Wu Zhixuan, Guo Yangyang, Qu Liangchen, Wang Xiaowu, Zhang Hewei
Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, First Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang Province, 325035, China.
Emergency Department, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang Province, 318000, China.
Sci Rep. 2024 Sep 16;14(1):21619. doi: 10.1038/s41598-024-72635-1.
The individual variation of carcinogenesis and drug response is influenced by the absorption, distribution, metabolism, and excretion (ADME) of drugs. The utilization of signatures derived from ADME-related genes holds potential for predicting prognosis and treatment response across diverse cancer types. Further investigation is required to completely understand the role of ADME-associated genes in breast cancer. A signature was constructed through the application of a least absolute shrinkage and selection operator regression model, employing prognostic differentially expressed genes found in both cancer tissue and normal tissue. To assess the robustness of the signature, verification analyses were carried out. RT-qPCR was utilized for the validation of gene expression related to risk. Subsequently, a nomogram was developed to enhance the clinical utility of our prognostic tool. The ADME signature, comprising four genes, was established and exhibited a robust association with the prognoses of individuals diagnosed with breast cancer. The nomogram was created by fusing the clinicopathological characteristics with the ADME signature. The ADME signature demonstrated remarkable superiority when compared to the performance of the other individual predictors. Additionally, the analysis of the immune microenvironment revealed that the ImmuneScores of the low-risk group were elevated. The variation in both the infiltration of immune cells and the expression of immune-related genes in the tissues differed among the two groups. For patients with breast cancer, the utilization of ADME signatures as biomarkers presents a significant reference point for prognosis and individualized treatment strategies.
癌症发生和药物反应的个体差异受药物的吸收、分布、代谢和排泄（ADME）影响。利用源自ADME相关基因的特征对于预测不同癌症类型的预后和治疗反应具有潜力。需要进一步研究以全面了解ADME相关基因在乳腺癌中的作用。通过应用最小绝对收缩和选择算子回归模型，利用在癌组织和正常组织中均发现的预后差异表达基因构建了一个特征。为评估该特征的稳健性，进行了验证分析。采用RT-qPCR验证与风险相关的基因表达。随后，开发了列线图以提高我们的预后工具的临床实用性。建立了包含四个基因的ADME特征，其与乳腺癌患者的预后显示出稳健的关联。通过将临床病理特征与ADME特征融合创建了列线图。与其他个体预测指标的性能相比，ADME特征表现出显著优势。此外，免疫微环境分析显示低风险组的免疫评分升高。两组之间组织中免疫细胞浸润和免疫相关基因表达的变化存在差异。对于乳腺癌患者，利用ADME特征作为生物标志物为预后和个体化治疗策略提供了重要参考点。
