Abstract The prevalence of nonalcoholic steatohepatitis (NASH) is rising annually, posing health and economic challenges, with limited treatments available. Diosgenin, a natural steroidal compound found in various plants, holds potential as a therapeutic candidate. Recent studies have confirmed diosgenin’s anti-inflammatory and metabolism-modulating properties. However, its therapeutic effects on NASH and the underlying mechanisms are still unclear. This study aims to explore diosgenin’s protective effects and pharmacological mechanisms against NASH using network pharmacology, molecular docking, and experimental validation. We gathered potential targets of diosgenin and NASH from various databases to generate protein-protein interaction (PPI) networks. GO and KEGG pathway enrichment analyses identified key targets and mechanisms. Molecular docking confirmed the binding capacity between diosgenin and core target proteins. Additionally, a NASH cell model was developed to validate the pharmacological effects of diosgenin. Our investigation identified nine key targets (ALB, AKT1, TP53, VEGFA, MAPK3, EGFR, STAT3, CASP3, IGF1) that interact with diosgenin. Molecular docking indicated potential bindings interactions, while enrichment analyses revealed that diosgenin may enhance fatty acid metabolism via the PI3K-Akt pathway. Cellular experiments confirmed that diosgenin activates this pathway, reduces SCD1 expression, and decreases triglyceride and IL-6 levels. Our study elucidates that diosgenin may ameliorate triglyceride deposition and inflammation through the PI3K-Akt pathway. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-025-95154-z. Keywords: Nonalcoholic steatohepatitis, Diosgenin, Network pharmacology, PI3K-Akt, SCD1 Subject terms: Diseases, Endocrinology Introduction With the global increase in the prevalence of metabolic syndrome, diabetesand obesity, the incidence of nonalcoholic fatty liver disease (NAFLD) has also increased dramatically, and NAFLD has become the most common type of chronic liver disease worldwide. Nonalcoholic steatohepatitis (NASH) is an aggressive inflammatory subtype of NAFLD associated with 5% or more hepatic steatosis, hepatocellular damage (ballooning) and inflammation, with or without fibrosis^[38]1,[39]2. 20% of NASH can progress to cirrhosis^[40]3 or even liver cancer^[41]4, and ultimately become the leading cause of liver transplantation^[42]3,[43]5. Hepatic steatosis, characterized by the accumulation of triglyceride vesicles in hepatocytes^[44]2, arises from an imbalance between triglyceride supply and demand in the liver. This imbalance is attributed to heightened hepatic de novo lipogenesis, augmented fatty acid uptake, and decreased fatty acid beta-oxidation and triglyceride output^[45]6,[46]7. The hepatic inflammatory response promotes liver fibrosis progression and is a key driver of cirrhosis^[47]8. Despite the dramatic public health concern that NASH poses, no effective therapeutic drugs have been approved for clinical use^[48]9,[49]10. Consequently, there is a pressing need to explore novel and effective therapeutic strategies and drugs to address this growing health concern. In recent years, significant progress has been made in the study of traditional Chinese medicine (TCM) for the treatment of NASH, providing valuable insights for the development of NASH therapeutics. The active component vitexin from Shan Zha ameliorates NASH by significantly reducing hepatic macrophage infiltration and downregulating the expression of molecules associated with triglyceride synthesis^[50]11. Alisol A from Ze Xie alleviates steatohepatitis by inhibiting oxidative stress and stimulating autophagy via the AMPK/mTOR pathway^[51]12, and Alisol B has been reported to reduce ROS levels and suppress inflammatory cytokine expression^[52]13. These findings highlight the potential of TCM in NASH treatment. Diosgenin is a steroidal compound found naturally in the matrix of plants such as fenugreek and wild yam^[53]14 that has anti-inflammatory, immunomodulatory, hypolipidemic, antiviral, antifungal, and anti-allergic effects^[54]15,[55]16. In recent years, diosgenin has drawn increasing amounts of attention in the treatment of various metabolic diseases, including diabetes^[56]17, osteoporosis^[57]18, and hyperlipidemia^[58]19. Recently, diosgenin was shown to improve hepatic lipid metabolism^[59]20 by interfering with cholesterol absorption and transport^[60]21, inhibiting triglyceride synthesis, accelerating the breakdown of free fatty acids^[61]22 and affecting liver-gut circulation^[62]23. Diosgenin has also been confirmed to exhibit anti-inflammatory effects by inhibiting inflammatory signals from macrophages^[63]24 and significantly attenuating the inflammatory response in obese adipose tissue^[64]25. However, whether diosgenin has a therapeutic effect on NASH, an inflammatory subtype of NAFLD, remains unclear. Network pharmacology, an emerging discipline based on systems biology theory, integrates various disciplines, such as polypharmacology, bioinformatics, and network analysis. In recent years, network pharmacology has been used to analyze the molecular associations between drugs and therapeutic objects from the overall perspective of the system level and biological network and to reveal the systemic pharmacological mechanism of drugs, serving as a guide for the development of new medicines and clinical therapy^[65]26,[66]27. Therefore, this study aimed to screen the potential targets and signaling pathways of diosgenin in NASH using network pharmacology. Further validation through cytological experiments was conducted to elucidate diosgenin’s effects on NASH and its pharmacological mechanisms. Materials and methods Network pharmacology analysis Prediction of the action targets of diosgenin We first used “diosgenin” as the search term to obtain the active target in the HERB database^[67]28 ([68]http://herb.ac.cn/). The 2D molecular structure formula of diosgenin was downloaded from the PubChem database^[69]29 ([70]https://pubchem.ncbi.nlm.nih.gov/) and retrieved from the PharmMapper database^[71]30,[72]31 ([73]http://www.lilab-ecust.cn/pharmmapper/). Furthermore, potential targets were predicted from the SwissTargetPrediction database^[74]32 ([75]http://www.swisstargetprediction.ch/). All the obtained targets were normalized for information using the UniProt database^[76]33 ([77]https://www.uniprot.org/), which was subsequently subjected to gene name analysis. Subsequently, all the targets retrieved from the above databases were merged, and duplicate values were removed to obtain the active targets. Identification of NASH-related targets NASH-related targets were comprehensively gathered using the search terms “non-alcoholic steatohepatitis” and “nonalcoholic steatohepatitis”. We searched the GeneCards database^[78]34 ([79]https://www.genecards.org), DisGeNET database^[80]35 ([81]http://www.disgenet.org), OMIM database^[82]36 ([83]http://www.omim.org), and TTD database^[84]37 ([85]http://db.idrblab.net) to identify putative targets. Finally, all the above targets were combined, and duplicate values were removed to obtain NASH-related targets. Analysis of overlapping targets of diosgenin in NASH target mapping The targets of the above two steps were imported into Venn mapping software^[86]38 ([87]http://www.bioinformatics.com.cn/static/others/jvenn/example.html) for visualization and mapping, after which the common targets were obtained. These targets are potential targets of diosgenin for the treatment of NASH. GO and KEGG pathway enrichment analysis The overlapping targets of diosgenin and NASH were imported into the DAVID database 6.8^[88]39 ([89]https://david.ncifcrf.gov/) for enrichment analysis of three aspects of GO enrichment: biological process (BP), molecular function (MF), and cellular component (CC) enrichment, as well as KEGG pathway enrichment^[90]40. The results were screened according to the criteria of a false discovery rate (FDR) < 0.05 and P < 0.05, and the top 10 pathways with the most highly enriched targets were selected for visualization via GO enrichment analysis and KEGG signaling pathway analysis via the “Microbiology Letter” tool^[91]38 ([92]http://www.bioinformatics.com.cn/). PPI network construction We submitted the overlapping targets to the STRING database^[93]41 ([94]https://string-db.org/) to predict functional protein association networks. For the protein–protein interaction (PPI) network constructed using the STRING database, the minimum interaction threshold was set to 0.4, with other parameters at default values. This generated a highly interconnected PPI network and derived combined PPI score data^[95]42. The composite score reflects protein interaction strength: higher scores indicate stronger interactions. The obtained network data were further imported into Cytoscape 3.7.2^[96]43 ([97]http://cytoscape.org/) software for the analysis of topological properties. The degree value indicates the number of direct interactions for a target, with higher values suggesting greater biological relevance and functional importance. Therefore, we used degree value analysis and ranking to screen the key targets of diosgenin in NASH. Molecular docking validation of the binding capacity between diosgenin and targets Protein structures were acquired from the RCSB Protein Data Bank (PDB) (Table [98]7). Use Pymol 2.3.0 to remove protein crystallization water and original ligands. Subsequently, import the protein structures into Autodock Tools (version 1.5.6) to add hydrogens, calculate charges, assign charges, specify atom types, and save the processed structures in the pdbqt format. The three-dimensional structure of diosgenin was retrieved from the PubChem database. This structure was subsequently imported into ChemBio3D Ultra 14.0 for energy minimization and saved in the mol2 format. The optimized diosgenin was then imported into Autodock Tools (version 1.5.6) to add hydrogens, calculate and assign charges, define rotatable bonds, and save in the pdbqt format. The molecular docking parameters of diosgenin with target proteins are shown in Table [99]7. Finally, Autodock Tools (version 1.5.6) was employed to compute the docking score, thereby assessing the degree of matching and docking activity between the target and its ligand. Based on prior studies, a binding affinity of less than − 4.25 kcal/mol is indicative of binding activity; a score below − 5.0 kcal/mol denotes enhanced binding activity; and a value lower than − 7 kcal/mol implies robust docking interactions between the ligand and the target^[100]44. The binding model was visualized by PyMol 2.3.0. Table 7. Molecular docking parameters and results of diosgenin with target proteins. Targets PDB ID Related parameter settings Types of interactions Interacting residues Bond lengths Binding energy(kcal/mol) center_x center_y center_z ALB 1N5U 29.0 10.8 15.0 Hydrogen bonds ARG-117 ARG-186 2.5 Å 2.5 Å − 19.4 AKT1 6NPZ -32.3 1.2 19.3 Hydrogen bonds LYS-179 ASP-439 2.5 Å 2.5Å − 8.9 CASP3 4JJE 49.8 22.7 57.9 Hydrogen bonds GLY-122 2.8 Å − 8.4 EGFR 2GS2 68.2 18.6 -40.3 Hydrogen bonds ASN-676 ALA-678 2.1 Å 2.6 Å − 8.4 IGF1 1IMX 18.4 21.1 25.7 Hydrogen bonds CYS-52 2.3 Å − 8.0 MAPK3 4QTB 37.1 54.4 50.1 Hydrogen bonds MET-125 2.1 Å − 10.1 TP53 4AGP 91.8 91.9 -45.4 Hydrogen bonds THR-231 2.7 Å − 7.7 VEGFA 4KZN 10.4 -4.1 22.4 Hydrogen bonds GLN-37 2.0 Å − 8.1 STAT3 6NJS -14.6 20.4 25.5 Hydrogen bonds TRP-510 ASP-502 2.8 Å 2.5 Å − 8.0 [101]Open in a new tab The search space dimensions are uniformly set to size_x: 60, size_y: 60, size_z: 60, with a grid spacing of 0.375 Å. The exhaustiveness parameter is fixed at 10, and all other parameters remain at their default settings. Cell experiments Construction and processing of cellular models HepG2 cells (National Model and Characteristic Experimental Cell Resource Bank/Chinese Academy of Sciences Typical Culture Repository Committee Cell Bank, China), a human hepatocellular carcinoma cell line, were cultured in MEM containing 10% fetal bovine serum (FND500; Shanghai Excell Biological Technology Co., Ltd., China). To construct the NASH cell model, HepG2 cells were treated with free fatty acids (FFAs; containing a palmitic acid to oleic acid molar ratio of 1:2; palmitic acid, P5585; Sigma, USA; oleic acid, O1383; Sigma, USA). The experimental groups were as follows: (i) normal control group: treated with the solvent BSA only; (ii) NASH model group: treated with FFA only; (iii) NASH + diosgenin group: Diosgenin (D1634, Sigma, USA) was added after FFA stimulation; and (iv) BSA + diosgenin group: Diosgenin was added in addition to the BSA treatment. Cell activity assessment The cytotoxic effects of FFA and diosgenin on HepG2 cells were detected by a CCK-8 assay (CK12, Beiren Chemical Technology Co., Ltd., China). HepG2 cells were seeded in 96-well plates at a density of 1 × 10^4/well, and media containing different concentrations of FFA (0, 0.1, 0.2, 0.3, 0.4, 0.5 mM) or diosgenin (0, 5, 10, 25, 50, 100 µM) were added for 24 h. CCK8 reagent was subsequently added, and the mixture was incubated at 37 °C for 1–2 h. The absorbance at a wavelength of 450 nm was subsequently determined. Cell viability was calculated as a percentage relative to untreated controls. Oil Red O staining The cells were cultured in 6-well plates (3 × 10^5 cells/well), treated with FFA and diosgenin, rinsed with PBS and then fixed in paraformaldehyde for 30 min. The triglycerides were stained with Oil Red O staining solution (O0625, Sigma‒Aldrich, USA), and the nuclei were stained with hematoxylin. After the slices were sealed with glycerol gelatin, the cells were observed under a microscope. Measurement of cellular triglyceride (TG), total cholesterol (TC), and free cholesterol (FC) levels The intracellular contents of TG, TC, and FC were determined according to the instructions in the kit (E1013-50/E1015-50/E1016-50; Beijing Pulley Gene Technology Co., Ltd., China). Supernatant IL-6 determination Enzyme-linked immunosorbent assays (ELISAs) were utilized to assess IL-6 levels in HepG2 cell culture supernatants following the instructions of an ELISA kit (EK106, UNIQUE Biologicals, China). Western blot analysis Cells were lysed using RIPA buffer, and total cellular proteins were extracted. Protein concentrations were determined with a BCA protein analysis kit. Proteins were separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). The proteins were subsequently transferred to PVDF membranes, blocked with 5% milk, and incubated with primary antibodies overnight at 4 °C. p-AKT (Ser473; #12694), AKT (#9272), FASN (#3180), ACC (#3676), and p-ACC (#11818) antibodies from CST; PI3K (PTM-635) from PTM Bio; CPT1α ([102]Ab128569); SCD1 (Ab1986) from Abcam; and GAPDH (#66000) from Proteintech were used. The blots were incubated with secondary antibodies for 1 h at room temperature. An Alpha Q detection system (GE Healthcare) was used for visualization. Statistical analysis Statistical analysis was performed using GraphPad Prism 8.0 software. The data are expressed as the mean ± standard deviation (x ± sd), and one-way ANOVA was used for multiple comparisons between groups. A P value < 0.05 was considered statistically significant. Results Network of diosgenin targets in NASH treatment A flow chart illustrating the methodology employed in this research is presented in Fig. [103]1. To elucidate the potential targets of diosgenin in the treatment of NASH, we first conducted a search in the HERB database, PharmMapper database, and SwissTargetPrediction database to determine the action targets of diosgenin; subsequently, all the obtained targets were combined, and duplicate values were deleted. Finally, 329 diosgenin action targets were obtained. A PPI network was constructed to analyze the relationships between diosgenin and its targets (Fig. [104]2). Fig. 1. [105]Fig. 1 [106]Open in a new tab Flow chart for studying the mechanism of diosgenin in NASH treatment. Fig. 2. [107]Fig. 2 [108]Open in a new tab Diosgenin-target network diagram. The search terms “non-alcoholic steatohepatitis” and “nonalcoholic steatohepatitis” were used to identify genes in the GeneCards database, OMIM database, TTD database, and DisGeNET database. After removing duplicates, 1240 NASH-related targets were identified (Table [109]1). To identify the key targets of NASH, we constructed a PPI network and performed network topology analysis. The findings revealed that the 5 targets with the highest degree values were EP300 (degree = 105), TP53 (degree = 100), RELA (degree = 91), STAT3 (degree = 89), and AKT1 (degree = 80) (Fig. [110]3). Table 1. Basic information on NASH disease targets in the protein interaction network map. Name Name of protein Degree Betweenness centrality Closeness centrality EP300 Histone acetyltransferase p300 105 0.06059 0.43534 TP53 Cellular tumor antigen p53 100 0.072305 0.418318 RELA Rela proto-oncogene, nf-kb subunit 91 0.051777 0.435583 STAT3 Signal transducer and activator of transcription 3 89 0.059363 0.422619 AKT1 RAC-alpha serine/threonine-protein kinase 80 0.043526 0.420797 JUN Transcription factor AP-1 79 0.031724 0.425845 MAPK3 Mitogen-activated protein kinase 3 76 0.03821 0.415426 CTNNB1 Catenin beta-1 75 0.040032 0.408046 RXRA Retinoic acid receptor RXR-alpha 70 0.047629 0.405083 TNF Tumor necrosis factor 67 0.034141 0.410405 ESR1 Estrogen receptor 63 0.019721 0.407833 HDAC1 Histone deacetylase 1/2 62 0.023119 0.394444 PIK3CA Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform 62 0.013745 0.396648 MYC Myc proto-oncogene protein 59 0.011523 0.401956 IL6 Interleukin-6 55 0.017332 0.400308 SMAD3 Mothers against decapentaplegic homolog 3 55 0.013719 0.38606 RPS27A Ubiquitin-40 S ribosomal protein S27a 53 0.012557 0.380049 TRAF6 TNF receptor-associated factor 6 53 0.025533 0.363087 EGFR Epidermal growth factor receptor 52 0.010025 0.392462 NFKB1 Nuclear factor NF-kappa-B p105 subunit 52 0.024978 0.394245 GRB2 Growth factor receptor-bound protein 2 51 0.015125 0.37476 JAK2 Tyrosine-protein kinase JAK2 51 0.012026 0.383031 NCOA1 Nuclear receptor coactivator 1 51 0.012763 0.380049 PTPN11 Tyrosine-protein phosphatase nonreceptor type 11 51 0.012051 0.382281 FOS Proto-oncogene c-Fos 50 0.006847 0.380604 SHC1 SHC-transforming protein 1 50 0.008924 0.394843 PPARGC1A Peroxisome proliferator-activated receptor gamma coactivator 1-alpha 49 0.023448 0.387978 NCOA2 Nuclear receptor coactivator 2 48 0.013544 0.373149 MAPK14 Mitogen-activated protein kinase 14 47 0.011388 0.398469 SP1 Transcription factor Sp1 47 0.014301 0.394245 FOXO1 Forkhead box protein O1 46 0.017711 0.395844 SMAD4 Mothers against decapentaplegic homolog 4 45 0.00857 0.381162 CAV1 Caveolin-1 43 0.01531 0.385489 FOXO3 Forkhead box protein O3 43 0.026149 0.393649 IL10 Interleukin-10 43 0.010475 0.391675 IL1B Interleukin-1 beta 43 0.012489 0.380049 PPARA Peroxisome proliferator-activated receptor alpha 43 0.00635 0.352278 HIF1A Hypoxia-inducible factor 1-alpha 42 0.015371 0.371021 STAT1 Signal transducer and activator of transcription 1-alpha/beta 42 0.007944 0.386251 TLR4 Toll-like receptor 4 42 0.008126 0.393253 VEGFA Vascular endothelial growth factor A 42 0.012763 0.383031 APOA1 Apolipoprotein A-I 41 0.028971 0.370493 CASP8 Caspase-8 39 0.003588 0.362413 CHUK Inhibitor of nuclear factor kappa-B kinase subunit alpha 39 0.014676 0.364783 TGFB1 Transforming growth factor beta-1 39 0.013755 0.382843 FN1 Fibronectin 1 38 0.004889 0.370669 IRS1 Insulin receptor substrate 1 38 0.034125 0.365466 AR Androgen receptor 37 0.010893 0.394046 IKBKB Inhibitor of nuclear factor kappa-B kinase subunit beta 37 0.0039 0.356784 IKBKG NF-kappa-B essential modulator 37 0.003234 0.357274 MAPK8 Mitogen-activated protein kinase 8/9/10 (c-jun n-terminal kinase) 37 0.007852 0.386634 CCND1 G1/S-specific cyclin-D1 36 0.009662 0.387401 PPARG Peroxisome proliferator-activated receptor gamma 36 0.006206 0.37476 RIPK1 Receptor-interacting serine/threonine-protein kinase 1 36 0.003094 0.358257 CDKN1A Cyclin-dependent kinase inhibitor 1 35 0.005917 0.369617 CXCL8 Interleukin-8 35 0.002845 0.375661 CREB1 Cyclic AMP-responsive element-binding protein 1 34 0.009021 0.367357 MDM2 E3 ubiquitin-protein ligase Mdm2 34 0.007742 0.371374 MTOR Serine/threonine-protein kinase mTOR 34 0.004772 0.38587 PRKCA Protein kinase C alpha type 33 0.016424 0.371021 TNFRSF1A Tumor necrosis factor receptor superfamily member 1 A 33 0.00471 0.365637 APOA2 Apolipoprotein A-II 32 0.011663 0.356296 LPL Lipoprotein lipase 32 0.021649 0.355485 CASP3 Caspase-3 31 0.008417 0.365295 HGF Hepatocyte growth factor 31 0.001757 0.343146 LIF Lif, interleukin 6 family cytokine 31 0.006575 0.363087 BIRC3 Baculoviral IAP repeat-containing protein 3 30 0.004853 0.373684 IL4 Interleukin-4 30 0.014666 0.365637 SIRT1 NAD-dependent protein deacetylase sirtuin-1 30 0.006677 0.365979 YWHAZ 14-3-3 protein zeta/delta 30 0.001686 0.339565 APOE Apolipoprotein E 29 0.007946 0.337365 CSF2 Granulocyte-macrophage colony-stimulating factor 29 0.009236 0.367876 INS Insulin 29 0.004597 0.357929 PTEN Phosphatase and tensin homolog 29 0.003939 0.364442 PTPN1 Tyrosine-protein phosphatase nonreceptor type 1 29 0.002263 0.35711 BIRC2 Baculoviral IAP repeat-containing protein 2 28 0.003464 0.355 CCL4 C-C motif chemokine 4 28 0.012084 0.362413 EGF Pro-epidermal growth factor 28 0.003778 0.356296 IGF1 Insulin-like growth factor I 28 0.003406 0.372615 IGF1R Insulin-like growth factor 1 receptor 28 0.004838 0.363256 IL1A Interleukin-1 alpha 28 0.003337 0.366151 LEP Leptin 28 0.01169 0.349597 MAP3K7 Mitogen-activated protein kinase kinase kinase 7 28 0.001717 0.336348 NR1H3 Oxysterols receptor LXR-alpha 28 0.001773 0.34991 UBE2D1 Ubiquitin-conjugating enzyme E2 D1 28 0.006291 0.358751 ATF2 Cyclic AMP-dependent transcription factor ATF-2 27 0.013327 0.365466 CCL2 C-C motif chemokine 2 27 0.005258 0.365295 CEBPB CCAAT/enhancer-binding protein beta 27 0.011374 0.361742 CXCR4 C-X-C chemokine receptor type 4 27 0.001768 0.365466 GSK3B Glycogen synthase kinase-3 beta 27 0.009884 0.352278 IRAK1 Interleukin-1 receptor-associated kinase 1 27 0.003 0.345728 PRKACA cAMP-dependent protein kinase catalytic subunit alpha 27 0.002067 0.346341 SYK Spleen associated tyrosine kinase 27 0.010146 0.356133 ACOX1 Peroxisomal acyl-coenzyme An oxidase 1 26 0.013433 0.355 APOB Apolipoprotein B-100 26 0.006104 0.348194 CEBPA CCAAT/enhancer-binding protein alpha 26 0.001805 0.371551 IRF7 Interferon regulatory factor 7 26 0.002043 0.370493 MYD88 Myeloid differentiation primary response protein MyD88 26 0.009959 0.305675 RPS6KB1 Ribosomal protein S6 kinase beta-1 26 0.003998 0.331072 ACTB Actin, cytoplasmic 1 25 0.029931 0.343901 ALB Serum albumin 25 0.011646 0.341794 ARRB1 Beta-arrestin-1 25 0.012045 0.355161 CCL5 C-C motif chemokine 5 25 0.004773 0.351485 EZH2 Histone-lysine N-methyltransferase EZH2 25 0.002717 0.365808 HMGB1 High mobility group protein B1 25 0.001531 0.351327 IL18 Interleukin-18 25 0.009815 0.356621 MMP1 Matrix metalloproteinase-1 (interstitial collagenase) 25 0.005839 0.362077 NCOA3 Nuclear receptor coactivator 3 25 0.007973 0.357601 PPP2CA Serine/threonine-protein phosphatase 2 A catalytic subunit alpha isoform 25 0.005942 0.363933 PRKCD Protein kinase C delta type 25 0.002079 0.347729 SMARCA4 Swi/snf related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 4 25 0.003847 0.347884 SREBF1 Sterol regulatory element-binding protein 1 25 0.008203 0.334332 CDK1 Cyclin-dependent kinase 1 24 0.001595 0.346495 CYP1A1 Cytochrome p450 family 1 subfamily a polypeptide 1 24 0.013317 0.282051 CYP2E1 Cytochrome P450 2E1 24 0.018464 0.312026 E2F1 Transcription factor E2F1 24 9.64E-04 0.343599 FASLG Tumor necrosis factor ligand superfamily member 6 24 0.004117 0.337803 MMP9 Matrix metalloproteinase-9 24 0.006283 0.341794 PIK3CB Phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha/beta/delta 24 0.006443 0.334189 CYP3A4 Cytochrome p450 family 3 subfamily a polypeptide 4 23 0.003778 0.366839 HDAC3 Histone deacetylase 3 23 0.011185 0.370142 IFNG Interferon gamma 23 0.004664 0.261729 IL17A Interleukin-17 A 23 0.001892 0.353234 NOS2 Nitric-oxide synthase, inducible 23 0.004536 0.343599 SQSTM1 Sequestosome-1 23 0.002112 0.344356 TLR2 Toll-like receptor 2 23 0.001207 0.338095 AGT Angiotensinogen 22 0.00649 0.350696 CD44 CD44 antigen 22 0.003654 0.348816 CXCL10 C-X-C motif chemokine 10 22 0.013108 0.327601 FAS Tumor necrosis factor receptor superfamily member 6 22 0.008915 0.354034 PDGFRB Platelet-derived growth factor receptor beta 22 0.00154 0.328981 PLG Plasminogen 22 0.001496 0.336348 AXIN1 Axin-1 21 0.00809 0.355485 CD40 Tumor necrosis factor receptor superfamily member 5 21 0.009242 0.351169 F2 Prothrombin 21 0.001553 0.342544 HSPA4 Heat shock protein family A member 4 21 0.001426 0.345575 SDC1 Syndecan-1 21 0.006216 0.34835 TGFBR1 TGF-beta receptor type-1 21 0.011372 0.336928 APP Amyloid-beta A4 protein 20 0.002587 0.337949 CCR5 C-C chemokine receptor type 5 20 0.01213 0.342094 CDK4 Cyclin-dependent kinase 4 20 0.002666 0.350381 GNAI2 Guanine nucleotide-binding protein G(i) subunit alpha-2 20 0.007359 0.362077 MET Hepatocyte growth factor receptor 20 0.001112 0.342094 MMP2 Matrix metalloproteinase-2 (gelatinase a) 20 0.001642 0.335481 RAF1 RAF proto-oncogene serine/threonine-protein kinase 20 0.003045 0.357765 UBE2D2 Ubiquitin-conjugating enzyme E2 D2 20 0.002802 0.327738 APOA5 Apolipoprotein A-V 19 0.014329 0.331494 CAT Catalase 19 0.005014 0.360739 CYCS Cytochrome c, somatic 19 6.59E-04 0.352278 HSPA5 78 kDa glucose-regulated protein 19 0.012956 0.336348 IRF1 Interferon regulatory factor 1 19 0.005713 0.343901 IRS2 Insulin receptor substrate 2 19 0.002173 0.312901 MAP3K5 Mitogen-activated protein kinase kinase kinase 5 19 0.002898 0.341048 NOTCH1 Neurogenic locus notch homolog protein 1 19 0.013169 0.327326 OSM Oncostatin-M 19 3.50E-04 0.332907 RUNX1 Runt-related transcription factor 1 19 6.44E-04 0.325281 APOC2 Apolipoprotein C-II 18 6.97E-04 0.349128 ARNT Aryl hydrocarbon receptor nuclear translocator 18 8.02E-04 0.346495 BID BH3-interacting domain death agonist 18 0.003645 0.356947 CPT1A Carnitine O-palmitoyltransferase 1, liver isoform 18 0.006185 0.33234 IFNB1 Interferon beta 18 0.001196 0.307844 IL13 Interleukin-13 18 0.001304 0.332482 TICAM1 TIR domain-containing adapter molecule 1 18 7.14E-04 0.31748 BCL2 Apoptosis regulator Bcl-2 17 7.70E-04 0.315046 CCL3 C-C motif chemokine 3 17 0.002488 0.337511 CYP1A2 Cytochrome p450 family 1 subfamily a polypeptide 2 17 0.003391 0.350067 FABP1 Fatty acid-binding protein, liver 17 0.003809 0.362413 FOXP3 Forkhead box protein P3 17 0.002394 0.346495 INSR Insulin receptor 17 0.003206 0.363933 LBP Lipopolysaccharide-binding protein 17 3.73E-04 0.335049 MAPK9 Mitogen-activated protein kinase 8/9/10 (c-jun n-terminal kinase) 17 0.001224 0.338388 NOS3 Nitric-oxide synthase, endothelial 17 8.90E-04 0.359908 NR1H2 Oxysterols receptor LXR-beta 17 5.50E-04 0.341645 PLAUR Urokinase plasminogen activator surface receptor 17 0.001616 0.344812 PRKAA1 5’-AMP-activated protein kinase catalytic subunit alpha-1 17 0.002876 0.246761 RPTOR Regulatory-associated protein of mTOR 17 8.12E-04 0.355971 RXRG Retinoic acid receptor RXR-gamma 17 0.004379 0.336493 SAA1 Serum amyloid A-1 protein 17 0.003159 0.332058 SMAD7 Mothers against decapentaplegic homolog 7 17 1.69E-04 0.314539 SOCS3 Suppressor of cytokine signaling 3 17 4.18E-04 0.327601 TBP TATA-box-binding protein 17 0.002204 0.323395 TERT Telomerase reverse transcriptase 17 0.002049 0.351327 TGFBR2 Transforming growth factor beta receptor 2 17 0.011353 0.320608 TIMP1 Metalloproteinase inhibitor 1 17 3.78E-04 0.340453 AOX1 Aldehyde oxidase 16 0.001369 0.330652 ATM Serine-protein kinase ATM 16 0.006589 0.261466 CD40LG CD40 ligand 16 3.75E-04 0.342244 CDKN2A Cyclin-dependent kinase inhibitor 2 A 16 0.002121 0.275582 CYP2B6 Cytochrome p450 family 2 subfamily b polypeptide 6 16 0.001511 0.324336 IL1R1 Interleukin-1 receptor type 1 16 7.46E-04 0.336783 IL6R Interleukin-6 receptor subunit alpha 16 7.63E-04 0.338095 LCN2 Neutrophil gelatinase-associated lipocalin 16 4.32E-04 0.340899 PRKAG1 5’-AMP-activated protein kinase subunit gamma-1 16 4.34E-04 0.339713 STK11 Serine/threonine-protein kinase STK11 16 9.30E-04 0.334189 YAP1 Yes1 associated transcriptional regulator 16 7.62E-04 0.322328 ADIPOQ Adiponectin, c1q and collagen domain containing 15 0.006522 0.341944 ATF4 Cyclic AMP-dependent transcription factor ATF-4 15 0.004779 0.336493 CD14 Monocyte differentiation antigen CD14 15 4.60E-04 0.306997 CETP Cholesteryl ester transfer protein 15 0.002635 0.306756 IL6ST Interleukin-6 receptor subunit beta 15 0.002196 0.333333 LDLR Low-density lipoprotein receptor 15 4.04E-04 0.300616 PLIN1 Perilipin-1 15 3.31E-04 0.313907 RIPK3 Receptor-interacting serine/threonine-protein kinase 3 15 0.001985 0.327189 RUNX3 Runt-related transcription factor 3 15 1.79E-04 0.299119 TIRAP Toll/interleukin-1 receptor domain-containing adapter protein 15 0.001781 0.353074 TNFRSF1B Tumor necrosis factor receptor superfamily member 1B 15 0.002083 0.334619 ADRBK1 Beta-adrenergic receptor kinase 1 14 9.13E-04 0.328427 APOC3 Apolipoprotein C-III 14 8.65E-04 0.328013 ARID1A AT-rich interactive domain-containing protein 1 A 14 0.003092 0.325688 ATG7 Ubiquitin-like modifier-activating enzyme ATG7 14 4.97E-04 0.350854 BIRC5 Baculoviral iap repeat-containing protein 5 14 0.00132 0.313152 BMP4 Bone morphogenetic protein 4 14 0.005492 0.319689 CASP6 Caspase-6 14 4.15E-04 0.254232 CASP7 Caspase-7 14 0.003764 0.326096 CTSD Cathepsin D 14 0.003031 0.33234 CYP2A6 Cytochrome P450 2A6 14 0.003525 0.331072 DDIT3 DNA damage-inducible transcript 3 protein 14 0.003229 0.328565 DNMT1 DNA (cytosine-5)-methyltransferase 1 14 0.002138 0.331072 LRP6 Low-density lipoprotein receptor-related protein 6 14 5.27E-05 0.281543 MUC1 Mucin-1 14 5.68E-04 0.335481 PCNA Proliferating cell nuclear antigen 14 9.57E-04 0.320739 PRKAA2 5’-AMP-activated protein kinase catalytic subunit alpha-2 14 0.002644 0.343901 PRKAB1 5’-AMP-activated protein kinase subunit beta-1 14 0.001289 0.33234 PSMD2 26 S proteasome non-ATPase regulatory subunit 2 14 3.64E-04 0.322328 SERPINE1 Plasminogen activator inhibitor 1 14 2.76E-04 0.304009 STUB1 E3 ubiquitin-protein ligase CHIP 14 0.001266 0.343297 TLR3 Toll-like receptor 3 14 0.004725 0.343297 TNFSF10 Tumor necrosis factor ligand superfamily member 10 14 0.003242 0.329536 TXN Thioredoxin 14 0.00339 0.352755 ABCA1 ATP-binding cassette subfamily A member 1 13 0.001789 0.334762 APOA4 Apolipoprotein A-IV 13 0.008703 0.338976 CCL19 C-C motif chemokine 19 13 6.57E-04 0.336348 CCNA2 Cyclin-A2 13 0.014638 0.285037 CD36 Platelet glycoprotein 4 13 0.003953 0.323798 EGR1 Early growth response protein 1 13 3.71E-04 0.279728 FGR Fgr proto-oncogene, src family tyrosine kinase 13 0.0021 0.360074 HFE2 Hemojuvelin 13 4.40E-04 0.34375 HSPA1A Heat shock protein family a member 1a 13 3.04E-04 0.318906 PSMD1 26 S proteasome non-ATPase regulatory subunit 1 13 0.002626 0.319166 PTGS2 Prostaglandin G/H synthase 2 13 8.35E-04 0.343146 RORA Rar-related orphan receptor alpha 13 5.80E-04 0.325281 SMURF1 E3 ubiquitin-protein ligase SMURF1 13 0.003353 0.32596 THBS1 Thrombospondin-1 13 0.021624 0.346803 TNFAIP3 Tumor necrosis factor alpha-induced protein 3 13 0.008247 0.31215 TNFRSF10B Tumor necrosis factor receptor superfamily member 10B 13 6.81E-04 0.329397 WNT3A Wingless-type mmtv integration site family, member 3 13 0.001737 0.339713 ADRB2 Beta-2 adrenergic receptor 12 1.36E-04 0.303655 CASP9 Caspase-9 12 0.010175 0.310784 CFTR Cystic fibrosis transmembrane conductance regulator 12 0.001665 0.318256 CYLD Ubiquitin carboxyl-terminal hydrolase CYLD 12 0.012709 0.282766 CYP2C19 Cytochrome P450 2C19 12 0.005289 0.311031 CYP2C9 Cytochrome p450 family 2 subfamily c polypeptide 9 12 0.001245 0.306876 E2F2 Transcription factor E2F2 12 0.001081 0.333333 EIF2AK2 Interferon-induced, double-stranded RNA-activated protein kinase 12 7.89E-04 0.327052 ESR2 Estrogen receptor beta 12 7.90E-04 0.329119 GCG Glucagon 12 0.009481 0.318776 GPIHBP1 Glycosylphosphatidylinositol-anchored high density lipoprotein-binding protein 1 12 0.001895 0.279628 HNF1A Hepatocyte nuclear factor 1-alpha 12 0.00121 0.275194 HNF4A Hepatocyte nuclear factor 4-alpha 12 1.16E-05 0.277837 ICAM1 Intercellular adhesion molecule 1 12 0.002659 0.277936 LAMA1 Laminin subunit alpha-1 12 8.66E-04 0.356621 LCAT Phosphatidylcholine-sterol acyltransferase 12 9.78E-04 0.335481 LPA Apolipoprotein(a) 12 2.32E-04 0.320476 MAOA Amine oxidase [flavin-containing] A 12 0.008542 0.319558 NLRP3 NACHT, LRR and PYD domains-containing protein 3 12 1.18E-04 0.327189 NR0B2 Nuclear receptor subfamily 0 group B member 2 12 1.74E-04 0.341794 NR1H4 Bile acid receptor 12 0.004472 0.338829 PARP1 Poly [ADP-ribose] polymerase 1 12 0.001463 0.336783 RAB5A RAB5A, member RAS oncogene family 12 0.001191 0.317867 SMPD1 Sphingomyelin phosphodiesterase 12 2.65E-04 0.347575 TFAP2A Transcription factor ap-2 alpha/beta 12 0.001197 0.268477 XBP1 X-box-binding protein 1 12 0.001009 0.343297 CFLAR CASP8 and FADD-like apoptosis regulator 11 4.26E-04 0.309184 DPP4 Dipeptidyl peptidase 4 11 7.18E-04 0.310908 EPHX1 Microsomal epoxide hydrolase 11 1.33E-04 0.328289 FASN Fatty acid synthase 11 7.78E-04 0.333476 HMOX1 Heme oxygenase 1 11 0.011 0.344356 IL3 Interleukin-3 11 7.32E-04 0.345575 LGALS3 Galectin-3 11 6.97E-05 0.333903 PIK3CG Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit gamma isoform 11 3.54E-04 0.288192 PIN1 Peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 11 2.83E-04 0.336059 PLAU Urokinase-type plasminogen activator 11 0.00207 0.308208 PLTP Phospholipid transfer protein 11 4.74E-04 0.327601 PRKCQ Protein kinase C theta type 11 0.00465 0.331072 PSME2 Proteasome activator subunit 2 (pa28 beta) 11 3.95E-04 0.326096 SHH Sonic hedgehog protein 11 0.005794 0.330932 TCF7L2 Transcription factor 7-like 2 11 0.001886 0.314412 VDR Vitamin D3 receptor 11 0.001104 0.322861 VIM Vimentin 11 3.14E-04 0.257841 VTN Vitronectin 11 0.001308 0.322994 APC Adenomatous polyposis coli protein 10 5.52E-04 0.287661 ATF3 Cyclic AMP-dependent transcription factor ATF-3 10 0.005804 0.288511 CD28 T-cell-specific surface glycoprotein CD28 10 0.001868 0.299348 CLU Clusterin 10 8.80E-04 0.337949 CXCR2 C-X-C chemokine receptor type 2 10 0.003524 0.32501 CYP2C8 Cytochrome p450 family 2 subfamily c polypeptide 8 10 0.003356 0.329119 CYP7A1 Cholesterol 7-alpha-monooxygenase 10 0.004325 0.337074 FABP4 Fatty acid-binding protein, adipocyte 10 5.79E-04 0.275 GADD45A Growth arrest and DNA damage-inducible protein GADD45 alpha 10 3.48E-04 0.314792 HSPB1 Heat shock protein family b (small) member 1 10 1.84E-04 0.281239 IDH1 Isocitrate dehydrogenase [nadp] cytoplasmic 10 0.003707 0.302245 IL1RN Interleukin-1 receptor antagonist protein 10 0.001224 0.32474 KLF4 Krueppel-like factor 4 10 0.003256 0.305436 LEPR Leptin receptor 10 1.48E-04 0.333333 LMNA Prelamin-A/C 10 0.005951 0.307844 MLXIPL Carbohydrate-responsive element-binding protein 10 0.001866 0.322994 MLYCD Malonyl-CoA decarboxylase, mitochondrial 10 9.97E-05 0.336203 NUP62 Nuclear pore glycoprotein p62 10 1.86E-04 0.293719 PKM Pyruvate kinase m1/2 10 4.27E-06 0.293941 PON1 Serum paraoxonase/arylesterase 1 10 0.001338 0.302011 PRDX1 Peroxiredoxin-1 10 0.001659 0.317997 SOD1 Superoxide dismutase, cu-zn family 10 0.00789 0.32596 SOD2 Superoxide dismutase [Mn], mitochondrial 10 6.48E-04 0.330093 TLR6 Toll-like receptor 6 10 5.28E-04 0.347729 VCAM1 Vascular cell adhesion protein 1 10 0.005844 0.305915 VLDLR Very low-density lipoprotein receptor 10 4.35E-04 0.323529 ACACA Acetyl-CoA carboxylase 1 9 0.002775 0.272125 ACACB Acetyl-CoA carboxylase 2 9 3.27E-04 0.274806 AHSG Alpha-2-HS-glycoprotein 9 3.84E-04 0.298547 ALDH2 Aldehyde dehydrogenase, mitochondrial 9 0.005948 0.262081 ANXA2 Annexin A2 9 0.00464 0.314412 BAX Apoptosis regulator BAX 9 0.001788 0.315046 BBC3 Bcl-2-binding component 3 9 9.93E-04 0.333191 CCR1 C-C chemokine receptor type 1 9 0.00504 0.315811 CLOCK Circadian locomoter output cycles protein kaput 9 9.78E-04 0.326505 COL1A1 Collagen alpha-1(I) chain 9 0.004237 0.333476 CRP C-reactive protein 9 0.003087 0.29494 CTLA4 Cytotoxic T-lymphocyte protein 4 9 0.004726 0.312026 CXCR3 C-X-C chemokine receptor type 3 9 0.001535 0.28023 DNMT3A DNA (cytosine-5)-methyltransferase 3 A 9 3.02E-04 0.301777 DYNLL1 Dynein light chain 1, cytoplasmic 9 2.44E-04 0.289796 EIF2S1 Eukaryotic translation initiation factor 2 subunit 1 9 0.003174 0.296507 FAT1 Protocadherin Fat 1 9 4.21E-04 0.315556 GH1 Somatotropin 9 2.88E-04 0.294384 HP Haptoglobin-related protein 9 0.002056 0.323798 KEAP1 Kelch-like ECH-associated protein 1 9 2.35E-04 0.316322 LIPC Hepatic triacylglycerol lipase 9 5.50E-04 0.311404 LRPPRC Leucine-rich PPR motif-containing protein, mitochondrial 9 0.004222 0.3005 MMP7 Matrix metalloproteinase-7 (matrilysin, uterine) 9 6.50E-05 0.284517 NFE2L2 Nuclear factor erythroid 2-related factor 2 9 0.00131 0.303655 PNPLA2 Patatin-like phospholipase domain-containing protein 2 9 0.002018 0.314792 PRKDC DNA-dependent protein kinase catalytic subunit 9 0.002852 0.311404 SREBF2 Sterol regulatory element-binding protein 2 9 2.73E-04 0.344356 SUFU Suppressor of fused homolog 9 6.08E-04 0.337511 TFF3 Trefoil factor 3 9 0.002731 0.316322 ULK1 Serine/threonine-protein kinase ULK1 9 1.38E-05 0.341346 WNT5A Wingless-type mmtv integration site family, member 5 9 1.99E-04 0.316836 WWTR1 WW domain-containing transcription regulator protein 1 9 0.003142 0.314412 ACVR2B Activin receptor type-2B 8 0.001094 0.303419 CCR2 C-C chemokine receptor type 2 8 0.006279 0.275 COL3A1 Collagen alpha-1(III) chain 8 0.004506 0.311031 CTGF Cellular communication network factor 2 8 2.74E-04 0.318386 CYP2J2 Cytochrome P450 2J2 8 0.002032 0.292948 ELAVL1 ELAV-like protein 1 8 2.41E-04 0.324201 ELK1 ETS domain-containing protein Elk-1 8 2.11E-04 0.316066 ENO1 Alpha-enolase 8 1.57E-04 0.316579 FDFT1 Squalene synthase 8 0.004483 0.253571 GHR Growth hormone receptor 8 9.68E-04 0.291636 GHRL Appetite-regulating hormone 8 4.82E-04 0.308696 GLUD1 Glutamate dehydrogenase 1, mitochondrial 8 0.003011 0.313403 GSK3A Glycogen synthase kinase-3 alpha 8 5.82E-04 0.306876 HIST1H4F Histone cluster 1 H4 family member f 8 0.002605 0.286395 HMGCR 3-hydroxy-3-methylglutaryl-coenzyme A reductase 8 0.002723 0.299923 HSD11B1 Corticosteroid 11-beta-dehydrogenase isozyme 1 8 8.33E-04 0.309798 IFNAR1 Interferon alpha/beta receptor 1 8 4.38E-04 0.29662 PDGFA Platelet-derived growth factor subunit A 8 5.74E-05 0.283382 PIK3CD Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit delta isoform 8 0.006201 0.295386 PKLR Pyruvate kinase PKLR 8 2.05E-04 0.323663 PLIN2 Perilipin-2 8 0.003818 0.308087 PPAP2B Phosphatidate phosphatase 8 2.76E-04 0.265737 PPP1R15A Protein phosphatase 1 regulatory subunit 15 A 8 3.44E-04 0.303066 PRKCE Protein kinase C epsilon type 8 4.85E-05 0.323128 RB1CC1 RB1-inducible coiled-coil protein 1 8 7.88E-04 0.270617 SIRT3 NAD-dependent protein deacetylase sirtuin-3, mitochondrial 8 7.84E-05 0.308696 SPHK1 Sphingosine kinase 1 8 5.01E-04 0.303537 SPP1 Secreted phosphoprotein 1 8 0.006449 0.310784 TRIB3 Tribbles pseudokinase 3 8 5.61E-05 0.31215 VCP Transitional endoplasmic reticulum ATPase 8 9.60E-04 0.301894 ACSL1 Long-chain-fatty-acid–CoA ligase 1 7 7.23E-04 0.316964 ADH1B Alcohol dehydrogenase 1b (class i), beta polypeptide 7 2.50E-04 0.286395 AGTR1 Type-1 angiotensin II receptor 7 3.58E-04 0.300616 AHR Aryl hydrocarbon receptor 7 0.003988 0.312026 ALDH1A1 Aldehyde dehydrogenase 1 family member a1 7 0.001207 0.31265 ATF6 Cyclic AMP-dependent transcription factor ATF-6 alpha 7 2.06E-04 0.238984 BRD4 Bromodomain-containing protein 4 7 5.78E-04 0.290767 C3 Complement C3 7 9.64E-04 0.329258 CXCL9 C-X-C motif chemokine 9 7 0.005562 0.279828 CYP17A1 Steroid 17-alpha-hydroxylase/17,20 lyase 7 5.25E-04 0.244751 CYP26B1 Cytochrome P450 26B1 7 8.19E-06 0.245675 CYP2D6 Cytochrome P450 2D6 7 0.002035 0.247074 DNMT3B DNA (cytosine-5)-methyltransferase 3B 7 0.005486 0.306395 EEF1A1 Elongation factor 1-alpha 1 7 5.55E-05 0.336059 EPAS1 Endothelial PAS domain-containing protein 1 7 8.61E-05 0.330372 FGFR4 Fibroblast growth factor receptor 4 7 1.66E-04 0.296507 GCLC Glutamate–cysteine ligase catalytic subunit 7 0.006436 0.264746 GOT1 Aspartate aminotransferase, cytoplasmic 7 2.07E-05 0.279028 GSTM1 Glutathione S-transferase Mu 1 7 0.001221 0.280129 H6PD GDH/6PGL endoplasmic bifunctional protein 7 0.004351 0.314539 LPIN1 Phosphatidate phosphatase LPIN1 7 6.23E-05 0.32447 NAMPT Nicotinamide phosphoribosyltransferase 7 6.29E-04 0.262257 NNMT Nicotinamide N-methyltransferase 7 0.002593 0.251692 PRSS1 Trypsin-1 7 0.006652 0.283279 RUVBL1 RuvB-like 1 7 3.54E-04 0.252017 SLC27A2 Very long-chain acyl-CoA synthetase 7 1.45E-04 0.32447 SLC2A4 Solute carrier family 2, facilitated glucose transporter member 4 7 7.51E-04 0.270617 USP7 Ubiquitin carboxyl-terminal hydrolase 7 7 0.001585 0.31265 ABCG1 ATP-binding cassette subfamily G member 1 6 1.57E-04 0.302713 ANGPTL3 Angiopoietin-related protein 3 6 0.007567 0.235738 ANXA1 Annexin A1 6 0 0.285662 BCL2A1 Bcl-2-related protein A1 6 0 0.300269 CDH2 Cadherin-2 6 1.67E-04 0.265646 CPT2 Carnitine O-palmitoyltransferase 2, mitochondrial 6 2.14E-04 0.295386 CSNK2B Casein kinase II subunit beta 6 0.001561 0.287344 ECH1 Delta(3,5)-Delta(2,4)-dienoyl-CoA isomerase, mitochondrial 6 3.88E-04 0.311155 ELANE Elastase, neutrophil expressed 6 2.47E-04 0.258011 ELOVL6 Elongation of very long-chain fatty acids protein 6 6 0.002777 0.302831 ERN1 Serine/threonine-protein kinase/endoribonuclease IRE1 6 2.06E-04 0.306997 FGF21 Fibroblast growth factor 21 6 1.60E-05 0.316451 GPI Glucose-6-phosphate isomerase 6 6.30E-05 0.325824 HNRNPK Heterogeneous nuclear ribonucleoprotein K 6 6.74E-04 0.274228 IFNA2 Interferon alpha-2 6 4.82E-04 0.320476 ITGA8 Integrin subunit alpha 8 6 1.11E-04 0.284 ITGAX Integrin subunit alpha x 6 0.002804 0.278531 KHDRBS1 KH domain-containing, RNA-binding, signal transduction-associated protein 1 6 0.001804 0.312901 LIPE Hormone-sensitive lipase 6 8.41E-05 0.324875 LOX Protein-lysine 6-oxidase 6 4.36E-04 0.302596 MMP10 Matrix metalloproteinase-10 (stromelysin 2) 6 3.23E-06 0.220809 PF4 Platelet factor 4 6 0.001281 0.275097 PPARD Peroxisome proliferator-activated receptor delta 6 0.004353 0.303891 PPIG Peptidyl-prolyl cis-trans isomerase G 6 0.002472 0.269032 ROCK1 Rho-associated protein kinase 1 6 1.41E-04 0.296733 SCD Stearoyl-coa desaturase (delta-9 desaturase) 6 6.54E-05 0.263406 SESN2 Sestrin-2 6 8.91E-05 0.320345 SULT2A1 Sulfotransferase family 2a member 1 6 8.79E-04 0.284103 TFRC Transferrin receptor protein 1 6 6.33E-05 0.29889 TIMP2 Metalloproteinase inhibitor 2 6 2.54E-04 0.309429 TLR9 Toll-like receptor 9 6 5.80E-05 0.259814 TP53BP1 TP53-binding protein 1 6 1.20E-04 0.268108 ABHD5 1-acylglycerol-3-phosphate O-acyltransferase ABHD5 5 1.16E-04 0.294828 AGER Advanced glycosylation end product-specific receptor 5 7.77E-04 0.259038 ATG3 Ubiquitin-like-conjugating enzyme ATG3 5 3.94E-04 0.264566 CCK Cholecystokinin 5 5.03E-04 0.316066 CIDEC Cell death inducing dffa like effector c 5 0.002996 0.297978 CNR1 Cannabinoid receptor 1 5 0.002743 0.302713 CP Ceruloplasmin 5 0.005791 0.271841 CSF1 Macrophage colony-stimulating factor 1 5 5.18E-04 0.287132 CYBA Cytochrome b-245, alpha polypeptide 5 7.89E-04 0.320476 CYP19A1 Aromatase 5 0.002287 0.282051 DGAT1 Diacylglycerol O-acyltransferase 1 5 6.22E-05 0.261817 DROSHA Ribonuclease 3 5 0.005159 0.270429 ELN Elastin 5 2.92E-05 0.264836 FBXW7 F-box/WD repeat-containing protein 7 5 4.13E-04 0.299119 FGF19 Fibroblast growth factor 19 5 8.46E-05 0.305915 G6PC Glucose-6-phosphatase 5 2.50E-04 0.287555 GIP Gastric inhibitory polypeptide 5 5.09E-04 0.289152 GPS2 G protein pathway suppressor 2 5 0.001438 0.270242 GPX1 Glutathione peroxidase 1 5 3.21E-04 0.289152 GSR Glutathione reductase, mitochondrial 5 0.002576 0.274806 IFIH1 Interferon-induced helicase C domain-containing protein 1 5 3.97E-04 0.293058 IGF2R Cation-independent mannose-6-phosphate receptor 5 1.36E-05 0.30484 INSIG1 Insulin-induced gene 1 protein 5 2.14E-05 0.307359 LMNB1 Lamin-B1 5 0.002829 0.270617 LOXL1 Lysyl oxidase-like protein 1 5 2.81E-04 0.231751 LOXL2 Lysyl oxidase homolog 2 5 2.81E-04 0.231751 LUM Lumican 5 3.82E-04 0.254646 MAP1LC3B Microtubule-associated proteins 1 A/1B light chain 3B 5 4.57E-05 0.294051 MAP3K11 Mitogen-activated protein kinase kinase kinase 11 5 3.24E-05 0.316964 MMP13 Matrix metalloproteinase-13 (collagenase 3) 5 3.44E-05 0.318906 MPO Myeloperoxidase 5 2.68E-04 0.293499 NPY Pro-neuropeptide Y 5 0.002951 0.300269 NR5A2 Nuclear receptor subfamily 5 group A member 2 5 9.86E-04 0.245829 PCOLCE Procollagen C-endopeptidase enhancer 1 5 2.40E-04 0.313277 PCSK9 Proprotein convertase subtilisin/kexin type 9 5 0.005266 0.263762 PHB Prohibitin 1 5 0.002928 0.276656 PINK1 Serine/threonine-protein kinase PINK1, mitochondrial 5 6.55E-06 0.219136 POLR2D DNA-directed RNA polymerase II subunit RPB4 5 6.55E-06 0.219136 PYCARD Apoptosis-associated speck-like protein containing a CARD 5 1.07E-05 0.256823 SIRT6 NAD-dependent protein deacetylase sirtuin-6 5 0.002575 0.274228 TALDO1 Transaldolase 1 5 1.34E-04 0.311776 THRB Thyroid hormone receptor beta 5 3.74E-04 0.303655 TLL1 Tolloid-like protein 1 5 0.00124 0.304483 TNFSF13B Tumor necrosis factor ligand superfamily member 13B 5 6.01E-04 0.303537 TP53BP2 Apoptosis-stimulating of p53 protein 2 5 5.22E-04 0.320608 TTR Transthyretin 5 8.54E-06 0.261117 UQCRB Cytochrome b-c1 complex subunit 7 5 1.41E-05 0.291309 USF1 Upstream stimulatory factor 1 5 1.02E-06 0.294828 AATF Apoptosis antagonizing transcription factor 4 0.001059 0.286606 ABCB4 Phosphatidylcholine translocator ABCB4 4 1.41E-05 0.282153 ACADM Medium-chain specific acyl-CoA dehydrogenase, mitochondrial 4 0 0.297071 ACAT1 Acetyl-CoA acetyltransferase, mitochondrial 4 1.39E-05 0.264477 ADAMTS5 A disintegrin and metalloproteinase with thrombospondin motifs 5 4 0 0.297071 ADH1A Alcohol dehydrogenase 1a (class i), alpha polypeptide 4 2.94E-04 0.245212 ADH1C Alcohol dehydrogenase 1c (class i), gamma polypeptide 4 9.57E-05 0.256402 ADH4 Alcohol dehydrogenase 4 (class ii), pi polypeptide 4 3.74E-05 0.229841 AFP Alpha-fetoprotein 4 3.74E-05 0.229841 AHCY Adenosylhomocysteinase 4 5.73E-05 0.2384 ASS1 Argininosuccinate synthase 4 1.71E-04 0.307844 ATP5B ATP synthase subunit beta, mitochondrial 4 0.002589 0.262786 BGLAP Osteocalcin 4 6.11E-04 0.28629 CCL4L1 C-C motif chemokine 4-like 4 3.93E-04 0.282051 CD163 Scavenger receptor cysteine-rich type 1 protein M130 4 0 0.249521 CEACAM1 Carcinoembryonic antigen-related cell adhesion molecule 1 4 0 0.249521 CES1 Liver carboxylesterase 1 4 9.05E-05 0.307238 CPB2 Carboxypeptidase B2 4 6.82E-05 0.309307 CYBB Cytochrome b-245 heavy chain 4 5.32E-04 0.277541 DGAT2 Diacylglycerol O-acyltransferase 2 4 0.00253 0.278431 DKK1 Dickkopf-related protein 1 4 4.04E-04 0.262081 DRD2 D(2) dopamine receptor 4 0 0.274324 EHMT1 [histone h3]-lysine9 n-trimethyltransferase ehmt 4 3.47E-04 0.284517 ENPP1 Ectonucleotide pyrophosphatase/phosphodiesterase family member 1 4 4.51E-04 0.279528 FST Follistatin 4 0.003299 0.285141 GATAD2A Transcriptional repressor p66-alpha 4 0.002606 0.245907 GLP1R Glucagon-like peptide 1 receptor 4 0.005437 0.300269 GPT Glutamic–pyruvic transaminase 4 9.10E-05 0.286185 GSTP1 Glutathione S-transferase P 4 9.37E-05 0.245443 HAMP Hepcidin 4 1.52E-04 0.280632 HFE Hereditary hemochromatosis protein 4 2.19E-05 0.280029 IDH2 Isocitrate dehydrogenase [NADP], mitochondrial 4 3.60E-04 0.296395 IL27 Interleukin-27 subunit alpha 4 2.52E-04 0.252506 INSIG2 Insulin-induced gene 2 protein 4 1.49E-05 0.282153 KLB Beta-klotho 4 7.20E-04 0.315939 KRT8 Keratin, type II cytoskeletal 8 4 8.55E-04 0.237747 MAT1A S-adenosylmethionine synthase isoform type-1 4 5.59E-04 0.301777 MEIS1 Homeobox protein Meis1 4 3.08E-04 0.247152 MST1 Hepatocyte growth factor-like protein 4 5.68E-06 0.318386 MT-CYB Cytochrome b 4 0 0.294495 MTTP Microsomal triglyceride transfer protein large subunit 4 2.42E-06 0.308452 NDUFA13 NADH dehydrogenase [ubiquinone] 1 alpha subcomplex subunit 13 4 5.80E-05 0.316066 NR1I2 Nuclear receptor subfamily 1 group I member 2 4 0.001109 0.313403 NR1I3 Nuclear receptor subfamily 1 group I member 3 4 0.00175 0.299463 PLA2G4A Cytosolic phospholipase A2 4 8.77E-04 0.308696 PNPLA3 Patatin-like phospholipase domain-containing protein 3 4 3.44E-05 0.243606 RETN Resistin 4 2.80E-05 0.305197 S100A9 Protein S100-A9 4 0 0.325281 SCAP Sterol regulatory element-binding protein cleavage-activating protein 4 1.76E-04 0.320608 SCARB1 Scavenger receptor class B member 1 4 0 0.309921 SELE E-selectin 4 3.69E-04 0.296507 SERPINA1 Alpha-1-antitrypsin 4 2.83E-06 0.294162 SH2B1 SH2B adapter protein 1 4 2.73E-04 0.222825 SNAI2 Zinc finger protein SNAI2 4 3.63E-05 0.22359 SPARC SPARC 4 0 0.305675 STK24 Serine/threonine-protein kinase 24 4 3.48E-06 0.260247 TOMM20 Mitochondrial import receptor subunit TOM20 homolog 4 3.48E-06 0.260247 TREM2 Triggering receptor expressed on myeloid cells 2 4 0 0.273364 TXNIP Thioredoxin-interacting protein 4 0.002573 0.26628 USF2 Upstream stimulatory factor 2 4 2.04E-04 0.311652 USP18 Ubl carboxyl-terminal hydrolase 18 4 0.005257 0.298776 ABCB11 Atp-binding cassette, subfamily b (mdr/tap), member 11 3 0.00188 0.241273 ACO1 Cytoplasmic aconitate hydratase 3 0.002692 0.244674 ACSL4 Long-chain-fatty-acid–CoA ligase 4 3 8.28E-04 0.228096 ACTA2 Actin, aortic smooth muscle 3 1.86E-05 0.263851 ADAMTS13 A disintegrin and metalloproteinase with thrombospondin motifs 13 3 4.64E-04 0.304959 ADAMTSL2 ADAMTS-like protein 2 3 7.74E-05 0.285245 AIFM1 Apoptosis-inducing factor 1, mitochondrial 3 4.92E-04 0.287979 ALDH4A1 Delta-1-pyrroline-5-carboxylate dehydrogenase, mitochondrial 3 1.04E-04 0.26403 ANGPT2 Angiopoietin-2 3 6.49E-05 0.252098 AOC3 Amine oxidase, copper containing 3 3 0 0.253489 ARC Activity-regulated cytoskeleton-associated protein 3 0 0.253489 AREG Amphiregulin 3 9.46E-05 0.258781 BMP6 Bone morphogenetic protein 6 3 1.74E-06 0.269589 C19orf80 Angiopoietin-like protein 8 3 1.77E-04 0.307238 CCL21 C-C motif chemokine 21 3 4.07E-04 0.213271 CD38 ADP-ribosyl cyclase/cyclic ADP-ribose hydrolase 1 3 4.87E-05 0.227498 CDKN3 Cyclin-dependent kinase inhibitor 3 3 2.93E-05 0.205689 CEACAM5 Carcinoembryonic antigen-related cell adhesion molecule 5 3 1.64E-06 0.203332 COX6B1 Cytochrome c oxidase subunit 6B1 3 9.40E-07 0.304246 CPS1 Carbamoyl-phosphate synthase [ammonia], mitochondrial 3 0 0.262698 CTRC Chymotrypsin-C 3 8.18E-05 0.250561 CTSB Cathepsin B 3 2.03E-04 0.271369 DDC Aromatic-l-amino-acid/l-tryptophan decarboxylase 3 4.50E-04 0.225592 DGCR8 DGCR8, microprocessor complex subunit 3 2.59E-04 0.284 DYSF Dysferlin 3 0 0.292181 FBL rRNA 2’-O-methyltransferase fibrillarin 3 0 0.300269 GDF11 Growth/differentiation factor 11 3 1.21E-04 0.299693 GLS Glutaminase kidney isoform, mitochondrial 3 3.57E-04 0.226969 GRN Granulin precursor 3 0.001773 0.289045 GTF2H1 General transcription factor IIH subunit 1 3 7.18E-04 0.268201 HAVCR2 Hepatitis A virus cellular receptor 2 3 4.92E-05 0.266462 HBA1 Hemoglobin subunit alpha 3 0 0.260594 IFNA1 Interferon alpha-1/13 3 1.08E-05 0.270992 IHH Indian hedgehog protein 3 0 0.271275 IL22 Interleukin-22 3 4.37E-05 0.266008 INHBA Inhibin beta A chain 3 0 0.264119 ITPR1 Inositol 1,4,5-trisphosphate receptor type 1 3 3.92E-05 0.265466 KRT18 Keratin, type I cytoskeletal 18 3 4.83E-05 0.236667 LAMB2 Laminin subunit beta-2 3 1.64E-04 0.271558 LATS2 Serine/threonine-protein kinase LATS2 3 6.05E-04 0.235809 LMNB2 Lamin-B2 3 1.09E-04 0.295833 MC4R Melanocortin receptor 4 3 0 0.247074 MIF Macrophage migration inhibitory factor 3 7.90E-04 0.217246 MLKL Mixed lineage kinase domain-like protein 3 0.002587 0.243986 MT-ND6 NADH-ubiquinone oxidoreductase chain 6 3 2.42E-05 0.287449 NDUFB3 NADH dehydrogenase [ubiquinone] 1 beta subcomplex subunit 3 3 8.09E-04 0.267741 NOX4 NADPH oxidase 4 3 1.16E-04 0.284517 NQO1 NAD(P)H dehydrogenase [quinone] 1 3 0.003238 0.295833 NRF1 Nuclear respiratory factor 1 3 0.002561 0.239277 NRP1 Neuropilin-1 3 7.14E-05 0.279328 OLR1 Oxidized low-density lipoprotein receptor 1 3 1.07E-05 0.253736 OSMR Oncostatin-M-specific receptor subunit beta 3 6.38E-05 0.28629 PCK2 Phosphoenolpyruvate carboxykinase [GTP], mitochondrial 3 0.8 0.714286 PDGFC Platelet-derived growth factor C 3 0 0.288511 PER2 Period circadian protein homolog 2 3 3.78E-05 0.294384 PPARGC1B Peroxisome proliferator-activated receptor gamma coactivator 1-beta 3 0.005115 0.266099 PRDX5 Peroxiredoxin-5, mitochondrial 3 1.81E-04 0.296845 PRSS3P2 Trypsin-2 3 0 0.222191 PTPRA Receptor-type tyrosine-protein phosphatase alpha 3 0.00105 0.278431 RBP4 Retinol-binding protein 4 3 0 0.275194 RDH5 11-cis retinol dehydrogenase 3 1.01E-04 0.28431 RDX Radixin 3 1.14E-04 0.299808 RSPO3 R-spondin-3 3 0.002562 0.261029 SERPINB2 Plasminogen activator inhibitor 2 3 2.96E-05 0.278035 SF3B1 Splicing factor 3B subunit 1 3 2.24E-06 0.302011 SHMT1 Serine hydroxymethyltransferase, cytosolic 3 5.20E-06 0.282153 SLC40A1 Solute carrier family 40 member 1 3 2.63E-05 0.278431 SOST Sclerostin 3 3.81E-05 0.274228 SRSF3 Serine/arginine-rich splicing factor 3 3 1.30E-04 0.262081 ST3GAL4 CMP-N-acetylneuraminate-beta-galactosamide-alpha-2,3-sialyltransferase 4 3 1.95E-04 0.278829 STAM Signal transducing adapter molecule 1 3 1.65E-04 0.256318 TFR2 Transferrin receptor protein 2 3 2.30E-05 0.232787 TLR5 Toll-like receptor 5 3 1.09E-06 0.232787 TMEM173 Stimulator of interferon genes protein 3 0 0.286816 TNFRSF4 Tumor necrosis factor receptor superfamily member 4 3 8.34E-05 0.271275 TREM1 Triggering receptor expressed on myeloid cells 1 3 6.58E-05 0.27471 TRIM33 E3 ubiquitin-protein ligase TRIM33 3 0 0.256992 ACE Angiotensin-converting enzyme 2 3.13E-05 0.298319 ACER3 Alkaline ceramidase 3 2 8.06E-04 0.257671 ACTC1 Actin, alpha cardiac muscle 1 2 0 0.214502 ADD1 Alpha-adducin 2 0 0.264656 ADIPOR1 Adiponectin receptor protein 1 2 0 0.243454 ADIPOR2 Adiponectin receptor protein 2 2 3.76E-05 0.266644 AKR1A1 Alcohol dehydrogenase [NADP(+)] 2 1.28E-05 0.295386 AKR1B1 Aldose reductase 2 4.50E-06 0.24391 AKR1B10 Aldo-keto reductase family 1 member B10 2 1.47E-05 0.2726 ALDH1B1 Aldehyde dehydrogenase 1 family member b1 2 0 0.257246 ANXA6 Annexin A6 2 0 0.257246 AVP Vasopressin-neurophysin 2-copeptin 2 2.48E-05 0.26314 B3GAT1 Galactosylgalactosylxylosylprotein 3-beta-glucuronosyltransferase 1 2 3.60E-06 0.301894 CCRL2 C-C chemokine receptor-like 2 2 6.35E-05 0.207823 CES2 Cocaine esterase 2 2.47E-04 0.220621 CTBP2 C-terminal-binding protein 2 2 0 0.220124 DICER1 Dicer 1, ribonuclease iii 2 0 0.220124 EIF6 Eukaryotic translation initiation factor 6 2 6.28E-05 0.267833 FFAR1 Free fatty acid receptor 1 2 1.16E-04 0.298092 FPR2 Formyl peptide receptor-like 2 1.89E-05 0.246917 FTH1 Ferritin heavy chain 2 4.19E-05 0.243606 GDF2 Growth/differentiation factor 2 2 4.65E-04 0.271936 GGT1 Glutathione hydrolase 1 proenzyme 2 8.23E-05 0.207437 GNMT Glycine N-methyltransferase 2 0.003247 0.2726 GPNMB Transmembrane glycoprotein NMB 2 5.08E-05 0.288938 GPR119 Glucose-dependent insulinotropic receptor 2 0 0.25218 GREM1 Gremlin-1 2 0 0.25218 GSDMD Gasdermin-D 2 0 0.29383 HBB Hemoglobin subunit beta 2 0 0.243378 HCFC1 Host cell factor 1 2 0.005115 0.238255 HMMR Hyaluronan mediated motility receptor 2 0.002561 0.192554 HNRNPU Heterogeneous nuclear ribonucleoprotein U 2 4.75E-05 0.283176 HSPA12A Heat shock protein family A member 12 A 2 6.52E-05 0.213446 IL11 Interleukin-11 2 4.43E-05 0.271463 IL13RA2 Interleukin-13 receptor subunit alpha-2 2 0.002561 0.251935 KLF6 Krueppel-like factor 6 2 0 0.309798 LGALS3BP Galectin-3-binding protein 2 0 0.300269 LMF1 Lipase maturation factor 1 2 1.22E-05 0.198073 LPCAT3 Lysophospholipid acyltransferase 5 2 7.00E-04 0.228764 LRPAP1 Alpha-2-macroglobulin receptor-associated protein 2 1.73E-05 0.198778 MKL1 MKL/myocardin-like protein 1 2 0.4 0.555556 MOCOS Molybdenum cofactor sulfurase 2 0 0.295163 MYOM2 Myomesin-2 2 2.33E-04 0.284725 NCAN Neurocan core protein 2 0 0.297184 NLRC3 NLR family CARD domain-containing protein 3 2 0 0.23913 NRG4 Pro-neuregulin-4, membrane-bound isoform 2 0.002561 0.208879 P2RX7 P2X purinoceptor 7 2 0 0.274035 PEMT Phosphatidylethanolamine N-methyltransferase 2 0.002561 0.190907 PLIN5 Perilipin-5 2 0 0.24202 POSTN Periostin 2 0 0.215985 PRPF8 Pre-mRNA-processing-splicing factor 8 2 1.37E-06 0.221938 PSPH Phosphoserine phosphatase 2 0 0.242321 RARRES2 Retinoic acid receptor responder protein 2 2 0 0.238182 SALL4 Sal-like protein 4 2 1.37E-04 0.272315 SART1 U4/U6.U5 tri-snRNP-associated protein 1 2 0 0.310044 SDF2L1 Stromal cell-derived factor 2-like protein 1 2 0 0.264387 SHBG Sex hormone-binding globulin 2 6.60E-07 0.25657 SLC10A1 Solute carrier family 10 (sodium/bile acid cotransporter), member 1 2 0 0.268754 SLC11A2 Natural resistance-associated macrophage protein 2 2 0 0.302596 SLC2A1 Solute carrier family 2, facilitated glucose transporter member 1 2 1.52E-04 0.218829 SLPI Secretory leukocyte peptidase inhibitor 2 5.22E-05 0.216164 SOCS7 Suppressor of cytokine signaling 7 2 2.20E-05 0.259296 SPINK1 Serine protease inhibitor Kazal-type 1 2 0.002561 0.295274 STK25 Serine/threonine-protein kinase 24/25/mst4 2 4.86E-05 0.275777 TCF4 Transcription factor 4 2 0.002561 0.242396 TF Serotransferrin 2 0.4 0.555556 TGM2 Protein-glutamine gamma-glutamyltransferase 2 2 0 0.264746 TNFRSF12A Tumor necrosis factor receptor superfamily member 12 A 2 0 0.210285 TRIM21 Tripartite motif-containing protein 21 2 0 0.210285 UBXN1 UBX domain-containing protein 1 2 0 0.274517 XDH Xanthine dehydrogenase/oxidase 2 0 0.270898 ABCC3 Canalicular multispecific organic anion transporter 2 1 5.41E-05 0.195152 ARRDC3 Arrestin domain-containing protein 3 1 0 0.196626 ATP5E ATP synthase subunit epsilon, mitochondrial 1 0 0.255982 ATP7B Copper-transporting ATPase 2 1 0 0.262081 ATP8B1 Phospholipid-transporting ATPase IC 1 0 0.262169 BCAT1 Branched-chain-amino-acid aminotransferase, cytosolic 1 0 0.237314 BCAT2 Branched-chain-amino-acid aminotransferase, mitochondrial 1 0 0.237314 CD34 Hematopoietic progenitor cell antigen CD34 1 0 0.255982 CD82 CD82 antigen 1 0 0.255982 CHST2 Carbohydrate sulfotransferase 2 1 0 0.234183 CIDEA Cell death inducing dffa like effector a 1 0 0.234183 CIDEB Cell death inducing dffa like effector b 1 0 0.282766 CMKLR1 Chemokine-like receptor 1 1 0 0.267649 CNR2 Cannabinoid receptor 2 1 0 0.197422 CPA1 Carboxypeptidase A1 1 0 0.213797 CPN1 Carboxypeptidase N catalytic chain 1 0 0.209383 CRABP2 Cellular retinoic acid-binding protein 2 1 0 0.209383 CRELD2 Cysteine rich with EGF like domains 2 1 0 0.218706 CTCFL Transcriptional repressor CTCFL 1 0 0.218706 CXCL16 C-X-C motif chemokine 16 1 0 0.215627 CXCL5 C-X-C motif chemokine 5 1 0 0.267741 CXCR5 C-X-C chemokine receptor type 5 1 0 0.282869 CXCR6 C-X-C chemokine receptor type 6 1 0 0.213039 DCTN4 Dynactin subunit 4 1 0 1 DECR1 2,4-dienoyl-CoA reductase, mitochondrial 1 0 1 DMGDH Dimethylglycine dehydrogenase, mitochondrial 1 0 0.232164 ECHS1 Enoyl-CoA hydratase, mitochondrial 1 0 0.161497 EDA Ectodysplasin-A 1 0 0.212923 EIF2AK1 Eukaryotic translation initiation factor 2-alpha kinase 1 1 0 0.212923 FABP2 Fatty acid-binding protein, intestinal 1 0 0.196182 FBXW5 F-box/WD repeat-containing protein 5 1 0 0.228096 FFAR4 Free fatty acid receptor 4 1 0 0.201289 FOXA3 Hepatocyte nuclear factor 3-gamma 1 0 1 FTL Ferritin light chain 1 0 1 GCGR Glucagon receptor 1 0 1 GCKR Glucokinase regulatory protein 1 0 1 GDF15 Growth differentiation factor 15 1 0 0.234043 GFER Growth factor, augmenter of liver regeneration 1 0 0.254979 GOLM1 Golgi membrane protein 1 1 0 0.23244 GPBAR1 G-protein coupled bile acid receptor 1 1 0 0.246684 GPLD1 Glycosylphosphatidylinositol specific phospholipase d1 1 0 0.201133 GPR55 G protein-coupled receptor 55 1 0 0.270711 GSTA4 Glutathione S-transferase A4 1 0 0.384615 GSTM2 Glutathione S-transferase Mu 2 1 0 0.256234 HDAC8 Histone deacetylase 8 1 0 0.208156 HS3ST1 [heparan sulfate]-glucosamine 3-sulfotransferase 1 1 0 0.259124 HSPA6 Heat shock 70 kDa protein 6 1 0 0.228764 HTR2A 5-hydroxytryptamine receptor 2 A 1 0 0.23251 IBTK Inhibitor of Bruton tyrosine kinase 1 0 0.289796 IFNA13 Interferon alpha-1/13 1 0 0.259382 IGFBP2 Insulin-like growth factor-binding protein 2 1 0 0.252587 IL19 Interleukin-19 1 0 1 IL20RA Interleukin-20 receptor subunit alpha 1 0 1 IL33 Interleukin-33 1 0 0.172826 INTU Protein inturned 1 0 0.25316 ITPR2 Inositol 1,4,5-trisphosphate receptor type 2 1 0 0.283691 KRT19 Keratin, type I cytoskeletal 19 1 0 0.160337 LIN28B Protein lin-28 homolog B 1 0 0.241796 LIPA Lysosomal acid lipase/cholesteryl ester hydrolase 1 0 0.235454 LTBP3 Latent-transforming growth factor beta-binding protein 3 1 0 0.249361 MBOAT7 Membrane bound o-acyltransferase domain containing 7 1 0 0.23717 MDK Midkine 1 0 0.242848 MERTK Tyrosine-protein kinase Mer 1 0 0.220809 MGAM Maltase-glucoamylase, intestinal 1 0 0.220809 MMP11 Matrix metalloproteinase-11 (stromelysin 3) 1 0 0.267741 MTHFR Methylenetetrahydrofolate reductase (nadph) 1 0 0.252098 NFE2L1 Nuclear factor erythroid 2-related factor 1 1 0 0.251773 NOX1 NADPH oxidase 1 1 0 0.235454 NPPB Natriuretic peptides B 1 0 1 NTS Neurotensin/neuromedin N 1 0 1 ORM1 Alpha-1-acid glycoprotein 1 1 0 0.262698 PANX1 Pannexin-1 1 0 0.207052 PDE4A cAMP-specific 3’,5’-cyclic phosphodiesterase 4 A 1 0 0.264119 PHGDH D-3-phosphoglycerate dehydrogenase 1 0 1 PRMT7 Protein arginine N-methyltransferase 7 1 0 1 RAG2 V(D)J recombination-activating protein 2 1 0 0.248805 RASSF1 Ras association domain-containing protein 1 1 0 0.239277 SAMM50 Sorting and assembly machinery component 50 homolog 1 0 0.217913 SERPINA12 Serpin A12 1 0 0.267741 SESN3 Sestrin 1/3 1 0 0.240086 SFRP4 Secreted frizzled-related protein 4 1 0 0.282869 SFRP5 Secreted frizzled-related protein 5 1 0 0.248726 SH3BP5 SH3 domain-binding protein 5 1 0 1 SI Sucrase-isomaltase, intestinal 1 0 1 SLAMF1 Signaling lymphocytic activation molecule 1 0 0.27695 SLC51A Organic solute transporter subunit alpha 1 0 0.215746 SLC5A2 Sodium/glucose cotransporter 2 1 0 0.228029 SLC6A3 Solute carrier family 6 (neurotransmitter transporter, dopamine) member 3 1 0 0.268754 SLC6A4 Sodium-dependent serotonin transporter 1 0 1 SLCO1A2 Solute carrier organic anion transporter family member 1A2 1 0 1 SRSF6 Splicing factor, arginine/serine-rich 4/5/6 1 0 0.250642 STAR Steroidogenic acute regulatory protein, mitochondrial 1 0 0.454545 TM6SF2 Transmembrane 6 superfamily member 2 1 0 0.265195 TMBIM6 Bax inhibitor 1 1 0 0.23717 TMED2 Transmembrane emp24 domain-containing protein 2 1 0 0.195152 TMSB4X Thymosin beta 4, x-linked 1 0 0.384615 TNC Tenascin 1 0 0.210342 TNFRSF6B Tumor necrosis factor receptor superfamily, member 6b, decoy 1 0 0.21527 TNIP3 TNFAIP3-interacting protein 3 1 0 0.279328 TRPC4AP Short transient receptor potential channel 4-associated protein 1 0 0.247937 TUBA8 Tubulin alpha-8 chain 1 0 0.276656 UBQLN4 Ubiquilin-4 1 0 0.230997 UCP1 Mitochondrial brown fat uncoupling protein 1 1 0 0.295051 UFM1 Ubiquitin-fold modifier 1 1 0 0.244674 UFSP2 Ufm1-specific protease 2 1 0 0.23753 VSIG4 V-set and immunoglobulin domain-containing protein 4 1 0 0.262698 WDR1 WD repeat-containing protein 1 1 0 0.245366 WTAP Pre-mRNA-splicing regulator WTAP 1 0 0.193126 ZCCHC11 Zinc finger cchc-type containing 11 1 0 0.24202 ZNF638 Zinc finger protein 638 1 0 1 [111]Open in a new tab Fig. 3. [112]Fig. 3 [113]Open in a new tab PPI network map of nonalcoholic steatohepatitis (NASH)-related targets. The size and color of the nodes. Are sorted in descending order by degree value from largest to smallest and from red to yellow. The figure displays targets with a degree of 9 or higher. Subsequently, mapping diosgenin targets (n = 329) to NASH targets (n = 1240) identified 114 overlapping targets (Table [114]2). Venn diagrams were generated for visualization purposes (Fig. [115]4A). The overlapping targets were imported into the STRING database to construct a PPI network. Additionally, network topology analysis was conducted using Cytoscape 7.3.2. As depicted in Fig. [116]4B, the PPI network contains a total of 114 nodes, and 9 key targets identified in the innermost circle based on the triple of the median degree value (≥ 57) are as follows: ALB (degree = 80), AKT1 (degree = 72), TP53 (degree = 66), VEGFA (degree = 64), MAPK3 (degree = 62), EGFR (degree = 59), STAT3 (degree = 59), CASP3 (degree = 58) and IGF1 (degree = 57). The above 9 targets are considered pivotal targets of diosgenin for NASH therapy. Table 2. Topological information on the diosgenin and NASH targets. Name Name of protein Degree Betweenness centrality Closeness centrality ALB RAC-alpha serine/threonine-protein kinase 80 0.105112 0.78169 AKT1 Aldose reductase 72 0.051785 0.74 TP53 Cellular tumor antigen p53 66 0.03787 0.711538 VEGFA Vascular endothelial growth factor A 64 0.028961 0.698113 MAPK3 Mitogen-activated protein kinase 3 62 0.040054 0.689441 EGFR Epidermal growth factor receptor 59 0.029649 0.680982 STAT3 Signal transducer and activator of transcription 3 59 0.04422 0.680982 CASP3 Branched-chain-amino-acid aminotransferase, mitochondrial 58 0.018328 0.668675 IGF1 Insulin-like growth factor I 57 0.026075 0.668675 PPARG Peroxisome proliferator-activated receptor gamma 56 0.038014 0.660714 ESR1 Estrogen receptor 55 0.022216 0.664671 MTOR Serine/threonine-protein kinase mTOR 50 0.0163 0.641619 MMP9 Matrix metalloproteinase-9 49 0.017083 0.634286 PPARA Peroxisome proliferator-activated receptor alpha 49 0.041892 0.634286 CCND1 G1/S-specific cyclin-D1 48 0.013186 0.634286 HIF1A Hypoxia-inducible factor 1-alpha 48 0.011672 0.630682 CAT Caspase-7 47 0.042191 0.634286 PTGS2 Prostaglandin G/H synthase 2 46 0.010637 0.627119 MAPK14 Mitogen-activated protein kinase 14 42 0.010223 0.61326 RELA Rela proto-oncogene, nf-kb subunit 40 0.010737 0.6 MAPK8 Mitogen-activated protein kinase 8/9/10 (c-jun n-terminal kinase) 39 0.005198 0.603261 NOS3 Nitric-oxide synthase, endothelial 39 0.006608 0.6 MMP2 Matrix metalloproteinase-2 (gelatinase a) 38 0.005338 0.596774 CYP3A4 Cytochrome p450 family 3 subfamily a polypeptide 4 37 0.028431 0.587302 IGF1R Insulin-like growth factor 1 receptor 37 0.003095 0.596774 MDM2 E3 ubiquitin-protein ligase Mdm2 37 0.005473 0.587302 STAT1 Signal transducer and activator of transcription 1-alpha/beta 37 0.007163 0.587302 GSK3B Glycogen synthase kinase-3 beta 36 0.002792 0.590426 HMOX1 Heme oxygenase 1 36 0.006587 0.593583 AR Amyloid-beta A4 protein 34 0.005159 0.584211 CDKN1A Cyclin-dependent kinase inhibitor 1 34 0.001527 0.57513 ICAM1 Intercellular adhesion molecule 1 34 0.004929 0.584211 JAK2 Tyrosine-protein kinase JAK2 34 0.004738 0.57513 APP Apolipoprotein A-II 33 0.005819 0.581152 SOD2 Superoxide dismutase [Mn], mitochondrial 33 0.016237 0.584211 GRB2 Growth factor receptor-bound protein 2 31 0.021606 0.569231 PTPN11 Tyrosine-protein phosphatase nonreceptor type 11 31 0.004814 0.572165 PARP1 Poly [ADP-ribose] polymerase 1 29 0.009258 0.563452 GSTP1 Glutathione S-transferase P 28 0.017789 0.566327 PDGFRB Platelet-derived growth factor receptor beta 28 0.0026 0.555 CDK4 Cyclin-dependent kinase 4 27 8.19E-04 0.549505 MET Hepatocyte growth factor receptor 26 9.08E-04 0.541463 ESR2 Estrogen receptor beta 25 0.002895 0.544118 PTPN1 Tyrosine-protein phosphatase nonreceptor type 1 25 0.001351 0.555 RXRA Retinoic acid receptor RXR-alpha 25 0.015233 0.538835 CYP19A1 Aromatase 24 0.004316 0.546798 FASN Fatty acid synthase 24 0.006278 0.546798 SOD1 Superoxide dismutase, cu-zn family 24 0.003079 0.552239 CYP2C9 Cytochrome p450 family 2 subfamily c polypeptide 9 22 0.006526 0.528571 SHH Sonic hedgehog protein 22 6.34E-04 0.528571 AKR1B1 Adenosine A2a receptor 21 0.004587 0.538835 CDK1 Cyclin-dependent kinase 1 21 8.05E-04 0.541463 F2 Prothrombin 21 0.005701 0.536232 GSR Glutathione reductase, mitochondrial 21 0.002246 0.541463 NQO1 NAD(P)H dehydrogenase [quinone] 1 21 0.001801 0.536232 INSR Insulin receptor 20 0.001153 0.528571 CASP7 Caspase-3 18 1.37E-04 0.521127 CTSB Cathepsin B 18 0.001531 0.526066 GSTA1 Glutathione S-transferase A1 18 0.00511 0.506849 GSTM1 Glutathione S-transferase Mu 1 18 0.005783 0.518692 NR1H4 Bile acid receptor 18 0.004847 0.533654 SERPINA1 Alpha-1-antitrypsin 18 0.003425 0.513889 TNFRSF10B Tumor necrosis factor receptor superfamily member 10B 18 5.97E-04 0.528571 DPP4 Dipeptidyl peptidase 4 17 0.001433 0.526066 PLA2G4A Cytosolic phospholipase A2 17 0.003383 0.523585 TGFBR1 TGF-beta receptor type-1 17 3.31E-04 0.511521 PIK3CG Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit gamma isoform 16 7.31E-04 0.497758 SYK Spleen associated tyrosine kinase 16 0.001011 0.523585 ABCC2 Atp-binding cassette, subfamily c (cftr/mrp), member 2 15 0.003047 0.495536 CYP2C8 Cytochrome p450 family 2 subfamily c polypeptide 8 15 0.001822 0.506849 VDR Vitamin D3 receptor 15 3.79E-04 0.521127 HMGCR 3-hydroxy-3-methylglutaryl-coenzyme A reductase 14 0.001999 0.506849 LCN2 Neutrophil gelatinase-associated lipocalin 14 0.001538 0.506849 MAPK9 Mitogen-activated protein kinase 8/9/10 (c-jun n-terminal kinase) 14 3.88E-04 0.495536 MMP13 Matrix metalloproteinase-13 (collagenase 3) 14 1.02E-04 0.513889 NR1I2 Nuclear receptor subfamily 1 group I member 2 14 0.002824 0.516279 PRKACA cAMP-dependent protein kinase catalytic subunit alpha 14 0.001497 0.5 SULT2A1 Sulfotransferase family 2a member 1 14 0.001889 0.480519 FGFR4 Fibroblast growth factor receptor 4 13 6.35E-04 0.502262 GSTM2 Glutathione S-transferase Mu 2 13 0.002412 0.488987 RAB5A RAB5A, member RAS oncogene family 13 4.01E-04 0.506849 CYP17A1 Steroid 17-alpha-hydroxylase/17,20 lyase 12 0.001418 0.474359 ELANE Elastase, neutrophil expressed 12 7.61E-04 0.486842 TTR Transthyretin 12 0.002221 0.493333 NR1H3 Oxysterols receptor LXR-alpha 11 9.67E-04 0.478448 PPARD Peroxisome proliferator-activated receptor delta 11 0.001725 0.478448 RBP4 Retinol-binding protein 4 11 0.001501 0.49115 APOA2 Aldehyde dehydrogenase, mitochondrial 10 0.001051 0.493333 FGR Fgr proto-oncogene, src family tyrosine kinase 10 0.001463 0.460581 CCR1 C-C chemokine receptor type 1 9 4.63E-04 0.449393 CES1 Liver carboxylesterase 1 9 6.94E-04 0.482609 SHBG Sex hormone-binding globulin 9 6.58E-04 0.476395 ALDH2 Serum albumin 8 0.001158 0.418868 HSD11B1 Corticosteroid 11-beta-dehydrogenase isozyme 1 8 3.41E-04 0.480519 NR1H2 Oxysterols receptor LXR-beta 8 2.96E-04 0.440476 NR1I3 Nuclear receptor subfamily 1 group I member 3 8 5.13E-04 0.478448 PRKCQ Protein kinase C theta type 8 6.05E-05 0.458678 NPC1L1 Niemann-Pick C1-like protein 1 7 7.58E-04 0.464435 S100A9 Protein S100-A9 7 4.28E-04 0.435294 THRB Thyroid hormone receptor beta 7 0.001281 0.478448 ACADM Abo, alpha 1-3-n-acetylgalactosaminyltransferase and alpha 1-3-galactosyltransferase 6 6.45E-04 0.458678 ADH1C Medium-chain specific acyl-CoA dehydrogenase, mitochondrial 6 2.90E-04 0.376271 ADORA2A Alcohol dehydrogenase 1c (class i), gamma polypeptide 6 0 0.478448 BCAT2 Androgen receptor 6 0.001248 0.430233 IL6ST Interleukin-6 receptor subunit beta 6 0 0.444 MAOA Amine oxidase [flavin-containing] A 5 8.13E-04 0.428571 CRABP2 Chitotriosidase-1 4 1.44E-04 0.437008 HTR2A 5-hydroxytryptamine receptor 2 A 4 2.91E-04 0.460581 HDAC8 Histone deacetylase 8 3 3.27E-05 0.433594 RORA Rar-related orphan receptor alpha 3 0 0.422053 YARS Tyrosine–tRNA ligase, cytoplasmic 3 1.07E-04 0.393617 MERTK Tyrosine-protein kinase Mer 1 0 0.363934 [117]Open in a new tab Fig. 4. [118]Fig. 4 [119]Open in a new tab Target screening of the effect of diosgenin on NASH. (A) Venn diagram of Diosgenin and NASH targets; (B) PPI network of Diosgenin and NASH intersection targets. The targets are sorted incrementally by degree-value, and the node colors and sizes are adjusted according to the degree values. Node colors from blue to orange and from small to large indicate progressively higher degree values. GO and KEGG signaling pathway enrichment analysis To further explore the potential mechanism of diosgenin in NASH treatment, an enrichment analysis of the 114 intersecting targets in the PPI network was performed employing the DAVID database. Using FDR < 0.05 and p-value < 0.05 as thresholds, 239 biological processes (BP), 32 cellular components (CC), and 66 molecular functions (MF) pathways were identified. The top 10 GO terms were ranked by Fold enrichment, and a bar graph was generated for visualization in Fig. [120]5A. The GO analysis revealed that the enriched BP pathways are primarily associated with metabolic regulation, oxidative stress and inflammation control (Table [121]3). The enriched CC pathways are mainly related to cell signaling, gene expression regulation and cellular secretion processes (Table [122]4). Meanwhile, the enriched MF pathways are predominantly involved in metabolic regulation, oxidative stress response, signal transduction, and gene expression regulation (Table [123]5). Among these, BP “positive regulation of fatty acid metabolic process (GO:0045923)” and “positive regulation of fatty acid oxidation (GO:0046321)” relate to Fatty acid metabolism, while BP “negative regulation of interferon-gamma-mediated signaling pathway (GO:0060336),” MF “superoxide dismutase activity (GO:0004784),” and “nitric-oxide synthase regulator activity (GO:0030235)” link to inflammation. Moreover, KEGG pathway enrichment analysis was performed to reveal the underlying signaling pathways involved. After removing the top 3 cancer-related pathways, the top 10 pathways were plotted in bubble diagrams according to the counts of hit targets and P values (Fig. [124]5B; Table [125]6). The primary pathway with the most enriched targets was the PI3K-Akt pathway (hsa04151). Based on this, we speculate that diosgenin could ameliorate fatty acid metabolism and inflammatory response in hepatocytes through the PI3K-Akt pathway to alleviate NASH progression. Fig. 5. [126]Fig. 5 [127]Open in a new tab GO and KEGG enrichment analyses of the overlapping targets of diosgenin in patients with NASH. (A) GO analysis revealed three aspects: biological process (BP), cellular component (CC), and molecular function (MF). The enriched targets were sorted in descending order according to Fold enrichment. (B) KEGG analysis was performed in descending order according to the number of potential targets enriched in the pathway. Table 3. Biological process of GO analysis. ID Biological process P value Fold enrichment FDR GO:0031281 Positive regulation of cyclase activity 9.97E−05 170.8672566 0.001967972 GO:0070141 Response to UV-A 9.97E−05 170.8672566 0.001967972 GO:1,901,687 Glutathione derivative biosynthetic process 1.99E−04 128.1504425 0.003474314 GO:0045923 Positive regulation of fatty acid metabolic process 3.30E−04 102.520354 0.005161332 GO:0070857 Regulation of bile acid biosynthetic process 3.30E−04 102.520354 0.005161332 GO:0010887 Negative regulation of cholesterol storage 2.70E−09 93.2003218 3.12E−07 GO:0061419 Positive regulation of transcription from RNA polymerase II promoter in response to hypoxia 4.93E−04 85.43362832 0.007379709 GO:0033591 Response to L-ascorbic acid 6.87E−04 73.22882427 0.009747128 GO:0046321 Positive regulation of fatty acid oxidation 6.87E−04 73.22882427 0.009747128 GO:0060336 Negative regulation of Interferon-gamma-mediated signaling pathway 9.13E−04 64.07522124 0.012266478 [128]Open in a new tab Table 4. Cellular component of GO analysis. ID Cellular component P value Fold enrichment FDR GO:0005901 Caveola 2.20E−07 18.73957621 7.13E−06 GO:0000307 Cyclin-dependent protein kinase holoenzyme complex 0.001376904 18.03684211 0.012623302 GO:0031093 Platelet alpha granule lumen 5.08E−04 13.46032993 0.005663372 GO:0090575 RNA polymerase II transcription factor complex 3.48E−07 13.41583297 8.78E−06 GO:0043235 Receptor complex 1.52E−11 12.46786321 3.46E−09 GO:0017053 Transcriptional repressor complex 0.005050691 11.45196324 0.036926523 GO:0000791 Euchromatin 0.005050691 11.45196324 0.036926523 GO:1,904,813 Ficolin-1-rich granule lumen 6.14E−04 8.727504244 0.00633631 GO:0034774 Secretory granule lumen 0.003743547 7.842105263 0.03034947 GO:0045121 Membrane raft 1.07E−05 7.157477026 1.86E−04 [129]Open in a new tab Table 5. Molecular function of GO analysis. ID Molecular function P value Fold enrichment GO:0034875 Caffeine oxidase activity 2.12E−04 124.1381579 GO:0004784 Superoxide dismutase activity 3.51E−04 99.31052632 GO:0043560 Insulin receptor substrate binding 1.58E−06 55.17251462 GO:0030235 Nitric-oxide synthase regulator activity 0.001245069 55.17251462 GO:0004879 RNA polymerase II transcription factor activity, ligand-activated sequence-specific DNA binding 1.23E−21 49.04223522 GO:0008144 Drug binding 0.001887347 45.14114833 GO:0004707 MAP kinase activity 1.11E−04 41.37938596 GO:0046965 Retinoid X receptor binding 1.60E−04 36.78167641 GO:0003707 Steroid hormone receptor activity 9.89E−06 35.98207475 GO:0005158 Insulin receptor binding 1.18E−05 34.48282164 [130]Open in a new tab Table 6. Basic information on the first 10 signaling pathways of the KEGG pathway analysis. KEGG ID Pathway name Count Enrichment p value hsa04151 PI3K-Akt signaling pathway 24 5.077904 1.38E−10 hsa05205 Proteoglycans in cancer 23 8.403312 1.50E−14 hsa05207 Chemical carcinogenesis - receptor activation 22 7.772546 3.17E−13 hsa05417 Lipid and atherosclerosis 21 7.315724 4.06E−12 hsa05208 Chemical carcinogenesis - reactive oxygen species 21 7.053277 8.02E−12 hsa01522 Endocrine resistance 20 15.28553 1.40E−17 hsa04010 MAPK signaling pathway 20 5.095176 7.61E−09 hsa05225 Hepatocellular carcinoma 19 8.47073 4.94E−12 hsa05163 Human cytomegalovirus infection 19 6.324811 6.60E−10 hsa04014 Ras signaling pathway 18 5.712642 1.02E−08 [131]Open in a new tab Molecular docking results To determine whether diosgenin can act on the nine core targets (ALB, AKT1, TP53, VEGFA, MAPK3, EGFR, STAT3, CASP3, IGF1), we conducted molecular docking studies. The results indicated that diosgenin exhibited binding affinity with all core targets. The other molecular docking results of diosgenin with target proteins are shown in Table [132]7 and the 3-dimensional map of the binding sites is shown in Fig. [133]6. The docking results demonstrate that diosgenin is capable of forming hydrogen bonds with these proteins, with bond lengths significantly shorter than the conventional 3.5 Å associated with standard hydrogen bonds. The binding energies are all below − 7 kcal/mol, which confirms a relatively strong binding ability and suggests potential interactions among them. Fig. 6. [134]Fig. 6 [135]Open in a new tab The 3-dimensional map of the binding sites between diosgenin and target proteins. (A) ALB. (B) AKT1, (C) TP53, (D) VEGFA, (E) MAPK3, (F) EGFR, (G) STAT3, (H) CASP3, (I) IGF1. Diosgenin is shown in green. Target proteins are displayed as white. The places where diosgenin and the target proteins are connected represent specific docking sites between diosgenin and target proteins. Assessment of HepG2 cell viability and morphology under FFA and diosgenin treatment Since the signaling pathway with the most enriched targets in the KEGG enrichment analysis was the PI3K-Akt pathway, and all of the nine core targets (ALB^[136]45, AKT1, TP53^[137]46, VEGFA^[138]47, MAPK3^[139]48, EGFR^[140]49, STAT3^[141]50, CASP3^[142]44, IGF1^[143]51) were related to the PI3K-Akt pathway, we thus hypothesized that diosgenin affects NASH by alleviating triglyceride deposition and the inflammatory response through the PI3K-Akt pathway. To confirm this hypothesis, HepG2 cells were treated with free fatty acids (FFAs) at a 1:2 molar ratio of palmitic acid (PA) to oleic acid (OA), aiming to simulate NASH in vitro^[144]52. Initially, the effective concentrations of both FFA and diosgenin were determined using the CCK-8 assay. The FFA concentration gradient ranged from 0.1 to 0.5 mM. The findings demonstrated that the viability of HepG2 cells was significantly inhibited when the concentration of PA in the FFA solution exceeded 0.1 mM (Fig. [145]7A). The highest concentration surpassing the median lethal dose (LD50), namely, the FFA containing 0.2 mM PA, was chosen for the subsequent experiments. Subsequently, HepG2 cells were treated with different concentrations of diosgenin at 0, 5, 10, 25, 50, and 100 µM. Figure [146]7B shows that there was no significant effect on cell viability at diosgenin concentrations ranging from 0 to 25 µM. However, cell viability was dramatically inhibited when the concentration of diosgenin was greater than 25 µM. Therefore, 5, 10, and 25 µM diosgenin were used in the following experiments. The morphology of the HepG2 cells in the various groups is depicted in Fig. [147]7C. The cell number decreased, and the cell morphology changed after 24 h of FFA treatment. The cell condition improved after 5 µM diosgenin treatment and was further enhanced in the 10 µM treatment group. However, 25 µM diosgenin treatment did not ameliorate these changes. Fig. 7. [148]Fig. 7 [149]Open in a new tab Free fatty acid and diosgenin concentration screening. (A) Effects of different concentrations of free fatty acids (FFA, PA-to-OA molar ratio of 1:2) on cell proliferation and toxicity; (B) cell survival after 24 h of incubation with different concentrations of diosgenin (µM); (C) cell morphology of HepG2 cells in different intervention states (× 50). Diosgenin alleviates triglyceride deposition and the inflammatory response in FFA-treated HepG2 cells To verify the effects of diosgenin on lipid metabolism in hepatocytes, we measured the intracellular TG, TC and FC levels in diosgenin-treated HepG2 cells. The intracellular TG content was significantly greater in the FFA-treated group than in the control group. Additionally, the intracellular TG content tended to decrease after 5 µM diosgenin treatment and further decreased after 10 µM diosgenin treatment (Fig. [150]8B). However, the concentration of 25 µM diosgenin did not significantly affect the lowering of TG levels. Consistent with these findings, Oil Red O staining (Fig. [151]8A) revealed that the intracellular lipid content was significantly greater in HepG2 cells after 24 h of FFA treatment than in normal controls, and the lipid content was reduced by the addition of 5 µM diosgenin. The intracellular lipid droplet content was significantly reduced after 10 µM diosgenin treatment. Although FFA treatment for 24 h did not increase cholesterol levels in the NASH cell model, treatment with 10 µM diosgenin for 24 h resulted in notable decreases in total cholesterol (TC) and total fibrin (FC) levels in the cells (Fig. [152]8C). Fig. 8. [153]Fig. 8 [154]Open in a new tab Diosgenin reduces cellular triglyceride deposition and attenuates FFA-induced inflammation. (A) Cellular Oil Red O staining (× 40) of HepG2 cells treated with FFA and different concentrations of diosgenin. (B) Determination of cellular triglyceride (TG) levels in HepG2 cells treated with FFA and different concentrations of diosgenin; (C) Determination of cellular total cholesterol (TC) and free cholesterol (FC) levels in HepG2 cells treated with FFA and different concentrations of diosgenin; (D) Determination of IL-6 levels in cell culture supernatants in HepG2 cells treated with FFA and different concentrations of diosgenin; (D) Determination of IL-6 in cell culture supernatant after treatment of HepG2 cells with FFA and different concentrations of diosgenin. The data are expressed as the mean ± standard deviation; * indicates P < 0.05, ** indicates P < 0.01, *** indicates P < 0.001, and **** indicates P < 0.0001. To assess the ameliorative effect of diosgenin on NASH-related inflammation, we also measured the inflammatory factor IL-6 in the culture supernatant of a diosgenin-treated NASH cell model using an enzyme-linked immunosorbent assay. The IL-6 concentration in the culture supernatant of FFA-treated cells was elevated nearly 1.5-fold compared with that in the normal control group, and treatment with diosgenin significantly reduced the elevated IL-6 level (Fig. [155]8D). Diosgenin ameliorates triglyceride deposition and inflammation through the PI3K-Akt pathway in FFA-treated HepG2 cells The results of the network pharmacological analysis showed that diosgenin might attenuate fatty acid metabolism through the PI3K-Akt pathway in NASH therapy. To further verify whether diosgenin activates the PI3K-Akt pathway and thus plays a role in the treatment of NASH, we examined the protein expression levels of PI3K, p-AKT, and AKT by western blotting. These findings indicate that the phosphorylation level of AKT decreased by approximately 32.7% in HepG2 cells treated with FFA, while diosgenin significantly enhanced the phosphorylation of AKT. However, there was no significant change in the phosphorylation of PI3K between the groups (Fig. [156]9A, B). We observed that the protein expression of stearoyl-coenzyme A desaturase-1 (SCD1), which is closely related to fatty acid synthesis, was increased in the NASH cell line, and its expression was significantly decreased after treatment with 10 µM diosgenin (Fig. [157]8E, F). However, there was no significant difference in the expression of other key proteins involved in fatty acid metabolism, such as FASN, ACC, p-ACC, SREBP1c and CPT1α (Fig. [158]9C–F). Fig. 9. [159]Fig. 9 [160]Open in a new tab Activation of the PI3K-Akt pathway by diosgenin activates lipid metabolism in hepatocytes. (A) Western blotting was used to detect PI3K, p-AKT, and AKT protein expression. (C) Western blotting was used to detect the protein expression of CPT1α, an indicator of fatty acid oxidation. (E) protein expression of other fatty acid metabolism-related indicators determined by Western blotting. (B, D, F) the data shown in the bar graphs indicate the mean ± standard deviation of three independent experiments. * indicates P < 0.05, ** indicates P < 0.01. Discussion In this study, we investigated the potential role that diosgenin plays in NASH remission. Network pharmacology was employed to predict the candidate therapeutic targets and signaling pathways of diosgenin in NASH, and then, experimental verification in HepG2 cells was conducted to further illustrate the pharmacological mechanism of diosgenin against NASH. Based on the results of the PPI network topology analysis, 9 key genes related to the association of diosgenin with NASH were screened, the most important of which was AKT1. The protein kinase AKT is a serine threonine protein kinase that is activated in response to different stimuli through a phosphatidylinositol 3 kinase (PI3K)-dependent mechanism^[161]53 and plays a central role in promoting cell proliferation, migration and transcription and inhibiting apoptosis. Akt1 is a subtype of the Akt family^[162]54,[163]55. Each Akt isoform plays a different role in metabolism and growth processes. However, Akt1 plays a key role in cell growth and survival^[164]56,[165]57. In addition, several studies have shown that AKT plays an important role in the regulation of lipid metabolism. The results of KEGG pathway analysis showed that the signaling pathway with the most enriched targets was the PI3K-Akt pathway (hsa04151). The PI3K-Akt signaling pathway functions in organism growth and key cellular processes such as glucose homeostasis, lipid metabolism, protein synthesis, cell proliferation and survival by mediating growth factor signaling^[166]58. Several studies have shown that the PI3K-Akt pathway plays an important role in regulating lipid metabolism. Brg1 regulates lipid metabolism in hepatocellular carcinoma by mediating GLMP expression through the PIK3AP1/PI3K/AKT pathway^[167]59. LAMP3 regulates hepatic lipid metabolism through activation of the PI3K/Akt pathway^[168]60. Scutellaria baicalensis and Radix Scutellariae improve glycolipid metabolism in T2DM rats by modulating the metabolic profile and the MAPK/PI3K/Akt signaling pathway^[169]61. In addition, the PI3K-Akt pathway plays a crucial role in the development of inflammation. The PTX3/TIST1 feedback loop regulates lipopolysaccharide-induced inflammation via the PI3K/Akt signaling pathway^[170]62. Moreover, macrolides reduce pulmonary and systemic inflammation in COPD by modulating the PI3K/Akt-Nrf2 pathway^[171]63. Our experimental results suggest that diosgenin may ameliorate steatosis and attenuate the inflammatory response in HepG2 cells by activating the PI3K-Akt pathway. Other core genes are discussed below. ALB is a carrier of fatty acids in the blood. It has been found that defective phosphorylation of TP53 at Ser312 leads to disruption of lipid metabolism, which causes fat accumulation and even the development of fatty liver^[172]64. Hepatocyte-derived VEGFA accelerates the progression of NAFLD to hepatocellular carcinoma through the activation of hepatic stellate cells^[173]65. MAPK3, or ERK1, plays key roles in many cell proliferation-related signaling pathways. EGFR is closely related to lipid rafts and plays an important role in the development of tumorigenesis^[174]66. Overexpression of STAT3 in the liver ameliorated hyperglycemia and hyperinsulinemia in insulin-resistant diabetic mice^[175]67. The protein encoded by the CASP3 gene is a cysteine-aspartate protease that plays a key role in the execution phase of apoptosis, and inhibition of CASP3 can reduce hepatocyte apoptosis and attenuate alcohol-induced liver injury^[176]68. It has been shown that IGF1 inhibits cholesterol accumulation in the liver of growth hormone-deficient mice through activation of ABCA1^[177]69. The above proteins play important roles in the liver and are likely to play an equal role in the development of NASH; moreover, whether diosgenin exerts effects on NASH through these key targets needs to be further confirmed. In recent years, diosgenin has received increasing attention for its efficacy in the treatment of various metabolic diseases and has been used to treat various cancers^[178]70, atherosclerosis^[179]71, skin diseases^[180]72, osteoporosis^[181]73, neurological diseases^[182]74, and metabolic diseases (obesity, diabetes, inflammation)^[183]75. It has been reported that diosgenin upregulates the expression of the caveolin-1 protein, which is closely related to cholesterol transport, and reduces intracellular cholesterol levels in human normal hepatocyte L02 cells^[184]76. In the present study, we investigated the triglyceride-lowering effect of diosgenin in a NASH cell model. Moreover, our experiments confirmed that diosgenin treatment significantly reduced intracellular cholesterol levels, which is consistent with the findings of the previous studies mentioned above. Fatty acid synthesis is an important process in lipid metabolism. Stearoyl coenzyme A desaturase 1 (SCD1) is the rate-limiting enzyme in the biosynthesis of monounsaturated fatty acids^[185]77. Both systemic SCD1 knockout mice and liver-specific SCD1 knockout mice exhibit decreased hepatic triglyceride accumulation and resistance to high-fat diet (HFD)- or high-carbohydrate diet-induced steatosis^[186]78–[187]80. Our experimental results suggest that diosgenin can reduce SCD1 protein expression in HepG2 cells and ameliorate hepatic lipids by reducing lipid synthesis. It is known that inflammatory cytokines play a key role in the pathogenesis and progression of NASH, leading to more severe fatty liver conditions^[188]81. It has been demonstrated that diosgenin can reduce proinflammatory and prosurvival signaling in cancer cells^[189]82. Our results showed that diosgenin significantly alleviated IL-6 secretion in a NASH cell model. These in vitro studies confirm diosgenin’s dual therapeutic effects: improving hepatocyte lipid metabolism and reducing inflammation in NASH. Network pharmacology provides potential drug targets and pathways through computational predictions. However, it has certain limitations, as it excessively relies on network models that cannot fully capture the highly complex and dynamic nature of biological systems. The combination of network pharmacology and in vivo and in vitro experiments not only validates the accuracy of predictions but also provides deeper insights into the multi-component and multi-target characteristics of natural products, thereby enhancing the scientific value and translational potential of the research^[190]83,[191]84. In summary, this study investigated the effects and mechanisms of action of diosgenin on NASH at the systemic level through network pharmacological analysis plus cellular experimental validation. Studies suggest that diosgenin may reduce SCD1 expression by activating the PI3K-Akt pathway in the treatment of NASH. This study lays a good foundation for further in-depth study of the mechanism of diosgenin-induced NASH and provides an important scientific basis for broader clinical application. However, further studies of the mechanism of diosgenin in the treatment of NASH are needed, and an animal model is needed for further research and exploration. Conclusion In this study, we employed a network pharmacology approach to investigate the effects and underlying mechanisms of diosgenin on NASH. Through screening of potential targets, construction of protein-protein interaction networks, and bioenrichment analysis, we found that diosgenin may alleviate NASH progression by enhancing fatty acid metabolism in hepatocytes via the PI3K-Akt pathway. Molecular docking results revealed that nine core targets not only exhibited strong binding affinity with diosgenin but also were reported to be associated with the PI3K-Akt pathway. Cellular experiments further confirmed our predictions, demonstrating that diosgenin reduced triglyceride accumulation and inflammatory responses in FFA-treated HepG2 cells through the PI3K-Akt pathway. This study lays a solid foundation for further exploration of the intrinsic mechanisms by which diosgenin ameliorates NASH and provides critical scientific support for its broader clinical application. As a natural compound, diosgenin holds promise as a novel therapeutic approach for NASH. Electronic supplementary material Below is the link to the electronic supplementary material. [192]Supplementary Material 1.^ (106.4MB, zip) Author contributions P.Y.G.: Writing—original draft, Validation, Data curation, Visualization. J.C.: Writing—original draft, Methodology, Investigation, Data curation. J.X.X.: Methodology, Data curation, Validation. H.Q.C.: Investigation, Validation. R.Z.: Methodology, Formal analysis. D.C.: Formal analysis, Investigation. Y.H.Z.: Writing—review & editing, Methodology, Resources, Supervision. S.S.S.: Writing—review & editing, Supervision, Funding acquisition, Resources, Conceptualization. All authors reviewed the manuscript. Funding This work was supported by the National Natural Science Foundation (82070818 and 82370868), Taishan Scholar Project of Shandong Province (tsqn202211330), the Natural Science Foundation of Shandong Province (ZR2024MH086, ZR2020MH037), together with Postdoctoral Innovation Project of Shandong Province (SDCX-ZG-202203058). Data availability Data is provided within the manuscript or supplementary information files. Declarations Competing interests The authors declare no competing interests. Footnotes Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Peiyuan Gu and Juan Chen have equally contributed to this work. Contributor Information Yuhan Zhang, Email: zyhan007@126.com. Shanshan Shao, Email: shaoshanshan11@126.com. References