Medical College, Jiangsu Vocational College of Medicine, YanCheng 224000, Jiangsu, China.
Contrast Media Mol Imaging. 2022 Jul 11;2022:8348780. doi: 10.1155/2022/8348780. eCollection 2022.
Predicting the risk of poor prognosis of breast cancer is crucial to treating breast cancer. This study investigated the prognostic assessment of 10 lipid metabolism-related genes constructed as breast cancer models based on this study.
The TCGA database was used to obtain clinical information and expression data of breast cancer patients, and GSEA analysis and univariate and multivariate Cox proportional risk regression models were performed to identify lipid metabolism genes closely associated with overall survival (OS) of breast cancer patients and to construct a prognostic risk score model based on lipid metabolism gene markers. The Kaplan-Meier method was used to analyze the survival status of patients with high and low-risk scores, and ROC curves assessed the accuracy of this risk score. Finally, the relationship between this risk score and clinicopathological characteristics of BRCA was analyzed in a stratified manner, and the validity of this risk score as an independent prognostic factor was determined using univariate and multivariate Cox regression analyses.
One hundred and forty-four differentially expressed lipid metabolism-related genes were identified in cancer and paracancerous tissues in BRCA, 21 of which were associated with overall survival (OS) in BRCA ( < 0.05). Univariate and multivariate Cox analyses revealed that age, grade, and risk score were independent prognostic factors for BRCA. Multivariate Cox regression analysis further identified APOL4, NR1H3, SLC25A5, APOL3, OSBPL1A, DYNLT1, IMMT, MAP2K6, ZDHHC8, and RAB2A lipid metabolism-related genes as independent prognostic markers for BRCA. A prognostic risk score model was developed by labeling lipid metabolism genes with these 10 genes, and patients with BRCA with high-risk scores in the model sample had significantly worse OS than those with low-risk ( < 0.01). The ROC curve area (AUC) of this risk score model was 0.712.
By mining the TCGA database, we identified 10 lipid metabolism-related genes APOL4, NR1H3, SLC25A5, APOL3, OSBPL1A, DYNLT1, IMMT, MAP2K6, ZDHHC8, and RAB2A, which are closely related to the prognosis of BRCA patients, and constructed a prognostic risk scoring system based on 10 lipid metabolism genes tags.
预测乳腺癌不良预后的风险对于治疗乳腺癌至关重要。本研究基于此研究构建了基于 10 个脂质代谢相关基因的乳腺癌模型，对其预后评估进行了研究。
利用 TCGA 数据库获取乳腺癌患者的临床信息和表达数据，进行 GSEA 分析及单因素和多因素 Cox 比例风险回归模型分析，筛选出与乳腺癌患者总生存期（OS）密切相关的脂质代谢基因，并基于脂质代谢基因标志物构建预后风险评分模型。采用 Kaplan-Meier 法分析高低风险评分患者的生存状态，ROC 曲线评估该风险评分的准确性。最后，对 BRCA 中该风险评分与临床病理特征的关系进行分层分析，并采用单因素和多因素 Cox 回归分析确定该风险评分作为独立预后因素的有效性。
在 BRCA 的癌与癌旁组织中鉴定出 144 个差异表达的脂质代谢相关基因，其中 21 个与 BRCA 的总生存（OS）相关（<0.05）。单因素和多因素 Cox 分析显示，年龄、分级和风险评分是 BRCA 的独立预后因素。多因素 Cox 回归分析进一步确定了 APOL4、NR1H3、SLC25A5、APOL3、OSBPL1A、DYNLT1、IMMT、MAP2K6、ZDHHC8 和 RAB2A 等脂质代谢相关基因是 BRCA 的独立预后标志物。通过标记该模型样本中具有这 10 个基因的脂质代谢基因，构建了一个预后风险评分模型，该模型中高风险评分的 BRCA 患者的 OS 明显差于低风险评分的患者（<0.01）。该风险评分模型的 ROC 曲线下面积（AUC）为 0.712。
通过挖掘 TCGA 数据库，我们确定了与 BRCA 患者预后密切相关的 10 个脂质代谢相关基因 APOL4、NR1H3、SLC25A5、APOL3、OSBPL1A、DYNLT1、IMMT、MAP2K6、ZDHHC8 和 RAB2A，并构建了基于 10 个脂质代谢基因标签的预后风险评分系统。
