Identification of the critical genes and signaling pathways in subcutaneous adipose tissue after bariatric surgery based on the GEO database

Identification of the critical genes and signaling pathways in subcutaneous adipose tissue after bariatric surgery based on the GEO database | 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 Article Identification of the critical genes and signaling pathways in subcutaneous adipose tissue after bariatric surgery based on the GEO database Chunjia Han, Yu Wang, Hao Wang, Rui Zhang, Junfeng Wang, Yabin Xia, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4127363/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 This study aimed to explore potential biomarkers and mechanisms following bariatric surgery. Two gene expression profiles from the Gene Expression Omnibus (GEO) were analysed to identify differentially expressed genes (DEGs) in subcutaneous adipose tissue (AT) post-bariatric surgery. Subsequently, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene-Set Enrichment Analysis (GSEA), and Protein-Protein Interaction (PPI) network analyses were employed to identify hub genes and associated pathways. Among the DEGs, 29 genes were downregulated. Enrichment analysis revealed that the downregulated DEGs significantly participated in inflammatory responses. GSEA provided comprehensive evidence that most genes were enriched in pro-inflammatory pathways before surgery, while after surgery, most genes were enriched in metabolism. In the PPI network, five key genes, including TREM2, MNDA, HP, C5AR1, and S100A8, were identified, with most validated as highly expressed in obesity by the Attie Lab Diabetes and another dataset, GSE72158. Bariatric surgery induces a significant shift from an obesity-promoting inflammatory state to an anti-inflammatory state, accompanied by improvements in adipocyte metabolic function. This represents a key mechanism for the enhancement of adipose tissue function following bariatric surgery. This study deepens the understanding of the benefits of bariatric surgery and provides potential biomarkers or therapeutic targets. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Gastroenterology Bariatric surgery obesity adipose tissue differentially expressed genes PPI network Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Obesity has emerged as a global epidemic [ 1 ], directly contributing to the onset of cardiovascular risk factors, including dyslipidemia, type 2 diabetes, hypertension, and sleep disorders [ 2 ]. According to the World Health Organization (WHO), projections estimate that by 2025, 2.7 billion adults will be overweight, with over 1 billion classified as obese [ 3 ]. Adipose tissue, exhibiting remarkable plasticity and activity, possesses functional pleiotropism and a high capacity for remodeling [ 4 ]. Beyond increased fat mass, obesity triggers inflammation in white adipose tissue (WAT), resulting in both local and systemic metabolic dysfunctions, notably insulin resistance (IR) [ 5 ]. The societal health and economic burdens imposed by obesity underscore the imperative to explore effective weight-loss treatment plans for implementation in clinical and public health contexts. Current drug therapies for obesity are limited in number and efficacy. Investigational drugs target various systems and tissues, including gastrointestinal hormones and adipose tissue. In addition to pharmacotherapy, noninvasive anti-obesity strategies such as novel drug delivery systems, vaccines, modulation of the gut microbiome, and gene therapy are being explored [ 6 ]. Common bariatric approaches in Asia, including sleeve gastrectomy (SG), Roux-en-Y gastric bypass (RYGB), and one-anastomosis gastric bypass (OAGB), performed laparoscopically, have shown more extensive and often more durable weight loss compared to lifestyle modification and obesity pharmacotherapy [ 7 ]. Studies in Asian and other populations indicate that bariatric surgery is more effective than conventional medical therapy in the long-term control of type 2 diabetes [ 8 ]. The principal mechanisms through which bariatric surgery influences obesity and its complications involve eating behaviours, altered gut hormone release, and changes in microbiota [ 9 ]. A prospective observational study identified an association between weight loss after bariatric surgery and changes in specific fatty acids [ 10 ]. Further research is warranted to elucidate the impact of bariatric surgery on lipid metabolism. Given the ongoing global health crisis posed by obesity, exploring potential biomarkers and mechanisms associated with the benefits of bariatric surgery is crucial for the development of individualized precision treatment in obese patients. With the advancement and widespread application of high-throughput system (HTS) technologies and the development of bioinformatics, numerous potential biomarkers have been uncovered. Chen obtained the dataset from the GEO database and analysed bariatric surgery using Weighted Gene Co-expression Network Analysis (WGCNA), Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Machine Recursive Feature Elimination (SVM-RFE) algorithms [ 11 ]. However, most bioinformatics analyses on bariatric surgery have not integrated 12 algorithms in CytoHubba in Cytoscape software and Upset. In this study, we identified Differentially Expressed Genes (DEGs) in subcutaneous adipose tissue after bariatric surgery by analysing two mRNA expression profiles from the GEO database. Employing 12 algorithms in CytoHubba combined with the Upset R package enabled us to identify key genes regulating adipose differentiation. Finally, the Attie Lab Diabetes and the external dataset GSE72158 were employed for further validation. Results 1 Identification of Differentially Expressed Genes (DEGs) after Bariatric Surgery In the pursuit of discerning DEGs pre- and post-bariatric surgery, and to standardise the dataset, 250 and 66 DEGs were respectively extracted from the GSE59034 and GSE53376 datasets (Fig. 1 ). The volcano plot illustrates the DEGs, revealing 250 in GSE59034, comprising 32 upregulated genes and 218 downregulated genes. In GSE53376, 66 DEGs were identified, encompassing 14 upregulated genes and 52 downregulated genes. Subsequently, heatmaps were constructed, with the GSE53376 heatmap based on DEGs with |logFC|>1 and the GSE59034 heatmap based on the top hundred DEGs with |logFC|>1. A Venn diagram was then utilised to identify 29 commonly expressed DEGs (Table 1 ) (Fig. 2 ). Table 1 Differentially expressed genes after bariatric surgery. DEGs Gene symbol Down-regulated SPP1,CHI3L1,SFRP4,PRG4,C3AR1,MSC,FPR3,NPR3,CD163,RANBP3L,HP,AQP9, RGS1,HSD11B1,CCR1,TREM2,IL18,GPR183,LYZ,CD180,DHRS9,C5AR1,CXCL16, CTSS,S100A8,CD53,EVI2A,MNDA,EVI2B 2 Gene Ontology and KEGG Pathway Enrichment Analyses after Bariatric Surgery The Gene Ontology (GO) enrichment results for biological processes (BP) indicate that differentially expressed genes are significantly enriched in the inflammatory response and cell activation involved in immune response. Results for cellular components (CC) reveal significant enrichment in the intrinsic component of the membrane, secretory granules, and secretory vesicles. In terms of molecular function (MF), enrichment is observed in G protein-coupled receptors and molecular transduction activity. The KEGG pathway enrichment results demonstrate that differentially expressed genes are enriched in the Staphylococcus aureus infection signaling pathway, cytokine-cytokine receptor interaction signaling pathway, complement and coagulation cascade lysosomes, and neuroactive ligand-receptor interaction signaling pathway (Fig. 3 ). 3 Gene Set Enrichment Analysis (GSEA) The objective of the GSEA analysis is to elucidate potential mechanisms associated with bariatric surgery. Samples were categorised into the pre-bariatric surgery group and the post-bariatric surgery group. The analysis reveals (Fig. 4 ) that the most significantly enriched pathways in the pre-bariatric surgery group encompass the chemokine signalling pathway, toll-like receptor signalling pathway, hematopoietic cell, lysosome, B cell receptor signalling pathway, cytokine-cytokine receptor interaction, natural killer cell-mediated cytotoxicity, NOD-like receptor signalling pathway, leukocyte transendothelial migration, T cell receptor signalling pathway, cell adhesion molecules, and Fc gamma receptor-mediated phagocytosis, among others. Additionally, pathways exhibiting a positive correlation with the post-bariatric surgery group and displaying significant differences include propionate metabolism, fatty acid metabolism, citrate cycle, glycerophospholipid metabolism, alanine, aspartate, and glutamate metabolism, as well as linoleic acid metabolism, among others. 4 Protein-Protein Interaction (PPI) Network Analysis and Key Gene Selection Based on disparities identified in STRING, the Protein-Protein Interaction (PPI) network of differentially expressed genes comprises 22 downregulated genes, forming a cluster of 22 nodes and 50 edges (Fig. 5 ). Protein interaction network graph data of differentially expressed genes, acquired from the STRING online database, were exported and subsequently analysed in Cytoscape software, resulting in an in-depth analysis of the protein interaction network graph by Cytoscape. The gene ranking methodology utilises 12 algorithms within the CytoHubba plugin to select the top 15 genes for each algorithm (Table 2 and Table 3 ). Gene selection utilises the UpsetR package in R software, as depicted in the gene set visualisation graph. Ultimately, MNDA, TREM2, C5AR1, S100A8, and HP are identified as key genes. Expression results of key genes before and after bariatric surgery indicate a varied reduction in postoperative expression compared to pre-surgery. Pearson correlation analysis of key genes reveals a positive correlation between the five genes in pairs. Table 2 CytoHubba plugin 12 algorithms get the top 15 genes(1) MCC DMNC Stress Radiality MNC EPC CTSS MNDA CD53 CCR1 CTSS CD163 MNDA C5AR1 CD163 CD163 CD163 CTSS CD53 TREM2 CCR1 CTSS CCR1 CD53 LYZ C3AR1 CTSS C3AR1 MNDA CCR1 C3AR1 LYZ C3AR1 CD53 CD53 LYZ CCR1 S100A8 LYZ LYZ C3AR1 MNDA CD163 EVI2B MNDA MNDA LYZ C3AR1 C5AR1 CD53 IL18 TREM2 C5AR1 TREM2 TREM2 AQP9 TREM2 IL18 TREM2 C5AR1 S100A8 GPR183 S100A8 C5AR1 IL18 S100A8 EVI2B HP C5AR1 S100A8 S100A8 EVI2B IL18 CXCL16 CHI3L1 EVI2B EVI2B IL18 AQP9 CD180 EVI2B HP AQP9 AQP9 HP EVI2A HP AQP9 GPR183 HP CHI3L1 CHI3L1 SPP1 CXCL16 HP CHI3L1 Table 3 CytoHubba plugin 12 algorithms get the top 15 genes(2) Degree EcCentricity ClusteringCoefficient Closeness BottleNeck Betweenness CD163 CD163 GPR183 CD163 CTSS CD53 CTSS IL18 CXCL16 CTSS CCR1 CD163 CD53 CTSS CD180 CD53 CD163 CCR1 CCR1 S100A8 EVI2A CCR1 CD53 CTSS MNDA C5AR1 RGS1 MNDA IL18 IL18 C3AR1 MNDA EVI2B C3AR1 MNDA C3AR1 LYZ C3AR1 AQP9 LYZ HP MNDA IL18 CD53 C5AR1 TREM2 S100A8 LYZ S100A8 AQP9 TREM2 IL18 EVI2B TREM2 C5AR1 CCR1 C3AR1 C5AR1 CHI3L1 S100A8 TREM2 TREM2 LYZ S100A8 TREM2 C5AR1 EVI2B LYZ MNDA EVI2B GPR183 CHI3L1 HP GPR183 S100A8 HP FPR3 HP AQP9 FPR3 HP AQP9 CXCL16 EVI2B CHI3L1 HP CTSS CHI3L1 C5AR1 SPP1 5 Cell Type Enrichment Analysis To comprehensively investigate alterations in immune cell infiltration levels post-bariatric surgery, this study employed xCell analysis to examine the relative abundance of immune cells, as well as changes in adipocytes and pre-adipocytes before and after surgery (Fig. 6 ). The findings unveiled a noteworthy decrease in adipocytes, pre-adipocytes, B cells, dendritic cells, macrophages, M1 macrophages, M2 macrophages, monocytes, and neutrophils post-surgery. The expressions of TREM2, HP, MNDA, and C5AR1 demonstrated positive correlations with macrophages, M1 macrophages, M2 macrophages, dendritic cells, and monocytes. Furthermore, the expression of S100A8 exhibited positive correlations with neutrophils and monocytes. Additionally, all five key genes demonstrated positive correlations with adipocytes or pre-adipocytes (Fig. 7 ). 6 Preliminary Validation of Key Genes The mRNA levels of hub genes in obesity underwent validation using the Attie Lab Diabetes Database. The findings revealed that, in comparison to the lean group, the expression of TREM2, HP, C5AR1, and S100A8 was up-regulated in the 10-week obese mice (Fig. 8 ). Subsequently, the GSE72158 dataset from the GEO database was employed to confirm the expression of MNDA, TREM2, HP, C5AR1, and S100A8 before and after surgery. The results (Fig. 9 ) indicated that following surgery, the expression of TREM2, MNDA, HP, C5AR1, and S100A8 exhibited varying degrees of reduction compared to pre-surgery levels. 7 Diagnostic Efficacy of Key Genes for Obesity Through a collective analysis of the obese and healthy populations in the GSE59034 dataset, ROC curves for hub genes were drawn using the pROC package. Subsequently, the area under the curve (AUC) was calculated to assess the diagnostic effectiveness of the hub genes (Fig. 10 ). The Area Under the Curve (AUC) for TREM2 stands at an impressive 0.918, indicating superior diagnostic performance for obesity. HP follows with a respectable AUC of 0.848, while MNDA exhibits a value of 0.828. C5AR1 shows a slightly lower but still commendable AUC of 0.879. S100A8, with an AUC of 0.762, indicates a moderate level of diagnostic utility. In light of these AUC values, it is evident that TREM2 is highly efficacious in diagnosing obesity, with HP, MNDA, C5AR1, and S100A8 also contributing to the diagnostic process, albeit to varying degrees. 8 Clinical Data Validate Changes in Patient Inflammatory Status Before and After Bariatric Surgery 8.1 Basic Information Clinical data from 200 obese patients, selected based on inclusion and exclusion criteria from 2020 to 2023, were analysed. The demographic details were as follows: a total of 200 cases, comprising 168 female patients (84%) and 32 male cases (16%). The mean body weight was (101.20 ± 16.59) kg, and the mean BMI was (36.81 ± 4.69) kg/m2. One hundred and forty patients completed a one-year follow-up. 8.2 Inclusion and Exclusion Criteria (1) Inclusion criteria: According to the "2019 Chinese Guidelines for the Surgical Treatment of Obesity and Type 2 Diabetes," patients with simple obesity with BMI ≥ 32.5 kg/m 2 or BMI ≥ 27.5 kg/m 2 and two metabolic syndromes or type 2 diabetes were included. Patients with no apparent surgical contraindications who agreed to participate in the clinical study were also considered. (2) Exclusion criteria: Non-compliance with medical advice and poor compliance, BMI below 25kg/m2, type 1 diabetes patients, and individuals unable to tolerate laparoscopic surgery. 8.3 Data Collection Clinical data, including BMI, neutrophil count (NEU), lymphocyte count (LYM), neutrophil-to-lymphocyte ratio (NLR), triglycerides (TG), high-density lipoprotein (HDL), and TG/HDL, were collected both before and one year after bariatric surgery(Table 4 ). Changes in BMI and markers of low-grade inflammation (NLR, TG/HDL) were observed before and after surgery. Table 4 Comparison of the observation indicators before and 1 year after surgery Index Pre-surgery Post-surgery P Example(n) 200 140 - height(m) 1.65 ± 0.07 1.65 ± 0.07 > 0.05 weight(kg) 101.20 ± 16.59 73.11 ± 9.68 < 0.01 BMI(Kg/m 2 ) 36.81 ± 4.69 26.62 ± 2.92 < 0.01 TG(mmol/L) 1.98(1.64) 1.50(0.11) < 0.01 HDL(mmol/L) 1.26(0.04) 1.40(0.10) 0.766 TG/HDL 1.50(1.31) 1.04(0.28) < 0.01 WBC(10 9 /L) 8.95 ± 2.03 6.25 ± 1.85 < 0.01 NEU(10 9 /L) 5.38 ± 1.57 3.91 ± 0.71 < 0.01 LYM(10 9 /L) 2.70(1.02) 2.70(0.35) < 0.01 NLR 1.86(0.95) 1.50(0.03) < 0.01 CRP(mg/L) 5.64(7.07) 2.95(1.00) 0.138 Discussion In the contemporary context of the global obesity pandemic, accumulating evidence indicates that obesity contributes to various complications, including cardiovascular disease, insulin resistance, type 2 diabetes (T2DM), hypertension, and hyperlipidemia [ 12 ]. Bariatric surgery, known for its effectiveness in weight loss, mitigating T2DM, improving cardiovascular risk factors, alleviating other obesity-related comorbidities, and enhancing long-term survival, stands as a potent therapeutic intervention. The primary mechanisms through which weight-loss surgery impacts obesity and its complications encompass dietary behavior modification, alterations in intestinal hormone release, and changes in the microbiota [ 13 ]. The molecular intricacies underlying the effects of bariatric surgery remain an area of clinical exploration. This study delves into the gene expression data of subcutaneous adipose tissue in obese patients, employing bioinformatics analyses to gain insights into the potential mechanisms of weight loss and metabolic improvement, with the aim of contributing findings for clinical treatment. Drawing from the GSE53376 and GSE59034 datasets, this study uncovers potential adipose tissue-related pathways both before and after bariatric surgery. Employing bioinformatics methodologies, we identify 29 downregulated differentially expressed genes. Gene Ontology (GO) enrichment analysis highlights that genes with reduced expression post-surgery are enriched in inflammatory response and cellular activation involved in the immune response, aligning with existing literature [ 14 – 16 ]. G protein-coupled receptors play a pivotal role in regulating various adipocyte functions, influencing processes such as lipolysis, heat production, glucose processing, and adipokine factor secretion. Their signalling significantly contributes to the regulation of adipose tissue metabolism, particularly in obesity, type 2 diabetes, and related comoridities [ 17 – 19 ]. G protein-coupled receptors are integral in maintaining energy homeostasis, influencing food intake, energy expenditure, glycemic control, and participating in cell division, growth, repair, protein synthesis, and immune responses [ 20 ]. Gene Set Enrichment Analysis (GSEA) outcomes support the notion that surgery-induced weight loss triggers a shift from a state of pro-inflammation to anti-inflammation, ultimately enhancing metabolic functions. GSEA reveals that, before bariatric surgery, most genes are predominantly enriched in chemokines, TOLL-like receptor, and B cell receptor signaling pathways—mechanisms integral to adipose tissue inflammation. Adipocytes release chemokines that intensify tissue inflammation, with specific chemokines like CCL2 and CCL5 exacerbating systemic inflammation by recruiting proinflammatory M1 macrophages, thereby contributing to obesity and its chronic complications [ 21 ]. Toll-like receptor signaling induces fatty acid synthesis, fostering chronic inflammation in adipose tissue and contributing to the metabolic syndrome associated with obesity [ 22 ]. B cells, through regulating T cell function and inflammatory factor secretion, can exacerbate the inflammatory response in obesity and T2DM [ 23 ]. GSEA provides additional support, revealing that the enrichment outcomes post-bariatric surgery are intricately linked with fatty acid and amino acid metabolism. Numerous obese individuals grapple with lipid metabolism disorders, notably abdominal obesity, elevating cardiometabolic risk due to atherogenic dyslipidemia [ 24 ]. Nutrient composition, particularly fatty acids, can significantly impact lipid levels. Adiponectin, exclusively expressed by adipocytes, exerts anti-inflammatory effects and enhances insulin sensitivity by promoting fatty acid oxidation. Most studies consistently report a substantial increase in adiponectin levels following laparoscopic sleeve gastrectomy [ 25 ]. A recent extensive meta-analysis demonstrated significant adiponectin increases at 6, 12, and 18 months post-weight loss [ 26 ]. Mounting evidence suggests that bariatric surgery contributes to enhanced production of anti-inflammatory adipokines and a concurrent reduction in proinflammatory adipokine production [ 27 ]. Branched-chain amino acid levels, including leucine, isoleucine, and valine, exhibit significant elevation in obese individuals with type 2 diabetes or insulin resistance [ 28 ]. Numerous studies affirm a decrease in the levels of most amino acids, including branched-chain amino acids, post-bariatric surgery. However, the levels of specific amino acids, such as glycine and serine, increase uniformly, irrespective of the surgical type [ 29 ]. Research underscores that metabolites like glucose, fatty acids, glutamine, and succinate, functioning as energy sources, can influence the activity of macrophages, neutrophils, and T cells. Obesity is characterized by a continuous supply of these metabolites. The observed metabolic shifts in nutrient supplies after bariatric surgery have the potential to modulate the immune system, playing a pivotal role in immune differentiation. Bariatric surgery, by curbing pathological immune activation and thereby diminishing the supply of nutrient metabolites, directly regulates inflammation. Consequently, it establishes an anti-inflammatory state, thereby enhancing metabolic function [ 30 ]. These mechanisms contribute to the comprehension of how bariatric surgery orchestrates a significant transition from inflammation to metabolism. TREM2, a membrane protein expressed on immune cells, is implicated in neurodegenerative diseases and cancer [ 31 ]. Metabolic symptoms, including adipocyte hypertrophy, hypercholesterolaemia, fat accumulation, and glucose intolerance, are observed in TREM2 knockout mice [ 32 ]. Exploring TREM2's role in metabolic diseases and obesity may unveil additional biological functions. MNDA governs bone marrow cell differentiation and myelodysplastic syndrome development [ 33 ]. It has been demonstrated that MNDA participates in inflammatory signaling pathways among proteins [ 34 ]. Subsequent studies indicate MNDA as an inflammatory gene with elevated expression in T1DM and hyperglycemia [ 35 ]. C5AR1 is closely linked to inflammation [ 36 ]. Adipose tissue reduction is noted in C5AR knockout mice irrespective of dietary changes [ 37 ]. Administering C5AR selective antagonists to diet-induced obese rats not only reduces body weight but also ameliorates insulin resistance and adipose tissue inflammation [ 38 ]. HP, a recognized inflammation marker [ 39 ], exhibits a positive correlation with body mass index in certain studies [ 40 ]. During early obesity phases, HP interacts with CCR2, facilitating monocyte entry into white adipose tissue. White adipose tissue macrophages enhance IL-6 and TNF α production, further inducing HP expression. Absence of HP diminishes adipose histiocytic inflammation, reducing obesity-related comorbidities. HP emerges as a potential therapeutic target [ 41 ]. S100A8 expedites macrophage migration, induces proinflammatory factor production, and triggers inflammatory responses in adipocytes. It is a pivotal component regulating the initial steps of the inflammatory cascade in obese adipose tissue. S100A8 antibodies effectively inhibit diet-induced inflammatory changes and play a crucial role in improving insulin resistance [ 42 ]. Collectively, these five key genes significantly contribute to obesity development. This study introduces potential therapeutic targets and biomarkers for obesity, offering a theoretical foundation for subsequent investigations. Key gene expression data were extracted from the Attie Lab database to confirm the association of these genes with obesity. Results revealed higher expression of most key genes in the obese group. The study presented in this paper possesses certain limitations. Firstly, the dissimilar follow-up durations of the two datasets (12 months for GSE53376 and 2 years for GSE59034) may have overlooked some biological information due to inadequate consideration of this temporal variation. Secondly, while the method employed by the Attie Lab diabetes database to confirm the elevated expression levels of key genes in obesity may not be optimal, it does provide partial confirmation of the correlation between key genes and obesity. However, the results lack more rigorous validation due to the absence of experimental verification. Thirdly, the data utilised in this study were sourced from public databases, and the data quality assessment was unfeasible. Additionally, the methods involving Affymetrix gene expression arrays, used in the data analysis of this study, have not been extensively employed. Lastly, the relatively small sample size of the dataset may impact the analysis of gene expression before and after bariatric surgery in obese patients. In summary, this study concludes that bariatric surgery induces a substantial transformation in obesity, shifting it from a pro-inflammatory state to an anti-inflammatory state, thereby enhancing adipocyte metabolic function. Five key genes, identified as highly expressed in obesity, shed light on potential mechanisms underlying adipose tissue function changes post weight-reducing surgery. These findings offer novel therapeutic targets for ameliorating adipocyte inflammation, suggesting alternative approaches for the non-surgical treatment of obesity and associated comorbidities. The study provides a theoretical foundation for future investigations. The modified gastrointestinal anatomy following surgery serves as an ideal model for observing changes in the signal transmission chain from the gut environment to the brain, aiding in elucidating the brain's activity relationship with weight loss. Real-time studies are essential to further unravel the physiological mechanisms of bariatric surgery, with the overarching aim of expanding obesity prevention and treatment strategies. Methods 1 Microarray Dataset Three datasets pertaining to alterations in the subcutaneous adipose tissue (AT) transcriptome following ba retrieved from the Gene Expression Omnibus (GEO) website (Table 5 ). GSE53376 and GSE59034 were selected for primary analyses, while the GSE72158 dataset served for subsequent validation. The platform for GSE53376 utilised GPL16686[HuGene-2_0-st] Affymetrix Human Gene 2.0 ST Array [transcript (gene) version]. This dataset comprised 16 samples of subcutaneous AT obtained from obese subjects, alongside an additional 16 subcutaneous AT samples collected shortly after bariatric surgery. On the other hand, the platform for GSE59034 employed GPL11532 [HuGene-1_1-st] Affymetrix Human Gene 1.1 ST Array [transcript (gene) version]. This dataset included subcutaneous AT samples from obese subjects before (n = 16) and after short-term bariatric surgery (n = 16), as well as samples from individuals maintaining a never-obese status (n = 16). Lastly, the platform for GSE72158 was GPL10558[Illumina HumanHT-12 V4.0], encompassing subcutaneous AT samples from obese subjects before (n = 40) and after short-term bariatric surgery (n = 40).riatric surgery were Table 5 Basic information of the dataset dataset year tissue sample platform GSE53376 2014 Subcutaneous adipose tissue before surgery (n = 16) and after surgery (n = 16) GPL16686[HuGene-2_0-st] Affymetrix Human Gene 2.0 ST Array [transcript (gene) version] GSE59034 2017 Subcutaneous adipose tissue before surgery (n = 16), after surgery (n = 16) and never obese people(n = 16) GPL11532[hugene-11.1-st] Affymetrix Human Gene 1.1 ST Array [transcript (gene) version] GSE72158 2015 Subcutaneous adipose tissue before surgery (n = 40) and after surgery (n = 40) GPL10558[Illumina HumanHT-12 V4.0] 2 Identification of Differentially Expressed Genes The R packages "GEOquery" (V2.66.0) and "limma" (V3.54.0) within the interactive online tool GEO2R ( https://www.ncbi.nlm.nih.gov/geo/geo2r ) were utilised for data normalisation and screening of Differentially Expressed Genes (DEGs) (|fold change| ≥ 1 and adj. p < .05). The resultant DEGs were presented visually through Heatmaps. The Draw Venn Diagram tool ( http://bioinformatics.psb.ugent.be/webtools/Venn/ ) was employed to identify common DEGs across the two datasets. 3 Functional and Pathway Enrichment Analysis Gene Ontology (GO) analysis was applied to discern biological attributes [ 43 ], encompassing biological processes (BP), cellular components (CC), and molecular functions (MF). KEGG analysis provided more comprehensive information on genome sequences and protein interactive networks [ 44 ]. Enrichment analysis was conducted using the SangerBox platform[ 45 ], with gene set sizes ranging from 5 to 5,000, and a threshold of statistical significance set at p-Value < .05 and FDR < 0.25. 4 Gene Set Enrichment Analysis The GSEA [ 46 ] software (4.3.2) was employed for further analysis of the genes identified in the GSE59034 dataset, offering a comprehensive screening of biological functions post-surgery. Each analysis included 1000 gene set permutations, and significance was established at a p-value < 5% and a false discovery rate (FDR) < 25%. 5 Construction of PPI Network and Identification of Key Genes The STRING [ 47 ] database ( http://www.string-db.org/ ) was utilised to construct a Protein-Protein Interaction (PPI) network of differentially expressed genes, identifying and predicting interactions between genes or proteins. The PPI network of differentially expressed genes was visualised using Cytoscape V3.9.1. Core nodes, indicative of greater impact in protein interaction networks, were determined using Cytohubba [ 48 ], employing 12 statistical analysis methods. The top 15 genes ranked by each algorithm were selected, filtered using the Upset package to identify key genes, and subjected to Pearson correlation analysis. 6 Immune Infiltration Analysis This study employed xCell[ 49 ] analysis to detect the distribution of immune cells in the tissue based on gene expression profiles, predicting the relative abundance of cell types. 7 Validation of Key Genes The Attie Lab Diabetes Database ( http://diabetes.wisc.edu ) facilitated a search for gene expression in six key tissues (islet, liver, adipose, hypothalamus, gastrocnemius, and soleus) in the mouse model of Type 2 diabetes, considering genetic obesity (lean vs. OB/OB), strain (B6 vs. BTBR), and age (4 vs. 10 weeks) (significance set at p < 0.05). This enabled verification of key gene expression in adipose tissue of both lean and obese mice at 4 and 10 weeks. Additionally, the dataset GSE72158 was utilised to validate the expression of key genes selected before and after surgery. 8 Assessment of the Diagnostic Effectiveness of Key Genes To validate the diagnostic efficacy of the key genes in identifying obesity within the GSE59034 gene sets, ROC logistic regression analysis was conducted. The Area Under the Receiver Operating Characteristic Curve (ROC-AUC) served as the criterion for assessment. 9 Clinical Data to Verify Changes in Inflammatory Status Before and After Bariatric Surgery Clinical data were collected both before and one year after bariatric surgery, encompassing Body Mass Index (BMI), neutrophil count (NEU), lymphocyte count (LYM), Neutrophil-to-Lymphocyte Ratio (NLR), triglycerides (TG), high-density lipoprotein (HDL), and the TG/HDL ratio. Observations focused on alterations in BMI and markers of inflammation (NLR, TG/HDL). SPSS 27.0 software (IBM SPSS Statistics 27 V27.0.1) facilitated data processing. A paired sample t-test was applied for normally distributed variables in the comparison of the two groups before and after surgery, while a paired sample Wilcoxon test was employed for non-normally distributed variables. A significance level of P < 0.05 denoted a statistically significant difference. Declarations Acknowledgement We are grateful to our team members for valuable discussion Author contributions statement H.H. and C.H. designed the research. C.H. conducted the search. Y.W. , Z.Z. and H.W. prepared figures. J.W. and Y.X. revised the article critically. W.W., Y.X. and H.Z. collected and analysed the clinical data. All the authors contributed to the manuscript writing, read and approved the final manuscript. Competing interests Each author declares that he or she has no commercial associations (e.g. consultancies, stock ownership, equity interest, patent/licensing arrangement etc.) that might pose a conflict of interest in connection with the submitted article. Data availability Any data and R script in this study can be obtained from the corresponding author upon reasonable request. The final manuscript was read and approved by all authors. In this study, publicly available datasets were analyzed. These are available on GEO (https://www.ncbi.nlm.nih.gov/). Attie Lab Database (http://diabetes.wisc.edu/) , STRING database (http://www.string-db.org/) and Sangerbox (http://sangerbox.com/home.html) are also freely available. Ethical Statement The study was approved by the Scientific research IRB of Wannan Medical College Yijishan Hospital. Written informed consent was obtained from all participants prior to their inclusion in the study. The authors affirm that participants' identities have been anonymized and that any potentially identifying information has been removed. The study adhered to the principles of ethical research, including the protection of participants' rights and welfare. The Ethics Approval Number is (2023)伦审研第(148). 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Aran, D. et al. xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome biology , 18 (1), 220. https://doi.org/10.1186/s13059-017-1349-1 (2017). Additional Declarations No competing interests reported. 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-4127363","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":298483417,"identity":"248535bd-4409-4bdb-b8a2-bf7f8e3dd78d","order_by":0,"name":"Chunjia Han","email":"","orcid":"","institution":"First Affiliated Hospital of Wannan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Chunjia","middleName":"","lastName":"Han","suffix":""},{"id":298483421,"identity":"b363c4e0-1c0e-4812-b36e-83497115433b","order_by":1,"name":"Yu Wang","email":"","orcid":"","institution":"First Affiliated Hospital of Wannan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Wang","suffix":""},{"id":298483425,"identity":"2a9d11f9-e85f-45a5-bd10-b5b77c10fbbd","order_by":2,"name":"Hao Wang","email":"","orcid":"","institution":"First Affiliated Hospital of Wannan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Wang","suffix":""},{"id":298483429,"identity":"14d655f0-e933-4164-a483-6b9f780625b6","order_by":3,"name":"Rui Zhang","email":"","orcid":"","institution":"First Affiliated Hospital of Wannan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Zhang","suffix":""},{"id":298483433,"identity":"106ef119-54f6-439b-847b-ba5cfb7b3b73","order_by":4,"name":"Junfeng Wang","email":"","orcid":"","institution":"First Affiliated Hospital of Wannan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Junfeng","middleName":"","lastName":"Wang","suffix":""},{"id":298483437,"identity":"0711666d-702c-4845-b0d3-ebc4701b9ca6","order_by":5,"name":"Yabin Xia","email":"","orcid":"","institution":"First Affiliated Hospital of Wannan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yabin","middleName":"","lastName":"Xia","suffix":""},{"id":298483442,"identity":"cc87d033-034b-4b9c-8661-1cf8a419fa73","order_by":6,"name":"Wei Wang","email":"","orcid":"","institution":"First Affiliated Hospital of Wannan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Wang","suffix":""},{"id":298483446,"identity":"d37f45f7-d773-4410-ab6e-8e56e71057aa","order_by":7,"name":"Yu Xia","email":"","orcid":"","institution":"First Affiliated Hospital of Wannan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Xia","suffix":""},{"id":298483449,"identity":"957465c5-811e-4e27-8489-3a6c3e6b9388","order_by":8,"name":"Houtao Zhang","email":"","orcid":"","institution":"First Affiliated Hospital of Wannan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Houtao","middleName":"","lastName":"Zhang","suffix":""},{"id":298483451,"identity":"25a226ae-af84-4e96-b5c5-1fd0c37e67da","order_by":9,"name":"He Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIie3RMQuCQBTA8YsDXU5uixMHv8ILhwr8MEpga6Nb2oVra1EfwrHx5K1+AKGl8AsIrQ5FY0ReW8P95veH93iEGMYfAjHK2x4EtShVqku1EiqBrUKb21Zc7WutxC4E6xLu7liAzlYjmXp5IQSgB8g65WTE52P1PZmfKjkDwADQKZV7JpPDMRpYrInzJgJcvJJJTSK4DCeZUIDrEtlVxYVesnEzSKgrGVGVZiIDAiHl1IIqq4XOLctbS/rnKzm29z4Nfe4NJO/Eb+OGYRjGZw+DHknZryxjMQAAAABJRU5ErkJggg==","orcid":"","institution":"First Affiliated Hospital of Wannan Medical College","correspondingAuthor":true,"prefix":"","firstName":"He","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2024-03-19 05:09:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4127363/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4127363/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56100690,"identity":"1f3f8c6f-acd4-4b08-87ce-92faaf4dfca0","added_by":"auto","created_at":"2024-05-08 14:35:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1019930,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Box plot of GSE53376, (b) Box plot of GSE59034, where the x-axis depicts sample symbols, the y-axis represents gene expression values, and the black line within the box plot denotes the median gene expression value. The purple colour designates the pre-bariatric surgery group, while the green colour represents the post-bariatric surgery group. (c) Volcano plot of GSE53376, (d) Volcano plot of GSE59034, with red dots indicating upregulated genes, green dots representing downregulated genes, and black dots signifying genes with no significant difference. (e) Heatmap of differential gene analysis for GSE53376, (f) Heatmap of differential gene analysis for GSE59034\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4127363/v1/a5349500bd61e1fe0dd58c79.png"},{"id":56099731,"identity":"fb72cf0c-7f13-4a5b-ba47-e0bb0bbc3223","added_by":"auto","created_at":"2024-05-08 14:27:11","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":21975,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagram of common differentially expressed genes from the two datasets.\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4127363/v1/57bcf16c089ca7e0604c51fd.jpeg"},{"id":56099733,"identity":"59f71425-3aff-46fa-a871-3df89ca20688","added_by":"auto","created_at":"2024-05-08 14:27:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3235016,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Biological process of the GO analysis. (b) Cellular Components of the GO analysis. (c) Molecular Function of the GO analysis. The x-axis label indicates the gene ratio, and the y-axis label represents GO terms. (d) KEGG analysis of the differentially expressed genes. The x-axis label denotes the gene ratio, and the y-axis label represents pathways.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4127363/v1/a46134d04930fd9768b27012.png"},{"id":56099732,"identity":"809c2722-b2ab-4a1b-9db7-cef74c792136","added_by":"auto","created_at":"2024-05-08 14:27:11","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2138975,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Chemokine Signalling Pathway, (b) Toll-Like Receptor Signalling Pathway, (c) B Cell Receptor Signalling Pathway, (d) Propionate Metabolism, (e) Citrate Cycle (TCA Cycle), (f) Fatty Acid Metabolism.\u003c/p\u003e","description":"","filename":"image4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4127363/v1/4a5a1531723cc91c8def1506.jpg"},{"id":56099738,"identity":"b5d7df45-6d67-484d-96a6-ec9eae7d9724","added_by":"auto","created_at":"2024-05-08 14:27:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":9354799,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Protein-Protein Interaction (PPI) Network of Differentially Expressed Genes. (b) Cytoscape used to construct the protein interaction networks of the differentially expressed genes. (c) Correlation between Key Genes: Pearson correlation analysis was conducted to calculate correlation coefficients. (d) UpsetR Package Plots for the visualization of gene sets. (e) Differential Expression of Key Genes before and after Surgery in GSE59034. The x-axis denotes each key gene, and the y-axis represents gene expression.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4127363/v1/fd8ad1183af2495e3536eeb5.png"},{"id":56099735,"identity":"15e43b6b-9602-43af-8139-c2add6a386f2","added_by":"auto","created_at":"2024-05-08 14:27:11","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":30997,"visible":true,"origin":"","legend":"\u003cp\u003eChanges of immune cell infiltration level and changes between adipocytes and preadipocytes, adipocytes, preadipocytes and immune infiltrating cells on the x-axis, and xCell fraction on the y-axis.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-4127363/v1/fcee8b3f796102e398d273c9.png"},{"id":56100691,"identity":"8d087c7d-7144-4104-8536-9bb5a2555a80","added_by":"auto","created_at":"2024-05-08 14:35:12","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3323319,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation of (a)C5AR1, (b)HP, (c)S100A8, (d)TREM2 and (e)MNDA expression levels and the degree of immune cell infiltration.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-4127363/v1/81be85fb2e8f8940fa73a6ef.png"},{"id":56099740,"identity":"3caf1d12-140c-452a-a75d-2506aa68fb44","added_by":"auto","created_at":"2024-05-08 14:27:12","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":550705,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression of genes (a) TREM2, (b) MNDA, (c) HP, (d) C5AR1, (e)S100A8, significantly up-regulated in the adipose of the 10-weeks obese diabetic mice. *P\u0026lt;0.05，**P\u0026lt;0.01，***P\u0026lt;0.001，****P\u0026lt;0.0001。\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-4127363/v1/32ad85c050b7e62e7ad8db89.png"},{"id":56099739,"identity":"b42e22b6-f062-43cb-bebf-7d126cf7ff24","added_by":"auto","created_at":"2024-05-08 14:27:11","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1189178,"visible":true,"origin":"","legend":"\u003cp\u003e(a) TREM2, (b)MNDA, (c) HP, (d) C5AR1, (e) S100A8 differential expression before and after surgery. \"a\" indicates after surgery, \"b\" indicates before surgery, and the y-axis represents gene expression.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-4127363/v1/afdc28faf22c3412aa1f8712.png"},{"id":56099737,"identity":"68b0d74c-c8df-4b3f-a73d-f5217eb762cf","added_by":"auto","created_at":"2024-05-08 14:27:11","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":47420,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve analysis\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-4127363/v1/8c0d22e6cac8864fcc22449f.png"},{"id":58507286,"identity":"f509eef1-23c2-4cba-adb8-bc1fb2248b34","added_by":"auto","created_at":"2024-06-17 14:59:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":22449750,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4127363/v1/17cb6d7d-590a-44ad-a60a-0c1cb359ab55.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of the critical genes and signaling pathways in subcutaneous adipose tissue after bariatric surgery based on the GEO database","fulltext":[{"header":"Introduction","content":"\u003cp\u003eObesity has emerged as a global epidemic [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], directly contributing to the onset of cardiovascular risk factors, including dyslipidemia, type 2 diabetes, hypertension, and sleep disorders [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. According to the World Health Organization (WHO), projections estimate that by 2025, 2.7\u0026nbsp;billion adults will be overweight, with over 1\u0026nbsp;billion classified as obese [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Adipose tissue, exhibiting remarkable plasticity and activity, possesses functional pleiotropism and a high capacity for remodeling [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Beyond increased fat mass, obesity triggers inflammation in white adipose tissue (WAT), resulting in both local and systemic metabolic dysfunctions, notably insulin resistance (IR) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The societal health and economic burdens imposed by obesity underscore the imperative to explore effective weight-loss treatment plans for implementation in clinical and public health contexts. Current drug therapies for obesity are limited in number and efficacy. Investigational drugs target various systems and tissues, including gastrointestinal hormones and adipose tissue. In addition to pharmacotherapy, noninvasive anti-obesity strategies such as novel drug delivery systems, vaccines, modulation of the gut microbiome, and gene therapy are being explored [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Common bariatric approaches in Asia, including sleeve gastrectomy (SG), Roux-en-Y gastric bypass (RYGB), and one-anastomosis gastric bypass (OAGB), performed laparoscopically, have shown more extensive and often more durable weight loss compared to lifestyle modification and obesity pharmacotherapy [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Studies in Asian and other populations indicate that bariatric surgery is more effective than conventional medical therapy in the long-term control of type 2 diabetes [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe principal mechanisms through which bariatric surgery influences obesity and its complications involve eating behaviours, altered gut hormone release, and changes in microbiota [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A prospective observational study identified an association between weight loss after bariatric surgery and changes in specific fatty acids [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Further research is warranted to elucidate the impact of bariatric surgery on lipid metabolism. Given the ongoing global health crisis posed by obesity, exploring potential biomarkers and mechanisms associated with the benefits of bariatric surgery is crucial for the development of individualized precision treatment in obese patients.\u003c/p\u003e \u003cp\u003eWith the advancement and widespread application of high-throughput system (HTS) technologies and the development of bioinformatics, numerous potential biomarkers have been uncovered. Chen obtained the dataset from the GEO database and analysed bariatric surgery using Weighted Gene Co-expression Network Analysis (WGCNA), Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Machine Recursive Feature Elimination (SVM-RFE) algorithms [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, most bioinformatics analyses on bariatric surgery have not integrated 12 algorithms in CytoHubba in Cytoscape software and Upset.\u003c/p\u003e \u003cp\u003eIn this study, we identified Differentially Expressed Genes (DEGs) in subcutaneous adipose tissue after bariatric surgery by analysing two mRNA expression profiles from the GEO database. Employing 12 algorithms in CytoHubba combined with the Upset R package enabled us to identify key genes regulating adipose differentiation. Finally, the Attie Lab Diabetes and the external dataset GSE72158 were employed for further validation.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e1 Identification of Differentially Expressed Genes (DEGs) after Bariatric Surgery\u003c/h2\u003e\n\u003cp\u003eIn the pursuit of discerning DEGs pre- and post-bariatric surgery, and to standardise the dataset, 250 and 66 DEGs were respectively extracted from the GSE59034 and GSE53376 datasets (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The volcano plot illustrates the DEGs, revealing 250 in GSE59034, comprising 32 upregulated genes and 218 downregulated genes. In GSE53376, 66 DEGs were identified, encompassing 14 upregulated genes and 52 downregulated genes. Subsequently, heatmaps were constructed, with the GSE53376 heatmap based on DEGs with |logFC|\u0026gt;1 and the GSE59034 heatmap based on the top hundred DEGs with |logFC|\u0026gt;1. A Venn diagram was then utilised to identify 29 commonly expressed DEGs (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDifferentially expressed genes after bariatric surgery.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDEGs\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGene symbol\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDown-regulated\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSPP1,CHI3L1,SFRP4,PRG4,C3AR1,MSC,FPR3,NPR3,CD163,RANBP3L,HP,AQP9,\u003c/p\u003e\n\u003cp\u003eRGS1,HSD11B1,CCR1,TREM2,IL18,GPR183,LYZ,CD180,DHRS9,C5AR1,CXCL16,\u003c/p\u003e\n\u003cp\u003eCTSS,S100A8,CD53,EVI2A,MNDA,EVI2B\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e2 Gene Ontology and KEGG Pathway Enrichment Analyses after Bariatric Surgery\u003c/h2\u003e\n\u003cp\u003eThe Gene Ontology (GO) enrichment results for biological processes (BP) indicate that differentially expressed genes are significantly enriched in the inflammatory response and cell activation involved in immune response. Results for cellular components (CC) reveal significant enrichment in the intrinsic component of the membrane, secretory granules, and secretory vesicles. In terms of molecular function (MF), enrichment is observed in G protein-coupled receptors and molecular transduction activity. The KEGG pathway enrichment results demonstrate that differentially expressed genes are enriched in the Staphylococcus aureus infection signaling pathway, cytokine-cytokine receptor interaction signaling pathway, complement and coagulation cascade lysosomes, and neuroactive ligand-receptor interaction signaling pathway (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003e3 Gene Set Enrichment Analysis (GSEA)\u003c/h2\u003e\n\u003cp\u003eThe objective of the GSEA analysis is to elucidate potential mechanisms associated with bariatric surgery. Samples were categorised into the pre-bariatric surgery group and the post-bariatric surgery group. The analysis reveals (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) that the most significantly enriched pathways in the pre-bariatric surgery group encompass the chemokine signalling pathway, toll-like receptor signalling pathway, hematopoietic cell, lysosome, B cell receptor signalling pathway, cytokine-cytokine receptor interaction, natural killer cell-mediated cytotoxicity, NOD-like receptor signalling pathway, leukocyte transendothelial migration, T cell receptor signalling pathway, cell adhesion molecules, and Fc gamma receptor-mediated phagocytosis, among others. Additionally, pathways exhibiting a positive correlation with the post-bariatric surgery group and displaying significant differences include propionate metabolism, fatty acid metabolism, citrate cycle, glycerophospholipid metabolism, alanine, aspartate, and glutamate metabolism, as well as linoleic acid metabolism, among others.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003e4 Protein-Protein Interaction (PPI) Network Analysis and Key Gene Selection\u003c/h2\u003e\n\u003cp\u003eBased on disparities identified in STRING, the Protein-Protein Interaction (PPI) network of differentially expressed genes comprises 22 downregulated genes, forming a cluster of 22 nodes and 50 edges (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Protein interaction network graph data of differentially expressed genes, acquired from the STRING online database, were exported and subsequently analysed in Cytoscape software, resulting in an in-depth analysis of the protein interaction network graph by Cytoscape. The gene ranking methodology utilises 12 algorithms within the CytoHubba plugin to select the top 15 genes for each algorithm (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Gene selection utilises the UpsetR package in R software, as depicted in the gene set visualisation graph. Ultimately, MNDA, TREM2, C5AR1, S100A8, and HP are identified as key genes. Expression results of key genes before and after bariatric surgery indicate a varied reduction in postoperative expression compared to pre-surgery. Pearson correlation analysis of key genes reveals a positive correlation between the five genes in pairs.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCytoHubba plugin 12 algorithms get the top 15 genes(1)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMCC\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDMNC\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStress\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eRadiality\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMNC\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEPC\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTSS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMNDA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCCR1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTSS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD163\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMNDA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC5AR1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD163\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD163\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD163\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTSS\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTREM2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCCR1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTSS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCCR1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD53\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLYZ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC3AR1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTSS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC3AR1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMNDA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCCR1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC3AR1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLYZ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC3AR1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLYZ\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCCR1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eS100A8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLYZ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLYZ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC3AR1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMNDA\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD163\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEVI2B\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMNDA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMNDA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLYZ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC3AR1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC5AR1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIL18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTREM2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC5AR1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTREM2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTREM2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAQP9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTREM2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIL18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTREM2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC5AR1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eS100A8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGPR183\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eS100A8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC5AR1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIL18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eS100A8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEVI2B\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC5AR1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eS100A8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eS100A8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEVI2B\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIL18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCXCL16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCHI3L1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEVI2B\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEVI2B\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIL18\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAQP9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEVI2B\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAQP9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAQP9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEVI2A\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAQP9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGPR183\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHP\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCHI3L1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCHI3L1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSPP1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCXCL16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCHI3L1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCytoHubba plugin 12 algorithms get the top 15 genes(2)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDegree\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEcCentricity\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eClusteringCoefficient\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCloseness\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eBottleNeck\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eBetweenness\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD163\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD163\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGPR183\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD163\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTSS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD53\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTSS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIL18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCXCL16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTSS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCCR1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD163\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTSS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD163\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCCR1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCCR1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eS100A8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEVI2A\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCCR1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTSS\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMNDA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC5AR1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRGS1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMNDA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIL18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIL18\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC3AR1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMNDA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEVI2B\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC3AR1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMNDA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC3AR1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLYZ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC3AR1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAQP9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLYZ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMNDA\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIL18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC5AR1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTREM2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eS100A8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLYZ\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eS100A8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAQP9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTREM2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIL18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEVI2B\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTREM2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC5AR1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCCR1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC3AR1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC5AR1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCHI3L1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eS100A8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTREM2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTREM2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLYZ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eS100A8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTREM2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC5AR1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEVI2B\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLYZ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMNDA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEVI2B\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGPR183\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCHI3L1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGPR183\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eS100A8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFPR3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHP\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAQP9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFPR3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAQP9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCXCL16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEVI2B\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCHI3L1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTSS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCHI3L1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC5AR1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSPP1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003e5 Cell Type Enrichment Analysis\u003c/h2\u003e\n\u003cp\u003eTo comprehensively investigate alterations in immune cell infiltration levels post-bariatric surgery, this study employed xCell analysis to examine the relative abundance of immune cells, as well as changes in adipocytes and pre-adipocytes before and after surgery (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). The findings unveiled a noteworthy decrease in adipocytes, pre-adipocytes, B cells, dendritic cells, macrophages, M1 macrophages, M2 macrophages, monocytes, and neutrophils post-surgery. The expressions of TREM2, HP, MNDA, and C5AR1 demonstrated positive correlations with macrophages, M1 macrophages, M2 macrophages, dendritic cells, and monocytes. Furthermore, the expression of S100A8 exhibited positive correlations with neutrophils and monocytes. Additionally, all five key genes demonstrated positive correlations with adipocytes or pre-adipocytes (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003e6 Preliminary Validation of Key Genes\u003c/h2\u003e\n\u003cp\u003eThe mRNA levels of hub genes in obesity underwent validation using the Attie Lab Diabetes Database. The findings revealed that, in comparison to the lean group, the expression of TREM2, HP, C5AR1, and S100A8 was up-regulated in the 10-week obese mice (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). Subsequently, the GSE72158 dataset from the GEO database was employed to confirm the expression of MNDA, TREM2, HP, C5AR1, and S100A8 before and after surgery. The results (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e) indicated that following surgery, the expression of TREM2, MNDA, HP, C5AR1, and S100A8 exhibited varying degrees of reduction compared to pre-surgery levels.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003e7 Diagnostic Efficacy of Key Genes for Obesity\u003c/h2\u003e\n\u003cp\u003eThrough a collective analysis of the obese and healthy populations in the GSE59034 dataset, ROC curves for hub genes were drawn using the pROC package. Subsequently, the area under the curve (AUC) was calculated to assess the diagnostic effectiveness of the hub genes (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e). The Area Under the Curve (AUC) for TREM2 stands at an impressive 0.918, indicating superior diagnostic performance for obesity. HP follows with a respectable AUC of 0.848, while MNDA exhibits a value of 0.828. C5AR1 shows a slightly lower but still commendable AUC of 0.879. S100A8, with an AUC of 0.762, indicates a moderate level of diagnostic utility. In light of these AUC values, it is evident that TREM2 is highly efficacious in diagnosing obesity, with HP, MNDA, C5AR1, and S100A8 also contributing to the diagnostic process, albeit to varying degrees.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e8 Clinical Data Validate Changes in Patient Inflammatory Status Before and After Bariatric Surgery\u003c/h2\u003e\n\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n\u003ch2\u003e8.1 Basic Information\u003c/h2\u003e\n\u003cp\u003eClinical data from 200 obese patients, selected based on inclusion and exclusion criteria from 2020 to 2023, were analysed. The demographic details were as follows: a total of 200 cases, comprising 168 female patients (84%) and 32 male cases (16%). The mean body weight was (101.20\u0026thinsp;\u0026plusmn;\u0026thinsp;16.59) kg, and the mean BMI was (36.81\u0026thinsp;\u0026plusmn;\u0026thinsp;4.69) kg/m2. One hundred and forty patients completed a one-year follow-up.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003e8.2 Inclusion and Exclusion Criteria\u003c/h2\u003e\n\u003cp\u003e(1) Inclusion criteria: According to the \"2019 Chinese Guidelines for the Surgical Treatment of Obesity and Type 2 Diabetes,\" patients with simple obesity with BMI\u0026thinsp;\u0026ge;\u0026thinsp;32.5 kg/m\u003csup\u003e2\u003c/sup\u003e or BMI\u0026thinsp;\u0026ge;\u0026thinsp;27.5 kg/m\u003csup\u003e2\u003c/sup\u003e and two metabolic syndromes or type 2 diabetes were included. Patients with no apparent surgical contraindications who agreed to participate in the clinical study were also considered.\u003c/p\u003e\n\u003cp\u003e(2) Exclusion criteria: Non-compliance with medical advice and poor compliance, BMI below 25kg/m2, type 1 diabetes patients, and individuals unable to tolerate laparoscopic surgery.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003e8.3 Data Collection\u003c/h2\u003e\n\u003cp\u003eClinical data, including BMI, neutrophil count (NEU), lymphocyte count (LYM), neutrophil-to-lymphocyte ratio (NLR), triglycerides (TG), high-density lipoprotein (HDL), and TG/HDL, were collected both before and one year after bariatric surgery(Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Changes in BMI and markers of low-grade inflammation (NLR, TG/HDL) were observed before and after surgery.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eComparison of the observation indicators before and 1 year after surgery\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eIndex\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePre-surgery\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePost-surgery\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExample(n)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e200\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e140\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eheight(m)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eweight(kg)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e101.20\u0026thinsp;\u0026plusmn;\u0026thinsp;16.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e73.11\u0026thinsp;\u0026plusmn;\u0026thinsp;9.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBMI(Kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36.81\u0026thinsp;\u0026plusmn;\u0026thinsp;4.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.62\u0026thinsp;\u0026plusmn;\u0026thinsp;2.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTG(mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.98(1.64)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.50(0.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHDL(mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.26(0.04)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.40(0.10)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.766\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTG/HDL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.50(1.31)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.04(0.28)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWBC(10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.95\u0026thinsp;\u0026plusmn;\u0026thinsp;2.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.25\u0026thinsp;\u0026plusmn;\u0026thinsp;1.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNEU(10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.38\u0026thinsp;\u0026plusmn;\u0026thinsp;1.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLYM(10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.70(1.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.70(0.35)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNLR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.86(0.95)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.50(0.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCRP(mg/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.64(7.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.95(1.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.138\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the contemporary context of the global obesity pandemic, accumulating evidence indicates that obesity contributes to various complications, including cardiovascular disease, insulin resistance, type 2 diabetes (T2DM), hypertension, and hyperlipidemia [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]. Bariatric surgery, known for its effectiveness in weight loss, mitigating T2DM, improving cardiovascular risk factors, alleviating other obesity-related comorbidities, and enhancing long-term survival, stands as a potent therapeutic intervention. The primary mechanisms through which weight-loss surgery impacts obesity and its complications encompass dietary behavior modification, alterations in intestinal hormone release, and changes in the microbiota [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]. The molecular intricacies underlying the effects of bariatric surgery remain an area of clinical exploration. This study delves into the gene expression data of subcutaneous adipose tissue in obese patients, employing bioinformatics analyses to gain insights into the potential mechanisms of weight loss and metabolic improvement, with the aim of contributing findings for clinical treatment.\u003c/p\u003e\n\u003cp\u003eDrawing from the GSE53376 and GSE59034 datasets, this study uncovers potential adipose tissue-related pathways both before and after bariatric surgery. Employing bioinformatics methodologies, we identify 29 downregulated differentially expressed genes. Gene Ontology (GO) enrichment analysis highlights that genes with reduced expression post-surgery are enriched in inflammatory response and cellular activation involved in the immune response, aligning with existing literature [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]. G protein-coupled receptors play a pivotal role in regulating various adipocyte functions, influencing processes such as lipolysis, heat production, glucose processing, and adipokine factor secretion. Their signalling significantly contributes to the regulation of adipose tissue metabolism, particularly in obesity, type 2 diabetes, and related comoridities [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. G protein-coupled receptors are integral in maintaining energy homeostasis, influencing food intake, energy expenditure, glycemic control, and participating in cell division, growth, repair, protein synthesis, and immune responses [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eGene Set Enrichment Analysis (GSEA) outcomes support the notion that surgery-induced weight loss triggers a shift from a state of pro-inflammation to anti-inflammation, ultimately enhancing metabolic functions. GSEA reveals that, before bariatric surgery, most genes are predominantly enriched in chemokines, TOLL-like receptor, and B cell receptor signaling pathways\u0026mdash;mechanisms integral to adipose tissue inflammation. Adipocytes release chemokines that intensify tissue inflammation, with specific chemokines like CCL2 and CCL5 exacerbating systemic inflammation by recruiting proinflammatory M1 macrophages, thereby contributing to obesity and its chronic complications [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]. Toll-like receptor signaling induces fatty acid synthesis, fostering chronic inflammation in adipose tissue and contributing to the metabolic syndrome associated with obesity [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. B cells, through regulating T cell function and inflammatory factor secretion, can exacerbate the inflammatory response in obesity and T2DM [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eGSEA provides additional support, revealing that the enrichment outcomes post-bariatric surgery are intricately linked with fatty acid and amino acid metabolism. Numerous obese individuals grapple with lipid metabolism disorders, notably abdominal obesity, elevating cardiometabolic risk due to atherogenic dyslipidemia [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. Nutrient composition, particularly fatty acids, can significantly impact lipid levels. Adiponectin, exclusively expressed by adipocytes, exerts anti-inflammatory effects and enhances insulin sensitivity by promoting fatty acid oxidation. Most studies consistently report a substantial increase in adiponectin levels following laparoscopic sleeve gastrectomy [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. A recent extensive meta-analysis demonstrated significant adiponectin increases at 6, 12, and 18 months post-weight loss [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]. Mounting evidence suggests that bariatric surgery contributes to enhanced production of anti-inflammatory adipokines and a concurrent reduction in proinflammatory adipokine production [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]. Branched-chain amino acid levels, including leucine, isoleucine, and valine, exhibit significant elevation in obese individuals with type 2 diabetes or insulin resistance [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]. Numerous studies affirm a decrease in the levels of most amino acids, including branched-chain amino acids, post-bariatric surgery. However, the levels of specific amino acids, such as glycine and serine, increase uniformly, irrespective of the surgical type [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]. Research underscores that metabolites like glucose, fatty acids, glutamine, and succinate, functioning as energy sources, can influence the activity of macrophages, neutrophils, and T cells. Obesity is characterized by a continuous supply of these metabolites. The observed metabolic shifts in nutrient supplies after bariatric surgery have the potential to modulate the immune system, playing a pivotal role in immune differentiation. Bariatric surgery, by curbing pathological immune activation and thereby diminishing the supply of nutrient metabolites, directly regulates inflammation. Consequently, it establishes an anti-inflammatory state, thereby enhancing metabolic function [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]. These mechanisms contribute to the comprehension of how bariatric surgery orchestrates a significant transition from inflammation to metabolism. TREM2, a membrane protein expressed on immune cells, is implicated in neurodegenerative diseases and cancer [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. Metabolic symptoms, including adipocyte hypertrophy, hypercholesterolaemia, fat accumulation, and glucose intolerance, are observed in TREM2 knockout mice [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]. Exploring TREM2's role in metabolic diseases and obesity may unveil additional biological functions. MNDA governs bone marrow cell differentiation and myelodysplastic syndrome development [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]. It has been demonstrated that MNDA participates in inflammatory signaling pathways among proteins [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]. Subsequent studies indicate MNDA as an inflammatory gene with elevated expression in T1DM and hyperglycemia [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e]. C5AR1 is closely linked to inflammation [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]. Adipose tissue reduction is noted in C5AR knockout mice irrespective of dietary changes [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]. Administering C5AR selective antagonists to diet-induced obese rats not only reduces body weight but also ameliorates insulin resistance and adipose tissue inflammation [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e]. HP, a recognized inflammation marker [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e], exhibits a positive correlation with body mass index in certain studies [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e]. During early obesity phases, HP interacts with CCR2, facilitating monocyte entry into white adipose tissue. White adipose tissue macrophages enhance IL-6 and TNF \u0026alpha; production, further inducing HP expression. Absence of HP diminishes adipose histiocytic inflammation, reducing obesity-related comorbidities. HP emerges as a potential therapeutic target [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e]. S100A8 expedites macrophage migration, induces proinflammatory factor production, and triggers inflammatory responses in adipocytes. It is a pivotal component regulating the initial steps of the inflammatory cascade in obese adipose tissue. S100A8 antibodies effectively inhibit diet-induced inflammatory changes and play a crucial role in improving insulin resistance [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eCollectively, these five key genes significantly contribute to obesity development. This study introduces potential therapeutic targets and biomarkers for obesity, offering a theoretical foundation for subsequent investigations.\u003c/p\u003e\n\u003cp\u003eKey gene expression data were extracted from the Attie Lab database to confirm the association of these genes with obesity. Results revealed higher expression of most key genes in the obese group.\u003c/p\u003e\n\u003cp\u003eThe study presented in this paper possesses certain limitations. Firstly, the dissimilar follow-up durations of the two datasets (12 months for GSE53376 and 2 years for GSE59034) may have overlooked some biological information due to inadequate consideration of this temporal variation. Secondly, while the method employed by the Attie Lab diabetes database to confirm the elevated expression levels of key genes in obesity may not be optimal, it does provide partial confirmation of the correlation between key genes and obesity. However, the results lack more rigorous validation due to the absence of experimental verification. Thirdly, the data utilised in this study were sourced from public databases, and the data quality assessment was unfeasible. Additionally, the methods involving Affymetrix gene expression arrays, used in the data analysis of this study, have not been extensively employed. Lastly, the relatively small sample size of the dataset may impact the analysis of gene expression before and after bariatric surgery in obese patients.\u003c/p\u003e\n\u003cp\u003eIn summary, this study concludes that bariatric surgery induces a substantial transformation in obesity, shifting it from a pro-inflammatory state to an anti-inflammatory state, thereby enhancing adipocyte metabolic function. Five key genes, identified as highly expressed in obesity, shed light on potential mechanisms underlying adipose tissue function changes post weight-reducing surgery. These findings offer novel therapeutic targets for ameliorating adipocyte inflammation, suggesting alternative approaches for the non-surgical treatment of obesity and associated comorbidities. The study provides a theoretical foundation for future investigations. The modified gastrointestinal anatomy following surgery serves as an ideal model for observing changes in the signal transmission chain from the gut environment to the brain, aiding in elucidating the brain's activity relationship with weight loss. Real-time studies are essential to further unravel the physiological mechanisms of bariatric surgery, with the overarching aim of expanding obesity prevention and treatment strategies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n\u003ch2\u003e1 Microarray Dataset\u003c/h2\u003e\n\u003cp\u003eThree datasets pertaining to alterations in the subcutaneous adipose tissue (AT) transcriptome following ba retrieved from the Gene Expression Omnibus (GEO) website (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). GSE53376 and GSE59034 were selected for primary analyses, while the GSE72158 dataset served for subsequent validation. The platform for GSE53376 utilised GPL16686[HuGene-2_0-st] Affymetrix Human Gene 2.0 ST Array [transcript (gene) version]. This dataset comprised 16 samples of subcutaneous AT obtained from obese subjects, alongside an additional 16 subcutaneous AT samples collected shortly after bariatric surgery. On the other hand, the platform for GSE59034 employed GPL11532 [HuGene-1_1-st] Affymetrix Human Gene 1.1 ST Array [transcript (gene) version]. This dataset included subcutaneous AT samples from obese subjects before (n\u0026thinsp;=\u0026thinsp;16) and after short-term bariatric surgery (n\u0026thinsp;=\u0026thinsp;16), as well as samples from individuals maintaining a never-obese status (n\u0026thinsp;=\u0026thinsp;16). Lastly, the platform for GSE72158 was GPL10558[Illumina HumanHT-12 V4.0], encompassing subcutaneous AT samples from obese subjects before (n\u0026thinsp;=\u0026thinsp;40) and after short-term bariatric surgery (n\u0026thinsp;=\u0026thinsp;40).riatric surgery were\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eBasic information of the dataset\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003edataset\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eyear\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003etissue\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003esample\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eplatform\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGSE53376\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSubcutaneous adipose tissue\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebefore surgery (n\u0026thinsp;=\u0026thinsp;16) and after surgery (n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGPL16686[HuGene-2_0-st] Affymetrix Human Gene 2.0 ST Array [transcript (gene) version]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGSE59034\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSubcutaneous adipose tissue\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebefore surgery (n\u0026thinsp;=\u0026thinsp;16), after surgery (n\u0026thinsp;=\u0026thinsp;16) and never obese people(n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGPL11532[hugene-11.1-st] Affymetrix Human Gene 1.1 ST Array [transcript (gene) version]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGSE72158\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSubcutaneous adipose tissue\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebefore surgery (n\u0026thinsp;=\u0026thinsp;40) and after surgery (n\u0026thinsp;=\u0026thinsp;40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGPL10558[Illumina HumanHT-12 V4.0]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n\u003ch2\u003e2 Identification of Differentially Expressed Genes\u003c/h2\u003e\n\u003cp\u003eThe R packages \"GEOquery\" (V2.66.0) and \"limma\" (V3.54.0) within the interactive online tool GEO2R (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/geo2r\u003c/span\u003e\u003c/span\u003e) were utilised for data normalisation and screening of Differentially Expressed Genes (DEGs) (|fold change| \u0026ge; 1 and adj. p\u0026thinsp;\u0026lt;\u0026thinsp;.05). The resultant DEGs were presented visually through Heatmaps. The Draw Venn Diagram tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinformatics.psb.ugent.be/webtools/Venn/\u003c/span\u003e\u003c/span\u003e) was employed to identify common DEGs across the two datasets.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n\u003ch2\u003e3 Functional and Pathway Enrichment Analysis\u003c/h2\u003e\n\u003cp\u003eGene Ontology (GO) analysis was applied to discern biological attributes [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e], encompassing biological processes (BP), cellular components (CC), and molecular functions (MF). KEGG analysis provided more comprehensive information on genome sequences and protein interactive networks [\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e]. Enrichment analysis was conducted using the SangerBox platform[\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e], with gene set sizes ranging from 5 to 5,000, and a threshold of statistical significance set at p-Value\u0026thinsp;\u0026lt;\u0026thinsp;.05 and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.25.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n\u003ch2\u003e4 Gene Set Enrichment Analysis\u003c/h2\u003e\n\u003cp\u003eThe GSEA [\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e] software (4.3.2) was employed for further analysis of the genes identified in the GSE59034 dataset, offering a comprehensive screening of biological functions post-surgery. Each analysis included 1000 gene set permutations, and significance was established at a p-value\u0026thinsp;\u0026lt;\u0026thinsp;5% and a false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;25%.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n\u003ch2\u003e5 Construction of PPI Network and Identification of Key Genes\u003c/h2\u003e\n\u003cp\u003eThe STRING [\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e] database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.string-db.org/\u003c/span\u003e\u003c/span\u003e) was utilised to construct a Protein-Protein Interaction (PPI) network of differentially expressed genes, identifying and predicting interactions between genes or proteins. The PPI network of differentially expressed genes was visualised using Cytoscape V3.9.1. Core nodes, indicative of greater impact in protein interaction networks, were determined using Cytohubba [\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e], employing 12 statistical analysis methods. The top 15 genes ranked by each algorithm were selected, filtered using the Upset package to identify key genes, and subjected to Pearson correlation analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n\u003ch2\u003e6 Immune Infiltration Analysis\u003c/h2\u003e\n\u003cp\u003eThis study employed xCell[\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e] analysis to detect the distribution of immune cells in the tissue based on gene expression profiles, predicting the relative abundance of cell types.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n\u003ch2\u003e7 Validation of Key Genes\u003c/h2\u003e\n\u003cp\u003eThe Attie Lab Diabetes Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://diabetes.wisc.edu\u003c/span\u003e\u003c/span\u003e) facilitated a search for gene expression in six key tissues (islet, liver, adipose, hypothalamus, gastrocnemius, and soleus) in the mouse model of Type 2 diabetes, considering genetic obesity (lean vs. OB/OB), strain (B6 vs. BTBR), and age (4 vs. 10 weeks) (significance set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This enabled verification of key gene expression in adipose tissue of both lean and obese mice at 4 and 10 weeks. Additionally, the dataset GSE72158 was utilised to validate the expression of key genes selected before and after surgery.\u003c/p\u003e\n\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\n\u003ch2\u003e8 Assessment of the Diagnostic Effectiveness of Key Genes\u003c/h2\u003e\n\u003cp\u003eTo validate the diagnostic efficacy of the key genes in identifying obesity within the GSE59034 gene sets, ROC logistic regression analysis was conducted. The Area Under the Receiver Operating Characteristic Curve (ROC-AUC) served as the criterion for assessment.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\n\u003ch2\u003e9 Clinical Data to Verify Changes in Inflammatory Status Before and After Bariatric Surgery\u003c/h2\u003e\n\u003cp\u003eClinical data were collected both before and one year after bariatric surgery, encompassing Body Mass Index (BMI), neutrophil count (NEU), lymphocyte count (LYM), Neutrophil-to-Lymphocyte Ratio (NLR), triglycerides (TG), high-density lipoprotein (HDL), and the TG/HDL ratio. Observations focused on alterations in BMI and markers of inflammation (NLR, TG/HDL). SPSS 27.0 software (IBM SPSS Statistics 27 V27.0.1) facilitated data processing. A paired sample t-test was applied for normally distributed variables in the comparison of the two groups before and after surgery, while a paired sample Wilcoxon test was employed for non-normally distributed variables. A significance level of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 denoted a statistically significant difference.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe are grateful to our team members for valuable discussion\u003c/p\u003e\n\u003ch2\u003eAuthor contributions statement\u003c/h2\u003e\n\u003cp\u003eH.H. and C.H. designed the research. C.H. conducted the search. Y.W. , Z.Z. and H.W. prepared figures. J.W. and Y.X. revised the article critically. W.W., Y.X. and H.Z. collected and analysed the clinical data. All the authors contributed to the manuscript writing, read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eEach author declares that he or she has no commercial associations (e.g. consultancies, stock ownership, equity interest, patent/licensing arrangement etc.) that might pose a conflict of interest in connection with the submitted article.\u003c/p\u003e\n\u003ch2\u003eData availability\u003c/h2\u003e\n\u003cp\u003eAny data and R script in this study can be obtained from the corresponding author upon reasonable request. The final manuscript was read and approved by all authors. In this study, publicly available datasets were analyzed. These are available on GEO (https://www.ncbi.nlm.nih.gov/). Attie Lab Database (http://diabetes.wisc.edu/) , STRING database (http://www.string-db.org/) and Sangerbox (http://sangerbox.com/home.html) are also freely available.\u003c/p\u003e\n\u003ch2\u003eEthical Statement\u003c/h2\u003e\n\u003cp\u003eThe study was approved by the Scientific research IRB of Wannan Medical College Yijishan Hospital. Written informed consent was obtained from all participants prior to their inclusion in the study. The authors affirm that participants\u0026apos; identities have been anonymized and that any potentially identifying information has been removed. The study adhered to the principles of ethical research, including the protection of participants\u0026apos; rights and welfare. The Ethics Approval Number is (2023)伦审研第(148).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRoberto, C. A. et al. 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H. et al. cytoHubba: identifying hub objects and sub-networks from complex interactome. \u003cem\u003eBMC systems biology\u003c/em\u003e, \u003cem\u003e8 Suppl 4\u003c/em\u003e(Suppl 4), S11. https://doi.org/10.1186/1752-0509-8-S4-S11 (2014).\u003c/li\u003e\n\u003cli\u003eAran, D. et al. xCell: digitally portraying the tissue cellular heterogeneity landscape. \u003cem\u003eGenome biology\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(1), 220. https://doi.org/10.1186/s13059-017-1349-1 (2017).\u003c/li\u003e\n\u003c/ol\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":"Bariatric surgery, obesity, adipose tissue, differentially expressed genes, PPI network","lastPublishedDoi":"10.21203/rs.3.rs-4127363/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4127363/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aimed to explore potential biomarkers and mechanisms following bariatric surgery. Two gene expression profiles from the Gene Expression Omnibus (GEO) were analysed to identify differentially expressed genes (DEGs) in subcutaneous adipose tissue (AT) post-bariatric surgery. Subsequently, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene-Set Enrichment Analysis (GSEA), and Protein-Protein Interaction (PPI) network analyses were employed to identify hub genes and associated pathways. Among the DEGs, 29 genes were downregulated. Enrichment analysis revealed that the downregulated DEGs significantly participated in inflammatory responses. GSEA provided comprehensive evidence that most genes were enriched in pro-inflammatory pathways before surgery, while after surgery, most genes were enriched in metabolism. In the PPI network, five key genes, including TREM2, MNDA, HP, C5AR1, and S100A8, were identified, with most validated as highly expressed in obesity by the Attie Lab Diabetes and another dataset, GSE72158. Bariatric surgery induces a significant shift from an obesity-promoting inflammatory state to an anti-inflammatory state, accompanied by improvements in adipocyte metabolic function. This represents a key mechanism for the enhancement of adipose tissue function following bariatric surgery. This study deepens the understanding of the benefits of bariatric surgery and provides potential biomarkers or therapeutic targets.\u003c/p\u003e","manuscriptTitle":"Identification of the critical genes and signaling pathways in subcutaneous adipose tissue after bariatric surgery based on the GEO database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-08 14:27:06","doi":"10.21203/rs.3.rs-4127363/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":"b772aa10-90d1-4b0d-8f6f-0b37bd11ae85","owner":[],"postedDate":"May 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":31493123,"name":"Health sciences/Biomarkers"},{"id":31493125,"name":"Health sciences/Diseases"},{"id":31493126,"name":"Health sciences/Gastroenterology"}],"tags":[],"updatedAt":"2024-06-17T14:51:24+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-08 14:27:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4127363","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4127363","identity":"rs-4127363","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}