[1]胡修东,臧洪婧,陈睿鹏.生物信息学分析构建三阴性乳腺癌基于基底膜相关基因的风险评分模型[J].现代检验医学杂志,2025,40(03):6-12.[doi:10.3969/j.issn.1671-7414.2025.03.002]
　HU Xiudong,ZANG Hongjing,CHEN Ruipeng.Construction of the Triple-negative Breast Cancer Risk Score Model Based on Basement Membrane-related Genes by Bioinformatics Analysis[J].Journal of Modern Laboratory Medicine,2025,40(03):6-12.[doi:10.3969/j.issn.1671-7414.2025.03.002]
点击复制

生物信息学分析构建三阴性乳腺癌基于基底膜相关基因的风险评分模型()

《现代检验医学杂志》[ISSN:/CN:]

卷:
第40卷
期数:
2025年03期
页码:
6-12
栏目:
论著
出版日期:
2025-05-15

文章信息/Info

Title:
Construction of the Triple-negative Breast Cancer Risk Score Model Based on Basement Membrane-related Genes by Bioinformatics Analysis
文章编号:
1671-7414（2025）03-006-07
作者:
胡修东1，臧洪婧2，陈睿鹏3
（1.贵港市人民医院，广西贵港 537100；2.中南大学湘雅二医院，长沙 410012；3.岳阳市人民医院/湖南师范大学附属岳阳医院，湖南岳阳 414022）
Author(s):
HU Xiudong1, ZANG Hongjing2, CHEN Ruipeng3
（1. Guigang People’s Hospital, Guangxi Guigang 537100, China；2. the Second Xiangya Hospital of Central South University, Changsha 410012, China；3. Yueyang People’s Hospital /Yueyang Hospital Affiliated to Hunan Normal University, Hunan Yueyang 414022, China)
关键词:
基底膜相关基因; 三阴性乳腺癌; 生物信息学; 风险评分模型
分类号:
R737.9；R730.43
DOI:
10.3969/j.issn.1671-7414.2025.03.002
文献标志码:
A
摘要:
目的旨在构建基底膜相关基因（BMRGs）在三阴性乳腺癌（TNBC）的风险评分模型。方法应用LASSO-COX回归的机器学习方法在癌症基因图谱(TCGA)队列建立基于BMRGs的TNBC风险评分模型,并在Yau-2010队列和国际乳腺癌协会的分子分类(METABRIC)队列进行验证。构建包含BMRGs风险评分和临床因素的列线图预测TNBC患者的生存预后。基于基因本体（GO）功能注释和京都基因和基因组百科全书（KEGG）、基因集富集分析(GSEA)探究风险亚组的蛋白功能富集差异。基于免疫肿瘤生物学研究(IOBR)程序包探究基于风险亚组的免疫浸润差异情况。应用突变注释格式工具（Maltools）程序包探究基因组改变情况。基于癌症治疗反应门户(CTRP)和单细胞公共信息在线数据库(CSEP）探究风险评分亚组的药物敏感性差异和单细胞表达状态。结果经Kaplan-Meier（KM）生存分析和LASSO-COX回归分析筛选出6个BMRGs构建的风险亚组与TNBC患者的预后密切相关（均P<0.001）。其中SDC1和ADAM9是预后不良因子，HAPLN1，FREM1，FBLN5和ITGB4是预后保护因子。整合BMRGs风险评分、肿瘤分期的列线图对TNBC患者的预后具有优秀的预测能力。蛋白功能分析显示高风险组的上调基因富集于神经活性配体受体的相互作用、合成各类生物复合物、参与免疫防御反应等通路途径和生物功能；相较于高风险组,低风险组的肥大细胞、细胞毒性淋巴细胞浸润程度更高；基因图谱显示高低风险亚组的最常突变基因并不完全一致；药物敏感度分析显示，高风险组患者对硼替佐米、氟伐他汀、哇巴因的敏感性较高(均P<0.05)；低风险组患者对尼达尼布、BRD-A86708339，凡德他尼的敏感性较高(均P<0.05)；单细胞分析显示：上述六个基因在TNBC的肿瘤细胞中均存在高表达。结论构建并验证基于BMRGs的风险评分，为预测TNBC患者的生存预后提供有效的生物学指标。
Abstract:
Objective To construct a risk score model for basement membrane-related genes (BMRGs) in triple-negative breast cancer (TNBC). Methods The TNBC risk score model based on BMRGs was established in the cancer genome atlas (TCGA) cohort by the LASSO-COX regression machine learning method, and verified in the Yau-2010 cohort and the molecular taxonomy of breast cancer international consortium (METABRIC) cohort. A nomogram containing BMRG risk score and clinical factors was constructed to predict the survival prognosis of TNBC patients. Based on the functional annotations of gene ontology (GO) and the Kyoto encyclopedia of genes and genomes, KEGG and Gene Set Enrichment Analysis (GSEA) were used to explore differences in protein functional enrichment in risk subgroups. The Immuno-Oncology Biological Research (IOBR) package was used to explore differences in immune infiltration based on risk subgroups. The Mutation Annotation Format Tools (Maltools) package was used to explore genomic changes. Finally, based on the cancer therapeutics response portal (CTRP) and the cancer single-cell expression map (CSEP) to explore differences in drug sensitivity and single-cell expression status in risk score subgroups. Results Six BMRGS-constructed risk subgroups were identified by Kaplan-Meier (KM) survival analysis and LASSO-COX regression analysis and were closely related to the prognosis of TNBC patients (all P<0.001). Among them, SDC1 and ADAM9 were poor prognostic factors, HAPLN1, FREM1, FBLN5 and ITGB4 were protective prognostic factors. The combination of BMRGs risk score and TNM tumor stage has excellent predictive ability for the prognosis of TNBC patients. Protein function analysis showed that the up-regulated genes in the high-risk group were enriched in the pathways and biological functions of neuroactive ligand-receptor interactions, synthesis of various biological complexes, and participation in immune defense responses. Compared with the high-risk group, the infiltration degree of mast cells and cytotoxic lymphocytes was higher in the low-risk group. The genetic map revealed that the most frequently mutated genes in the high-low-risk subgroups were not identical Drug sensitivity analysis showed that patients in the high-risk group had higher sensitivity to bortezomib, fluvastatin, and ouabain(all P<0.05). Patients in the low-risk group had higher sensitivity to nintedanib, BRD-A86708339, and vandetanib(all P<0.05). Single-cell analysis showed that the above six genes were highly expressed in TNBC tumor cells. Conclusion The risk score based on BMRGs was constructed and validated to provide effective biological indicators for predicting the survival and prognosis of TNBC patients.

参考文献/References:

[1] HARBECK N, GNANT M．Breast cancer[J]. Lancet （London，England）,2017,389(10074):1134-1150.
[2] LEON-FERRE R A, GOETZ M P. Advances in systemic therapies for triple negative breast cancer [J]. BMJ，2023，381：e071674.
[3] KATSURA C, OGUNMWONYI I, KANKAM H K, et al. Breast cancer: presentation, investigation and management[J]. British Journal of Hospital Medicine (London，England：2005), 2022, 83(2): 1-7.
[4] LU Boya, NATARAJAN E, BALAJI RAGHAVENDRAN H R, et al. Molecular classification, treatment, and genetic biomarkers in triple-negative breast cancer: a review[J]. Technology in Cancer Research & Treatment, 2023, 22:15330338221145246.
[5] BANERJEE S, LO Wencheng, MAJUMDER P, et al. Multiple roles for basement membrane proteins in cancer progression and EMT[J]. European Journal of Cell Biology, 2022, 101(2): 151220.
[6] JAYADEV R, MORAIS M R P T, ELLINGFORD J M, et al. A basement membrane discovery pipeline uncovers network complexity, regulators, and human disease associations[J]. Science Advances, 2022, 8(20):eabn2265.
[7] 刁迅,范绮雨,耿良栋,等.基于生物信息学分析双硫死亡相关基因PDLIM1 mRNA在多种肿瘤中的表达及临床应用价值[J].现代检验医学杂志,2024,39 (1):36-42, 54. DIAO Xun, FAN Qiyu, GENG Liangdong, et al. Analysis of expression in disulfidptosis-related gene PDLIM1 mRNA in various tumors and its clinical application value based on bioinformatics [J]. Journal of Modern Laboratory Medicine, 2024, 39(1): 36-42, 54.
[8] 邵波,汪进,万水,等.基于生物信息学构建膀胱尿路上皮癌的ceRNA网络以及关键mRNA与免疫功能分析[J].现代检验医学杂志,2024,39(1):29-35, 66. SHAO Bo, WANG Jin, WAN Shui, et al. Construction of ceRNA network and analysis of key mRNA and immune function for bladder urothelial carcinoma based on bioinformatics[J]. Journal of Modern Laboratory Medicine, 2024, 39(1): 29-35, 66.
[9] 王结珍,梁培松.基于TCGA数据库构建肝细胞癌双硫死亡相关基因(DRGs)预后风险模型及评价[J].现代检验医学杂志,2024,39(2):86-90. WANG Jiezhen, LIANG Peisong. Construction and evaluation of the disulfidptosis-related genes(DRGs) prognostic risk model for hepatocellular carcinoma based on TCGA database[J]. Journal of Modern Laboratory Medicine, 2024, 39(2): 86-90.
[10] DING Ruili, ZHAO Chuanbing, JING Yixin, et al. Basement membrane-related regulators for prediction of prognoses and responses to diverse therapies in hepatocellular carcinoma[J]. BMC Medical Genomics, 2023, 16(1): 81.
[11] CAI Jiehui, ZHANG Xinkang, XIE Wanchun, et al. Identification of a basement membrane-related gene signature for predicting prognosis and estimating the tumor immune microenvironment in breast cancer[J]. Frontiers in Endocrinology, 2022, 13: 1065530.
[12] BAI Yi, WEI Chunlian, ZHONG Yuxin, et al. Development and validation of a prognostic nomogram for gastric cancer based on DNA methylation-driven differentially expressed genes[J]. International Journal of Biological Sciences, 2020, 16(7): 1153-1165.
[13] LI Yanze, XU Kai, ZHANG Ye, et al. Identification of a basement membrane-related genes signature with immune correlation in bladder urothelial carcinoma and verification in vitro[J]. BMC Cancer, 2023, 23(1): 1021.
[14] ZHANG Yurong, LI Tingting, LIU Huanqing, et al. Function and prognostic value of basement membrane-related genes in lung adenocarcinoma[J]. Frontiers in Pharmacology, 2023, 14: 1185380.
[15] WIEDMANN L, DE ANGELIS RIGOTTI F, VAQUERO-SIGUERO N, et al. HAPLN1 potentiates peritoneal metastasis in pancreatic cancer[J]. Nature Communications, 2023, 14(1): 2353.
[16] ZHANG Tiancheng, LI Xiang, HE Yani, et al. Cancer-associated fibroblasts-derived HAPLN1 promotes tumour invasion through extracellular matrix remodeling in gastric cancer[J]. Gastric Cancer，2022, 25(2): 346-359.
[17] WANG Yao, XU Xiaoyue, MARSHALL J E, et al. Loss of hyaluronan and proteoglycan link protein-1 induces tumorigenesis in colorectal cancer[J]. Frontiers in Oncology, 2021, 11: 754240.
[18] KAUR A, ECKER B L, DOUGLASS S M, et al. Remodeling of the collagen matrix in aging skin promotes melanoma metastasis and affects immune cell motility[J]. Cancer Discovery, 2019, 9(1): 64-81.
[19] XU Xiaoyi, GUO Wenjing, PAN Shihua, et al. TILRR (FREM1 isoform 2) is a prognostic biomarker correlated with immune infiltration in breast cancer[J]. Aging, 2020, 12(19): 19335-19351.
[20] LI Hanning, LI Xingrui, L? Zhengtao, et al. Elevated expression of FREM1 in breast cancer indicates favorable prognosis and high-level immune infiltration status[J]. Cancer Medicine, 2020, 9(24): 9554-9570.
[21] BIAN Xiulan, YIN Shengjie, YIN Xin, et al. Clinical and biological significances of FBLN5 in gastric cancer[J]. Cancers, 2023, 15(2): 553.
[22] XIAO Wei, ZHOU Shumin, XU Hua, et al. Nogo-B promotes the epithelial-mesenchymal transition in HeLa cervical cancer cells via Fibulin-5[J]. Oncology Reports, 2022, 47(5): 100.
[23] RUAN Shasha, LIN Ming, ZHU Yong, et al. Integrin β4-targeted cancer immunotherapies inhibit tumor growth and decrease metastasis[J]. Cancer Research, 2020, 80(4): 771-783.
[24] YANG Haoyu, XU Zixuan, PENG Yuqian, et al. Integrin β4 as a potential diagnostic and therapeutic tumor marker[J]. Biomolecules, 2021, 11(8): 1197.
[25] JANG T H, HUANG W C, TUNG S L, et al. MicroRNA-485-5p targets keratin 17 to regulate oral cancer stemness and chemoresistance via the integrin/FAK/Src/ERK/β-catenin pathway[J]. Journal of Biomedical Science, 2022, 29(1): 42.
[26] CHEN Kai, WANG Yazhou, HOU Yuting, et al. Single cell RNA-seq reveals the CCL5/SDC1 receptor-ligand interaction between T cells and tumor cells in pancreatic cancer[J]. Cancer Letters, 2022, 545: 215834.
[27] LUO Pan, GONG Yanfei, WENG Jie, et al. CircKIF4A combines EIF4A3 to stabilize SDC1 expression to activate c-src/FAK and promotes TNBC progression[J]. Cellular Signalling, 2023, 108: 110690.
[28] LIAO Shiyao, LIU Chang, ZHU Guiying, et al. Relationship between SDC1 and cadherin signalling activation in cancer[J]. Pathology, Research and Practice, 2020, 216(1): 152756.
[29] CHOU Chengwei, HUANG Yukai, KUO Tingting, et al. An overview of Adam9: structure, activation, and regulation in human diseases[J]. International Journal of Molecular Sciences, 2020, 21(20): 7790.
[30] ZHOU Rui, CHO W C S, MA V, et al. Adam9 mediates triple-negative breast cancer progression via AKT/NF-κB pathway [J]. Frontiers in Medicine (Lausanne), 2020, 7: 214.
?

相似文献/References:

[1]王英英,朱　娇.三阴性乳腺癌患者血清miR-146a，miRlet-7a水平表达与临床病理特征和化疗疗效的相关性研究[J].现代检验医学杂志,2021,36(06):60.[doi:10.3969/j.issn.1671-7414.2021.06.012]
　WANG Ying-ying,ZHU Jiao.Correlation of Serum miR-146a and miR let-7a Expression with Clinicopathological Characteristics and Chemotherapy Efficacy in Patients with Triple Negative Breast Cancer[J].Journal of Modern Laboratory Medicine,2021,36(03):60.[doi:10.3969/j.issn.1671-7414.2021.06.012]
[2]肖姗姗,李 越,周艳阳,等.基于TCGA 数据库构建三阴性乳腺癌预后相关的ceRNA 调控网络及分析[J].现代检验医学杂志,2023,38(01):83.[doi:10.3969/j.issn.1671-7414.2023.01.016]
　XIAO Shan-shan,LI Yue,ZHOU Yan-yang,et al.Construction and Analysis of ceRNA Regulatory Network Related to Prognosis of Triple Negative Breast Cancer Based on TCGA Database[J].Journal of Modern Laboratory Medicine,2023,38(03):83.[doi:10.3969/j.issn.1671-7414.2023.01.016]
[3]张 玲,徐海燕,王强力,等.三阴性乳腺癌组织中lncRNA HCG11 mRNA和miR-4465水平表达与临床病理特征及预后的相关性研究[J].现代检验医学杂志,2025,40(01):84.[doi:10.3969/j.issn.1671-7414.2025.01.016]
　ZHANG Ling,XU Haiyan,WANG Qiangli,et al.Correlation between lncRNA HCG11 mRNA and miR-4465 Expression Levels and Clinicopathological Features and Prognosis in Triple-negative Breast Cancer Tissues[J].Journal of Modern Laboratory Medicine,2025,40(03):84.[doi:10.3969/j.issn.1671-7414.2025.01.016]
[4]王佩佩,章笑甜,凌志明,等.EGFP报告基因PiggyBac载体在三阴性乳腺癌MDA-MB-231细胞系电转染条件优化[J].现代检验医学杂志,2025,40(03):189.[doi:10.3969/j.issn.1671-7414.2025.03.035]
　WANG Peipei,ZHANG Xiaotian,LING Zhiming,et al.Optimization of Electrotransfection Conditions of EGFP Reporter Gene PiggyBac Vector in Triple Negative Breast Cancer MDA-MB-231 Cell Line[J].Journal of Modern Laboratory Medicine,2025,40(03):189.[doi:10.3969/j.issn.1671-7414.2025.03.035]
[5]杨良权,于 淼,姜 茜,等.miR-371a-5p靶向抑制LZTS2促进三阴性乳腺癌细胞增殖、迁移、侵袭和上皮间充质转化[J].现代检验医学杂志,2026,41(02):70.[doi:10.3969/j.issn.1671-7414.2026.02.012]
　YANG Liangquan,YU Miao,JIANG Qian,et al.MiR-371a-5p Targeted Inhibition of LZTS2 Promoted the Proliferation, Migration, Invasion and Epithelial Mesenchymal Transition of Triple-Negative Breast Cancer Cells[J].Journal of Modern Laboratory Medicine,2026,41(03):70.[doi:10.3969/j.issn.1671-7414.2026.02.012]

备注/Memo

备注/Memo:
基金项目：国家自然科学基金青年项目（项目编号：82102805）。
作者简介：胡修东（1994-），男，学士，住院医师，研究方向：肿瘤相关数据挖掘分析, E-mail：751374946@qq.com。
通讯作者：陈睿鹏，男，硕士，E-mail：709486949@qq.com。
更新日期/Last Update: 2025-05-15