CXCL12可作为2型糖尿病合并慢性阻塞性肺疾病的潜在治疗靶点

doi:10.12122/j.issn.1673-4254.2025.01.13

摘要/Abstract

摘要：

目的探讨2型糖尿病与慢性阻塞性肺疾病（COPD）的关键基因与免疫学共同机制，并研究潜在的治疗靶点。方法利用基因表达综合数据库（GEO）获取COPD与T2DM的基因表达谱。筛选出共同差异表达基因，并对其进行富集分析和蛋白-蛋白相互网络构建后，综合4种拓扑算法与Friends分析得到候选基因。进行数据集和疾病验证集中候选基因的差异表达验证，得到最终靶点基因，通过受试者工作特征曲线（ROC）评估诊断特征的准确性，通过临床收集的共患病患者资料和血液标本验证靶基因的表达和肺功能相关性。基于CIBERSORT算法分析数据集样品中22种免疫细胞丰度，通过相关性分析靶点基因与22种免疫细胞的关系。使用DGIdb数据库筛选药物-基因互作关系信息和可药用的基因。最后对进行基因集富集分析。结果选取两种疾病175个共同差异表达基因进分析，结果显示其主要富集于免疫与炎症相关通路，最终筛选趋化因子配体12（CXCL12）作为最终靶基因，其表达在两种疾病中均明显上升（P<0.05），且在ROC曲线中表现出较优异效能，并在COPD共患2型糖尿病患者的血液得到验证。CXCL12表达与肺功能的相关性也得到验证。免疫浸润分析结果显示，CXCL12与CD8⁺T细胞、γδT细胞和静息肥大细胞呈正相关，而与静息NK细胞和嗜酸性粒细胞呈负相关，差异均具有统计学意义（P<0.01）。GESA分析显示“细胞因子-细胞因子受体相互作用”为与CXCL12高表达密切相关的活化途径。药物-基因检测显示，与CXCL12相关的药物多为非靶向，有较大的细胞毒性，还有进一步改良的空间。结论 CXCL12可能是COPD与T2DM共同的关键发病基因，靶向CXCL12药物可能是未来COPD和T2DM共病患者更为合理、有效的药物治疗新方向。

关键词: 2型糖尿病, 慢性阻塞性肺疾病, 生物信息学分析, 趋化因子配体12, 靶点分析

Abstract:

Objective To identify the key genes and immunological pathways shared by type 2 diabetes mellitus (T2DM) and chronic obstructive pulmonary disease (COPD) and explore the potential therapeutic targets of T2DM complicated by COPD. Methods GEO database was used for analyzing the gene expression profiles in T2DM and COPD to identify the common differentially expressed genes (DEGs) in the two diseases. A protein-protein interaction network was constructed to identify the candidate hub genes, which were validated in datasets and disease sets to obtain the target genes. The diagnostic accuracy of these target genes was assessed with ROC analysis, and their expression levels and association with pulmonary functions were investigated using clinical data and blood samples of patients with T2DM and COPD. The abundance of 22 immune cells was analyzed with CIBERSORT algorithm, and their relationship with the target genes was examined using correlation analysis. DGIdb database was used for analyzing the drug-gene interactions and the druggable genes followed by gene set enrichment analysis. Results We identified a total of 175 common DEGs in T2DM and COPD, mainly enriched in immune- and inflammation-related pathways. Among these genes, CXCL12 was identified as the final target gene, whose expression was elevated in both T2DM and COPD (P<0.05) and showed good diagnostic efficacy. Immune cell infiltration correlation analysis showed significant correlations of CXCL12 with various immune cells (P<0.01). GESA analysis showed that high CXCL12 expression was significantly correlated with "cytokine-cytokine receptor interaction". Drug-gene analysis showed that most of CXCL12-related drugs were not targeted drugs with significant cytotoxicity. Conclusion CXCL12 is a potential common key pathogenic gene of COPD and T2DM, and small-molecule targeted drugs against CXCL12 can provide a new strategy for treatment T2DM complicated by COPD.

Key words: type 2 diabetes mellitus, chronic obstructive pulmonary disease, bioinformatics analysis, CXCL12, drug target analysis

许怀文, 翁丽, 薛鸿. CXCL12可作为2型糖尿病合并慢性阻塞性肺疾病的潜在治疗靶点[J]. 南方医科大学学报, 2025, 45(1): 100-109.

Huaiwen XU, Li WENG, Hong XUE. CXCL12 is a potential therapeutic target for type 2 diabetes mellitus complicated by chronic obstructive pulmonary disease[J]. Journal of Southern Medical University, 2025, 45(1): 100-109.

图/表 8

表1 研究所用数据集信息

Tab.1 Information of the datasets used in this study

Database	Molecule	Tissue	Subgroup	Number	Platform
GSE76925	mRNA	Lung tissue samples	COPD Non-COPD	111 40	GPL10558
GSE76894	mRNA	Islet samples	T2DM Non-diabetic	19 84	GPL570
GSE37768	mRNA	Lung tissue samples	COPD Non-COPD	20 18	GPL570
GSE25724	mRNA	islet samples	T2DM Non-diabetic	7 7	GPL96

图 1 疾病相关共同易感基因的筛选

Fig.1 Screening of the common differentially expressed genes in COPD and T2DM. A: Volcano plots of gene expression changes in GSE76925. B: Volcano plots of gene expression changes in GSE76894. C: Venn diagrams of the intersected DEGs.

图2 共易感基因的GO和KEGG分析

Fig.2 Functional enrichment analyses of the common differentially expressed genes in COPD and T2DM using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). A: KEGG analysis of the upregulated genes. B: KEGG analysis of the downregulated genes. C: GO analysis of the upregulated genes. D: GO analysis of the downregulated genes.

图3 PPI网络构建与候选hub基因鉴定

Fig.3 Protein-protein interaction network and identification of the candidate hub genes. A: PPI network diagram (Red: Upregulated genes; Green: downregulated genes). B: The top20 core gene networks predicted and explored using 4 topology algorithms. C: Venn diagram of the core genes identified by the 4 topology algorithms. D: Friend analysis of 10 common genes.

图4 候选hub基因的验证和诊断效能评价

Fig.4 Validation of the candidate hub genes and evaluation of their diagnostic effectiveness. A: Hub-gene expression in the analyzing datasets. B: Hub-gene expression in the validating datasets. C: Hub-gene ROC curves and AUC statistics in the analyzing datasets and validating datasets. *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001.

图5 CXCL12表达水平的临床血液标本检测及分析

Fig.5 Validation of CXCL12 expressions in clinical blood samples. A: Elevated CXCL12 in patients with T2DM complicated by COPD (n=12,*P<0.05). B: Correlation between CXCL12 and FEV1pred% in patients with T2DM complicated by COPD (n=20).

图6 候选Hub基因与免疫细胞浸润关联分析

Fig.6 Correlation analysis of the candidate hub genes and immune cells in COPD and T2DM. A: Violin plot of the distribution of immune cells in GSE76925. B: Violin plot of the distribution of immune cells in GSE76894. C: Heat map of the hub genes and immune infiltration in GSE76925. D: Heat map of the hub genes and immune infiltration in GSE76894. *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001.

图7 药物-基因检测与CXCL12单基因GSEA分析

Fig.7 Drug-gene analysis (A) and GSEA enrichment analysis of CXCL12 gene expression (B).

参考文献 35

1	Agustí A, Celli BR, Criner GJ, et al. Global initiative for chronic obstructive lung disease 2023 report: gold executive summary[J]. Am J Respir Crit Care Med, 2023, 207(7): 819-37.
2	Rana JS, Khan SS, Lloyd-Jones DM, et al. Changes in mortality in top 10 causes of death from 2011 to 2018[J]. J Gen Intern Med, 2021, 36(8): 2517-8.
3	López-Campos JL, Tan W, Soriano JB. Global burden of COPD[J]. Respirology, 2016, 21(1): 14-23.
4	Castañ-Abad MT, Montserrat-Capdevila J, Godoy P, et al. Diabetes as a risk factor for severe exacerbation and death in patients with COPD: a prospective cohort study[J]. Eur J Public Health, 2020, 30(4): 822-7.
5	Stolz D, Mkorombindo T, Schumann DM, et al. Towards the elimination of chronic obstructive pulmonary disease: a Lancet Commission[J]. Lancet, 2022, 400(10356): 921-72.
6	Gayle A, Dickinson S, Poole C, et al. Incidence of type II diabetes in chronic obstructive pulmonary disease: a nested case-control study[J]. NPJ Prim Care Respir Med, 2019, 29(1): 28.
7	Barrett T, Wilhite SE, Ledoux P, et al. NCBI GEO: archive for functional genomics data sets: update[J]. Nucleic Acids Res, 2013, 41(Database issue): D991-5.
8	Ritchie ME, Phipson B, Wu D, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies[J]. Nucleic Acids Res, 2015, 43(7): e47.
9	Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis[J]. BMC Bioinformatics, 2008, 9: 559.
10	Szklarczyk D, Gable AL, Nastou KC, et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets[J]. Nucleic Acids Res, 2021, 49(D1): D605-12.
11	Chin CH, Chen SH, Wu HH, et al. cytoHubba: identifying hub objects and sub-networks from complex interactome[J]. BMC Syst Biol, 2014, 8(): S11.
12	Yu GC, Li F, Qin YD, et al. GOSemSim: an R package for measuring semantic similarity among GO terms and gene products[J]. Bioinformatics, 2010, 26(7): 976-8.
13	Robin X, Turck N, Hainard A, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves[J]. BMC Bioinformatics, 2011, 12: 77.
14	Chen BB, Khodadoust MS, Liu CL, et al. Profiling tumor infiltrating immune cells with CIBERSORT[J]. Methods Mol Biol, 2018, 1711: 243-59.
15	Cotto KC, Wagner AH, Feng YY, et al. DGIdb 3.0: a redesign and expansion of the drug-gene interaction database[J]. Nucleic Acids Res, 2018, 46(D1): D1068-73.
16	Katsiki N, Steiropoulos P, Papanas N, et al. Diabetes mellitus and chronic obstructive pulmonary disease: an overview[J]. and, 2021, 129(10): 699-704.
17	Mannino DM, Thorn D, Swensen A, et al. Prevalence and outcomes of diabetes, hypertension and cardiovascular disease in COPD[J]. Eur Respir J, 2008, 32(4): 962-9.
18	Dupin I, Thumerel M, Maurat E, et al. Fibrocyte accumulation in the airway walls of COPD patients[J]. Eur Respir J, 2019, 54(3): 1802173.
19	Murdoch C, Thornhill MH, Fang HY, et al. CXCL12/CXCR4 axis mediates recruitment of monocytes to oral cancer spheroids [J]. Oral Dis, 2012, 18(6): 8-18.
20	Elena T, James O, Ann H, et al. Caveolin-1 regulates CXCR4/CXCL12-dependent monocyte recruitment in scleroderma patients and in a murine model of interstitial lung disease[J]. Clin Experim Rheumatol, 2010, 28(5): S79-80.
21	Hao WD, Li MX, Pang YM, et al. Increased chemokines levels in patients with chronic obstructive pulmonary disease: correlation with quantitative computed tomography metrics[J]. Br J Radiol, 2021, 94(1118): 20201030.
22	Aydin Ozgur B, Coskunpinar E, Bilgic Gazioglu S, et al. Effects of complement regulators and chemokine receptors in type 2 diabetes[J]. Immunol Invest, 2021, 50(5): 478-91.
23	Alagpulinsa DA, Cao JJL, Sobell D, et al. Harnessing CXCL12 signaling to protect and preserve functional β-cell mass and for cell replacement in type 1 diabetes[J]. Pharmacol Ther, 2019, 193: 63-74.
24	Yin QZ, Sun KX, Xiang X, et al. Identification of novel CXCL12 genetic polymorphisms associated with type 2 diabetes mellitus: a Chinese sib-pair study[J]. Genet Test Mol Biomarkers, 2019, 23(7): 435-41.
25	Gredic M, Sharma V, Hadzic S, et al. iNOS deletion in alveolar epithelium cannot reverse the elastase-induced emphysema in mice[J]. Cells, 2022, 12(1): 125.
26	Choy JC, Yi T, Rao DA, et al. CXCL12 induction of inducible nitric oxide synthase in human CD8 T cells[J]. J Heart Lung Transplant, 2008, 27(12): 1333-9.
27	Teicher BA, Fricker SP. CXCL12 (SDF-1)/CXCR4 pathway in cancer[J]. Clin Cancer Res, 2010, 16(11): 2927-31.
28	Pallett LJ, Swadling L, Diniz M, et al. Tissue CD14⁺CD8⁺ T cells reprogrammed by myeloid cells and modulated by LPS[J]. Nature, 2023, 614(7947): 334-42.
29	Lim K, Hyun YM, Lambert-Emo K, et al. Neutrophil trails guide influenza-specific CD8⁺ T cells in the airways[J]. Science, 2015, 349(6252): aaa4352.
30	Zhong T, Li XY, Lei K, et al. CXCL12-CXCR4 mediates CD57⁺ CD8⁺ T cell responses in the progression of type 1 diabetes[J]. J Autoimmun, 2024, 143: 103171.
31	Barwinska D, Oueini H, Poirier C, et al. AMD3100 ameliorates cigarette smoke-induced emphysema-like manifestations in mice[J]. Am J Physiol Lung Cell Mol Physiol, 2018, 315(3): L382-6.
32	Cazzola M, Rogliani P, Ora J, et al. Hyperglycaemia and chronic obstructive pulmonary disease[J]. Diagnostics, 2023, 13(21): 3362.
33	Au PCM, Tan KCB, Lam DCL, et al. Association of sodium-glucose cotransporter 2 inhibitor vs dipeptidyl peptidase-4 inhibitor use with risk of incident obstructive airway disease and exacerbation events among patients with type 2 diabetes in Hong Kong [J]. JAMA Netw Open, 2023, 6(1): e2251177.
34	Pradhan R, Lu S, Yin H, et al. Novel antihyperglycaemic drugs and prevention of chronic obstructive pulmonary disease exacerbations among patients with type 2 diabetes: population based cohort study[J]. BMJ, 2022, 379: e071380.
35	Janssens R, Struyf S, Proost P. The unique structural and functional features of CXCL12[J]. Cell Mol Immunol, 2018, 15(4): 299-311.

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