PDF(Pubmed)
Abstract：
BACKGROUND: Breast cancer is the most common malignancy in women, with its prognosis varying greatly according to its subtype. Triple-negative breast cancer (TNBC) has the worst prognosis among all subtypes. Glycosylation is a critical factor influencing the prognosis of patients with TNBC. Our aim is to develop a tumor prognosis model by analyzing genes related to glycosylation to predict patient outcomes.
METHODS: The dataset used in this study was downloaded from the Cancer Genome Atlas Program (TCGA) database, and predictive genes were identified through Cox one-way regression analysis. The model genes with the highest risk scores among the 18 samples were obtained by lasso regression analysis to establish the model. We analyzed the pathways affecting the progression of TNBC and discovered key genes for subsequent research.
RESULTS: Our model was constructed using data from TCGA database and validated through Kaplan-Meier curve analysis and Receiver Operating Characteristic (ROC) curve assessment. Our analysis revealed that a high expression of tumor-related chemokines in the high-risk group may be associated with poor tumor prognosis. Furthermore, we conducted a random survival forest analysis and identified two significant genes, namely DPM2 and PINK1, which have been selected for further investigation.
CONCLUSIONS: The prognostic analysis model, developed based on the glycosylation genes in TNBC, exhibits excellent validation efficacy. This model is valuable for the prognostic analysis of patients with TNBC.
METHODS: The dataset used in this study was downloaded from the Cancer Genome Atlas Program (TCGA) database, and predictive genes were identified through Cox one-way regression analysis. The model genes with the highest risk scores among the 18 samples were obtained by lasso regression analysis to establish the model. We analyzed the pathways affecting the progression of TNBC and discovered key genes for subsequent research.
RESULTS: Our model was constructed using data from TCGA database and validated through Kaplan-Meier curve analysis and Receiver Operating Characteristic (ROC) curve assessment. Our analysis revealed that a high expression of tumor-related chemokines in the high-risk group may be associated with poor tumor prognosis. Furthermore, we conducted a random survival forest analysis and identified two significant genes, namely DPM2 and PINK1, which have been selected for further investigation.
CONCLUSIONS: The prognostic analysis model, developed based on the glycosylation genes in TNBC, exhibits excellent validation efficacy. This model is valuable for the prognostic analysis of patients with TNBC.
摘要：
背景：乳腺癌是女性最常见的恶性肿瘤，其预后根据其亚型差异很大。三阴性乳腺癌（TNBC）在所有亚型中预后最差。糖基化是影响TNBC患者预后的关键因素。我们的目的是通过分析与糖基化相关的基因来预测患者的预后，从而建立肿瘤预后模型。
方法：本研究中使用的数据集从癌症基因组图谱计划（TCGA）数据库下载，并通过Cox单向回归分析鉴定预测基因。通过Lasso回归分析获得18个样本中风险得分最高的模型基因建立模型。我们分析了影响TNBC进展的途径，并发现了后续研究的关键基因。
结果：我们的模型是使用TCGA数据库的数据构建的，并通过Kaplan-Meier曲线分析和受试者工作特征（ROC）曲线评估进行了验证。我们的分析表明，高危人群中肿瘤相关趋化因子的高表达可能与不良肿瘤预后有关。此外，我们进行了随机生存森林分析，并确定了两个重要的基因，即DPM2和PINK1，已被选择用于进一步研究。
结论：预后分析模型，基于TNBC中的糖基化基因开发，表现出优异的验证功效。该模型对TNBC患者的预后分析具有重要价值。
方法：本研究中使用的数据集从癌症基因组图谱计划（TCGA）数据库下载，并通过Cox单向回归分析鉴定预测基因。通过Lasso回归分析获得18个样本中风险得分最高的模型基因建立模型。我们分析了影响TNBC进展的途径，并发现了后续研究的关键基因。
结果：我们的模型是使用TCGA数据库的数据构建的，并通过Kaplan-Meier曲线分析和受试者工作特征（ROC）曲线评估进行了验证。我们的分析表明，高危人群中肿瘤相关趋化因子的高表达可能与不良肿瘤预后有关。此外，我们进行了随机生存森林分析，并确定了两个重要的基因，即DPM2和PINK1，已被选择用于进一步研究。
结论：预后分析模型，基于TNBC中的糖基化基因开发，表现出优异的验证功效。该模型对TNBC患者的预后分析具有重要价值。
