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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 (06) : 336 -345. doi: 10.3877/cma.j.issn.1674-0807.2022.06.002

论著

基于糖酵解相关基因模型的乳腺癌患者预后及免疫功能综合分析
张锦1, 郑瑾2, 叶陈晓3, 陈海滔4, 李欣荣3, 肖海娟1, 郭勇5,†()   
  1. 1. 712000　咸阳，陕西中医药大学附属肿瘤医院三病区
    2. 710038　西安，空军军医大学第二附属医院中医科
    3. 310053　杭州，浙江中医药大学第一临床医学院
    4. 310022　杭州，中国科学院大学附属肿瘤医院中西医结合科
    5. 310003　杭州，浙江中医药大学附属第一医院肿瘤科
  • 收稿日期:2021-11-12 出版日期:2022-12-01
  • 通信作者: 郭勇
  • 基金资助:
    国家自然科学基金面上项目(81973805)

Comprehensive analysis of prognosis and immune function of breast cancer patients based on glycolysis related gene model

Jin Zhang1, Jin Zheng2, Chenxiao Ye3, Haitao Chen4, Xinrong Li3, Haijuan Xiao1, Yong Guo5,†()   

  1. 1. Department of Ward 3, Cancer Hospital Affiliated to Hospital of Shaanxi University of Traditional Chinese Medicine, Xianyang 712000, China
    2. Department of Traditional Chinese Medicine, Second Hospital Affiliated to Air Force Medical University, Xi’an 710038, China
    3. First Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou 310053, China
    4. Department of Integrated Chinese and Western Medicine, Cancer Hospital, University of Chinese Academy of Science, Hangzhou 310022, China
    5. Department of Oncology, First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou 310003, China
  • Received:2021-11-12 Published:2022-12-01
  • Corresponding author: Yong Guo
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引用本文：

张锦, 郑瑾, 叶陈晓, 陈海滔, 李欣荣, 肖海娟, 郭勇. 基于糖酵解相关基因模型的乳腺癌患者预后及免疫功能综合分析[J]. 中华乳腺病杂志(电子版), 2022, 16(06): 336-345.

Jin Zhang, Jin Zheng, Chenxiao Ye, Haitao Chen, Xinrong Li, Haijuan Xiao, Yong Guo. Comprehensive analysis of prognosis and immune function of breast cancer patients based on glycolysis related gene model[J]. Chinese Journal of Breast Disease(Electronic Edition), 2022, 16(06): 336-345.

目的

探讨肿瘤有氧糖酵解对乳腺癌患者预后及其免疫微环境的影响。

方法

从癌症基因组图谱(TCGA)数据库中获取1988年1月至2013年12月1 097例乳腺癌组织及112例癌旁正常组织的mRNA表达谱和相应临床数据。利用基因集富集分析(GSEA)软件筛选具有显著差异性的糖酵解相关基因(GRG)。利用单/多因素Cox回归分析及最小绝对收缩和选择算法(LASSO)筛选出与OS相关的GRG后生成6-GRG模型并绘制列线图。按模型算得中位风险值划分高/低风险组，提取不同组患者的相关GRG表达量后生成热图并进行了Kaplan-Meier生存分析。受试者操作特征(ROC)曲线分析及校准图用以验证模型准确性。用实时荧光定量PCR分析三阴性乳腺癌细胞系MDA-MB-231及正常人乳腺上皮细胞系MCF-10A中相关GRG的表达量。最后将不同风险组的差异表达基因(DEG)进行基因本体论(GO)及京都基因与基因组百科全书(KEGG)分析及免疫微环境分析。

结果

共有6个GRG与OS相关，包括CACNA1H(HR＝1.12，95%CI：1.03~1.20，P＝0.009)、SDC1(HR＝1.22，95%CI：1.06~1.40，P＝0.008)、SDC3(HR＝0.74，95%CI：0.61~0.90，P＝0.003)、NUP43(HR＝1.42，95%CI：1.07~1.90，P＝0.015)、PGK1(HR＝1.74，95%CI：1.36~2.20，P<0.001)，CHST1(HR＝1.14，95%CI：1.03~1.30，P＝0.009)，从而构建了6-GRG预后模型。根据模型测算的中位风险值，分为低风险组(n＝576)和高风险组(n＝521)。基因表达热图发现高风险组中SDC3表达减少，其余5个基因表达增加。生存分析发现低风险组患者的总生存率高于高风险组(χ2＝7.314，P<0.001)，低风险组与高风险组患者的总生存期分别为(17.23±0.89)年和(8.75±1.71)年。校准图显示该模型对患者1、3、5年OS的预测准确率曲线与理想曲线(45度角灰线)贴合。ROC曲线显示，乳腺癌患者1、3、5年OS的曲线下面积分别为0.68、0.71和0.72，提示本模型具有较好的预测精准性。相较于正常乳腺细胞MCF-10A，乳腺癌MDA-MB-231细胞中CACNA1H、SDC1、NUP43、CHST1、PGK1均表达增高(F＝15.36、30.73、1.08、14.92、12.93，P均<0.050)，而SDC3的表达减少(F＝17.50，P＝0.038)。GO及KEGG结果显示不同风险组中DEG多在免疫学功能或通路显著富集。低风险组患者的正性免疫细胞比例高于高风险组，包括初始B细胞[0.037(0.011，0.095)比0.031(0.004，0.069)，Z＝－3.928，P＝0.012]、记忆性B细胞[0.010(0.002，0.104)比0.004(0.001，0.411), Z＝－5.175，P<0.001]、CD8+T细胞[0.103(0.077，0.329)比0.073(0.012，0.136)，Z＝－4.904，P<0.001]、滤泡辅助性T细胞[0.068(0.000，0.117)比0.057(0.001，0.128)，Z＝－2.363，P<0.001]、γδT细胞[0.017(0.000，0.180)比0.010(0.000，0.140)，Z＝－1.491，P＝0.001]、活化自然杀伤细胞[0.031(0.000，0.141)比0.021(0.000，0.099)，Z＝－1.667，P<0.001]、单核细胞[0.017(0.000，0.101)比0.015(0.000，0.085)，Z＝－1.093，P＝0.047]、中性粒细胞[0.048(0.011，0.122)比0.021(0.008，0.069)，Z＝－2.776，P<0.001]。

结论

6-GRG预后模型具有良好的预测效能，乳腺癌患者的高糖酵解水平与不良预后及抗肿瘤免疫的功能下降密切相关。

关键词: 乳腺肿瘤, 糖酵解, 预后, 变态反应和免疫学, 计算生物学
Objective

To investigate the effect of tumor aerobic glycolysis on the prognosis and immune microenvironment of breast cancer patients.

Methods

The mRNA expression profiles of 1 097 cases of breast cancer tissues and 112 cases of normal adjacent tissues and corresponding clinical data were obtained from The Cancer Genome Atlas (TCGA) database from January 1988 to December 2013. The gene set engineering analysis (GSEA) software was used to screen out glycolysis-related genes (GRGs) with a significant difference. The single/multiple factor Cox regression analysis and the least absolute contraction and selection algorithm (LASSO) regression method were used to find GRGs related to OS, and then the 6-GRG model was generated and the nomogram was drawn. The patients were divided into high risk and low risk groups according to the median risk value of the model. The expression of related GRGs in different groups was extracted to generate heat maps and the Kaplan-Meier method was conducted for survival analysis. The receiver operating characteristic (ROC) curve analysis and calibration chart were used to verify the accuracy of the model, and single/multiple factor regression analysis is used to judge the independence of the model. The validity of the model was also verified by RT qPCR cell experiment. Finally, GO, KEGG analysis and immune microenvironment analysis were conducted for differentially expressed genes(DEGs) of different risk groups.

Results

Six GRGs were significantly related with OS, including CACNA1H (HR=1.12, 95%CI: 1.03-1.20, P=0.009), SDC1 (HR=1.22, 95%CI: 1.06-1.40, P=0.008), SDC3 (HR=0.74, 95%CI: 0.61-0.90, P=0.003), NUP43 (HR=1.42, 95%CI: 1.07-1.90, P=0.015), PGK1 (HR=1.74, 95%CI: 1.36-2.20, P<0.001), CHST1(HR=1.14, 95%CI: 1.03-1.30, P=0.009). Thus, we established a prognostic model of 6-GRG. According to the median risk value measured by the model, the patients were divided into low-risk group (n=576) and high-risk groups (n=521). The heat map showed that the expression of SDC3 in high-risk group decreased, and the expression of other 5 genes increased. The survival analysis showed that the overall survival rate of patients in low-risk group was significantly higher than that in high-risk group (χ2=7.314, P<0.001), and the overall survival of patients was (17.23±0.89) years in low-risk group and (8.75±1.71) years and high-risk group. The calibration plot shows that the predictive curve of the model for 1-, 3- and 5-year OS fitted the ideal curve (gray line at 45-degree angle). The ROC curve showed that the area under the curve (AUC)of 1-, 3-and 5-year OS was 0.68, 0.71 and 0.72, respectively, indicating a good prediction accuracy of this model. Compared with normal breast cell line MCF-10A, the expression of CACNA1H, SDC1, NUP43, CHST1 and PGK1 in breast cancer cell line MDA-MB-231 significantly increased (F=15.36, 30.73, 1.08, 14.92, 12.93, all P<0.050), while the expression of SDC3 significantly decreased (F=17.50, P=0.038). The GO and KEGG analysis indicated that most of DEGs in different risk groups were significantly enriched in immunological functions or pathways. The patients in low-risk group had significantly higher proportion of positive immune cells compared with high-risk group, including initial B cells [0.037 (0.011, 0.095) vs 0.031 (0.004, 0.069), Z=-3.928, P=0.012], memory B cells [0.010 (0.002, 0.104) vs 0.004 (0.001, 0.411), Z=-5.175, P<0.001], CD8+ T cells [0.103 (0.077, 0.329) vs 0.073 (0.012, 0.136), Z=-4.904, P<0.001], follicular helper T cells [0.068 (0.000, 0.117) vs 0.057(0.001, 0.128), Z=-2.363, P<0.001], γδ T cells [0.017 (0.000, 0.180) vs 0.010 (0.000, 0.140) Z=-1.491, P=0.001], activated natural killer cells [0.031 (0.000, 0.141) vs 0.021 (0.000, 0.099), Z=-1.667, P<0.001], monocytes [0.017 (0.000, 0.101) vs 0.015 (0.000, 0.085), Z=-1.093, P=0.047], and neutrophils [0.048 (0.011, 0.122) vs 0.021 (0.008, 0.069), Z=-2.776, P<0.001].

Conclusions

The 6-GRG prognostic model has good predictive performance. The high glycolysis level in breast cancer patients is closely related to poor prognosis and decreased anti-tumor immunity.

Key words: Breast neoplasms, Glycolysis, Prognosis, Allergy and immunology, Computational biology
表1 1 097例乳腺癌患者的临床病理特征
临床特征 例数 构成比(%)
性别    
  女 1 084 98.9
  男 13 1.1
年龄    
  ≥60岁 513 46.8
  <60岁 583 53.2
T分期    
  T1 281 25.6
  T2 635 57.9
  T3 138 12.6
  T4 43 3.9
N分期    
  N0 516 47.0
  N1 364 33.2
  N2 120 10.9
  N3 97 8.8
M分期    
  M0 912 83.1
  M1 22 2.0
  未知 163 14.9
肿瘤分期    
  Ⅰ期 183 16.7
  Ⅱ期 621 56.6
  Ⅲ期 249 22.7
  Ⅳ期 20 1.8
  未知 24 2.2
ER    
  阴性 238 21.7
  阳性 808 73.7
  未知 51 4.6
PR    
  阴性 344 31.4
  阳性 699 63.7
  未知 54 4.9
HER-2    
  阴性 564 51.4
  阳性 164 14.9
  未知 179 16.3
生存状态    
  存活 948 86.5
  死亡 149 13.5
  未知 190 17.3
表2 实时荧光定量PCR引物序列
基因名 正向序列(5′-3′) 反向序列(5′-3′)
SDC1 AGCAACACCGAGACTGCTTTT GTGCGGATGAGATGTGACAG
SDC3 AGAGGCCGGTGGATCTTGA CTCCTGCTCGAAGTAGCCAGA
PGK-1 ATGTCGCTTTCCAACAAGCTG GCTCCATTGTCCAAGCAGAAT
CHST1 TACACAGCCATCCGCACTTTC CGGCTCGAACAGGTAGAAGAC
NUP43 CTTGGATTCTGACGGAGGGTT CGAAGCAGCTACAATTCTTTCCT
CACNA1H CGTGACACTGGGCATGTTC CCACCATCTTGATAACCATCTCC
β-actin TGCTGTCCCTGTATGCCTCTG TGATGTCACGCACGATTTCC
表3 TCGA数据库中1 097例乳腺癌样本的基因集富集分析结果
糖酵解相关基因集 基因数量 标准化富集分数 FDR
HALLMARK_GLYCOLYSIS 200 1.807 0.030
REACTOME_GLYCOLYSIS 72 1.730 0.047
GLYCOLYSIS_GLUCONEOGENESIS 51 0.725 0.106
图1 糖酵解相关预测模型的LASSO回归分析　a图所示GRG的切点优化图；b图所示GRG的10倍交叉验证图注：根据图a的红色趋势线的最低区域范围内选定一个合理值12；b图中红色的虚线同折线有12个交点，代表12个基因(PGK1、PGAM1、PMM2、CHST1、GFPT1、SDC1、CHST1、HS6ST2、SDC3、CACNA1H、NUP88、NUP43)；GRG为糖酵解相关基因
表4 1 097例乳腺癌患者总生存的多因素Cox回归分析结果
基因 回归系数 HR 95%置信区间 P值
PGK1 0.63 1.74 1.36~2.20 <0.001
SDC1 0.21 1.22 1.06~1.40 0.008
CHST1 0.10 1.14 1.03~1.30 0.009
SDC3 －0.32 0.74 0.61~0.90 0.003
CACNA1H 0.10 1.12 1.03~1.20 0.009
NUP43 0.44 1.42 1.07~1.90 0.015
图2 6种糖酵解相关基因在不同风险组乳腺癌患者中的表达热图注：基因表达量的高低用颜色的深浅表示
图3 高/低风险组乳腺癌患者的总生存曲线比较
图4 依据6-GRG预测模型绘制的1 097例乳腺癌患者总生存率预测列线图注：预测模型中的各变量对应第一行得分值，各变量得分值相加获得总得分，总得分下引垂线查得各年生存率
图5 6-GRG预测模型预测1 097例乳腺癌患者总生存率的准确性验证　a、b、c图分别所示患者1、3、5年总生存率预测校准图；d图为患者1、3、5年总生存率的受试者操作特征曲线注：GRG为糖酵解相关基因；AUC为曲线下的面积值；1、3、5年AUC＝0.68、0.71、0.72
表5 1 097例乳腺癌患者总生存的单因素与多因素分析
临床特征 单因素分析 多因素分析
HR值 95%置信区间 P值 Z-分数 HR值 95%置信区间 P值 Z-分数
年龄 1.94 1.41~2.68 <0.001 4.03 1.95 1.40~2.72 <0.001 3.930
肿瘤分期 2.68 1.94~3.72 <0.001 5.95 1.99 1.24~3.20 0.005 2.834
T分期 1.63 1.14~2.35 0.008 2.65 0.88 0.55~1.40 0.577 －0.558
N分期 2.35 1.65~3.34 <0.001 4.77 1.52 1.00~2.30 0.048 1.978
M分期 1.94 1.31~2.89 0.001 3.28 1.38 0.89~2.16 0.151 1.435
6-GRG风险评分 2.72 2.11~3.51 <0.001 7.72 2.39 1.84~3.09 <0.001 6.588
表6 在MDA-MB-231细胞和MCF-10A细胞中6种糖酵解相关基因的表达比较
细胞系 SDC1 PGK1 CHST1 NUP43 CACNA1H SDC3
MDA-MB-231 8.77±9.60 5.27±1.50 2.24±1.31 3.05±0.60 2.13±0.12 0.26±0.26
MCF-10A 1.00±0.91 1.00±0.44 1.00±0.33 1.00±0.72 1.00±0.50 1.00±0.53
F值 30.73 12.93 14.92 1.08 15.36 17.50
P值 0.028 0.002 0.047 0.019 0.015 0.038
图6 不同风险组乳腺癌患者差异表达基因的总体分布和富集分析　a图所示为高风险组与低风险组差异表达基因的火山图；b、c、d图分别所示为高风险与低风险组的差异表达基因在分子功能、细胞成分和生物学过程中的富集分析
图7 146个差异表达基因的富集通路分析
表7 不同风险组乳腺癌患者的肿瘤免疫微环境差异分析
分组 例数 纯度评分 免疫评分 基质评分
低风险组 576 1 576.411±141.236 940.737±90.113 738.256±130.046
高风险组 521 1 066.532±177.916 616.771±72.599 696.004±122.705
F值   52.733 38.144 61.785
P值   <0.001 <0.001 0.281
表8 不同风险组乳腺癌患者的免疫细胞比例比较[M(P25，P75)]
分组 例数 初始B细胞 记忆性B细胞 CD8+T细胞 滤泡辅助性T细胞 γδT细胞 活化NK细胞 单核细胞 中性粒细胞
低风险组 576 0.037(0.011，0.095) 0.010(0.002，0.104) 0.103(0.077，0.329) 0.068(0.000，0.117) 0.017(0.000，0.180) 0.031(0.000，0.141) 0.017(0.000，0.101) 0.048(0.011，0.122)
高风险组 521 0.031(0.004，0.069) 0.004(0.001，0.411) 0.073(0.012，0.136) 0.057(0.001，0.128) 0.010(0.000，0.140) 0.021(0.000，0.099) 0.015(0.000，0.085) 0.021(0.008，0.069)
Z值   －3.928 －5.175 －4.904 －2.363 －1.491 －1.667 －1.093 －2.776
P值   0.012 <0.001 <0.001 <0.001 0.001 <0.001 0.047 <0.001
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