{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T17:43:46Z","timestamp":1791049426785,"version":"4.1.0"},"reference-count":61,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T00:00:00Z","timestamp":1700438400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biomolecules"],"abstract":"<jats:p>(1) Background: A large and diverse microbial population exists in the human intestinal tract, which supports gut homeostasis and the health of the host. Short-chain fatty acid (SCFA)-secreting microbes also generate several metabolites with favorable regulatory effects on various malignancies and immunological inflammations. The involvement of intestinal SCFAs in kidney diseases, such as various kidney malignancies and inflammations, has emerged as a fascinating area of study in recent years. However, the mechanisms of SCFAs and other metabolites produced by SCFA-producing bacteria against kidney cancer and inflammation have not yet been investigated. (2) Methods: We considered 177 different SCFA-producing microbial species and 114 metabolites from the gutMgene database. Further, we used different online-based database platforms to predict 1890 gene targets associated with metabolites. Moreover, DisGeNET, OMIM, and Genecard databases were used to consider 13,104 disease-related gene targets. We used a Venn diagram and various protein\u2212protein interactions (PPIs), KEGG pathways, and GO analyses for the functional analysis of gene targets. Moreover, the subnetwork of protein\u2212protein interactions (through string and cytoscape platforms) was used to select the top 20% of gene targets through degree centrality, betweenness centrality, and closeness centrality. To screen the possible candidate compounds, we performed an analysis of the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of metabolites and then found the best binding affinity using molecular docking simulation. (3) Results: Finally, we found the key gene targets that interact with suitable compounds and function against kidney cancer and inflammation, such as MTOR (with glycocholic acid), PIK3CA (with 11-methoxycurvularin, glycocholic acid, and isoquercitrin), IL6 (with isoquercitrin), PTGS2 (with isoquercitrin), and IGF1R (with 2-amino-1-methyl-6-phenylimidazo[4,5-b] pyridine, isoquercitrin), showed a lower binding affinity. (4) Conclusions: This study provides evidence to support the positive effects of SCFA-producing microbial metabolites that function against kidney cancer and inflammation and makes integrative research proposals that may be used to guide future studies.<\/jats:p>","DOI":"10.3390\/biom13111678","type":"journal-article","created":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T10:32:32Z","timestamp":1700476352000},"page":"1678","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["A Network Pharmacology and Molecular-Docking-Based Approach to Identify the Probable Targets of Short-Chain Fatty-Acid-Producing Microbial Metabolites against Kidney Cancer and Inflammation"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7385-5443","authenticated-orcid":false,"given":"Md. Rezaul","family":"Karim","sequence":"first","affiliation":[{"name":"Department of Biopharmaceutical Biotechnology, College of Life Science, Kyung Hee University, Yongin-si 17104, Republic of Korea"},{"name":"Department of Biotechnology and Genetic Engineering, Faculty of Biological Sciences, Islamic University, Kushtia 7003, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6646-6394","authenticated-orcid":false,"given":"Md. Niaj","family":"Morshed","sequence":"additional","affiliation":[{"name":"Department of Biopharmaceutical Biotechnology, College of Life Science, Kyung Hee University, Yongin-si 17104, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Safia","family":"Iqbal","sequence":"additional","affiliation":[{"name":"Department of Biopharmaceutical Biotechnology, College of Life Science, Kyung Hee University, Yongin-si 17104, Republic of Korea"},{"name":"Department of Microbiology, Varendra Institute of Biosciences, Affiliated University of Rajshahi, Natore, Rajshahi 6400, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shahnawaz","family":"Mohammad","sequence":"additional","affiliation":[{"name":"Graduate School of Biotechnology, College of Life Science, Kyung Hee University, Yongin-si 17104, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8427-7140","authenticated-orcid":false,"given":"Ramya","family":"Mathiyalagan","sequence":"additional","affiliation":[{"name":"Graduate School of Biotechnology, College of Life Science, Kyung Hee University, Yongin-si 17104, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2394-4728","authenticated-orcid":false,"given":"Deok Chun","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Biopharmaceutical Biotechnology, College of Life Science, Kyung Hee University, Yongin-si 17104, Republic of Korea"},{"name":"Hanbangbio Inc., Yongin-si 17104, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yeon Ju","family":"Kim","sequence":"additional","affiliation":[{"name":"Graduate School of Biotechnology, College of Life Science, Kyung Hee University, Yongin-si 17104, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9961-6451","authenticated-orcid":false,"given":"Joon Hyun","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Veterinary International Medicine, College of Veterinary Medicine, Chungnam National University, Daejeon 34134, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong Uk","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Biopharmaceutical Biotechnology, College of Life Science, Kyung Hee University, Yongin-si 17104, Republic of Korea"},{"name":"AIBIOME, 6, Jeonmin-ro 30beon-gil, Yuseong-gu 34052, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"272","DOI":"10.3892\/ol.2020.12135","article-title":"Impact of inflammation and immunotherapy in renal cell carcinoma","volume":"20","author":"Shi","year":"2020","journal-title":"Oncol. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.etap.2018.08.018","article-title":"Hazardous effects of chemical pesticides on human health-Cancer and other associated disorders","volume":"63","author":"Sabarwal","year":"2018","journal-title":"Environ. Toxicol. Pharmacol."},{"key":"ref_3","unstructured":"Chhikara, B.S., and Parang, K. (2022). Global Cancer Statistics 2022: The trends projection analysis. Chem. Biol. Lett., 10."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Galdiero, M.R., Marone, G., and Mantovani, A. (2018). Cancer Inflammation and Cytokines. Cold Spring Harb. Perspect Biol., 10.","DOI":"10.1101\/cshperspect.a028662"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"102673","DOI":"10.1016\/j.dnarep.2019.102673","article-title":"Inflammation-induced DNA damage, mutations and cancer","volume":"83","author":"Kay","year":"2019","journal-title":"DNA Repair"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1543","DOI":"10.7150\/thno.15625","article-title":"Role of the CXCL8-CXCR1\/2 Axis in Cancer and Inflammatory Diseases","volume":"7","author":"Ha","year":"2017","journal-title":"Theranostics"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Brighi, N., Farolfi, A., Conteduca, V., Gurioli, G., Gargiulo, S., Gall\u00e0, V., Schepisi, G., Lolli, C., Casadei, C., and De Giorgi, U. (2019). The Interplay between Inflammation, Anti-Angiogenic Agents, and Immune Checkpoint Inhibitors: Perspectives for Renal Cell Cancer Treatment. Cancers, 11.","DOI":"10.3390\/cancers11121935"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1038\/icb.2016.126","article-title":"Targeting cancer-related inflammation in the era of immunotherapy","volume":"95","author":"Nakamura","year":"2017","journal-title":"Immunol. Cell Biol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3851","DOI":"10.1200\/JCO.2016.72.1985","article-title":"Safety and Efficacy of Nivolumab in Combination with Ipilimumab in Metastatic Renal Cell Carcinoma: The CheckMate 016 Study","volume":"35","author":"Hammers","year":"2017","journal-title":"J. Clin. Oncol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Hsu, C.N., and Tain, Y.L. (2022). Chronic Kidney Disease and Gut Microbiota: What Is Their Connection in Early Life?. Int. J. Mol. Sci., 23.","DOI":"10.3390\/ijms23073954"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1038\/s41581-018-0018-2","article-title":"The gut microbiota and the brain-gut-kidney axis in hypertension and chronic kidney disease","volume":"14","author":"Yang","year":"2018","journal-title":"Nat. Rev. Nephrol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"F1211","DOI":"10.1152\/ajprenal.00298.2018","article-title":"Chronic kidney disease and the gut microbiome","volume":"316","author":"Hobby","year":"2019","journal-title":"Am. J. Physiol. Renal Physiol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"921","DOI":"10.1007\/s00467-016-3392-7","article-title":"Gut microbiome and kidney disease: A bidirectional relationship","volume":"32","author":"Shatat","year":"2017","journal-title":"Pediatr. Nephrol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3531","DOI":"10.2147\/DDDT.S150825","article-title":"Gut microbiota-derived short-chain fatty acids and kidney diseases","volume":"11","author":"Li","year":"2017","journal-title":"Drug Des. Devel. Ther."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3047","DOI":"10.1073\/pnas.1010529108","article-title":"Normal gut microbiota modulates brain development and behavior","volume":"108","author":"Wang","year":"2011","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1126\/science.1179721","article-title":"Metabolic syndrome and altered gut microbiota in mice lacking Toll-like receptor 5","volume":"328","author":"Aitken","year":"2010","journal-title":"Science"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Uronis, J.M., M\u00fchlbauer, M., Herfarth, H.H., Rubinas, T.C., Jones, G.S., and Jobin, C. (2009). Modulation of the intestinal microbiota alters colitis-associated colorectal cancer susceptibility. PLoS ONE, 4.","DOI":"10.1371\/journal.pone.0006026"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"14691","DOI":"10.1073\/pnas.1005963107","article-title":"Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa","volume":"107","author":"Cavalieri","year":"2010","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"D795","DOI":"10.1093\/nar\/gkab786","article-title":"gutMGene: A comprehensive database for target genes of gut microbes and microbial metabolites","volume":"50","author":"Cheng","year":"2022","journal-title":"Nucleic Acids Res."},{"key":"ref_20","first-page":"D845","article-title":"The DisGeNET knowledge platform for disease genomics: 2019 update","volume":"48","author":"Ronzano","year":"2020","journal-title":"Nucleic Acids Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"D514","DOI":"10.1093\/nar\/gki033","article-title":"Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders","volume":"33","author":"Hamosh","year":"2005","journal-title":"Nucleic Acids Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1.30.31","DOI":"10.1002\/cpbi.5","article-title":"The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses","volume":"54","author":"Stelzer","year":"2016","journal-title":"Curr. Protoc. Bioinform."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"W214","DOI":"10.1093\/nar\/gkq537","article-title":"The GeneMANIA prediction server: Biological network integration for gene prioritization and predicting gene function","volume":"38","author":"Donaldson","year":"2010","journal-title":"Nucleic Acids Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2628","DOI":"10.1093\/bioinformatics\/btz931","article-title":"ShinyGO: A graphical gene-set enrichment tool for animals and plants","volume":"36","author":"Ge","year":"2020","journal-title":"Bioinformatics"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1523","DOI":"10.1038\/s41467-019-09234-6","article-title":"Metascape provides a biologist-oriented resource for the analysis of systems-level datasets","volume":"10","author":"Zhou","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e3751","DOI":"10.7717\/peerj.3751","article-title":"Gene expression profiling and functional analysis reveals that p53 pathway-related gene expression is highly activated in cancer cells treated by cold atmospheric plasma-activated medium","volume":"5","author":"Shi","year":"2017","journal-title":"PeerJ"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1038\/s41596-019-0128-8","article-title":"Protocol Update for large-scale genome and gene function analysis with the PANTHER classification system (v.14.0)","volume":"14","author":"Mi","year":"2019","journal-title":"Nat. Protoc."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Siki\u0107, M., Tomi\u0107, S., and Vlahovicek, K. (2009). Prediction of protein-protein interaction sites in sequences and 3D structures by random forests. PLoS Comput. Biol., 5.","DOI":"10.1371\/journal.pcbi.1000278"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"147648","DOI":"10.1155\/2014\/147648","article-title":"Protein-protein interaction detection: Methods and analysis","volume":"2014","author":"Rao","year":"2014","journal-title":"Int. J. Proteom."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"W234","DOI":"10.1093\/nar\/gkz240","article-title":"NetworkAnalyst 3.0: A visual analytics platform for comprehensive gene expression profiling and meta-analysis","volume":"47","author":"Zhou","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.biosystems.2014.11.005","article-title":"CytoNCA: A cytoscape plugin for centrality analysis and evaluation of protein interaction networks","volume":"127","author":"Tang","year":"2015","journal-title":"Biosystems"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"42717","DOI":"10.1038\/srep42717","article-title":"SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules","volume":"7","author":"Daina","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"W5","DOI":"10.1093\/nar\/gkab255","article-title":"ADMETlab 2.0: An integrated online platform for accurate and comprehensive predictions of ADMET properties","volume":"49","author":"Xiong","year":"2021","journal-title":"Nucleic Acids Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4066","DOI":"10.1021\/acs.jmedchem.5b00104","article-title":"pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures","volume":"58","author":"Pires","year":"2015","journal-title":"J. Med. Chem."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"W257","DOI":"10.1093\/nar\/gky318","article-title":"ProTox-II: A webserver for the prediction of toxicity of chemicals","volume":"46","author":"Banerjee","year":"2018","journal-title":"Nucleic Acids Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.addr.2016.05.007","article-title":"BDDCS, the Rule of 5 and drugability","volume":"101","author":"Benet","year":"2016","journal-title":"Adv. Drug Deliv. Rev."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1002\/pro.3307","article-title":"The human protein atlas: A spatial map of the human proteome","volume":"27","author":"Thul","year":"2018","journal-title":"Protein Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"E7348","DOI":"10.1073\/pnas.1707304114","article-title":"Immunogenicity and structures of a rationally designed prefusion MERS-CoV spike antigen","volume":"114","author":"Pallesen","year":"2017","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4049","DOI":"10.1038\/s41598-021-83626-x","article-title":"Structure based pharmacophore modeling, virtual screening, molecular docking and ADMET approaches for identification of natural anti-cancer agents targeting XIAP protein","volume":"11","author":"Opo","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"11448","DOI":"10.1038\/s41598-020-68224-7","article-title":"A bioinformatics investigation into molecular mechanism of Yinzhihuang granules for treating hepatitis B by network pharmacology and molecular docking verification","volume":"10","author":"Zhang","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"W363","DOI":"10.1093\/nar\/gky473","article-title":"CASTp 3.0: Computed atlas of surface topography of proteins","volume":"46","author":"Tian","year":"2018","journal-title":"Nucleic Acids Res."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Kemmish, H., Fasnacht, M., and Yan, L. (2017). Fully automated antibody structure prediction using BIOVIA tools: Validation study. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0177923"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1007\/978-1-4939-2269-7_19","article-title":"Small-molecule library screening by docking with PyRx","volume":"1263","author":"Dallakyan","year":"2015","journal-title":"Methods Mol. Biol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1002\/jcc.21334","article-title":"AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading","volume":"31","author":"Trott","year":"2010","journal-title":"J. Comput. Chem."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S0169-409X(00)00129-0","article-title":"Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings","volume":"46","author":"Lipinski","year":"2001","journal-title":"Adv. Drug Deliv. Rev."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1046\/j.1365-2885.2002.00442.x","article-title":"Free drug fraction vs. free drug concentration: A matter of frequent confusion","volume":"25","author":"Toutain","year":"2002","journal-title":"J. Vet. Pharmacol. Ther."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1038\/s41581-022-00544-5","article-title":"Belzutifan: A novel therapy for von Hippel\u2013Lindau disease","volume":"18","author":"Zhou","year":"2022","journal-title":"Nat. Rev. Nephrol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"843","DOI":"10.2147\/TCRM.S3426","article-title":"Levofloxacin in the treatment of complicated urinary tract infections and acute pyelonephritis","volume":"4","author":"McGregor","year":"2008","journal-title":"Ther. Clin. Risk Manag."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1016\/j.eururo.2022.08.019","article-title":"Epidemiology of Renal Cell Carcinoma: 2022 Update","volume":"82","author":"Bukavina","year":"2022","journal-title":"Eur. Urol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1249","DOI":"10.1039\/C5NP00005J","article-title":"Network pharmacology applications to map the unexplored target space and therapeutic potential of natural products","volume":"32","author":"Kibble","year":"2015","journal-title":"Nat. Prod. Rep."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"5","DOI":"10.2174\/138920021801170119204832","article-title":"Protein-Protein Interaction (PPI) Network: Recent Advances in Drug Discovery","volume":"18","author":"Athanasios","year":"2017","journal-title":"Curr. Drug Metab."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.2174\/156802605774297038","article-title":"Strategy of utilizing in vitro and in vivo ADME tools for lead optimization and drug candidate selection","volume":"5","author":"Balani","year":"2005","journal-title":"Curr. Top. Med. Chem."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1038\/nrd1549","article-title":"Docking and scoring in virtual screening for drug discovery: Methods and applications","volume":"3","author":"Kitchen","year":"2004","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1950018","DOI":"10.1142\/S0219633619500184","article-title":"Study of CXCR4 chemokine receptor inhibitors using QSPR and molecular docking methodologies","volume":"18","author":"Arian","year":"2019","journal-title":"J. Theor. Comput. Chem."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"165","DOI":"10.3892\/or.2016.4794","article-title":"Isoquercitrin inhibits bladder cancer progression in vivo and in vitro by regulating the PI3K\/Akt and PKC signaling pathways","volume":"36","author":"Chen","year":"2016","journal-title":"Oncol. Rep."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"840","DOI":"10.3892\/or.2014.3626","article-title":"Isoquercitrin inhibits the progression of pancreatic cancer in vivo and in vitro by regulating opioid receptors and the mitogen-activated protein kinase signalling pathway","volume":"33","author":"Chen","year":"2015","journal-title":"Oncol. Rep."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2377","DOI":"10.3892\/or.2014.3099","article-title":"Isoquercitrin inhibits the progression of liver cancer in vivo and in vitro via the MAPK signalling pathway","volume":"31","author":"Huang","year":"2014","journal-title":"Oncol. Rep."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Lee, E.-H., Park, H.-J., Jung, H.-Y., Kang, I.-K., Kim, B.-O., and Cho, Y.-J. (2022). Isoquercitrin isolated from newly bred Green ball aapple peel in lipopolysaccharide-stimulated macrophage regulates NF-\u03baB inflammatory pathways and cytokines. 3 Biotech, 12.","DOI":"10.1007\/s13205-022-03118-1"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Ge, X., Huang, S., Ren, C., and Zhao, L. (2023). Taurocholic Acid and Glycocholic Acid Inhibit Inflammation and Activate Farnesoid X Receptor Expression in LPS-Stimulated Zebrafish and Macrophages. Molecules, 28.","DOI":"10.3390\/molecules28052005"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Ha, T.M., Ko, W., Lee, S.J., Kim, Y.-C., Son, J.-Y., Sohn, J.H., Yim, J.H., and Oh, H. (2017). Anti-Inflammatory Effects of Curvularin-Type Metabolites from a Marine-Derived Fungal Strain Penicillium sp. SF-5859 in Lipopolysaccharide-Induced RAW264.7 Macrophages. Mar. Drugs, 15.","DOI":"10.3390\/md15090282"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1186\/s12986-016-0111-0","article-title":"2-amino-1-methyl-6-phenylimidazo(4,5-b)pyridine (PhIP) induces gene expression changes in JAK\/STAT and MAPK pathways related to inflammation, diabetes and cancer","volume":"13","author":"Rogers","year":"2016","journal-title":"Nutr. Metab."}],"container-title":["Biomolecules"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2218-273X\/13\/11\/1678\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:26:33Z","timestamp":1760131593000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2218-273X\/13\/11\/1678"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,20]]},"references-count":61,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["biom13111678"],"URL":"https:\/\/doi.org\/10.3390\/biom13111678","relation":{},"ISSN":["2218-273X"],"issn-type":[{"value":"2218-273X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,20]]}}}