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Research Paper Volume 14, Issue 20 pp 8568—8580

Identification and validation of genetic signature associated with aging in chronic obstructive pulmonary disease

Shanshan Chen1, *, , Yuan Zhan1, *, , Jinkun Chen2, , Jixing Wu1, , Yiya Gu1, , Qian Huang1, , Zhesong Deng1, , Xiaojie Wu3, , Yongman Lv4, , Jungang Xie1, &, ,

  • 1 Department of Respiratory and Critical Care Medicine, National Clinical Research Center of Respiratory Disease, Key Laboratory of Pulmonary Diseases of Health Ministry, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, China
  • 2 Department of Science, Western University, London, Ontario N6A 3K7, Canada
  • 3 Department of Respiratory and Critical Care Medicine, Wuhan No.1 Hospital, Wuhan Hospital of Traditional Chinese and Western Medicine, Wuhan 430022, China
  • 4 Health Management Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, China
* Equal contribution

Received: March 16, 2022       Accepted: October 12, 2022       Published: October 28, 2022      

https://doi.org/10.18632/aging.204358
How to Cite

Copyright: © 2022 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Aging plays an essential role in the development for chronic obstructive pulmonary disease (COPD). The aim of this study was to identify and validate the potential aging-related genes of COPD through bioinformatics analysis and experimental validation. Firstly, we compared the gene expression profiles of aged and young COPD patients using two datasets (GSE76925 and GSE47460) from Gene Expression Omnibus (GEO), and identified 244 aging-related different expressed genes (DEGs), with 132 up-regulated and 112 down-regulated. Then, by analyzing the data for cigarette smoke-induced COPD mouse model (GSE125521), a total of 783 DEGs were identified between aged and young COPD mice, with 402 genes increased and 381 genes decreased. Additionally, functional enrichment analysis revealed that these DEGs were actively involved in COPD-related biological processes and function pathways. Meanwhile, six genes were identified as the core aging-related genes in COPD after combining the human DEGs and mouse DEGs. Eventually, five out of six core genes were validated to be up-regulated in the lung tissues collected from aged COPD patients than young COPD patients, namely NKG7, CKLF, LRP4, GDPD3 and CXCL9. Thereinto, the expressions of NKG7 and CKLF were negatively associated with lung function. These results may expand the understanding for aging in COPD.

Introduction

Chronic obstructive pulmonary disease (COPD) is a progressive and debilitating respiratory disease that causes a heavy burden both medically and economically [1]. By 2050, the life expectancy of the global population will be extended, and about 15% of the world’s population will reach 70 years or more [2]. According to the Global Burden of Disease Study (the GBD study), the incidence and mortality of COPD patients over 70 years old are significantly higher than those of young patients. It can be discovered that aging increases the clinical incidence and treatment difficulty of COPD [3–6]. Therefore, a better understanding of the pathophysiology of COPD and knowing how the aging process is related to the COPD development are beneficial and necessary for the clinical treatment of this disease.

Although the association between aging and COPD has been repeatedly proposed, the mechanism of this association remains unclear [5, 6]. Lopez-Otin et al. summarized 9 manifestations associated with aging, including genomic instability, telomere loss, epigenetic changes, loss of protein homeostasis, demodulating nutritional sensing, mitochondrial dysfunction, cell senescence, stem cell failure and changes in intercellular communication [7]. Studies have shown that the telomere length of patients with COPD is shorter than that of healthy people [8, 9]. In addition, cigarette smoke has been shown to induce the expression of aging markers in lung epithelial cells and fibroblasts, including senescence-related secretory phenotypes, p21 and p16 [10, 11]. However, although it is generally believed that there is a link between aging and COPD, the underlying mechanism is not clear [12–14].

In the current study, in order to explore the underlying mechanism between aging and COPD development, using gene expression profiles of COPD patients and COPD model mice, we analyzed the genetic signature related to aging and corresponding enrichment biological processes, as well as the validations in the clinical specimens.

Results

DEGs associated with aging in COPD patients

To determine the aging-related core gene profiles of COPD patients, two microarray datasets were obtained from GEO. The information included in the study was listed in Table 1. According to age criteria, this study included 11 young patients with COPD and 66 aged patients with COPD, and the detailed information regarding the included subjects can be accessed in Supplementary File 1. Comparing the gene expression profiles of the two groups, 244 DEGs were generated, of which 132 were up-regulated and 112 were down-regulated (Figure 1A). In order to further understand the functions related to the 244 DEGs, GO enrichment analysis was carried out, including BP, MF and CC. In BP analysis, the DEGs were found to associate with “collagen fibril organization”, “extracellular matrix organization”, and “regulation of potassium ion transmembrane transport” (Figure 1B). In the CC category, the DEGs were related to the “extracellular space”, “extracellular region”, and “cell surface” (Figure 1C). Moreover, DEGs were enriched in the MF category related to “calcium ion binding”, “heparin binding”, and “protease binding” (Figure 1D). For KEGG pathway enrichment analysis, the top three significant KEGG pathways of the DEGs included “PI3K-AKT signaling pathway”, “Cytokine-cytokine receptor interaction”, and “Focal adhesion” (Figure 1E).

Table 1. Characteristics of microarray datasets included.

DatasetPlatformSamplesSample number (Young group)Sample number (Aged group)
GSE76925GPL10558Lung tissue of COPD patient212
GSE47460GPL14550Lung tissue of COPD patient954
GSE125521GPL6885Lung tissue of CS-exposed mouse33
COPD, chronic obstructive pulmonary disease; CS, cigarette smoke.
DEGs associated with aging in COPD patients. (A) Volcano-plot of the 244 age-associated DEGs in COPD patients. Red circle: upregulated genes; blue circles: downregulated genes. (B) Top 10 GO biological processes analysis of the 244 age-associated DEGs. Outer gray circle: a scatter plot for each term of the logFC of the assigned genes; red circles: upregulated genes; blue circles: downregulated genes. (C) All GO cellular components analysis of the 244 age-associated DEGs. (D) Top 11 GO molecular functions analysis of the 244 age-associated DEGs. (E) Top 7 significantly (P

Figure 1. DEGs associated with aging in COPD patients. (A) Volcano-plot of the 244 age-associated DEGs in COPD patients. Red circle: upregulated genes; blue circles: downregulated genes. (B) Top 10 GO biological processes analysis of the 244 age-associated DEGs. Outer gray circle: a scatter plot for each term of the logFC of the assigned genes; red circles: upregulated genes; blue circles: downregulated genes. (C) All GO cellular components analysis of the 244 age-associated DEGs. (D) Top 11 GO molecular functions analysis of the 244 age-associated DEGs. (E) Top 7 significantly (P<0.05) enriched KEGG pathways. DEGs, differently expressed genes; COPD, chronic obstructive pulmonary disease; GO, gene ontology; FC, fold change; KEGG, Kyoto Encyclopedia of Genes and Genomes.

DEGs associated with aging in COPD model mice

To further identify the expression characteristics related to aging in COPD model mice, we obtained a dataset from GEO and compared the DEGs between aged and young COPD mice, and the detailed information regarding the included subjects can be accessed in Supplementary File 2. A total of 783 DEGs were identified, of which 402 genes increased and 381 genes decreased (Figure 2A). Slightly different from the results obtained from patients with COPD, the top three BP terms were enriched in “extracellular matrix organization”, “cell adhesion”, and “inflammatory response” (Figure 2B). In the CC category, the DEGs were associated with “proteinaceous extracellular matrix”, “extracellular exosome”, “extracellular matrix”, “basement membrane”, and “extracellular region” (Figure 2C). “Calcium ion binding”, “actin binding”, “enzyme binding”, and “transcription factor binding” were the most important MF terms (Figure 2D). Furthermore, KEGG pathway analysis showed enrichments in “PI3K-Akt signaling pathway”, “Focal adhesion”, and “ECM-receptor interaction” (Figure 2E).

Age-associated DEGs in COPD model mice. (A) Volcano plot of the 783 age-associated DEGs. Red circle: upregulated genes; blue circles: downregulated genes. (B) Top 10 GO biological processes of the 783 age-associated DEGs. Outer gray circle: a scatter plot for each term of the logFC of the assigned genes; red circles: upregulated genes; blue circles: downregulated genes. (C) All GO cellular components of the 783 age-associated DEGs. (D) Top 13 GO molecular functions of the 783 age-associated DEGs. (E) Top 13 significantly (P

Figure 2. Age-associated DEGs in COPD model mice. (A) Volcano plot of the 783 age-associated DEGs. Red circle: upregulated genes; blue circles: downregulated genes. (B) Top 10 GO biological processes of the 783 age-associated DEGs. Outer gray circle: a scatter plot for each term of the logFC of the assigned genes; red circles: upregulated genes; blue circles: downregulated genes. (C) All GO cellular components of the 783 age-associated DEGs. (D) Top 13 GO molecular functions of the 783 age-associated DEGs. (E) Top 13 significantly (P<0.05) enriched KEGG pathways. DEGs, differently expressed genes; COPD, chronic obstructive pulmonary disease; GO, gene ontology; FC, fold change; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Core DEGs associated with aging in COPD

To comprehensively explore the gene expression profile in COPD related to aging, we combined the results of COPD patients and COPD model mice. As shown in the Venn diagram, six common DEGs were observed both in aged COPD patients and aged COPD mice (Figure 3A). The heat maps showed the gene expression profile of six aging-related genes in COPD patients and COPD mice (Figure 3B, 3C). Details of the six core genes were shown in Table 2.

Common age-associated DEGs in COPD. (A) A Venn diagram of DEGs of patients with COPD patients and COPD model mice. (B) Heatmap of the six core genes in young and aged COPD patients. (C) Heatmap of the six core genes in young and aged COPD model mice. DEGs, differently expressed genes; COPD, chronic obstructive pulmonary disease.

Figure 3. Common age-associated DEGs in COPD. (A) A Venn diagram of DEGs of patients with COPD patients and COPD model mice. (B) Heatmap of the six core genes in young and aged COPD patients. (C) Heatmap of the six core genes in young and aged COPD model mice. DEGs, differently expressed genes; COPD, chronic obstructive pulmonary disease.

Table 2. Detailed information of 6 core genes.

HumanMouse
GeneFull nameIDLog2FCP valueGeneIDLog2FCP value
NKG7natural killer cell granule protein 748180.719060.00841Nkg7723100.559680.01462
CKLFchemokine like factor511920.559550.01459Cklf2459780.679810.00104
LRP4LDL receptor related protein 440380.659210.02236Lrp4228357-0.622720.00088
MLF1myeloid leukemia factor 14291-0.615190.04810Mlf1173490.724950.00051
GDPD3Glycerophosphodiester phosphodiesterase domain containing 3791530.536650.01607Gdpd3686160.634280.00119
CXCL9C-X-C motif chemokine ligand 942831.158050.02108Cxcl9173290.667880.03170
Human, Aged COPD patient vs Young COPD patient; Mouse, Aged CS-expose mouse vs Young CS-expose mouse; FC, fold change.

Subject characteristics

The clinical characteristics of all subjects were displayed in Table 3. In all, 32 subjects were included in the current study containing 16 young COPD patients and 16 aged COPD patients. The subjects presented no obvious differences by gender, body mass index (BMI) and smoke status. The patients in aged COPD group had significantly lower FEV1% of predicted and FEV1/FVC, compared with those in the young COPD group. Meanwhile, the aged COPD group had a greater proportion of patients with higher GOLD grade (including grade II and III), relative to the young COPD group. Collectively, the aged COPD patients had worse lung function and more severe disease progression.

Table 3. Clinical characteristics of subjects.

Young COPD (n=16)Aged COPD (n=16)P value
Age (years)47.88 (3.05)72.31 (2.33)<0.0001
Gender (male/female)9/711/50.72
BMI (kg/m2)23.16 (2.05)22.99 (2.50)0.84
Non-smokers/smokers8/85/110.47
FEV1 (% predicted)94.58 (19.83)70.83 (19.63)0.0019
FEV1/FVC (%)66.79 (2.67)58.94 (8.11)0.0009
GOLD Grading
I125
II48
III03
IV00
Data are expressed as mean (SD). P-values were calculated using chi-square test or Student’s t-test. COPD, chronic obstructive pulmonary disease; BMI, body mass index; FEV1, forced expiratory volume in one second; FVC, forced vital capacity.

Validations of the aging-related DEGs

The expressions of the 6 aging-related DEGs (NKG7, CKLF, LRP4, MLF1, GDPD3 and CXCL9) were validated in the lung tissues from aged COPD patients and young COPD patients by qRT-PCR. Compared with young COPD patients, the expressions of NKG7, CKLF, LRP4, GDPD3 and CXCL9 were significantly enhanced in aged COPD patients, whereas there’s no marked difference in terms of MLF1 expression (Figure 4). Thereafter, the correlation analysis between lung function and the expression of confirmed core genes was performed. Thereinto, NKG7 and CKLF were negatively correlated with lung function (Figure 5).

The expression of the 6 core genes in the lungs of COPD patients. The relative mRNA expressions of six genes in the young COPD patients and aged COPD patients were shown, including (A) NKG7, (B) CKLF, (C) LRP4, (D) MLF1, (E) GOPD3, (F) CXCL9. Results were expressed as median (P25 quartile, P75 quartile); n=16 for young COPD and n=16 for aged COPD; *P

Figure 4. The expression of the 6 core genes in the lungs of COPD patients. The relative mRNA expressions of six genes in the young COPD patients and aged COPD patients were shown, including (A) NKG7, (B) CKLF, (C) LRP4, (D) MLF1, (E) GOPD3, (F) CXCL9. Results were expressed as median (P25 quartile, P75 quartile); n=16 for young COPD and n=16 for aged COPD; *P<0.05, **P<0.01, ***P<0.001. COPD, chronic obstructive pulmonary disease; NKG7, natural killer cell granule protein 7; CKLF, chemokine like factor; LRP4, LDL receptor related protein 4; MLF1, myeloid leukemia factor 1; GDPD3, glycerophosphodiester phosphodiesterase domain containing 3; CXCL9, C-X-C motif chemokine ligand 9.

The association analysis between the critical aging-related DEGs and lung function in COPD patients. The lung function was negatively associated with (A) NKG7 expression and (B) CKLF expression, whereas no significant associations with the expressions of (C) LRP4, (D) GOPD3 and (E) CXCL9. DEGs, differently expressed genes; COPD, chronic obstructive pulmonary disease; FEV1 (% predicted), forced expiratory volume in 1 s (FEV1) % predicted; NKG7, natural killer cell granule protein 7; CKLF, chemokine like factor; LRP4, LDL receptor related protein 4; GDPD3, glycerophosphodiester phosphodiesterase domain containing 3; CXCL9, C-X-C motif chemokine ligand 9.

Figure 5. The association analysis between the critical aging-related DEGs and lung function in COPD patients. The lung function was negatively associated with (A) NKG7 expression and (B) CKLF expression, whereas no significant associations with the expressions of (C) LRP4, (D) GOPD3 and (E) CXCL9. DEGs, differently expressed genes; COPD, chronic obstructive pulmonary disease; FEV1 (% predicted), forced expiratory volume in 1 s (FEV1) % predicted; NKG7, natural killer cell granule protein 7; CKLF, chemokine like factor; LRP4, LDL receptor related protein 4; GDPD3, glycerophosphodiester phosphodiesterase domain containing 3; CXCL9, C-X-C motif chemokine ligand 9.

Discussion

Aging is a strong risk factor and independent prognostic biomarker for progressive COPD [15]. However, there is a lack of comprehensive analysis based on gene expression profiles to investigate the role of aging in COPD. In this study, using two human COPD datasets, we identified 244 aging-related DEGs in COPD patients. Similarly, through the analysis of the dataset of COPD model mice, we identified 783 DEGs related to aging. Subsequently, GO and KEGG analyses were conducted regarding these DEGs respectively. Finally, through the combination of DEGs between human and mouse datasets, we identified six common aging-related genes in COPD, of which five genes were confirmed to be up-regulated in clinical specimens, and the expressions of two genes were negatively correlated with lung function in COPD patients.

In this study, 244 aging-related DEGs in COPD patients were involved in the regulation of potassium channel transport in BP function. Previous studies have also shown that potassium exchange was slower in patients with chronic obstructive pulmonary disease than in healthy individuals, taking at least 48 hours [16]. Qiunan Zuo et al. also found that COPD was related to “collagen fibril organization” and “extracellular matrix organization” [17]. Presynaptic components involved most of the top five enriched CC terms, indicating that the nervous system could play an important role in aged patients with COPD. Animal studies have shown that α 2-adrenoceptor agonists may help reduce airway responsiveness in chronic obstructive pulmonary disease, especially when nerve-mediated airway reflexes may be triggered [18]. In addition, a randomized controlled trial found that lumbar percutaneous nerve stimulation improved motor performance in patients with COPD [19]. Platelet growth factor is a significantly enriched MF term. Genes related to platelet growth factor activity were observed in elderly-related diseases [20]. KEGG analysis showed that the DEGs were enriched in PI3K-AKT signaling pathway. Some studies have shown that PTEN/PI3K/AKT can regulate the polarization of macrophages in emphysema mice [21] and induce apoptosis of alveolar epithelial cells in COPD by regulating autophagy through PI3K/AKT/mTOR pathway [22]. Extracellular matrix promotes the proliferation, migration and adhesion of airway smooth muscle cells in rat model of chronic obstructive pulmonary disease through up-regulation of PI3K/AKT signal pathway [22].

Different from the DEGs from COPD patients, the GO enrichment of DEGs in COPD model mice was significant in extracellular matrix (ECM) organization and inflammatory response. Sustained ECM accumulation was thought to be closely involved in the development of several diseases, including COPD, making it difficult to reverse the disease progression of these diseases [23, 24]. Although the relationship between ECM disorders and the aging process is widely recognized, the underlying mechanism remains unclear. The extensive induction of inflammation was revealed to be associated with the remodeling of the epigenome and transcriptome landscape of mouse aging [25]. Age-related chronic inflammation is the main cause of different chronic diseases with the increase of age [26, 27]. There is extensive evidence that persistent inflammation exists in the elderly and that age-related inflammation can occur in the lungs [28].

In our study, we identified six critical aging-related genes in COPD through the bioinformatic analysis with combination of human DEGs and mouse DEGs, and five out of these six genes were validated to be up-regulated in the lung tissues of aged COPD patients, of which NKG7 and CKLF presented remarkably negative associations with lung function. Meanwhile, these coincident genes were more or less related to inflammatory response. In specific, NKG7 was reported to be expressed in various immune cells including activated T cells and NK cells [29], and played important roles in multiple immune-related diseases, like controlling intratumor T-cell accumulation and activation, the cytotoxicity of lymphocytes and regulation of inflammation [30–32]. Future researches are required to validate the possible regulation of NKG7 in airway inflammation, the typical phenotype in COPD pathogenesis. Moreover, CKLF was a novel cytokine which had a crucial role in immune and inflammatory responses [33]. The enhanced CKLF expression was observed to associate with excess production of numerous inflammatory cytokines and graver autoimmune injury in rheumatoid arthritis and asthma [34, 35]. And meanwhile CKLF was also reported to be involved in chemokine activity and neutrophil chemotaxis [36, 37]. In addition, it has been reported that CXCL9 is also associated with chemokine activity and neutrophil chemotaxis [38, 39]. Many studies have proved that neutrophil chemotaxis is simultaneously one of the most important hallmarks and closely related to COPD development [40, 41]. Moreover, CXCL9 was an inflammatory cytokine that played an important role in multiple inflammatory diseases. CXCL9 has been reported to be upregulated in inflammatory colitis [42], and the increased CXCL9 expression was obviously associated with the survival and prognosis of patients with idiopathic pulmonary arterial hypertension (PAH) and chronic thromboembolic pulmonary arterial hypertension (CTEPAH) [43]. Besides, LRP4 and GDPD3 were reported to exert important regulations in lung development. LRP4 is a regulator of Wnt signaling, regulating the differentiation of goblet cells during the lung development and repairment [44, 45]. And meanwhile, goblet cell metaplasia and mucus hypersecretion are the critical features of respiratory diseases, including COPD [46, 47]. In addition, tobacco smoke exposure can promote the disease progress by affecting the expression of Huntingtin-Interacting Protein 1 (HIP1), which could influence GDPD3 expression [48, 49]. Therefore, these 5 aging-related genes are more or less associated with COPD development. But the specific roles of these genes in COPD merit more investigations, which may provide new insights for seeking the therapeutic targets for aged COPD patients.

There are still some limitations in this study. First, we cannot evaluate the role of other aging markers in COPD, such as telomere loss, epigenetic changes and genomic instability, which can be more beneficial to understanding the roles of aging in COPD development. While we only explored the role of aging in COPD from the perspective of related gene transcription. Secondly, the number of clinical samples included in our study is limited, and we need to confirm our conclusions in a larger COPD cohort. Third, we only verified the expression level of the differentially expressed aging-related genes in clinical samples, but did not explore the potential mechanism of these genes in COPD by in vitro cell experiments and in vivo mouse models. Therefore, on the basis of the above studies, considering the key role of these six aging-related genes, further research may focus on exploring the corresponding mechanisms underlying the association between aging and COPD.

In summary, it is generally believed that aging is a dependent risk factor for COPD. From the point of view of gene expression profile, the pathogenesis of COPD in young patients and aged patients is not exactly the same. Through the combination of bioinformatic analysis, clinical experiment validations and correlation analysis with lung function, our results may provide new potential therapeutic targets for COPD patients with old age.

Materials and Methods

Microarray data acquisition and process

To identify the genes associated with aging in COPD patients, two microarray datasets (GSE76925 and GSE47460) were downloaded from Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo) [50, 51]. Young COPD patients were defined as ≤ 50 years old, and aged COPD patients were defined as ≥ 70 years old. To explore age-related genes in COPD model mice, we downloaded a microarray dataset (GSE125521) from GEO. In this dataset, the mice were divided into young group (4-month-old) and aged group (15-month-old) respectively. The details of the GSE datasets were summarized in Table 1. The preprocessing expression matrix and detector annotation files of three GSE datasets were obtained from the GEO repository. The official genetic symbols were used to list probes from different datasets. The multiple expression results of the gene were replaced by the median of the expression results. All the log2 folding changes (log2FC) of the expressed results were normalized using limma R packages [52]. Different expression genes (DEGs) were screened according to the cut-off values of p<0.05 and |log2FC|>0.5. Gene ontology (GO) function analysis and Kyoto encyclopedia of genes and genomes (KEGG) analysis were also. In order to identify the DEGs between the young and aged groups, the limma R packages (http://www.bioconductor.org/packages) was used to perform the negative binomial distribution method according to the two standard deviations where the absolute value of FC was greater than the median of FC. According to the hypergeometric distribution algorithm, the pathway enrichment analysis was carried out by querying GO biological process (BP), molecular function (MF) and cellular composition (CC) by DAVID (https://david.ncifcrf.gov). The same method was used in KEGG analysis. The cut-off value was p<0.05. Bioinformatics and Evolutionary Genomics (http://bioinformatics.psb.ugent.be/webtools/Venn) was used to select central or core genes.

Clinical subjects

All lung tissues in this study were recruited from Tongji Hospital, Wuhan, China, between 2019 and 2021. All the patients involved were negative for SARS-CoV2 examinations. Thereinto, the lung specimens were collected from patients who underwent surgical resection for pulmonary lump in the department of thoracic surgery. All included participants were classified into two groups based on age, namely young COPD (≤ 50 years) and aged COPD (≥ 70 years). COPD was diagnosed according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2021 criteria. Patients with a post-bronchodilator forced expiratory volume in 1 s (FEV1)/forced vital capacity ratio of less than 70% were enrolled. Participants were excluded if they suffered from asthma, severe lung infections, or other obstructive lung diseases.

Quantitative real-time polymerase chain reaction (qRT-PCR)

Total RNA of the lung tissue was extracted by RNAiso plus kit (TaKaRa) and reversely transcribed to cDNA using the cDNA RT-PCR Kit (Takara, Japan). The mRNA expression was assessed by real-time quantitative polymerase chain reaction (RT-qPCR) using TB GreenR Premix Ex TaqTM II (Takara, Japan) on BioRad CFX384 (Bio-Rad, CA, United States). The parameters were as follows: 40 cycles at 95° C for 10s, 59° C for 20s, and 72° C for 30s. Data were analyzed using the 2−ΔΔCt method with β-actin as control. The primers were as follow: NKG7 (5’-CCAGAAGCCCTGAGCTTATCCC-3’ and 5’-AGTGAGCACCCAGGCTCAGGG-3’), CKLF (5’-TCGCTTCGCAGAACCTACTCA-3’ and 5’-TATTTTCGGCTGCACGTTATCC-3’), LRP4 (5’-GCCGCCAAGTCATTATCT-3’ and 5’-TCAGCACCTTCCTCTTACT-3’), MLF1 (5’-TCGTTTTTCCAATCTGTCCGC-3’ and 5’-GATACTGAGCAAGTCTCTTCC-3’), GDPD3 (5’-GCCAGTCGGGCCTAAACAG-3’ and 5’- GTCCTCCAGACGAACCATGC-3’), CXCL9 (5’- TGCAAGGAACCCCAGTAGTGA-3’ and 5’- GGTGGATAGTCCCTTGGTTGG-3’) and β-actin (5’- AGAAAATCTGGCACCACACCT-3’ and 5’- GATAGCACAGCCTGGATAGCA-3’).

Statistical analysis

Data analysis was carried out by Rstudio (version 4.1.2) and GraphPad Prism 8 Software (GraphPad Software, San Diego, CA, United States). The microarray data were analyzed by different R packages. Results were expressed as mean ± SD or median (P25 quartile, P75 quartile). Statistical significance was determined using Student’s t-test for two groups. The correlations were analyzed by Pearson correlation. A two-sided p-value <0.05 was considered as statistically significant.

Abbreviations

COPD: chronic obstructive pulmonary disease; GEO: gene expression omnibus; DEGs: differently expressed genes; GO: gene ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; BP: biological process; CC: cell component; MF: molecular function; NKG7: natural killer cell granule protein 7; CKLF: chemokine like factor; LRP4: LDL receptor related protein 4; MLF1: myeloid leukemia factor 1; GDPD3: glycerophosphodiester phosphodiesterase domain containing 3; CXCL9: C-X-C motif chemokine ligand 9; ECM: extracellular matrix; PAH: pulmonary arterial hypertension; CTEPAH: chronic thromboembolic pulmonary arterial hypertension; HIP1: huntingtin-interacting protein 1.

Author Contributions

JX had full access to the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: JX. Acquisition, analysis and interpretation of data: SC, YZ, JC, JW, YG, QH, ZD, XW and YL. Performing the experiments: SC and YZ. Drafting of the manuscript: SC and YZ. Revision of the manuscript: JX. Statistical analysis: SC and YZ. Supervision: JX. All authors read and approved the final manuscript.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Ethical Statement and Consent

This study was approved by the Ethics Committees of the Tongji Hospital (TJ-IRB20210346) and written informed consent was obtained from all subjects.

Funding

This study was supported by the National Natural Science Foundation of China (No. 82170049, 81800041, 81973986, and 82070032), Health Research Fund of Wuhan (No. WX21Q07), Leading talents of public health in Hubei Province (2022SCZ047) and Health and family planning research project of Hubei (No. WJ2019M116).

Editorial Note

&

This corresponding author has a verified history of publications using a personal email address for correspondence.

References

  • 1. May SM, Li JT. Burden of chronic obstructive pulmonary disease: healthcare costs and beyond. Allergy Asthma Proc. 2015; 36:4–10. https://doi.org/10.2500/aap.2015.36.3812 [PubMed]
  • 2. Divo MJ, Martinez CH, Mannino DM. Ageing and the epidemiology of multimorbidity. Eur Respir J. 2014; 44:1055–68. https://doi.org/10.1183/09031936.00059814 [PubMed]
  • 3. Huybrechts KF, Bateman BT, Pawar A, Bessette LG, Mogun H, Levin R, Li H, Motsko S, Scantamburlo Fernandes MF, Upadhyaya HP, Hernandez-Diaz S. Maternal and fetal outcomes following exposure to duloxetine in pregnancy: cohort study. BMJ. 2020; 368:m237. https://doi.org/10.1136/bmj.m237 [PubMed]
  • 4. Xie M, Liu X, Cao X, Guo M, Li X. Trends in prevalence and incidence of chronic respiratory diseases from 1990 to 2017. Respir Res. 2020; 21:49. https://doi.org/10.1186/s12931-020-1291-8 [PubMed]
  • 5. Buist AS, McBurnie MA, Vollmer WM, Gillespie S, Burney P, Mannino DM, Menezes AM, Sullivan SD, Lee TA, Weiss KB, Jensen RL, Marks GB, Gulsvik A, Nizankowska-Mogilnicka E, and BOLD Collaborative Research Group. International variation in the prevalence of COPD (the BOLD Study): a population-based prevalence study. Lancet. 2007; 370:741–50. https://doi.org/10.1016/S0140-6736(07)61377-4 [PubMed]
  • 6. Ito K, Barnes PJ. COPD as a disease of accelerated lung aging. Chest. 2009; 135:173–80. https://doi.org/10.1378/chest.08-1419 [PubMed]
  • 7. López-Otín C, Blasco MA, Partridge L, Serrano M, Kroemer G. The hallmarks of aging. Cell. 2013; 153:1194–217. https://doi.org/10.1016/j.cell.2013.05.039 [PubMed]
  • 8. Savale L, Chaouat A, Bastuji-Garin S, Marcos E, Boyer L, Maitre B, Sarni M, Housset B, Weitzenblum E, Matrat M, Le Corvoisier P, Rideau D, Boczkowski J, et al. Shortened telomeres in circulating leukocytes of patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2009; 179:566–71. https://doi.org/10.1164/rccm.200809-1398OC [PubMed]
  • 9. Alder JK, Guo N, Kembou F, Parry EM, Anderson CJ, Gorgy AI, Walsh MF, Sussan T, Biswal S, Mitzner W, Tuder RM, Armanios M. Telomere length is a determinant of emphysema susceptibility. Am J Respir Crit Care Med. 2011; 184:904–12. https://doi.org/10.1164/rccm.201103-0520OC [PubMed]
  • 10. Tsuji T, Aoshiba K, Nagai A. Cigarette smoke induces senescence in alveolar epithelial cells. Am J Respir Cell Mol Biol. 2004; 31:643–9. https://doi.org/10.1165/rcmb.2003-0290OC [PubMed]
  • 11. Nyunoya T, Monick MM, Klingelhutz A, Yarovinsky TO, Cagley JR, Hunninghake GW. Cigarette smoke induces cellular senescence. Am J Respir Cell Mol Biol. 2006; 35:681–8. https://doi.org/10.1165/rcmb.2006-0169OC [PubMed]
  • 12. Chilosi M, Carloni A, Rossi A, Poletti V. Premature lung aging and cellular senescence in the pathogenesis of idiopathic pulmonary fibrosis and COPD/emphysema. Transl Res. 2013; 162:156–73. https://doi.org/10.1016/j.trsl.2013.06.004 [PubMed]
  • 13. MacNee W. Is Chronic Obstructive Pulmonary Disease an Accelerated Aging Disease? Ann Am Thorac Soc. 2016 (Suppl 5); 13:S429–37. https://doi.org/10.1513/AnnalsATS.201602-124AW [PubMed]
  • 14. Tuder RM, Kern JA, Miller YE. Senescence in chronic obstructive pulmonary disease. Proc Am Thorac Soc. 2012; 9:62–3. https://doi.org/10.1513/pats.201201-012MS [PubMed]
  • 15. Beghé B, Cerri S, Fabbri LM, Marchioni A. COPD, Pulmonary Fibrosis and ILAs in Aging Smokers: The Paradox of Striking Different Responses to the Major Risk Factors. Int J Mol Sci. 2021; 22:9292. https://doi.org/10.3390/ijms22179292 [PubMed]
  • 16. Telfer N, Weiner JM, Merrill Q. Distribution of sodium and potassium in chronic obstructive pulmonary disease. Am Rev Respir Dis. 1975; 111:166–76. https://doi.org/10.1164/arrd.1975.111.2.166 [PubMed]
  • 17. Zuo Q, Wang Y, Yang D, Guo S, Li X, Dong J, Wan C, Shen Y, Wen F. Identification of hub genes and key pathways in the emphysema phenotype of COPD. Aging (Albany NY). 2021; 13:5120–35. https://doi.org/10.18632/aging.202432 [PubMed]
  • 18. Groeben H, Mitzner W, Brown RH. Effects of the alpha2-adrenoceptor agonist dexmedetomidine on bronchoconstriction in dogs. Anesthesiology. 2004; 100:359–63. https://doi.org/10.1097/00000542-200402000-00026 [PubMed]
  • 19. Bonnevie T, Gravier FE, Prieur G, Combret Y, Debeaumont D, Patout M, Lamia B, Muir JF, Médrinal C, Cuvelier A. Lumbar transcutaneous electrical nerve stimulation to improve exercise performance in COPD patients. Eur Respir J. 2019; 54:1900784. https://doi.org/10.1183/13993003.00784-2019 [PubMed]
  • 20. Ouyang L, Zhang K, Chen J, Wang J, Huang H. Roles of platelet-derived growth factor in vascular calcification. J Cell Physiol. 2018; 233:2804–14. https://doi.org/10.1002/jcp.25985 [PubMed]
  • 21. Lu J, Xie L, Liu C, Zhang Q, Sun S. PTEN/PI3k/AKT Regulates Macrophage Polarization in Emphysematous mice. Scand J Immunol. 2017; 85:395–405. https://doi.org/10.1111/sji.12545 [PubMed]
  • 22. Zhang F, Ma H, Wang ZL, Li WH, Liu H, Zhao YX. The PI3K/AKT/mTOR pathway regulates autophagy to induce apoptosis of alveolar epithelial cells in chronic obstructive pulmonary disease caused by PM2.5 particulate matter. J Int Med Res. 2020; 48:300060520927919. https://doi.org/10.1177/0300060520927919 [PubMed]
  • 23. Genschmer KR, Russell DW, Lal C, Szul T, Bratcher PE, Noerager BD, Abdul Roda M, Xu X, Rezonzew G, Viera L, Dobosh BS, Margaroli C, Abdalla TH, et al. Activated PMN Exosomes: Pathogenic Entities Causing Matrix Destruction and Disease in the Lung. Cell. 2019; 176:113–26.e15. https://doi.org/10.1016/j.cell.2018.12.002 [PubMed]
  • 24. Liu G, Philp AM, Corte T, Travis MA, Schilter H, Hansbro NG, Burns CJ, Eapen MS, Sohal SS, Burgess JK, Hansbro PM. Therapeutic targets in lung tissue remodelling and fibrosis. Pharmacol Ther. 2021; 225:107839. https://doi.org/10.1016/j.pharmthera.2021.107839 [PubMed]
  • 25. Benayoun BA, Pollina EA, Singh PP, Mahmoudi S, Harel I, Casey KM, Dulken BW, Kundaje A, Brunet A. Remodeling of epigenome and transcriptome landscapes with aging in mice reveals widespread induction of inflammatory responses. Genome Res. 2019; 29:697–709. https://doi.org/10.1101/gr.240093.118 [PubMed]
  • 26. Stepanova M, Rodriguez E, Birerdinc A, Baranova A. Age-independent rise of inflammatory scores may contribute to accelerated aging in multi-morbidity. Oncotarget. 2015; 6:1414–21. https://doi.org/10.18632/oncotarget.2725 [PubMed]
  • 27. Ferrucci L, Corsi A, Lauretani F, Bandinelli S, Bartali B, Taub DD, Guralnik JM, Longo DL. The origins of age-related proinflammatory state. Blood. 2005; 105:2294–9. https://doi.org/10.1182/blood-2004-07-2599 [PubMed]
  • 28. Cho SJ, Stout-Delgado HW. Aging and Lung Disease. Annu Rev Physiol. 2020; 82:433–59. https://doi.org/10.1146/annurev-physiol-021119-034610 [PubMed]
  • 29. Turman MA, Yabe T, McSherry C, Bach FH, Houchins JP. Characterization of a novel gene (NKG7) on human chromosome 19 that is expressed in natural killer cells and T cells. Hum Immunol. 1993; 36:34–40. https://doi.org/10.1016/0198-8859(93)90006-m [PubMed]
  • 30. Duizendstra AA, van der Grift MV, Boor PP, Noordam L, de Knegt RJ, Peppelenbosch MP, Betjes MGH, Litjens NHR, Kwekkeboom J. Current Tolerance-Associated Peripheral Blood Gene Expression Profiles After Liver Transplantation Are Influenced by Immunosuppressive Drugs and Prior Cytomegalovirus Infection. Front Immunol. 2022; 12:738837. https://doi.org/10.3389/fimmu.2021.738837 [PubMed]
  • 31. Ng SS, De Labastida Rivera F, Yan J, Corvino D, Das I, Zhang P, Kuns R, Chauhan SB, Hou J, Li XY, Frame TCM, McEnroe BA, Moore E, et al. The NK cell granule protein NKG7 regulates cytotoxic granule exocytosis and inflammation. Nat Immunol. 2020; 21:1205–18. https://doi.org/10.1038/s41590-020-0758-6 [PubMed]
  • 32. Li XY, Corvino D, Nowlan B, Aguilera AR, Ng SS, Braun M, Cillo AR, Bald T, Smyth MJ, Engwerda CR. NKG7 Is Required for Optimal Antitumor T-cell Immunity. Cancer Immunol Res. 2022; 10:154–61. https://doi.org/10.1158/2326-6066.CIR-20-0649 [PubMed]
  • 33. Zhang M, Xu Y, Liu Y, Cheng Y, Zhao P, Liu H, Wang Y, Ma X. Chemokine-Like Factor 1 (CKLF-1) is Overexpressed in Keloid Patients: A Potential Indicating Factor for Keloid-Predisposed Individuals. Medicine (Baltimore). 2016; 95:e3082. https://doi.org/10.1097/MD.0000000000003082 [PubMed]
  • 34. Li G, Wang D, Sun M, Li G, Hu J, Zhang Y, Yuan Y, Ji H, Chen N, Liu G. Discovery and optimization of novel 3-piperazinylcoumarin antagonist of chemokine-like factor 1 with oral antiasthma activity in mice. J Med Chem. 2010; 53:1741–54. https://doi.org/10.1021/jm901652p [PubMed]
  • 35. Keith JC Jr, Albert LM, Leathurby Y, Follettie M, Wang L, Borges-Marcucci L, Chadwick CC, Steffan RJ, Harnish DC. The utility of pathway selective estrogen receptor ligands that inhibit nuclear factor-kappa B transcriptional activity in models of rheumatoid arthritis. Arthritis Res Ther. 2005; 7:R427–38. https://doi.org/10.1186/ar1692 [PubMed]
  • 36. Liu Y, Liu L, Zhou Y, Zhou P, Yan Q, Chen X, Ding S, Zhu F. CKLF1 Enhances Inflammation-Mediated Carcinogenesis and Prevents Doxorubicin-Induced Apoptosis via IL6/STAT3 Signaling in HCC. Clin Cancer Res. 2019; 25:4141–54. https://doi.org/10.1158/1078-0432.CCR-18-3510 [PubMed]
  • 37. Chen C, Chu SF, Ai QD, Zhang Z, Chen NH. CKLF1/CCR5 axis is involved in neutrophils migration of rats with transient cerebral ischemia. Int Immunopharmacol. 2020; 85:106577. https://doi.org/10.1016/j.intimp.2020.106577 [PubMed]
  • 38. Tokunaga R, Zhang W, Naseem M, Puccini A, Berger MD, Soni S, McSkane M, Baba H, Lenz HJ. CXCL9, CXCL10, CXCL11/CXCR3 axis for immune activation - A target for novel cancer therapy. Cancer Treat Rev. 2018; 63:40–7. https://doi.org/10.1016/j.ctrv.2017.11.007 [PubMed]
  • 39. Vanheule V, Janssens R, Boff D, Kitic N, Berghmans N, Ronsse I, Kungl AJ, Amaral FA, Teixeira MM, Van Damme J, Proost P, Mortier A. The Positively Charged COOH-terminal Glycosaminoglycan-binding CXCL9(74-103) Peptide Inhibits CXCL8-induced Neutrophil Extravasation and Monosodium Urate Crystal-induced Gout in Mice. J Biol Chem. 2015; 290:21292–304. https://doi.org/10.1074/jbc.M115.649855 [PubMed]
  • 40. Wu J, Hillier C, Komenda P, Lobato de Faria R, Levin D, Zhang M, Lin F. A Microfluidic Platform for Evaluating Neutrophil Chemotaxis Induced by Sputum from COPD Patients. PLoS One. 2015; 10:e0126523. https://doi.org/10.1371/journal.pone.0126523 [PubMed]
  • 41. Kaur M, Singh D. Neutrophil chemotaxis caused by chronic obstructive pulmonary disease alveolar macrophages: the role of CXCL8 and the receptors CXCR1/CXCR2. J Pharmacol Exp Ther. 2013; 347:173–80. https://doi.org/10.1124/jpet.112.201855 [PubMed]
  • 42. Bergemalm D, Andersson E, Hultdin J, Eriksson C, Rush ST, Kalla R, Adams AT, Keita ÅV, D’Amato M, Gomollon F, Jahnsen J, Ricanek P, Satsangi J, et al, and IBD Character Consortium. Systemic Inflammation in Preclinical Ulcerative Colitis. Gastroenterology. 2021; 161:1526–39.e9. https://doi.org/10.1053/j.gastro.2021.07.026 [PubMed]
  • 43. Koudstaal T, van Uden D, van Hulst JAC, Heukels P, Bergen IM, Geenen LW, Baggen VJM, van den Bosch AE, van den Toorn LM, Chandoesing PP, Kool M, Boersma E, Hendriks RW, Boomars KA. Plasma markers in pulmonary hypertension subgroups correlate with patient survival. Respir Res. 2021; 22:137. https://doi.org/10.1186/s12931-021-01716-w [PubMed]
  • 44. Kim HT, Yin W, Nakamichi Y, Panza P, Grohmann B, Buettner C, Guenther S, Ruppert C, Kobayashi Y, Guenther A, Stainier DYR. WNT/RYK signaling restricts goblet cell differentiation during lung development and repair. Proc Natl Acad Sci USA. 2019; 116:25697–706. https://doi.org/10.1073/pnas.1911071116 [PubMed]
  • 45. Jing H, Chen P, Hui T, Yu Z, Zhou J, Fei E, Wang S, Ren D, Lai X, Li B. Synapse-specific Lrp4 mRNA enrichment requires Lrp4/MuSK signaling, muscle activity and Wnt non-canonical pathway. Cell Biosci. 2021; 11:105. https://doi.org/10.1186/s13578-021-00619-z [PubMed]
  • 46. Ma J, Rubin BK, Voynow JA. Mucins, Mucus, and Goblet Cells. Chest. 2018; 154:169–76. https://doi.org/10.1016/j.chest.2017.11.008 [PubMed]
  • 47. Cohn L. Mucus in chronic airway diseases: sorting out the sticky details. J Clin Invest. 2006; 116:306–8. https://doi.org/10.1172/JCI27690 [PubMed]
  • 48. Niimori-Kita K, Ogino K, Mikami S, Kudoh S, Koizumi D, Kudoh N, Nakamura F, Misumi M, Shimomura T, Hasegawa K, Usui F, Nagahara N, Ito T. Identification of nuclear phosphoproteins as novel tobacco markers in mouse lung tissue following short-term exposure to tobacco smoke. FEBS Open Bio. 2014; 4:746–54. https://doi.org/10.1016/j.fob.2014.08.002 [PubMed]
  • 49. Wijayatunge R, Holmstrom SR, Foley SB, Mgbemena VE, Bhargava V, Perez GL, McCrum K, Ross TS. Deficiency of the Endocytic Protein Hip1 Leads to Decreased Gdpd3 Expression, Low Phosphocholine, and Kypholordosis. Mol Cell Biol. 2018; 38:e00385–18. https://doi.org/10.1128/MCB.00385-18 [PubMed]
  • 50. Conlon TM, John-Schuster G, Heide D, Pfister D, Lehmann M, Hu Y, Ertüz Z, Lopez MA, Ansari M, Strunz M, Mayr C, Angelidis I, Ciminieri C, et al. Inhibition of LTβR signalling activates WNT-induced regeneration in lung. Nature. 2020; 588:151–6. https://doi.org/10.1038/s41586-020-2882-8 [PubMed]
  • 51. Guan Q, Tian Y, Zhang Z, Zhang L, Zhao P, Li J. Identification of Potential Key Genes in the Pathogenesis of Chronic Obstructive Pulmonary Disease Through Bioinformatics Analysis. Front Genet. 2021; 12:754569. https://doi.org/10.3389/fgene.2021.754569 [PubMed]
  • 52. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015; 43:e47. https://doi.org/10.1093/nar/gkv007 [PubMed]
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Corresponding Author

Jungang Xie
xiejjgg@hotmail.com
https://orcid.org/0000-0001-9037-3027

Keywords
chronic obstructive pulmonary disease aging bioinformatics analysis genetic signature
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