Analysis of gene expression and immune infiltration in white adipose tissue of patients with obesity: bioinformatics analysis and meta-analysis

Analysis of gene expression and immune infiltration in white adipose tissue of patients with obesity: bioinformatics analysis and meta-analysis | 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 Analysis of gene expression and immune infiltration in white adipose tissue of patients with obesity: bioinformatics analysis and meta-analysis Qiuying Liu, Yifan Ren, Linfeng He, Tianshu Zeng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3937597/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 The physiological and pathological process of obesity involves inflammation and immunity. The alterations in the number and function of immune cells may have an effect on systemic inflammation and homeostasis. This study aimed to explore the different biological processes and immune infiltration landscape in obesity. Methods Nine obesity-related datasets were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs)in adipose tissues were identified by “limma” R package or GEO2R and then Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed. Meanwhile, we conducted the immune infiltration analysis with gene expression data and Meta-analysis was performed based on the results of immune infiltration. Finally, we selected hub genes and tried to find out the connection between hub genes and immune infiltration. Results 46 common DEGs were identified, among which the up-regulated genes were involved in biological processes such as the regulation of hemopoiesis, leukocyte differentiation, activation and migration, cell adhesion, cytokine secretion, and interactions. Immune infiltration analysis showed that the percentages of monocytes and macrophages were increased in obesity, while there was no significant difference in neutrophils. The obese patients had a higher proportion of CD4 T cells, induced regulatory T (iTreg) cells, T follicular helper (Tfh) cells, T helper 2 (Th2) cells, T regulatory type 1 (Tr1) cells, and natural killer (NK) cells, and lower levels of CD8 T cells, B cells, CD8 naive cells, exhausted T (Tex) cells, and γδ T cells compared with the controls. PTPRC、ITGAX、CD86、MMP9、ITGB2、CCR1、TLR8、CCL19、SPP1、TREM2 were identified as hub genes. Conclusion In obesity, genes related to immunity and inflammation are upregulated in adipose tissue, and the function and abundance of immune cells are changed. There are more monocytes and macrophages in obese people than those in non-obese individuals, and there are also differences in lymphocytes and their subsets. obesity immune infiltration white adipose tissue bioinformatics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Obesity prevalence is increasing worldwide. Diseases such as hyperlipidemia, hypertension, atherosclerosis, diabetes, and even cancer closely related to obesity have made it an important disease that endangers global public health security(Swinburn et al., 2011, Desharnais et al., 2022). Adipose tissue is a key to obesity-related inflammation(Cildir et al., 2013). The adipose tissue of mammals mainly includes white adipose tissue, beige adipose tissue, and brown adipose tissue. Among them, the accumulation of white adipose tissue, especially visceral adipose tissue, plays a key role in the occurrence of obesity-related metabolic diseases(Lee et al., 2013). The study of white adipose tissue inflammation is helpful to explore the pathogenesis of obesity and provide a reference for the prevention and treatment of metabolic diseases. In this study, we searched the Gene Expression Omnibus (GEO) database for obesity-related studies. 9 studies were finally included after screening. The differentially expressed genes (DEGs) in adipose tissue of the obese group compared with the control group in each study were obtained by the analysis of sequencing data. Enrichment analysis showed that genes were upregulated in pathways related to inflammation and immunity in obesity. Immune infiltration analysis was performed at the same time and it was found that the immune infiltration score of obese people was higher than that of the control group, and monocytes and macrophages were more than those of non-obese people, with lymphocytes and their subsets also different. We also identified 10 hub genes in obesity for turther study. Methods Dataset retrieval and screening We searched the GEO database using "obesity" as the keyword, and the species were limited to humans. The retrieved studies were screened with certain inclusion and exclusion criteria. Inclusion criteria: 1. The study was conducted between 2013 and 2023; 2. There were both obese and control groups in the study; 3. Whole transcriptome sequencing data was included; 4. The samples were white adipose tissue; 5. The data was available and analyzable; 6. The sample size was sufficient (the sample size of each group was more than 5). Studies that were too old, had few samples or included only small interfering RNA (siRNA) or long noncoding RNA (lncRNA) sequencing were excluded. The datasets were first screened based on the title and introduction, and further screening was conducted by reading the full text of the study. Downloading of gene expression data and identification of DEGs We used the “GEOquery” package to get raw datasets and annotation data files. DEGs were screened with the threshold of log2 fold change |log2FC| >1 and adjusted P-value < 0.05, by GEO2R and 'limma' package(Ritchie et al., 2015 ). Robust rank aggregationanalysis of common DEGs DEGs from different datasets were analyzed by Robust Rank Aggregation(RRA) with the 'RobustRankAggreg' package to obtain robust DEGs, and then we drew the heatmap with 'pheatmap' package(Kolde et al., 2012 ). Enrichment analysis of DEGs Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis of the robust DEGs were performed by ‘org.Hs.eg.db’ package and ‘clusterProfiler’ package, and then ‘ggplot2’ package was used to draw the bubble charts. Construction of Protein-Protein Interaction network and identification of hub genes We constructed a protein-protein interaction (PPI) network of robust DEGs by the STRING database version 12(Szklarczyk et al., 2023 ). Hub genes were screened according to the number of neighbors. Immune infiltration analysis of each dataset ImmuCellAi, based on single sample gene set enrichment analysis (ssGSEA), was used to analyze the gene expression matrix of each dataset. The rank value of each gene was calculated based on the expression profile, simulating the flow cytometry process. The abundance of different cells was predicted by a stratification strategy (Miao et al., 2020 ). Statistical analysis of immune infiltration results The original data of immune infiltration analysis in each dataset were obtained to calculate the mean and standard deviation of the abundance of different types of immune cells and the total immune infiltration score in each dataset. Review Manager software (version 5.4) was used for Meta-analysis. We applied the χ2 test to determine whether there was heterogeneity among the studies. If P > 0.05 and I 2 < 50%, the heterogeneity would be considered small and we would use the fixed effect model, otherwise the random effect model would be used. Correlation analysis between hub genes and immune infiltration We conducted Spearman’s rank correlation analysis of the hub genes and immune infiltration using “psych” package and “ggcorrplot” package. Results Datasets retrieval and screening results 724 datasets were retrieved from the GEO database, with "obesity" as the keyword and the “Homo sapiens” as filter conditions for “Organism”. According to inclusion and exclusion criteria, 14 datasets were screened by title and introduction, and 9 studies ( GSE151839, GSE141432, GSE162653, GSE94753, GSE59034, GSE199063, GSE159924, GSE156906, GSE152991)were finally included in this study by reading the full text, as shown in Fig. 1 and Table 1(Walker et al., 2020 , Fryk et al., 2021 , Fisk et al., 2022 , Kulyté et al., 2017 , Petrus et al., 2018 , Kerr et al., 2022 , Beals et al., 2021 , Fuchs et al., 2021 , Cifarelli et al., 2020 ). 9 studies were from five different countries, and all of them were conducted in the past 10 years. The research content included the whole transcriptome sequencing of human subcutaneous white adipose tissue, as shown in Table 1. DEGs 9 datasets were analyzed separately to identify their DEGs, and the DEGs were later ranked by RRA. Finally, we got 46 robust DEGs, of which, 32 were upregulated and 14 downregulated in the obese group. Among the upregulated genes, PTPRC, CCL19, KYNU, POPDC3, TNMD, IL2RG, NCEH1, CD86, FPR3, and UCHL1 were endowed with the highest RRA scores while downregulated genes included SLC27A2, CA3, SPX, RORB, AZGP1, GPAT3, ALPK3, CIDEA, STOX1, COL6A6, C6, EYA1, SYT17, and GSDM. The robust DEGs were shown as a heatmap in Fig. 2 Enrichment analysis KEGG and GO enrichment analysis were performed on the upregulated robust DEGs respectively, and the results are shown in Fig. 3. GO enrichment results revealed that the upregulated robust DEGs in the obese group were mainly enriched in biological processes such as the regulation of hemopoiesis, leukocyte differentiation, activation and migration, cell adhesion, cytokine secretion, and interaction. KEGG enrichment analysis also showed that they were enriched in pathways concerning inflammation and immunity. There were too few downregulated robust DEGs to analyze. Construction of Protein-Protein Interaction (PPI) network and identification of hub genes To highlight the hub genes in robust DEGs and figure out the relationship between different genes, we constructed the PPI network (Fig. 4a). 10 genes (PTPRC、ITGAX、CD86、MMP9、ITGB2、CCR1、TLR8、CCL19、SPP1、TREM2) were selected as the hub genes according to the number of neighbors (Fig. 4b). Immune infiltration analysis Immune infiltration analysis was performed on each of the 9 datasets, as shown in Fig. 5 and supplemental materials. In most studies, the immune infiltration scores of obesities were higher than those of the control group (Fig. 5). In detail, monocytes, macrophages, and CD4 T cells in the obese group were more than those in the control group in most datasets. However, there were more CD8 T cells and γδ T cells in the control group. There were no significant differences in other subsets of lymphocytes in a single dataset. Forest plot of immune infiltration results It was not difficult to conclude that the immune infiltration score of the obese group was higher than that of the control group (Fig. 6), and the abundance of various immune cells was also different, according to the meta-analysis of the results of immune infiltration. Among the cells with higher abundance, the abundance of monocytes and macrophages was higher in the obese group than in the control group, and there was no significant difference in neutrophils (Fig. 6). The abundance of CD4 T cells in the obese group was higher than that in the control group, while the abundance of CD8 T cells was higher in the control group as shown in Fig. 5. There were more induced regulatory T (iTreg) cells, T follicular helper (Tfh) cells, T helper 2 (Th2) cells, T regulatory type 1 (Tr1) cells, and natural killer (NK) cells in the obese group, but more B cells, CD8naive cells, CD8T cells, exhausted T (Tex) cells, and γδ T cells in the control group (supplemental materials). Subgroup analyses The abundance of monocytes and macrophages in adipose tissue was higher in the obese group than in the control group, but there was heterogeneity. The abundance of neutrophils was also huge, and the overall meta-analysis showed no significant difference between the obese and control groups. Subgroup analysis was performed on the three types of cells by region. In five studies from European countries, obese people had more neutrophils than controls, and the case was the opposite in Asian populations. Asian obese individuals had more monocytes and macrophages than controls, with no heterogeneity (Figs. 7). Correlation analysis between hub genes and immune infiltration To explore the connection between hub genes and immune cells, we conducted Spearman’s rank correlation analysis. Most of the hub genes were positively correlated with immune infiltration score and the abundance of macrophages, CD8 T cells, and Tex cells, and were negatively correlated with DC cells, CD4 T cells, γδ T cells, Th2 cells, and Tfh cells (Fig. 8). Discussion Transcriptome sequencing is an efficient transcriptomics research method, which can obtain high-throughput gene expression data. However, a single study provides less information for further study because of its sample size. GEO database contains high-throughput sequencing data from all over the world. We applied the meta-analysis method to summarize multiple GEO datasets to seek evidence-based medical evidence with higher quality and tried to find a more reliable direction for obesity-related diseases. Immune infiltration analysis is a classic method for tumor immunity research that has been gradually applied to the study of non-tumor diseases nowadays, but there have been few studies on metabolic diseases such as obesity. Here, we comprehensively searched the GEO database, targeting obesity, for deep mining and immunological analysis with sufficient high-throughput data. We noticed that the upregulated genes mainly enriched in pathways related to inflammation and immunity. GO analysis demonstrated that the upregulated genes involved in the regulation of interleukin-8(IL-8) production and tumor necrosis factor (TNF) superfamily cytokine production, positive regulation of immune effector process, regulation of leukocyte differentiation, activation, and migration. Similarly, the upregulated genes enriched in pathways concerned with immunity and inflammation by KEGG analysis, such as cytokine-cytokine receptor interaction, primary immunodeficiency, cell adhesion molecules, and toll-like receptor signaling pathway. Studies have shown that the secretion of inflammatory cytokines and the infiltration of immune cells in adipose tissue change during obesity(Kahn et al., 2019 ). The infiltration and M1 polarization of macrophages contribute to the increased level of TNF-α in both plasma and adipose tissue in obesity, also, adiponectin which is related to the downregulation of TNF- α is reduced in obesity(Park et al., 2005 , Hotamisligil et al., 1993 , Arita et al., 1999 ). The circulating and adipose tissue IL-8 levels in obese patients are higher than those in lean subjects(Bruun et al., 2004 , Straczkowski et al., 2002 ). Free fatty acids such as palmitic acid from daily diet can activate downstream inflammation-related signaling pathways by binding to toll-like receptors(Shi et al., 2006 , Wu and Ballantyne, 2020 ). These studies are consistent with our enrichment results, indicating that pathways related to inflammation and immunity are altered in obesity. In the above pathways related to inflammation and immunity, different cells in adipose tissue are involved. In obesity, the type, abundance and function of immune cells will change(Koenen et al., 2021 ).Our immune infiltration analysis showed that the abundance of monocytes, macrophages, CD4 + T cells, NK cells, Th2 cells, Tfh cells and iTreg cells in obese people were higher than those in normal weight people, while the abundance of B cells, CD8 + T cells, and γδ T cells were lower. Early neutrophil infiltration was increased in the adipose tissue of obese mice, which correlated with increased IL-8 production(Elgazar-Carmon et al., 2008 , Bruun et al., 2001). Under the condition of hypoxia in obesity, the Hif-1α pathway and the IKK/NF-κB pathway are activated, promoting the secretion of chemokines such as MCP-1, and subsequently, more monocytes migrate and settle down(Haase et al., 2014 , Amano et al., 2014 ). In addition to the changes in quantity, there are also changes in the phenotype of macrophages, which means a shift from M0 or M2 macrophages to M1 macrophages.M1 macrophages secrete IL-6, IL-1β, and TNF-α that contribute to inflammation(Weisberg et al., 2003 , Wu and Ballantyne, 2020 , Amano et al., 2014 ). Blocking the trafficking of inflammatory monocytes into adipose tissue protected mice from obese-associated inflammation and insulin resistance(Arkan et al., 2005 , Weisberg et al., 2006 ). In addition, recent studies have shown that macrophages in adipose tissue can be divided into many subsets in detail, such as sympathetic neuron-associated macrophages (SAMs), lipid-associated macrophages(LAMs), metabolically activated macrophages (MMe), possessing their own cellular metabolic status and functional characteristics in obesity(Korf et al., 2020 ). In the aspect of lymphocytes, there were increased amounts of CD4 T cells and CD8 T cells in both abdominal subcutaneous and visceral adipose tissues in obese people, accompanied by increased secretion of inflammatory cytokines such as IL-10 secreted by CD4 T cells(Yang et al., 2010 , Winer et al., 2009 ). Treg cells in the visceral adipose tissue of mice regulated adipose tissue inflammation and insulin resistance and a declining number of them was noticed in both obese mice and humans(Feuerer et al., 2009 ). NK cells in epididymal adipose tissue of obese mice increased and secreted inflammatory cytokines such as IFN-γ and TNF-α, which promoted M1 polarization of macrophages and subsequently promoted insulin resistance in obese mice(Lee et al., 2016 ). γδ T cells are highly plastic and play different roles in different immune states, mainly involved in anti-infection and anti-tumor immunity(Carding and Egan, 2002 ). γδ T cells decreased and macrophages increased in subcutaneous adipose tissue of mice fed a long-term ketogenic diet, accompanied by increased inflammation and worsening glycemic control. However, γδ T cells increased in epicardial adipose tissue of HFD-induced obese mice(Goldberg et al., 2020 , Mehta et al., 2015 ). In obesity, B cells modulate T cells and inflammatory macrophages and produce pathogenic IgG antibodies, leading to systemic inflammation and insulin resistance(Winer et al., 2011 , DeFuria et al., 2013). The changing trends of some cells are consistent with the existing research, while the results of other cells are different or less studied. Subcutaneous adipose tissue and visceral adipose tissue have different functions in metabolic inflammation, and research on different tissues or species may lead to different results. We constructed the PPI network analysis of the robust DEGs, and 10 genes were finally filtered as the hub genes. PTPRC, ITGAX, CD86, and ITGB2 are expressed in lymphocytes, granulocytes, and macrophages, and they are involved in the differentiation, movement, interaction, adhesion, and signal transduction of leukocytes(Al Barashdi et al., 2021 , Cutolo et al., 2022 , Zhang et al., 2020 ). CCR1 and CCL19 are members of the chemokine and chemokine receptor family. CCR1 regulates the recruitment and activation of macrophages, and the CCR7 /CCL19 axis works in T cell and DC cell migration and coordinates the immune response(Li et al., 2024 , Salem et al., 2021 ). These regulations of leukocyte markers or chemokine gene expression may indicate changes in immune cell infiltration in tissues. Association analysis showed that these genes were positively correlated with immune infiltration score and the abundance of macrophages, CD8 T cells, and Tex cells, and were negatively correlated with DC cells, CD4 T cells, γδ T cells, Th2 cells, and Tfh cells. The hub genes are not only related to the results of immune infiltration, but also participate in atherosclerosis, anti-tumor, and anti-infection immunity. MMP9 is related to oxidative stress, which can disrupt the basement membrane, and alter the stabilization of atherosclerotic plaque(Jaoude and Koh, 2016 , Cabral-Pacheco et al., 2020 ). Macrophage-derived SPP1 promotes atrial disease, like atrial fibrillation(Hulsmans et al., 2023). TLR8 is mainly expressed in bone marrow and T cells, inducing the production of type I interferon, IL-6、TNF-α, which contribute to anti-tumor and anti-infection immunity(Wang et al., 2020 ). TREM expressed in visceral adipocytes of obese mice can promote phagocytosis and regulate lipid catabolism to maintaining metabolic homeostasis(Jaitin et al., 2019 , Deczkowska et al., 2020 ). Metabolic inflammation is a significant part of the pathological changes in obesity, and it contributes to insulin resistance, elevated blood glucose levels, and atherosclerosis. Due to alterations in the immune system, obese people show different characteristics from others in anti-infection immunity and tumor susceptibility(Bapat et al., 2022 , Bhupathiraju and Hu, 2016 ). Therefore, the intervention of inflammatory and immune pathways may play a positive role in the prevention and treatment of obesity-related diseases. Visceral adipose tissue is more significant for metabolic inflammation, but there are few studies included in GEO database, which cannot be further discussed. We will continue to pay attention to the research progress in this area. Conclusion In this study, we confirmed that obesity involves inflammation and immunity. Notably, we have identified 10 hub genes and showed the immune infiltration alterations in obesity which may help to explain the the pathological mechanisms of obesity and provide potential therapeutic orientations. Declarations Author Contribution Study concept and design: Tianshu Zeng and Qiuying Liu; Data acquisition and statistical analysis: Qiuying Liu and Yifan Ren; Statistical interpretation: Qiuying Liu and Linfeng He. All authors have read and approved the final manuscript. References AL BARASHDI, M. A., ALI, A., MCMULLIN, M. F. & MILLS, K. 2021. Protein tyrosine phosphatase receptor type C (PTPRC or CD45). J Clin Pathol, 74 , 548-552. AMANO, S. U., COHEN, J. 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Obesity is associated with macrophage accumulation in adipose tissue. J Clin Invest, 112 , 1796-808. WINER, D. A., WINER, S., SHEN, L., WADIA, P. P., YANTHA, J., PALTSER, G., TSUI, H., WU, P., DAVIDSON, M. G., ALONSO, M. N., LEONG, H. X., GLASSFORD, A., CAIMOL, M., KENKEL, J. A., TEDDER, T. F., MCLAUGHLIN, T., MIKLOS, D. B., DOSCH, H. M. & ENGLEMAN, E. G. 2011. B cells promote insulin resistance through modulation of T cells and production of pathogenic IgG antibodies. Nat Med, 17 , 610-7. WINER, S., CHAN, Y., PALTSER, G., TRUONG, D., TSUI, H., BAHRAMI, J., DORFMAN, R., WANG, Y., ZIELENSKI, J., MASTRONARDI, F., MAEZAWA, Y., DRUCKER, D. J., ENGLEMAN, E., WINER, D. & DOSCH, H. M. 2009. Normalization of obesity-associated insulin resistance through immunotherapy. Nat Med, 15 , 921-9. WU, H. & BALLANTYNE, C. M. 2020. Metabolic Inflammation and Insulin Resistance in Obesity. Circ Res, 126 , 1549-1564. YANG, H., YOUM, Y. H., VANDANMAGSAR, B., RAVUSSIN, A., GIMBLE, J. M., GREENWAY, F., STEPHENS, J. M., MYNATT, R. L. & DIXIT, V. D. 2010. Obesity increases the production of proinflammatory mediators from adipose tissue T cells and compromises TCR repertoire diversity: implications for systemic inflammation and insulin resistance. J Immunol, 185 , 1836-45. ZHANG, X., DONG, Y., ZHAO, M., DING, L., YANG, X., JING, Y., SONG, Y., CHEN, S., HU, Q. & NI, Y. 2020. ITGB2-mediated metabolic switch in CAFs promotes OSCC proliferation by oxidation of NADH in mitochondrial oxidative phosphorylation system. Theranostics, 10 , 12044-12059. Table 1 Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files supplementalpictures.docx table1.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3937597","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":271702389,"identity":"2eead2ee-6e44-4806-af43-d1ce0db1f2a3","order_by":0,"name":"Qiuying Liu","email":"","orcid":"","institution":"Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Qiuying","middleName":"","lastName":"Liu","suffix":""},{"id":271702390,"identity":"fe30b844-c3fc-402e-a924-f10eacebde0f","order_by":1,"name":"Yifan Ren","email":"","orcid":"","institution":"Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yifan","middleName":"","lastName":"Ren","suffix":""},{"id":271702391,"identity":"4028336f-c8d5-4cc1-aa0e-8e52cf8cf613","order_by":2,"name":"Linfeng He","email":"","orcid":"","institution":"Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Linfeng","middleName":"","lastName":"He","suffix":""},{"id":271702392,"identity":"2ca2fb03-04c3-496f-a532-66d83f18eed2","order_by":3,"name":"Tianshu Zeng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYDACZgglZ4DCxQd4oGqMgVoYG4jTAqUTNxCtxZ6dO/nDxx216dsl0p8/YKiwTmxgP3uAgMN4NxjOPHM8d+eMHMMGhjPpiQ08eQkEtSTzth3L3XAjh7GBse1wYoMEjwFBLYf/th1LN7iR/rCB8R9xWjY2M7bVJBjcSDBsYGwgRsth3s2MvW0HDDeceWM4I+FYunEbTw5+Lez9Zzd/+NlWJ29wPP3Bhw811rL97Gfwa4GCwxAqAYjZiFEPBHVEqhsFo2AUjIIRCQDL4UZo+j1O3wAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Tianshu","middleName":"","lastName":"Zeng","suffix":""}],"badges":[],"createdAt":"2024-02-07 17:29:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3937597/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3937597/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50928208,"identity":"d751f504-cedd-430e-8d5e-693abe6fc5bc","added_by":"auto","created_at":"2024-02-09 17:18:42","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":107980,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDatasets retrieval and screening\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3937597/v1/d14f9ecd367af08e1e0fda93.jpg"},{"id":50928212,"identity":"08789856-a39f-4f85-90bd-fc0c7dcc08af","added_by":"auto","created_at":"2024-02-09 17:18:42","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":181148,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe heatmap of robust DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHeatmap of robust DEG expression changes in obese and control samples. Red indicates high gene expression, and blue indicates low gene expression. Gradation of color represents the value of |log2FC|.\u003c/p\u003e","description":"","filename":"figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3937597/v1/704cfcaf106d853be3eb2e1a.jpg"},{"id":50928214,"identity":"687c34b0-4439-49f2-8374-787e36b64e61","added_by":"auto","created_at":"2024-02-09 17:18:42","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":620449,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKEGG and GO enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKEGG enrichment pathways (a) and Top GO enrichment pathways (b). BP, biological processes; CC, cellular components; MF, molecular function.\u003c/p\u003e","description":"","filename":"figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3937597/v1/5d2c0d6176e4750ed2b95bb9.jpg"},{"id":50928217,"identity":"445d1829-e1de-415a-9d74-2710c7f4e0ca","added_by":"auto","created_at":"2024-02-09 17:18:42","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1776589,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePPI network and adjacent nodes of robust DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a)The ppi network of robust DEGs. (b) The number of adjacent nodes of robust DEGs.\u003c/p\u003e","description":"","filename":"figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3937597/v1/b81050e2eb2a2088ccf326db.jpg"},{"id":50928215,"identity":"c1b6f048-b5d1-450c-aa85-c10470cc0373","added_by":"auto","created_at":"2024-02-09 17:18:42","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1574421,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune infiltration analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInfiltration of different immune cells in GSE151839(a), GSE152991(b), GSE162653(c), GSE199063(d). The immune infiltration scores of different datasets(e). *, \u003cem\u003eP\u003c/em\u003e-value\u0026lt;0.05; **, \u003cem\u003eP\u003c/em\u003e-value\u0026lt;0.01; ***, \u003cem\u003eP\u003c/em\u003e-value\u0026lt;0.001; ****, \u003cem\u003eP\u003c/em\u003e-value\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3937597/v1/77b4b27a863e454be23599b1.jpg"},{"id":50928210,"identity":"2a9c1293-54d0-4ab4-ba02-ac42d4b4ad1c","added_by":"auto","created_at":"2024-02-09 17:18:42","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3165570,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot of immune infiltration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eForest plot of the combined Std. Mean Difference for immune infiltration score(a) and the abundance of monocytes(b), macrophages(c), neutrophils(d), CD4 T cells(e), and CD8 T cells(f) in obese group and control group.\u003c/p\u003e","description":"","filename":"figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3937597/v1/a7a76fbb3514d652ae6ac1b2.jpg"},{"id":50928216,"identity":"820afa00-612b-4c39-9531-6df144720c05","added_by":"auto","created_at":"2024-02-09 17:18:42","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2599380,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubgroup analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSubgroup analyses by region of monocytes(a), macrophages(b), and neutrophils(c).\u003c/p\u003e","description":"","filename":"figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3937597/v1/98d4d81ec2d1a696d39490cc.jpg"},{"id":50928218,"identity":"afe36948-15d7-4a2f-8838-9645a4f9e541","added_by":"auto","created_at":"2024-02-09 17:18:42","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":245905,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation analysis of hub gene and immune cell content in all obese patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRed indicates positively correlation, blue indicates negatively correlation, and white indicates that there is no significant correlation.\u003c/p\u003e","description":"","filename":"figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3937597/v1/c255c408019e8fa01a6cd478.jpg"},{"id":50954818,"identity":"f9289ab2-bbc0-4864-b91c-1b2b5133e4d7","added_by":"auto","created_at":"2024-02-10 16:09:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1384223,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3937597/v1/717af684-12cd-4a3b-8504-f35c284a2f91.pdf"},{"id":50928211,"identity":"978a5427-ea3b-463d-ac0d-c70532a68ec5","added_by":"auto","created_at":"2024-02-09 17:18:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1336928,"visible":true,"origin":"","legend":"","description":"","filename":"supplementalpictures.docx","url":"https://assets-eu.researchsquare.com/files/rs-3937597/v1/6ab32b6085d817a83a67c29d.docx"},{"id":50928209,"identity":"a5f59f7b-4f04-4b57-a7a8-8823f5f92477","added_by":"auto","created_at":"2024-02-09 17:18:42","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":58007,"visible":true,"origin":"","legend":"","description":"","filename":"table1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3937597/v1/794cb346cea53d8893b4378c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of gene expression and immune infiltration in white adipose tissue of patients with obesity: bioinformatics analysis and meta-analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eObesity prevalence is increasing worldwide. Diseases such as hyperlipidemia, hypertension, atherosclerosis, diabetes, and even cancer closely related to obesity have made it an important disease that endangers global public health security(Swinburn et al., 2011, Desharnais et al., 2022).\u003c/p\u003e\n\u003cp\u003eAdipose tissue is a key to obesity-related inflammation(Cildir et al., 2013). The adipose tissue of mammals mainly includes white adipose tissue, beige adipose tissue, and brown adipose tissue. Among them, the accumulation of white adipose tissue, especially visceral adipose tissue, plays a key role in the occurrence of obesity-related metabolic diseases(Lee et al., 2013). The study of white adipose tissue inflammation is helpful to explore the pathogenesis of obesity and provide a reference for the prevention and treatment of metabolic diseases.\u003c/p\u003e\n\u003cp\u003eIn this study, we searched the Gene Expression Omnibus (GEO) database for obesity-related studies. 9 studies were finally included after screening. The differentially expressed genes (DEGs) in adipose tissue of the obese group compared with the control group in each study were obtained by the analysis of sequencing data. Enrichment analysis showed that genes were upregulated in pathways related to inflammation and immunity in obesity. Immune infiltration analysis was performed at the same time and it was found that the immune infiltration score of obese people was higher than that of the control group, and monocytes and macrophages were more than those of non-obese people, with lymphocytes and their subsets also different. We also identified 10 hub genes in obesity for turther study.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eDataset retrieval and screening\u003c/h2\u003e \u003cp\u003eWe searched the GEO database using \"obesity\" as the keyword, and the species were limited to humans. The retrieved studies were screened with certain inclusion and exclusion criteria. Inclusion criteria: 1. The study was conducted between 2013 and 2023; 2. There were both obese and control groups in the study; 3. Whole transcriptome sequencing data was included; 4. The samples were white adipose tissue; 5. The data was available and analyzable; 6. The sample size was sufficient (the sample size of each group was more than 5). Studies that were too old, had few samples or included only small interfering RNA (siRNA) or long noncoding RNA (lncRNA) sequencing were excluded. The datasets were first screened based on the title and introduction, and further screening was conducted by reading the full text of the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDownloading of gene expression data and identification of DEGs\u003c/h2\u003e \u003cp\u003eWe used the \u0026ldquo;GEOquery\u0026rdquo; package to get raw datasets and annotation data files. DEGs were screened with the threshold of log2 fold change |log2FC| \u0026gt;1 and adjusted P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, by GEO2R and 'limma' package(Ritchie et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRobust rank aggregationanalysis of common DEGs\u003c/h3\u003e\n\u003cp\u003eDEGs from different datasets were analyzed by Robust Rank Aggregation(RRA) with the 'RobustRankAggreg' package to obtain robust DEGs, and then we drew the heatmap with 'pheatmap' package(Kolde et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eEnrichment analysis of DEGs\u003c/h2\u003e \u003cp\u003eKyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis of the robust DEGs were performed by \u0026lsquo;org.Hs.eg.db\u0026rsquo; package and \u0026lsquo;clusterProfiler\u0026rsquo; package, and then \u0026lsquo;ggplot2\u0026rsquo; package was used to draw the bubble charts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of Protein-Protein Interaction network and identification of hub genes\u003c/h2\u003e \u003cp\u003eWe constructed a protein-protein interaction (PPI) network of robust DEGs by the STRING database version 12(Szklarczyk et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Hub genes were screened according to the number of neighbors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eImmune infiltration analysis of each dataset\u003c/h2\u003e \u003cp\u003eImmuCellAi, based on single sample gene set enrichment analysis (ssGSEA), was used to analyze the gene expression matrix of each dataset. The rank value of each gene was calculated based on the expression profile, simulating the flow cytometry process. The abundance of different cells was predicted by a stratification strategy (Miao et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis of immune infiltration results\u003c/h2\u003e \u003cp\u003eThe original data of immune infiltration analysis in each dataset were obtained to calculate the mean and standard deviation of the abundance of different types of immune cells and the total immune infiltration score in each dataset. Review Manager software (version 5.4) was used for Meta-analysis. We applied the χ2 test to determine whether there was heterogeneity among the studies. If P\u0026thinsp;\u0026gt;\u0026thinsp;0.05 and I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;50%, the heterogeneity would be considered small and we would use the fixed effect model, otherwise the random effect model would be used.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis between hub genes and immune infiltration\u003c/h2\u003e \u003cp\u003eWe conducted Spearman\u0026rsquo;s rank correlation analysis of the hub genes and immune infiltration using \u0026ldquo;psych\u0026rdquo; package and \u0026ldquo;ggcorrplot\u0026rdquo; package.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDatasets retrieval and screening results\u003c/h2\u003e \u003cp\u003e724 datasets were retrieved from the GEO database, with \"obesity\" as the keyword and the \u0026ldquo;Homo sapiens\u0026rdquo; as filter conditions for \u0026ldquo;Organism\u0026rdquo;. According to inclusion and exclusion criteria, 14 datasets were screened by title and introduction, and 9 studies ( GSE151839, GSE141432, GSE162653, GSE94753, GSE59034, GSE199063, GSE159924, GSE156906, GSE152991)were finally included in this study by reading the full text, as shown in Fig.\u0026nbsp;1 and Table\u0026nbsp;1(Walker et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Fryk et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Fisk et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Kulyt\u0026eacute; et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Petrus et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Kerr et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Beals et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Fuchs et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Cifarelli et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). 9 studies were from five different countries, and all of them were conducted in the past 10 years. The research content included the whole transcriptome sequencing of human subcutaneous white adipose tissue, as shown in Table\u0026nbsp;1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDEGs\u003c/h2\u003e \u003cp\u003e9 datasets were analyzed separately to identify their DEGs, and the DEGs were later ranked by RRA. Finally, we got 46 robust DEGs, of which, 32 were upregulated and 14 downregulated in the obese group. Among the upregulated genes, PTPRC, CCL19, KYNU, POPDC3, TNMD, IL2RG, NCEH1, CD86, FPR3, and UCHL1 were endowed with the highest RRA scores while downregulated genes included SLC27A2, CA3, SPX, RORB, AZGP1, GPAT3, ALPK3, CIDEA, STOX1, COL6A6, C6, EYA1, SYT17, and GSDM. The robust DEGs were shown as a heatmap in Fig.\u0026nbsp;2\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eEnrichment analysis\u003c/h2\u003e \u003cp\u003eKEGG and GO enrichment analysis were performed on the upregulated robust DEGs respectively, and the results are shown in Fig.\u0026nbsp;3. GO enrichment results revealed that the upregulated robust DEGs in the obese group were mainly enriched in biological processes such as the regulation of hemopoiesis, leukocyte differentiation, activation and migration, cell adhesion, cytokine secretion, and interaction. KEGG enrichment analysis also showed that they were enriched in pathways concerning inflammation and immunity. There were too few downregulated robust DEGs to analyze.\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of Protein-Protein Interaction (PPI) network and identification of hub genes\u003c/h2\u003e \u003cp\u003eTo highlight the hub genes in robust DEGs and figure out the relationship between different genes, we constructed the PPI network (Fig.\u0026nbsp;4a). 10 genes (PTPRC、ITGAX、CD86、MMP9、ITGB2、CCR1、TLR8、CCL19、SPP1、TREM2) were selected as the hub genes according to the number of neighbors (Fig.\u0026nbsp;4b).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eImmune infiltration analysis\u003c/h2\u003e \u003cp\u003eImmune infiltration analysis was performed on each of the 9 datasets, as shown in Fig.\u0026nbsp;5 and supplemental materials. In most studies, the immune infiltration scores of obesities were higher than those of the control group\u003c/p\u003e \u003cp\u003e(Fig.\u0026nbsp;5). In detail, monocytes, macrophages, and CD4 T cells in the obese group were more than those in the control group in most datasets. However, there were more CD8 T cells and γδ T cells in the control group. There were no significant differences in other subsets of lymphocytes in a single dataset.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eForest plot of immune infiltration results\u003c/h2\u003e \u003cp\u003eIt was not difficult to conclude that the immune infiltration score of the obese group was higher than that of the control group (Fig.\u0026nbsp;6), and the abundance of various immune cells was also different, according to the meta-analysis of the results of immune infiltration. Among the cells with higher abundance, the abundance of monocytes and macrophages was higher in the obese group than in the control group, and there was no significant difference in neutrophils (Fig.\u0026nbsp;6). The abundance of CD4 T cells in the obese group was higher than that in the control group, while the abundance of CD8 T cells was higher in the control group as shown in Fig.\u0026nbsp;5. There were more induced regulatory T (iTreg) cells, T follicular helper (Tfh) cells, T helper 2 (Th2) cells, T regulatory type 1 (Tr1) cells, and natural killer (NK) cells in the obese group, but more B cells, CD8naive cells, CD8T cells, exhausted T (Tex) cells, and γδ T cells in the control group (supplemental materials).\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eSubgroup analyses\u003c/h2\u003e \u003cp\u003eThe abundance of monocytes and macrophages in adipose tissue was higher in the obese group than in the control group, but there was heterogeneity. The abundance of neutrophils was also huge, and the overall meta-analysis showed no significant difference between the obese and control groups. Subgroup analysis was performed on the three types of cells by region. In five studies from European countries, obese people had more neutrophils than controls, and the case was the opposite in Asian populations. Asian obese individuals had more monocytes and macrophages than controls, with no heterogeneity (Figs.\u0026nbsp;7).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eCorrelation analysis between hub genes and immune infiltration\u003c/h2\u003e \u003cp\u003eTo explore the connection between hub genes and immune cells, we conducted Spearman\u0026rsquo;s rank correlation analysis.\u003c/p\u003e \u003cp\u003eMost of the hub genes were positively correlated with immune infiltration score and the abundance of macrophages, CD8 T cells, and Tex cells, and were negatively correlated with DC cells, CD4 T cells, γδ T cells, Th2 cells, and Tfh cells (Fig.\u0026nbsp;8).\u003c/p\u003e \u003c/div\u003e "},{"header":"Discussion","content":"\u003cp\u003eTranscriptome sequencing is an efficient transcriptomics research method, which can obtain high-throughput gene expression data. However, a single study provides less information for further study because of its sample size. GEO database contains high-throughput sequencing data from all over the world. We applied the meta-analysis method to summarize multiple GEO datasets to seek evidence-based medical evidence with higher quality and tried to find a more reliable direction for obesity-related diseases. Immune infiltration analysis is a classic method for tumor immunity research that has been gradually applied to the study of non-tumor diseases nowadays, but there have been few studies on metabolic diseases such as obesity. Here, we comprehensively searched the GEO database, targeting obesity, for deep mining and immunological analysis with sufficient high-throughput data.\u003c/p\u003e \u003cp\u003eWe noticed that the upregulated genes mainly enriched in pathways related to inflammation and immunity. GO analysis demonstrated that the upregulated genes involved in the regulation of interleukin-8(IL-8) production and tumor necrosis factor (TNF) superfamily cytokine production, positive regulation of immune effector process, regulation of leukocyte differentiation, activation, and migration. Similarly, the upregulated genes enriched in pathways concerned with immunity and inflammation by KEGG analysis, such as cytokine-cytokine receptor interaction, primary immunodeficiency, cell adhesion molecules, and toll-like receptor signaling pathway. Studies have shown that the secretion of inflammatory cytokines and the infiltration of immune cells in adipose tissue change during obesity(Kahn et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The infiltration and M1 polarization of macrophages contribute to the increased level of TNF-α in both plasma and adipose tissue in obesity, also, adiponectin which is related to the downregulation of TNF- α is reduced in obesity(Park et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, Hotamisligil et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1993\u003c/span\u003e, Arita et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). The circulating and adipose tissue IL-8 levels in obese patients are higher than those in lean subjects(Bruun et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2004\u003c/span\u003e, Straczkowski et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Free fatty acids such as palmitic acid from daily diet can activate downstream inflammation-related signaling pathways by binding to toll-like receptors(Shi et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, Wu and Ballantyne, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These studies are consistent with our enrichment results, indicating that pathways related to inflammation and immunity are altered in obesity.\u003c/p\u003e \u003cp\u003eIn the above pathways related to inflammation and immunity, different cells in adipose tissue are involved. In obesity, the type, abundance and function of immune cells will change(Koenen et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).Our immune infiltration analysis showed that the abundance of monocytes, macrophages, CD4\u0026thinsp;+\u0026thinsp;T cells, NK cells, Th2 cells, Tfh cells and iTreg cells in obese people were higher than those in normal weight people, while the abundance of B cells, CD8\u0026thinsp;+\u0026thinsp;T cells, and γδ T cells were lower. Early neutrophil infiltration was increased in the adipose tissue of obese mice, which correlated with increased IL-8 production(Elgazar-Carmon et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Bruun et al., 2001). Under the condition of hypoxia in obesity, the Hif-1α pathway and the IKK/NF-κB pathway are activated, promoting the secretion of chemokines such as MCP-1, and subsequently, more monocytes migrate and settle down(Haase et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Amano et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In addition to the changes in quantity, there are also changes in the phenotype of macrophages, which means a shift from M0 or M2 macrophages to M1 macrophages.M1 macrophages secrete IL-6, IL-1β, and TNF-α that contribute to inflammation(Weisberg et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2003\u003c/span\u003e, Wu and Ballantyne, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Amano et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Blocking the trafficking of inflammatory monocytes into adipose tissue protected mice from obese-associated inflammation and insulin resistance(Arkan et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, Weisberg et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In addition, recent studies have shown that macrophages in adipose tissue can be divided into many subsets in detail, such as sympathetic neuron-associated macrophages (SAMs), lipid-associated macrophages(LAMs), metabolically activated macrophages (MMe), possessing their own cellular metabolic status and functional characteristics in obesity(Korf et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In the aspect of lymphocytes, there were increased amounts of CD4 T cells and CD8 T cells in both abdominal subcutaneous and visceral adipose tissues in obese people, accompanied by increased secretion of inflammatory cytokines such as IL-10 secreted by CD4 T cells(Yang et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Winer et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Treg cells in the visceral adipose tissue of mice regulated adipose tissue inflammation and insulin resistance and a declining number of them was noticed in both obese mice and humans(Feuerer et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). NK cells in epididymal adipose tissue of obese mice increased and secreted inflammatory cytokines such as IFN-γ and TNF-α, which promoted M1 polarization of macrophages and subsequently promoted insulin resistance in obese mice(Lee et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). γδ T cells are highly plastic and play different roles in different immune states, mainly involved in anti-infection and anti-tumor immunity(Carding and Egan, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). γδ T cells decreased and macrophages increased in subcutaneous adipose tissue of mice fed a long-term ketogenic diet, accompanied by increased inflammation and worsening glycemic control. However, γδ T cells increased in epicardial adipose tissue of HFD-induced obese mice(Goldberg et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Mehta et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In obesity, B cells modulate T cells and inflammatory macrophages and produce pathogenic IgG antibodies, leading to systemic inflammation and insulin resistance(Winer et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, DeFuria et al., 2013). The changing trends of some cells are consistent with the existing research, while the results of other cells are different or less studied. Subcutaneous adipose tissue and visceral adipose tissue have different functions in metabolic inflammation, and research on different tissues or species may lead to different results.\u003c/p\u003e \u003cp\u003eWe constructed the PPI network analysis of the robust DEGs, and 10 genes were finally filtered as the hub genes. PTPRC, ITGAX, CD86, and ITGB2 are expressed in lymphocytes, granulocytes, and macrophages, and they are involved in the differentiation, movement, interaction, adhesion, and signal transduction of leukocytes(Al Barashdi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Cutolo et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Zhang et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). CCR1 and CCL19 are members of the chemokine and chemokine receptor family. CCR1 regulates the recruitment and activation of macrophages, and the CCR7 /CCL19 axis works in T cell and DC cell migration and coordinates the immune response(Li et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Salem et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These regulations of leukocyte markers or chemokine gene expression may indicate changes in immune cell infiltration in tissues. Association analysis showed that these genes were positively correlated with immune infiltration score and the abundance of macrophages, CD8 T cells, and Tex cells, and were negatively correlated with DC cells, CD4 T cells, γδ T cells, Th2 cells, and Tfh cells. The hub genes are not only related to the results of immune infiltration, but also participate in atherosclerosis, anti-tumor, and anti-infection immunity. MMP9 is related to oxidative stress, which can disrupt the basement membrane, and alter the stabilization of atherosclerotic plaque(Jaoude and Koh, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Cabral-Pacheco et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Macrophage-derived SPP1 promotes atrial disease, like atrial fibrillation(Hulsmans et al., 2023). TLR8 is mainly expressed in bone marrow and T cells, inducing the production of type I interferon, IL-6、TNF-α, which contribute to anti-tumor and anti-infection immunity(Wang et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). TREM expressed in visceral adipocytes of obese mice can promote phagocytosis and regulate lipid catabolism to maintaining metabolic homeostasis(Jaitin et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Deczkowska et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Metabolic inflammation is a significant part of the pathological changes in obesity, and it contributes to insulin resistance, elevated blood glucose levels, and atherosclerosis. Due to alterations in the immune system, obese people show different characteristics from others in anti-infection immunity and tumor susceptibility(Bapat et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Bhupathiraju and Hu, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Therefore, the intervention of inflammatory and immune pathways may play a positive role in the prevention and treatment of obesity-related diseases.\u003c/p\u003e \u003cp\u003eVisceral adipose tissue is more significant for metabolic inflammation, but there are few studies included in GEO database, which cannot be further discussed. We will continue to pay attention to the research progress in this area.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we confirmed that obesity involves inflammation and immunity. Notably, we have identified 10 hub genes and showed the immune infiltration alterations in obesity which may help to explain the the pathological mechanisms of obesity and provide potential therapeutic orientations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eStudy concept and design: Tianshu Zeng and Qiuying Liu; Data acquisition and statistical analysis: Qiuying Liu and Yifan Ren; Statistical interpretation: Qiuying Liu and Linfeng He. All authors have read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAL BARASHDI, M. A., ALI, A., MCMULLIN, M. F. \u0026amp; MILLS, K. 2021. Protein tyrosine phosphatase receptor type C (PTPRC or CD45). \u003cem\u003eJ Clin Pathol,\u003c/em\u003e 74\u003cstrong\u003e,\u003c/strong\u003e 548-552.\u003c/li\u003e\n\u003cli\u003eAMANO, S. U., COHEN, J. L., VANGALA, P., TENCEROVA, M., NICOLORO, S. M., YAWE, J. 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ITGB2-mediated metabolic switch in CAFs promotes OSCC proliferation by oxidation of NADH in mitochondrial oxidative phosphorylation system. \u003cem\u003eTheranostics,\u003c/em\u003e 10\u003cstrong\u003e,\u003c/strong\u003e 12044-12059.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"obesity, immune infiltration, white adipose tissue, bioinformatics","lastPublishedDoi":"10.21203/rs.3.rs-3937597/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3937597/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe physiological and pathological process of obesity involves inflammation and immunity. The alterations in the number and function of immune cells may have an effect on systemic inflammation and homeostasis. This study aimed to explore the different biological processes and immune infiltration landscape in obesity.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eNine obesity-related datasets were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs)in adipose tissues were identified by \u0026ldquo;limma\u0026rdquo; R package or GEO2R and then Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed. Meanwhile, we conducted the immune infiltration analysis with gene expression data and Meta-analysis was performed based on the results of immune infiltration. Finally, we selected hub genes and tried to find out the connection between hub genes and immune infiltration.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e46 common DEGs were identified, among which the up-regulated genes were involved in biological processes such as the regulation of hemopoiesis, leukocyte differentiation, activation and migration, cell adhesion, cytokine secretion, and interactions. Immune infiltration analysis showed that the percentages of monocytes and macrophages were increased in obesity, while there was no significant difference in neutrophils. The obese patients had a higher proportion of CD4 T cells, induced regulatory T (iTreg) cells, T follicular helper (Tfh) cells, T helper 2 (Th2) cells, T regulatory type 1 (Tr1) cells, and natural killer (NK) cells, and lower levels of CD8 T cells, B cells, CD8 naive cells, exhausted T (Tex) cells, and γδ T cells compared with the controls. PTPRC、ITGAX、CD86、MMP9、ITGB2、CCR1、TLR8、CCL19、SPP1、TREM2 were identified as hub genes.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn obesity, genes related to immunity and inflammation are upregulated in adipose tissue, and the function and abundance of immune cells are changed. There are more monocytes and macrophages in obese people than those in non-obese individuals, and there are also differences in lymphocytes and their subsets.\u003c/p\u003e","manuscriptTitle":"Analysis of gene expression and immune infiltration in white adipose tissue of patients with obesity: bioinformatics analysis and meta-analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-09 17:18:37","doi":"10.21203/rs.3.rs-3937597/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":"6a4525b1-ce2e-4fd4-8133-d781d0ba3837","owner":[],"postedDate":"February 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-10T16:01:44+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-09 17:18:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3937597","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3937597","identity":"rs-3937597","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}