Exploration and validation of biomarkers related to bile acid metabolism in obese patients

doi:10.21203/rs.3.rs-4975305/v1

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Exploration and validation of biomarkers related to bile acid metabolism in obese patients

https://doi.org/10.21203/rs.3.rs-4975305/v1

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Background

Studies have shown that bile acids can effectively improve metabolism and play an anti-obesity role. However, the mechanism of bile acid-related genes in obesity has not been fully elucidated.

Methods

Differential analysis was implemented to acquire differentially expressed genes (DEGs) between obesity (Obese) and Nonobese samples. The critical module genes were identified by the weighted gene co-expression network analysis (WGCNA). Overlapping genes derived from intersecting DEGs, bile acid metabolism genes, and critical module genes. Biomarkers identified using three ML algorithms and intersection process. Nomogram constructed for predicting disease probabilities. Biomarker functions and pathways determined by enrichment analysis. miRNA-mRNA and mRNA-TF networks created.

Results

59 DEGs identified between Obese and Nonobese samples; yellow module deemed critical. 13 overlapping genes found via intersection analysis. PEMT, CP, and SLC27A2 identified as biomarkers via three three machine learning algorithms, used to construct a nomogram for predicting obesity disease probabilities. These biomarkers primarily involved in ER lumen, protein-lipid complex, and FA transmembrane transport activities. mRNA-miRNA network showed CP regulated by hsa-miR-592; TF-mRNA network indicated CP, PEMT, and SLC27A2 regulated by HNF4A, MLXIPL, and TCF2. RT-qPCR results showed PEMT and CP up-regulated in obese mouse tissues, while SLC27A2 expression was lower than in non-obese samples.

Conclusion

Three biomarkers (CP, PEMT, SLC27A2) linked to obesity, involved in bile acid synthesis/accumulation, impacting energy metabolism, glucose/lipid metabolism, etc. Study offers clinical significance for obesity diagnosis.

Bile acid metabolism

Obesity

biomarkers

Energy metabolism

Glucose and lipid metabolism

Insulin resistance

Obesity refers to a certain degree of obvious overweight and fat layer is too thick, especially the accumulation of triglycerides (Engin 2017, Mayoral 2020). Obesity increases the risk of many diseases, such as cardiovascular disease, diabetes, depression, etc., as well as cancer, and increases the symptoms of the disease(Piche 2020, Purdy 2021). There has been a sharp increase in the prevalence of obesity in the past decades (Cakir 2022, Finucane 2011). The number of obese or overweight people worldwide is about two billion people(Bertazzi 1989, Finucane 2011). Therefore, how to curb obesity has become a public health problem to be solved urgently.

There are many causes of obesity, and many metabolic pathways are involved in the regulation of obesity, and at present, it is agreed that the occurrence of human obesity is caused by the imbalance of energy metabolism in the body(Bluher 2019, Goran 2000). In addition, glucose, lipid metabolism, and gut microflora also play an important role(Gomes 2018, Hilton 2023, Liu 2021, Nagarajan 2022). Bile acid is a cholesterol derivative synthesized in the liver, which is involved in many important physiological and metabolic reactions in the body(Jiao 2022, Li 2014). In recent years, bile acid has been found as an important signal regulatory molecule, playing an important role in glucose and lipid metabolism, energy consumption and regulating the immune function of the body (Jia 2021, Lefebvre 2009, McGlone 2019). Specifically, bile acid can enhance the activity of intracellular type 2 deiodinase by specifically binding to TGR5 on the cell surface, prompting thyroid hormone T4 into T3 (the main endogenous active substance in body energy regulation), further accelerate the body energy metabolism and fat consumption, to inhibit the increase of weight(Chavez-Talavera 2017, Donkers 2020, Mullur 2014). In addition, bile acid can able to interact with the gut microflora of the host and to effect development of obesity (Ramirez-Perez 2017, Wei 2020). Therefore, bile acids are likely to provide for the prevention and treatment of obesity and its related diseases. However, the role of bile acid metabolism genes (the gene level) in obesity is still unclear.

In this study, we used obesity-related public database data and biologic informatics analysis to excavate the bile acid metabolism genes with diagnostic value for obesity, explore the function of biomarkers and regulatory mechanisms, and further deepened the understanding of the role of bile acid metabolism in obesity.

2.1 Data sources

The datasets of Obesity were achieved from GEO online database. The GSE25401 dataset contained 30 Obesity (Obese) samples and 26 Nonobese samples, and these samples were utilized as the training set. Moreover, the external validation set GSE151839 had 10 Obese samples and 10 Nonobese samples. All the samples were abdominal subcutaneous adipose tissue. A total of 1746 bile acid metabolism genes with relevance score > 10 were achieved using Genecards online database (https://pathcards.genecards.org/).

2.2 Differential expression analysis and the WGCNA

In our study, DEGs between Obese and Nonobese samples were acquired by the limma (v 3.52.4) (Ritchie 2015) package (|Log2FC| > 1 and p.adj < 0.05). Moreover, A heat map and a volcano map of DEGs were plotted by pheatmap (v 1.0.12) and ggplot2 (v 3.3.6) (Ito 2013) packages, respectively. Furthermore, the WGCNA was performed on the samples in the training set to screen out the critical module. Firstly, outlier samples were eliminated to secure the precision of the analysis by sample clustering. An appropriate soft threshold (β) was selected to make the engagement among genes conforms to the scale-free distribution to the maximum extent. Then, these genes were classified into several modules. The Obese was used as trait, and the relationships between the modules and traits were computed. Next, the module that most relevant to the trait was defined as the critical module.

2.3 Screening of overlapping genes

The bile acid metabolism genes, DEGs, and the critical module genes were crossed to achieve the overlapping genes. In addition, for further studying the related biological functions and signaling pathways of the overlapping genes, the Gene ontology (GO) (p.adjust < 0.05) and Kyoto Encyclopedia of Genes and Genomes (KEGG) (p.value < 0.05) enrichment analyses were conducted by the clusterProfiler (v 4.4.4) package (Wu 2021). Furthermore, a protein-protein interactions (PPI) network of those overlapping genes was created using the GeneMINIA database (http://genemania.org/) to forecast the interactions of the overlapping genes.

2.4 Identification and verification of biomark

Based on the crossover genes, the Least absolute shrinkage and selection operator (LASSO), Random Forest (RF) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) algorithms were implemented on the overlapping genes to acquire the feature genes, respectively. Furthermore, the feature genes identified in the LASSO, RF and SVM-RFE algorithms were crossed to acquire the biomarkers. Furthermore, according to the above biomarkers, a nomogram for forecasting disease probability rates of Obesity patients were created. Moreover, calibration curve was drawn to evaluate the precision of the prediction model. Besides, the receiver operating characteristic (ROC) curves for biomarkers were drawn, and the biomarkers with the area under the curve (AUC) value greater than 0.7 were considered that have diagnostic value. Meanwhile, the expression of the biomarkers was verified in the validation set and training set.

2.5 Analysis of relationship and functional similarity

In order to further explore the interaction relationship of the biomarkers, the association among the biomarkers was analyzed based on the expression of them in the training set. In addition, the functional similarity of biomarkers was analyzed using the GOSemSim (v 2.22.0) package (Yu 2010).

2.6 Enrichment analysis of biomarkers

To investigate the related biological functions and pathways of the biomarkers, moreover, the 'c2.cp.kegg.v7.5.1.symbols.gmt' and 'c5.go.v2022.1.Hs.entrez.gmt' were utilized as the background gene set, and then the GSEA was conducted using the clusterProfiler (v 4.4.4) package (Wu 2021) (|NES| > 1, NOM P < 0.05, and q < 0.25).

2.7 The regulation network and the drug prediction

The miRNAs corresponding to the above biomarkers were forecasted using the miRDB (http://mirdb.org), miRmap (https://mirmap.ezlab.org/), and miRWalk (http://mirwalk.uni-hd.de/) online databases. The miRNAs in those three databases were crossed to acquire the co-miRNAs of each biomarker, and the mRNA-miRNA network was created. Moreover, the TFs of the biomarkers were forecasted by the ChEA3 (https://amp.pharm.mssm.edu/ChEA3) online database. The TF-mRNA regulatory network was created. Besides, the potential drugs of those above biomarkers were acquired through the DGIdb (www.dgidb.org) database. Moreover, the mRNA-drugs network was created.

2.8 Animal model producing and real-time quantitative polymerase chain reaction (RT-qPCR)

The experimental animals were 16 clean male C57BL/6J mice, 8 weeks old, body weight (20 ± 3.5)g, to be purchased from Sichuan University Laboratory Animal Center, which have been clean grade standard feeding. After the purchase, all mice were fed normal diet (SFP rat diet) for 1 week, and were randomly divided into 2 groups for feeding: Normal group of 6, continued to be fed the normal diet; model group of 10 mice which were planned to be modeled and fed high-fat diet to produce diet-induced obese (DIO) model. The feed were improved to: common feed 60%, lard 12%, sucrose 5%, milk powder 5%, peanut 5%, egg 10%, sesame oil 2% and 1% salt, commissioned by Beijing Huafukang Biotechnology Co., LTD. During the modeling period, all mice were free to eat and drink water, the feed and water were changed once a day, and the weight of mice was weighed once a week. After 8 weeks of high-fat feeding, 5 DIO mice were screened to final models from the model group according to the criteria of obese mice whose body weight exceeded 20% of the average body weight of the normal diet feeding mice of the normal group. 5 normal mice were screened to normal group with one died.

To confirm the regulation of important genes in tissues from obese samples, we performed RT-qPCR with Obese samples and 5 Nonobese samples. Total RNA has been purified with TRIZol (Thermo Fisher, Shanghai, CN), which was then used to inverse-transcribe mRNA into cDNA. Thereafter, qPCR analysis was carried out using 2xUniversal Blue SYBR Green qPCR Master Mix, and the PCR primer design is available in Supplementary Online resource 1. Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) was utilised as the internal reference gene, whilst the expression levels of pivotal genes were measured using the 2^−ΔΔCt method..

2.9 Statistical Analysis

All bioinformatics analyses were carried out in R language. The data of different groups were compared by Wilcoxon test. For comparisons, a student t-test was adopted in the current study.

3.1 Acquisition of DEGs and critical module

There were 59 DEGs between Obese and Nonobese samples (Fig. 1a, Online resource 2). The expression heat map of Obese-associated DEGs was shown in Fig. 1b. In addition, the samples clustering result demonstrated that there were no outlier samples (Fig. 1c). When the soft threshold was 6, the genes conforms to a scale-free distribution to the greatest extent possible. A total of 6 modules associated with traits were identified (Fig. 1d). The yellow module had the highest and significant correlation with traits, thus we identified it as the critical module (|Cor| = 0.45 and p. value < 0.05). There were 843 genes in the module which were utilized for subsequent analysis (Fig. 1e).

3.2 A total of 13 overlapping genes were identified

According to the intersection, 13 overlapping genes were identified (Fig. 2a). The enrichment analysis demonstrate that overlapping genes mainly participated in ‘regulation of superoxide anion generation’, ‘low-density lipoprotein particle’, ‘cargo receptor activity’, ‘endoplasmic reticulum lumen’, ‘protein-lipid complex’, ‘fatty acid transmembrane transporter activity’, and etc. GO items, and ‘Leukocyte transendothelial migration’, ‘Phagosome’, and etc. KEGG pathways. (Fig. 2b-c, Online resource3-4). Besides, a PPI network was created, and we found that the ITGB2 had a higher connectivity degree in the network (Fig. 2d).

3.3 PEMT, CP and SLC27A2 were screened out as the biomarkers of obesity

There were six feature genes including PEMT, CP, MMP9, SLC27A2, DHRS9 and HP were detected by the LASSO algorithm (Fig. 3a). Six feature genes (PLA2G7, SLC27A2, MMP9, CP, PEMT and IL1RN) were acquired using the SVM-RFE algorithm (Fig. 3b). According to the RF algorithm, six feature genes such as PEMT, HP, CP, SLC27A2, ITGB2 and MSR1 were identified by the RF algorithm (Fig. 3c). Furthermore, three biomarkers including PEMT, CP and SLC27A2 were achieved by the intersection (Fig. 3d). In addition, a nomogram for disease diagnostic prediction in the Obese patients was created based on PEMT, CP and SLC27A2 (Fig. 3e). The calibration curve was plotted based on the above nomogram, indicated that the predictive ability of the model was favorable (Fig. 3f). Besides, in the training set GSE25401, we found the AUC values of PEMT, CP and SLC27A2 were all greater than 0.75. Moreover, in the validation set GSE151839, the AUC values of PEMT and SLC27A2 were all greater than 0.85, and the combined AUC value was 1. Therefore, all the three biomarkers had a favorable diagnostic value for Obese patients. (Fig. 4a-b). The expression trends of the three biomarkers were significantly different between Obese and Nonobese groups, and the trends in different datasets were consistent (Fig. 4c-d).

3.4 Significant positive correlation of PEMT with CP and significant negative correlation with SLC27A2

There was a significant positive correlation between PEMT and CP (cor = 0.69), moreover, PEMT had a significantly negatively correlated with SLC27A2 (cor = -0.48) (Fig. 4e-f). The similarity of these biomarkers were all less than 0.3, demonstrated that the similarity between these biomarkers was low (Fig. 4g).

3.5 Biomarkers were primarily enriched in glycolipid metabolism-related pathways

We performed GSEA on the above biomarkers, according to the GO enrichment analysis, we found that PEMT mainly participated in ‘glycerophospholipid metabolic process’, ‘aminoglycan metabolic process’, ‘glycoprotein metabolic processes’ and so on (Fig. 5a, Online resource 5). CP primarily participated in ‘superoxide anion generation’, ‘regulation of superoxide anion generation’, ‘organic anion transport’ and so on (Fig. 5b, Online resource 6). SLC27A2 mainly enriched in ‘antigen processing and presentation of exogenous peptide antigen’, and ‘antigen processing and presentation of exogenous peptide antigen via MHC class II’ and so on (Fig. 5c, Online resource 7). Moreover, the KEGG enrichment analysis demonstrated that PEMT was enriched in pathways involved in glycan biosynthesis and degradation, such as ‘other glycan degradation’, ‘N glycan biosynthesis’ and ‘O glycan biosynthesis’, CP was mainly associated with ‘hematopoietic cell lineage’, and ‘Lysosome’ KEGG pathways, and SLC27A2 was mainly involved in ‘glycosylphosphatidylinositol GPI anchor biosynthesis’, ‘biosynthesis of unsaturated fatty acids’, ‘amino sugar and nucleotide sugar metabolism’ KEGG pathways. (Fig. 5d-f, Online resource 8–10).

3.6 The regulation network and the drug prediction of biomarkers

According to the intersection, there were 33 miRNAs, 4 miRNAs and 11 miRNAs forecasted based on CP, PEMT, and SLC27A2, respectively (Fig. 6a-c). The mRNA-miRNA network was created, CP was regulated by hsa-miR-592 and so on, and we found that SLC27A2 was regulated by hsa-miR-429, etc. (Fig. 6d, Online resource 11) A total of 238 TFs were screened out, and the TF-mRNA network was created, it was found that CP, PEMT, and SLC27A2 were all regulated by HNF4A, MLXIPL, and TCF24 (Fig. 6e, Online resource 12). Besides, the mRNA-drug regulatory network was created including 18 nodes and 16 edges, such as Isoflavone, Sontuzumab, etc. (Fig. 6f, Online resource 13).

3.7 Verification of gene expression

RT-qPCR results have revealed significant up-regulation of PEMT and CP gene expression levels in the obese samples compared to nonobese samples, the expression level of SLC27A2 was lower in comparison to that of nonobese samples. These results were consistent with the bioinformatics analysis, confirming the reliability of RNA sequencing results (Fig. 7a-c).

Bile acid metabolism has been shown to play an important role in the progression of obesity, which is associated with the energy metabolism, glucose and lipid metabolism, gut microflora interactions, and etc. (Donkers 2020, McGlone 2019, Wei 2020) Therefore, we further explored the regulation of bile acid metabolism at the gene level. In this study, we downloaded the mRNA transcriptomic data from obese patients and normal subjects in public databases, and obtained 59 DEGs and 843 obesity related module genes by bioinformatics analyses. Then, based on 13 overlapping genes, we obtained three bile acid metabolism related biomarkers of obesity, namely PEMT, CP and SLC27A2, and further constructed a diagnostic model of obesity.

PEMT (Phosphatidylethanolamine N-Methyltransferase) is related to glycerophospholipid biosynthesis and one-carbon metabolism and related pathways (Li 2023). In mice, the reduced expression of PEMT could catalyze the production of phosphatidylcholine, causing steatosis, inflammation, and etc. In humans, common PEMT variants impair phosphatidylcholine synthesis (Piras 2022). As we all know, choline is one of the components of the bile acid, which deficiency causes hepatic fat accumulation (Zeisel 1991). Therefore, we could expect that the reduction in PEMT is able to cause fat accumulation leading to obesity by impairing the synthesis of choline and bile acids. Notably, PEMT is overexpressed in obese patients, perhaps because of the existence of feedback regulatory mechanisms in this gene, but so far, no study has demonstrated this.

CP (Ceruloplasmin) is a protein coding gene that has been implicated in the transport of inorganic cations/anions and amino acids/oligopeptides and regulation of insulin-like growth factor (IGF) transport and uptake by insulin-like growth factor binding proteins (IGFBPs) (Liu 2022, Safavi 2012), which was consistent with our GSEA results. Erik Arner et al. have pointed that CP is a novel adipokine which is overexpressed in adipose tissue of obesity subjects and in obesity-associated cancer cells (Arner 2014). Oh Yoen Kim et al. have also showed that elevated plasma CP levels were significantly associated with obesity, which could be suggested to be a marker of obesity (Kim 2011). In addition, Zhidong Liu et al. have pointed out that CP was associated with the formation of free radicals in tissues during inflammation and infection, and has the diagnostic effects on metabolic diseases (including obesity)(Liu 2022). In our results, the gene expression level of CP was significantly higher in the obesity group than that in normal group, which is consistent with previous studies. Encourspiringly, the most recent study by Quanxin Jiang et al. have showed that the deletion of CP effectively remodeled bile acid metabolism by upregulating Cyp7a1 and Cyp8b1, which subsequently leads to enhanced bile acid, further decreased lipid accumulation and curbed inflammation (Jiang 2024). In other words, perhaps we could think that the significantly increasing of CP in serum and tissues of obesity patients might further affect the accumulation of bile acids by hindering or disrupting the bile acid metabolism, and finally leading to lipid accumulation.

SLC27A2 (Solute Carrier Family 27 Member 2) was associated with fatty acid metabolism and the synthesis of bile acids and bile salts, and many studies have showed that SLC27A2 could promote fat differentiation and development (Wu 2018, Yang 2004). Undoubtedly, the expression of SLC27A2 in peripheral blood mononuclear cells also could be a molecular marker for overweight development (Caimari 2010). Interestingly, Wenjing Wu et al. have suggested that LGALS12 knockdown could inhibit adipogenesis by downregulating lipogenic genes and activating the PKA-Erk1/2 signaling pathway (Wu 2018). Romina Mancinelli et al. have suggestted that PKA-Erk1/2TGF-β1/VEGF axis played an important role in the accumulation of bile acids (Mancinelli 2022). Therefore, we speculate that SLC27A2, as well as CP, is associated with the accumulation of bile acids. Interestingly, all of these three genes were associated with insulin sensitivity (Liu 2022, Wan 2019, Yang 2011). We have mentioned earlier that bile acids could exert the regulation of glucose metabolism and the influence on insulin sensitivity through TGR5 (Chavez-Talavera 2017), which further implies the correctness of our speculation about the mechanism of biomarkers affecting obesity.

Next, we predicted that there were 33 miRNAs, 4 miRNAs and 11 miRNAs of CP, PEMT, and SLC27A2, respectively, and totals of 238 TFs were screened out, specially, HNF4A, MLXIPL, and TCF2 could regulate CP, PEMT, and SLC27A2 simultaneously. Lei Chen et al. have suggested that HNF4 could regulate SLC27A2 and impact on fatty acid oxidation (Chen 2020), which was in agreement with our findings. Besides, Qi Yin et al. have pointed that HNF4A was associated with lipid metabolic homeostasis (Yin 2022). A Louise Hunter et al. have pointed out that HNF4A could modulate glucocorticoid action and further impact the regulation of energy metabolism and inflammation (Hunter 2022). Yuhui Wang et al. have showed that MLXIPL was involved in the transcriptional regulation of liver fat, and was significantly associated with energy metabolism and insulin resistance of obesity (Wang 2015). These results all imply the possibility that these TFs regulate biomarkers to influence the progression of obesity through fatty acid metabolism. Furthermore, we also predicted 16 targeted drugs of biomarkers, among them, Isoflavone (Nakai 2020, Perez-Torres 2021), Penicillamine (Stickford 2020), Anakinra (Maculewicz 2022), Ampicillin (Awujoola 2023), Protoporphyrin (Galbraith 1990), Isoproterenol (Nureki 2022) and etc. have been reported that either for clinical remission or treatment of obesity. Our study provides a further rationale for these drugs in clinically treating obesity, namely that they act by targeting biomarkers.

In conclusion, this study obtained three biomarkers (CP, PEMT and SLC27A2) related to bile acid metabolism in obesity, which were associated with the synthesis or accumulation of bile acids, and further affected the progression of obesity by affecting energy metabolism, glucose and lipid metabolism, insulin resistance and etc. Moreover, we constructed a new diagnostic model of obesity based on these three biomarkers, and provided further theoretical support for clinical treatment of obesity with soflavone, Penicillamine, and so on. Nevertheless, our hypothesis on the regulatory mechanism of biomarkers has not been confirmed, we will further verify the regulatory role of TFs (HNF4A, MLXIPL, and TCF2) on CP, PEMT, SLC27A2 through cell experiments, and finally construct a complete regulatory axis of bile acid metabolism affecting obesity.

Abbreviations	Full name
DEGs	Differentially Expressed Genes
WGCNA	Weighted Gene Co-expression network Analysis
GO	Gene Ontology
KEGG	Kyoto Encyclopedia of Genes and Genomes
PPI	Protein-protein Interactions
LASSO	Least Absolute Shrinkage and Selection Operator
RF	Random Forest
SVM-RFE	Support Vector Machine-Recursive Feature Elimination
ROC	Receiver Operating Characteristic
AUC	Area under the Curve
DIO	Diet-induced Obese
GADPH	Glyceraldehyde 3-phosphate Dehydrogenase
PEMT	Phosphatidylethanolamine N-Methyltransferase
CP	Ceruloplasmin
IGF	Insulin-like Growth Factor
IGFBPs	Insulin-like Growth Factor Binding Proteins
SLC27A2	Solute Carrier Family 27 Member 2

Acknowledgments: We would like to express our sincere gratitude to all individuals and organizations who supported and assisted us throughout this research. This work was supported by National Natural Science Foundation of China (82374404).

7.1 Funding

This work was supported by National Natural Science Foundation of China (82374404).

7.2 Competing interests

The authors have no relevant financial or non-financial interests to disclose.

7.3 Author contributions

The authors confirm contribution to the paper as follows: study conception and design: Chenchen Wei; data collection: Chenchen Wei, Quanyu Du; analysis and interpretation of results: Chenchen Wei, Quanyu Du and Xiaoxu Fu; the animal experiments: Chenchen Wei, Hongying Fu and Yuwei Zhang; draft manuscript preparation: Chenchen Wei, Hongying Fu and Yuwei Zhang. All authors reviewed the results and approved the final version of the manuscript.

7.4 Data availability

The datasets analysed during the current study are available in the [Gene Expression Omnibus (GEO)] repository, [https://www.ncbi.nlm.nih.gov/geo/] and the [Genecards online] database repository, [https://pathcards.genecards.org/].

7.5 Ethics approval

The animal experiments were approved by the Institutional Animal Care and Use Committee of Chengdu University of Traditional Chinese Medicine (2024023).

7.6 Consent to Participate

7.7 Consent to Publish

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Exploration and validation of biomarkers related to bile acid metabolism in obese patients

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Abstract

Background

Methods

Results

Conclusion

Figures

1 Introduction

2 Material and Methods

2.1 Data sources

2.2 Differential expression analysis and the WGCNA

2.3 Screening of overlapping genes

2.4 Identification and verification of biomark

2.5 Analysis of relationship and functional similarity

2.6 Enrichment analysis of biomarkers

2.7 The regulation network and the drug prediction

2.8 Animal model producing and real-time quantitative polymerase chain reaction (RT-qPCR)

2.9 Statistical Analysis

3 Results

3.1 Acquisition of DEGs and critical module

3.2 A total of 13 overlapping genes were identified

3.3 PEMT, CP and SLC27A2 were screened out as the biomarkers of obesity

3.4 Significant positive correlation of PEMT with CP and significant negative correlation with SLC27A2

3.5 Biomarkers were primarily enriched in glycolipid metabolism-related pathways

3.6 The regulation network and the drug prediction of biomarkers

3.7 Verification of gene expression

4 Discussion

Abbreviations

Declarations

References

Online resource 5

Additional Declarations

Supplementary Files

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