Glycolysis Related Genes in Osteoporosis: Screening for Potential Prevention Targets

Glycolysis Related Genes in Osteoporosis: Screening for Potential Prevention Targets | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Glycolysis Related Genes in Osteoporosis: Screening for Potential Prevention Targets Xing-Bo Hu, Jing-Ze Yang, Jin Zhang, Jun Hu, Xiao-Feng Yuan, Juan Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3782121/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Osteoporosis is a metabolic bone disorder that globally affects more than 200 million people. Glycolysis seemingly important for bone resorption. We aimed to investigate glycolysis-related differentially expressed genes (GRDEGs) that might be potential targets for osteoporosis. Methods Differential expression analysis of GSE56815 from the Gene Expression Omnibus (GEO) database was performed. A Venn diagram was used to obtain the overlapping GRDEGs. The enrichment pathway analysis was performed and the hub genes were obtained. The abundance of immune cells was estimated utilizing the CIBERSORT algorithm. Results Utilizing the limma package and the Venn diagram, 154 GRDEGs were obtained. The GO and KEGG enrichment analysis of GRDEGs indicated several enriched terms related to regulation of JAK-STAT cascade and canonical glycolysis. As for GSEA enrichment analysis, they were significantly enriched in the NF_KB, glycolysis, Wnt and Hedgehog pathway. In the protein-protein interaction network, the hub differentially expressed genes, such as CTNNB1, HK3, MPI, HKDC1, PFKL, PTEN were obtained, which were correlated with the abundance of infiltrating T follicular helper cells. The hub genes MPI was significantly correlated with the invasion abundance of Macrophages M0 and Macrophages M2. Conclusion Our study reveals the potential role of GRDEGs in osteoporosis through bioinformatics analysis. The screened hub genes, CTNNB1, HK3, MPI, HKDC1, PFKL and PTEN might be therapeutic targets for patients with osteoporosis and novelly provide a theoretical basis for the early prevention of osteoporosis. Osteoporosis glycolysis bioinformatic analysis GEO hub genes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. INTRODUCTION Osteoporosis is a chronic metabolic disease caused by the imbalance between bone formation and resorption in the bone remodeling process in which osteoclasts resorb bone tissues while osteoblasts generate and mineralize new bone matrix, both of which are regulated by a variety of biological factors[ 1 ]. As estimated, there are more than 200 million people suffering from osteoporosis worldwide[ 2 ]. The National Osteoporosis Foundation estimates that more than 10 million people over age 50 in the U.S.A. have osteoporosis and another 34 million are at risk for the disease[ 3 ]. Similar to females encountering postmenopausal osteoporosis, recent studies have shown that estrogen plays a crucial role in age-mediated male osteoporosis[ 4 ]. Statistically, osteoporosis primarily affects postmenopausal women and elderly men, with 30% of women and 20% of men aged 50 years old predicted to experience an osteoporosis-related fracture in their lifetime[ 5 ]. It is the sequela of osteoporosis and most particularly the occurrence of osteoporotic fracture that makes osteoporosis a serious medical condition[ 6 ]. Bone metabolism is a dynamic process of bone remodeling. Its balance is complicated, with oxidative metabolism seemingly important for osteoclast differentiation and glycolysis important for bone resorption[ 7 ]. Glycolysis generates ATP but also provides metabolic intermediates that enter many other metabolic pathways[ 8 ]. Our study implicates glycolytic metabolism as a novel therapeutic target for bone anabolism in age-related bone loss and osteoporosis. We believe that the developmental origins of osteoporosis could novelly provide a theoretical basis for the early prevention of osteoporosis. In this study, with tie aid of bioinformatics, we analyzed two publicly available microarray datasets retrieved from Gene Expression Omnibus (GEO). We screened out glycolysis-related differentially expressed genes (GRDEGs), conducted Gene Oncology/Kyoto Encyclopedia of Genes and Genomes annotations, protein-protein interaction network, analysis of immune infiltration and ROC curve, with the hope of finding potential therapeutic agents that could provide clues for early intervention of bone metabolism and prevention of osteoporosis. 2. MATERIALS AND METHODS 2.1 Data Source We downloaded osteoporosis related datasets GSE56815 and GSE13850[ 9 ] from the GEO[ 10 ] database through the GEO query package[ 11 ]. Two datasets came from Homo sapiens, and the data platform were GPL96 [HG-U133A] Affymetrix Human Genome U133A Array. The dataset GSE56815 contained a total of 80 samples, including 40 normal samples and 40 osteoporosis samples. The dataset GSE13850 included 40 samples, among which 10 were classified into normal group, and 10 samples of osteoporosis. We normalized the data through the limma package[ 12 ]. We collected glycolysis-related genes (GRGs) from the GeneCards database[ 13 ], the GeneCards database ( https://www.genecards.org/ ) provides comprehensive information about human genes. In the GeneCards database, 133 GRGs were obtained by using "glycolysis" as the search keyword and only retaining GRGs with "Protein Coding" and Relevance score > 2. We also used the word "glycolysis" as the search keyword to obtain 835 GRGs from the Molecular Signatures Database (MSigDB)[ 14 ], and obtained a total of 895 GRGs after combining and deduplicating, see table S1 . 2.2 Osteoporosis Related Differentially Expressed Genes In order to identify the potential mechanism and relevant biological characteristics and pathways of differential genes in osteoporosis, differences between the datasets GSE56815 and GSE13850 were analyzed using the limma package[ 12 ]. Differentially expressed genes (DEGs) between different groups (Normal/OP) were obtained. |logFC| > 0 and p 0 and p < 0.05 were up regulated genes, logFC < 0 and p < 0.05 were down regulated genes. In order to obtain GRDEGs associated with osteoporosis, the intersection of DEGs and GRGs of dataset GSE56815 was made and the Venn diagram was drawn. The results of the difference analysis were displayed by drawing volcano map and heatmap of R package ggplot2. 2.3 Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Enrichment Analyses of DEGs Gene Ontology (GO)[ 15 ] analysis is a common method for large-scale functional enrichment studies, including biological process (BP), molecular function (MF) and cellular component (CC). Kyoto Encyclopedia of Genes and Genomes (KEGG)[ 16 ] is a widely used database storing information on genomes, biological pathways, diseases, and drugs. We used clusterProfiler R package[ 17 ] to conduct GO annotation analysis on GRDEGs, and the entry screening criteria were p < 0.05 and FDR value < 0.05, which was considered statistically significant. P value correction method was Benjamini-Hochberg. 2.4 Gene Set Enrichment Analysis GSEA (Gene Set Enrichment Analysis)[ 18 ] is used to evaluate the distribution trend of genes from a predefined gene set in the gene list sorted with phenotypic relevance to judge their contribution to phenotype. In this study, logFC values were first used as molecular sequencing to assess significant enrichment in predefined gene sets. Then, the clusterProfiler package was used for enrichment analysis of all genes associated with phenotypes. The parameters used in this GSEA were as follows, the seed was 2020, the count was 1000, each gene set contained at least 10 genes and at most 500 genes, and the p value correction method was Benjamini-Hochberg. We obtained the gene set "h.all.v7.4.symbols.gmt" from MSigDB for GSEA analysis of genes expression in GSE56815. The screening criteria for significant enrichment were p < 0.05 and FDR value < 0.25. 2.5 Protein-Protein Interaction Network (PPI Network) The protein-protein interaction network is made up of individual proteins interacting with each other. STRING Database[ 19 ] is a database that searches for known and predicted protein interactions. In this study, we used STRING database to construct protein-protein interaction network associated with GRDEGs and visualized PPI network models using Cytoscape. Tightly connected local regions in the PPI network, which might represent molecular complexes with specific biological functions. By utilizing the in cytoHubba plugin[ 20 ] DEGREE (Degree Correlation), MNC (Maximum Neighborhood Component), MCC (Maximal Clique Centrality), EPC (Edge Percolated Component) and Density of Maximum Neighborhood Component (DMNC), five algorithms, selected the top 10 common GRDEGs as key genes (mRNA). 2.6 Analysis of Immune Infiltration CIBERSORT ( https://cibersort.stanford.edu/ )[ 21 ] is based on linear support vector regression principle of human immune cells subtype to the expression of matrix convolution of a R/web tools. It could evaluate the infiltrating state of immune cells in sequencing samples based on the gene expression characteristics of 22 known immune cell subtypes. In this study, CIBERSORT algorithm was used to evaluate the infiltrating state of immune cells in dataset GSE56815, and Spearman correlation was used to calculate the correlation between various immune cells. Subsequently, Spearman correlation analysis was used to determine the correlation between hub genes and immune infiltrating cells. Then, the correlation between immune cells and GRDEGs was calculated by combining the gene expression matrix of osteoporosis dataset, and the correlation heatmap was drawn by R-Pheatmap. 2.7 ROC Curve Receiver operating characteristic curve (ROC) is a coordinate schema analysis tool that could be used to select the best model, discard the suboptimal or set the optimal threshold within the same model. ROC curve is a comprehensive index reflecting the sensitivity and specificity of continuous variables, and the correlation between sensitivity and specificity is reflected by the composition method. The area value under the ROC curve is generally between 0.5 and 1. The closer the AUC is to 1, the better the diagnostic effect. AUC has low accuracy between 0.5 and 0.7, certain accuracy between 0.7 and 0.9, and high accuracy above 0.9. We used the pROC package to plot ROC curves of hub genes in different groups (Normal/OP) in two datasets and calculated the area under the curve (AUC) to assess the diagnostic effect of hub genes expression on disease. 2.8 Statistical Analysis All data processing and analysis in this paper were based on R software (Version 4.1.2). For the comparison of two groups of continuous variables, the independent Student t test was used to estimate the statistical significance of normally distributed variables. The Mann-Whitney U test (Wilcoxon rank sum test) was used to analyze the difference between variables with non-normal distribution. The Chi-square test or Fisher's exact test is used to compare and analyze statistical significance between two sets of categorical variables. If not specified specifically, the correlation coefficients between different molecules were calculated by Spearman correlation analysis. All p values reported from statistical tests are two-sided., and p < 0.05 was considered statistically significant. 3. RESULTS Figure 1 The design flow chart of this study. GRGs:Glycolysis-related genes. DEGs:Differentially expressed genes. GSEA:Gene Set Enrichment Analysis. GO:Gene Ontology. KEGG:Kyoto Encyclopedia of Genes and Genomes. PPI network:Protein-protein interaction network. ROC:Receiver operating characteristic curve. GRDEGs:Glycolysis-related differentially expressed genes. 3.1 Data Standardization and Analysis of Osteoporosis Related DEGs This study mainly uses bioinformatics methods to explore the biological characteristics of osteoporosis, and the overall analysis flow chart is shown in Figure 1. In the datasets GSE56815 and GSE13850, subjects were divided into osteoporosis group and normal control group. Datasets GSE56815 and GSE13850 were standardized respectively, and data cleaning operations such as annotation probes were performed, and a boxplot was drawn for data distribution before and after standardization (Figure 2A-B, 2C-D). Figure 2 Boxplot before and after correction of osteoporosis datasets GSE56815 and GSE13850 A. Boxplot of gene expression distribution among samples of dataset GSE56815 before correction. B. Boxplot of gene expression distribution among samples of the corrected dataset GSE56815. C. Boxplot of gene expression distribution among samples of dataset GSE13850 before correction. D. Boxplot of gene expression distribution among samples of the corrected dataset GSE13850. Blue represents the normal group and red represents the group of osteoporosis. In order to analyze the difference in gene expression values between the osteoporosis and normal group in the datasets GSE56815 and GSE13850, we used the limma package for differential analysis to obtain the DEGs in the data, and the results were as follows, in dataset GSE56815, 1280 DEGs met the requirements of high expression in disease group (low expression in normal group, logFC >0, up-regulated gene) and 1019 met the requirements of low expression in disease group (high expression in normal group, logFC < 0, down-regulated gene). We mapped the results of the variance analysis into a volcano map (Figure 3A). In the GSE13850 dataset, 461 met the requirements of high expression in disease group (low expression in normal group, logFC > 0, up-regulated gene), and 499 met the requirements of low expression in disease group (high expression in normal group, logFC < 0, down-regulated gene). A volcano map (Figure 3B) was drawn from the results of the variance analysis of this dataset. In order to obtain GRDEGs, We took intersection of DEGs of dataset GSE56815 in which| logFC | > 0 and p < 0.05 and GRGs, a total of 154 GRDEGs and mapped the Venn diagram (figure 3C). We analyzed the expression differences among different groups in the dataset GSE56815 (Figure 3D) and used R package pheatmap to draw heatmap to show the analysis results. The first 5 up-regulated DEGs and the first 5 down-regulated DEGs (FOLR3, DPP8, GZMB, DMTF1, FOXO3, RUNX1T1, DMP1, THPO, PAWR, PRLR) were found by selecting the logFC column in ascending and descending order from the results of differential analysis. The results showed that the clustering degree of these 10 DEGs was obvious in different groups of dataset GSE56815. We also analyzed the expression differences among different groups in the dataset GSE13850 (Figure 3E) and used R package pheatmap to draw heatmap to show the analysis results. The first 5 up-regulated DEGs and the first 5 down-regulated DEGs (LY96, NRIP1, ARMC1, CYB5R4, TRIB2, ITSN1, TPTE, GNAL, TRPC6, GPR1) were found by selecting the logFC column in ascending and descending order. The results showed that the clustering degree of these 10 DEGs were obvious in different groups of dataset GSE13850. Figure 3. Differential gene analysis of osteoporosis dataset A. Volcano map of differential gene of dataset GSE56815. B. Volcano map of differential gene of dataset GSE13850. C. Venn diagram of DEGs and GRGs in dataset GSE56815. D. Differential gene heatmap of dataset GSE56815. E. Differential gene heatmap of dataset GSE13850. DEGs: differentially expressed genes. GRGs: Glycolysis-related genes. 3.2 GRDEGs Functional Enrichment Analysis and Pathway Enrichment Analysis In order to analyze the biological processes, molecular functions, cellular components, biological pathways and their relationship with osteoporosis in 154 GRDEGs, the Gene Ontology (GO) (Table 1) and Kyoto Encyclopedia of Genes and Genomes (KEGG) (Table 2) enrichment analyses of GRDEGs were conducted firstly. The screening criteria for enrichment items were p < 0.05 and FDR value < 0.05, which was considered to be statistically significant. The results of GO functional enrichment analysis and KEGG enrichment analysis were shown in bubble diagram (Figure 4A-B) and ring network diagram (Figure 4C-D). Then, the GO functional enrichment analysis results of GRDEGs combined with logFC were shown in a bar chart (Figure 4E-F). The results showed that 154 GRDEGs were mainly enriched in JAK-STAT cascade (GO 0007259), regulation of JAK-STAT cascade (GO 0046425), negative regulation of JAK-STAT cascade (GO 0046426), canonical glycolysis (GO 0061621) in biological process, host (GO 0018995), nuclear pore (GO 0005643), vesicle lumen (GO 0031983), region of cytosol (GO 0099522) in cellular component, isomerase activity (GO 0016853), vitamin B6 binding (GO 0070279), vitamin binding (GO 0019842), NAD binding (GO: 0051287) and other molecular functions. The results of KEGG enrichment analysis mainly included glycolysis/gluconeogenesis (hsa00010), autophagy-animal (hsa04140), carbon metabolism (hsa01200), pyruvate metabolism (hsa00620), beta-alanine metabolism (hsa00410), galactose metabolism (hsa00052), prostate cancer (hsa05215) and other signaling pathways. Figure 4 GRDEGs functional enrichment analysis (GO) and pathway enrichment analysis (KEGG) A-B. The results of GO functional enrichment analysis (A) and KEGG pathway enrichment analysis (B) by GRDEGs are shown in bubble graphs. C-D. The results of GO functional enrichment analysis (C) and KEGG pathway enrichment analysis (D) are shown in the ring network diagram. E-F. GO functional enrichment analysis (E) and KEGG pathway enrichment analysis (F) of GRDEGs are shown in bar chart. GRDEGs: Glycolysis-related differentially expressed genes. GO: Gene Ontology. BP: Biological process. CC: Cellular component. MF: Molecular function. KEGG: The Kyoto Encyclopedia of Genes and Genomes. The screening criteria for GO and KEGG enrichment items were p < 0.05 and FDR value < 0.25. 3.3 GSEA of Osteoporosis Dataset To determine the effect of gene expression levels in the normal and osteoporosis group, we analyzed the association between gene expression in the dataset GSE56815 and the biological processes involved, cellular components affected, and molecular functions performed by GSEA (Table 3). The results showed as follows (Figure 5A) that DEGs in GSE56815 were significantly enriched in the NF_KB pathway (Figure 5B), glycolysis pathway (Figure 5C), Wnt pathway (Figure 5D), Hedgehog pathway (Figure 5E) and others. Figure 5 GSEA of dataset GSE56815 A. Four main biological characteristics of GSEA in dataset GSE56815. The DEGs in B-E. GSE56815 were significantly enriched in the NF_KB pathway (Figure 5B), glycolysis pathway (Figure 5C), Wnt pathway (Figure 5D), Hedgehog pathway (Figure 5E). 3.4 Protein-Protein Interaction Network (PPI Network) Protein-protein interaction analysis was performed on 154 GRDEGs using the STRING database, with a minimum required interaction score higher than 0.9 was used as the standard, and the protein-protein interaction network (PPI network) was constructed, and the network diagram of protein-protein interaction was drawn (Figure 6A). Then cytoHubba plugin of Cytoscape software was used to analyze the six hub genes (CTNNB1, HK3, MPI, HKDC1, PFKL,PTEN, mRNA) in the top 10 selected by MCC, MNC, EPC, DEGREE and DMNC algorithms which were as key genes (hub genes, mRNA) (Figure 6B) and mapped protein-protein interaction networks (Figure 6C). Figure 6 Protein-protein interaction network (PPI network) A. PPI network of GRDEGs. B. Venn diagram of common genes of the top 10 GRDEGs were selected under the five algorithms of MCC, MNC, EPC, DEGREE and DMNC. C. PPI network of 6 hub genes. PPI network: Protein-protein interaction network. GRDEGs: Glycolysis-related differentially expressed genes. DEGREE: Degree Correlation. MNC: Maximum Neighborhood Component. MCC: Maximal Clique Centrality. EPC: Edge Percolated Component. DMNC: Density of Maximum Neighborhood Component. 3.5 Expression Analysis of GRDEGs We analyzed the expression difference of 6 GRDEGs (CTNNB1, HK3, MPI, HKDC1, PFKL, PTEN) among different groups (Normal/OP) in the dataset GSE56815 respectively (Figure 7A), and the results showed that the expression levels of 2 GRDEGs (CTNNB1, PFKL) in different groups of the dataset GSE56815 were highly statistically significant ( p <0.01). The expression levels of 4 GRDEGs (HK3, MPI, HKDC1, PTEN) were statistically significant ( p < 0.05). We drew 6 GRDEGs (CTNNB1, HK3, MPI, HKDC1, PFKL, PTEN) ROC curve in dataset GSE56815, and the results were shown (Figure 7B-G). As can be seen from the figure, HK3 (AUC = 0.667, Figure 7B), CTNNB1 (AUC = 0.682, Figure 7D), PTEN (AUC = 0.653, Figure 7E), MPI (AUC = 0.650, Figure 7F), HKDC1 (AUC = 0.635, Figure 7G) showed low diagnostic value among different groups (Normal/OP) in dataset GSE56815. The expression level of PFKL (AUC = 0.701, Figure 7C) in dataset GSE56815 showed certain diagnostic value. In order to further explore the expression differences of 6 GRDEGs (CTNNB1, HK3, MPI, HKDC1, PFKL, PTEN), we performed ROC verification in the dataset GSE13850. As can be seen from the figure, the expression level of PTEN (AUC = 0.740, Figure 7H) in dataset GSE13850 showed certain diagnostic value among different groups (Normal/OP). PFKL (AUC = 0.640, Figure 7I), MPI (AUC = 0.630, Figure 7J), HK3 (AUC = 0.630, Figure 7K), CTNNB1 (AUC = 0.620, Figure 7L), HKDC1 (AUC = 0.505, Figure 7M) showed low diagnostic value. FIG. 7 Expression of GRDEGs in dataset A. Expression differences of 6 GRDEGs in different groups (Normal/OP) of the dataset GSE56815 were analyzed. B-G. ROC curve analysis of DEGs, HK3 (B), PFKL (C), CTNNB1 (D), PTEN (E), MPI (F), HKDC1 (G) in dataset GSE56815. H-M. ROC curve analysis of DEGs, PTEN (H), PFKL (I), MPI (J), HK3 (K), CTNNB1 (L), HKDC1 (M) in dataset GSE13850. The symbol * is equivalent to p < 0.05, which means statistically significant. The symbol ** is equivalent to p < 0.01, indicating high statistical significance. GRDEGs: Glycolysis-related differentially expressed genes. ROC: Receiver operating characteristic curve. The closer the AUC is to 1 in the ROC curve, the better the diagnostic effect is. The accuracy of AUC is low between 0.5 and 0.7. AUC has a certain accuracy between 0.7 and 0.9. 3.6 Immunoinfiltration Analysis of Osteoporosis Dataset (CIBERSORT) We used CIBERSORT algorithm to calculate the correlation between expression profile data of 22 kinds of immune cells in different groups (Normal/OP) in the dataset GSE56815. According to the results of immune infiltration analysis, we plotted the infiltration of 22 types of immune cells in each sample of dataset GSE56815 (Figure 8A) in a bar chart. We showed the correlation between the abundance of immune cell infiltration with the correlation heatmap (Figure 8B). The results showed that the infiltration abundance of memory B cells was significantly correlated with that of naive B cells, CD4 memory activated T cells, Monocytes, Macrophages M2 ( p < 0.01). The abundance of infiltrating immune cells was significantly correlated with memory B cells and gamma-delta T cells ( p < 0.05). We also used correlation heatmaps to show the correlation between 6 GRDEGs (CTNNB1, HK3, MPI, HKDC1, PFKL, PTEN) and the abundance of immune cell infiltration (Figure 8C). The results showed that there was a significant correlation between hub genes (CTNNB1 HK3, MPI, HKDC1, PFKL, PTEN) and the abundance of infiltrating T follicular helper cells ( p < 0.01). The hub gene MPI was significantly correlated with the infiltrating abundance of Macrophages M0 and Macrophages M2 ( p < 0.05). Figure 8 CIBERSORT analysis of immunoinfiltration in dataset GSE56815 A. Bar chart showing the immunoinfiltration results of 22 kinds of immune cells in dataset GSE56815. B. The correlation analysis results between the abundance of immune cell infiltration were shown in the heatmap. C. Heatmap of correlation analysis results of GRDEGs and immune cell expression in dataset GSE56815. The symbol * is equivalent to p < 0.05, which means statistically significant. The symbol ** is equivalent to p < 0.01, indicating high statistical significance. GRDEGs: Glycolysis-related differentially expressed genes. OP: Osteoporosis. Table 1. GO enrichment analysis results of GRDEGs. ONTOLOGY ID Description GeneRatio BgRatio pvalue p.adjust qvalue BP GO:0016052 carbohydrate catabolic process 24/147 199/18670 8.31e-22 1.61e-18 1.31e-18 BP GO:0006090 pyruvate metabolic process 22/147 154/18670 1.06e-21 1.61e-18 1.31e-18 BP GO:0006165 nucleoside diphosphate phosphorylation 20/147 134/18670 3.35e-20 3.41e-17 2.77e-17 BP GO:0046939 nucleotide phosphorylation 20/147 136/18670 4.55e-20 3.47e-17 2.82e-17 BP GO:0042866 pyruvate biosynthetic process 19/147 119/18670 8.13e-20 4.96e-17 4.03e-17 CC GO:0018995 host 8/149 73/19717 7.82e-08 6.44e-06 5.52e-06 CC GO:0043657 host cell 8/149 73/19717 7.82e-08 6.44e-06 5.52e-06 CC GO:0044215 other organism 8/149 77/19717 1.19e-07 6.44e-06 5.52e-06 CC GO:0044216 other organism cell 8/149 77/19717 1.19e-07 6.44e-06 5.52e-06 CC GO:0044217 other organism part 8/149 77/19717 1.19e-07 6.44e-06 5.52e-06 MF GO:0050662 coenzyme binding 13/148 291/17697 1.03e-06 3.10e-04 2.77e-04 MF GO:0005536 glucose binding 4/148 11/17697 1.48e-06 3.10e-04 2.77e-04 MF GO:0017056 structural constituent of nuclear pore 5/148 28/17697 3.22e-06 4.48e-04 4.02e-04 MF GO:0048029 monosaccharide binding 6/148 75/17697 3.87e-05 0.004 0.004 MF GO:0016853 isomerase activity 7/148 158/17697 3.69e-04 0.031 0.028 GRDEGs:Glycolysis-related differentially expressed genes. GO:Gene Ontology. BP:Biological process. CC:Cellular component. MF:Molecular function. Table 2. KEGG enrichment analysis results of GRDEGs. ONTOLOGY ID Description GeneRatio BgRatio pvalue p.adjust qvalue KEGG hsa01200 Carbon metabolism 17/102 118/8076 6.64e-14 1.60e-11 1.25e-11 KEGG hsa00010 Glycolysis / Gluconeogenesis 12/102 67/8076 2.87e-11 3.46e-09 2.71e-09 KEGG hsa00020 Citrate cycle (TCA cycle) 7/102 30/8076 6.68e-08 5.37e-06 4.20e-06 KEGG hsa01230 Biosynthesis of amino acids 9/102 75/8076 3.60e-07 2.17e-05 1.70e-05 KEGG hsa04910 Insulin signaling pathway 11/102 137/8076 1.07e-06 5.16e-05 4.03e-05 GRDEGs:Glycolysis-related differentially expressed genes. KEGG:Kyoto Encyclopedia of Genes and Genomes. Table 3. GSEA of dataset GSE56815. Description setSize enrichmentScore NES pvalue p.adjust REACTOME_TNFR2_NON_CANONICAL_NF_KB_PATHWAY 90 0.342838269 1.342041373 0.046683047 0.269949792 WP_COMPUTATIONAL_MODEL_OF_AEROBIC_GLYCOLYSIS 12 -0.630758854 -1.528063408 0.045714286 0.268686869 ST_WNT_BETA_CATENIN_PATHWAY 30 -0.513219512 -1.521698728 0.028021016 0.206369761 REACTOME_HEDGEHOG_ON_STATE 70 0.365468646 1.380768448 0.037209302 0.239687272 WP_VEGFAVEGFR2_SIGNALING_PATHWAY 382 -0.384226162 -1.690505693 0.001461988 0.065017973 REACTOME_NEUTROPHIL_DEGRANULATION 402 -0.497153795 -2.192682565 0.001468429 0.065017973 PID_PDGFRB_PATHWAY 119 -0.477253699 -1.836503002 0.001602564 0.065017973 REACTOME_CELLULAR_SENESCENCE 117 -0.471952164 -1.810317865 0.001602564 0.065017973 REACTOME_SIGNALING_BY_NTRKS 130 -0.527310648 -2.048546521 0.001610306 0.065017973 WP_TGFBETA_SIGNALING_PATHWAY 125 -0.436228939 -1.680876234 0.001620746 0.065017973 REACTOME_SIGNALING_BY_VEGF 99 -0.471579767 -1.765944536 0.001636661 0.065017973 REACTOME_ANTIMICROBIAL_PEPTIDES 40 -0.732774608 -2.325756934 0.001782531 0.065017973 BIOCARTA_INTEGRIN_PATHWAY 32 -0.606302895 -1.81260648 0.001785714 0.065017973 PID_ARF6_PATHWAY 32 -0.575529195 -1.720605255 0.001785714 0.065017973 PID_IL8_CXCR2_PATHWAY 32 -0.61154306 -1.828272505 0.001785714 0.065017973 GSEA: Gene Set Enrichment Analysis. 4. DISCUSSION As the global aging process continues to accelerate, the number of osteoporosis patients increases, the most serious complication of osteoporosis is osteoporotic fracture, and the incidence of spinal fractures is the highest, that makes osteoporosis a serious medical condition[ 22 ]. Therefore, the ability to diagnose osteoporosis before fractures occur, and timely treatment of osteoporosis are important[ 23 ]. The drugs current used for treating osteoporosis are efficient, but far from ideal, making an urgent need to find new pharmacological agents that could treat osteoporosis efficiently without serious side-effects[ 24 ]. Several anabolic agents have been approved for use in severe cases of osteoporosis[ 25 ]. However, the role of glycolysis in the development of osteoporosis has been poorly studied. We believe that the developmental origins of osteoporosis could novelly provide a theoretical basis for the early prevention of osteoporosis. In this study, we selected two microarray datasets from the GEO database to screen for DEGs in osteoporosis and performed GO and KEGG enrichment analyses as well as PPI analyses to clarify the molecular mechanism of the DEGs. Then we obtained GRDEGs of osteoporosis by analyzing the gene expression profiles, there were 154 GRDEGs. To understand the roles of these GRDEGs in osteoporosis, we further performed GO enrichment analysis and found that the biological processes mainly involved in JAK-STAT cascade and canonical glycolysis. GRDEGs were involved in regards to the molecular functions to isomerase activity, vitamin B6 binding, vitamin binding, NAD binding. Besides, KEGG enrichment analysis demonstrated that the pathways correlated to GRDEGs also included glycolysis/gluconeogenesis. For GSEA enrichment analysis, they were significantly enriched in the NF_KB pathway, glycolysis pathway, Wnt pathway and Hedgehog pathway. The JAK/STAT pathway is a signaling cascade that has a prominent role in oncogenes, tumor progression, angiogenesis, cell motility, immune response, and stem cell differentiation[ 26 ]. Thus, the regulation of JAK/STAT signal transduction is important to prevent abnormal signal transduction that leads to disease progression. Glycolysis is a process involved a series of glycolytic enzymes and closely related to an inflammatory functional phenotype in macrophages, and it is known that classical inflammatory M1 macrophages heavily dependent on glycolysis to produce ATP[ 27 ]. Classically activated macrophages induce aerobic glycolysis that results in lactate production and increased synthesis and secretion of inflammatory cytokines[ 28 ]. Upregulation of the canonical WNT pathway plays a pivotal role in metabolism and particularly in the aerobic glycolysis[ 29 ]. As can be seen in protein-protein interaction network of GRDEGs by STRING, 6 hub genes for glycolysis in osteoporosis which were CTNNB1, HK3, MPI, HKDC1, PFKL, PTEN, were identified by Cytoscape. As for expression analysis, the expression level of PFKL in dataset GSE56815 showed certain diagnostic value and PTEN in dataset GSE13850 showed certain diagnostic value among different groups (Normal/OP). In the event of osteoporosis, 22 kinds of immune cells were recruited to the site of inflammation repairing the tissue to eventually restore homeostasis. In this study, GSE56815 was downloaded and immune cells analysis was carried out to screen out the hub genes associated with immune cells. The results showed that the infiltration abundance of memory B cells was significantly correlated with that of naive B cells, CD4 memory activated T cells, Monocytes, Macrophages M2. The abundance of infiltrating immune cells was significantly correlated with memory B cells and gamma-delta T cells. There was a significant correlation between hub genes (CTNNB1 HK3, MPI, HKDC1, PFKL, PTEN) and the abundance of infiltrating T follicular helper cells. The hub gene MPI was significantly correlated with the infiltrating abundance of Macrophages M0 and Macrophages M2. Glycolysis promote the inflammation through multiple mechanism, metabolic reprogramming of macrophages promises to be a new target for macrophage-mediated inflammatory diseases[ 30 ]. In mammals, M1 macrophages show metabolic reprogramming toward glycolysis, while M2 macrophages rely on oxidative phosphorylation to generate energy[ 31 ]. Glycolysis is also required for macrophage M2 differentiation[ 32 ]. Perturbation of the normal balance of M1/M2 macrophages appears to be an important factor in osteoporosis pathogenesis[ 33 ]. Based on these evidences, development of therapeutic agents that potentiate M2 macrophage populations in bone might be useful to improve osteoporosis clinical outcome. There are some limitations which should be mentioned. Firstly, the strict inclusion and exclusion criteria are a double-edged sword. Although it ensures that the datasets included in the discovery stage have good homogeneity, which might limit the discovery of differential genes. Secondly, the sample sizes of included datasets were not too large, however, after verified by dataset GSE13850, the results are highly reliable. Lastly, these glycolysis-related genes differentially expressed in osteoporosis, especially the hub genes and signaling pathways, which need to be further verified by cell experiments, and clinical samples. CONCLUSION Based on our current study, our research provided a bioinformatics analysis of glycolytic metabolism as a novel therapeutic target for bone anabolism in bone loss and osteoporosis. The screened hub GRDEGs, CTNNB1 HK3, MPI, HKDC1, PFKL, PTEN, might potentially be used as targets for the early prevention of osteoporosis. Declarations Acknowledgements We acknowledge the support of the contributors in uploading datasets and providing the GEO platform. Author contributions XB Hu and JZ Yang carried out the studies, participated in collecting data, and drafted the manuscript，J Zhang and J Hu helped to analyse the data and draft the manuscript. J Zhang and XF Yuan participated in its design. All authors contributed to data analysis, drafting and revising the article, gave final approval of the version to be published, and agree to be accountable for all aspects of the work. Funding This study was supported by the Yunnan Province Applied Basic Research Program (No.202101AT070276), the Yunnan Provincial Department of Science and Technology-Kunming Medical University Applied Basic Research Joint Project(No.202001AY07001-209), and the Kunming Health Science and Technology Personnel Training Project - Ten Hundred Thousand Project Training Plan(2021-SW Province-08). Availability of data and materials The datasets generated and analysed during the current study (GSE56815 and GSE13850) are available in the National Center for Biotechnology Information Gene Expression Omnibus (GEO) repository. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References Zhang D, Miranda M, Li X, Han J, Sun Y, Rojas N, He S, Hu M, Lin L, Li X, Ke HZ, Qin YX. Retention of osteocytic micromorphology by sclerostin antibody in a concurrent ovariectomy and functional disuse model. Ann N Y Acad Sci. 2019;1442(1):91–103. Yan X, Wu H, Wu Z, Hua F, Liang D, Sun H, Yang Y, Huang D, Bian JS. The New Synthetic H(2)S-Releasing SDSS Protects MC3T3-E1 Osteoblasts against H(2)O(2)-Induced Apoptosis by Suppressing Oxidative Stress, Inhibiting MAPKs, and Activating the PI3K/Akt Pathway. Front Pharmacol. 2017;8:07. Stevens JA, Rudd RA. 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Identification of gene biomarkers in patients with postmenopausal osteoporosis. Mol Med Rep. 2019;19(2):1065–73. Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, Yefanov A, Lee H, Zhang N, Robertson CL, Serova N, Davis S, Soboleva A. NCBI GEO: archive for functional genomics data sets–update. Nucleic Acids Res. 2013;41(Database issue):D991–995. Chodary Khameneh S, Razi S, Shamdani S, Uzan G, Naserian S. Weighted correlation network analysis revealed novel long non-coding RNAs for colorectal cancer. Sci Rep. 2022;12(1):2990. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47. Fishilevich S, Nudel R, Rappaport N, Hadar R, Plaschkes I, Iny Stein T, Rosen N, Kohn A, Twik M, Safran M, Lancet D, Cohen D. GeneHancer: genome-wide integration of enhancers and target genes in GeneCards. 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Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol. 2014;8(Suppl 4):11. Newman AM, Steen CB, Liu CL, Gentles AJ, Chaudhuri AA, Scherer F, Khodadoust MS, Esfahani MS, Luca BA, Steiner D, Diehn M, Alizadeh AA. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat Biotechnol. 2019;37(7):773–82. Li Z, Wang Y, Xu Y, Xu W, Zhu X, Chen C. Efficacy analysis of percutaneous pedicle screw fixation combined with percutaneous vertebroplasty in the treatment of osteoporotic vertebral compression fractures with kyphosis. J Orthop Surg Res. 2020;15(1):53. Oo WM, Naganathan V, Bo MT, Hunter DJ. Clinical utilities of quantitative ultrasound in osteoporosis associated with inflammatory rheumatic diseases. Quant Imaging Med Surg. 2018;8(1):100–13. Chen K, Lv ZT, Cheng P, Zhu WT, Liang S, Yang Q, Parkman VA, Zhou CH, Jing XZ, Liu H, Wang YT, Lin H, Liao H, Chen AM. Boldine Ameliorates Estrogen Deficiency-Induced Bone Loss via Inhibiting Bone Resorption. Front Pharmacol. 2018;9:1046. Raut N, Wicks SM, Lawal TO, Mahady GB. Epigenetic regulation of bone remodeling by natural compounds. Pharmacol Res. 2019;147:104350. Yang L, Wang R, Ma Z, Xiao Y, Nan Y, Wang Y, Lin S, Zhang YJ. Porcine Reproductive and Respiratory Syndrome Virus Antagonizes JAK/STAT3 Signaling via nsp5, Which Induces STAT3 Degradation. J Virol. 2017; 91(3). Italiani P, Mosca E, Della Camera G, Melillo D, Migliorini P, Milanesi L, Boraschi D. Profiling the Course of Resolving vs. Persistent Inflammation in Human Monocytes: The Role of IL-1 Family Molecules. Front Immunol. 2020;11:1426. Palmer CS, Cherry CL, Sada-Ovalle I, Singh A, Crowe SM. Glucose Metabolism in T Cells and Monocytes: New Perspectives in HIV Pathogenesis. EBioMedicine. 2016; 6:31–41. Lecarpentier Y, Schussler O, Hebert JL, Vallee A. Multiple Targets of the Canonical WNT/beta-Catenin Signaling in Cancers. Front Oncol. 2019;9:1248. Zeng H, Qi X, Xu X, Wu Y. TAB1 regulates glycolysis and activation of macrophages in diabetic nephropathy. Inflamm Res. 2020;69(12):1215–34. Wentzel AS, Janssen JJE, de Boer VCJ, van Veen WG, Forlenza M, Wiegertjes GF. Fish Macrophages Show Distinct Metabolic Signatures Upon Polarization. Front Immunol. 2020;11:152. Yu Q, Wang Y, Dong L, He Y, Liu R, Yang Q, Cao Y, Wang Y, Jia A, Bi Y, Liu G. Regulations of Glycolytic Activities on Macrophages Functions in Tumor and Infectious Inflammation. Front Cell Infect Microbiol. 2020;10:287. Becker L, Nguyen L, Gill J, Kulkarni S, Pasricha PJ, Habtezion A. Age-dependent shift in macrophage polarisation causes inflammation-mediated degeneration of enteric nervous system. Gut. 2018;67(5):827–36. Additional Declarations No competing interests reported. Supplementary Files tableS1.Glycolysisrelatedgenes..xlsx table S1. Glycolysis-related differentially expressed genes. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3782121","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":263620245,"identity":"857fe71d-83b4-44bb-8c0a-a6b3e85b56cd","order_by":0,"name":"Xing-Bo Hu","email":"","orcid":"","institution":"The First People's Hospital of Kunming","correspondingAuthor":false,"prefix":"","firstName":"Xing-Bo","middleName":"","lastName":"Hu","suffix":""},{"id":263620246,"identity":"08518204-8173-4b02-b8fe-6106859849e0","order_by":1,"name":"Jing-Ze Yang","email":"","orcid":"","institution":"The First People's Hospital of Kunming","correspondingAuthor":false,"prefix":"","firstName":"Jing-Ze","middleName":"","lastName":"Yang","suffix":""},{"id":263620247,"identity":"d8af4151-272d-4eff-a839-b967e712acab","order_by":2,"name":"Jin Zhang","email":"","orcid":"","institution":"The First People's Hospital of Kunming","correspondingAuthor":false,"prefix":"","firstName":"Jin","middleName":"","lastName":"Zhang","suffix":""},{"id":263620248,"identity":"c852510a-62cf-45ae-aa52-069f3f9443d2","order_by":3,"name":"Jun Hu","email":"","orcid":"","institution":"The First People's Hospital of Kunming","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Hu","suffix":""},{"id":263620249,"identity":"96025843-1ca3-4abb-92c1-415ecf1344ad","order_by":4,"name":"Xiao-Feng Yuan","email":"","orcid":"","institution":"The First People's Hospital of Kunming","correspondingAuthor":false,"prefix":"","firstName":"Xiao-Feng","middleName":"","lastName":"Yuan","suffix":""},{"id":263620250,"identity":"32688bd2-5806-4323-b7b3-b3a9ecf3019a","order_by":5,"name":"Juan Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIie3PsUvDQBTH8TsO0uXE9XekmH8hJZBURPxXWjpkySwtiD0IOAVcFYt/w00Bt5ODTAXXOmksOFsQiYOgN7okGQXvOzx48D7DI8Tl+ovBDm7Hw+4Zcxzs9ydUR+F4PY6E7EVsTMdYXMynqosEN/lr/T40QeLpCR5vQRVh9cumhdBVlURDbkZ3hdaj6xIsIV4UZS2EYRL74Iaqzb2ciRLeoeSe30Y8pB+WnKinLTFfK/BQdxCOLBZv3EyVrqgUEugkQHbqE57OlF4zggqhyDt+Ca7SUnwWR8c/ZNDg7Hx5OcjrbRuxsb3i19pxbqNN0+PK5XK5/m/fJ1JMheBLZ5oAAAAASUVORK5CYII=","orcid":"","institution":"First Affiliated Hospital of Kunming Medical University","correspondingAuthor":true,"prefix":"","firstName":"Juan","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2023-12-20 13:48:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3782121/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3782121/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49080198,"identity":"f8e0a00b-fdb4-4c9c-b3da-610a7bacafc4","added_by":"auto","created_at":"2024-01-02 19:54:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":10593,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe design flow chart of this study.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGRGs:Glycolysis-related genes. DEGs:Differentially expressed genes. GSEA:Gene Set Enrichment Analysis. GO:Gene Ontology. KEGG:Kyoto Encyclopedia of Genes and Genomes. PPI network:Protein-protein interaction network. ROC:Receiver operating characteristic curve. GRDEGs:Glycolysis-related differentially expressed genes.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3782121/v1/1aba98362fc7797ba65b5ef8.png"},{"id":49080199,"identity":"9ef33bbb-6df6-412e-9282-632eea7c705f","added_by":"auto","created_at":"2024-01-02 19:54:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":542841,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBoxplot before and after correction of osteoporosis datasets GSE56815 and GSE13850\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Boxplot of gene expression distribution among samples of dataset GSE56815 before correction. B. Boxplot of gene expression distribution among samples of the corrected dataset GSE56815. C. Boxplot of gene expression distribution among samples of dataset GSE13850 before correction. D. Boxplot of gene expression distribution among samples of the corrected dataset GSE13850. Blue represents the normal group and red represents the group of osteoporosis.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3782121/v1/0df24c1e1f384657632fb73f.png"},{"id":49080200,"identity":"8d085f01-ad91-4025-9d2e-f2dfea1e5812","added_by":"auto","created_at":"2024-01-02 19:54:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":506267,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential gene analysis of osteoporosis dataset\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Volcano map of differential gene of dataset GSE56815. B. Volcano map of differential gene of dataset GSE13850. C. Venn diagram of DEGs and GRGs in dataset GSE56815. D. Differential gene heatmap of dataset GSE56815. E. Differential gene heatmap of dataset GSE13850. DEGs: differentially expressed genes. GRGs: Glycolysis-related genes.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3782121/v1/a778a08de327bd2a3afa2537.png"},{"id":49081499,"identity":"29587104-01d3-4024-a5a7-e35526018f88","added_by":"auto","created_at":"2024-01-02 20:02:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1176748,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGRDEGs functional enrichment analysis (GO) and pathway enrichment analysis (KEGG)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA-B. The results of GO functional enrichment analysis (A) and KEGG pathway enrichment analysis (B) by GRDEGs are shown in bubble graphs. C-D. The results of GO functional enrichment analysis (C) and KEGG pathway enrichment analysis (D) are shown in the ring network diagram. E-F. GO functional enrichment analysis (E) and KEGG pathway enrichment analysis (F) of GRDEGs are shown in bar chart.\u003c/p\u003e\n\u003cp\u003eGRDEGs: Glycolysis-related differentially expressed genes. GO: Gene Ontology. BP: Biological process. CC: Cellular component. MF: Molecular function. KEGG: The Kyoto Encyclopedia of Genes and Genomes. The screening criteria for GO and KEGG enrichment items were \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 and FDR value \u0026lt; 0.25.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3782121/v1/b54323f016133dce352f5824.png"},{"id":49080203,"identity":"ac4375a1-90e3-4e88-910c-0d4a6b8bd712","added_by":"auto","created_at":"2024-01-02 19:54:49","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":432573,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGSEA of dataset GSE56815\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Four main biological characteristics of GSEA in dataset GSE56815. The DEGs in B-E. GSE56815 were significantly enriched in the NF_KB pathway (Figure 5B), glycolysis pathway (Figure 5C), Wnt pathway (Figure 5D), Hedgehog pathway (Figure 5E).\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3782121/v1/ff9bf999fa24f30d0b08ac96.png"},{"id":49080206,"identity":"86cbcdd0-4a98-46cb-a63c-bd24a1a4a837","added_by":"auto","created_at":"2024-01-02 19:54:49","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1022036,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProtein-protein interaction network (PPI network)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. PPI network of GRDEGs. B. Venn diagram of common genes of the top 10 GRDEGs were selected under the five algorithms of MCC, MNC, EPC, DEGREE and DMNC. C. PPI network of 6 hub genes. PPI network: Protein-protein interaction network. GRDEGs: Glycolysis-related differentially expressed genes. DEGREE: Degree Correlation. MNC: Maximum Neighborhood Component. MCC: Maximal Clique Centrality. EPC: Edge Percolated Component. DMNC: Density of Maximum Neighborhood Component.\u003c/p\u003e","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-3782121/v1/7ab4bb5ebc934a718e869357.png"},{"id":49080202,"identity":"e2b43f59-aa75-49bd-86b3-a9364685e127","added_by":"auto","created_at":"2024-01-02 19:54:49","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":147358,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression of GRDEGs in dataset\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Expression differences of 6 GRDEGs in different groups (Normal/OP) of the dataset GSE56815 were analyzed. B-G. ROC curve analysis of DEGs, HK3 (B), PFKL (C), CTNNB1 (D), PTEN (E), MPI (F), HKDC1 (G) in dataset GSE56815. H-M. ROC curve analysis of DEGs, PTEN (H), PFKL (I), MPI (J), HK3 (K), CTNNB1 (L), HKDC1 (M) in dataset GSE13850. The symbol * is equivalent to \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, which means statistically significant. The symbol ** is equivalent to \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, indicating high statistical significance. GRDEGs: Glycolysis-related differentially expressed genes. ROC: Receiver operating characteristic curve. The closer the AUC is to 1 in the ROC curve, the better the diagnostic effect is. The accuracy of AUC is low between 0.5 and 0.7. AUC has a certain accuracy between 0.7 and 0.9.\u003c/p\u003e","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-3782121/v1/c91d55ecfe83623faa9e19f9.png"},{"id":49080205,"identity":"95ba3ba2-1777-4471-874c-a7acd6ab651d","added_by":"auto","created_at":"2024-01-02 19:54:49","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":723579,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCIBERSORT analysis of immunoinfiltration in dataset GSE56815\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Bar chart showing the immunoinfiltration results of 22 kinds of immune cells in dataset GSE56815. B. The correlation analysis results between the abundance of immune cell infiltration were shown in the heatmap. C. Heatmap of correlation analysis results of GRDEGs and immune cell expression in dataset GSE56815. The symbol * is equivalent to \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, which means statistically significant. The symbol ** is equivalent to \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, indicating high statistical significance. GRDEGs: Glycolysis-related differentially expressed genes. OP: Osteoporosis.\u003c/p\u003e","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-3782121/v1/4b159a34f9cb15f7ba9a4db3.png"},{"id":67388247,"identity":"d4d394c2-a0ce-4fca-815e-eb69ce5af4f8","added_by":"auto","created_at":"2024-10-24 10:31:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2269825,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3782121/v1/8f852d9a-836b-4d16-9541-694e7764e76c.pdf"},{"id":49081498,"identity":"6e09e0aa-edc6-49a9-916a-d1f89e970b99","added_by":"auto","created_at":"2024-01-02 20:02:49","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19693,"visible":true,"origin":"","legend":"\u003cp\u003etable S1. Glycolysis-related differentially expressed genes.\u003c/p\u003e","description":"","filename":"tableS1.Glycolysisrelatedgenes..xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3782121/v1/531ab431d4bf9b8182c75e53.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Glycolysis Related Genes in Osteoporosis: Screening for Potential Prevention Targets","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eOsteoporosis is a chronic metabolic disease caused by the imbalance between bone formation and resorption in the bone remodeling process in which osteoclasts resorb bone tissues while osteoblasts generate and mineralize new bone matrix, both of which are regulated by a variety of biological factors[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As estimated, there are more than 200\u0026nbsp;million people suffering from osteoporosis worldwide[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The National Osteoporosis Foundation estimates that more than 10\u0026nbsp;million people over age 50 in the U.S.A. have osteoporosis and another 34\u0026nbsp;million are at risk for the disease[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Similar to females encountering postmenopausal osteoporosis, recent studies have shown that estrogen plays a crucial role in age-mediated male osteoporosis[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Statistically, osteoporosis primarily affects postmenopausal women and elderly men, with 30% of women and 20% of men aged 50 years old predicted to experience an osteoporosis-related fracture in their lifetime[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. It is the sequela of osteoporosis and most particularly the occurrence of osteoporotic fracture that makes osteoporosis a serious medical condition[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBone metabolism is a dynamic process of bone remodeling. Its balance is complicated, with oxidative metabolism seemingly important for osteoclast differentiation and glycolysis important for bone resorption[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Glycolysis generates ATP but also provides metabolic intermediates that enter many other metabolic pathways[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Our study implicates glycolytic metabolism as a novel therapeutic target for bone anabolism in age-related bone loss and osteoporosis. We believe that the developmental origins of osteoporosis could novelly provide a theoretical basis for the early prevention of osteoporosis.\u003c/p\u003e \u003cp\u003eIn this study, with tie aid of bioinformatics, we analyzed two publicly available microarray datasets retrieved from Gene Expression Omnibus (GEO). We screened out glycolysis-related differentially expressed genes (GRDEGs), conducted Gene Oncology/Kyoto Encyclopedia of Genes and Genomes annotations, protein-protein interaction network, analysis of immune infiltration and ROC curve, with the hope of finding potential therapeutic agents that could provide clues for early intervention of bone metabolism and prevention of osteoporosis.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data Source\u003c/h2\u003e \u003cp\u003eWe downloaded osteoporosis related datasets GSE56815 and GSE13850[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] from the GEO[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] database through the GEO query package[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Two datasets came from Homo sapiens, and the data platform were GPL96 [HG-U133A] Affymetrix Human Genome U133A Array. The dataset GSE56815 contained a total of 80 samples, including 40 normal samples and 40 osteoporosis samples. The dataset GSE13850 included 40 samples, among which 10 were classified into normal group, and 10 samples of osteoporosis. We normalized the data through the limma package[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe collected glycolysis-related genes (GRGs) from the GeneCards database[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], the GeneCards database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) provides comprehensive information about human genes. In the GeneCards database, 133 GRGs were obtained by using \"glycolysis\" as the search keyword and only retaining GRGs with \"Protein Coding\" and Relevance score\u0026thinsp;\u0026gt;\u0026thinsp;2. We also used the word \"glycolysis\" as the search keyword to obtain 835 GRGs from the Molecular Signatures Database (MSigDB)[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and obtained a total of 895 GRGs after combining and deduplicating, see table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Osteoporosis Related Differentially Expressed Genes\u003c/h2\u003e \u003cp\u003eIn order to identify the potential mechanism and relevant biological characteristics and pathways of differential genes in osteoporosis, differences between the datasets GSE56815 and GSE13850 were analyzed using the limma package[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Differentially expressed genes (DEGs) between different groups (Normal/OP) were obtained. |logFC| \u0026gt; 0 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for the standard selected genes for further study. logFC\u0026thinsp;\u0026gt;\u0026thinsp;0 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were up regulated genes, logFC\u0026thinsp;\u0026lt;\u0026thinsp;0 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were down regulated genes.\u003c/p\u003e \u003cp\u003eIn order to obtain GRDEGs associated with osteoporosis, the intersection of DEGs and GRGs of dataset GSE56815 was made and the Venn diagram was drawn. The results of the difference analysis were displayed by drawing volcano map and heatmap of R package ggplot2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Enrichment Analyses of DEGs\u003c/h2\u003e \u003cp\u003eGene Ontology (GO)[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] analysis is a common method for large-scale functional enrichment studies, including biological process (BP), molecular function (MF) and cellular component (CC). Kyoto Encyclopedia of Genes and Genomes (KEGG)[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] is a widely used database storing information on genomes, biological pathways, diseases, and drugs. We used clusterProfiler R package[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] to conduct GO annotation analysis on GRDEGs, and the entry screening criteria were \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and FDR value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, which was considered statistically significant. \u003cem\u003eP\u003c/em\u003e value correction method was Benjamini-Hochberg.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4\u003c/b\u003e Gene Set Enrichment Analysis\u003c/h2\u003e \u003cp\u003eGSEA (Gene Set Enrichment Analysis)[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] is used to evaluate the distribution trend of genes from a predefined gene set in the gene list sorted with phenotypic relevance to judge their contribution to phenotype. In this study, logFC values were first used as molecular sequencing to assess significant enrichment in predefined gene sets. Then, the clusterProfiler package was used for enrichment analysis of all genes associated with phenotypes. The parameters used in this GSEA were as follows, the seed was 2020, the count was 1000, each gene set contained at least 10 genes and at most 500 genes, and the \u003cem\u003ep\u003c/em\u003e value correction method was Benjamini-Hochberg. We obtained the gene set \"h.all.v7.4.symbols.gmt\" from MSigDB for GSEA analysis of genes expression in GSE56815. The screening criteria for significant enrichment were \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and FDR value\u0026thinsp;\u0026lt;\u0026thinsp;0.25.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Protein-Protein Interaction Network (PPI Network)\u003c/h2\u003e \u003cp\u003eThe protein-protein interaction network is made up of individual proteins interacting with each other. STRING Database[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] is a database that searches for known and predicted protein interactions. In this study, we used STRING database to construct protein-protein interaction network associated with GRDEGs and visualized PPI network models using Cytoscape. Tightly connected local regions in the PPI network, which might represent molecular complexes with specific biological functions. By utilizing the in cytoHubba plugin[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] DEGREE (Degree Correlation), MNC (Maximum Neighborhood Component), MCC (Maximal Clique Centrality), EPC (Edge Percolated Component) and Density of Maximum Neighborhood Component (DMNC), five algorithms, selected the top 10 common GRDEGs as key genes (mRNA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Analysis of Immune Infiltration\u003c/h2\u003e \u003cp\u003eCIBERSORT (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cibersort.stanford.edu/\u003c/span\u003e\u003cspan address=\"https://cibersort.stanford.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] is based on linear support vector regression principle of human immune cells subtype to the expression of matrix convolution of a R/web tools. It could evaluate the infiltrating state of immune cells in sequencing samples based on the gene expression characteristics of 22 known immune cell subtypes. In this study, CIBERSORT algorithm was used to evaluate the infiltrating state of immune cells in dataset GSE56815, and Spearman correlation was used to calculate the correlation between various immune cells. Subsequently, Spearman correlation analysis was used to determine the correlation between hub genes and immune infiltrating cells. Then, the correlation between immune cells and GRDEGs was calculated by combining the gene expression matrix of osteoporosis dataset, and the correlation heatmap was drawn by R-Pheatmap.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 ROC Curve\u003c/h2\u003e \u003cp\u003eReceiver operating characteristic curve (ROC) is a coordinate schema analysis tool that could be used to select the best model, discard the suboptimal or set the optimal threshold within the same model. ROC curve is a comprehensive index reflecting the sensitivity and specificity of continuous variables, and the correlation between sensitivity and specificity is reflected by the composition method. The area value under the ROC curve is generally between 0.5 and 1. The closer the AUC is to 1, the better the diagnostic effect. AUC has low accuracy between 0.5 and 0.7, certain accuracy between 0.7 and 0.9, and high accuracy above 0.9. We used the pROC package to plot ROC curves of hub genes in different groups (Normal/OP) in two datasets and calculated the area under the curve (AUC) to assess the diagnostic effect of hub genes expression on disease.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Statistical Analysis\u003c/h2\u003e \u003cp\u003eAll data processing and analysis in this paper were based on R software (Version 4.1.2). For the comparison of two groups of continuous variables, the independent Student t test was used to estimate the statistical significance of normally distributed variables. The Mann-Whitney U test (Wilcoxon rank sum test) was used to analyze the difference between variables with non-normal distribution. The Chi-square test or Fisher's exact test is used to compare and analyze statistical significance between two sets of categorical variables. If not specified specifically, the correlation coefficients between different molecules were calculated by Spearman correlation analysis. All p values reported from statistical tests are two-sided., and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cp\u003e\u003cstrong\u003eFigure 1 The design flow chart of this study.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGRGs:Glycolysis-related genes. DEGs:Differentially expressed genes.\u0026nbsp;GSEA:Gene Set Enrichment Analysis. GO:Gene Ontology. KEGG:Kyoto Encyclopedia of Genes and Genomes.\u0026nbsp;PPI network:Protein-protein interaction network.\u0026nbsp;ROC:Receiver operating characteristic curve. GRDEGs:Glycolysis-related differentially expressed genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1 Data Standardization and Analysis of Osteoporosis Related DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study mainly uses bioinformatics methods to explore the biological characteristics of osteoporosis, and the overall analysis flow chart is shown in Figure 1. In the datasets GSE56815 and GSE13850, subjects were divided into osteoporosis group and normal control group. Datasets GSE56815 and GSE13850 were standardized respectively, and data cleaning operations such as annotation probes were performed, and a boxplot was drawn for data distribution before and after standardization (Figure 2A-B, 2C-D).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2 Boxplot before and after correction of osteoporosis datasets GSE56815 and GSE13850\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Boxplot of gene expression distribution among samples of dataset GSE56815 before correction. B. Boxplot of gene expression distribution among samples of the corrected dataset GSE56815. C. Boxplot of gene expression distribution among samples of dataset GSE13850 before correction. D. Boxplot of gene expression distribution among samples of the corrected dataset GSE13850. Blue represents the normal group and red represents the group of osteoporosis.\u003c/p\u003e\n\u003cp\u003eIn order to analyze the difference in gene expression values between the osteoporosis and normal group in the datasets GSE56815 and GSE13850, we used the limma package for differential analysis to obtain the DEGs in the data, and the results were as follows, in dataset GSE56815, 1280 DEGs met the requirements of high expression in disease group (low expression in normal group, logFC \u0026gt;0, up-regulated gene) and 1019 met the requirements of low expression in disease group (high expression in normal group, logFC \u0026lt; 0, down-regulated gene). We mapped the results of the variance analysis into a volcano map (Figure 3A). In the GSE13850 dataset, 461 met the requirements of high expression in disease group (low expression in normal group, logFC \u0026gt; 0, up-regulated gene), and 499 met the requirements of low expression in disease group (high expression in normal group, logFC \u0026lt; 0, down-regulated gene). A volcano map (Figure 3B) was drawn from the results of the variance analysis of this dataset.\u003c/p\u003e\n\u003cp\u003eIn order to obtain GRDEGs, We took intersection of DEGs of dataset GSE56815 in which| logFC | \u0026gt; 0 and \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 and GRGs, a total of 154 GRDEGs and mapped the Venn diagram (figure 3C).\u003c/p\u003e\n\u003cp\u003eWe analyzed the expression differences among different groups in the dataset GSE56815 (Figure 3D) and used R package pheatmap to draw heatmap to show the analysis results. The first 5 up-regulated DEGs and the first 5 down-regulated DEGs (FOLR3, DPP8, GZMB, DMTF1, FOXO3, RUNX1T1, DMP1, THPO, PAWR, PRLR) were found by selecting the logFC column in ascending and descending order from the results of differential analysis. The results showed that the clustering degree of these 10 DEGs was obvious in different groups of dataset GSE56815.\u003c/p\u003e\n\u003cp\u003eWe also analyzed the expression differences among different groups in the dataset GSE13850 (Figure 3E) and used R package pheatmap to draw heatmap to show the analysis results. The first 5 up-regulated DEGs and the first 5 down-regulated DEGs (LY96, NRIP1, ARMC1, CYB5R4, TRIB2, ITSN1, TPTE, GNAL, TRPC6, GPR1) were found by selecting the logFC column in ascending and descending order. The results showed that the clustering degree of these 10 DEGs were obvious in different groups of dataset GSE13850.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3. Differential gene analysis of osteoporosis dataset\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Volcano map of differential gene of dataset GSE56815. B. Volcano map of differential gene of dataset GSE13850. C. Venn diagram of DEGs and GRGs in dataset GSE56815. D. Differential gene heatmap of dataset GSE56815. E. Differential gene heatmap of dataset GSE13850. DEGs: differentially expressed genes. GRGs: Glycolysis-related genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 GRDEGs Functional Enrichment Analysis and Pathway Enrichment Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to analyze the biological processes, molecular functions, cellular components, biological pathways and their relationship with osteoporosis in 154 GRDEGs, the Gene Ontology (GO) (Table 1) and Kyoto Encyclopedia of Genes and Genomes (KEGG) (Table 2) enrichment analyses of GRDEGs were conducted firstly. The screening criteria for enrichment items were \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 and FDR value \u0026lt; 0.05, which was considered to be statistically significant. The results of GO functional enrichment analysis and KEGG enrichment analysis were shown in bubble diagram (Figure 4A-B) and ring network diagram (Figure 4C-D). Then, the GO functional enrichment analysis results of GRDEGs combined with logFC were shown in a bar chart (Figure 4E-F).\u003c/p\u003e\n\u003cp\u003eThe results showed that 154 GRDEGs were mainly enriched in JAK-STAT cascade (GO 0007259), regulation of JAK-STAT cascade (GO 0046425), negative regulation of JAK-STAT cascade (GO 0046426), canonical glycolysis (GO 0061621) in biological process, host (GO 0018995), nuclear pore (GO 0005643), vesicle lumen (GO 0031983), region of cytosol (GO 0099522) in cellular component, isomerase activity (GO 0016853), vitamin B6 binding (GO 0070279), vitamin binding (GO 0019842), NAD binding (GO: 0051287) and other molecular functions. The results of KEGG enrichment analysis mainly included glycolysis/gluconeogenesis (hsa00010), autophagy-animal (hsa04140), carbon metabolism (hsa01200), pyruvate metabolism (hsa00620), beta-alanine metabolism (hsa00410), galactose metabolism (hsa00052), prostate cancer (hsa05215) and other signaling pathways.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 4 GRDEGs functional enrichment analysis (GO) and pathway enrichment analysis (KEGG)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA-B. The results of GO functional enrichment analysis (A) and KEGG pathway enrichment analysis (B) by GRDEGs are shown in bubble graphs. C-D. The results of GO functional enrichment analysis (C) and KEGG pathway enrichment analysis (D) are shown in the ring network diagram. E-F. GO functional enrichment analysis (E) and KEGG pathway enrichment analysis (F) of GRDEGs are shown in bar chart.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGRDEGs: Glycolysis-related differentially expressed genes. GO: Gene Ontology. BP: Biological process. CC: Cellular component. MF: Molecular function. KEGG: The Kyoto Encyclopedia of Genes and Genomes. The screening criteria for GO and KEGG enrichment items were \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 and FDR value \u0026lt; 0.25.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 GSEA of Osteoporosis Dataset\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo determine the effect of gene expression levels in the normal and osteoporosis group, we analyzed the association between gene expression in the dataset GSE56815 and the biological processes involved, cellular components affected, and molecular functions performed by GSEA (Table 3). The results showed as follows (Figure 5A) that DEGs in GSE56815 were significantly enriched in the NF_KB pathway (Figure 5B), glycolysis pathway (Figure 5C), Wnt pathway (Figure 5D), Hedgehog pathway (Figure 5E) and others.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 5 GSEA of dataset GSE56815\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Four main biological characteristics of GSEA in dataset GSE56815. The DEGs in B-E. GSE56815 were significantly enriched in the NF_KB pathway (Figure 5B), glycolysis pathway (Figure 5C), Wnt pathway (Figure 5D), Hedgehog pathway (Figure 5E).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Protein-Protein Interaction Network (PPI Network)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProtein-protein interaction analysis was performed on 154 GRDEGs using the STRING database, with a minimum required interaction score higher than 0.9 was used as the standard, and the protein-protein interaction network (PPI network) was constructed, and the network diagram of protein-protein interaction was drawn (Figure 6A). Then cytoHubba plugin of Cytoscape software was used to analyze the six hub genes (CTNNB1, HK3, MPI, HKDC1, PFKL,PTEN, mRNA) in the top 10 selected by MCC, MNC, EPC, DEGREE and DMNC algorithms which were as key genes (hub genes, mRNA) (Figure 6B) and mapped protein-protein interaction networks (Figure 6C).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 6 Protein-protein interaction network (PPI network)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. PPI network of GRDEGs. B. Venn diagram of common genes of the top 10 GRDEGs were selected under the five algorithms of MCC, MNC, EPC, DEGREE and DMNC. C. PPI network of 6 hub genes. PPI network: Protein-protein interaction network. GRDEGs: Glycolysis-related differentially expressed genes. DEGREE: Degree Correlation. MNC: Maximum Neighborhood Component. MCC: Maximal Clique Centrality. EPC: Edge Percolated Component. DMNC: Density of Maximum Neighborhood Component.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Expression Analysis of GRDEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analyzed the expression difference of 6 GRDEGs (CTNNB1, HK3, MPI, HKDC1, PFKL, PTEN) among different groups (Normal/OP) in the dataset GSE56815 respectively (Figure 7A), and the results showed that the expression levels of 2 GRDEGs (CTNNB1, PFKL) in different groups of the dataset GSE56815 were highly statistically significant (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt;0.01). The expression levels of 4 GRDEGs (HK3, MPI, HKDC1, PTEN) were statistically significant (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003eWe drew 6 GRDEGs (CTNNB1, HK3, MPI, HKDC1, PFKL, PTEN) ROC curve in dataset GSE56815, and the results were shown (Figure 7B-G). As can be seen from the figure, HK3 (AUC = 0.667, Figure 7B), CTNNB1 (AUC = 0.682, Figure 7D), PTEN (AUC = 0.653, Figure 7E), MPI (AUC = 0.650, Figure 7F), HKDC1 (AUC = 0.635, Figure 7G) showed low diagnostic value among different groups (Normal/OP) in dataset GSE56815. The expression level of PFKL (AUC = 0.701, Figure 7C) in dataset GSE56815 showed certain diagnostic value.\u003c/p\u003e\n\u003cp\u003eIn order to further explore the expression differences of 6 GRDEGs (CTNNB1, HK3, MPI, HKDC1, PFKL, PTEN), we performed ROC verification in the dataset GSE13850. As can be seen from the figure, the expression level of PTEN (AUC = 0.740, Figure 7H) in dataset GSE13850 showed certain diagnostic value among different groups (Normal/OP). PFKL (AUC = 0.640, Figure 7I), MPI (AUC = 0.630, Figure 7J), HK3 (AUC = 0.630, Figure 7K), CTNNB1 (AUC = 0.620, Figure 7L), HKDC1 (AUC = 0.505, Figure 7M) showed low diagnostic value.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFIG. 7 Expression of GRDEGs in dataset\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Expression differences of 6 GRDEGs in different groups (Normal/OP) of the dataset GSE56815 were analyzed. B-G. ROC curve analysis of DEGs, HK3 (B), PFKL (C), CTNNB1 (D), PTEN (E), MPI (F), HKDC1 (G) in dataset GSE56815. H-M. ROC curve analysis of DEGs, PTEN (H), PFKL (I), MPI (J), HK3 (K), CTNNB1 (L), HKDC1 (M) in dataset GSE13850. The symbol * is equivalent to \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, which means statistically significant. The symbol ** is equivalent to \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, indicating high statistical significance. GRDEGs: Glycolysis-related differentially expressed genes. ROC: Receiver operating characteristic curve. The closer the AUC is to 1 in the ROC curve, the better the diagnostic effect is. The accuracy of AUC is low between 0.5 and 0.7. AUC has a certain accuracy between 0.7 and 0.9.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Immunoinfiltration Analysis of Osteoporosis Dataset (CIBERSORT)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used CIBERSORT algorithm to calculate the correlation between expression profile data of 22 kinds of immune cells in different groups (Normal/OP) in the dataset GSE56815. According to the results of immune infiltration analysis, we plotted the infiltration of 22 types of immune cells in each sample of dataset GSE56815 (Figure 8A) in a bar chart. We showed the correlation between the abundance of immune cell infiltration with the correlation heatmap (Figure 8B). The results showed that the infiltration abundance of memory B cells was significantly correlated with that of \u0026nbsp;naive B cells, CD4 memory activated T cells, Monocytes, Macrophages M2 (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01). The abundance of infiltrating immune cells was significantly correlated with memory B cells and gamma-delta T cells (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003eWe also used correlation heatmaps to show the correlation between 6 GRDEGs (CTNNB1, HK3, MPI, HKDC1, PFKL, PTEN) and the abundance of immune cell infiltration (Figure 8C). The results showed that there was a significant correlation between hub genes (CTNNB1 HK3, MPI, HKDC1, PFKL, PTEN) and the abundance of\u0026nbsp;infiltrating T\u0026nbsp;follicular\u0026nbsp;helper\u0026nbsp;cells\u0026nbsp;(\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01). The hub gene MPI was significantly correlated with the infiltrating abundance of Macrophages M0 and Macrophages M2 (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 8 CIBERSORT analysis of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eimmunoinfiltration\u0026nbsp;in dataset GSE56815\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Bar chart showing the immunoinfiltration results of 22 kinds of immune cells in dataset GSE56815. B. The correlation analysis results between the abundance of immune cell infiltration were shown in the heatmap. C. Heatmap of correlation analysis results of GRDEGs and immune cell expression in dataset GSE56815. The symbol * is equivalent to \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, which means statistically significant. The symbol ** is equivalent to \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, indicating high statistical significance. GRDEGs: Glycolysis-related differentially expressed genes. OP: Osteoporosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. GO enrichment analysis results of GRDEGs.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"664\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.984962406015038%\"\u003e\n \u003cp\u003eONTOLOGY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.030075187969924%\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.18796992481203%\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.330827067669173%\"\u003e\n \u003cp\u003eGeneRatio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003eBgRatio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003epvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003ep.adjust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003eqvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.984962406015038%\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.030075187969924%\"\u003e\n \u003cp\u003eGO:0016052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.18796992481203%\"\u003e\n \u003cp\u003ecarbohydrate catabolic process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.330827067669173%\"\u003e\n \u003cp\u003e24/147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e199/18670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e8.31e-22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e1.61e-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e1.31e-18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.984962406015038%\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.030075187969924%\"\u003e\n \u003cp\u003eGO:0006090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.18796992481203%\"\u003e\n \u003cp\u003epyruvate metabolic process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.330827067669173%\"\u003e\n \u003cp\u003e22/147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e154/18670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e1.06e-21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e1.61e-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e1.31e-18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.984962406015038%\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.030075187969924%\"\u003e\n \u003cp\u003eGO:0006165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.18796992481203%\"\u003e\n \u003cp\u003enucleoside diphosphate phosphorylation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.330827067669173%\"\u003e\n \u003cp\u003e20/147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e134/18670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e3.35e-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e3.41e-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e2.77e-17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.984962406015038%\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.030075187969924%\"\u003e\n \u003cp\u003eGO:0046939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.18796992481203%\"\u003e\n \u003cp\u003enucleotide phosphorylation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.330827067669173%\"\u003e\n \u003cp\u003e20/147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e136/18670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e4.55e-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e3.47e-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e2.82e-17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.984962406015038%\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.030075187969924%\"\u003e\n \u003cp\u003eGO:0042866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.18796992481203%\"\u003e\n \u003cp\u003epyruvate biosynthetic process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.330827067669173%\"\u003e\n \u003cp\u003e19/147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e119/18670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e8.13e-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e4.96e-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e4.03e-17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.984962406015038%\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.030075187969924%\"\u003e\n \u003cp\u003eGO:0018995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.18796992481203%\"\u003e\n \u003cp\u003ehost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.330827067669173%\"\u003e\n \u003cp\u003e8/149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e73/19717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e7.82e-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e6.44e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e5.52e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.984962406015038%\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.030075187969924%\"\u003e\n \u003cp\u003eGO:0043657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.18796992481203%\"\u003e\n \u003cp\u003ehost cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.330827067669173%\"\u003e\n \u003cp\u003e8/149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e73/19717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e7.82e-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e6.44e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e5.52e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.984962406015038%\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.030075187969924%\"\u003e\n \u003cp\u003eGO:0044215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.18796992481203%\"\u003e\n \u003cp\u003eother organism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.330827067669173%\"\u003e\n \u003cp\u003e8/149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e77/19717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e1.19e-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e6.44e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e5.52e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.984962406015038%\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.030075187969924%\"\u003e\n \u003cp\u003eGO:0044216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.18796992481203%\"\u003e\n \u003cp\u003eother organism cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.330827067669173%\"\u003e\n \u003cp\u003e8/149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e77/19717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e1.19e-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e6.44e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e5.52e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.984962406015038%\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.030075187969924%\"\u003e\n \u003cp\u003eGO:0044217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.18796992481203%\"\u003e\n \u003cp\u003eother organism part\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.330827067669173%\"\u003e\n \u003cp\u003e8/149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e77/19717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e1.19e-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e6.44e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e5.52e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.984962406015038%\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.030075187969924%\"\u003e\n \u003cp\u003eGO:0050662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.18796992481203%\"\u003e\n \u003cp\u003ecoenzyme binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.330827067669173%\"\u003e\n \u003cp\u003e13/148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e291/17697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e1.03e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e3.10e-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e2.77e-04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.984962406015038%\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.030075187969924%\"\u003e\n \u003cp\u003eGO:0005536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.18796992481203%\"\u003e\n \u003cp\u003eglucose binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.330827067669173%\"\u003e\n \u003cp\u003e4/148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e11/17697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e1.48e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e3.10e-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e2.77e-04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.984962406015038%\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.030075187969924%\"\u003e\n \u003cp\u003eGO:0017056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.18796992481203%\"\u003e\n \u003cp\u003estructural constituent of nuclear pore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.330827067669173%\"\u003e\n \u003cp\u003e5/148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e28/17697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e3.22e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e4.48e-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e4.02e-04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.984962406015038%\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.030075187969924%\"\u003e\n \u003cp\u003eGO:0048029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.18796992481203%\"\u003e\n \u003cp\u003emonosaccharide binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.330827067669173%\"\u003e\n \u003cp\u003e6/148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e75/17697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e3.87e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.984962406015038%\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.030075187969924%\"\u003e\n \u003cp\u003eGO:0016853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.18796992481203%\"\u003e\n \u003cp\u003eisomerase activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.330827067669173%\"\u003e\n \u003cp\u003e7/148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e158/17697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e3.69e-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eGRDEGs:Glycolysis-related differentially expressed genes. GO:Gene Ontology. BP:Biological process. CC:Cellular component. MF:Molecular function.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. KEGG enrichment analysis results of GRDEGs.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"664\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.027149321266968%\"\u003e\n \u003cp\u003eONTOLOGY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.91553544494721%\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.764705882352942%\"\u003e\n \u003cp\u003eGeneRatio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.669683257918551%\"\u003e\n \u003cp\u003eBgRatio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.312217194570136%\"\u003e\n \u003cp\u003epvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.312217194570136%\"\u003e\n \u003cp\u003ep.adjust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.312217194570136%\"\u003e\n \u003cp\u003eqvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.027149321266968%\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.91553544494721%\"\u003e\n \u003cp\u003ehsa01200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\"\u003e\n \u003cp\u003eCarbon metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.764705882352942%\"\u003e\n \u003cp\u003e17/102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.669683257918551%\"\u003e\n \u003cp\u003e118/8076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.312217194570136%\"\u003e\n \u003cp\u003e6.64e-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.312217194570136%\"\u003e\n \u003cp\u003e1.60e-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.312217194570136%\"\u003e\n \u003cp\u003e1.25e-11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.027149321266968%\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.91553544494721%\"\u003e\n \u003cp\u003ehsa00010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\"\u003e\n \u003cp\u003eGlycolysis / Gluconeogenesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.764705882352942%\"\u003e\n \u003cp\u003e12/102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.669683257918551%\"\u003e\n \u003cp\u003e67/8076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.312217194570136%\"\u003e\n \u003cp\u003e2.87e-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.312217194570136%\"\u003e\n \u003cp\u003e3.46e-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.312217194570136%\"\u003e\n \u003cp\u003e2.71e-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.027149321266968%\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.91553544494721%\"\u003e\n \u003cp\u003ehsa00020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\"\u003e\n \u003cp\u003eCitrate cycle (TCA cycle)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.764705882352942%\"\u003e\n \u003cp\u003e7/102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.669683257918551%\"\u003e\n \u003cp\u003e30/8076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.312217194570136%\"\u003e\n \u003cp\u003e6.68e-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.312217194570136%\"\u003e\n \u003cp\u003e5.37e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.312217194570136%\"\u003e\n \u003cp\u003e4.20e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.027149321266968%\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.91553544494721%\"\u003e\n \u003cp\u003ehsa01230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\"\u003e\n \u003cp\u003eBiosynthesis of amino acids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.764705882352942%\"\u003e\n \u003cp\u003e9/102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.669683257918551%\"\u003e\n \u003cp\u003e75/8076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.312217194570136%\"\u003e\n \u003cp\u003e3.60e-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.312217194570136%\"\u003e\n \u003cp\u003e2.17e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.312217194570136%\"\u003e\n \u003cp\u003e1.70e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.027149321266968%\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.91553544494721%\"\u003e\n \u003cp\u003ehsa04910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\"\u003e\n \u003cp\u003eInsulin signaling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.764705882352942%\"\u003e\n \u003cp\u003e11/102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.669683257918551%\"\u003e\n \u003cp\u003e137/8076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.312217194570136%\"\u003e\n \u003cp\u003e1.07e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.312217194570136%\"\u003e\n \u003cp\u003e5.16e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.312217194570136%\"\u003e\n \u003cp\u003e4.03e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eGRDEGs:Glycolysis-related differentially expressed genes. KEGG:Kyoto Encyclopedia of Genes and Genomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. GSEA of dataset GSE56815.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"637\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.95924764890282%\" valign=\"bottom\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\" valign=\"bottom\"\u003e\n \u003cp\u003esetSize\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.705329153605014%\" valign=\"bottom\"\u003e\n \u003cp\u003eenrichmentScore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.18808777429467%\" valign=\"bottom\"\u003e\n \u003cp\u003eNES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.18808777429467%\" valign=\"bottom\"\u003e\n \u003cp\u003epvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.344827586206897%\" valign=\"bottom\"\u003e\n \u003cp\u003ep.adjust\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.95924764890282%\"\u003e\n \u003cp\u003eREACTOME_TNFR2_NON_CANONICAL_NF_KB_PATHWAY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.705329153605014%\"\u003e\n \u003cp\u003e0.342838269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.18808777429467%\"\u003e\n \u003cp\u003e1.342041373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.18808777429467%\"\u003e\n \u003cp\u003e0.046683047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.344827586206897%\"\u003e\n \u003cp\u003e0.269949792\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.95924764890282%\"\u003e\n \u003cp\u003eWP_COMPUTATIONAL_MODEL_OF_AEROBIC_GLYCOLYSIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.705329153605014%\"\u003e\n \u003cp\u003e-0.630758854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.18808777429467%\"\u003e\n \u003cp\u003e-1.528063408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.18808777429467%\"\u003e\n \u003cp\u003e0.045714286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.344827586206897%\"\u003e\n \u003cp\u003e0.268686869\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.95924764890282%\"\u003e\n \u003cp\u003eST_WNT_BETA_CATENIN_PATHWAY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.705329153605014%\"\u003e\n \u003cp\u003e-0.513219512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.18808777429467%\"\u003e\n \u003cp\u003e-1.521698728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.18808777429467%\"\u003e\n \u003cp\u003e0.028021016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.344827586206897%\"\u003e\n \u003cp\u003e0.206369761\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.95924764890282%\"\u003e\n \u003cp\u003eREACTOME_HEDGEHOG_ON_STATE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.705329153605014%\"\u003e\n \u003cp\u003e0.365468646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.18808777429467%\"\u003e\n \u003cp\u003e1.380768448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.18808777429467%\"\u003e\n \u003cp\u003e0.037209302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.344827586206897%\"\u003e\n \u003cp\u003e0.239687272\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.95924764890282%\"\u003e\n \u003cp\u003eWP_VEGFAVEGFR2_SIGNALING_PATHWAY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\"\u003e\n \u003cp\u003e382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.705329153605014%\"\u003e\n \u003cp\u003e-0.384226162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.18808777429467%\"\u003e\n \u003cp\u003e-1.690505693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.18808777429467%\"\u003e\n \u003cp\u003e0.001461988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.344827586206897%\"\u003e\n \u003cp\u003e0.065017973\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.95924764890282%\"\u003e\n \u003cp\u003eREACTOME_NEUTROPHIL_DEGRANULATION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\"\u003e\n \u003cp\u003e402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.705329153605014%\"\u003e\n \u003cp\u003e-0.497153795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.18808777429467%\"\u003e\n \u003cp\u003e-2.192682565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.18808777429467%\"\u003e\n \u003cp\u003e0.001468429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.344827586206897%\"\u003e\n \u003cp\u003e0.065017973\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.95924764890282%\"\u003e\n \u003cp\u003ePID_PDGFRB_PATHWAY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.705329153605014%\"\u003e\n \u003cp\u003e-0.477253699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.18808777429467%\"\u003e\n \u003cp\u003e-1.836503002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.18808777429467%\"\u003e\n \u003cp\u003e0.001602564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.344827586206897%\"\u003e\n \u003cp\u003e0.065017973\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.95924764890282%\"\u003e\n \u003cp\u003eREACTOME_CELLULAR_SENESCENCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.705329153605014%\"\u003e\n \u003cp\u003e-0.471952164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.18808777429467%\"\u003e\n \u003cp\u003e-1.810317865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.18808777429467%\"\u003e\n \u003cp\u003e0.001602564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.344827586206897%\"\u003e\n \u003cp\u003e0.065017973\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.95924764890282%\"\u003e\n \u003cp\u003eREACTOME_SIGNALING_BY_NTRKS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.705329153605014%\"\u003e\n \u003cp\u003e-0.527310648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.18808777429467%\"\u003e\n \u003cp\u003e-2.048546521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.18808777429467%\"\u003e\n \u003cp\u003e0.001610306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.344827586206897%\"\u003e\n \u003cp\u003e0.065017973\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.95924764890282%\"\u003e\n \u003cp\u003eWP_TGFBETA_SIGNALING_PATHWAY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.705329153605014%\"\u003e\n \u003cp\u003e-0.436228939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.18808777429467%\"\u003e\n \u003cp\u003e-1.680876234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.18808777429467%\"\u003e\n \u003cp\u003e0.001620746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.344827586206897%\"\u003e\n \u003cp\u003e0.065017973\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.95924764890282%\"\u003e\n \u003cp\u003eREACTOME_SIGNALING_BY_VEGF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.705329153605014%\"\u003e\n \u003cp\u003e-0.471579767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.18808777429467%\"\u003e\n \u003cp\u003e-1.765944536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.18808777429467%\"\u003e\n \u003cp\u003e0.001636661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.344827586206897%\"\u003e\n \u003cp\u003e0.065017973\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.95924764890282%\"\u003e\n \u003cp\u003eREACTOME_ANTIMICROBIAL_PEPTIDES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.705329153605014%\"\u003e\n \u003cp\u003e-0.732774608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.18808777429467%\"\u003e\n \u003cp\u003e-2.325756934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.18808777429467%\"\u003e\n \u003cp\u003e0.001782531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.344827586206897%\"\u003e\n \u003cp\u003e0.065017973\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.95924764890282%\"\u003e\n \u003cp\u003eBIOCARTA_INTEGRIN_PATHWAY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.705329153605014%\"\u003e\n \u003cp\u003e-0.606302895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.18808777429467%\"\u003e\n \u003cp\u003e-1.81260648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.18808777429467%\"\u003e\n \u003cp\u003e0.001785714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.344827586206897%\"\u003e\n \u003cp\u003e0.065017973\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.95924764890282%\"\u003e\n \u003cp\u003ePID_ARF6_PATHWAY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.705329153605014%\"\u003e\n \u003cp\u003e-0.575529195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.18808777429467%\"\u003e\n \u003cp\u003e-1.720605255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.18808777429467%\"\u003e\n \u003cp\u003e0.001785714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.344827586206897%\"\u003e\n \u003cp\u003e0.065017973\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.95924764890282%\"\u003e\n \u003cp\u003ePID_IL8_CXCR2_PATHWAY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.705329153605014%\"\u003e\n \u003cp\u003e-0.61154306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.18808777429467%\"\u003e\n \u003cp\u003e-1.828272505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.18808777429467%\"\u003e\n \u003cp\u003e0.001785714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.344827586206897%\"\u003e\n \u003cp\u003e0.065017973\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eGSEA: Gene Set Enrichment Analysis.\u003c/p\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eAs the global aging process continues to accelerate, the number of osteoporosis patients increases, the most serious complication of osteoporosis is osteoporotic fracture, and the incidence of spinal fractures is the highest, that makes osteoporosis a serious medical condition[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Therefore, the ability to diagnose osteoporosis before fractures occur, and timely treatment of osteoporosis are important[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The drugs current used for treating osteoporosis are efficient, but far from ideal, making an urgent need to find new pharmacological agents that could treat osteoporosis efficiently without serious side-effects[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Several anabolic agents have been approved for use in severe cases of osteoporosis[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, the role of glycolysis in the development of osteoporosis has been poorly studied. We believe that the developmental origins of osteoporosis could novelly provide a theoretical basis for the early prevention of osteoporosis.\u003c/p\u003e \u003cp\u003eIn this study, we selected two microarray datasets from the GEO database to screen for DEGs in osteoporosis and performed GO and KEGG enrichment analyses as well as PPI analyses to clarify the molecular mechanism of the DEGs. Then we obtained GRDEGs of osteoporosis by analyzing the gene expression profiles, there were 154 GRDEGs. To understand the roles of these GRDEGs in osteoporosis, we further performed GO enrichment analysis and found that the biological processes mainly involved in JAK-STAT cascade and canonical glycolysis. GRDEGs were involved in regards to the molecular functions to isomerase activity, vitamin B6 binding, vitamin binding, NAD binding. Besides, KEGG enrichment analysis demonstrated that the pathways correlated to GRDEGs also included glycolysis/gluconeogenesis. For GSEA enrichment analysis, they were significantly enriched in the NF_KB pathway, glycolysis pathway, Wnt pathway and Hedgehog pathway. The JAK/STAT pathway is a signaling cascade that has a prominent role in oncogenes, tumor progression, angiogenesis, cell motility, immune response, and stem cell differentiation[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Thus, the regulation of JAK/STAT signal transduction is important to prevent abnormal signal transduction that leads to disease progression. Glycolysis is a process involved a series of glycolytic enzymes and closely related to an inflammatory functional phenotype in macrophages, and it is known that classical inflammatory M1 macrophages heavily dependent on glycolysis to produce ATP[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Classically activated macrophages induce aerobic glycolysis that results in lactate production and increased synthesis and secretion of inflammatory cytokines[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Upregulation of the canonical WNT pathway plays a pivotal role in metabolism and particularly in the aerobic glycolysis[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs can be seen in protein-protein interaction network of GRDEGs by STRING, 6 hub genes for glycolysis in osteoporosis which were CTNNB1, HK3, MPI, HKDC1, PFKL, PTEN, were identified by Cytoscape. As for expression analysis, the expression level of PFKL in dataset GSE56815 showed certain diagnostic value and PTEN in dataset GSE13850 showed certain diagnostic value among different groups (Normal/OP).\u003c/p\u003e \u003cp\u003eIn the event of osteoporosis, 22 kinds of immune cells were recruited to the site of inflammation repairing the tissue to eventually restore homeostasis. In this study, GSE56815 was downloaded and immune cells analysis was carried out to screen out the hub genes associated with immune cells. The results showed that the infiltration abundance of memory B cells was significantly correlated with that of naive B cells, CD4 memory activated T cells, Monocytes, Macrophages M2. The abundance of infiltrating immune cells was significantly correlated with memory B cells and gamma-delta T cells. There was a significant correlation between hub genes (CTNNB1 HK3, MPI, HKDC1, PFKL, PTEN) and the abundance of infiltrating T follicular helper cells. The hub gene MPI was significantly correlated with the infiltrating abundance of Macrophages M0 and Macrophages M2.\u003c/p\u003e \u003cp\u003eGlycolysis promote the inflammation through multiple mechanism, metabolic reprogramming of macrophages promises to be a new target for macrophage-mediated inflammatory diseases[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In mammals, M1 macrophages show metabolic reprogramming toward glycolysis, while M2 macrophages rely on oxidative phosphorylation to generate energy[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Glycolysis is also required for macrophage M2 differentiation[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Perturbation of the normal balance of M1/M2 macrophages appears to be an important factor in osteoporosis pathogenesis[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Based on these evidences, development of therapeutic agents that potentiate M2 macrophage populations in bone might be useful to improve osteoporosis clinical outcome.\u003c/p\u003e \u003cp\u003eThere are some limitations which should be mentioned. Firstly, the strict inclusion and exclusion criteria are a double-edged sword. Although it ensures that the datasets included in the discovery stage have good homogeneity, which might limit the discovery of differential genes. Secondly, the sample sizes of included datasets were not too large, however, after verified by dataset GSE13850, the results are highly reliable. Lastly, these glycolysis-related genes differentially expressed in osteoporosis, especially the hub genes and signaling pathways, which need to be further verified by cell experiments, and clinical samples.\u003c/p\u003e "},{"header":"CONCLUSION","content":" \u003cp\u003eBased on our current study, our research provided a bioinformatics analysis of glycolytic metabolism as a novel therapeutic target for bone anabolism in bone loss and osteoporosis. The screened hub GRDEGs, CTNNB1 HK3, MPI, HKDC1, PFKL, PTEN, might potentially be used as targets for the early prevention of osteoporosis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the support of the contributors in uploading datasets and providing the GEO platform.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXB Hu and JZ Yang carried out the studies, participated in collecting data, and drafted the manuscript，J Zhang and J Hu helped to analyse the data and draft the manuscript. J Zhang and XF Yuan participated in its design. All authors contributed to data analysis, drafting and revising the article, gave final approval of the version to be published, and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Yunnan Province Applied Basic Research Program\u003c/p\u003e\n\u003cp\u003e(No.202101AT070276), the Yunnan Provincial Department of Science and Technology-Kunming Medical University Applied Basic Research Joint Project(No.202001AY07001-209), and the Kunming Health Science and Technology Personnel Training Project - Ten Hundred Thousand Project Training Plan(2021-SW Province-08).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study (GSE56815 and GSE13850) are available in the National Center for Biotechnology Information Gene Expression Omnibus (GEO) repository.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZhang D, Miranda M, Li X, Han J, Sun Y, Rojas N, He S, Hu M, Lin L, Li X, Ke HZ, Qin YX. 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Gut. 2018;67(5):827\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e\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":"Osteoporosis, glycolysis, bioinformatic analysis, GEO, hub genes","lastPublishedDoi":"10.21203/rs.3.rs-3782121/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3782121/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eOsteoporosis is a metabolic bone disorder that globally affects more than 200\u0026nbsp;million people. Glycolysis seemingly important for bone resorption. We aimed to investigate glycolysis-related differentially expressed genes (GRDEGs) that might be potential targets for osteoporosis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eDifferential expression analysis of GSE56815 from the Gene Expression Omnibus (GEO) database was performed. A Venn diagram was used to obtain the overlapping GRDEGs. The enrichment pathway analysis was performed and the hub genes were obtained. The abundance of immune cells was estimated utilizing the CIBERSORT algorithm.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eUtilizing the limma package and the Venn diagram, 154 GRDEGs were obtained. The GO and KEGG enrichment analysis of GRDEGs indicated several enriched terms related to regulation of JAK-STAT cascade and canonical glycolysis. As for GSEA enrichment analysis, they were significantly enriched in the NF_KB, glycolysis, Wnt and Hedgehog pathway. In the protein-protein interaction network, the hub differentially expressed genes, such as CTNNB1, HK3, MPI, HKDC1, PFKL, PTEN were obtained, which were correlated with the abundance of infiltrating T follicular helper cells. The hub genes MPI was significantly correlated with the invasion abundance of Macrophages M0 and Macrophages M2.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur study reveals the potential role of GRDEGs in osteoporosis through bioinformatics analysis. The screened hub genes, CTNNB1, HK3, MPI, HKDC1, PFKL and PTEN might be therapeutic targets for patients with osteoporosis and novelly provide a theoretical basis for the early prevention of osteoporosis.\u003c/p\u003e","manuscriptTitle":"Glycolysis Related Genes in Osteoporosis: Screening for Potential Prevention Targets","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-02 19:54:44","doi":"10.21203/rs.3.rs-3782121/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":"0320695a-f61c-483b-bdd5-324eb0bcb88b","owner":[],"postedDate":"January 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-25T05:08:35+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-02 19:54:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3782121","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3782121","identity":"rs-3782121","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}