Machine learning-based bioinformatics analysis of common hub genes associated with oxidative stress and immune infiltration in COPD and atherosclerosis Running title: Bioinformatics analysis of common hub OS genes in COPD and Atherosclerosis

Machine learning-based bioinformatics analysis of common hub genes associated with oxidative stress and immune infiltration in COPD and atherosclerosis Running title: Bioinformatics analysis of common hub OS genes in COPD and Atherosclerosis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Machine learning-based bioinformatics analysis of common hub genes associated with oxidative stress and immune infiltration in COPD and atherosclerosis Running title: Bioinformatics analysis of common hub OS genes in COPD and Atherosclerosis 金海 全, Weijie Fan, 仕森 李, Huaijin Xie, BiChen Quan, Shanghai Li, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4013922/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Chronic obstructive pulmonary disease (COPD) and atherosclerosis (AS) are both chronic irreversible diseases in the aged population, with oxidative stress (OS) and immune activation as the pathological basis. This study explored the common hub gene associated with OS and immune cell infiltration in AS and COPD. Methods Genes associated with AS were identified by the differentially expressed genes (DEGs) analysis and weighted gene co‑expression network analysis (WGCNA) in the GSE100927 dataset. Genes associated with COPD were analyzed by WGCNA in the GSE76925 dataset. Functional enrichment analysis was carried out by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). The common hub OS-related genes were analyzed by the intersection of the WGCNA modules of AS and COPD and OS‑related genes, protein–protein interaction (PPI), and lasso regression. The diagnostic value of the hub common genes was assessed by receiver operating characteristic analysis. The association of the hub common genes with immune infiltration in AS and COPD was analyzed by the Spearman correlation method. Results A total of 455 DEGs (336 upregulated genes and 139 downregulated genes) were identified in GSE100927. The turquoise module of WGCNA in GSE100927 and the yellow module of WGCNA in GSE76925, which are the most relevant modules, were intersected and obtained 25 common OS-related genes between AS and COPD. Those common OS-related genes were enriched in signaling pathways related to immunity and OS. Two hub common OS-related genes (SELL and MMP9) were identified and showed good diagnostic value in AS and COPD. The Spearman correlation analysis showed that the hub common OS-related genes positively or negatively correlated with various infiltrating immune cells. Conclusion Our study identified the common hub genes (SELL and MMP9) associated with OS and immune infiltration in AS and COPD, providing candidate therapeutic targets for AS combined with COPD. Atherosclerosis Machine learning Chronic obstructive pulmonary disease WGCNA Immune infiltration Oxidative stress Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease prevalent in the aged population, which has a great adverse effect on the quality of life of patients and is currently one of the main causes of death in the world [ 1 ]. The large medical and economic burden of COPD is largely attributed to the management of its comorbidities and other related chronic diseases, among which atherosclerosis (AS), the leading cause of coronary heart disease, peripheral vascular disease, and stroke, is a common complication of COPD [ 2 ]. The association between COPD and atheromatous cardiovascular disease (ACD) has been observed clinically. The severity and intensity of coronary atherosclerosis increased with the increase of COPD severity [ 3 ]. COPD is associated with poor prognosis with coronary artery disease percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG) [ 4 ]. The comorbid of ACD is a key factor that resulted in the deaths of COPD, about 30% of the patients with COPD die from cardiovascular performance [ 5 ]. However, the pathogenesis of COPD complicated with AS is complex and largely unknown. AS and COPD share many common risk factors, including a history of smoking, persistent inflammation, high oxidative stress (OS) load, and genetics [ 6 ]. Because of this, the pathological mechanism of COPD and AS comorbidity is considered to be complex and multifactorial. Until now, persistent local (i.e., pulmonary, vascular) and systemic inflammation and OS due to exposure to cigarette smoke are considered to be the common pathophysiological link driving the progression of ACD and COPD [ 2 , 7 ]. Exposure to cigarette smoke has harmful pulmonary and systemic effects, including inflammation, OS, lung endothelial dysfunction and the improvement of circulation coagulant medium [ 8 , 9 ], which is the basis for the development of chronic comorbidities. In addition, pulmonary hypertension driven by COPD is also an important factor in cardiovascular injury [ 10 ]. Pulmonary inflammation and pulmonary hypertension caused by COPD increase the risk of these patients developing AS [ 11 ]. Some studies have found through cell and animal experiments that increasing anti-inflammatory and antioxidant activities can significantly reduce systemic and pulmonary inflammation and OS, help protect lung function, and reduce the occurrence and severity of comorbid atherosclerosis [ 12 ]. Because of these phenomena and the development of biological information technology, several studies have attempted to explore the genetic pathological mechanisms underlying the comorbidity of COPD and AS. A large-scale cross-trait genome-wide association study found the common genetic traits shared by COPD and cardiac traits cardiovascular diseases [ 13 ]. Common biological processes and signaling pathways may be related to the occurrence of COPD and OA. This work using bioinformatics methods for identification of potential candidate genes and comprehensive analysis, will deepen our understanding of gene regulation in OS and immune response in comorbid COPD and AS and may provide a promising candidate for AD biomarkers and therapeutic targets. 2 Materials and methods 2.1 Data set Atherosclerosis (AS) dataset GSE100927 (GPL17077) and COPD dataset GSE76925 (GPL10558) were downloaded from the GEO database. 2.2 Identification of differentially expressed genes (DEGs) in AS The Limma package was employed to screen the differentially expressed genes (DEGs) in AS with |logFC|>1 and adj.P.Val < 0.05 as the criterion, which were subsequently visualized by the volcano plot and heatmap by using “ggplot2” and “pheatmap” R packages. The OS-related DEGs were identified by the intersection of DEGs and OS genes obtained from the literature [ 14 ] by using the “VennDiagram” R package [ 15 ]. 2.3 Weighted gene co‑expression network analysis (WGCNA). WGCNA was performed to describe gene patterns association with AS or COPD using the R package [ 16 ] based on the gene expression profiles extracted from GSE100927 and GSE76925. According to the criteria of approximate scale-free topology, the best soft-thresholding power β was obtained. The network analysis was constructed by transforming the adjacency matrix into a topological overlap matrix. The module color was generated based on the degree of dissimilarity. The intersection of AS_WGCNA, COPD_WGCNA and OS genes in key modules was carried out by using the “VennDiagram” R package [ 15 ]. The shared genes were defined as OS-related DEGs, which were used for subsequent analysis. 2.4 Functional enrichment analysis. DEG functional enrichment analyses were conducted by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) by using the clusterProfiler package [ 17 ]. GO enrichment pathway encompasses biological processes, cellular components, and molecular function. A P < 0.05 was recognized as the statistical significance. 2.5 Protein–protein interaction (PPI) network construction. The OS-related DEGs were uploaded to the Search Tools for the Retrieval of Interacting Genes (STRING, http://www.string-db.org/ ). Confidence > 0.4 was the cut-off criterion for the PPI network analysis. The key gene modules were screened by MCODE plug-in (degree cutoff = 2, node score cutoff = 0.2, K-core = 2, and max depth = 100) in Cytoscape ( https://cytoscape.org ) in the PPI network. The key module genes were hub genes. Subsequently, the least absolute shrinkage and selection operator (LASSO) Cox regression analysis was conducted to further identify hub genes by using “glmnet” package R [ 18 ]. The intersection of AS_LASSO and COPD_LASSO was carried out by using the “VennDiagram” R package [ 15 ] to identify the common hub genes. 2.6 Validation of a diagnostic nomogram diagram for AS and COPD We constructed a nomogram to predict whether the common hub genes can act as disease risk factors by using the rms package in R software [ 19 ]. Based on multivariate Cox analysis, the point scale in the nomogram was used to assign values to each variable. We determined the number of points for each variable using a horizontal line and calculated the total number of points for each patient by summing the number of points for all variables and normalizing the distribution from 0 to 100. 2.7 Receiver operating characteristic (ROC) curve analysis. To test the diagnostic value of the common hub genes, we performed ROC curve and area under the curve (AUC) analysis by using the “pROC” package [ 20 ]. The genes with an AUC value of more than 0.7 were defined as available markers for disease diagnosis. 2.8 Immune infiltration Identification of immune cell infiltration using tumor immune infiltration assay (CIBERSORT) R package [ 21 ] and comparison between disease (AS or COPD) and healthy controls using Wilcoxon assay. The P -value < 0.05 represented a significant difference. 2.9 Statistical analysis R software (v.4.1.0) was run to analyze all statistical data. Spearman correlation was performed to test the correlation of the common hub genes with immune cell infiltration. A P -value or if necessary adjusted P -value < 0.05 represented statistical significance. 3 Results 3.1 Identification of DEGs and WGCNA analysis in AS The bioinformatics analysis was performed with the GSE100927 data set and found 455 DEGs, including 336 upregulated genes and 139 downregulated genes in AS patients, compared with the healthy controls, as shown by the Volcano plots and heatmap (Figs. 1 A and 1 B). Based on the module-trait relationships, the turquoise module was the one with the highest positive relevance (Cor = 0.74, p = 6e-19) and the blue module was the one with the highest negative relevance (Cor=-0.55, p = 2e-09) to AS in GSE100927 dataset (Fig. 1 C). The turquoise module of WGCNA in GSE100927 was selected for intersection with DEGs and obtained 429 important genes (Fig. 1 D). GO enrichment showed that those important genes were mainly enriched in leukocyte-mediated immunity, positive regulation of leukocyte activation, positive regulation of cytokine production, endocytic vesicle and immune receptor activity (Fig. 1 E). The KEGG enrichment analysis revealed that those important genes were related to pathways involved in Tuberculosis, Phagosome, and Cytokine-cytokine receptor interaction. (Fig. 1 F). 3.2 The common key genes associated with OS in COPD and AS Based on the mode-trait relationship, we found that the turquoise module had the most negative relevance in COPD (Cor=-0.34, p = 2e-05), while the yellow module had the most positive relevance in COPD (Cor = 35, p = 9e-06) in the GSE76925 dataset (Fig. 2 A). Therefore, the yellow module was used for downstream intersection analysis. The turquoise module of WGCNA in GSE100927 and the yellow module of WGCNA in GSE76925 were selected and intersected with 1398 OS-related genes and obtained 25 key OS-related DEGs (Fig. 2 B). GO enrichment analysis suggested that the key OS-related DEGs were enriched in positive regulation of leukocyte activation, leukocyte, response to OS, immune response-regulating signaling pathway, neuronal cell body, immune receptor activity and cardiac muscle cell action potential repolarization cytokine receptor activity (Fig. 2 C). The KEGG enrichment analysis revealed that the key OS-related DEGs were enriched in Viral protein interaction with cytokine and cytokine receptors, Cytokine-cytokine receptor interaction and chemokine signaling pathway (Fig. 2 D). 3.3 Screen of hub OS-related genes PPI network was constructed with 25 key OS-related DEGs (Fig. 3 A), among which 8 hub genes (CD28, CXCL10, SELL, IL2RA, CXCR3, CD38, MMP9, and CXCR4) were screened by MCODE plug-in Cytoscape (Figs. 3 A and 3 B). Three genes (CXCR3, MMP9, and SELL) were obtained after filtering by the lasso regression model construction with the 8 hub genes in the GSE100927 data set (AS) (Fig. 3 C and 3 D). Four genes (CXCL10, IL2RA, MMP9, and SELL) were obtained after filtering by the lasso regression model construction with the 8 hub genes in the GSE76925 data set (COPD) (Fig. 3 E and 3 F). The Venn diagram displayed 2 common hub genes (MMP9 and SELL) between AS and COPD (Fig. 3 G). 3.4 Evaluation of the diagnostic value of the common hub genes in AS and COPD Nomogram diagram of the two common hub genes SELL and MMP9 in COPD and AS. (Fig. 4 A). We evaluated the diagnostic performance of the two hub genes by plotting the ROC curves of the GSE100927 and GSE76925 data sets. The AUC values of the two hub genes (SELL and MMP9) were 0.692 and 0.707 in the GSE76925 data set (COPD) and 0.787 and 0.942 in the GSE100927 data set (AS) (Fig. 4 B), indicating that the two common hub genes possessed favorable diagnostic values in COPD and AS. 3.5 Correlation analysis of the common hub genes with immune infiltration in AS and COPD The profile of immune infiltration in COPD and AS was explored by using ssGSEA. Stacked bar charts exhibited the distribution of 22 infiltrating immune cells in AS (Fig. 5 A). The Vioplot displayed a significantly increased abundance of B cell memory, T cell CD4 memory resting, T cell gamma delta, Monocytes M0, and Mast cell activated and the significantly decreased abundance of B cell native, Plasma cells, Monocytes, Macrophages M1, Dendritic cell activated, and Mast cell resting in AS patients, compared with the controls (Fig. 5 B). The spearman correlation analysis demonstrated that the common hub genes (SELL and MMP9) positively correlated with B cell memory, T cell CD4 memory resting, T cell gamma delta, Monocytes M0, and Mast cell activated, negatively correlated with B cell native, Plasma cells, Monocytes, Macrophages M1, Dendritic cell activated, and Mast cell resting, and showed the opposite correlation in Neutrophils (Fig. 5 C). Stacked bar charts exhibited the distribution of 22 infiltrating immune cells in COPD (Fig. 5 D). The Vioplot displayed a significantly increased abundance of Plasma cells, T cell CD8 cells, T cell gamma delta, and Monocytes M0 and a significantly decreased abundance of T cell CD4 memory resting, Monocytes, and Dendritic cell activated in COPD patients, compared with the controls (Fig. 5 E). The Spearman correlation analysis showed that the common hub genes (SELL and MMP9) positively correlated with B cell native, T cells CD8, and T cell gamma delta, negatively correlated with NK resting, T cell CD4 memory resting, Macrophages M2 and B cell memory, and showed the opposite correlation in Neutrophils (Fig. 5 F). 4 Discussion In-depth research on the OS and immune-related pathological mechanisms underlying COPD and AS is a prerequisite for the development of effective interventions against this comorbidity. identified 25 common OS-related DEGs by WGCNA, which enriched in response to OS and T cell adaptive immune response, and further identified 2 common hub genes (MMP9 and SELL) by PPI, which possessed favorable diagnostic values and correlated with differentially infiltrated immune cells in AS and COPD. Among the few known pathological mechanisms shared by COPD and AS, increased OS is considered to be the key factor. In COPD patients, increased OS driven by the external environment (exogenous oxidants in cigarette smoke and air pollution) and intrapulmonary airway factors (endogenous generation of reactive oxygen species by inflammatory and structural cells) associated with COPD plays a crucial role in the pathological mechanism of the disease [ 22 ]. OS induces the activation of the pro-inflammatory transcription factor NF-κB pathway, which can lead to the activation of epithelial and inflammatory macrophages. By inducing the activation of the transforming growth factor pathway, it promotes the epithelial-mesenchymal transition of lung epithelial cells, leading to small airway fibrosis [ 23 ] and also by increasing the expression of MMP9, promotes the development of emphysema in COPD [ 24 , 25 ]. Increased OS also reflects an imbalance between oxidant and antioxidant defense mechanisms in COPD patients. It is the imbalance between oxidant and antioxidant defense mechanisms that magnifies the local inflammatory process, worsening cardiovascular health, and leading to COPD-associated cardiovascular dysfunction and mortality. As early as more than a decade ago, Topsakal et al. found that the severity and intensity of atherosclerosis in COPD patients increased, and speculated that chronic OS and inflammation associated with COPD may be the cause of driving coronary atherosclerosis in these patients [ 26 ]. The occurrence of COPD patients with AS has also been continuously found in subsequent clinical studies [ 27 , 28 ]. A metabolomics analysis provided evidence for this phenomenon and found that the carnitine/acylcarnitine ratio of COPD patients was lower than that of healthy controls, suggesting the existence of atherosclerosis susceptibility and OS caused by insufficient fatty acid β-oxidation [ 29 ]. Therefore, studying signaling pathways that better coordinate oxidation-antioxidant balance disorders is essential to help optimize the management of these two diseases to improve patient outcomes. We identified 25 common key OS-related DEGs (CD28, CXCL10, SELL, IL2RA, CXCR3, CD38, MMP9, and CXCR4, etc.) shared by COPD and AS. Functional analysis showed that common key genes were enriched in OS, immunity, inflammatory pathway, inflammatory factor, and chemokine pathway. Our findings reinforce the role of OS and immune activation in driving the process of COPD and AS comorbidity. Given the limited preliminary data, the molecular mechanisms of these common genes in the pathogenesis of comorbid COPD and AS are still unclear, and more targeted studies are expected to reveal their important roles in the link between the two. Using computer learning, we obtained 2 common hub OS-genes (MMP9 and SELL) between AS and COPD and determined their value as diagnostic markers for COPD and AS. MMP9 belongs to a member of the matrix metalloproteinases (MMPs) proteolytic enzyme family, which is involved in a series of physiological and pathological processes from reproductive development, morphogenesis, angiogenesis, inflammation, cancer cell metastasis [ 30 ]. MMP9 can be used as a biomarker for mitochondrial metabolism disorder and OS [ 31 ]. The plasma levels of MMP9 were increased in COPD and AS patients [ 32 , 33 ]. MMP9 gene polymorphism is associated with the susceptibility of COPD and AS [ 34 – 36 ]. Since its activation acts on many inflammatory substrates, it is suspected to promote the development of chronic inflammation-related COPD and AS diseases [ 37 , 38 ]. The SELL gene encodes L-selectin (CD62L), which is responsible for recruiting inflammatory immune cells to transendothelial migration. The variation of this gene is a genetic risk factor for atherosclerosis-related and inflammatory diseases [ 39 ]. SELL gene dysfunction (decreased L-selectin expression) has been reflected in COPD and AS patients [ 40 , 41 ]. Through correlation analysis, we found that SELL and MMP9 had common positive correlated inflammatory immune cells in AS and COPD, including T cell gamma delta and Mast cell activated, negative correlated immune cells, and T cell gamma delta and mast cell activated. Including macrophage M1 and Dendritic cell activated. Therefore, MMP9 and SELL are expected to be diagnostic markers and therapeutic targets for the diagnosis of COPD and AS comorbidity. 5 Conclusions In conclusion, based on the overlapping OS-related DEGs in the pathogenesis of COPD and AS, we identified 2 hub genes (MMP9 and SELL) with good diagnostic value for the comorbid of COPD and AS. Our findings highlight the importance of genetic factors in OS and inflammatory immune responses and provide new insights into future therapeutic targets for the comorbid of COPD and AS. Further research is needed to elucidate the clinical utility of biomarkers and therapeutic targets in the comorbid of COPD and AS and to determine the generalizability of our findings. Declarations Author Contribution RH, SL, and WL conceived and designed the study and approved the final draft. JQ provided scientific supervision. SL and WF authored and reviewed drafts of the manuscript. BQ and HX prepared the figures and analyzed the data. All authors have read and approved of the manuscript. Data Availability All the data are available upon reasonable request from corresponding author. GSE100927（https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE100927）GSE76925（https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE76925） References Guthrie, A., Chronic Obstructive Pulmonary Disease Series Part 4: Identifying, Managing, and Preventing Exacerbations. Sr Care Pharm, 2023. 38 (9): p. 361-369. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4013922","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276668960,"identity":"3c67c2de-2d20-4052-9bed-d1da6e3dfe4f","order_by":0,"name":"金海 全","email":"","orcid":"","institution":"Affiliated Hospital of Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"金海","middleName":"","lastName":"全","suffix":""},{"id":276668961,"identity":"19296086-c3df-4158-9b77-b7a823feeb25","order_by":1,"name":"Weijie Fan","email":"","orcid":"","institution":"Affiliated Hospital of Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Weijie","middleName":"","lastName":"Fan","suffix":""},{"id":276668962,"identity":"b0bdedb0-4939-406d-a759-055c0930408c","order_by":2,"name":"仕森 李","email":"","orcid":"","institution":"Affiliated Hospital of Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"仕森","middleName":"","lastName":"李","suffix":""},{"id":276668963,"identity":"f3f1ecde-f7e1-47d1-a31b-6522fb35a679","order_by":3,"name":"Huaijin Xie","email":"","orcid":"","institution":"Affiliated Hospital of Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Huaijin","middleName":"","lastName":"Xie","suffix":""},{"id":276668964,"identity":"1594d5a7-362b-4802-9ea8-a1bb288c2e3d","order_by":4,"name":"BiChen Quan","email":"","orcid":"","institution":"Affiliated Hospital of Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"BiChen","middleName":"","lastName":"Quan","suffix":""},{"id":276668965,"identity":"d8b70870-afd7-42c8-ae84-1759ad08a0e2","order_by":5,"name":"Shanghai Li","email":"","orcid":"","institution":"Affiliated Hospital of Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shanghai","middleName":"","lastName":"Li","suffix":""},{"id":276668966,"identity":"502a320f-36e0-421c-bc8a-0691dcded337","order_by":6,"name":"Ruina Huang","email":"","orcid":"","institution":"Affiliated Hospital of Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ruina","middleName":"","lastName":"Huang","suffix":""},{"id":276668967,"identity":"a43d719d-4c23-4271-8b02-1fd5ffc622ba","order_by":7,"name":"Weijun Liang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYBACPmYwxcbDz9588AGDARFa2KBaZCR7jiUbEKcFStsY3PAxkyDKYWzszM8efm3j4zG4wWBW+aPgjjwD++GjG/A7jM3cWLaNjUfydkPabR6DZ4YNPGlpNwj4xUxachsbD9+dA8duMxgcZmyQ4DEjoIX9G1gLw43EtsIfBoftidDCYyb5EahF4EYyGwOPweFEYrSUSTP+A/ql5xizNFBLchshv/DzH98m+ePMMXt+9v6PH3/8OWzbz374GF4tIMDMw3AMyV5CykGA8QdDDTHqRsEoGAWjYKQCACENQ8cxQtxFAAAAAElFTkSuQmCC","orcid":"","institution":"Affiliated Hospital of Guangdong Medical University","correspondingAuthor":true,"prefix":"","firstName":"Weijun","middleName":"","lastName":"Liang","suffix":""}],"badges":[],"createdAt":"2024-03-04 18:33:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4013922/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4013922/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52213766,"identity":"0dc9bd8a-8c40-4ea5-99ef-9fe942db0b86","added_by":"auto","created_at":"2024-03-08 02:46:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":741221,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of DEGs in AS. A, Volcano plots visualize the identified DEGs in AS. Red dots and green dots correspond to upregulated and downregulated genes, respectively. B, Heatmap of DEGs. C, WGCNA analysis. D, Venn diagram of overlapped important genes of WGCNA turquoise module and DEGs. E, GO functional enrichment analysis of the important genes. F, KEGG functional enrichment analysis of the important genes.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4013922/v1/86d4698ab49d620ecafccd4a.png"},{"id":52213706,"identity":"127ec1db-77ce-4a29-843d-71bd75c273a3","added_by":"auto","created_at":"2024-03-08 02:38:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":424122,"visible":true,"origin":"","legend":"\u003cp\u003eThe common key genes related to OS in COPD and AS. A, WGCNA analysis in the GSE76925 dataset. B, Venn diagram of overlapped common OS-related genes between the turquoise module of WGCNA in GSE100927 and the yellow module of WGCNA in GSE76925. C, GO enrichment analysis of the common OS-related key genes. D, KEGG enrichment analysis of the common OS-related key genes.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4013922/v1/59c33da6d0dcdc42f9a2af81.png"},{"id":52215785,"identity":"4de6622f-60e0-46bd-a0c4-db60ab86208b","added_by":"auto","created_at":"2024-03-08 02:54:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":775777,"visible":true,"origin":"","legend":"\u003cp\u003eScreen of common hub OS-related genes. A and B, The PPI network is constructed with 25 common OS-related genes (A) and screens 8 hub common genes (B). C and D, Lasso regression analysis of 8 hub common genes in the GSE100927 dataset (AS). E and F, Lasso regression analysis of 8 hub common genes in the GSE76925 dataset (COPD). G, Venn diagram of common hub OS-related genes intersected between AS and COPD.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4013922/v1/d72e40b41cbb91bd71f4d028.png"},{"id":52213707,"identity":"676fd0d4-4cc9-4547-af1d-053e7a9fb666","added_by":"auto","created_at":"2024-03-08 02:38:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":160557,"visible":true,"origin":"","legend":"\u003cp\u003eThe diagnostic value of the two hub genes in AS and COPD. A, Nomogram diagram of SELL and MMP9 in COPD and AS data sets, respectively. B, ROC curve showed the AUC areas of SELL and MMP9 in COPD and AS data sets, respectively.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4013922/v1/d7900493863b9f8f36c0de78.png"},{"id":52213710,"identity":"bb95249e-e0b6-4ef2-bc69-7477f3e535bd","added_by":"auto","created_at":"2024-03-08 02:38:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1006701,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis of common hub genes with immune infiltration in AS and COPD. A, Stacked bar charts exhibit the different types of infiltrated immune cells between AS and normal controls. B, Vioplot of comparison of differentially infiltrated immune cell types between AS and normal controls. C, Spearman correlation analysis between the hub genes and immune cells in AS. D, Stacked bar charts exhibit the different types of infiltrated immune cells between COPD and normal controls. E, Vioplot of comparison of differentially infiltrated immune cell types between COPD and normal controls. F, Spearman correlation analysis between the hub genes and immune cells in COPD.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4013922/v1/45649e36f1a4764b1bcdc75e.png"},{"id":54851733,"identity":"b6b5f520-ee6a-48fd-a219-be44dbb89183","added_by":"auto","created_at":"2024-04-17 16:35:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1834776,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4013922/v1/f60ea518-aef5-438f-9c18-6da5bba9af9e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Machine learning-based bioinformatics analysis of common hub genes associated with oxidative stress and immune infiltration in COPD and atherosclerosis Running title: Bioinformatics analysis of common hub OS genes in COPD and Atherosclerosis","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eChronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease prevalent in the aged population, which has a great adverse effect on the quality of life of patients and is currently one of the main causes of death in the world [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The large medical and economic burden of COPD is largely attributed to the management of its comorbidities and other related chronic diseases, among which atherosclerosis (AS), the leading cause of coronary heart disease, peripheral vascular disease, and stroke, is a common complication of COPD [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The association between COPD and atheromatous cardiovascular disease (ACD) has been observed clinically. The severity and intensity of coronary atherosclerosis increased with the increase of COPD severity [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. COPD is associated with poor prognosis with coronary artery disease percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The comorbid of ACD is a key factor that resulted in the deaths of COPD, about 30% of the patients with COPD die from cardiovascular performance [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, the pathogenesis of COPD complicated with AS is complex and largely unknown.\u003c/p\u003e \u003cp\u003eAS and COPD share many common risk factors, including a history of smoking, persistent inflammation, high oxidative stress (OS) load, and genetics [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Because of this, the pathological mechanism of COPD and AS comorbidity is considered to be complex and multifactorial. Until now, persistent local (i.e., pulmonary, vascular) and systemic inflammation and OS due to exposure to cigarette smoke are considered to be the common pathophysiological link driving the progression of ACD and COPD [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Exposure to cigarette smoke has harmful pulmonary and systemic effects, including inflammation, OS, lung endothelial dysfunction and the improvement of circulation coagulant medium [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], which is the basis for the development of chronic comorbidities. In addition, pulmonary hypertension driven by COPD is also an important factor in cardiovascular injury [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Pulmonary inflammation and pulmonary hypertension caused by COPD increase the risk of these patients developing AS [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Some studies have found through cell and animal experiments that increasing anti-inflammatory and antioxidant activities can significantly reduce systemic and pulmonary inflammation and OS, help protect lung function, and reduce the occurrence and severity of comorbid atherosclerosis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Because of these phenomena and the development of biological information technology, several studies have attempted to explore the genetic pathological mechanisms underlying the comorbidity of COPD and AS. A large-scale cross-trait genome-wide association study found the common genetic traits shared by COPD and cardiac traits cardiovascular diseases [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Common biological processes and signaling pathways may be related to the occurrence of COPD and OA. This work using bioinformatics methods for identification of potential candidate genes and comprehensive analysis, will deepen our understanding of gene regulation in OS and immune response in comorbid COPD and AS and may provide a promising candidate for AD biomarkers and therapeutic targets.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data set\u003c/h2\u003e \u003cp\u003eAtherosclerosis (AS) dataset GSE100927 (GPL17077) and COPD dataset GSE76925 (GPL10558) were downloaded from the GEO database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Identification of differentially expressed genes (DEGs) in AS\u003c/h2\u003e \u003cp\u003eThe Limma package was employed to screen the differentially expressed genes (DEGs) in AS with |logFC|\u0026gt;1 and adj.P.Val\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as the criterion, which were subsequently visualized by the volcano plot and heatmap by using \u0026ldquo;ggplot2\u0026rdquo; and \u0026ldquo;pheatmap\u0026rdquo; R packages. The OS-related DEGs were identified by the intersection of DEGs and OS genes obtained from the literature [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] by using the \u0026ldquo;VennDiagram\u0026rdquo; R package [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Weighted gene co‑expression network analysis (WGCNA).\u003c/h2\u003e \u003cp\u003eWGCNA was performed to describe gene patterns association with AS or COPD using the R package [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] based on the gene expression profiles extracted from GSE100927 and GSE76925. According to the criteria of approximate scale-free topology, the best soft-thresholding power β was obtained. The network analysis was constructed by transforming the adjacency matrix into a topological overlap matrix. The module color was generated based on the degree of dissimilarity. The intersection of AS_WGCNA, COPD_WGCNA and OS genes in key modules was carried out by using the \u0026ldquo;VennDiagram\u0026rdquo; R package [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The shared genes were defined as OS-related DEGs, which were used for subsequent analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Functional enrichment analysis.\u003c/h2\u003e \u003cp\u003eDEG functional enrichment analyses were conducted by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) by using the clusterProfiler package [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. GO enrichment pathway encompasses biological processes, cellular components, and molecular function. A \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was recognized as the statistical significance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Protein\u0026ndash;protein interaction (PPI) network construction.\u003c/h2\u003e \u003cp\u003eThe OS-related DEGs were uploaded to the Search Tools for the Retrieval of Interacting Genes (STRING, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.string-db.org/\u003c/span\u003e\u003cspan address=\"http://www.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Confidence\u0026thinsp;\u0026gt;\u0026thinsp;0.4 was the cut-off criterion for the PPI network analysis. The key gene modules were screened by MCODE plug-in (degree cutoff\u0026thinsp;=\u0026thinsp;2, node score cutoff\u0026thinsp;=\u0026thinsp;0.2, K-core\u0026thinsp;=\u0026thinsp;2, and max depth\u0026thinsp;=\u0026thinsp;100) in Cytoscape (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cytoscape.org\u003c/span\u003e\u003cspan address=\"https://cytoscape.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) in the PPI network. The key module genes were hub genes. Subsequently, the least absolute shrinkage and selection operator (LASSO) Cox regression analysis was conducted to further identify hub genes by using \u0026ldquo;glmnet\u0026rdquo; package R [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The intersection of AS_LASSO and COPD_LASSO was carried out by using the \u0026ldquo;VennDiagram\u0026rdquo; R package [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] to identify the common hub genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Validation of a diagnostic nomogram diagram for AS and COPD\u003c/h2\u003e \u003cp\u003eWe constructed a nomogram to predict whether the common hub genes can act as disease risk factors by using the rms package in R software [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Based on multivariate Cox analysis, the point scale in the nomogram was used to assign values to each variable. We determined the number of points for each variable using a horizontal line and calculated the total number of points for each patient by summing the number of points for all variables and normalizing the distribution from 0 to 100.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Receiver operating characteristic (ROC) curve analysis.\u003c/h2\u003e \u003cp\u003eTo test the diagnostic value of the common hub genes, we performed ROC curve and area under the curve (AUC) analysis by using the \u0026ldquo;pROC\u0026rdquo; package [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The genes with an AUC value of more than 0.7 were defined as available markers for disease diagnosis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Immune infiltration\u003c/h2\u003e \u003cp\u003eIdentification of immune cell infiltration using tumor immune infiltration assay (CIBERSORT) R package [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and comparison between disease (AS or COPD) and healthy controls using Wilcoxon assay. The \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 represented a significant difference.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Statistical analysis\u003c/h2\u003e \u003cp\u003eR software (v.4.1.0) was run to analyze all statistical data. Spearman correlation was performed to test the correlation of the common hub genes with immune cell infiltration. A \u003cem\u003eP\u003c/em\u003e-value or if necessary adjusted \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 represented statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Identification of DEGs and WGCNA analysis in AS\u003c/h2\u003e \u003cp\u003eThe bioinformatics analysis was performed with the GSE100927 data set and found 455 DEGs, including 336 upregulated genes and 139 downregulated genes in AS patients, compared with the healthy controls, as shown by the Volcano plots and heatmap (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Based on the module-trait relationships, the turquoise module was the one with the highest positive relevance (Cor\u0026thinsp;=\u0026thinsp;0.74, p\u0026thinsp;=\u0026thinsp;6e-19) and the blue module was the one with the highest negative relevance (Cor=-0.55, p\u0026thinsp;=\u0026thinsp;2e-09) to AS in GSE100927 dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). The turquoise module of WGCNA in GSE100927 was selected for intersection with DEGs and obtained 429 important genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). GO enrichment showed that those important genes were mainly enriched in leukocyte-mediated immunity, positive regulation of leukocyte activation, positive regulation of cytokine production, endocytic vesicle and immune receptor activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). The KEGG enrichment analysis revealed that those important genes were related to pathways involved in Tuberculosis, Phagosome, and Cytokine-cytokine receptor interaction. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2 The common key genes associated with OS in COPD and AS\u003c/h2\u003e \u003cp\u003eBased on the mode-trait relationship, we found that the turquoise module had the most negative relevance in COPD (Cor=-0.34, p\u0026thinsp;=\u0026thinsp;2e-05), while the yellow module had the most positive relevance in COPD (Cor\u0026thinsp;=\u0026thinsp;35, p\u0026thinsp;=\u0026thinsp;9e-06) in the GSE76925 dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Therefore, the yellow module was used for downstream intersection analysis. The turquoise module of WGCNA in GSE100927 and the yellow module of WGCNA in GSE76925 were selected and intersected with 1398 OS-related genes and obtained 25 key OS-related DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). GO enrichment analysis suggested that the key OS-related DEGs were enriched in positive regulation of leukocyte activation, leukocyte, response to OS, immune response-regulating signaling pathway, neuronal cell body, immune receptor activity and cardiac muscle cell action potential repolarization cytokine receptor activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The KEGG enrichment analysis revealed that the key OS-related DEGs were enriched in Viral protein interaction with cytokine and cytokine receptors, Cytokine-cytokine receptor interaction and chemokine signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Screen of hub OS-related genes\u003c/h2\u003e \u003cp\u003ePPI network was constructed with 25 key OS-related DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), among which 8 hub genes (CD28, CXCL10, SELL, IL2RA, CXCR3, CD38, MMP9, and CXCR4) were screened by MCODE plug-in Cytoscape (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Three genes (CXCR3, MMP9, and SELL) were obtained after filtering by the lasso regression model construction with the 8 hub genes in the GSE100927 data set (AS) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Four genes (CXCL10, IL2RA, MMP9, and SELL) were obtained after filtering by the lasso regression model construction with the 8 hub genes in the GSE76925 data set (COPD) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). The Venn diagram displayed 2 common hub genes (MMP9 and SELL) between AS and COPD (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Evaluation of the diagnostic value of the common hub genes in AS and COPD\u003c/h2\u003e \u003cp\u003eNomogram diagram of the two common hub genes SELL and MMP9 in COPD and AS. (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). We evaluated the diagnostic performance of the two hub genes by plotting the ROC curves of the GSE100927 and GSE76925 data sets. The AUC values of the two hub genes (SELL and MMP9) were 0.692 and 0.707 in the GSE76925 data set (COPD) and 0.787 and 0.942 in the GSE100927 data set (AS) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), indicating that the two common hub genes possessed favorable diagnostic values in COPD and AS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Correlation analysis of the common hub genes with immune infiltration in AS and COPD\u003c/h2\u003e \u003cp\u003eThe profile of immune infiltration in COPD and AS was explored by using ssGSEA. Stacked bar charts exhibited the distribution of 22 infiltrating immune cells in AS (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The Vioplot displayed a significantly increased abundance of B cell memory, T cell CD4 memory resting, T cell gamma delta, Monocytes M0, and Mast cell activated and the significantly decreased abundance of B cell native, Plasma cells, Monocytes, Macrophages M1, Dendritic cell activated, and Mast cell resting in AS patients, compared with the controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The spearman correlation analysis demonstrated that the common hub genes (SELL and MMP9) positively correlated with B cell memory, T cell CD4 memory resting, T cell gamma delta, Monocytes M0, and Mast cell activated, negatively correlated with B cell native, Plasma cells, Monocytes, Macrophages M1, Dendritic cell activated, and Mast cell resting, and showed the opposite correlation in Neutrophils (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eStacked bar charts exhibited the distribution of 22 infiltrating immune cells in COPD (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). The Vioplot displayed a significantly increased abundance of Plasma cells, T cell CD8 cells, T cell gamma delta, and Monocytes M0 and a significantly decreased abundance of T cell CD4 memory resting, Monocytes, and Dendritic cell activated in COPD patients, compared with the controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). The Spearman correlation analysis showed that the common hub genes (SELL and MMP9) positively correlated with B cell native, T cells CD8, and T cell gamma delta, negatively correlated with NK resting, T cell CD4 memory resting, Macrophages M2 and B cell memory, and showed the opposite correlation in Neutrophils (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eIn-depth research on the OS and immune-related pathological mechanisms underlying COPD and AS is a prerequisite for the development of effective interventions against this comorbidity. identified 25 common OS-related DEGs by WGCNA, which enriched in response to OS and T cell adaptive immune response, and further identified 2 common hub genes (MMP9 and SELL) by PPI, which possessed favorable diagnostic values and correlated with differentially infiltrated immune cells in AS and COPD.\u003c/p\u003e \u003cp\u003eAmong the few known pathological mechanisms shared by COPD and AS, increased OS is considered to be the key factor. In COPD patients, increased OS driven by the external environment (exogenous oxidants in cigarette smoke and air pollution) and intrapulmonary airway factors (endogenous generation of reactive oxygen species by inflammatory and structural cells) associated with COPD plays a crucial role in the pathological mechanism of the disease [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. OS induces the activation of the pro-inflammatory transcription factor NF-κB pathway, which can lead to the activation of epithelial and inflammatory macrophages. By inducing the activation of the transforming growth factor pathway, it promotes the epithelial-mesenchymal transition of lung epithelial cells, leading to small airway fibrosis [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and also by increasing the expression of MMP9, promotes the development of emphysema in COPD [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Increased OS also reflects an imbalance between oxidant and antioxidant defense mechanisms in COPD patients. It is the imbalance between oxidant and antioxidant defense mechanisms that magnifies the local inflammatory process, worsening cardiovascular health, and leading to COPD-associated cardiovascular dysfunction and mortality. As early as more than a decade ago, Topsakal et al. found that the severity and intensity of atherosclerosis in COPD patients increased, and speculated that chronic OS and inflammation associated with COPD may be the cause of driving coronary atherosclerosis in these patients [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The occurrence of COPD patients with AS has also been continuously found in subsequent clinical studies [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. A metabolomics analysis provided evidence for this phenomenon and found that the carnitine/acylcarnitine ratio of COPD patients was lower than that of healthy controls, suggesting the existence of atherosclerosis susceptibility and OS caused by insufficient fatty acid β-oxidation [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Therefore, studying signaling pathways that better coordinate oxidation-antioxidant balance disorders is essential to help optimize the management of these two diseases to improve patient outcomes. We identified 25 common key OS-related DEGs (CD28, CXCL10, SELL, IL2RA, CXCR3, CD38, MMP9, and CXCR4, etc.) shared by COPD and AS. Functional analysis showed that common key genes were enriched in OS, immunity, inflammatory pathway, inflammatory factor, and chemokine pathway. Our findings reinforce the role of OS and immune activation in driving the process of COPD and AS comorbidity. Given the limited preliminary data, the molecular mechanisms of these common genes in the pathogenesis of comorbid COPD and AS are still unclear, and more targeted studies are expected to reveal their important roles in the link between the two.\u003c/p\u003e \u003cp\u003eUsing computer learning, we obtained 2 common hub OS-genes (MMP9 and SELL) between AS and COPD and determined their value as diagnostic markers for COPD and AS. MMP9 belongs to a member of the matrix metalloproteinases (MMPs) proteolytic enzyme family, which is involved in a series of physiological and pathological processes from reproductive development, morphogenesis, angiogenesis, inflammation, cancer cell metastasis [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. MMP9 can be used as a biomarker for mitochondrial metabolism disorder and OS [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The plasma levels of MMP9 were increased in COPD and AS patients [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. MMP9 gene polymorphism is associated with the susceptibility of COPD and AS [\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Since its activation acts on many inflammatory substrates, it is suspected to promote the development of chronic inflammation-related COPD and AS diseases [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The SELL gene encodes L-selectin (CD62L), which is responsible for recruiting inflammatory immune cells to transendothelial migration. The variation of this gene is a genetic risk factor for atherosclerosis-related and inflammatory diseases [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. SELL gene dysfunction (decreased L-selectin expression) has been reflected in COPD and AS patients [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Through correlation analysis, we found that SELL and MMP9 had common positive correlated inflammatory immune cells in AS and COPD, including T cell gamma delta and Mast cell activated, negative correlated immune cells, and T cell gamma delta and mast cell activated. Including macrophage M1 and Dendritic cell activated. Therefore, MMP9 and SELL are expected to be diagnostic markers and therapeutic targets for the diagnosis of COPD and AS comorbidity.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eIn conclusion, based on the overlapping OS-related DEGs in the pathogenesis of COPD and AS, we identified 2 hub genes (MMP9 and SELL) with good diagnostic value for the comorbid of COPD and AS. Our findings highlight the importance of genetic factors in OS and inflammatory immune responses and provide new insights into future therapeutic targets for the comorbid of COPD and AS. Further research is needed to elucidate the clinical utility of biomarkers and therapeutic targets in the comorbid of COPD and AS and to determine the generalizability of our findings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eRH, SL, and WL conceived and designed the study and approved the final draft. JQ provided scientific supervision. SL and WF authored and reviewed drafts of the manuscript. BQ and HX prepared the figures and analyzed the data. All authors have read and approved of the manuscript.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data are available upon reasonable request from corresponding author. 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This study explored the common hub gene associated with OS and immune cell infiltration in AS and COPD.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eGenes associated with AS were identified by the differentially expressed genes (DEGs) analysis and weighted gene co‑expression network analysis (WGCNA) in the GSE100927 dataset. Genes associated with COPD were analyzed by WGCNA in the GSE76925 dataset. Functional enrichment analysis was carried out by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). The common hub OS-related genes were analyzed by the intersection of the WGCNA modules of AS and COPD and OS‑related genes, protein\u0026ndash;protein interaction (PPI), and lasso regression. The diagnostic value of the hub common genes was assessed by receiver operating characteristic analysis. The association of the hub common genes with immune infiltration in AS and COPD was analyzed by the Spearman correlation method.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 455 DEGs (336 upregulated genes and 139 downregulated genes) were identified in GSE100927. The turquoise module of WGCNA in GSE100927 and the yellow module of WGCNA in GSE76925, which are the most relevant modules, were intersected and obtained 25 common OS-related genes between AS and COPD. Those common OS-related genes were enriched in signaling pathways related to immunity and OS. Two hub common OS-related genes (SELL and MMP9) were identified and showed good diagnostic value in AS and COPD. The Spearman correlation analysis showed that the hub common OS-related genes positively or negatively correlated with various infiltrating immune cells.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur study identified the common hub genes (SELL and MMP9) associated with OS and immune infiltration in AS and COPD, providing candidate therapeutic targets for AS combined with COPD.\u003c/p\u003e","manuscriptTitle":"Machine learning-based bioinformatics analysis of common hub genes associated with oxidative stress and immune infiltration in COPD and atherosclerosis Running title: Bioinformatics analysis of common hub OS genes in COPD and Atherosclerosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-08 02:38:16","doi":"10.21203/rs.3.rs-4013922/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0b410a2f-4132-45bc-8a5a-70d460461cdf","owner":[],"postedDate":"March 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-17T16:35:38+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-08 02:38:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4013922","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4013922","identity":"rs-4013922","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}