WJCR World Journal of Cancer Research 2164-9049 Scientific Research Publishing 10.12677/WJCR.2023.132011 WJCR-64483 WJCR20230200000_13786362.pdf 医药卫生 MMP11基因对肺腺癌诊断和预后的影响 Effect of MMP11 Gene on Diagnosis and Prognosis of Lung Adenocarcinoma 赵 强 2 1 李 兴广 3 1 中国科学院大学宁波华美医院，浙江 宁波；中国科学院大学宁波生命与健康产业研究院，浙江 宁波 天津市肿瘤医院空港医院肿瘤精准检测与转化中心，天津 null 07 04 2023 13 02 69 78 © Copyright 2014 by authors and Scientific Research Publishing Inc. 2014 This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/

目的：分析MMP11基因在肺腺癌中的表达特征，并评估其对肺腺癌诊断和预后的影响。方法：从TCGA数据库中下载肺腺癌转录组数据，利用生物信息学方法对MMP11在肺腺癌中的表达特征进行分析、并将该基因的表达特征与临床信息进行关联分析，进而对肺腺癌诊断及预后的影响进行系统分析。结果：MMP11基因在肺癌中高表达，对肺腺癌的诊断具有重要价值，但对肺腺癌预后的影响还有待于进一步研究。结论：MMP11基因有可能成为肺腺癌诊断相关的重要生物标记物。 Objective: To analyze the characteristics of MMP11 expression in lung adenocarcinoma and evaluate the impact on diagnosis and prognosis of lung adenocarcinoma. Method: The transcriptomic data of lung adenocarcinoma were downloaded from the TCGA database, and the expression characteristics of MMP11 in lung adenocarcinoma were analyzed by bioinformatic method, and then the expression characteristics of MMP11 were correlated with clinical information, so as to systematically analyze its influence on lung adenocarcinoma’ diagnosis and prognosis. Result: MMP11 gene is highly expressed in lung cancer and plays a significant role in the diagnosis of lung adenocarcinoma, but its effect on the prognosis of lung adenocarcinoma remains to be further elucidated. Conclusion: MMP11 gene may be an important biomarker in the diagnosis of lung adenocarcinoma.

MMP11，肺腺癌，诊断，预后, MMP11 Lung Adenocarcinoma Diagnosis Prognosis 摘要

目的：分析MMP11基因在肺腺癌中的表达特征，并评估其对肺腺癌诊断和预后的影响。方法：从TCGA数据库中下载肺腺癌转录组数据，利用生物信息学方法对MMP11在肺腺癌中的表达特征进行分析、并将该基因的表达特征与临床信息进行关联分析，进而对肺腺癌诊断及预后的影响进行系统分析。结果：MMP11基因在肺癌中高表达，对肺腺癌的诊断具有重要价值，但对肺腺癌预后的影响还有待于进一步研究。结论：MMP11基因有可能成为肺腺癌诊断相关的重要生物标记物。

 关键词

MMP11，肺腺癌，诊断，预后

 Effect of MMP11 Gene on Diagnosis and Prognosis of Lung Adenocarcinoma<sup> </sup>

Qiang Zhao1*, Xingguang Li2,3#

1Center for Precion Medicine and Translational Research, Tianjin Cancer Hospital Airport Hospital, Tianjin

2Ningbo Huamei Hospital, University of Chinese Academy of Sciences, Ningbo Zhejiang

3Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo Zhejiang

Received: Mar. 24th, 2023; accepted: Apr. 14th, 2023; published: Apr. 25th, 2023

 ABSTRACT

Objective: To analyze the characteristics of MMP11 expression in lung adenocarcinoma and evaluate the impact on diagnosis and prognosis of lung adenocarcinoma. Method: The transcriptomic data of lung adenocarcinoma were downloaded from the TCGA database, and the expression characteristics of MMP11 in lung adenocarcinoma were analyzed by bioinformatic method, and then the expression characteristics of MMP11 were correlated with clinical information, so as to systematically analyze its influence on lung adenocarcinoma’ diagnosis and prognosis. Result: MMP11 gene is highly expressed in lung cancer and plays a significant role in the diagnosis of lung adenocarcinoma, but its effect on the prognosis of lung adenocarcinoma remains to be further elucidated. Conclusion: MMP11 gene may be an important biomarker in the diagnosis of lung adenocarcinoma.

Keywords:MMP11, Lung Adenocarcinoma, Diagnosis, Prognosis

Copyright © 2023 by author(s) and beplay安卓登录

This work is licensed under the Creative Commons Attribution International License (CC BY 4.0).

http://creativecommons.org/licenses/by/4.0/

 1. 引言

肺癌是造成癌症相关死亡的主要原因，其病理分型大致可以分为两类：非小细胞肺癌(Non-small cell lung cancer, NSCLC)和小细胞肺癌，其中非小细胞肺癌占肺癌总数的85%左右。非小细胞肺癌又可以分为：腺癌、鳞癌和大细胞癌，其中以肺腺癌(Lung adenocarcinoma, LUAD)最为常见，占肺癌总数的一半左右。手术、放疗和化疗是肺癌治疗的三种常用方法，然而，局部治疗方法，包括手术和放射治疗，对晚期转移患者效果较差。目前，化疗是晚期转移性肺癌的主要治疗方法，但化疗药物对肿瘤细胞的靶向性不强，在杀伤肿瘤细胞的同时，对人体正常组织细胞的损害很大，导致其副作用过大，如恶心、呕吐、腹泻、便秘、心肺损伤等 [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ] 。同时，多次用药会导致肿瘤细胞耐药 [ 6 ] 。

最近几年，随着“精准医学”概念的兴起，越来越多的针对特定基因的靶向药物被应用于临床，且取得了较好的临床效果，为很多肿瘤尤其是肺癌的治疗带来了革命性的变化 [ 7 ] [ 8 ] 。同时，很多对肿瘤起到驱动作用的基因被陆续开发为肿瘤治疗的靶点，如EGFR、VEGF、ALK、MET、KRAS、ROS1和HER2等，靶向药物通过特异性杀伤肿瘤细胞或者改变肿瘤微环境对肿瘤的发生发展起到明显的抑制作用 [ 9 ] - [ 15 ] 。在肺癌的治疗领域，针对肺癌相关特定基因的靶向治疗药物已被陆续开发出来，包括细胞生长因子受体抑制剂、血管生成抑制剂、信号转导抑制剂等 [ 16 ] [ 17 ] [ 18 ] 。虽然这些靶向药物取得了较好的临床效果，极大地延长了病人的生存期，但随着肿瘤细胞耐药性的出现，肺癌的治疗仍面临较大挑战，临床上对新型靶向治疗药物的需求仍然较大 [ 19 ] [ 20 ] [ 21 ] 。

基因组学和生物信息学的发展为肿瘤致病机理研究提供了有效的方法，被证明是一种寻找肿瘤相关基因靶点的可靠方法 [ 21 ] [ 22 ] 。此外，国内外很多公共数据库中存储了大量的肿瘤相关基因的表达数据，也为破译肿瘤发生发展的分子机制提供了较为方便和有效的方法，如TCGA (The Cancer Gene Atlas, https://gdc.cancer.gov/)数据库包含了大量由微阵列或高通量测序产生的转录组数据。胶原和纤维组织代谢过程被很多研究证明与肿瘤的侵袭转移密切相关，基质金属蛋白酶(MMPs)被认为是参与胶原和纤维组织代谢过程的重要基因，现在，在人类基因组中已经发现了24种MMPs基因，研究较多的主要有MMP2、MMP3、MMP7、MMP9、MMP10、MMP12、MMP23等，MMPs不仅对肿瘤微环境发挥调节作用，对肿瘤的侵袭和转移过程也同样发挥重要调控作用 [ 23 ] [ 24 ] [ 25 ] 。MMP11，也被称为基质溶血素-3，是基质金属蛋白酶家族的一个重要成员，通常分泌到细胞外发挥其酶活性作用，可在患者血清中检测到 [ 26 ] [ 27 ] 。在本研究中，我们从TCGA数据库中下载肺腺癌相关基因表达数据，通过生物信息学方法对MMP11在肺腺癌中的表达特征、及在肿瘤的诊断和预后评估过程中的作用进行了分析，系统评估了MMP11作为肺腺癌在诊断和预后评估中的价值。

 2. 材料和方法 2.1. 肺腺癌相关转录组数据的下载和获取

从TCGA公共数据库中下载594例肺腺癌RNAseq转录组数据(TCGA-LUAD)，将FPKM (fregments per kilobase per million)格式的RNAseq数据转换成TPM (transcripts per million reads)格式并进行log2转化。

 2.2. 统计分析

R语言(3.6.3版本)用于数据的统计分析，ggplot2包(3.3.3版本)用于数据的可视化，pROC包用于ROC曲线的绘制，卡方检验(Chi-square test)用于MMP11表达量和临床病理特征之间的关联分析。Kaplan-Meier 分析和Cox多因素分析用于MMP11表达量和预后之间的关联分析。

 3. 结果 3.1. 肺腺癌患者的临床特征分析

从TCGA公共数据库中下载肺腺癌患者的临床特征相关信息，临床特征包括病人的年龄、性别、总生存期、病理分期、远端转移情况、生存状况等。我们利用卡方检验(Chi-square test)对肺腺癌中MMP11基因表达与临床特征进行了关联分析，以MMP11的表达中位数作为分界线，将入组的肺腺癌患者分为MMP11高表达组和低表达组，结果显示MMP11基因与肺腺癌中不同的T分期、N分期、M分期、病理组织学分期、性别、年龄、人种、肿瘤部位等临床特征在统计学上无明显差异(p > 0.05) (表1)。

Correlation analysis between MMP11 gene and clinical features of lung adenocarcinom
特征 MMP11低表达 MMP11高表达 p
n 267 268
T分期，n (%) 0.532
T1 94 (17.7%) 81 (15.2%)
T2 140 (26.3%) 149 (28%)
T3 22 (4.1%) 27 (5.1%)
T4 8 (1.5%) 11 (2.1%)
N分期，n (%) 0.263
N0 179 (34.5%) 169 (32.6%)
N1 41 (7.9%) 54 (10.4%)
N2 35 (6.7%) 39 (7.5%)
N3 0 (0%) 2 (0.4%)
M分期，n (%) 0.842
M0 187 (48.4%) 174 (45.1%)
M1 14 (3.6%) 11 (2.8%)
病理分期，n (%) 0.418
I 153 (29%) 141 (26.8%)
II 56 (10.6%) 67 (12.7%)
III 38 (7.2%) 46 (8.7%)
IV 15 (2.8%) 11 (2.1%)
性别，n (%) 0.280
女 136 (25.4%) 150 (28%)
男 131 (24.5%) 118 (22.1%)
人种，n (%) 0.387
亚洲 5 (1.1%) 2 (0.4%)
黑人 30 (6.4%) 25 (5.3%)
白人 199 (42.5%) 207 (44.2%)
年龄，n (%) 0.537
≤65 123 (23.8%) 132 (25.6%)
>65 134 (26%) 127 (24.6%)
肿瘤部位，n (%) 0.474
肺中央 30 (15.9%) 32 (16.9%)
肺周边 70 (37%) 57 (30.2%)
吸烟年限，n (%) 1.000
<40 98 (26.6%) 90 (24.4%)
≥40 95 (25.7%) 86 (23.3%)
吸烟，n (%) 0.112
否 31 (6%) 44 (8.4%)
是 232 (44.5%) 214 (41.1%)
是否存活，n (%) 0.338
存活 177 (33.1%) 166 (31%)
死亡 90 (16.8%) 102 (19.1%)
年龄中位数 66 (60, 72) 65 (58, 73) 0.827

表1. MMP11基因与肺腺癌临床特征的关联分析

 3.2. MMP11基因在肺腺癌中高表达

我们对TCGA数据库中MMP11基因在肺腺癌的表达水平进行了分析，发现MMP11基因在肺腺癌组织中的表达水平明显高于正常组织(p < 0.001) (图1)。此外，对不同亚组中MMP11基因的表达情况进行统计分析，发现不同性别(图2(A))、年龄(图2(B))、病理分期(图2(C))、T分期(图2(D))和M分期(图2(E))等中MMP11基因的表达情况无显著性差异(p > 0.05)，但与患者是否吸烟相关(图2(F))，MMP11基因在非吸烟患者中的表达高于吸烟患者，两者的差值中位数为0.624 (95%CI, 0.058~1.178)，差异具有统计学意义(p < 0.05)。但根据不同吸烟年限对吸烟患者的MMP11基因进行进一步统计分析发现，吸烟年限 < 40组的表达中位数(上下四分位数)为4.294 (95%CI, 3.112~5.631)，≥40组的中位数(上下四分位数)为4.294 (95%CI, 3.128~5.755)进行分组，在统计学上无显著性差异(p > 0.05)。

图1. MMP11基因在肺腺癌中的表达水平。(A) MMP11基因在肺腺癌组织和正常组织中的表达水平；(B) MMP11基因在肺腺癌组织和配对的临近正常组织中的表达水平

图2. MMP11表达水平和肺腺癌临床病理特征的关联分析。MMP11表达水平和性别(A)、年龄(B)、病理分期(C)、T分期(D)、M分期(E)、吸烟(F)和吸烟年限(G)的关联分析。ns，p ≥ 0.05；*，p < 0.05

 3.3. MMP11基因的表达有助于肺腺癌中的诊断

在本研究中，我们利用受试者工作特征曲线(ROC)对MMP11在肺腺癌中的诊断价值进行了评估，结果表明，MMP11的曲线下面积(Area Under The Curve, AUC)为0.970 (图3)。此外，我们还对MMP11在肺腺癌不同病理分期中的ROC进行了进一步的分析，发现其在I期、II期、III期和IV期肺腺癌中的AUC分别为0.964、0.981、0.979和0.965 (图3)。

图3. 受试者工作特征曲线(ROC)评估MMP11基因在肺腺癌诊断中的作用。MMP11基因在正常组织和肿瘤组织(A)、I期(B)、II期(C)、III期(D)、IV期(E)肺腺癌中的ROC分析

 3.4. MMP11基因对肺腺癌预后的影响

KM生存曲线分析结果显示，MMP11的表达对肺腺癌病人的总生存期(OS)没有显著影响(p > 0.05)，为了能够更充分的研究MMP11的表达对肺腺癌OS的影响，我们依据不同的病理分期进行分组，分别研究了其对不同分组肺腺癌OS的影响，发现MMP11的表达对I/II、III/IV、T1/T2、T3/T4、N0/N1、N2/N3、M0/M1分期的OS均没有显著的影响(p > 0.05) (图4)。单因素Cox回归分析显示，虽然不同的病理组织分期(II/IIIIV)、T分期(T2/T3)、M分期(M1)能够影响肺腺癌病人的OS，但MMP11的表达对OS没有影响。多因素Cox回归分析显示MMP11的表达是一个影响OS的非独立因素(表2)。

Univariate and multivariate Cox regression analysis of clinical features and overall survival (OS) of lung adenocarcinom
特征 总数(N) 单因素分析 多因素分析
风险比(95%CI) p值 风险比(95%CI) p值
年龄 516
≤65 255 参考
>65 261 1.223 (0.916~1.635) 0.172
性别 526
女 280 参考
男 246 1.070 (0.803~1.426) 0.642
病理分期 518
I 290 参考
II 121 2.418 (1.691~3.457) <0.001 0.873 (0.436~1.749) 0.702
III 81 3.544 (2.437~5.154) <0.001 1.431 (0.548~3.738) 0.465
IV 26 3.790 (2.193~6.548) <0.001 1.789 (0.807~3.967) 0.152
T分期 523
T1 175 参考
T2 282 1.521 (1.068~2.166) 0.020 1.562 (0.991~2.463) 0.055
T3 47 2.937 (1.746~4.941) <0.001 3.421 (1.685~6.946) <0.001
T4 19 3.326 (1.751~6.316) <0.001 1.995 (0.891~4.469) 0.093
N分期 510
N0 343 参考
N1 94 2.381 (1.695~3.346) <0.001 2.348 (1.231~4.478) 0.010
N2 71 3.108 (2.136~4.521) <0.001 2.145 (0.909~5.064) 0.082
N3 2 0.000 (0.000~Inf) 0.994 0.000 (0.000~Inf) 0.993
M分期 377
M0 352 参考
M1 25 2.136 (1.248~3.653) 0.006
MMP11 526
低表达 262 参考
高表达 264 1.219 (0.915~1.626) 0.176

表2. 肺腺癌临床特征与总生存期(OS)的单因素和多因素Cox回归分析

图4. MMP11基因表达与肺腺癌总体生存期(OS)的KM曲线分析。MMP11基因表达与肺腺癌(A)、I/II期(B)、III/IV期(C)、T1/T2 (D)、T3/T4 (E)、N0/N1 (F)、N2/N3 (G)、M0/M1 (H)总体生存期(OS)的KM曲线分析

 4. 讨论

生物信息学分析显示，相对于正常组织，MMP11基因在肺腺癌组织中高表达，对不同亚组中MMP11基因的表达与性别、年龄、病理分期、T分期和M分期无关，但吸烟是一个能够影响MMP11表达的重要因素，MMP11基因在非吸烟患者中的表达高于吸烟患者。ROC分析显示，MMP11的高表达有助于肺腺癌的诊断，其在I期、II期、III期和IV期肺腺癌中的AUC分别为0.964、0.981、0.979和0.965。为了进一步研究MMP11基因对肺腺癌预后的影响，我们利用KM生存曲线分析和单/多因素Cox回归分析系统论证了MMP11基因的表达对肺腺癌OS的影响，KM生存曲线分析显示，MMP11基因的表达对肺腺癌I/II、III/IV、T1/T2、T3/T4、N0/N1、N2/N3、M0/M1分期的OS均没有显著的影响，但单因素Cox回归分析显示，不同的病理组织分期(II/III/IV)、T分期(T2/T3)、M分期(M1)能够影响肺腺癌病人的OS，MMP11的表达对OS没有影响，多因素Cox回归分析显示MMP11的表达是一个影响OS的非独立因素。

 文章引用

赵 强,李兴广. MMP11基因对肺腺癌诊断和预后的影响Effect of MMP11 Gene on Diagnosis and Prognosis of Lung Adenocarcinoma[J]. 世界肿瘤研究, 2023, 13(02): 69-78. https://doi.org/10.12677/WJCR.2023.132011

 参考文献 References Nagasaka, M. and Gadgeel, S.M. (2018) Role of Chemotherapy and Targeted Therapy in Early-Stage Non-Small Cell Lung Cancer. Expert Review of Anticancer Therapy, 18, 63-70. https://doi.org/10.1080/14737140.2018.1409624 Pirker, R. (2020) Chemotherapy Remains a Cornerstone in the Treatment of Nonsmall Cell Lung Cancer. Current Opinion in Oncology, 32, 63-67. https://doi.org/10.1097/CCO.0000000000000592 Imyanitov, E.N., Iyevleva, A.G. and Levchenko, E.V. (2021) Molecular Testing and Targeted Therapy for Non-Small Cell Lung Cancer: Current Status and Perspectives. Critical Reviews in Oncology/Hematology, 157, Article ID: 103194. https://doi.org/10.1016/j.critrevonc.2020.103194 Reck, M. and Gatzemeier, U. (2004) Chemotherapy in Stage-IV NSCLC. Lung Cancer, 45, S217-S222. https://doi.org/10.1016/j.lungcan.2004.07.972 Klastersky, J. and Awada, A. (2012) Milestones in the Use of Chemotherapy for the Management of Non-Small Cell Lung Cancer (NSCLC). Critical Reviews in Oncology/Hematology, 81, 49-57. https://doi.org/10.1016/j.critrevonc.2011.02.002 Lemjabbar-Alaoui, H., Hassan, O.U., Yang, Y.-W. and Buchanan, P. (2015) Lung Cancer: Biology and Treatment Options. Biochimica et Biophysica Acta (BBA)-Reviews on Cancer, 1856, 189-210. https://doi.org/10.1016/j.bbcan.2015.08.002 Moreira, A.L. and Eng, J. (2014) Personalized Therapy for Lung Cancer. CHEST, 146, 1649-1657. https://doi.org/10.1378/chest.14-0713 Fang, B., Mehran, R.J., Heymach, J.V. and Swisher, S.G. (2015) Predictive Biomarkers in Precision Medicine and Drug Development against Lung Cancer. Chinese Journal of Cancer, 34, Article No. 26. https://doi.org/10.1186/s40880-015-0028-4 Yang, H., Jiang, P., Liu, D., et al. (2019) Matrix Metalloproteinase 11 Is a Potential Therapeutic Target in Lung Adenocarcinoma. Molecular Therapy-Oncolytics, 14, 82-93. https://doi.org/10.1016/j.omto.2019.03.012 Liu, S.V., Subramaniam, D., Cyriac, G.C., Abdul-Khalek, F.J. and Giaccone, G. (2014) Emerging Protein Kinase Inhibitors for Non-Small Cell Lung Cancer. Expert Opinion on Emerging Drugs, 19, 51-65. https://doi.org/10.1517/14728214.2014.873403 Dempke, W.C., Suto, T. and Reck, M. (2010) Targeted Therapies for Non-Small Cell Lung Cancer. Lung Cancer, 67, 257-274. https://doi.org/10.1016/j.lungcan.2009.10.012 Liu, X., Newton, R.C. and Scherle, P.A. (2011) Development of c-MET Pathway Inhibitors. Expert Opinion on Investigational Drugs, 20, 1225-1241. https://doi.org/10.1517/13543784.2011.600687 Rolfo, C., Ruiz, R., Giovannetti, E., et al. (2015) Entrectinib: A Potent New TRK, ROS1, and ALK Inhibitor. Expert Opinion on Investigational Drugs, 24, 1493-1500. https://doi.org/10.1517/13543784.2015.1096344 Mazieres, J., Barlesi, F., Filleron, T., et al. (2016) Lung Cancer Patients with HER2 Mutations Treated with Chemotherapy and HER2-Targeted Drugs: Results from the European EUHER2 Cohort. Annals of Oncology, 27, 281-286. https://doi.org/10.1093/annonc/mdv573 Liu, D., Vokes, N.I. and Van Allen, E.M. (2017) Toward Molecularly Driven Precision Medicine in Lung Adenocarcinoma. Cancer Discovery, 7, 555-557. https://doi.org/10.1158/2159-8290.CD-17-0355 Tiseo, M., Loprevite, M. and Ardizzoni, A. (2004) Epidermal Growth Factor Receptor Inhibitors: A New Prospective in the Treatment of Lung Cancer. Current Medicinal Chemistry-Anti-Cancer Agents, 4, 139-148. https://doi.org/10.2174/1568011043482106 Cabebe, E. and Wakelee, H. (2007) Role of Anti-Angiogenesis Agents in Treating NSCLC: Focus on Bevacizumab and VEGFR Tyrosine Kinase Inhibitors. Current Treatment Options in Oncology, 8, 15-27. https://doi.org/10.1007/s11864-007-0022-4 Yagui-Beltrán, A., He, B., Raz, D., Kim, J. and Jablons, D.M. (2006) Novel Therapies Targeting Signaling Pathways in Lung Cancer. Thoracic Surgery Clinics, 16, 379-396. https://doi.org/10.1016/j.thorsurg.2006.07.001 Wu, D., Wang, D.C., Cheng, Y., et al. (2017) Roles of Tumor Heterogeneity in the Development of Drug Resistance: A Call for Precision Therapy. Seminars in Cancer Biology, 42, 13-19. https://doi.org/10.1016/j.semcancer.2016.11.006 Sacco, J.J., Al-Akhrass, H. and Wilson, C.M. (2016) Challenges and Strategies in Precision Medicine for Non-Small-Cell Lung Cancer. Current Pharmaceutical Design, 22, 4374-4385. https://doi.org/10.2174/1381612822666160603014932 Lizardi, P.M., Forloni, M. and Wajapeyee, N. (2011) Genome-Wide Approaches for Cancer Gene Discovery. Trends in Biotechnology, 29, 558-568. https://doi.org/10.1016/j.tibtech.2011.06.003 Lee, E. and Moon, A. (2016) Identification of Biomarkers for Breast Cancer Using Databases. Journal of Cancer Prevention, 21, 235-242. https://doi.org/10.15430/JCP.2016.21.4.235 Bassiouni, W., Ali, M. A. and Schulz, R. (2021) Multifunctional Intracellular Matrix Metalloproteinases: Implications in Disease. The FEBS Journal, 288, 7162-7182. https://doi.org/10.1111/febs.15701 Murphy, G. and Nagase, H. (2008) Progress in Matrix Metalloproteinase Research. Molecular Aspects of Medicine, 29, 290-308. https://doi.org/10.1016/j.mam.2008.05.002 Quintero-Fabián, S., Arreola, R., Becerril-Villanueva, E., et al. (2019) Role of Matrix Metalloproteinases in Angiogenesis and Cancer. Frontiers in Oncology, 9, Article 1370. https://doi.org/10.3389/fonc.2019.01370 Zhang, X., Huang, S., Guo, J., et al. (2016) Insights into the Distinct Roles of MMP-11 in Tumor Biology and Future Therapeutics. International Journal of Oncology, 48, 1783-1793. https://doi.org/10.3892/ijo.2016.3400 Wang, Y., Wei, Y., Fan, X., et al. (2020) MicroRNA-125b as a Tumor Suppressor by Targeting MMP11 in Breast Cancer. Thoracic Cancer, 11, 1613-1620. https://doi.org/10.1111/1759-7714.13441
Baidu
map