Development and validation of a breast cancer survival prediction model based on perioperative anesthesia-related drug target genes and analysis of immune microenvironment and drug sensitivity

IF 3.1 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry Pub Date : 2025-09-16 DOI:10.1016/j.compbiolchem.2025.108681

Dongmei Yu , Jiajia Li , Wenjing Ma , Yue Pei , Yingchao Qi , Tong Yu , Wenkai Li , Xiaohan Sun , Jingyan Zhang , Xuantonghe Li , Longyan Liang , Yunen Liu , Yichen Wang

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Abstract

Introduction

This study aimed to create a survival prediction model for breast cancer(BC) using perioperative anesthesia - related drug target genes(PARDTGs). It explored their immune microenvironment and drug sensitivity for personalized therapy.

Methods

Transcriptomic sequencing data of BC were downloaded from The Cancer Genome Atlas (TCGA) database. Common PARDTGs were retrieved from the DrugBank and ChemBL databases. Transcriptomic data were analyzed to identify differentially expressed PARDTGs (DE-PARDTGs) using rigorous statistical thresholds. A total of 101 machine learning algorithms were applied to construct PARDTG-based survival prediction models. Patients were stratified into high- and low-risk groups based on model-derived risk scores. Model performance was validated using an independent dataset from the Gene Expression Omnibus (GEO). Clinical-pathological correlations, immune profiling, and mutational landscapes were compared between risk groups in the TCGA-BRCA cohort. Drug sensitivity to commonly used therapies was predicted via transcriptomic correlations.

Results

We identified five DE - PARDTGs (PTGS2, TACR1, ADRB1, ABCB1, ACKR3) for a BC prognostic model. Receiver Operating Characteristic - Area Under the Curves（ROC - AUCs) for 1 -, 3 -, 5 - year overall survival(OS) were 0.722, 0.730, 0.691. TACR1 and ADRB1 high - expression meant better prognosis. Risk groups differed in immunity, with TACR1 correlating with immune checkpoints and drug sensitivity. Conclusions: The PARDTG - based model predicts BC survival independently. TACR1, key to immune response and drug sensitivity, could be a new therapeutic target. These results stress the importance of focusing on perioperative anesthesia - related drug targets in BC research.

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基于围手术期麻醉相关药物靶基因、免疫微环境及药物敏感性分析的乳腺癌生存预测模型的建立与验证

前言：本研究旨在利用围手术期麻醉相关药物靶基因（PARDTGs）建立乳腺癌（BC）患者的生存预测模型。探讨其免疫微环境和药物敏感性，为个体化治疗提供依据。方法：从癌症基因组图谱（TCGA）数据库下载BC的转录组测序数据。从DrugBank和ChemBL数据库中检索常见的partgs。使用严格的统计阈值分析转录组学数据以识别差异表达的PARDTGs （DE-PARDTGs）。共使用101种机器学习算法构建基于pardtg的生存预测模型。根据模型衍生的风险评分将患者分为高风险组和低风险组。使用来自基因表达Omnibus （GEO）的独立数据集验证模型的性能。比较TCGA-BRCA队列中危险组之间的临床病理相关性、免疫谱和突变景观。通过转录组学相关性预测对常用疗法的药物敏感性。结果：我们确定了5个DE - PARDTGs （PTGS2, TACR1, ADRB1, ABCB1, ACKR3）用于BC预后模型。1年、3年、5年总生存期（OS）的受试者工作特征曲线下面积（ROC - aus）分别为0.722、0.730、0.691。TACR1、ADRB1高表达预后较好。风险组在免疫方面存在差异，TACR1与免疫检查点和药物敏感性相关。结论：基于PARDTG的模型可独立预测BC存活。TACR1是免疫反应和药物敏感性的关键，可能成为新的治疗靶点。这些结果强调了在BC研究中关注围手术期麻醉相关药物靶点的重要性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computational Biology and Chemistry 生物-计算机：跨学科应用

CiteScore

6.10

自引率

3.20%

发文量

142

审稿时长

24 days

期刊介绍： Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions with a major computational component in the areas of nucleic acid and protein sequence research, molecular evolution, molecular genetics (functional genomics and proteomics), theory and practice of either biology-specific or chemical-biology-specific modeling, and structural biology of nucleic acids and proteins are particularly welcome. Exceptionally high quality research work in bioinformatics, systems biology, ecology, computational pharmacology, metabolism, biomedical engineering, epidemiology, and statistical genetics will also be considered. Given their inherent uncertainty, protein modeling and molecular docking studies should be thoroughly validated. In the absence of experimental results for validation, the use of molecular dynamics simulations along with detailed free energy calculations, for example, should be used as complementary techniques to support the major conclusions. Submissions of premature modeling exercises without additional biological insights will not be considered. Review articles will generally be commissioned by the editors and should not be submitted to the journal without explicit invitation. However prospective authors are welcome to send a brief (one to three pages) synopsis, which will be evaluated by the editors.