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《中华肺部疾病杂志》[ISSN:1006-6977/CN:61-1281/TN]

卷:

期数:

2023年01期

页码:

20-25

栏目:

论著

出版日期:

2023-02-20

文章信息/Info

Title:

Analysis of differential genes that affect the efficacy of Nivolumab and Pembrolizumab in the treatment of non-small cell lung cancer based on GEO database

作者:

李一然; 王玉秀; 朱研; 王梦; 刘颖; 闫文锦; 徐兴祥; 闵凌峰

225001 扬州,大连医科大学附属苏北医院,南京大学医学院附属苏北医院, 扬州大学医学院附属苏北人民医院呼吸与危重症医学科

Author(s):

Li Yiran;  Wang Yuxiu;  Zhu Yan;  Wang Meng;  Liu Ying;  Yan Wenjin;  Xu Xingxiang;  Min Lingfeng.

The Department of Respiratory and Critical Care Medicine of North Jiangsu People's Hospital Affiliated to Dalian Medical University, North Jiangsu Hospital Affiliated to Nanjing University Medical College, North Jiangsu Hospital affiliated to Yangzhou Medical University, Yangzhou 225001, China

关键词:

纳武单抗;  派姆单抗;  非小细胞肺癌;  差异表达基因

Keywords:

Nivolumab;  Pembrolizumab;  Non-small cell lung cancer;  Differentially expressed genes

分类号:

R734.2

DOI:

10.3877/cma.j.issn.1674-6902.2023.01.005

摘要:

目的 筛选影响纳武单抗和派姆单抗治疗非小细胞肺癌(non-small cell lung cancer, NSCLC)疗效的差异基因,为免疫治疗药物的选择及治疗预后提供参考。方法 通过GEO数据库搜索“Nivolumab”、“Pembrolizumab”找到目的芯片,下载免疫治疗相关表达芯片“GSE93157”,筛选NSCLC相关样本共35个,利用R语言数据包对样本进行表达差异基因进行聚类分析。对差异基因进行基因功能注释GO分析和KEGG通路分析,构建蛋白相互作用网络,筛选枢纽基因进行生存分析,确定影响不同抗程序性细胞死亡蛋白1药物治疗的关键基因。结果 筛选出影响纳武单抗治疗疗效差异基因共58个,其中免疫相关基因25个; 影响派姆单抗治疗疗效差异基因231个,免疫相关基因82个。基于两种药物免疫相关差异基因的蛋白互作网络提示纳武单抗共得到2个子网络,主要模块共11个节点,51个边; 派姆单抗共得到4个子网络,主要模块共24个节点,231个边。影响两种药物治疗疗效的前10位主要免疫相关基因生存分析,显示生存差异具有统计学意义(P<0.05)的基因,与纳武单抗相关的免疫差异基因为CD5、CD22、CR2、CD40LG。与派姆单抗相关的免疫差异基因为CTLA4、SELL、IL7、CD40LG、CD2、IL7R。结论 纳武单抗治疗NSCLC患者的关键免疫相关差异基因CD5、CD22、CR2; 派姆单抗治疗NSCLC的关键免疫相关差异基因为CTLA4、SELL、IL7、CD2、IL7R。两种药物共同免疫相关差异基因为CD40LG,有望成为抗PD-1抑制剂治疗NSCLC预后预测的潜在生物标志物及靶点。

Abstract:

Objective Using gene expression microarray database and bioinformatics methods to screen the differential genes that affect the efficacy of Nivolumab and Pembrolizumab in the treatment of non-small cell lung cancer, to provide references for the selection of these two commonly used immunotherapy drugs in clinical practice and the prognosis of treatment. Methods Searching "Nivolumab" and "Pembrolizumab" through the GEO database to find the target chip, downloading the immunotherapy-related expression chip "GSE93157", screening out a total of 35 non-small cell lung cancer-related samples, and using the R language data package to perform differential gene expression and analyzing and performing cluster analysis on differential genes. Performing gene function annotation GO analysis and KEGG pathway analysis on differential genes, constructing protein interaction network, screening out pivot genes for survival analysis, and identifing key genes that affect different anti-programmed cell death protein 1 drug treatments. Results By analyzing the differential genes of NSCLC patients with different therapeutic effects(clinical benefits and disease progression)of Nivolumab or Pembrolizumab, a total of 58 differential genes were screened out that affect the therapeutic efficacy of Nivolumab or Pembrolizumab, including immune-related genes 25; 231 differential genes and 82 immune-related genes that affect the therapeutic effect of Pembrolizumab. These differential genes are involved in different biological processes. The protein interaction network based on two kinds of drug immune-related differential genes prompted a total of 2 sub-networks for Nivolumab, with a total of 11 nodes and 51 edges for the main modules; for Pembrolizumab, a total of 4 sub-networks, the main module There are a total of 24 nodes and 231 edges. The survival analysis of the top 10 main immune-related genes that affect the efficacy of the two drugs was conducted, and genes with statistically significant differences in survival(P<0.05)were selected. The results showed that the immune differential genes related to Nivolumab were CD5, CD22, CR2, CD40LG. The immune differential genes related to Pembrolizumab are CTLA4, SELL, IL7, CD40LG, CD2, IL7R. Conclusion We analyzed the key immune-related differential genes CD5, CD22, and CR2 for Nivolumab in the treatment of NSCLC patients through bioinformatics; the key immune-related differential genes for Pembrolizumab in the treatment of NSCLC patients are CTLA4, SELL, IL7, CD2 IL7R. The two drugs share the common immune-related differential gene CD40LG. They are expected to become potential biomarkers and targets for predicting the efficacy of anti-PD-1 inhibitors in the treatment of NSCLC patients.

参考文献/References:

1 王洪武, 金发光. 晚期非小细胞肺癌多域整合治疗策略[J/CD]. 中华肺部疾病杂志(电子版), 2022, 15(4): 457-461.
2 王 园, 杨 懿, 牟云飞, 等. 可切除非小细胞肺癌患者术后奥西替尼靶向治疗分析[J/CD]. 中华肺部疾病杂志(电子版), 2022, 15(5): 567-660.
3 Duma N, Santana-Davila R, Molina JR. Non-small cell lung cancer:Epidemiology, screening, diagnosis, and treatment[J]. Mayo Clin Proc, 2019, 94(8): 1623-1640.
4 Wang L, Ma Q, Yao R, et al. Current status and development of anti-PD-1/PD-L1 immunotherapy for lung cancer[J]. Int Immunopharmacol, 2020, 79: 106088.
5 Ojlert AK, Halvorsen AR, Nebdal D, et al. The immune microenvironment in non-small cell lung cancer is predictive of prognosis after surgery[J]. Mol Oncol, 2019, 13(5): 1166-1179.
6 Bianco A, Perrotta F, Barra G, et al. Prognostic factors and biomarkers of responses to immune checkpoint inhibitors in lung cancer[J]. Int J Mol Sci, 2019, 20(19): 4931.
7 Chardin D, Paquet M, Schiappa R, et al. Baseline metabolic tumor volume as a strong predictive and prognostic biomarker in patients with non-small cell lung cancer treated with PD1 inhibitors: a prospective study[J]. J Immunother Cancer, 2020, 8(2): e000645.
8 Hao D, Liu J, Chen M, et al. Immunogenomic analyses of advanced serous ovarian cancer reveal immune score is a strong prognostic factor and an indicator of chemosensitivity[J]. Clin Cancer Res, 2018, 24(15): 3560-3571.
9 Riaz N, Havel JJ, Makarov V, et al. Tumor and microenvironment evolution during immunotherapy with nivolumab[J]. Cell, 2017, 171(4): 934-949.
10 Wei F, Zhong S, Ma Z, et al. Strength of PD-1 signaling differentially affects T-cell effector functions[J]. Proc Natl Acad Sci U S A, 2013, 110(27): E2480-E2489.
11 Wang Q, Li P, Wu W. A systematic analysis of immune genes and overall survival in cancer patients[J]. BMC Cancer, 2019, 19(1): 1225.
12 Burger JA, Wiestner A. Targeting B cell receptor signalling in cancer:preclinical and clinical advances[J]. Nat Rev Cancer, 2018, 18(3): 148-167.
13 Mitchell S, Vargas J, Hoffmann A. Signaling via the NFkappaB system[J]. Wiley Interdiscip Rev Syst Biol Med, 2016, 8(3): 227-241.
14 Scott O, Roifman CM. NF-kappaB pathway and the Goldilocks principle:Lessons from human disorders of immunity and inflammation[J]. J Allergy Clin Immunol, 2019, 143(5): 1688-1701.
15 DiDonato JA, Mercurio F, Karin M. NF-kappaB and the link between inflammation and cancer[J]. Immunol Rev, 2012, 246(1): 379-400.
16 Ji Z, He L, Regev A, et al. Inflammatory regulatory network mediated by the joint action of NF-kB, STAT3, and AP-1 factors is involved in many human cancers[J]. Proc Natl Acad Sci U S A, 2019, 116(19): 9453-9462.
17 Pencik J, Pham HT, Schmoellerl J, et al. JAK-STAT signaling in cancer: From cytokines to non-coding genome[J]. Cytokine, 2016, 87: 26-36.
18 Owen KL, Brockwell NK, Parker BS. JAK-STAT signaling: A double-edged sword of immune regulation and cancer progression[J]. Cancers(Basel), 2019, 11(12): 2002.
19 Anthoney N, Foldi I, Hidalgo A. Toll and Toll-like receptor signalling in development[J]. Development, 2018, 145(9): dev156018.
20 Shcheblyakov DV, Logunov DY, Tukhvatulin AI, et al. Toll-like receptors(TLRs): The role in tumor progression[J]. Acta Naturae, 2010, 2(3): 21-29.
21 Ohadian MS, Nowroozi MR. Toll-like receptors: The role in bladder cancer development, progression and immunotherapy[J]. Scand J Immunol, 2019, 90(6): e12818.
22 Moreno-Manuel A, Jantus-Lewintre E, Simoes I, et al. CD5 and CD6 as immunoregulatory biomarkers in non-small cell lung cancer[J]. Transl Lung Cancer Res, 2020, 9(4): 1074-1083.
23 Pop LM, Barman S, Shao C, et al. A reevaluation of CD22 expression in human lung cancer[J]. Cancer Res, 2014, 74(1): 263-271.
24 Tuscano JM, Kato J, Pearson D, et al. CD22 antigen is broadly expressed on lung cancer cells and is a target for antibody-based therapy[J]. Cancer Res, 2012, 72(21): 5556-5565.
25 Holers VM, Kulik L. Complement receptor 2, natural antibodies and innate immunity: Inter-relationships in B cell selection and activation[J]. Mol Immunol, 2007, 44(1-3): 64-72.
26 Hannan JP. The structure-function relationships of complement receptor Type 2(CR2; CD21)[J]. Curr Protein Pept Sci, 2016, 17(5): 463-487.
27 Buchbinder EI, Desai A. CTLA-4 and PD-1 Pathways: Similarities, differences, and implications of their inhibition[J]. Am J Clin Oncol, 2016, 39(1): 98-106.
28 Kagamu H, Kitano S, Yamaguchi O, et al. CD4(+)T-cell immunity in the peripheral blood correlates with response to anti-PD-1 therapy[J]. Cancer Immunol Res, 2020, 8(3): 334-344.
29 Zhang XH, Wang HM, Jin L, et al. Effect of interleukin-7 on the anti-tumor function of CD8(+)T cells in patients with non-small cell lung cancer][J]. Zhonghua Yi Xue Za Zhi, 2018, 98(30): 2429-2433.
30 Liu ZH, Wang MH, Ren HJ, et al. Interleukin 7 signaling prevents apoptosis by regulating bcl-2 and bax via the p53 pathway in human non-small cell lung cancer cells[J]. Int J Clin Exp Pathol, 2014, 7(3): 870-881.
31 Ming J, Zhang QF, Jiang YD, et al. Interleukin 7 and its receptor promote cell proliferation and induce lymphangiogenesis in non-small cell lung cancer [J]. Zhonghua Bing Li Xue Za Zhi, 2012, 41(8): 511-518.
32 Binder C, Cvetkovski F, Sellberg F, et al. CD2 Immunobiology[J]. Front Immunol, 2020,11: 1090.
33 McNerney ME, Kumar V. The CD2 family of natural killer cell receptors[J]. Curr Top Microbiol Immunol, 2006, 298: 91-120.
34 Ivetic A, Hoskins GH, Hart SJ. L-selectin: A Major Regulator of Leukocyte Adhesion, Migration and Signaling[J]. Front Immunol, 2019, 10: 1068.
35 Watson HA, Durairaj R, Ohme J, et al. L-Selectin enhanced T cells improve the efficacy of cancer immunotherapy[J]. Front Immunol, 2019, 10: 1321.
36 Kuhn NF, Purdon TJ, van Leeuwen DG, et al. CD40 Ligand-modified chimeric antigen receptor T cells enhance antitumor function by eliciting an endogenous antitumor response[J]. Cancer Cell, 2019, 35(3): 473-488.
37 Seijkens T, Engel D, Tjwa M, et al. The role of CD154 in haematopoietic development[J]. Thromb Haemost, 2010, 104(4): 693-701.
38 Hassan GS, Stagg J, Mourad W. Role of CD154 in cancer pathogenesis and immunotherapy[J]. Cancer Treat Rev, 2015, 41(5): 431-440.

备注/Memo

备注/Memo:

通信作者: 闵凌峰, Email: minlingfeng@126.com

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更新日期/Last Update: 2023-02-20