Identification of Macrophage Polarization-Related Genes As Biomarkers of Chronic Obstructive Pulmonary Disease Based on Bioinformatics Analyses

Hindawi BioMed Research International Volume 2021, Article ID 9921012, 17 pages https://doi.org/10.1155/2021/9921012

Research Article Identification of Macrophage Polarization-Related Genes as Biomarkers of Chronic Obstructive Pulmonary Disease Based on Bioinformatics Analyses

Yalin Zhao , Meihua Li, Yanxia Yang, Tao Wu, Qingyuan Huang, Qinghua Wu, and Chaofeng Ren

Respiratory and Critical Care Medicine, Kunming First People’s Hospital, Kunming, Yunnan Province, China

Correspondence should be addressed to Yalin Zhao; [email protected] and Chaofeng Ren; [email protected]

Received 31 March 2021; Accepted 4 June 2021; Published 21 June 2021

Academic Editor: Nagarajan Raju

Copyright © 2021 Yalin Zhao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Objectives. Chronic obstructive pulmonary disease (COPD) is characterized by lung inflammation and remodeling. Macrophage polarization is associated with inflammation and tissue remodeling, as well as immunity. Therefore, this study attempts to investigate the diagnostic value and regulatory mechanism of macrophage polarization-related genes for COPD by bioinformatics analysis and to provide a new theoretical basis for experimental research. Methods. The raw gene expression profile dataset (GSE124180) was collected from the Gene Expression Omnibus (GEO) database. Next, a weighted gene coexpression network analysis (WGCNA) was conducted to screen macrophage polarization-related genes. The differentially expressed genes (DEGs) between the COPD and normal samples were generated using DESeq2 v3.11 and overlapped with the macrophage polarization-related genes. Moreover, functional annotations of overlapped genes were conducted by Database for Annotation, Visualization and Integrated Discovery (DAVID) Bioinformatics Resource. The immune-related genes were selected, and their correlation with the differential immune cells was analyzed by Pearson. Finally, receiver operating characteristic (ROC) curves were used to verify the diagnostic value of genes. Results. A total of 4922 coexpressed genes related to macrophage polarization were overlapped with the 203 DEGs between the COPD and normal samples, obtaining 25 genes related to COPD and macrophage polarization. GEM, S100B, and GZMA of them participated in the immune response, which were considered the candidate biomarkers. GEM and S100B were significantly correlated with marker genes of B cells which had a significant difference between the COPD and normal samples. Moreover, GEM was highly associated with the genes in the PI3K/Akt/GSK3β signaling pathway, regulation of actin cytoskeleton, and calcium signaling pathway based on a Pearson correlation analysis of the candidate genes and the genes in the B cell receptor signaling pathway. PPI network analysis also indicated that GEM might participate in the regulation of the PI3K/Akt/GSK3β signaling pathway. The ROC curve showed that GEM possessed an excellent accuracy in distinguishing COPD from normal samples. Conclusions. The data provide a transcriptome-based evidence that GEM is related to COPD and macrophage polarization likely contributes to COPD diagnosis. At the same time, it is hoped that in-depth functional mining can provide new ideas for exploring the COPD pathogenesis.

1. Introduction ity rates of COPD are still growing [4]. Worse, although COPD has long been considered treatable, the diagnosis Chronic obstructive pulmonary disease (COPD), an inflam- and treatment of COPD in the clinical practice remain to matory disease of the lung mainly caused by smoking tobacco be improved [5, 6]. Currently, the diagnosis of COPD mainly cigarettes and environmental exposure from burning bio- depends on the use of spirometry by identifying pulmonary mass fuels or air pollution [1, 2], has become a major health dysfunction [7], which is full of limitations, such as the detec- problem around the world [3]. It has been reported that tion of the early stage of COPD. Moreover, although some COPD killed about 3 million people in 2016, and the mortal- blood-related biomarkers were related to exacerbations, 2 BioMed Research International progression, or mortality of COPD, it was unknown whether stroke [22] and schizophrenia [23]. Moreover, all of these they could be selected as the diagnostic biomarkers [8]. On researches suggested that screening biomarkers for diagnosis the other hand, even if the traditional treatments including and treatment of complex disease by WGCNA was effective. lung volume reduction surgery for COPD have made great In the present study, we firstly downloaded the raw gene strides, there were still some uncontrollable complications expression profile dataset which included COPD samples [9]. In addition, even if some pulmonary rehabilitation pro- and normal samples from the Gene Expression Omnibus grams, such as exercise training, education, and behavior (GEO) database (https://www.ncbi.nlm.nih.gov/geo/query/ change followed by patient-tailored therapies have been acc.cgi). Next, WGCNA was carried out to screen macro- regarded as promising measures for improving the COPD phage polarization-related genes. Finally, we identified hub patients, a few patients still cannot gain the benefit from the genes as the novel diagnostic biomarkers for COPD and fur- exercise training [10]. Hence, it is very wishful for further ther analyzed their molecular mechanisms in COPD, which parsing the molecular mechanism and recognizing the novel will contribute to the treatment of COPD. biomarkers of COPD. With the development of bioinformatics, recent studies 2. Materials and Methods have revealed that COPD susceptibility is associated with the genes’ expression [11–14]. For example, it has been sug- 2.1. Dataset Acquisition. The raw gene expression profile gested that the expression of B3GNT, LAF4, and ARHGEF10 dataset (GSE124180, only peripheral blood samples) was can predict the frequent exacerbations of COPD [11]. More- collected from the GEO database [24]. The dataset based on over, IL6 and SOCS3 also have been suggested to play a key GPL16791 platform included 6 COPD samples and 15 role in COPD and can be used as the therapeutic targets of normal samples. COPD [12]. Moreover, Zhang et al. also found that TLR2 and CD79A may be used as the potential biomarkers for 2.2. WGCNA. The weighted gene coexpression network the clinical severity of COPD and related to the inflammatory analysis was performed using WGCNA R package (v1.69) responses of COPD [13]. More importantly, it has been in the COPD and normal samples [25]. A total of 35 revealed that COPD can be subdivided into three molecular macrophage polarization genes (Supplementary Table 1) subtypes based on the gene expression profiles of 213 from the MsigDB database (https://www.gsea-msigdb.org/ COPD-related genes [14]. Therefore, bioinformatics analysis gsea/msigdb) were considered different traits to investigate of gene expression profiling may contribute to screening the coexpressed genes related to macrophage polarization novel biomarkers of COPD resulting in improving the treat- genes. Soft thresholding was then applied by raising ment of COPD. correlation values to a power of 14 to amplify disparity Increasing evidences have proposed that macrophage between strong and weak correlations. The soft thresholding which is an important effector cell for the innate immune power was chosen to achieve approximately scale-free response plays a crucial role in COPD [15–17]. A recent network topology, as recommended for biological networks study has demonstrated that COPD patients exhibit more [26, 27]. The resulting signed adjacency matrix was used to macrophages in the bronchial alveolar lavage fluid, along calculate topological overlap matrix (TO), which was then the airways and lung parenchyma compared to the normal hierarchically clustered with (1-TO) as a distance measure. samples [15, 16]. Besides, Traves et al. also found more blood Genes were then assigned into coexpression modules by monocyte-derived macrophages into the airspaces of COPD dynamic tree cutting algorithm requiring minimal module size [17]. More importantly, emerging evidences showed that of 100 genes [28]. The modules with highly correlated eigen- macrophage polarization may be associated with COPD genes (correlation above 0.6) were merged. Module eigengene [18–20]. For example, a previous study has suggested an (ME) is the first principal component of the gene expression increase in proinflammatory M1 macrophages in the small values within a module and is used to summarize the module’s airways of COPD compared to controls, but a reciprocal expression. The Pearson correlation between each gene and decrease in M2 macrophages [18]. On the other hand, the ME was then calculated. This value is called module member- results of Eapen et al. suggested that lncRNA MIR155HG ship (MM) and represents how close a particular gene is to a modulated the progression of COPD by inducing the module. Finally, each gene was assigned to a module for which granulocyte-macrophage colony-stimulating factor-mediated it had the highest MM. The module with the highest absolute M1/M2 macrophage polarization [19]. In addition, Shaykhiev value of the correlation coefficient with the traits was chosen as et al. suggested that the reprogramming for alveolar macro- the key module for subsequent analysis. phage polarization likely contributes to COPD pathogenesis [20]. Nevertheless, the research focusing on the molecular reg- 2.3. Differential Expression Analysis and the Candidate Gene ulation mechanism of macrophage polarization in COPD is Identification. The comparison of differential expressions inadequate. between the COPD and normal samples was generated using p ∣ ∣ : With the development and extensive applications of bio- DESeq2 v3.11 [29]. The value < 0.05 and log2FC >0 8 informatics, the weighted gene coexpression network analy- were considered the cutoff value. The candidate genes were sis (WGCNA) has become an important and effective identified by overlapping the DEGs and the genes in the method to screen hub genes for complex disease [21]. In key module, getting 25 genes related to COPD and macro- recent years, WGCNA has been performed to identify genes phage polarization, and their expressions were displayed by which are related to clinical features in many diseases, such as a heatmap in the COPD and normal samples. BioMed Research International 3

Scale independence Mean connectivity

14 5000 1 15 16 17 0.8 3 4 5 7 8 9 18 19 6 10 11 12 13 20 4000

2 0.6 3000

0.4 2000 2 Mean connectivity

0.2 1000 3 4 5 6 0.0 7 8 9 1 0 10 11 12 13 14 15 16 17 18 19 20 5 10 15 20 5 10 15 20 Soft threshold (power) Soft threshold (power) (a) Gene dendrogram and module colors 1.0

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Merged dynamic

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Module–trait relationships

–0.25 0.31 –0.36 0.19 –0.21 0.18 –0.19 0.26 –0.23 –0.25 –0.27 0.41 –0.25 –0.19 0.04 0.35 0.097 –0.14 –0.27 –0.23 –0.19 0.18 –0.2 0.21 0.082 0.26 0.21 0.32 0.23 0.22 –0.27 0.34 –0.2 0.079 0.014 MEdarkturquoise (0.3) (0.2) (0.1) (0.4) (0.4) (0.4) (0.4) (0.3) (0.3) (0.3) (0.2) (0.07) (0.3) (0.4) (0.9) (0.1) (0.7) (0.6) (0.2) (0.3) (0.4) (0.4) (0.4) (0.4) (0.7) (0.3) (0.4) (0.2) (0.3) (0.3) (0.2) (0.1) (0.4) (0.7) (1)

–0.8 0.77 –0.81 0.82 –0.82 0.91 –0.71 0.96 –0.79 –0.74 –0.71 0.86 –0.84 –0.73 0.86 0.75 0.92 –0.81 –0.75 –0.75 –0.78 0.89 –0.78 0.9 –0.89 0.85 0.87 0.88 0.73 0.87 –0.79 0.8 –0.77 0.9 0.9 MEblack (1e–05) (4e–05) (9e–06) (5e–06) (6e–06) (1e–08) (3e–04) (1e–11) (2e–05) (1e–04) (3e–04) (7e–07) (2e–06) (2e–04) (7e–07) (1e–04) (6e–09) (1e–05) (8e–05) (9e–05) (3e–05) (6e–08) (3e–05) (3e–08) (8e–08) (4e–06) (3e–07) (2e–07) (2e–04) (3e–07) (2e–05) (2e–05) (4e–05) (4e–08) (3e–08)

–0.26 0.51 –0.17 –0.085 –0.18 –0.0036 –0.24 0.21 –0.25 –0.31 –0.17 0.085 –0.018 –0.23 0.15 0.25 0.09 –0.17 –0.14 –0.15 –0.087 0.22 –0.18 0.43 0.34 0.39 0.13 0.022 0.23 0.077 –0.23 0.19 –0.27 0.099 0.22 MEroyalblue (0.3) (0.02) (0.5) (0.7) (0.4) (1) (0.3) (0.4) (0.3) (0.2) (0.5) (0.7) (0.9) (0.3) (0.5) (0.3) (0.7) (0.5) (0.5) (0.5) (0.7) (0.3) (0.4) (0.05) (0.1) (0.08) (0.6) (0.9) (0.3) (0.7) (0.3) (0.4) (0.2) (0.7) (0.3) 0.5 –0.081 –0.15 –0.0086 –0.049 –0.046 –0.064 –0.21 –0.16 –0.059 0.049 –0.024 –0.16 –0.015 –0.08 –0.0056 0.012 –0.22 –0.024 –0.034 –0.21 –0.069 –0.088 –0.088 0.037 0.12 –0.11 –0.08 –0.0079 0.00071 –0.17 0.12 0.074 –0.12 –0.082 –0.16 MEpurple (0.7) (0.5) (1) (0.8) (0.8) (0.8) (0.4) (0.5) (0.8) (0.8) (0.9) (0.5) (1) (0.7) (1) (1) (0.3) (0.9) (0.9) (0.4) (0.8) (0.7) (0.7) (0.9) (0.6) (0.6) (0.7) (1) (1) (0.5) (0.6) (0.8) (0.6) (0.7) (0.5)

–0.016 –0.077 –0.074 –0.12 –0.012 –0.12 –0.041 –0.079 –0.025 –0.14 –0.075 –0.11 0.19 0.32 –0.073 –0.014 –0.038 –0.081 0.099 0.15 –0.064 –0.051 0.18 0.14 –0.13 –0.059 –0.0085 –0.11 –0.29 –0.19 –0.12 –0.25 –0.029 –0.13 –0.063 MElightgreen (0.9) (0.7) (0.8) (0.6) (1) (0.6) (0.9) (0.7) (0.9) (0.6) (0.8) (0.6) (0.4) (0.2) (0.8) (1) (0.9) (0.7) (0.7) (0.5) (0.8) (0.8) (0.4) (0.5) (0.6) (0.8) (1) (0.6) (0.2) (0.4) (0.6) (0.3) (0.9) (0.6) (0.8)

0.31 –0.16 0.22 –0.052 0.31 –0.17 0.091 –0.06 0.36 –0.046 –0.068 –0.19 0.12 0.36 –0.16 –0.065 –0.19 –0.028 –0.26 –0.18 –0.12 –0.17 0.24 –0.23 –0.14 –0.25 –0.0051 –0.05 –0.2 –0.099 0.12 –0.14 –0.036 –0.091 –0.13 MEpink (0.2) (0.5) (0.3) (0.8) (0.2) (0.5) (0.7) (0.8) (0.1) (0.8) (0.8) (0.4) (0.6) (0.1) (0.5) (0.8) (0.4) (0.9) (0.2) (0.4) (0.6) (0.5) (0.3) (0.3) (0.6) (0.3) (1) (0.8) (0.4) (0.7) (0.6) (0.5) (0.9) (0.7) (0.6) 0 –0.13 –0.016 –0.0014 0.24 –0.065 –0.047 0.18 –0.058 –0.16 –0.049 0.05 –0.0034 0.16 –0.099 –0.062 –0.28 0.11 0.076 –0.075 0.041 0.0016 –0.13 0.063 0.031 –0.1 0.049 –0.058 –0.15 0.17 –0.098 –0.027 –0.033 –0.059 –0.075 0.12 MEdarkgreen (0.6) (0.9) (1) (0.3) (0.8) (0.8) (0.4) (0.8) (0.5) (0.9) (0.8) (1) (0.5) (0.7) (0.8) (0.2) (0.6) (0.7) (0.7) (0.9) (1) (0.6) (0.8) (0.9) (0.7) (0.8) (0.8) (0.5) (0.4) (0.7) (0.9) (0.9) (0.8) (0.7) (0.6)

0.17 –0.19 0.27 –0.32 0.28 –0.23 0.12 –0.13 0.098 0.16 0.19 –0.0099 0.32 0.23 –0.18 –0.36 –0.16 0.32 0.3 0.53 0.36 –0.23 0.31 –0.15 –0.11 –0.15 –0.18 –0.27 –0.11 –0.36 –0.096 –0.18 0.17 –0.14 –0.13 MEdarkgrey (0.5) (0.4) (0.2) (0.2) (0.2) (0.3) (0.6) (0.6) (0.7) (0.5) (0.4) (1) (0.2) (0.3) (0.4) (0.1) (0.5) (0.2) (0.2) (0.01) (0.1) (0.3) (0.2) (0.5) (0.6) (0.5) (0.4) (0.2) (0.6) (0.1) (0.7) (0.4) (0.5) (0.5) (0.6)

0.26 –0.28 0.22 –0.35 0.2 –0.38 0.45 –0.41 0.22 0.52 0.57 –0.33 0.37 0.26 –0.35 –0.41 –0.41 0.39 0.291 0.17 0.48 –0.5 0.37 –0.36 –0.46 –0.3 –0.53 –0.39 –0.25 –0.49 0.26 –0.34 0.64 –0.46 –0.44 MEmidnightblue (0.3) (0.2) (0.3) (0.08) (0.4) (0.09) (0.04) (0.07) (0.3) (0.01) (0.007) (0.1) (0.1) (0.3) (0.1) (0.07) (0.07) (0.08) (0.2) (0.5) (0.03) (0.02) (0.09) (0.1) (0.04) (0.02) (0.01) (0.08) (0.3) (0.02) (0.3) (0.1) (0.002) (0.03) (0.05) –0.5 0.92 –0.69 0.95 –0.81 0.94 –0.85 0.7 –0.84 0.89 0.75 –0.76 –0.72 0.88 0.71 –0.68 –0.83 –0.79 0.84 0.92 0.86 0.75 –0.86 –0.83 –0.82 –0.81 –0.7 –0.84 –0.84 –0.63 –0.78 0.88 –0.77 0.75 –0.71 –0.69 MEbrown (5e–09) (5e–04) (4e–11) (8e–06) (1e–10) (1e–06) (4e–04) (1e–06) (7e–08) (8e–05) (6e–05) (2e–04) (1e–07) (3e–04) (7e–04) (4e–06) (2e–05) (2e–06) (5e–09) (1e–07) (1e–04) (5e–07) (4e–06) (6e–06) (8e–06) (4e–04) (2e–06) (2e–06) (0.002 (3e–05) (2e–07) (5e–05) (1e–04) (3e–04) (5e–04)

0.3 –0.13 0.34 –0.32 0.31 –0.34 0.15 –0.39 0.28 0.16 0.14 –0.25 0.2 0.38 –0.26 –0.22 –0.35 0.3 0.22 0.13 0.55 –0.33 0.16 –0.3 –0.23 –0.22 –0.34 –0.35 –0.24 –0.18 0.33 –0.58 0.24 –0.3 –0.36 MElightcyan (0.2) (0.6) (0.1) (0.2) (0.2) (0.1) (0.5) (0.08) (0.2) (0.6) (0.6) (0.3) (0.4) (0.09) (0.3) (0.3) (0.1) (0.2) (0.3) (0.6) (0.01) (0.1) (0.5) (0.2) (0.3) (0.3) (0.1) (0.1) (0.3) (0.4) (0.1) (0.005) (0.3) (0.2) (0.1)

–0.22 0.14 –0.14 –0.33 –0.12 –0.13 0.46 –0.27 –0.26 –0.27 –0.034 –0.18 0.35 –0.084 –0.25 –0.26 –0.071 0.48 0.013 –0.081 0.41 –0.081 0.43 –0.18 –0.22 –0.077 –0.16 –0.23 –0.17 –0.19 –0.05 –0.46 0.46 –0.39 –0.25 MEgrey (0.3) (0.6) (0.5) (0.1) (0.6) (0.6) (0.04) (0.2) (0.2) (0.2) (0.9) (0.4) (0.1) (0.7) (0.3) (0.2) (0.8) (0.03) (1) (0.7) (0.07) (0.7) (0.05) (0.4) (0.3) (0.7) (0.5) (0.3) (0.5) (0.4) (0.8) (0.03) (0.04) (0.08) (0.3) SFI1 MAF CBX6 ARSK FGD2 GLO1 ENO2 ACSSI LIN7C SH2B2 PINK1 CRIPT ZNF85 RAB10 COMT OCEL1 SOAT1 PRDX2 EFHD2 LCORL MYOIF HAUS2 SNHG6 ATG4D ADAM8 PTGER2 HMGN3 QRICH1 SLC35A1 LYPLALI MALAT1 FAMI35A FAM241A IGHMBP2 TMEM267 (c)

Figure 1: Construction weighted gene coexpression network and identiﬁcation of modules related to the markers of macrophage polarization. (a) Determination the optimal soft threshold to conform to the scale-free distribution. (b) Dendrogram of genes clustered based on the highly correlated eigengenes (correlation above 0.6). 4 BioMed Research International

178178 25 4897 2 − log10 ( p value)

0 DEGs Black module −5 0 log2 fold change

Significant Down No Up (a) (b) 4 Type C10orf105 MIR626 RNF43 HESX1 GASK1A PRDM6 2 DIP2A–IT1 HLA–V NEBL S100B UTS2 EIF2AK3–DT 0 ZNF676 CENPS RUNDC3B GEM SPINK2 CBR3 NEFL −2 FZD6 LOC100505715 HMMR SNORD116–1 GZMA PROX2 −4 Type COPD Normal (c)

Figure 2: Identiﬁcation of genes associated with COPD and macrophage polarization. (a) Volcano plot showed the DEGs between COPD samples and normal samples. (b) Overlapping genes between DEGs and macrophage polarization-related genes. (c) Heatmap exhibited the diﬀerential expression of the candidate genes between COPD samples and normal samples.

2.4. The Functional Annotations and Correlation Analysis of genes) were selected from the 25 genes, and Pearson analysis the Candidate Genes. DAVID bioinformatics resource was used to investigate the correlation of the candidate genes (v6.8), an online website (https://david.ncifcrf.gov/), was and the diﬀerential immune cells between the COPD and devoted to conduct the functional annotations of the 25 normal samples, as well as marker genes and pathways of dif- genes [30]. The immune-related genes (as the candidate ferential immune cells. BioMed Research International 5

Table 1: The results of GO functional annotation and KEGG pathways for the 25 candidate genes.

ID Biological process KEGG pathway hsa00590: arachidonic acid metabolism, hsa00980: GO:0042376 ~ phylloquinone catabolic process, GO:0050890 ~ CBR3 metabolism of xenobiotics by cytochrome P450, hsa01100: cognition, GO:0055114 ~ oxidation-reduction process metabolic pathways GO:0001736 ~ establishment of planar polarity, GO:0001843 ~ neural tube closure, GO:0001942 ~ hair follicle development, GO:0007186 ~ G-protein coupled receptor signaling pathway, GO:0007223 ~ Wnt signaling pathway, calcium modulating pathway, GO:0007275 ~ multicellular organism development, hsa04310: Wnt signaling pathway, hsa04390: Hippo GO:0030168 ~ platelet activation, GO:0033278 ~ cell signaling pathway, hsa04550: signaling pathways proliferation in midbrain, GO:0035567 ~ non-canonical Wnt regulating pluripotency of stem cells, hsa04916: FZD6 signaling pathway, GO:0035880 ~ embryonic nail plate melanogenesis, hsa05166:HTLV-I infection, hsa05200: morphogenesis, GO:0042472 ~ inner ear morphogenesis, pathways in cancer, hsa05205: proteoglycans in cancer, GO:0043433 ~ negative regulation of sequence-specific DNA hsa05217: basal cell carcinoma binding transcription factor activity, GO:0060071 ~ Wnt signaling pathway, planar cell polarity pathway, GO:0090090 ~ negative regulation of canonical Wnt signaling pathway, GO:1904693 ~ midbrain morphogenesis GO:0000165 ~ MAPK cascade, GO:0000226 ~ microtubule cytoskeleton organization, GO:0008089 ~ anterograde axonal transport, GO:0008090 ~ retrograde axonal transport, GO:0009636 ~ response to toxic substance, GO:0014012 ~ peripheral nervous system axon regeneration, GO:0019896 ~ axonal transport of mitochondrion, GO:0021510 ~ spinal cord development, GO:0021766 ~ hippocampus development, GO:0021987 ~ cerebral cortex development, GO:0031133 ~ regulation of axon diameter, GO:0033693 ~ neurofilament bundle assembly, GO:0040011 ~ locomotion, GO:0043434 ~ NEFL response to peptide hormone, GO:0043524 ~ negative hsa05014: amyotrophic lateral sclerosis (ALS), regulation of neuron apoptotic process, GO:0043547 ~ positive regulation of GTPase activity, GO:0045105 ~ intermediate filament polymerization or depolymerization, GO:0045109 ~ intermediate filament organization, GO:0048812 ~ neuron projection morphogenesis, GO:0050772 ~ positive regulation of axonogenesis, GO:0050885 ~ neuromuscular process controlling balance, GO:0051258 ~ protein polymerization, GO:0051412 ~ response to corticosterone, GO:0061564 ~ axon development, GO:1903935 ~ response to sodium arsenite, GO:1903937 ~ response to acrylamide GO:0006351 ~ transcription, DNA-templated, GO:0006355 ~ ZNF676 regulation of transcription, DNA-templated GO:0000122 ~ negative regulation of transcription from RNA polymerase II promoter, GO:0006351 ~ transcription, DNA- PROX2 templated, GO:0030182 ~ neuron differentiation, GO:0045944 ~ positive regulation of transcription from RNA polymerase II promoter, GO:0055007 ~ cardiac muscle cell differentiation GO:0000086 ~ G2/M transition of mitotic cell cycle, HMMR hsa04512: ECM-receptor interaction GO:0030214 ~ hyaluronan catabolic process GO:0006955 ~ immune response, GO:0007067 ~ mitotic nuclear division, GO:0007165 ~ signal transduction, GO:0007166 ~ cell surface receptor signaling pathway, GEM GO:0007264 ~ small GTPase mediated signal transduction, GO:0051276 ~ chromosome organization, GO:0051310 ~ metaphase plate congression GO:0006351 ~ transcription, DNA-templated, GO:0007420 ~ brain development,GO:0030916 ~ otic vesicle formation, hsa04550: signaling pathways regulating pluripotency HESX1 GO:0043584 ~ nose development, GO:0045892 ~ negative of stem cells regulation of transcription, DNA-templated, GO:0048853 ~ forebrain morphogenesis, 6 BioMed Research International

Table 1: Continued.

ID Biological process KEGG pathway GO:0006351 ~ transcription, DNA-templated, GO:0022008 ~ neurogenesis, GO:0034968 ~ histone lysine methylation, PRDM6 GO:0045892 ~ negative regulation of transcription, DNA- templated, GO:0051151 ~ negative regulation of smooth muscle cell differentiation RUNDC3B GO:0007409 ~ axonogenesis, GO:0007417 ~ central nervous system development, GO:0007611 ~ learning or memory, GO:0007613 ~ memory, GO:0008283 ~ cell proliferation, GO:0008284 ~ positive regulation of cell proliferation, GO:0008360 ~ regulation of cell shape, GO:0043065 ~ positive regulation of apoptotic process, GO:0043123 ~ positive regulation of I-kappaB kinase/NF-kappaB signaling, S100B GO:0045087 ~ innate immune response, GO:0048168 ~ regulation of neuronal synaptic plasticity, GO:0048708 ~ astrocyte differentiation, GO:0051384 ~ response to glucocorticoid, GO:0051597 ~ response to methylmercury, GO:0060291 ~ long-term synaptic potentiation, GO:0071456 ~ cellular response to hypoxia, GO:2001015 ~ negative regulation of skeletal muscle cell differentiation GO:0000712 ~ resolution of meiotic recombination intermediates, GO:0006281 ~ DNA repair, GO:0006312 ~ mitotic recombination, GO:0006974 ~ cellular response to DNA damage stimulus, GO:0007062 ~ sister chromatid cohesion, GO:0007067 ~ mitotic nuclear division, GO:0031297 ~ CENPS hsa03460: Fanconi anemia pathway replication fork processing, GO:0031398 ~ positive regulation of protein ubiquitination, GO:0034080 ~ CENP-A containing nucleosome assembly, GO:0036297 ~ interstrand cross-link repair, GO:0051301 ~ cell division, GO:0051382 ~ kinetochore assembly C10orf105 GO:0006915 ~ apoptotic process, GO:0006955 ~ immune response, GO:0019835 ~ cytolysis, GO:0032078 ~ negative regulation of endodeoxyribonuclease activity, GO:0043065 ~ GZMA positive regulation of apoptotic process, GO:0043392 ~ negative hsa04080: neuroactive ligand-receptor interaction regulation of DNA binding, GO:0051354 ~ negative regulation of oxidoreductase activity, GO:0051603 ~ proteolysis involved in cellular protein catabolic process NEBL GO:0071691 ~ cardiac muscle thin filament assembly GO:0016055 ~ Wnt signaling pathway, GO:0016567 ~ protein ubiquitination, GO:0030178 ~ negative regulation of Wnt signaling pathway, GO:0038018 ~ Wnt receptor catabolic RNF43 process, GO:0042787 ~ protein ubiquitination involved in ubiquitin-dependent protein catabolic process, GO:0072089 ~ stem cell proliferation, GO:0002176 ~ male germ cell proliferation, GO:0007286 ~ spermatid development, GO:0009566 ~ fertilization, GO:0043065 ~ positive regulation of apoptotic process, SPINK2 GO:0060046 ~ regulation of acrosome reaction, GO:0072520 ~ seminiferous tubule development, GO:1900004 ~ negative regulation of serine-type endopeptidase activity BioMed Research International 7

Table 1: Continued.

ID Biological process KEGG pathway GO:0001666 ~ response to hypoxia, GO:0003105 ~ negative regulation of glomerular filtration, GO:0006936 ~ muscle contraction, GO:0007204 ~ positive regulation of cytosolic calcium ion concentration, GO:0007268 ~ chemical synaptic transmission, GO:0008217 ~ regulation of blood pressure, GO:0010459 ~ negative regulation of heart rate, GO:0010460 ~ positive regulation of heart rate, GO:0010763 ~ positive regulation of fibroblast migration, GO:0010841 ~ positive regulation of circadian sleep/wake cycle, wakefulness, GO:0032224 ~ positive regulation of synaptic transmission, cholinergic, GO:0032967 ~ positive regulation of collagen biosynthetic process, GO:0033574 ~ response to testosterone, UTS2 GO:0035811 ~ negative regulation of urine volume, GO:0035814 ~ negative regulation of renal sodium excretion, GO:0042312 ~ regulation of vasodilation, GO:0042493 ~ response to drug, GO:0045597 ~ positive regulation of cell differentiation, GO:0045766 ~ positive regulation of angiogenesis,GO:0045776 ~ negative regulation of blood pressure, GO:0045777 ~ positive regulation of blood pressure, GO:0045909 ~ positive regulation of vasodilation, GO:0046005 ~ positive regulation of circadian sleep/wake cycle, REM sleep,GO:0046676 ~ negative regulation of insulin secretion,GO:0048146 ~ positive regulation of fibroblast proliferation,

2.5. Construction of PPI Network and LASSO Model. To fur- shown by a volcano plot (Figure 2(a)), including 82 ther evaluate the functions of the candidate genes, the PPI upregulated genes (Supplementary Table 3) and 121 network was constructed by IPA (v01-18-05) to explore the downregulated genes (Supplementary Table 4). The DEGs gene-related pathways. Besides, a LASSO model was estab- were overlapped with the 4922 eignegenes in the black lished by the candidate genes using the glmnet R package module (Figure 2(b)), obtaining 25 genes as the candidate (v4.0-2) [31]. The ROC curve was drawn by the pROC R genes related to COPD and macrophage polarization. The package (v1.16.12) and used to analyze the ability of the candidate genes were listed in Supplementary Table 5, and model and gene to distinguish the COPD and normal their expression was shown by a heatmap which exhibited samples [32]. The higher the area under the curve (AUC), an obvious difference between the COPD and normal the stronger the predictive capacity. samples (Figure 2(c)). Almost all of the candidate genes in COPD samples had lower expressions compared to that in 3. Results normal samples, except for C10orf105 which was highly expressed in COPD samples, suggesting that it enabled 3.1. Selection of Coexpressed Genes Related to Macrophage these candidate genes related to macrophage polarization to Polarization. The coexpression network of the 21 samples commendably distinguish COPD from normal samples. was divided into 26 modules. We had chosen the soft threshold power 14 (marked with blue) to define the adjacency 3.3. Functional Annotations of the Candidate Genes. To fur- matrix based on the criterion of approximate scale-free ther explore the functions of the candidate genes, func- topology (Figure 1(a)). The modules whose eigengenes were tional annotations were conducted by DAVID. Only 18 correlated above 0.6 would be merged (Figure 1(b)). Subse- of the 25 candidate genes received corresponding func- quently, the analysis of the relationship of the modules and tional annotations. As shown in Table 1, GEM, S100B, the traits suggested that multiple modules (black, brown, and GZMA participated in the immune response, which and midnight blue) were closely related to one or more traits, were considered the candidate genes for subsequent analy- especially the black module, which was the one with the ses. Macrophages are traditional innate immunocytes, as highest correlation coefficient strongly associated with the well as being involved into adaptive immune response. macrophage polarization (Figure 1(c)). The black module Macrophage polarization is a dynamic process, macro- contained 4922 eignegenes (Supplementary Table 2). Thus, phages switch reversibly between M1 (proinflammatory) the black module was selected as the coexpressed genes phenotype and M2 (anti-inflammatory) phenotype which related to macrophage polarization. is implicated in the immune response [33]. Therefore, we next would attempt to investigate the correlation between 3.2. Candidate Gene Identification. A total of 203 DEGs was the candidate genes related to macrophage polarization generated between the COPD and normal samples and and immunity. 8 BioMed Research International

⁎ 0.100 ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns

0.075

0.050

xCell score Type 0.025 Control COPD 0.000 iDC CLP cDC aDC pDC NKT CMP Tregs B − cells NK cells T1 cells T2 cells Tgd cells Basophils Mast cells CD4+Tcm CD8+Tcm CD8+Tem CD4 +Tem Pro B − cells Neutrophils CD8+T − cells CD4+T − cells Macrophages Naive B − cells Megakaryocytes Memory B − cells Macrophages M1 Macrophages M2 CD8+naive T − cells CD8+naive T − cells CD4+memory T − cells Class − switched memory B cells

Immunocyte

Type Control COPD (a) GEM S100B GZMA 0.08 0.08 0.08 R = −0.15, p = 0.53 R = 0.024, p = 0.92 R = 0.13, p = 0.59 0.06 0.06 0.06

0.04 0.04 0.04

Immunocyte 0.02 Immunocyte 0.02 Immunocyte 0.02

0.00 0.00 0.00 0 2 4 6 0 25 50 75 100 125 200 400 600 Expression Expression Expression (b)

Figure 3: Continued. BioMed Research International 9

Correlation

S100B ⁎ 0.25

GZMA 0

−0.25 GEM ⁎ ⁎ CR2 BLK SPIB CD19 CD72 CCR9 TC1A BLNK GLDC GNG7 PNOC FCRL2 COCH QRSL1 ABCB4 MS4A1 SCN3A MEF2C BACH2 BCL11A MICAL3 FAM30A SLC15A2 OSBPL10 TNFRSF17 HLA − DOB

⁎ p < 0.05 ⁎⁎ p < 0.01 (c)

Figure 3: Correlation between candidate biomarkers and immune-infiltrated cells. (a) Box plot displayed the immune cell infiltration levels between COPD samples and normal samples. (b) Bubble plots showed the correlation between candidate biomarkers and immune-infiltrated cells. (c) Heatmap exhibited the correlation between candidate biomarkers and the marker genes of B cells.

It was worth noting that some genes were involved in the 3.5. Involvement of the Candidate Genes in the B Cell Receptor process of neuronal activity, such as CBR3, FZD6, NEFL, Signaling Pathway. The B cell receptor signaling pathway is PROX2, PRDM6, S100B, and GZMA. NEFL was found to be of great importance for B cell survival and proliferation. involved in amyotrophic lateral sclerosis- (ALS-) related sig- The B cell receptor aberrantly expressed on B cells contrib- naling pathways at the same time. Here, we speculated that utes to the multiple disease pathogenesis, and its signaling when COPD occurred, these genes were regulated by macro- pathway is currently the target of several therapeutic strate- phage polarization, which in turn affected motor neuron gies [36]. Therefore, we investigated the correlation of the activity, resulting in motor neuron atrophy, and motor weak- candidate genes with the genes in the B cell receptor signaling ness and atrophy (like ALS). This may be the reason why pathway which were obtained from the Kyoto Encyclopedia patients with COPD often accompany unexplained skeletal of Genes and Genomes (KEGG) database (Figure 4(a)). The muscle atrophy [34]. result indicated that GEM was highly associated with the genes in the PI3K/Akt/GSK3β signaling pathway (AKT and GSK3B, all p values < 0.05), regulation of the actin cytoskele- 3.4. Relationship of the Candidate Genes with Immune Cells. ton (BCR, LYN, and RAC, all p values < 0.05), and the cal- To explore the correlation between the candidate genes and cium signaling pathway (SYK, PLCG2, NFAT5, and immunity, we analyzed the score of immune cells in the NFATC1, all p values < 0.05) and might be partially related COPD and normal samples, showing that only the score of κ fi ff to the NF- B signaling pathway (only MALT1, IKBKB, B cells had a signi cant di erence between the COPD and NFKB2, and IKB, all p values < 0.05). Besides, no correlation normal samples. Compared with normal samples, score of between GEM and the MAPK signaling pathway was found the B cells was significantly reduced in the COPD samples fi p (Figure 4(b)). Genes and pathways signi cantly related to ( value < 0.05, Figure 3(a)). But, GEM, S100B and GZMA GEM were marked in Figure 4(a). These results indicated that were not significantly associated with the score of B cells p GEM might regulate B cell activity through the (all value > 0.05, Figure 3(b)), which might be caused by PI3K/Akt/GSK3β signaling pathway, regulation of actin too small samples. To further study the correlation between cytoskeleton, and calcium signaling pathway, as well as the the candidate genes and B cells, we downloaded the 26 NF-κB signaling pathway. marker genes from a literature with Immunology Journal [35]. Correlation of the candidate genes with the 26 marker 3.6. Construction of PPI Network. To further verify GEM-reg- genes were displayed by a heatmap (Figure 3(c)), suggesting ulated genes in pathways, all genes were used to construct a that GEM was significantly negatively correlated with two PPI network based on the screening of DEGs (Figure 5(a)), marker molecules (BLK and MICAL3, all p values < 0.05), in which GEM, S100B, and GZMA were signed. In the net- S100B was significantly positively correlated with one marker work, red represents upregulated genes, and green represents molecule (GNG7, p value < 0.05), while GZMA was not cor- downregulated genes. Gray indicates that the element is a related with any marker molecules. Thus, it can be seen that predicted element. The solid line represents the proteins that GEM and S100B may participate in the progression of COPD have been experimentally confirmed to have physical contact via regulating activity of B cells. with each other, and the dotted line represents that the 10 BioMed Research International

B cell receptor signaling pathway

Co-inhibitor PIR-B CD22 SHP1 CD72 −p

SYK +p

Antigen BCR +p � Ig LYN Bam32 Ig� +p +p +p −p ? Regulation of actin BLNK VAV Rac BTK cytoske lton +p −p DNA +p PLC-y2 O O CaN NFAT O IP3 Ca2+ +p GRB2 Calcium signaling pathway BCAP B cell ontogeny SOS Autoimmunity +p +p +p DNA RasGRP3 Ras Raf MEK1/2 Erk AP1 O Anergy O Immuno response DAG Co-inhibitor MAPK signaling Ig production FcgRIIB SHIP pathway GC formation +p PKC� CALMA1 Co-stimulator BCL-10 +p � MALT1 IKK NF�B O LEU13 +p IKK� DNA +p +p CD81 −p IKK� I�B CD19 Survival PI3K O AKT NF-� B signaling CD21 PIP3 pathway VAV CD19-21 complex +p GSK3� Ubiquitin mediated Complement and proteolysis coagulation cascade

04662 6/3/19 (c) Kanehisa laboratories (a) Correlation

S100B 0.25

0.00 GZMA ⁎ ⁎⁎ ⁎⁎ ⁎⁎ ⁎ ⁎⁎ ⁎⁎⁎⁎ ⁎ ⁎⁎ −0.25

− GEM ⁎⁎ ⁎ ⁎ ⁎⁎⁎⁎ ⁎⁎ ⁎⁎⁎⁎⁎ ⁎ ⁎ ⁎⁎ ⁎⁎ 0.50

−0.75 IKB SYK RAS JUN RAF BCR LYN AKT CD19 CD22 CD72 CD81 GRB2 KRAS AKT1 BCAP NRAS BLNK BCL10 CHUK IKBKB GSK3B PLCG2 NFKB1 NFKB2 DAPP1 NFAT5 PRKCB MALT1 MAPK1 PIK3CB NFKBIB FCGR2B NFATC1 NFATC2 MAP2K1 MAP2K2 RASGRP3

⁎ p < 0.05 ⁎⁎ p < 0.01 (b)

Figure 4: Association between candidate biomarkers and B cell receptor signaling pathway. (a) Regulation network of the B cell receptor signaling pathway. (b) Heatmap exhibited the correlation between candidate biomarkers and the genes in the B cell receptor signaling pathway. interaction between the proteins is physically combined acted with the most genes. This was consistent with our without being conﬁrmed by the interaction experiment. above results that GEM might participate in the regulation The color of the line has no special meaning, and the arrow of the PI3K/Akt/GSK3β signaling pathway. represents the object of action. Then, to show the interactions of GEM with other genes more clearly, we individually 3.7. Diagnostic Value of GEM. To investigate the accuracy of selected the PPI network of GEM. As shown in Figure 5(b), the genes for distinguishing the COPD samples from normal GEM was associated with Akt which was a gene that inter- samples, we ﬁrstly constructed a LASSO model using the BioMed Research International 11

(a)

Figure 5: Continued. 12 BioMed Research International

GALK1 GCM1 GEM FSH Growth hormone Creb HESX1 CNKSR1 Hsp27 CLEC12B IER3 CG LRRN3 Akt Lh ALPL

MAP2K1/2

14-3-3

MELK

Estrogen receptor

MIPOL1 TPD52L1

MIR155HG TNFSF11 N-cor TM4SF1 NEBL SUSD5 NEFL S100B NKX3-1 REM2 PRDM6 PMP22 OCLN

(b)

Figure 5: Construction of PPI network. (a) PPI network for candidate biomarkers and other DEGs. (b) PPI network for GEM and other DEGs. three candidate genes (Figure 6(a)). The accuracy of the screen novel and reliable biomarkers to improve the diagno- LASSO model was verified by the ROC curve, and the AUC sis of COPD. was 0.717 (Figure 6(b)). Meanwhile, we plotted the other Notably, there are some researches that focused on the ROC curves with only GEM, and the AUC was 0:844 > GSE124180 dataset. For example, the authors uploaded the 0:717 (Figure 6(c)). These results suggested that GEM, rather GSE124180 dataset mainly compared the correlation of gene than the three candidate genes, possessed an excellent accu- expression across COPD-relevant tissues, including large- racy in distinguishing COPD from normal samples. airway epithelium, alveolar macrophage, and peripheral blood, and explored the biological functions of overlapped genes among three tissues [42]. On the other hand, Baschal 4. Discussion et al. compared the gene expressions across the lower airway, sinus, and middle ear tissues using the GSE124180 dataset COPD, characterized by persistent airflow obstruction, is an and other data [43]. Different from these two researches, irreversible and preventable disease [37]. Although from our study mainly focused on the role of macrophage 1990 to 2015, the death rate of COPD has gone down [38, polarization in COPD and identification of macrophage 39], COPD will become the third leading cause of death polarization-related genes as the biomarkers of COPD based worldwide in 2030 [40]. However, the diagnosis of COPD on the peripheral blood of the GSE124180 dataset. mainly relied on the spirometry values and clinical symp- In our study, we identified 25 genes related to COPD and toms, which is full of continual underdiagnosis [41]. Hence, macrophage polarization, which were considered candidate it is urgent to further study the molecular mechanism and genes for further analysis (Figure 2(b), Supplementary BioMed Research International 13

3 3332 3 3 3 3 3 3 3 3 3 3 3 3 1 0 3 2 0.45 (B) −1 (A) −2

0.35 −3

−4 Coefficients 0.25 −5 Misclassification error

−6

0.15 −7 1 −10 −8 −6 −4 −2 −10 −8 −6 −4 −2 Log lambda Log(�) (a)

1.0 1.0

0.8 0.8

0.6 0.6

AUC: 0.717 AUC: 0.844

Sensitivity 0.4 Sensitivity 0.4

0.2 0.2

0.0 0.0

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 1 – specificity 1 – specificity (b) (c)

Figure 6: Investigation of the accuracy of the candidate biomarkers for distinguishing the COPD samples from normal samples. (a) Construction of the LASSO model based on 3 candidate biomarkers: (A) image showed the log (lambda) value of the 3 candidate biomarkers and (B) image showed the distribution of the log (lambda) value in the LASSO model. (c) ROC curve for the LASSO model. (d) ROC curve for the GEM.

Table 5). Moreover, to further explore the functions of the 25 induce inflammatory response via FZD5 and Wnt3a can candidate genes, we performed the GO annotation and mediate anti-inflammatory effects by the Wnt/β-catenin KEGG enrichment analyses. We found that for the signaling pathway [47]. Therefore, theses 25 genes might be biological process, these 25 candidate genes mainly associated with the COPD. involved in immune response, signal transduction, cell Clearly, the results of functional annotations for the 25 proliferation, negative regulation of skeletal muscle cell genes suggested that GEM, S100B, and GZMA participated differentiation, and so on (Table 1). The analysis of KEGG in the immune response and were selected as the hub candi- pathway enrichment showed that these 25 genes mainly date genes (Table 1). GEM, a small GTP binding, is a member related to metabolic pathways, Wnt signaling pathway, of RAS superfamily of monomeric G-proteins [48]. It has Hippo signaling pathway, amyotrophic lateral sclerosis, been suggested that GEM is involved in signal transduction ECM-receptor interaction, and so on (Table 1). Increasing [49]. Although it was not reported that GEM was related to evidence has revealed that obesity can affect the morbidity COPD in recent studies, it has been suggested that GEM is of COPD [44, 45]. Thus, we speculated that metabolic related to the skeletal muscle for type 2 diabetes [49]. Thus, pathways may play an important role in COPD. In we speculated that GEM might be related to skeletal muscle addition, it has been suggested that Wnt signaling is dysfunction of COPD. S100B, a member of the S100 protein associated with the inflammatory response in lung alveolar family for Ca2+-binding proteins of the EF-hand type, is epithelial cells [46]. Especially, in macrophages, Wnt5a can increasingly being studied in the central nervous system 14 BioMed Research International

[50]. Moreover, a recent study has proved that S100B is asso- Finally, we found GEM could more precisely distinguish ciated with the reparative process in acute muscle injury and COPD from normal samples than the LASSO model muscular dystrophy by regulating the M1/M2 macrophage obtained by combined GEM, S100B, and GZMA levels [51]. In addition, it has been also found that S100B (Figure 6(c)). Hence, GEM could act as an independent upregulated TNF-α and M1 markers in RAW264.7 macro- diagnostic biomarker for COPD. phages [52]. S100B could participate in the FGFR1- mediated inflammatory response in osteoarthritis [53]. 5. Conclusions Besides, S100B was necessary for the progression of vascular immune responses in neonatally infected rat brains [54]. In summary, our study for the first time analyzes the role of GZMA, a member of the serine protease family, may play a macrophage polarization-related genes in COPD by key role for the pathophysiology of different inflammatory WGCNA. In the present study, we found that the GEM, disorders by acting as a proinflammatory mediator [55–57]. S100B, and GZMA might be the novel therapeutic targets Garzón-Tituaña et al. also found that GZMA could serve as for COPD and GEM could act as an independent diagnostic a proinflammatory mediator in macrophages by inducing biomarker for COPD. Our findings may contribute to further the TLR4-dependent expression of IL-6 and TNF-α [58]. understanding the molecular mechanism and improving the On the other hand, it has been reported that GZMA is clinical diagnosis and treatment for COPD. However, there involved in the COPD [59]. Notably, S100B and GZMA were are still many limitations in this study, for instance, sample associated with ALS-related signaling pathways. It has been limitation and experiment limitation. Therefore, further demonstrated that muscle dysfunction is an important com- experimental studies are essential for further verifying the plication for COPD patients. Generally, COPD patients functions and mechanisms of GEM, S100B, and GZMA in showed greater susceptibility muscle fatigue of the lower limb COPD. compared to healthy people [60–62]. Moreover, muscle mass loss or atrophy usually lead to the muscle dysfunction in Data Availability COPD patients [34, 63]. Thus, GEM, S100B, and GZMA might be involved in the muscle dysfunction for COPD The GSE124180 dataset analyzed in this study was available patients by ALS-related signaling pathways and might partic- in the GEO database. ipate in the inflammatory response in COPD by regulating macrophage polarization, and these genes might act as the Conflicts of Interest potential therapeutic targets for muscle atrophy of COPD fl patients. The authors declare that they have no con icts of interest. Interestingly, GEM was significantly negatively correlated with BLK and MICAL3, which are the two marker molecules Authors’ Contributions for B cells, and S100B was significantly positively correlated with the GNG7 marker for B cells (Figure 3(c)). Moreover, Chaofeng Ren conceived and designed the research. Yalin we found that GEM was highly associated with the genes in Zhao, Meihua Li, Yanxia Yang, Tao Wu, Qingyuan Huang, the PI3K/Akt/GSK3β signaling pathway, regulation of actin and Qinghua Wu downloaded and analyzed the data. Yalin cytoskeleton, and calcium signaling pathway and might be Zhao wrote the draft manuscript, and all authors approved partially related to the NF-κB signaling pathway the manuscript for publication. (Figure 4(a)). Especially, GEM was related to the Akt in the PPI network (Figure 5(b)). These findings suggested that Acknowledgments GEM and S100B might participate in the progression of The work was supported by the Kunming Science and COPD via regulating the activity of B cells and GEM might Technology Bureau (2020-1-N-026). participate in the regulation of the PI3K/Akt/GSK3β signaling pathway. Apart from the macrophages, T lymphocytes also played an important role in COPD [64]. For example, Supplementary Materials Gosman et al. revealed that the number of B cells in bronchial Supplementary Table 1: 35 macrophage polarization marker biopsies of COPD was increased [65]. It has been revealed fl genes from the MsigDB database. Supplementary Table 2: that glycyrrhizic acid could alleviate in ammatory lung dis- 4922 genes in black module. Supplementary Table 3: 82 ease, including chronic obstructive pulmonary disease by β upregulated genes in COPD samples compared to normal promoting the downstream PI3K/Akt/GSK3 signaling samples. Supplementary Table 4: 121 downregulated genes pathway [66]. Furthermore, compelling evidence has sug- in COPD samples compared to normal samples. Supplemen- gested that NF-κB signaling plays a key role in the airway fl tary Table 5: overlapping genes between DEGs and macro- in ammation, including COPD [67]. Kim et al. also found phage polarization-related genes. (Supplementary Materials) that PI3K/Akt and NF-κB signaling pathways were involved in the emphysematous change in COPD [68]. Taken References together, these findings further suggested that S100B and GEM might serve as the therapeutic target and might become [1] P. J. Barnes, P. G. Burney, E. K. Silverman et al., “Chronic the significant targets for drug identification and drug obstructive pulmonary disease,” Nature Reviews Disease designing of COPD. Primers, vol. 1, no. 1, p. 76, 2015. BioMed Research International 15

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