切换至 "中华医学电子期刊资源库"
高级检索
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
全文 ( ) 下载PDF ( ) 导出引用
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/OL]. 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/OL]. 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
[1]
Hanahan D，Weinberg RA. Hallmarks of cancer: the next generation[J]. Cell，2011，144(5): 646-674.
[2]
Vander Heiden MG，Cantley LC，Thompson CB. Understanding the Warburg effect: the metabolic requirements of cell proliferation[J]. Science，2009，324(5930): 1029-1033.
[3]
Jiang Z，Liu Z，Li M，et al. Increased glycolysis correlates with elevated immune activity in tumor immune microenvironment[J]. EBioMedicine，2019，42: 431-442.
[4]
Justus C，Sanderlin E，Yang L. Molecular connections between cancer cell metabolism and the tumor microenvironment[J]．Int J Mol Sci，2015，16(12): 11 055-11 086.
[5]
Ganapathy-Kanniappan S. Linking tumor glycolysis and immune evasion in cancer: Emerging concepts and therapeutic opportunities[J]. Biochim Biophys Acta Rev Cancer，2017，1868(1): 212-220.
[6]
Demichele A，Yee D，Esserman L. Mechanisms of resistance to neoadjuvant chemotherapy in breast cancer[J]. N Engl J Med，2017，377(23): 2287-2289.
[7]
Bao Z，Li M，Wang J，et al. Prognostic value of a nine-gene signature in glioma patients based on mRNA expression profiling[J]. CNS Neurosci Ther, 2014，20(2): 112-118.
[8]
Hu T，Zhao G，Liu Y，et al. A machine learning approach to differentiate two specific breast cancer subtypes using androgen receptor pathway genes[J]. Technol Cancer Res Treat，2021，20: 2091197508.
[9]
Long M，Hou W，Liu Y，et al. A histone acetylation modulator gene signature for classification and prognosis of breast cancer[J]. Curr Oncol, 2021，28(1): 928-939.
[10]
FKantor O，Bao J，Jaskowiak N，et al. The prognostic value of the AJCC 8th edition staging system for patients undergoing neoadjuvant chemotherapy for breast cancer[J]．Ann Surg Oncol，2020，27(2): 352-358.
[11]
Suárez-Fariñas M，Lowes MA，Zaba LC，et al. Evaluation of the psoriasis transcriptome across different studies by gene set enrichment analysis (GSEA)[J]. PLoS One，2010，5(4): e10247.
[12]
Liberzon A，Subramanian A，Pinchback R，et al. Molecular signatures database (MSigDB) 3.0[J]. Bioinformatics，2011，27(12): 1739-1740.
[13]
Wang H，Lengerich BJ，Aragam B，et al. Precision Lasso: accounting for correlations and linear dependencies in high-dimensional genomic data[J]. Bioinformatics，2019，35(7): 1181-1187.
[14]
Hu T，Chen Y，Liu Y，et al. Classification of PR-positive and PR-negative subtypes in ER-positive and HER2-negative breast cancers based on pathway scores[J]. BMC Med Res Methodol，2021，21(1)：108.
[15]
Núñez E，Steyerberg EW，Núñez J. Estrategias para la elaboración de modelos estadísticos de regresión [Regression modeling strategies] [J]. Rev Esp Cardiol, 2011，64(6): 501-507.
[16]
Heagerty PJ，Lumley T，Pepe MS. Time-dependent ROC curves for censored survival data and a diagnostic marker[J]. Biometrics，2000，56(2): 337-344.
[17]
Livak KJ，Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method[J]. Methods，2001，25(4): 402-408.
[18]
Yoshihara K，Shahmoradgoli M，Martinez E，et al. Inferring tumour purity and stromal and immune cell admixture from expression data[J]. Nat Commun，2013，4: 2612.
[19]
Newman AM，Liu CL，Green MR，et al. Robust enumeration of cell subsets from tissue expression profiles[J]. Nat Methods, 2015，12(5): 453-457.
[20]
Fu D，He C，Wei J，et al. PGK1 is a potential survival biomarker and invasion promoter by regulating the HIF-1α-mediated epithelial-mesenchymal transition process in breast cancer[J]. Cell Phy Biochem，2018，51(5): 2434-2444.
[21]
Van Schaftingen E，Veiga-Da-Cunha M，Linster CL. Enzyme complexity in intermediary metabolism[J]. J Inherit Metab Dis, 2015，38(4): 721-727.
[22]
Brown TP，Ganapathy V. Lactate/GPR81 signaling and proton motive force in cancer: Role in angiogenesis, immune escape, nutrition, and Warburg phenomenon[J]. Pharmacol Ther, 2020，206：107 451.
[23]
Zhang C，Gou X，He W，et al. A glycolysis-based 4-mRNA signature correlates with the prognosis and cell cycle process in patients with bladder cancer [J]. Cancer Cell Int, 2020, 20：177.
[24]
Jiang F，Wu C，Wang M，et al. Identification of novel cell glycolysis related gene signature predicting survival in patients with breast cancer[J]. Sci Rep，2021, 11(1)：3986.
[25]
Gaude E，Frezza C. Tissue-specific and convergent metabolic transformation of cancer correlates with metastatic potential and patient survival[J]. Nat Commun, 2016, 7：13 041.
[26]
Cui X，Jing X，Yi Q，et al. Clinicopathological and prognostic significance of SDC1 overexpression in breast cancer[J]. Oncotarget，2017，8(67): 111 444-111 455.
[27]
Tian C，Zhou S，Yi C. High NUP43 expression might independently predict poor overall survival in luminal A and in HER2+ breast cancer[J]. Future Oncol，2018，14(15): 1431-1442.
[28]
Zhao X，Graves C，Ames SJ，et al. Mechanism of regulation and suppression of melanoma invasiveness by novel retinoic acid receptor-γtarget gene carbohydrate sulfotransferase 10[J]. Cancer Res，2009，69(12): 5218-5225.
[29]
Phan NN，Wang C，Chen C，et al. Voltage-gated calcium channels: Novel targets for cancer therapy[J]. Oncol Lett, 2017，14(2): 2059-2074.
[30]
Bespalov MM，Sidorova YA，Tumova S，et al. Heparan sulfate proteoglycan syndecan-3 is a novel receptor for GDNF, neurturin, and artemin[J]. J Cell Biol，2011，192(1): 153-169.
[31]
Donnelly RP，Finlay DK. Glucose, glycolysis and lymphocyte responses[J]. Mol Immunol，2015，68(2): 513-519.
[1] 周荷妹, 金杰, 叶建东, 夏之一, 王进进, 丁宁. 罕见成人肋骨郎格汉斯细胞组织细胞增生症被误诊为乳腺癌术后骨转移一例[J/OL]. 中华乳腺病杂志(电子版), 2024, 18(06): 380-383.
[2] 韩萌萌, 冯雪园, 马宁. 乳腺癌改良根治术后桡神经损伤1例[J/OL]. 中华普外科手术学杂志(电子版), 2025, 19(01): 117-118.
[3] 许杰, 李亚俊, 韩军伟. 两种入路下腹腔镜根治性全胃切除术治疗超重胃癌的效果比较[J/OL]. 中华普外科手术学杂志(电子版), 2025, 19(01): 19-22.
[4] 高杰红, 黎平平, 齐婧, 代引海. ETFA和CD34在乳腺癌中的表达及与临床病理参数和预后的关系研究[J/OL]. 中华普外科手术学杂志(电子版), 2025, 19(01): 64-67.
[5] 李代勤, 刘佩杰. 动态增强磁共振评估中晚期低位直肠癌同步放化疗后疗效及预后的价值[J/OL]. 中华普外科手术学杂志(电子版), 2025, 19(01): 100-103.
[6] 梁孟杰, 朱欢欢, 王行舟, 江航, 艾世超, 孙锋, 宋鹏, 王萌, 刘颂, 夏雪峰, 杜峻峰, 傅双, 陆晓峰, 沈晓菲, 管文贤. 联合免疫治疗的胃癌转化治疗患者预后及术后并发症分析[J/OL]. 中华普外科手术学杂志(电子版), 2024, 18(06): 619-623.
[7] 张志兆, 王睿, 郜苹苹, 王成方, 王成, 齐晓伟. DNMT3B与乳腺癌预后的关系及其生物学机制[J/OL]. 中华普外科手术学杂志(电子版), 2024, 18(06): 624-629.
[8] 王玲艳, 高春晖, 冯雪园, 崔鑫淼, 刘欢, 赵文明, 张金库. 循环肿瘤细胞在乳腺癌新辅助及术后辅助治疗中的应用[J/OL]. 中华普外科手术学杂志(电子版), 2024, 18(06): 630-633.
[9] 赵林娟, 吕婕, 王文胜, 马德茂, 侯涛. 超声引导下染色剂标记切缘的梭柱型和圆柱型保乳区段切除术的效果研究[J/OL]. 中华普外科手术学杂志(电子版), 2024, 18(06): 634-637.
[10] 李伟, 宋子健, 赖衍成, 周睿, 吴涵, 邓龙昕, 陈锐. 人工智能应用于前列腺癌患者预后预测的研究现状及展望[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(06): 541-546.
[11] 刘郁, 段绍斌, 丁志翔, 史志涛. miR-34a-5p 在结肠癌患者的表达及其与临床特征及预后的相关性研究[J/OL]. 中华消化病与影像杂志(电子版), 2024, 14(06): 485-490.
[12] 陈倩倩, 袁晨, 刘基, 尹婷婷. 多层螺旋CT 参数、癌胚抗原、错配修复基因及病理指标对结直肠癌预后的影响[J/OL]. 中华消化病与影像杂志(电子版), 2024, 14(06): 507-511.
[13] 曾明芬, 王艳. 急性胰腺炎合并脂肪肝患者CT 与彩色多普勒超声诊断参数与其病情和预后的关联性研究[J/OL]. 中华消化病与影像杂志(电子版), 2024, 14(06): 531-535.
[14] 沈炎, 张俊峰, 唐春芳. 预后营养指数结合血清降钙素原、胱抑素C及视黄醇结合蛋白对急性胰腺炎并发急性肾损伤的预测价值[J/OL]. 中华消化病与影像杂志(电子版), 2024, 14(06): 536-540.
[15] 王景明, 王磊, 许小多, 邢文强, 张兆岩, 黄伟敏. 腰椎椎旁肌的研究进展[J/OL]. 中华临床医师杂志(电子版), 2024, 18(09): 846-852.
阅读次数
全文


摘要

版权所有 中华医学会 中华医学电子音像出版社有限责任公司  京ICP备14006079号-1  网络出版服务许可证：(署)网出证(京)字第075号