生物信息学分析鉴定骨髓增殖性肿瘤发生发展的免疫调控因子

doi:10.19586/j.2095-2341.2023.0160

生物技术进展 ›› 2024, Vol. 14 ›› Issue (3): 492-500.DOI: 10.19586/j.2095-2341.2023.0160

• 研究论文 • 上一篇

生物信息学分析鉴定骨髓增殖性肿瘤发生发展的免疫调控因子

梁一鹏¹^,²(), 王迪¹^,², 宋昊泽¹^,², 石莉红¹^,²(), 佟静媛¹^,²()

^1.中国医学科学院血液病医院（中国医学科学院血液学研究所）北京协和医学院，血液与健康全国重点实验室，国家血液系统疾病临床医学研究中心，天津市血液病细胞治疗研究重点实验室，细胞生态海河实验室，天津 300020
^2.天津医学健康研究院，天津 301600

收稿日期:2023-12-12 接受日期:2024-02-27 出版日期:2024-05-25 发布日期:2024-06-18
通讯作者: 石莉红,佟静媛
作者简介:梁一鹏 E-mail: liangyipeng@ihcams.ac.cn
基金资助:
国家自然科学基金项目(82100152);中国医学科学院医学与健康科技创新工程项目(2021-12M-1-073)

Identification of Immunoregulatory Factors in the Development of Myeloproliferative Neoplasms by Bioinformatics

Yipeng LIANG¹^,²(), Di WANG¹^,², Haoze SONG¹^,², Lihong SHI¹^,²(), Jingyuan TONG¹^,²()

^1.State Key Laboratory of Experimental Hematology，National Clinical Research Center for Blood Diseases，Tianjin Key Laboratory of Cell Therapy for Blood Diseases，Haihe Laboratory of Cell Ecosystem，Institute of Hematology & Blood Diseases Hospital，Chinese Academy of Medical Sciences & Peking Union Medical College，Tianjin 300020，China
^2.Tianjin Institutes of Health Science，Tianjin 301600，China

Received:2023-12-12 Accepted:2024-02-27 Online:2024-05-25 Published:2024-06-18
Contact: Lihong SHI,Jingyuan TONG

摘要/Abstract

摘要：

骨髓增殖性肿瘤是一组由造血干细胞异质性增殖引起的疾病，主要包括真红细胞增多症、原发性血小板增多症和原发性骨髓纤维化。在骨髓增殖性肿瘤患者和小鼠模型中都表现出炎症反应紊乱，而过度的炎症反应会推进骨髓增殖性肿瘤的发生和进展。深入研究骨髓增殖性肿瘤的免疫炎症机理，对其控制有着至关重要的意义。从基因表达综合数据库中寻找合适的骨髓增殖性肿瘤的基因表达谱芯片（GSE174060），其中骨髓增殖性肿瘤患者有50个，正常供者有15个。利用limma分析，筛选获得1 269个差异表达基因（|log₂FC|≥0.5，P_.adj<0.05），包括810个上调基因和459个下调基因。随后将差异基因与免疫相关基因集取交集获得了128个免疫相关基因，包括108个上调基因和20个下调基因。基因功能注释分析发现这些基因主要富集在免疫应答、趋化、白细胞介素信号传导、中性粒细胞脱颗粒、适应性免疫等通路。蛋白互作网络分析中，首先利用Cytoscape软件构建这些基因的重要模块，主要包括2个重要模块，模块一包括11个节点（node）和62个连接（edge），模块二包括9个节点和36个连接；然后通过Cytohubba插件确定了10个免疫相关关键HUB基因（IL1B、JAK2、CXCL10、ICAM1、CX3CR1、TLR4、MMP9、CD4、CCR1、LYN）。利用GSE103237数据集对这10个基因进行基因表达分析，发现其中7个基因在骨髓增殖性肿瘤组中表达上调，验证了HUB基因的重要性。综上，研究结果有助于发现骨髓增殖性肿瘤中重要的免疫炎症因子，为肿瘤治疗提供一定的策略和依据。

关键词: 骨髓增殖性肿瘤, 免疫相关HUB基因, 慢性炎症

Abstract:

Myeloproliferative neoplasms （MPN） represent a group of chronic myeloproliferative disorders initiated by the hyperproliferation of hematopoietic stem cells， giving rise to conditions such as polycythemia vera， essential thrombocythemia， and primary myelofibrosis. In both afflicted patients and murine models， the inflammatory response exhibits dysregulation， and an undue inflammatory reaction serves to foster the initiation and progression of myeloproliferative tumors. A comprehensive exploration of the immunoinflammatory mechanisms in MPN is essential for the advancement of its treatment. To investigate the mechanism， we utilized gene expression profiling microarrays （accession number GSE174060） retrieved from the Gene Expression omnibus database， including 50 patients with myeloproliferative tumor and 15 normal donors. Using limma analysis， 1 269 differentially expressed genes （DEGs） were identified （|log₂FC|≥0.5 and P_.adjust<0.05）， including 810 up-regulated genes and 459 down-regulated genes. We subsequently identified 128 immune-related genes， comprising 108 up-regulated genes and 20 down-regulated genes. This selection was made by intersecting the set of differentially expressed genes with the set of immune-related genes. Notably， these genes were predominantly enriched in signaling pathways associated with the inflammatory response and chemotaxis， as established through gene function annotation analysis. To gain insights into the interplay of these immune-related genes， we utilized Cytoscape software to construct protein interaction networks. This analysis uncovered two prominent modules： Module 1， comprising 11 nodes and 62 edges， and Module 2， consisting of 9 nodes and 36 edges. Additionally， we identified 10 critical immune-related HUB genes （IL1B， JAK2， CXCL10， ICAM1， CX3CR1， TLR4， MMP9， CD4， CCR1， LYN） using the Cytohubba plugin. The research further validated the importance of these HUB genes through gene expression analysis using the GSE103237 dataset. In conclusion， this study contributes to the identification of pivotal immune-inflammatory factors in MPN and enhances the understanding of the molecular mechanisms driving inflammation in MPN， providing a foundation for developing targeted strategies and treatment approaches for MPN.

Key words: myeloproliferative neoplasms, immune-related HUB genes, chronic inflammation

中图分类号:

梁一鹏, 王迪, 宋昊泽, 石莉红, 佟静媛. 生物信息学分析鉴定骨髓增殖性肿瘤发生发展的免疫调控因子[J]. 生物技术进展, 2024, 14(3): 492-500.

Yipeng LIANG, Di WANG, Haoze SONG, Lihong SHI, Jingyuan TONG. Identification of Immunoregulatory Factors in the Development of Myeloproliferative Neoplasms by Bioinformatics[J]. Current Biotechnology, 2024, 14(3): 492-500.

图/表 11

图1 实验流程设计图

Fig. 1 Schematic diagram of experimental process

图2 MPN患者与健康对照之间的主成分分析

Fig. 2 Principal component analysis between MPN and HC

图3 MPN与HC之间差异基因的火山图

Fig. 3 Volcanic map of differential genes between MPN and HC

图4 GO功能富集与Hallmark通路富集图

Fig. 4 Barplot of GO and Hallmark

图5 MPN患者与健康对照之间上调和下调的基因之间富集免疫特征基因的维恩图分析

Fig. 5 Venn diagram analysis of immune characteristic genes enriched between up-regulated and down-regulated genes between MPN and HC

图6 差异基因与免疫相关基因之间维恩图分析以及免疫相关基因的表达热图注：热图中颜色代表基因表达程度，越红表达越高；数字表示归一化后的表达量，数字越大表达量越高。

Fig. 6 Venn diagram analysis between differential genes and immune-related genes and heatmap of immune-related genes expression

图7 GO功能富集与REACTOME通路富集图

Fig. 7 Barplot of GO and REACTOME

图8 免疫相关基因的模块分析

Fig. 8 MCODE analysis of immune-related genes

图9 免疫相关基因的HUB基因分析注：HUB基因的互作网络，颜色深浅代表计算分数的高低，越深代表分数越高。

Fig. 9 HUB analysis of immune-related genes

图10 HUB基因的表达水平

Fig. 10 Expression of HUB genes

图11 验证HUB基因的表达水平

Fig. 11 Verification of HUB genes expression

参考文献 34

1	JAMIESON C H, GOTLIB J, DUROCHER J A, et al.. The JAK2 V617F mutation occurs in hematopoietic stem cells in polycythemia vera and predisposes toward erythroid differentiation[J]. Proc. Natl. Acad. Sci. USA, 2006, 103(16): 6224-6229.
2	GRINFELD J, NANGALIA J, BAXTER E J, et al.. Classification and personalized prognosis in myeloproliferative neoplasms[J]. N. Engl. J. Med., 2018, 379(15): 1416-1430.
3	CAMPBELL P J, GREEN A R. The myeloproliferative disorders[J]. N. Engl. J. Med., 2006, 355(23): 2452-2466.
4	TYNER J W, BUMM T G, DEININGER J, et al.. CYT387, a novel JAK2 inhibitor, induces hematologic responses and normalizes inflammatory cytokines in murine myeloproliferative neoplasms[J]. Blood, 2010, 115(25): 5232-5240.
5	TEFFERI A, VAIDYA R, CARAMAZZA D, et al.. Circulating interleukin (IL)-8, IL-2R, IL-12, and IL-15 levels are independently prognostic in primary myelofibrosis: a comprehensive cytokine profiling study[J]. J. Clin. Oncol., 2011, 29(10): 1356-1363.
6	HOERMANN G, GREINER G, VALENT P. Cytokine regulation of microenvironmental cells in myeloproliferative neoplasms[J/OL]. Mediat. Inflamm., 2015, 2015: 869242[2024-04-07]. .
7	EDGAR R, DOMRACHEV M, LASH A E. Gene expression omnibus: NCBI gene expression and hybridization array data repository[J]. Nucleic Acids Res., 2002, 30(1): 207-210.
8	BAUMEISTER J, MAIÉ T, CHATAIN N, et al.. Early and late stage MPN patients show distinct gene expression profiles in CD34⁺ cells[J]. Ann. Hematol., 2021, 100(12): 2943-2956.
9	ZINI R, GUGLIELMELLI P, PIETRA D, et al.. CALR mutational status identifies different disease subtypes of essential thrombocythemia showing distinct expression profiles[J/OL]. Blood Cancer J., 2017, 7(12): 638[2024-04-07]. .
10	RITCHIE M E, PHIPSON B, WU D, et al.. Limma powers differential expression analyses for RNA-sequencing and microarray studies[J/OL]. Nucleic Acids Res., 2015, 43(7): e47[2024-04-07]. .
11	ZHOU Y, ZHOU B, PACHE L, et al.. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets[J/OL]. Nat. Commun., 2019, 10(1): 1523[2024-04-07]. .
12	SZKLARCZYK D, GABLE A L, LYON D, et al.. STRING V11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets[J]. Nucleic Acids Res., 2019, 47(D1): 607-613.
13	SHANNON P, MARKIEL A, OZIER O, et al.. Cytoscape: a software environment for integrated models of biomolecular interaction networks[J]. Genome Res., 2003, 13(11): 2498-2504.
14	BADER G D, HOGUE C W V. An automated method for finding molecular complexes in large protein interaction networks[J/OL]. BMC Bioinformatics, 2003, 4: 2[2024-04-07]. .
15	HCHIN C, CHEN S H, WU H H, et al.. CytoHubba: identifying hub objects and sub-networks from complex interactome[J/OL]. BMC Syst. Biol., 2014, 8(S4): S11[2024-04-07]. .
16	UDDIN M P, MAMUN MAL, AFJAL M I, et al.. Information-theoretic feature selection with segmentation-based folded principal component analysis (PCA) for hyperspectral image classification[J]. Int. J. Remote. Sens., 2021, 42(1): 286-321.
17	MARIN OYARZÚN C P, CARESTIA A, LEV P R, et al.. Neutrophil extracellular trap formation and circulating nucleosomes in patients with chronic myeloproliferative neoplasms[J/OL]. Sci. Rep., 2016, 6: 38738[2024-04-07]. .
18	王迪,赵艳红,梁一鹏,等.CXCL2在巨核细胞分化过程中的功能研究[J].生物技术进展,2023,13(2):257-263.
	WANG D, ZHAO Y H, LIANG Y P, et al.. Research on the function of CXCL2 during megakaryocyte differentiation[J]. Curr. Biotechnol., 2023, 13(2): 257-263.
19	KLEPPE M, KOCHE R, ZOU L, et al.. Dual targeting of oncogenic activation and inflammatory signaling increases therapeutic efficacy in myeloproliferative neoplasms[J]. Cancer Cell, 2018, 33(4): 785-787.
20	MENDEZ LUQUE L F, BLACKMON A L, RAMANATHAN G, et al.. Key role of inflammation in myeloproliferative neoplasms: instigator of disease initiation, progression. and symptoms[J]. Curr. Hematol. Malig. Rep., 2019, 14(3): 145-153.
21	KLEPPE M, KWAK M, KOPPIKAR P, et al.. JAK-STAT pathway activation in malignant and nonmalignant cells contributes to MPN pathogenesis and therapeutic response[J]. Cancer Discov., 2015, 5(3): 316-331.
22	FLEISCHMAN A G, AICHBERGER K J, LUTY S B, et al.. TNFα facilitates clonal expansion of JAK2 V617F positive cells in myeloproliferative neoplasms[J]. Blood, 2011, 118(24): 6392-6398.
23	SKOV V, LARSEN T S, THOMASSEN M, et al.. Whole-blood transcriptional profiling of interferon-inducible genes identifies highly upregulated IFI₂7 in primary myelofibrosis[J]. Eur. J. Haematol., 2011, 87(1): 54-60.
24	BAXTER E J, SCOTT L M, CAMPBELL P J, et al.. Acquired mutation of the tyrosine kinase JAK2 in human myeloproliferative disorders[J]. Lancet, 2005, 365(9464): 1054-1061.
25	LARSEN T S, CHRISTENSEN J H, HASSELBALCH H C, et al.. The JAK2 V617F mutation involves B- and T-lymphocyte lineages in a subgroup of patients with Philadelphia-chromosome negative chronic myeloproliferative disorders[J]. Br. J. Haematol., 2007, 136(5): 745-751.
26	RAHMAN M F, YANG Y, LE B T, et al.. Interleukin-1 contributes to clonal expansion and progression of bone marrow fibrosis in JAK 2 V617F-induced myeloproliferative neoplasm[J/OL]. Nat. Commun., 2022, 13(1): 5347[2024-04-07]. .
27	STONE M J, HAYWARD J A, HUANG C, et al.. Mechanisms of regulation of the chemokine-receptor network[J/OL]. Int. J. Mol. Sci., 2017, 18(2): 342[2024-04-07]. .
28	DUNBAR A J, KIM D, LU M, et al.. CXCL8/CXCR2 signaling mediates bone marrow fibrosis and is a therapeutic target in myelofibrosis[J]. Blood, 2023, 141(20): 2508-2519.
29	AUGOFF K, HRYNIEWICZ-JANKOWSKA A, TABOLA R, et al.. MMP9: a tough target for targeted therapy for cancer[J/OL]. Cancers, 2022, 14(7): 1847[2024-04-07]. .
30	VUKOTIĆ M, KAPOR S, DRAGOJEVIĆ T, et al.. Inhibition of proinflammatory signaling impairs fibrosis of bone marrow mesenchymal stromal cells in myeloproliferative neoplasms[J]. Exp. Mol. Med., 2022, 54(3): 273-284.
31	MOLITOR D C A, BOOR P, BUNESS A, et al.. Macrophage frequency in the bone marrow correlates with morphologic subtype of myeloproliferative neoplasm[J]. Ann. Hematol., 2021, 100(1): 97-104.
32	SCHATTNER M. Platelet TLR4 at the crossroads of thrombosis and the innate immune response[J]. J. Leukoc. Biol., 2019, 105(5): 873-880.
33	MARÍN OYARZÚN C P, GLEMBOTSKY A C, GOETTE N P, et al.. Platelet toll-like receptors mediate thromboinflammatory responses in patients with essential thrombocythemia[J/OL]. Front. Immunol., 2020, 11: 705[2024-04-07]. .
34	TAKEDA Y, NAKASEKO C, TANAKA H, et al.. Direct activation of STAT5 by ETV6-LYN fusion protein promotes induction of myeloproliferative neoplasm with myelofibrosis[J]. Br. J. Haematol., 2011, 153(5): 589-598.

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生物信息学分析鉴定骨髓增殖性肿瘤发生发展的免疫调控因子

Identification of Immunoregulatory Factors in the Development of Myeloproliferative Neoplasms by Bioinformatics

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