient outcomes and immune status. UCSC Xena (<a href="http://xena.ucsc.edu/">http://xena.ucsc.edu/</a>) and GEO (<a href="https://www.ncbi.nlm.nih.gov/geo/">https://www.ncbi.nlm.nih.gov/geo/</a>) databases were used to obtain RNA-seq data and corresponding clinical information. The classification of OSRG subtypes was performed using consensus cluster. The oxidative stress related scoring (OSRS) model was established combining Lasso regression and multivariable Cox regression. The analysis of tumor mutation burden (TMB) and somatic mutation were carried out using the R package ‘maftools’. Python package ‘pySCENIC’ was used to construct and analyze the transcription factor network. Additionally, immune infiltration was analyzed using R packages ‘CIBERSORT’ and ‘ESTIMATE’. Three OSRG subgroups were identified and the Differentially Expressed Genes (DEGs) among them were enriched in humoral immunity, cytokine communication and drug metabolism pathways. OSRS model was established based on the DEGs and revealed association with patients’ overall survival, somatic mutations, immune statuses, and drug resistance. Finally, transcription factor TFAP2B was identified as a key regulatory factor in high OSRS cells, and associated with a negative prognostic outcome in Basal-like breast cancer patients.</p>

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Comprehensive analysis of breast cancer oxidative stress related gene signature: a combination of bulk and single-cell RNA sequencing analysis

  • 期刊论文

  • 发表日期：

    2025年4月24日

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  • Yuheng Shao,
  • Yumeng Zhang,
  • Jie Chen,
  • Liang Yang,
  • Meihong Wu,
  • Zhiyuan Fan,
  • Zhigang Zhuang

摘要

Oxidative stress influences the tumor microenvironment, driving breast cancer progression and drug resistance. This study aimed to develop a prognostic gene signature based on oxidative stress-related genes (OSRGs) to assess patient outcomes and immune status. UCSC Xena (http://xena.ucsc.edu/) and GEO (https://www.ncbi.nlm.nih.gov/geo/) databases were used to obtain RNA-seq data and corresponding clinical information. The classification of OSRG subtypes was performed using consensus cluster. The oxidative stress related scoring (OSRS) model was established combining Lasso regression and multivariable Cox regression. The analysis of tumor mutation burden (TMB) and somatic mutation were carried out using the R package ‘maftools’. Python package ‘pySCENIC’ was used to construct and analyze the transcription factor network. Additionally, immune infiltration was analyzed using R packages ‘CIBERSORT’ and ‘ESTIMATE’. Three OSRG subgroups were identified and the Differentially Expressed Genes (DEGs) among them were enriched in humoral immunity, cytokine communication and drug metabolism pathways. OSRS model was established based on the DEGs and revealed association with patients’ overall survival, somatic mutations, immune statuses, and drug resistance. Finally, transcription factor TFAP2B was identified as a key regulatory factor in high OSRS cells, and associated with a negative prognostic outcome in Basal-like breast cancer patients.