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山东大学学报 (医学版) ›› 2022, Vol. 60 ›› Issue (3): 51-58.doi: 10.6040/j.issn.1671-7554.0.2021.0821

• • 上一篇    

基于数据库LKB1突变肺腺癌DNA异常甲基化位点构建的预后风险模型

郑昊天1*,王光辉1,2*,赵小刚3,王亚东1,曾榆凯1,杜贾军1,2   

  • 发布日期:2022-03-09
  • 通讯作者: 杜贾军. E-mail:dujiajun@sdu.edu.cn
  • 基金资助:
    山东省自然科学基金(ZR2021MH192);济南市临床医学科技创新计划(202019058)

A prognostic risk model for LKB1 mutant lung adenocarcinoma based on aberrant DNA methylation sites

ZHENG Haotian1*, WANG Guanghui1,2*, ZHAO Xiaogang3, WANG Yadong1, ZENG Yukai1, DU Jiajun1,2   

  1. 1. Institute of Oncology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250021, Shandong, China;
    2. Department of Thoracic Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250021, Shandong, China;
    3. Department of Thoracic Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250033, Shandong, China
  • Published:2022-03-09

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摘要： 目的 构建DNA甲基化相关的肝激酶B1(LKB1)突变肺腺癌预后风险模型。 方法 下载并分析癌症基因组图谱(TCGA)数据库中RNA和甲基化测序数据。筛选甲基化调控显著影响预后的差异表达基因,构建预后风险模型,将LKB1突变肺腺癌患者分为高风险组和低风险组,并进行相关功能学分析。 结果 筛选出3个低甲基化高表达的预后相关基因并构建LKB1突变肺腺癌的预后风险模型。多因素COX回归分析表明,Risk score可作为独立预测因子(HR>2,P<0.001)。受试者工作特征曲线证实,Risk score比其他临床病理特征有更好的生存预测能力。功能分析表明,高风险LKB1突变肺腺癌患者促癌通路激活、免疫细胞浸润程度明显高于低风险患者。 结论 在LKB1突变肺腺癌中发掘了3个因异常甲基化而表达失调的分子标记物,据此构建的预后风险模型可以准确筛选LKB1突变肺腺癌患者中的高风险人群,提供生存预测,为LKB1突变肺腺癌的分子机制研究及临床预后分析提供新思路。

关键词: 肝激酶B1, 肺腺癌, DNA甲基化, 预后风险模型, 癌症基因组图谱

Abstract: Objective To construct a methylation-related prognostic risk model of liver kinase B1(LKB1)mutant lung adenocarcinoma. Methods The RNA and methylation sequencing data from the The Cancer Genome Atlas(TCGA)database were downloaded and analyzed. Differentially expressed genes that significantly affected the prognosis and were regulated by different methylation sites were screened out to construct a prognostic risk model. Then, the LKB1 mutant lung adenocarcinoma patients were divided into the high-risk group and low-risk group and the corresponding function was analyzed. Results Three prognostic-related genes with low methylation levels and high expression levels were screened out and a prognostic risk model of LKB1 mutant lung adenocarcinoma was constructed. Multivariate COX regression analysis showed that Risk score could be used as an independent predictor(HR>2, P<0.001). The receiver operating characteristic(ROC)curve confirmed that the Risk score had better survival predictive ability than other clinicopathological characteristics. The cancer-promoting pathway was activated in the high-risk group and the degree of immune cell infiltration was significantly higher than that in the low-risk group. Conclusion Three biomarkers due to aberrant methylation are discovered in LKB1 mutant lung adenocarcinomas. The prognostic risk model can accurately screen the high-risk population among patients with LKB1 mutant lung adenocarcinoma, and provides accurate survival predictions. The study provides new ideas for the molecular mechanism and the clinical prognosis analysis of LKB1 mutant lung adenocarcinoma.

Key words: Liver kinase B1, Lung adenocarcinoma, DNA methylation, Prognostic risk model, The Cancer Genome Atlas

中图分类号: 

  • R574
[1] Hirsch FR, Scagliotti GV, Mulshine JL, et al. Lung cancer: current therapies and new targeted treatments [J]. The Lancet, 2017, 389(10066): 299-311.
[2] Jordan EJ, Kim HR, Arcila ME, et al. Prospective comprehensive molecular characterization of lung adenocarcinomas for efficient patient matching to approved and emerging therapies [J]. Cancer Discov, 2017, 7(6): 596-609.
[3] Yang Z, Liu B, Lin T, et al. Multiomics analysis on DNA methylation and the expression of both messenger RNA and microRNA in lung adenocarcinoma [J]. J Cell Physiol, 2019, 234(5): 7579-7586.
[4] Gerlinger M, Rowan AJ, Horswell S, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing [J]. N Engl J Med, 2012, 366(10): 883-892.
[5] Navin N, Kendall J, Troge J, et al. Tumour evolution inferred by single-cell sequencing [J]. Nature, 2011, 472(7341): 90-94.
[6] Cancer Genome Atlas Research Network. Comprehensive molecular profiling of lung adenocarcinoma [J]. Nature, 2014, 511(7511): 543-550.
[7] Ding L, Getz G, Wheeler DA, et al. Somatic mutations affect key pathways in lung adenocarcinoma [J]. Nature, 2008, 455(7216): 1069-1075.
[8] Shackelford DB, Shaw RJ. The LKB1-AMPK pathway: metabolism and growth control in tumour suppression [J]. Nat Rev Cancer, 2009, 9(8): 563-575.
[9] 马晴. LKB1抑制MMP1表达和肺癌细胞侵袭的分子机制 [D].天津:天津医科大学, 2016.
[10] Wu D, Gong C, Su C. Genome-wide analysis of differential DNA methylation in Silver-Russell syndrome [J]. Sci China Life Sci, 2017, 60(7): 692-699.
[11] Ferry L, Fournier A, Tsusaka T, et al. Methylation of DNA ligase 1 by G9a/GLP recruits UHRF1 to replicating DNA and regulates DNA methylation [J]. Mol Cell, 2017, 67(4): 550-565.e5.
[12] Zheng X, Zhang N, Wu HJ, et al. Estimating and accounting for tumor purity in the analysis of DNA methylation data from cancer studies [J]. Genome Biol, 2017, 18(1): 17.
[13] 谭玉娥, 刘鑫. IRF4、ELMO1、CLIP4和MSC启动子甲基化水平在胃癌早期筛查中的应用价值分析 [J]. 河北医药, 2021, 43(2): 182-186. TAN Yue, LIU Xin. Application value of IRF4, ELMO1 and CLIP4 promoter methylation levels in early screening of gastric cancer [J]. Hebei Medical Journal, 2021, 43(2): 182-186.
[14] Rajaraman P, Anderson BO, Basu P, et al. Recommendations for screening and early detection of common cancers in India [J]. The Lancet Oncology, 2015, 16(7): e352-e361.
[15] Wei JH, Haddad A, Wu KJ, et al. A CpG-methylation-based assay to predict survival in clear cell renal cell carcinoma [J]. Nat Commun, 2015, 6: 8699. doi: 10.1038/ncomms9699.
[16] Heyn H, Vidal E, Ferreira HJ, et al. Epigenomic analysis detects aberrant super-enhancer DNA methylation in human cancer [J]. Genome Biol, 2016, 17: 11. doi: 10.1186/s13059-016-0879-2.
[17] 朱良宇, 孙宏瑜, 周谦, 等. 利用数据库资料分析DNA甲基化调控AC004540.4表达水平与肝细胞癌预后相关性 [J]. 中华肿瘤防治杂志, 2021, 28(3): 205-211. ZHU Liangyu, SUN Hongyu, ZHOU Qian, et al. Analysis of the relationship of DNA methylation AX004540.4 expression with prognosis of hepatocellular carcinoma with database data [J]. Chinese Journal of Cancer Prevention and Treatment, 2021, 28(3): 205-211.
[18] Fang Q, Chen H. Development of a novel autophagy-related prognostic signature and nomogram for hepatocellular carcinoma [J]. Front Oncol, 2020, 10: 591356. doi: 10.3389/fonc.2020.591356. eCollection 2020.
[19] Li X, Jin F, Li Y. A novel autophagy-related lncRNA prognostic risk model for breast cancer [J]. J Cell Mol Med, 2021, 25(1): 4-14.
[20] Hinshaw DC, Shevde LA. The tumor microenvironment innately modulates cancer progression [J]. Cancer Research, 2019, 79(18): 4557-4566.
[21] Pitt JM, Marabelle A, Eggermont A, et al. Targeting the tumor microenvironment: removing obstruction to anticancer immune responses and immunotherapy [J]. Ann Oncol, 2016, 27(8): 1482-1492.
[22] Xu JY, Zhang C, Wang X, et al. Integrative proteomic characterization of human lung adenocarcinoma [J]. Cell, 2020, 182(1): 245-261.e17.
[23] Saito M, Suzuki H, Kono K, et al. Treatment of lung adenocarcinoma by molecular-targeted therapy and immunotherapy [J]. Surg Today, 2018, 48(1): 1-8.
[24] 高向征, 梁可莹, 梅圣圣, 等. 联合靶向免疫检查点CD47与PDL1的抗肿瘤研究进展 [J]. 中国细胞生物学学报, 2021, 43(4): 896-904. GAO Xiangzheng, LIANG Keying, MEI Shengsheng, et al. Anti-tumor progress on dual blockage of immune checkpoints CD47 and PDL1 [J]. Chinese Journal of Cell Biology, 2021, 43(4): 896-904.
[25] Cha YJ, Kim HR, Lee CY, et al. Clinicopathological and prognostic significance of programmed cell death ligand-1 expression in lung adenocarcinoma and its relationship with p53 status [J]. Lung Cancer, 2016, 97: 73-80. doi: 10.1016/j.lungcan.2016.05.001.
[26] Teglasi V, Reiniger L, Fabian K, et al. Evaluating the significance of density, localization, and PD-1/PD-L1 immunopositivity of mononuclear cells in the clinical course of lung adenocarcinoma patients with brain metastasis [J]. Neuro Oncol, 2017, 19(8): 1058-1067.
[27] Thakur C, Chen F. Connections between metabolism and epigenetics in cancers [J]. Semin Cancer Biol, 2019, 57: 52-58. doi: 10.1016/j.semcancer.2019.06.006.
[28] Liu X, Fu J, Bi H, et al. DNA methylation of SFRP1, SFRP2, and WIF1 and prognosis of postoperative colorectal cancer patients [J]. BMC Cancer, 2019, 19(1): 1212.
[29] Liu P, Shen JK, Hornicek FJ, et al. Wnt inhibitory factor 1(WIF1)methylation and its association with clinical prognosis in patients with chondrosarcoma [J]. Sci Rep, 2017, 7(1): 1580.
[30] Stewart DJ. Wnt signaling pathway in non-small cell lung cancer [J]. J Natl Cancer Inst, 2014, 106(1): djt356.
[31] Marjanovic I, Karan-Djurasevic T, Kostic T, et al. Prognostic significance of combined BAALC and MN1 gene expression level in acute myeloid leukemia with normal karyotype [J]. Int J Lab Hematol, 2021, 43(3): 433-440.
[32] Hagag AA, Elshehaby WA, Hablas NM, et al. Role of BAALC gene in prognosis of acute lymphoblastic leukemia in Egyptian children [J]. Indian J Hematol Blood Transfus, 2018, 34(1): 54-61.
[33] Morita K, Masamoto Y, Kataoka K, et al. BAALC potentiates oncogenic ERK pathway through interactions with MEKK1 and KLF4 [J]. Leukemia, 2015, 29(11): 2248-2256.
[34] Zhang H, Wang Y, Zhang W, et al. BAALC-AS1/G3BP2/c-Myc feedback loop promotes cell proliferation in esophageal squamous cell carcinoma [J]. Cancer Commun(Lond), 2021, 41(3): 240-257.
[35] Zhang Y, Tang B, Song J, et al. Lnc-PDZD7 contributes to stemness properties and chemosensitivity in hepatocellular carcinoma through EZH2-mediated ATOH8 transcriptional repression [J]. J Exp Clin Cancer Res, 2019, 38(1): 92.
[36] Cassetta L, Pollard JW. Targeting macrophages: therapeutic approaches in cancer [J]. Nat Rev Drug Discov, 2018, 17(12): 887-904.
[37] Di Vito C, Mikulak J, Zaghi E, et al. NK cells to cure cancer [J]. Semin Immunol, 2019, 41: 101272. doi: 10.1016/j.smim.2019.03.004.
[38] Thommen DS, Schumacher TN. T cell dysfunction in cancer [J]. Cancer Cell, 2018, 33(4): 547-562.
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