Zhou Jian, Qian Weiwei, Huang Cuiliu, Mai Cunjun, Lai Yimei, Lin Zhiqin, Lai Guie
Department of Neurosurgery Center, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Emergency Department, Shangjinnanfu Hospital, West China Hospital, Sichuan University, Chengdu, China.
Gland Surg. 2022 Oct;11(10):1673-1682. doi: 10.21037/gs-22-486.
Breast cancers characterized by triple-negative status tend to be more malignant and have a poorer prognosis. A risk model for predicting breast cancer risk should be developed.
We obtained gene expression and clinical characteristics data using the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and The Cancer Genome Atlas (TCGA) database. Differential gene screening between patients with triple-negative breast cancer (TNBC) and non-triple-negative breast cancers (NTNBC) was performed according to the "edgeR" filter criteria. Univariate and multivariate Cox regression analyses were used to construct a risk model and identify prognosis-related genes. XCELL, TIMER, EPIC, QUANTISEQ, MCPCOUNTER, EPIC, CIBERSORT-ABS, and CIBERSORT software programs were used to determine the extent of tumor immune cell infiltration. To evaluate the clinical responses to breast cancer treatment, the half maximal inhibitory concentration (IC50s) of common chemotherapeutics were calculated using "pRRophetic" and "ggplot2". Cell proliferation was assayed using cell counting kit-8 (CCK8) and 5-Ethynyl-2'-deoxyuridine (EdU) Cell Proliferation Kit. A dual-luciferase reporter assay confirmed the gene regulatory relationship of sex determining region Y-box 10 (SOX10).
An assessment model was established for Keratin23 (KRT23) and non-specific cytotoxic cell receptor 1 (NCCRP1) using the univariate and multivariate Cox regression analyses. In addition, high expression levels of KRT23 and NCCRP1 indicated high proliferation and poor prognosis. We also found that the gene expression patterns of multiple genes were significantly more predictive of risks and have a higher level of consistency when assessing risk. experiments showed that the expressions of KRT23 and NCCRP1 were increased in TNBCs and promoted cell proliferation. Mechanically, the dual-luciferase reporter assay confirmed that SOX10 regulated the expressions of KRT23 and NCCRP1. The risk score model revealed a close relationship between the expressions of KRT23 and NCCRP1, the tumor immune microenvironment, and chemotherapeutics.
In conclusion, we constructed a risk assessment model to predict the risk of TNBC patients, which acted as a potential predictor for chemosensitivity.
三阴性乳腺癌具有更高的恶性程度和更差的预后。因此，需要建立一个预测乳腺癌风险的模型。
我们使用临床蛋白质组肿瘤分析联盟（CPTAC）和癌症基因组图谱（TCGA）数据库获取基因表达和临床特征数据。根据“edgeR”筛选标准，对三阴性乳腺癌（TNBC）患者和非三阴性乳腺癌（NTNBC）患者进行差异基因筛选。使用单变量和多变量Cox回归分析构建风险模型并识别预后相关基因。使用XCELL、TIMER、EPIC、QUANTISEQ、MCPCOUNTER、EPIC、CIBERSORT-ABS和CIBERSORT软件程序来确定肿瘤免疫细胞浸润程度。为了评估乳腺癌治疗的临床反应，使用“pRRophetic”和“ggplot2”计算常用化疗药物的半数最大抑制浓度（IC50）。使用细胞计数试剂盒-8（CCK8）和5-乙炔基-2'-脱氧尿苷（EdU）细胞增殖试剂盒检测细胞增殖。双荧光素酶报告基因检测证实了性别决定区Y盒10（SOX10）的基因调控关系。
通过单变量和多变量Cox回归分析，建立了角蛋白23（KRT23）和非特异性细胞毒性细胞受体1（NCCRP1）的评估模型。此外，KRT23和NCCRP1的高表达表明高增殖和预后不良。我们还发现，在评估风险时，多个基因的基因表达模式对风险的预测性更强，一致性更高。实验表明，KRT23和NCCRP1在TNBC中表达增加并促进细胞增殖。机制上，双荧光素酶报告基因检测证实SOX10调节KRT23和NCCRP1的表达。风险评分模型揭示了KRT23和NCCRP1的表达、肿瘤免疫微环境和化疗药物之间的密切关系。
总之，我们构建了一个风险评估模型来预测TNBC患者的风险，该模型可作为化疗敏感性的潜在预测指标。
