Wang Ye, Zhong Pei, Wang Congjun, Huang Weijia, Yang Hong
Internet Hospital Operation Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
First clinical college of medicine, Guangxi Medical University, Nanning, China.
BMC Cancer. 2024 Dec 18;24(1):1533. doi: 10.1186/s12885-024-13326-y.
Sarcopenia, an age-related syndrome characterized by a decline in muscle mass, not only affects patients' quality of life but may also increase the risk of breast cancer recurrence and reduce survival rates. Therefore, investigating the genetic mechanisms shared between breast cancer and sarcopenia is significant for the prevention, diagnosis, and treatment of breast cancer.
This study downloaded gene expression datasets and clinical data related to breast cancer and skeletal muscle aging from the GEO database. Data preprocessing, integration, differential gene identification, functional enrichment analysis, and construction of protein-protein interaction networks were performed using R language. Subsequently, COX proportional hazards model analysis and survival analysis were conducted, and survival curves and nomograms were generated. The expression levels of genes in tissues were detected using qRT-PCR, and the Radiant DICOM viewer software was used to delineate the pectoralis major muscle area in CT images.
We identified 152 differentially expressed genes (P < .05) and 226 sarcopenia-related genes (r > .4) associated with skeletal muscle aging. The TCGA-BRCA dataset revealed 106 genes associated with breast cancer (P < .05, logFC = 1). Functional enrichment analysis indicated significant enrichment in cell proliferation and growth pathways. The PPI network identified critical molecules involved in muscle aging and tumor progression. After dimensionality reduction, a strong correlation was observed between the expression of the muscle aging-related gene set and the prognosis of breast cancer patients (P < .01). The expression of SLC38A1 identified through multivariate COX analysis was significantly associated with poor prognosis in breast cancer patients (P = .03). Incorporating SLC38A1 expression, the prognostic model precisely forecasted breast cancer survival (P < .01). External validation confirmed the higher expression of the SLC38A1 gene in breast cancer tissues compared to adjacent non-cancerous tissues (P < .01). The SLC38A1 index, calculated in combination with the patient's age and BMI, can optimize the prognostic prediction model, providing a powerful tool for personalized treatment of breast cancer.
High SLC38A1 gene expression was significantly associated with poor prognosis in breast cancer patients. The combination of SLC38A1 expression and the pectoralis major muscle area provided an optimized prognostic prediction model, offering a potential tool for personalized breast cancer treatment.
肌肉减少症是一种与年龄相关的综合征，其特征是肌肉量下降，不仅影响患者的生活质量，还可能增加乳腺癌复发风险并降低生存率。因此，研究乳腺癌与肌肉减少症之间共享的遗传机制对乳腺癌的预防、诊断和治疗具有重要意义。
本研究从GEO数据库下载了与乳腺癌和骨骼肌衰老相关的基因表达数据集及临床数据。使用R语言进行数据预处理、整合、差异基因鉴定、功能富集分析以及蛋白质-蛋白质相互作用网络构建。随后进行COX比例风险模型分析和生存分析，并生成生存曲线和列线图。使用qRT-PCR检测组织中基因的表达水平，并使用Radiant DICOM viewer软件在CT图像中勾勒胸大肌区域。
我们鉴定出152个差异表达基因（P < 0.05）和226个与骨骼肌衰老相关的基因（r > 0.4）。TCGA-BRCA数据集显示106个与乳腺癌相关的基因（P < 0.05，logFC = 1）。功能富集分析表明在细胞增殖和生长途径中显著富集。蛋白质-蛋白质相互作用网络确定了参与肌肉衰老和肿瘤进展的关键分子。降维后，观察到肌肉衰老相关基因集的表达与乳腺癌患者的预后之间存在强相关性（P < 0.01）。通过多变量COX分析鉴定出的SLC38A1的表达与乳腺癌患者的不良预后显著相关（P = 0.03）。纳入SLC38A1表达后，预后模型精确预测了乳腺癌生存率（P < 0.01）。外部验证证实乳腺癌组织中SLC38A1基因的表达高于相邻非癌组织（P < 0.01）。结合患者年龄和BMI计算的SLC38A1指数可优化预后预测模型，为乳腺癌个性化治疗提供有力工具。
高SLC38A1基因表达与乳腺癌患者的不良预后显著相关。SLC38A1表达与胸大肌区域的结合提供了优化的预后预测模型，为乳腺癌个性化治疗提供了潜在工具。
