A Novel Effective Models for Identifying BRCA Patients and Optimizing Clinical Treatments.

IF 2.6 4区医学 Q3 CHEMISTRY, MEDICINAL

Anti-cancer agents in medicinal chemistry Pub Date : 2024-12-17 DOI:10.2174/0118715206336019241119070155

Yi Luo, Li Liu, Zeyu Hou, Daigang Xiong, Rui Chen

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引用次数: 0

Abstract

Objective: This study aimed to develop an effective model that identifies high-risk breast cancer (BRCA) patients and optimizes clinical treatments.

Methods: This study includes five public datasets, TCGA-BRCA as the training dataset and other cohorts as the validation datasets. Machine learning algorithms for finding key tumor-associated immune gene pairs (TAIGPs). These TAIGPs were used to construct tumor-associated immune gene pair index (TAIGPI) by multivariate analysis and further validated on the validation datasets. In addition, the differences in clinical prognosis, biological characteristics, and treatment benefits between high and low TAIGPI groups were further analyzed.

Results: The TAIGPI was established by 36 TAIGPs. Better clinical outcomes in the low TAIGPI patients, with consistent results, were also obtained in the validation datasets. The study showed that patients in the low TAIGPI group had a high infiltration of immune cells and low proliferative activity of tumor cells. In contrast, patients in the high TAIGPI group exhibited low infiltration of immune cells and high proliferative activity of tumor cells. In addition, patients in the low TAIGPI group are more likely to benefit from chemotherapy, adjuvant chemotherapy, or immunotherapy.

Conclusions: The TAIGPI can be an effective predictive strategy for the clinical prognosis of breast cancer patients, providing new insights into personalized treatment options for breast cancer patients.

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一种新的识别BRCA患者和优化临床治疗的有效模型。

目的：本研究旨在建立一种有效的识别高危乳腺癌（BRCA）患者并优化临床治疗的模型。方法：本研究包括5个公共数据集，TCGA-BRCA作为训练数据集，其他队列作为验证数据集。寻找关键肿瘤相关免疫基因对（TAIGPs）的机器学习算法。通过多变量分析构建肿瘤相关免疫基因对指数（TAIGPI），并在验证数据集上进一步验证。并进一步分析高、低TAIGPI组在临床预后、生物学特性、治疗获益等方面的差异。结果：36个TAIGPs建立TAIGPI。在验证数据集中，低TAIGPI患者也获得了更好的临床结果，结果一致。研究表明，低TAIGPI组患者免疫细胞浸润高，肿瘤细胞增殖活性低。相反，高TAIGPI组患者表现出低免疫细胞浸润和高肿瘤细胞增殖活性。此外，低TAIGPI组的患者更有可能从化疗、辅助化疗或免疫治疗中获益。结论：TAIGPI可作为乳腺癌患者临床预后的有效预测策略，为乳腺癌患者的个性化治疗方案提供新的见解。

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

Anti-cancer agents in medicinal chemistry ONCOLOGY-CHEMISTRY, MEDICINAL

CiteScore

5.10

自引率

3.60%

发文量

323

审稿时长

4-8 weeks

期刊介绍： Formerly: Current Medicinal Chemistry - Anti-Cancer Agents. Anti-Cancer Agents in Medicinal Chemistry aims to cover all the latest and outstanding developments in medicinal chemistry and rational drug design for the discovery of anti-cancer agents. Each issue contains a series of timely in-depth reviews and guest edited issues written by leaders in the field covering a range of current topics in cancer medicinal chemistry. The journal only considers high quality research papers for publication. Anti-Cancer Agents in Medicinal Chemistry is an essential journal for every medicinal chemist who wishes to be kept informed and up-to-date with the latest and most important developments in cancer drug discovery.