基于数据库构建乳腺癌焦亡相关基因的预后风险模型

doi:10.6040/j.issn.1671-7554.0.2021.1339

摘要/Abstract

摘要： 目的探究焦亡相关差异表达基因(DEGs)在乳腺癌中预后价值并构建预后风险模型。方法从癌症基因组图谱(TCGA)和肿瘤基因表达数据库(GEO)官网下载乳腺癌的基因测序、临床数据,筛选焦亡相关DEGs。将乳腺癌患者进行聚类分析。在TCGA队列中以最小绝对收缩和选择算子(LASSO)方法建立模型。利用Kaplan-Meier生存曲线、受试者工作特征曲线(ROC)、单因素及多因素Cox回归独立预后因素分析等评价该模型。GEO队列为验证集。通过GO、KEGG、ssGSEA分析风险DEGs的富集情况。结果筛选出焦亡相关DEGs,聚类分析可见C2组总生存期(OS)延长,差异有统计学意义(P=0.020)。该模型K-M生存分析显示,高风险组OS缩短(TCGA队列中P<0.001,GEO队列中P=0.018)。ROC曲线下面积(AUC)表明该模型具有一定预测能力。单因素、多因素Cox回归分析表明,年龄、M、N分期和风险评分为OS的独立预测因子。GO、 KEGG富集与ssGSEA分析证实了风险相关DEGs与免疫炎症因子和通路有关。结论本研究构建了由9个焦亡相关基因组成的乳腺癌预后风险模型,为乳腺癌患者的风险预后评估提供了参考。

关键词: 乳腺癌, 焦亡, 免疫, 预后, 预测模型

Abstract: Objective To explore the prognostic value of pyroptosis-related differentially expressed genes(DEGs)in breast cancer and to construct a prognostic risk model. Methods Gene sequencing and clinical data of breast cancer were downloaded from The Cancer Genome Atlas(TCGA)and Gene Expression Ominibus(GEO)to screen for pyroptosis-related DEGs. A cluster analysis was performed on breast cancer patients. The model of TCGA cohort was established by the least absolute shrinkage and selection operator(LASSO)method, which was then evaluated with Kaplan-Meier survival curve, receiver operating characteristic curve(ROC), univariate and multivariate Cox regression independent prognostic factor analysis. The GEO cohort was used as the validation set. Enrichment of DGEs was analyzed with GO, KEGG, and ssGSEA. Results Pyroptosis-related DEGs were screened, cluster analysis showed that the overall survival(OS)of C2 group was prolonged, and the difference was statistically significant?(P=0.020). K-M survival analysis showed that OS was shortened in the high-risk group(P<0.001 in the TCGA cohort, P=0.018 in the GEO cohort). The area under the ROC curve(AUC)showed that the model had certain predictive ability. Univariate and multivariate Cox regression showed that age, M and N stage, and risk score were independent predictors of OS. GO, KEGG and ssGSEA analyses confirmed that pyroptosis-related DEGs were related to immune inflammatory factors and pathways. Conclusion This study constructed a prognostic risk model of breast cancer composed of 9 pyroptosis-related genes, which can provide reference for the risk and prognosis assessment of breast cancer patients.

Key words: Breast cancer, Pyroptosis, Immunity, Prognosis, Prediction model

中图分类号:

R737.9

贺士卿,李皖皖,董书晴,牟婧怡,刘宇莹,魏思雨,刘钊,张家新. 基于数据库构建乳腺癌焦亡相关基因的预后风险模型[J]. 山东大学学报 (医学版), 2022, 60(8): 34-43.

HE Shiqing, LI Wanwan, DONG Shuqing, MOU Jingyi, LIU Yuying, WEI Siyu, LIU Zhao, ZHANG Jiaxin. Construction of a prognostic risk model of pyroptosis-related genes in breast cancer based on database[J]. Journal of Shandong University (Health Sciences), 2022, 60(8): 34-43.

参考文献

[1] Siegel RL, Miller KD, Fuchs HE, et al. Cancer Statistics, 2021 [J]. CACancer J Clin, 2021, 71(1): 7-33.
[2] Britt KL, Cuzick J, Phillips KA. Key steps for effective breast cancer prevention [J]. NatRev Cancer, 2020, 20(8): 417-436.
[3] Barzaman K, Karami J, Zarei Z, et al. Breast cancer: biology, biomarkers, and treatments [J]. Int Immunopharmacol, 2020, 84: 106535. doi: 10.1016/j.intimp.2020.106535.
[4] Liu X, Lieberman J. A mechanistic understanding of pyroptosis: the fiery death triggered by invasive infection [J]. Adv Immunol 2017, 135: 81-117. doi: 10.1016/bs.ai.2017.02.002.
[5] 胡颖超, 杨硕. 细胞焦亡的研究进展[J]. 南京医科大学学报(自然科学版), 2021, 41(8): 1245-1251. HU Yingchao, YANG Shuo. Advance in research of phroptosis [J]. 2021, 41(8): 1245-1251.
[6] Tan Y, Chen Q, Li X, et al. Pyroptosis: a new paradigm of cell death for fighting against cancer [J]. J Exp Clin Cancer Res, 2021, 40(1): 153.
[7] Ni J, Peng Y, Yang FL, et al. Overexpression of CLEC3A promotes tumor progression and poor prognosis in breast invasive ductal cancer [J]. OncoTargets Ther, 2018, 11: 3303-12. doi: 10.2147/OTT.S161311.
[8] Bakaeean B, Gholamin M, Tabatabaee Yazdi SA, et al. Novel biomarkers aim at detecting metastatic sentinel lymph nodes in breast cancer [J]. Iran Biomed J, 2020, 24(3): 183-191.
[9] Ren C, Pan R, Hou L, et al. Suppression of CLEC3A inhibits osteosarcoma cell proliferation and promotes their chemosensitivity through the AKT1/mTOR/HIF1α signaling pathway [J]. Mol Med Rep, 2020, 21(4): 1739-1748.
[10] Tsunezumi J, Higashi S, Miyazaki K. Matrilysin(MMP-7)cleaves C-type lectin domain family 3 member A(CLEC3A)on tumor cell surface and modulates its cell adhesion activity [J]. J Cell Biochem, 2009, 106(4): 693-702.
[11] Miki M, Oono T, Fujimori N, et al. CLEC3A, MMP7, and LCN2 as novel markers for predicting recurrence in resected G1 and G2 pancreatic neuroendocrine tumors [J]. Cancer Med, 2019, 8(8): 3748-3760.
[12] Qi T, Qu J, Tu C, et al. Super-enhancer associated five-gene risk score model predicts overall survival in multiple myeloma patients [J]. Front Cell Dev Biol, 2020, 8: 596777. doi: 10.3389/fcell.2020.596777.
[13] Zhang G, Liu Y, Dong F, et al. Transcription/expression of KLRB1 gene as a prognostic indicator in human esophageal squamous cell carcinoma [J]. Comb Chem High Throughput Screen, 2020, 23(7): 667-674.
[14] Qin R, Cao L, Ye C, et al. A novel prognostic prediction model based on seven immune-related RNAs for predicting overall survival of patients in early cervical squamous cell carcinoma [J]. BMC Med Genomics, 2021, 14(1): 49.
[15] Ma C, Luo H, Cao J, et al. Identification of a novel tumor microenvironment-associated eight-gene signature for prognosis prediction in lung adenocarcinoma [J]. Front Mol Biosci, 2020, 7: 571641. doi: 10.3389/fmolb.2020.571641.
[16] Zheng M, Mullikin H, Hester A, et al. Development and validation of a novel 11-gene prognostic model for serous ovarian carcinomas based on lipid metabolism expression profile [J]. Int J Mol Sci, 2020, 21(23): 9169.
[17] Zhang Y, Di X, Chen G, et al. An immune-related signature that to improve prognosis prediction of breast cancer [J]. Am J Cancer Res, 2021, 11(4): 1267-1285.
[18] Park YJ, Ryu H, Choi G, et al. IL-27 confers a protumorigenic activity of regulatory T cells via CD39 [J]. Proc Natl Acad Sci U S A, 2019, 116(8): 3106-3111.
[19] Wang L, Liu J, Tai J, et al. A prospective study revealing the role of an immune-related eRNA, WAKMAR2, in breast cancer [J]. Sci Rep, 2021, 11(1): 15328.
[20] Ignacio RMC, Gibbs CR, Kim S, et al. Serum amyloid A predisposes inflammatory tumor microenvironment in triple negative breast cancer [J]. Oncotarget, 2019, 10(4): 511-526.
[21] Jiao J, Jiang L, Luo Y. N6-methyladenosine-related RNA signature predicting the prognosis of ovarian cancer [J]. Recent Pat Anticancer Drug Discov, 2021, 16(3): 407-416.
[22] Liang J, Zhao W, Tong P, et al. Comprehensive molecular characterization of inhibitors of apoptosis proteins(IAPs)for therapeutic targeting in cancer [J]. BMC Med Genomics, 2020, 13(1): 7.
[23] Cao C, Lin S, Zhi W, et al. LOXL2 expression status is correlated with molecular characterizations of cervical carcinoma and associated with poor cancer survival via epithelial-mesenchymal transition(EMT)phenotype [J]. Front Oncol, 2020, 10: 284. doi: 10.3389/fonc.2020.00284.
[24] Shahi P, Wang CY, Chou J, et al. GATA3 targets semaphorin 3B in mammary epithelial cells to suppress breast cancer progression and metastasis [J]. Oncogene, 2017, 36(40): 5567-5575.
[25] Milioli HH, Sousa KS, Kaviski R, et al. Comparative proteomics of primary breast carcinomas and lymph node metastases outlining markers of tumor invasion [J]. Cancer Genomics Proteomics, 2015, 12(2): 89-101.

多维度评价

Viewed

Full text

Abstract

Cited

Shared

Discussed