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<p>A pivotal determinant of tumor therapy efficacy lies in the absorption, distribution, metabolism, and excretion (ADME) processes that govern drug disposition within the body. We intended to establish a prognostic model incorporating ADME-related genes to forecast the survival rate and therapeutic response in gastric cancer (GC) patients. By integrating Cox regression and LASSO analysis for dimensionality reduction and feature selection, we identified a stable five-gene signature with significant prognostic value. Subsequently, the stability of the model was verified. A nomogram incorporating these genes was constructed and integrated with a clinicopathological feature prediction system to improve its clinical applicability. The results revealed a robust correlation between ADME-related genes and the survival outcomes of GC patients. The ADME-based gene signature serves as a robust prognostic biomarker for evaluating the survival outcomes. Furthermore, immune cell infiltration and functional analyses demonstrated distinct patterns between the two strata, with the high-risk stratum exhibiting superior drug sensitivity. Finally, in vitro validation experiments using AGS and HGC-27 cell lines confirmed that elevated <i>CYP2A6</i> expression promotes the progression of GC. This finding indicates that <i>CYP2A6</i> may be a new biomarker in the therapeutic management of the disease.</p>

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Development of an ADME gene signature for prognostic and therapeutic stratification in gastric cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月15日

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  • Dandan Wang,
  • Xuejian Wang,
  • Lin Chen,
  • Huiying Wang,
  • Xiangfei Meng,
  • Zhenwei Miao,
  • Yang Zhao

摘要

A pivotal determinant of tumor therapy efficacy lies in the absorption, distribution, metabolism, and excretion (ADME) processes that govern drug disposition within the body. We intended to establish a prognostic model incorporating ADME-related genes to forecast the survival rate and therapeutic response in gastric cancer (GC) patients. By integrating Cox regression and LASSO analysis for dimensionality reduction and feature selection, we identified a stable five-gene signature with significant prognostic value. Subsequently, the stability of the model was verified. A nomogram incorporating these genes was constructed and integrated with a clinicopathological feature prediction system to improve its clinical applicability. The results revealed a robust correlation between ADME-related genes and the survival outcomes of GC patients. The ADME-based gene signature serves as a robust prognostic biomarker for evaluating the survival outcomes. Furthermore, immune cell infiltration and functional analyses demonstrated distinct patterns between the two strata, with the high-risk stratum exhibiting superior drug sensitivity. Finally, in vitro validation experiments using AGS and HGC-27 cell lines confirmed that elevated CYP2A6 expression promotes the progression of GC. This finding indicates that CYP2A6 may be a new biomarker in the therapeutic management of the disease.