Cholesterol Metabolism Pathway, the Main Target of Coffee

Document Type : Original paper


1 Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

2 Laser Application in Medical Sciences Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

3 Cell Therapy and Regenerative Medicine Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.

4 Critical Care Quality Improvement Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.


Background and objectives: Coffee as a common drink for many people has been evaluated in the preset study due to its relationship with cancer risk or prevention, regulation of cholesterol level, and anti-oxidant properties. The dysregulated genes in liver of high-fat dieted mice which were treated with coffee were evaluated via network analysis to explore molecular mechanism of the event. Methods: Data were downloaded from gene expression omnibus (GEO) and the significant differentially expressed genes (DEGs) were analyzed via protein-protein interaction (PPI) network analysis by Cytoscape V.3.7.2. The Selected DEGs were enriched via gene ontology by ClueGO. Results of PPI network analysis and gene ontology enrichment were interpreted together to find the critical genes and pathways. Results: Hmgcr, Hmgcs1, Msmo1, Nsdhl, Lss, Fdps, Idi1, Mvd, Ppara, and Hsp90aa1 were identified as the central targeted genes while “cholesterol metabolism pathway” was introduced as the main affected pathway. Conclusion: Final analysis led to determine Hmgcr, Hmgcs1, Msmo1, Nsdhl, Lss, Fdps, Idi1, and Mvd as key dysregulated genes which are related to the most biological terms of “cholesterol metabolism pathway”.   


Main Subjects

  • Nkondjock A. Coffee consumption and the risk of cancer: an overview. Cancer Lett. 2009; 277(2): 121–
  • Chang HC, Nfor ON, Ho CC, Chen PH, Kung YY, Hsu SY, Tantoh DM, Liaw YC, Hsieh CF, Liaw Changes in high-density lipoprotein cholesterol levels in relation to coffee consumption among Taiwanese adults. J Multidiscip Healthc. 2020; 13: 1427–1432.
  • Hu S, Liu K, Luo H, Xu D, Chen L, Zhang L, Wang Caffeine programs hepatic SIRT1-related cholesterol synthesis and hypercholesterolemia via A2AR/cAMP/PKA pathway in adult male offspring rats. Toxicology. 2019; 418: 11–21.
  • Lebeau PF, Byun JH, Platko K, Saliba P, Sguazzin M, MacDonald ME, Paré G, Steinberg GR, Janssen LG, Igdoura SA, Tarnopolsky MA, Chen SRW, Seidah NG, Magolan J, Austin Caffeine blocks SREBP2-induced hepatic PCSK9 expression to enhance LDLR-mediated cholesterol clearance. Nat Commun. 2022; 13(1): 1–17.
  • Ostrowski J, Wyrwicz LS. Integrating genomics, proteomics and bioinformatics in translational studies of molecular medicine. Expert Rev Mol Diagn. 2009; 9(6): 623–630.
  • Doll S, Gnad F, Mann M. The case for proteomics and phospho‐proteomics in personalized cancer medicine. Proteomics Clin Appl. 2019; 13(2): 1–10.
  • Zhang T, Guo J, Gu J, Wang Z, Wang G, Li H, Wang Identifying the key genes and microRNAs in colorectal cancer liver metastasis by bioinformatics analysis and in vitro experiments. Oncol Rep. 2019; 41(1): 279–291.
  • Athanasios A, Charalampos V, Vasileios T. Protein-protein interaction (PPI) network: recent advances in drug discovery. Curr Drug Metab. 2017; 18(1): 5–10.
  • Vella D, Marini S, Vitali F, Di Silvestre D, Mauri G, Bellazzi R. MTGO: PPI network analysis via topological and functional module identification. Sci Rep. 2018; 8(1): 1–13.
  • Safari Alighiarloo N, Taghizadeh M, Rezaei Tavirani M, Goliaei B, Peyvandi AA. Protein-protein interaction networks (PPI) and complex diseases. Gastroenterol Hepatol Bed Bench. 2014; 7(1): 17–31.
  • Rezaei Tavirani M, Arjmand B, Razzaghi M, Ahmadzadeh A. 50S Ribosomal proteins family is the main target of cinnamon extract: a network analysis. Res J Pharmacogn. 2021; 8(2): 63–68.
  • Zamanian Azodi M, Rezaei Tavirani M, Esmaeili S, Arjmand B, Jahani Sherafat S. Evaluation of anticancer effect of ghost pepper: a bioinformatics assessment. Res J Pharmacogn. 2021; 8(3): 77–82.
  • Arjmand B, Khodadoost M, Razzaghi M, Ahmadzadeh A, Rezaei Tavirani S. Assessment of molecular mechanism of saffron anti-stress property. Res J Pharmacogn. 2021; 8(3): 25–31.
  • Rezaei Tavirani M, Rezaei Tavirani M, Mansouri V, Rostami Nejad M, Rezaei Tavirani M. Protein-protein interaction network analysis for a biomarker panel related to human esophageal adenocarcinoma. Asian Pac J Cancer Prev. 2017; 18(12): 3357–3363.
  • Ye J, Li L, Hu Z. Exploring the molecular mechanism of action of Yinchen Wuling powder for the treatment of hyperlipidemia, using network pharmacology, molecular docking, and molecular dynamics simulation. Biomed Res Int. 2021; Article ID 9965906.
  • Mlecnik B, Galon J, Bindea G. Automated exploration of gene ontology term and pathway networks with ClueGO-REST. Bioinformatics. 2019; 35(19): 3864–3866.
  • Zhang Y, Gu L, PuYang J, Liu M, Xia Q, Jiang W, Cao Systems bioinformatic approach to determine the pharmacological mechanisms of radix Astragali and radix Angelicae sinensis in idiopathic pulmonary fibrosis. Pharmacogn Mag. 2021; 17(76): 708–718.
  • Xu H, Zhou S, Tang Q, Xia H, Bi F. Cholesterol metabolism: new functions and therapeutic approaches in cancer. Biochim Biophys Acta Rev Cancer. 2020; Article ID 188394.
  • Min HK, Kapoor A, Fuchs M, Mirshahi F, Zhou H, Maher J, Kellum J, Warnick R, Contos MJ, Sanyal Increased hepatic synthesis and dysregulation of cholesterol metabolism is associated with the severity of nonalcoholic fatty liver disease. Cell Metab. 2012; 15(5): 665–674.
  • Rezaei Tavirani M, Rezaei Tavirani M, Akbari Z, Hajimehdipoor H. Prediction of coffee effects in rats with healthy and NAFLD conditions based on protein-protein interaction network analysis. Res J Pharmacogn. 2019; 6(4): 7–15.
  • Chen CC, Xie XM, Zhao XK, Zuo S, Li HY. Krüppel-like factor 13 promotes HCC progression by transcriptional regulation of HMGCS1-mediated cholesterol synthesis. J Clin Transl Hepatol. 2022; Article ID 00370.
  • Shi X, Sun X, Xu P, Zhang C, Zhang M, Yuan X, Jiang J, Jin K, Chen C, Zuo Q, Zhang Y, Li B. HMGCS1 promotes male differentiation of chicken embryos by regulating the generate of cholesterol. All Life. 2021; 14(1): 577–587.
  • Cheng Y, Meng Y, Li S, Cao D, Ben S, Qin C, Hua L, Cheng Genetic variants in the cholesterol biosynthesis pathway genes and risk of prostate cancer. Gene. 2021; Article ID145432.
  • Wang IH, Huang TT, Chen JL, Chu LW, Ping YH, Hsu KW, Huang KH, Fang WL, Lee C, Chen CF, Liao CC, Hsieh RH, Yeh Mevalonate pathway enzyme HMGCS1 contributes to gastric cancer progression. Cancers. 2020; 12(5): 1–22.
  • Ma X, Bai Y, Liu K, Han Y, Zhang J, Liu Y, Hou X, Hao E, Hou Y, Bai Ursolic acid inhibits the cholesterol biosynthesis and alleviates high fat diet-induced hypercholesterolemia via irreversible inhibition of HMGCS1 in vivo. Phytomedicine. 2022: Article ID 154233.
  • Xiang Z, Valenza M, Cui L, Leoni V, Jeong HK, Brilli E, Zhang J, Peng Q, Duan W, Reeves SA, Cattaneo E, Krainc Peroxisome-proliferator-activated receptor gamma coactivator 1 α contributes to dysmyelination in experimental models of Huntington's disease. J Neurosci. 2011; 31(26): 9544–9553.
  • Kang H, Oh T, Bahk YY, Kim GH, Kan SY, Shin DH, Kim JH, Lim HSF1 regulates mevalonate and cholesterol biosynthesis pathways. Cancers. 2019; 11(9): 1–19.
  • Mori M, Li G, Abe I, Nakayama J, Guo Z, Sawashita J, Ugawa T, Nishizono S, Serikawa T, Higuchi K, Shumiya Lanosterol synthase mutations cause cholesterol deficiency-associated cataracts in the Shumiya cataract rat. J Clin Invest. 2006; 116(2): 395–404.
  • Fang CY, Chen MC, Chang TH, Wu CC, Chang JP, Huang HD, Ho WC, Wang YZ, Pan KL, Lin YS, Huang YK, Chen CJ, Lee Idi1 and Hmgcs2 are affected by stretch in HL-1 atrial myocytes. Int J Mol Sci. 2018; 19(12): 1–14.
  • Chen M, Zhao Y, Yang X, Zhao Y, Liu Q, Liu Y, Hou Y, Sun H, Jin NSDHL promotes triple-negative breast cancer metastasis through the TGFβ signaling pathway and cholesterol biosynthesis. Breast Cancer Res Treat. 2021; 187(2): 349–362.
  • Xin Y, Li C, Guo Y, Xiao R, Zhang H, Zhou G. RNA-Seq analysis reveals a negative role of MSMO1 with a synergized NSDHL expression during adipogenesis of 3T3-L1. Biosci Biotechnol Biochem. 2019; 83(4): 641–652.