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中华乳腺病杂志(电子版)

中华乳腺病杂志(电子版) ›› 2022, Vol. 16 ›› Issue (02) : 74 -83. doi: 10.3877/cma.j.issn.1674-0807.2022.02.002

论著

基于NR3C2相关免疫调节基因的乳腺癌预后模型构建与验证
张哲1, 樊俊1, 刘文斌2, 徐琰1,†()   
  1. 1. 400042　重庆，陆军军医大学附属陆军特色医学中心乳腺甲状腺外科
    2. 400038　重庆，陆军军医大学军事预防医学院毒理学研究所
  • 收稿日期:2021-08-13 出版日期:2022-04-01
  • 通信作者: 徐琰
  • 基金资助:
    陆军军医大学临床技术创新培育项目(CX2019LC120)

Construction and validation of breast cancer prognostic model based on NR3C2-related immunomodulator genes

Zhe Zhang1, Jun Fan1, Wenbin Liu2, Yan Xu1,†()   

  1. 1. Department of Breast and Thyroid Surgery, Army Medical Center, Army Medical University, Chongqing 400042, China
    2. Institute of Toxicology, College of Preventive Medicine, Army Medical University, Chongqing 400038, China
  • Received:2021-08-13 Published:2022-04-01
  • Corresponding author: Yan Xu
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引用本文：

张哲, 樊俊, 刘文斌, 徐琰. 基于NR3C2相关免疫调节基因的乳腺癌预后模型构建与验证[J]. 中华乳腺病杂志(电子版), 2022, 16(02): 74-83.

Zhe Zhang, Jun Fan, Wenbin Liu, Yan Xu. Construction and validation of breast cancer prognostic model based on NR3C2-related immunomodulator genes[J]. Chinese Journal of Breast Disease(Electronic Edition), 2022, 16(02): 74-83.

目的

通过生物信息学方法分析NR3C2基因在乳腺癌中的免疫作用并构建预后模型。

方法

(1)分别以癌症基因组图谱(TCGA)数据库中的乳腺癌队列和基因综合表达(GEO)数据库中的GSE42568队列作为训练集(113例癌旁样本和1 019例乳腺癌样本)和测试集(17例癌旁样本和104例乳腺癌样本)，比较上述2个队列中NR3C2在癌旁样本和乳腺癌样本中的mRNA表达；通过TCGA队列、Kaplan-Meier plotter队列(4 929例乳腺癌样本)分析NR3C2表达对无复发生存期(RFS)的影响。(2)利用基因集富集分析(GSEA)探讨NR3C2潜在的生物学功能，通过单样本基因集富集分析(ssGSEA)定量评估24种免疫细胞，以皮尔森系数计算NR3C2和24种免疫细胞及70个免疫调节基因的相关性。(3)通过多元逐步Cox回归的方法在TCGA队列中构建NR3C2相关免疫调节基因的预后模型，按照中位风险值将TCGA队列分为高、低风险组，比较2组的无复发生存率，采用受试者操作特征曲线(ROC)计算模型的敏感度和特异度，并在GSE42568队列中进行验证；结合其他临床参数，通过多因素Cox回归分析该模型的独立预后性能。(4)在TCGA队列中，基于临床分期和风险值构建列线图，利用校准曲线对其准确性进行评价，通过时间相关曲线下面积(tAUC)比较不同指标预测的准确度。(5)为了验证NR3C2基因在mRNA和蛋白水平上的表达是否一致，本研究另外收集了2021年9月于陆军特色医学中心乳腺甲状腺外科进行手术切除的3例乳腺癌患者的临床组织样本，通过Western blot实验检测其癌旁组织和癌组织中NR3C2的蛋白表达量。

结果

(1)TCGA样本分析结果显示：与癌旁组织相比，NR3C2的mRNA表达量在乳腺癌组织中显著下降(2.59±0.43比0.98±0.62，t＝35.990，P<0.001)。在GSE42568队列中，乳腺癌组织中NR3C2的mRNA表达量比癌旁组织显著下降(5.35±1.47比3.32±1.12，t＝7.096，P<0.001)。生存分析结果显示：在TCGA队列、Kaplan-Meier plotter队列中，NR3C2表达和乳腺癌患者的RFS呈正相关(HR＝0.667、0.725，95%CI：0.458~0.972、0.653~0.804，P均<0.050)。(2)GSEA结果提示：NR3C2主要参与JAK-STAT和TGF-β等免疫相关信号通路。相关性分析发现：NR3C2的mRNA表达和19种免疫细胞的浸润程度及43个免疫调节基因的表达均显著相关(P均<0.050)。(3)将上述43个NR3C2相关的免疫调节基因纳入Cox回归分析，构建了13个免疫调节基因组成的预后模型，风险截断值为0.988。生存分析提示在TCGA队列及GSE42568队列中，高风险组的RFS明显低于低风险组(HR＝2.682、2.389，95%CI：1.839~3.910、1.343~4.248，P均<0.010)；AUC为0.758、0.618(95%CI：0.662~0.857、0.545~0.758，敏感度：0.833、0.538，特异度：0.614、0.714，P均<0.010)。多因素Cox回归分析发现该模型的风险值可作为乳腺癌独立的预后因子(HR＝1.259、1.163，95%CI：1.187~1.336、1.068~1.266，P均<0.001)。(4)基于临床分期和风险值构建的列线图可以预测乳腺癌患者3年、5年和8年的RFS，校准曲线提示其具有较好的预测准确性，tAUC提示其优于临床分期和预后模型。(5)Western blot实验结果显示：NR3C2的蛋白表达量在乳腺癌组织中显著降低。

结论

NR3C2是乳腺癌患者潜在的免疫治疗靶点和预后生物标志物。

关键词: 乳腺肿瘤, 免疫, 预后, NR3C2蛋白，人
Objective

To analyze the immune implication of NR3C2 gene in breast cancer using bioinformatics methods and construct a prognostic model.

Methods

(1) The breast cancer cohort in the Cancer Genome Atlas (TCGA) database and the GSE42568 cohort in the Gene Expression Omnibus (GEO) database were used as training set (113 paracancerous samples and 1 019 breast cancer samples) and testing set (17 paracancerous samples and 104 breast cancer samples), respectively. The mRNA level of NR3C2 was compared between adjacent tissue and tumor tissue samples in the above 2 cohorts. The effect of NR3C2 expression on recurrence-free survival (RFS) was analyzed in cohorts of TCGA and Kaplan-Meier plotter(4 929 breast cancer samples), respectively. (2) The Gene Set Enrichment Analysis (GSEA) was employed to explore the potential biological functions of NR3C2. The 24 immune cells were quantitively accessed by single sample gene set enrichment analysis (ssGSEA) and the correlation of NR3C2 with 24 immune cells and 70 immunomodulator genes were conducted by Pearson coefficient. (3) A prognostic model was constructed by NR3C2-related immunomodulator genes in the TCGA cohort through multivariate stepwise Cox regression. The TCGA cohort was divided into two groups (high-risk group and low-risk group) by median risk value and RFS was compared between two groups. The sensitivity and specificity of the model was calculated using receiver operating characteristic (ROC) curves and validated in the GSE42568 cohort. Combined with other clinical parameters, the independent prognostic performance of this model was analyzed by multivariate Cox regression. (4) A nomogram was constructed based on pathological stage and risk value in TCGA cohort. The calibration curve was used to evaluate its accuracy and the predictive accuracy of different parameters was compared by time-dependent area under curve (tAUC). (5) In order to verify whether the NR3C2 mRNA expression is consistent with NR3C2 protein expression, we collected clinical specimens from three breast cancer patients who underwent surgical resection in the Department of Breast and Thyroid Surgery of the Army Medical Center in September 2021. The protein expression of NR3C2 in the paracancerous tissue and cancer tissue was detected by Western blot analysis.

Results

(1) In TCGA cohort, the mRNA expression of NR3C2 in breast cancer tissue was significantly lower than that in paracancerous tissues (2.59±0.43 vs 0.98±0.62, t=35.990, P<0.001). In GSE42568 cohort, the mRNA expression of NR3C2 in breast cancer tissue was significantly lower than that in paracancerous tissues (5.35±1.47 vs 3.32±1.12, t=7.096, P<0.001). The results of survival analysis showed that in TCGA cohort and Kaplan-Meier plotter cohort, NR3C2 expression was positively correlated with RFS in breast cancer patients (HR=0.667, 0.725; 95%CI: 0.458-0.972, 0.653-0.804; both P<0.050). (2) GSEA results suggested that NR3C2 was mostly involved in JAK-STAT and TGF-β signaling pathways related to immunity. Correlation analysis found that the mRNA expression of NR3C2 was significantly correlated with 19 immune cells and 43 immunomodulator genes (all P<0.050). (3) The 43 NR3C2-related immunomodulator genes were included in Cox regression to construct a prognostic model which composed of 13 immunomodulator genes with a risk cutoff value equal to 0.988. Survival analysis showed that in TCGA cohort and GSE42568 cohort, the RFS in high-risk group was significantly lower than that in low-risk group (HR=2.682, 2.389; 95%CI: 1.839-3.910, 1.343-4.248; both P<0.010). ROC indicated that the AUC was 0.758 and 0.618 in TCGA cohort and GSE42568 cohort, respectively (95%CI: 0.662-0.857, 0.545-0.758; sensitivity: 0.833, 0.538; specificity: 0.614, 0.714; both P<0.010). Multivariate Cox regression showed that the risk score of this model could serve as an independent prognostic factor for breast cancer in TCGA cohort and GSE42568 cohort (HR=1.259, 1.163; 95%CI: 1.187-1.336, 1.068-1.266; both P<0.001). (4) The nomogram constructed based on the pathological stage and risk value could predict the 3-year, 5-year and 8-year RFS of breast cancer patients. The calibration curve suggested that it has good predictive accuracy and tAUC indicated that it is superior to pathological stage and a prognostic model. (5) Western blot analysis showed that the protein expression of NR3C2 was significantly decreased in breast cancer tissues.

Conclusion

NR3C2 is a potential immunotherapeutic target and prognostic biomarker in breast cancer patients.

Key words: Breast neoplasms, Immunity, Prognosis, NR3C2 protein, human
表1 TCGA队列中1 019例乳腺癌患者临床病理特征
临床病理特征 指标值
年龄(岁，±s) 57.9±12.8
临床分期[例(%)]  
  Ⅰ~Ⅱ期 762 (74.8)
  Ⅲ~Ⅳ期 236 (23.2)
  未知 21 (2.1)
无复发生存时间(月，±s) 40.4±35.8
结局状态[例(%)]  
  有复发 109 (10.7)
  无复发 910 (89.3)
表2 GSE42568队列中104例乳腺癌患者临床病理特征
临床病理特征 指标值
年龄(岁，±s) 58.8±11.7
组织学分级[例(%)]  
  1~2级 51 (49.0)
  3级 53 (51.0)
无复发时间(月，±s) 54.4±32.4
结局状态[例(%)]  
  有复发 48 (46.2)
  无复发 56 (53.8)
图1 NR3C2基因表达量不同的乳腺癌患者的无复发生存分析　a、b图分别所示在TCGA队列及Kaplan-Meier plotter队列中NR3C2高/低表达乳腺癌患者的无复发生存曲线注：NR3C2基因高表达组与低表达组比较，HR＝0.667、0.725，95%CI：0.458~0.972、0.653~0.804，P均<0.050
表3 TCGA数据库中1 019例乳腺癌样本的GSEA结果
通路名称 基因数量 标准化富集分数 FDR值
JAK-STAT信号通路 155 2.21 <0.001
TGF-β信号通路 85 2.08 <0.001
趋化因子信号通路 184 1.90 <0.001
T细胞受体信号通路 107 1.83 <0.001
B细胞受体信号通路 75 1.73 <0.001
卵母细胞减数分裂 111 -1.64 <0.001
错配修复 23 -2.11 <0.001
细胞周期 124 -2.04 <0.001
氧化磷酸化 101 -2.39 <0.001
DNA复制 36 -2.43 <0.001
图2 TCGA数据库中1 019例乳腺癌样本的单样本基因集富集分析结果　a、b图分别表示免疫细胞、免疫调节基因与NR3C2 mRNA表达量之间的相关性
图3 TCGA队列中高风险组与低风险组乳腺癌患者的无复发生存曲线比较注：高风险组与低风险组比较，HR＝2.682，95%CI: 1.839~3.910，P<0.001
图4 乳腺癌预后模型在TCGA队列中的ROC注：根据风险值绘制1年时间点的ROC，95%CI：0.662~0.857，P<0.001；ROC为受试者操作特征曲线
图5 GSE42568队列中高风险组与低风险组乳腺癌患者的无复发生存曲线比较注：高风险组与低风险组比较，HR＝2.389，95%CI：1.343~4.248，P＝0.003
图6 乳腺癌预后模型在GSE42568队列中的ROC注：根据风险值绘制1年时间点的ROC，95%CI：0.545~0.758，P＝0.008；ROC为受试者操作特征曲线
表4 乳腺癌患者NR3C2和免疫调节基因之间相关性的多元逐步Cox回归分析结果
基因 回归系数 风险比 95%置信区间 P值
CD40LG 0.509 1.664 1.033~2.679 0.036
CD96 -0.606 0.546 0.319~0.933 0.027
CXCL12 -0.290 0.748 0.568~0.986 0.039
ENTPD1 0.201 1.223 0.973~1.537 0.085
KLRK1 0.207 1.230 0.955~1.584 0.110
LGALS9 -0.281 0.755 0.574~0.994 0.045
MICB -0.202 0.817 0.663~1.006 0.058
NT5E 0.257 1.292 1.039~1.607 0.021
PVR 0.214 1.238 0.984~1.558 0.068
TNFRSF13C -0.253 0.777 0.567~1.065 0.116
TNFRSF4 0.535 1.707 1.308~2.227 <0.001
TNFRSF8 -0.320 0.726 0.505~1.043 0.084
TNFSF9 0.153 1.165 0.990~1.372 0.066
表5 TCGA队列中1 019例乳腺癌患者无复发生存的多因素Cox回归结果
变量 B值 Wald值 风险比 95%置信区间 P值
年龄 -0.004 0.235 0.996 0.980~1.012 0.628
临床分期 0.735 26.648 2.085 1.578~2.756 <0.001
风险值 0.231 58.263 1.259 1.187~1.336 <0.001
表6 GSE42568队列中104例乳腺癌患者无复发生存的多因素Cox回归结果
变量 B值 Wald值 风险比 95%置信区间 P值
年龄 -0.002 0.037 0.998 0.973~1.023 0.847
组织学分级 0.764 8.077 2.147 1.268~3.638 0.004
风险值 0.151 12.143 1.163 1.068~1.266 <0.001
图7 基于临床分期和风险值构建的TCGA队列中1 019例乳腺癌患者列线图注：各协变量的预测概率映射到图形中从0到100分的刻度，各协变量累积的总分数对应患者3、5、8年的无复发生存率
图8 TCGA队列中1 019例乳腺癌患者生存率预测列线图在3、5、8年时间点的校准曲线注：横轴代表列线图的预测生存率，纵轴代表实际生存率，对角线(虚线)表示两者完全一致，观察指标为RFS
图9 TCGA队列中1 019例乳腺癌患者的临床分期、预后模型和列线图在不同时间点的曲线下面积比较
图10 Western blot实验检测NR3C2基因的蛋白表达情况注：GAPDH为内参照物；N为癌旁组织；T为乳腺癌组织
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