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Background <p>Luminal B breast cancer (LBBC) is characterized by marked molecular heterogeneity and unfavorable therapeutic outcomes, underscoring the need for concise and biologically interpretable biomarkers that can support reliable prognostic stratification. However, existing biomarker signatures are often redundant, weakly generalizable, and insufficiently validated in multi-omics regulatory consistency.</p> Methods <p>Using a minimum prognostic-redundancy feature identification strategy, we identified an immune-related four-gene prognostic biomarker signature and determined its optimal classification scheme. A molecular subtyping model was subsequently developed and externally validated to distinguish two prognostic immune subtypes: the immune-barren type (IBT) and the immune-enriched type (IET). Functional enrichment analysis, tumor immune microenvironment profiling, and multi-omics integrative consistency assessments were performed to elucidate the biological characteristics and regulatory underpinnings of the predicted subtypes. Moreover, the clinical independence, prognostic robustness, and cross-cohort reproducibility of the model were comprehensively evaluated, and independent clinical variables were incorporated to construct an individualized decision-support tool.</p> Results <p>The four-gene biomarker signature demonstrated strong prognostic discrimination and subtype classification capability (max AUC = 0.86, <i>P</i> &lt; 0.05). Across extensive multi-source external cohorts (<i>n</i> = 1,841), the molecular subtyping model robustly stratified patients into immune-related subtypes with significantly different survival outcomes and consistently high predictive performance (ACC ≥ 0.93, AUC ≥ 0.97, all <i>P</i> &lt; 0.001), while remaining independent of conventional clinical variables (<i>P</i> &lt; 0.001). Furthermore, the predicted subtypes exhibited concordant immune functional characteristics and multi-omics regulatory consistency (all <i>P</i> &lt; 0.05). In addition, the individualized decision-support tool showed significant prognostic relevance (<i>P</i> = 0.004) and favorable comparative performance relative to previously reported prognostic models (AUC = 0.86).</p> Conclusion <p>This immune-related molecular subtyping model, derived from a four-gene prognostic biomarker signature and supported by multi-omics integrative evidence, provides a robust and clinically independent model for prognostic stratification in LBBC. These findings offer a biologically interpretable basis for individualized risk assessment and may facilitate precision-oriented clinical decision support.</p>

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Development and validation of an immune-related molecular subtyping model based on a four-gene prognostic biomarker signature and multi-omics integrative analysis in Luminal B breast cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月10日

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  • Junqi Long,
  • Bo Liu,
  • Jianqiang Li,
  • Xin Wang,
  • Jiashuai Xu,
  • Gege Li,
  • Yining Chen,
  • Xiaohan Tian,
  • Shuangtao Zhao

摘要

Background

Luminal B breast cancer (LBBC) is characterized by marked molecular heterogeneity and unfavorable therapeutic outcomes, underscoring the need for concise and biologically interpretable biomarkers that can support reliable prognostic stratification. However, existing biomarker signatures are often redundant, weakly generalizable, and insufficiently validated in multi-omics regulatory consistency.

Methods

Using a minimum prognostic-redundancy feature identification strategy, we identified an immune-related four-gene prognostic biomarker signature and determined its optimal classification scheme. A molecular subtyping model was subsequently developed and externally validated to distinguish two prognostic immune subtypes: the immune-barren type (IBT) and the immune-enriched type (IET). Functional enrichment analysis, tumor immune microenvironment profiling, and multi-omics integrative consistency assessments were performed to elucidate the biological characteristics and regulatory underpinnings of the predicted subtypes. Moreover, the clinical independence, prognostic robustness, and cross-cohort reproducibility of the model were comprehensively evaluated, and independent clinical variables were incorporated to construct an individualized decision-support tool.

Results

The four-gene biomarker signature demonstrated strong prognostic discrimination and subtype classification capability (max AUC = 0.86, P < 0.05). Across extensive multi-source external cohorts (n = 1,841), the molecular subtyping model robustly stratified patients into immune-related subtypes with significantly different survival outcomes and consistently high predictive performance (ACC ≥ 0.93, AUC ≥ 0.97, all P < 0.001), while remaining independent of conventional clinical variables (P < 0.001). Furthermore, the predicted subtypes exhibited concordant immune functional characteristics and multi-omics regulatory consistency (all P < 0.05). In addition, the individualized decision-support tool showed significant prognostic relevance (P = 0.004) and favorable comparative performance relative to previously reported prognostic models (AUC = 0.86).

Conclusion

This immune-related molecular subtyping model, derived from a four-gene prognostic biomarker signature and supported by multi-omics integrative evidence, provides a robust and clinically independent model for prognostic stratification in LBBC. These findings offer a biologically interpretable basis for individualized risk assessment and may facilitate precision-oriented clinical decision support.