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image of Identifying Novel Therapeutic Opportunities for Dilated Cardiomyopathy: A Bioinformatics Approach to Drug Repositioning and Herbal Medicine Prediction

Abstract

Background

Dilated Cardiomyopathy (DCM) is a debilitating cardiovascular disorder that challenges current therapeutic strategies. The exploration of novel drug repositioning opportunities through gene expression analysis offers a promising avenue for discovering effective treatments.

Objective

This study aims to identify potential drug repositioning opportunities and lead compounds for DCM treatment by optimizing gene expression characteristics using published data.

Methods

Our approach involved analyzing DCM expression profiles from the Gene Expression Omnibus database and identifying differentially expressed genes with GEO2R. A protein interaction network was constructed using the STRING database and visualized with Cytoscape. Enrichment analyses were conducted on these genes through the Omicshare platform, followed by the identification of candidate compounds the Connectivity Map (CMAP) and validation through molecular docking. The Coremine Medical database was utilized to predict potential herbal medicines.

Results

We identified 29 differentially expressed genes, highlighting MYH6, NPPA, and NPPB as central to DCM pathology. Enrichment analyses indicated significant impacts on biological processes, such as organ morphogenesis and inflammatory responses. The AGE-RAGE signaling pathway was notably affected. From over 6,100 compounds analyzed, tenoxicam emerged as a promising candidate, with Radix Salviae Miltiorrhizae (Danshen) being suggested as a potential herbal treatment.

Conclusion

This study underscores the utility of bioinformatics in uncovering new therapeutic candidates for DCM, offering a foundational step towards novel drug development.

© 2025 Bentham Science Publishers
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2025-01-15
2025-03-26

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References

  1. Pinto Y.M. Elliott P.M. Arbustini E. Adler Y. Anastasakis A. Böhm M. Duboc D. Gimeno J. de Groote P. Imazio M. Heymans S. Klingel K. Komajda M. Limongelli G. Linhart A. Mogensen J. Moon J. Pieper P.G. Seferovic P.M. Schueler S. Zamorano J.L. Caforio A.L.P. Charron P. Proposal for a revised definition of dilated cardiomyopathy, hypokinetic non-dilated cardiomyopathy, and its implications for clinical practice: a position statement of the ESC working group on myocardial and pericardial diseases. Eur. Heart J. 2016 37 23 1850 1858 10.1093/eurheartj/ehv727 26792875
    [Google Scholar]
  2. Eda Y. Nabeta T. Iikura S. Takigami Y. Fujita T. Iida Y. Ikeda Y. Ishii S. Ako J. Non‐dilated left ventricular cardiomyopathy vs. dilated cardiomyopathy: clinical background and outcomes. ESC Heart Fail. 2024 11 3 1463 1471 10.1002/ehf2.14711 38320776
    [Google Scholar]
  3. Ascione R. Lim K.H.H. Chamberlain M. Al-Ruzzeh S. Angelini G.D. Early and late results of partial left ventriculectomy: single center experience and review of the literature. J. Card. Surg. 2003 18 3 190 196 10.1046/j.1540‑8191.2003.02017.x 12809391
    [Google Scholar]
  4. Mathew T. Williams L. Navaratnam G. Rana B. Wheeler R. Collins K. Harkness A. Jones R. Knight D. O’Gallagher K. Oxborough D. Ring L. Sandoval J. Stout M. Sharma V. Steeds R.P. Diagnosis and assessment of dilated cardiomyopathy: a guideline protocol from the British Society of Echocardiography. Echo Res. Pract. 2017 4 2 G1 G13 10.1530/ERP‑16‑0037 28592613
    [Google Scholar]
  5. Correction. J. Clin. Diagn. Res. 2022 16 10 ZZ01 10.7860/JCDR/2022/9624.16771 36827650
    [Google Scholar]
  6. Tsujii N Miyazaki A Sakaguchi H Kagisaki K Yamamoto T Matsuoka M High incidence of dilated cardiomyopathy after right ventricular inlet pacing in patients with congenital complete atrioventricular block. J. Japan. Circul. Soci. 2016 80 1251 1258 10.1253/circj.CJ‑15‑1122
    [Google Scholar]
  7. Halliday B.P. Cleland J.G.F. Goldberger J.J. Prasad S.K. Personalizing risk stratification for sudden death in dilated cardiomyopathy. Circulation 2017 136 2 215 231 10.1161/CIRCULATIONAHA.116.027134 28696268
    [Google Scholar]
  8. Ivanov B. Djordjevic I. Sabashnikov A. Sindhu D. Hink S. Eghbalzadeh K. Gerfer S. Gaisendrees C. Schlachtenberger G. Rustenbach C. Seuthe K. Regnier K. Mader N. Pfister R. Zeriouh M. Rahmanian P. Wahlers T. Impact of ischaemic and dilated cardiomyopathy on short-term and long-term survival after ventricular assist device implantation: a single-centre experience. Heart Lung Circ. 2022 31 3 383 389 10.1016/j.hlc.2021.08.017 34598889
    [Google Scholar]
  9. Lotfi Shahreza M. Ghadiri N. Mousavi S.R. Varshosaz J. Green J.R. A review of network-based approaches to drug repositioning. Brief. Bioinform. 2018 19 5 878 892 10.1093/bib/bbx017 28334136
    [Google Scholar]
  10. Iwata M. Hirose L. Kohara H. Liao J. Sawada R. Akiyoshi S. Tani K. Yamanishi Y. Pathway-based drug repositioning for cancers: computational prediction and experimental validation. J. Med. Chem. 2018 61 21 9583 9595 10.1021/acs.jmedchem.8b01044 30371064
    [Google Scholar]
  11. Xu M. Li W. He J. Wang Y. Lv J. He W. Chen L. Zhi H. DDCM: A computational strategy for drug repositioning based on support-vector regression algorithm. Int. J. Mol. Sci. 2024 25 10 5267 10.3390/ijms25105267 38791306
    [Google Scholar]
  12. Tzvetkov N.T. Kirilov K. Matin M. Atanasov A.G. Natural product drug discovery and drug design: two approaches shaping new pharmaceutical development. Nephrol. Dial. Transplant. 2024 39 3 375 378 10.1093/ndt/gfad208 37708048
    [Google Scholar]
  13. Luo H. Li M. Yang M. Wu F.X. Li Y. Wang J. Biomedical data and computational models for drug repositioning: a comprehensive review. Brief. Bioinform. 2021 22 2 1604 1619 10.1093/bib/bbz176 32043521
    [Google Scholar]
  14. Sakate R. Kimura T. Drug target gene-based analyses of drug repositionability in rare and intractable diseases. Sci. Rep. 2021 11 1 12338 10.1038/s41598‑021‑91428‑4 34117295
    [Google Scholar]
  15. Rawat S. Subramaniam K. Subramanian S.K. Subbarayan S. Dhanabalan S. Chidambaram S.K.M. Stalin B. Roy A. Nagaprasad N. Aruna M. Tesfaye J.L. Badassa B. Krishnaraj R. Drug repositioning using computer-aided drug design (CADD). Curr. Pharm. Biotechnol. 2024 25 3 301 312 10.2174/1389201024666230821103601 37605405
    [Google Scholar]
  16. Ece A. Computer-aided drug design. BMC Chem. 2023 17 1 26 10.1186/s13065‑023‑00939‑w 36964610
    [Google Scholar]
  17. Orro A. Uggeri M. Rusnati M. Urbinati C. Pedemonte N. Pesce E. Moscatelli M. Padoan R. Cichero E. Fossa P. D’Ursi P. In silico drug repositioning on F508del-CFTR: A proof-of-concept study on the AIFA library. Eur. J. Med. Chem. 2021 213 113186 10.1016/j.ejmech.2021.113186 33472120
    [Google Scholar]
  18. Lamb J. Crawford E.D. Peck D. Modell J.W. Blat I.C. Wrobel M.J. Lerner J. Brunet J.P. Subramanian A. Ross K.N. Reich M. Hieronymus H. Wei G. Armstrong S.A. Haggarty S.J. Clemons P.A. Wei R. Carr S.A. Lander E.S. Golub T.R. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 2006 313 5795 1929 1935 10.1126/science.1132939 17008526
    [Google Scholar]
  19. Li F. Zhuo L. Xie F. Luo H. Li Y. Lin H. Li X. Exploration of small molecule compounds targeting abdominal aortic aneurysm based on CMap database and molecular dynamics simulation. Vascular 2024 ••• 17085381241273289 10.1177/17085381241273289 39155144
    [Google Scholar]
  20. Du C. Wang S. Shi X. Jing P. Wang H. Wang L. Identification of senescence related hub genes and potential therapeutic compounds for dilated cardiomyopathy via comprehensive transcriptome analysis. Comput. Biol. Med. 2024 179 108901 10.1016/j.compbiomed.2024.108901 39029429
    [Google Scholar]
  21. Deng S. Shen S. Liu K. El-Ashram S. Alouffi A. Cenci-Goga B.T. Ye G. Cao C. Luo T. Zhang H. Li W. Li S. Zhang W. Wu J. Chen C. Integrated bioinformatic analyses investigate macrophage-M1-related biomarkers and tuberculosis therapeutic drugs. Front. Genet. 2023 14 1041892 10.3389/fgene.2023.1041892 36845395
    [Google Scholar]
  22. Brum A.M. van de Peppel J. Nguyen L. Aliev A. Schreuders-Koedam M. Gajadien T. van der Leije C.S. van Kerkwijk A. Eijken M. van Leeuwen J.P.T.M. van der Eerden B.C.J. Using the Connectivity Map to discover compounds influencing human osteoblast differentiation. J. Cell. Physiol. 2018 233 6 4895 4906 10.1002/jcp.26298 29194609
    [Google Scholar]
  23. Hu Z.R. Zheng W.J. Yan Q. Hu W.W. Sun X.S. Bioinformatic analysis of differentially expressed genes and Chinese medicine prediction for ulcerative colitis. Zhongguo Zhongyao Zazhi 2020 45 7 1684 1690 32489050
    [Google Scholar]
  24. Wei L. Yin F. Chen C. Li L. Expression of integrin α‑6 is associated with multi drug resistance and prognosis in ovarian cancer. Oncol. Lett. 2019 17 4 3974 3980 10.3892/ol.2019.10056 30930993
    [Google Scholar]
  25. Cao Y. Kong L.B. Huang X. Li X.L. Chang J.L. Gao Y. Screening of key genes and pathways of ischemic stroke and prediction of traditional Chinese medicines based on bioinformatics. Zhongguo Zhongyao Zazhi 2021 46 7 1803 1812 33982485
    [Google Scholar]
  26. Edgar R. Domrachev M. Lash A.E. Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002 30 1 207 210 10.1093/nar/30.1.207 11752295
    [Google Scholar]
  27. Meng T. Liu J. Chang H. Qie R. Reverse predictive analysis of Rhizoma Pinelliae and Rhizoma Coptidis on differential miRNA target genes in lung adenocarcinoma. Medicine (Baltimore) 2023 102 7 e32999 10.1097/MD.0000000000032999 36800601
    [Google Scholar]
  28. Franceschini A. Szklarczyk D. Frankild S. Kuhn M. Simonovic M. Roth A. Lin J. Minguez P. Bork P. von Mering C. Jensen L.J. STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 2013 41 Database issue D808 D815 23203871
    [Google Scholar]
  29. Shannon P. Markiel A. Ozier O. Baliga N.S. Wang J.T. Ramage D. Amin N. Schwikowski B. Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003 13 11 2498 2504 10.1101/gr.1239303 14597658
    [Google Scholar]
  30. Noohi M. Hakemi-vala M. Nowroozi J. Fatemi S.R. Dezfulian M. Prediction of blood miRNA-mRNA regulatory network in gastric cancer. Rep. Biochem. Mol. Biol. 2021 10 2 243 256 10.52547/rbmb.10.2.243 34604414
    [Google Scholar]
  31. Mu H. Chen J. Huang W. Huang G. Deng M. Hong S. Ai P. Gao C. Zhou H. OmicShare tools: A zero‐code interactive online platform for biological data analysis and visualization. iMeta 2024 3 5 e228 10.1002/imt2.228 39429881
    [Google Scholar]
  32. Li Y. Gao S. Guo Z. Chen Z. Wei Y. Li Y. Ba Y. Liu Z. Bao H. Screening of potential drugs for the treatment of diabetic kidney disease using single-cell transcriptome sequencing and connectivity map data. Biochem. Biophys. Res. Commun. 2024 725 150263 10.1016/j.bbrc.2024.150263 38905995
    [Google Scholar]
  33. Wehr M.M. Reamon-Buettner S.M. Ritter D. Knebel J. Niehof M. Escher S.E. A comparison of the TempO-Seq and Affymetrix microarray platform using RTqPCR validation. BMC Genomics 2024 25 1 669 10.1186/s12864‑024‑10586‑7 38961363
    [Google Scholar]
  34. Peng K Liu R Yu Y Liang L Yu S Xu X Identification and validation of cetuximab resistance associated long noncoding RNA biomarkers in metastatic colorectal cancer. Biomed. Pharmacother. 2018 97 1138 1146 10.1016/j.biopha.2017.11.031
    [Google Scholar]
  35. Kim S. Thiessen P.A. Bolton E.E. Chen J. Fu G. Gindulyte A. Han L. He J. He S. Shoemaker B.A. Wang J. Yu B. Zhang J. Bryant S.H. Pubchem substance and compound databases. Nucleic Acids Res. 2016 44 D1 D1202 D1213 10.1093/nar/gkv951 26400175
    [Google Scholar]
  36. Lynch M.L. Dudek M.F. Bowman S.E.J. A Searchable Database of Crystallization Cocktails in the PDB: Analyzing the Chemical Condition Space. New York, NY Patterns 2020 1 4 100024 10.1016/j.patter.2020.100024
    [Google Scholar]
  37. Santos K.B. Guedes I.A. Karl A.L.M. Dardenne L.E. Highly flexible ligand docking: benchmarking of the dockthor program on the LEADS-PEP protein–peptide data set. J. Chem. Inf. Model. 2020 60 2 667 683 10.1021/acs.jcim.9b00905 31922754
    [Google Scholar]
  38. Mooers BHM Brown ME Templates for writing PyMOL scripts. Protein Sci. 2021 20 1 262 269 10.1002/pro.3997 33179363
    [Google Scholar]
  39. Wu D. Wang X. Yang J. Wu Y. Liu Y.N. Xiu C.K. Wang J.L. Liu Y.Q. Lei Y. Scr eening of key genes and pathways of brain aging and prediction of potential effective Chinese medicinals: based on bioinformatics. Zhongguo Zhongyao Zazhi 2021 46 21 5701 5709 34951224
    [Google Scholar]
  40. Yang G. Chen S. Ma A. Lu J. Wang T. Identification of the difference in the pathogenesis in heart failure arising from different etiologies using a microarray dataset. Clinics (São Paulo) 2017 72 10 600 608 10.6061/clinics/2017(10)03 29160422
    [Google Scholar]
  41. Fang C. Lv Z. Yu Z. Wang K. Xu C. Li Y. Wang Y. Exploration of dilated cardiomyopathy for biomarkers and immune microenvironment: evidence from RNA-seq. BMC Cardiovasc. Disord. 2022 22 1 320 10.1186/s12872‑022‑02759‑7 35850644
    [Google Scholar]
  42. Chen N Zhang L Zhong Z Zhang W Gong Q Xu N PARP9 affects myocardial function through TGF-β/Smad axis and pirfenidone. Biomol. Biomed. 2024 24 5 1199 1215 10.17305/bb.2024.10246 39213416
    [Google Scholar]
  43. Ruan Y. Chen X.H. Jiang F. Liu Y.G. Liang X.L. Lv B.M. Zhang H.Y. Zhang Q.Y. Agent clustering strategy based on metabolic flux distribution and transcriptome expression for novel drug development. Biomedicines 2021 9 11 1640 10.3390/biomedicines9111640 34829869
    [Google Scholar]
  44. Sheldon C. Kessinger C.W. Sun Y. Kontaridis M.I. Ma Q. Hammoud S.S. Gao Z. Zhang H. Lin Z. Myh6 promoter-driven Cre recombinase excises floxed DNA fragments in a subset of male germline cells. J. Mol. Cell. Cardiol. 2023 175 62 66 10.1016/j.yjmcc.2022.12.005 36584478
    [Google Scholar]
  45. López-Unzu M.A. Durán A.C. Soto-Navarrete M.T. Sans-Coma V. Fernández B. Differential expression of myosin heavy chain isoforms in cardiac segments of gnathostome vertebrates and its evolutionary implications. Front. Zool. 2019 16 1 18 10.1186/s12983‑019‑0318‑9 31198434
    [Google Scholar]
  46. Lin J. Chen Z. Yang L. Liu L. Yue P. Sun Y. Zhao M. Guo X. Hu X. Zhang Y. Zhang H. Li Y. Guo Y. Dong E. Cas9/AAV9-mediated somatic mutagenesis uncovered the cell-autonomous role of sarcoplasmic/endoplasmic reticulum calcium ATPase 2 in murine cardiomyocyte maturation. Front. Cell Dev. Biol. 2022 10 864516 10.3389/fcell.2022.864516 35433671
    [Google Scholar]
  47. Razmara E. Garshasbi M. Whole-exome sequencing identifies R1279X of MYH6 gene to be associated with congenital heart disease. BMC Cardiovasc. Disord. 2018 18 1 137 10.1186/s12872‑018‑0867‑4 29969989
    [Google Scholar]
  48. Jiang J. Wakimoto H. Seidman J.G. Seidman C.E. Allele-specific silencing of mutant Myh6 transcripts in mice suppresses hypertrophic cardiomyopathy. Science 2013 342 6154 111 114 10.1126/science.1236921 24092743
    [Google Scholar]
  49. Grassini D.R. Lagendijk A.K. De Angelis J.E. Da Silva J. Jeanes A. Zettler N. Bower N.I. Hogan B.M. Smith K.A. Nppa and Nppb act redundantly during zebrafish cardiac development to confine AVC marker expression and reduce cardiac jelly volume. Development 2018 145 12 dev.160739 10.1242/dev.160739 29752386
    [Google Scholar]
  50. Menon A. Hong L. Savio-Galimberti E. Sridhar A. Youn S.W. Zhang M. Kor K. Blair M. Kupershmidt S. Darbar D. Electrophysiologic and molecular mechanisms of a frameshift NPPA mutation linked with familial atrial fibrillation. J. Mol. Cell. Cardiol. 2019 132 24 35 10.1016/j.yjmcc.2019.05.004 31077706
    [Google Scholar]
  51. Houweling A. Vanborren M. Moorman A. Christoffels V. Expression and regulation of the atrial natriuretic factor encoding gene during development and disease. Cardiovasc. Res. 2005 67 4 583 593 10.1016/j.cardiores.2005.06.013 16002056
    [Google Scholar]
  52. Man J.C.K. van Duijvenboden K. Krijger P.H.L. Hooijkaas I.B. van der Made I. de Gier-de Vries C. Wakker V. Creemers E.E. de Laat W. Boukens B.J. Christoffels V.M. Genetic dissection of a super enhancer controlling the Nppa-Nppb cluster in the heart. Circ. Res. 2021 128 1 115 129 10.1161/CIRCRESAHA.120.317045 33107387
    [Google Scholar]
  53. Man J. Barnett P. Christoffels V.M. Structure and function of the Nppa–Nppb cluster locus during heart development and disease. Cell. Mol. Life Sci. 2018 75 8 1435 1444 10.1007/s00018‑017‑2737‑0 29302701
    [Google Scholar]
  54. Cheval L. Bakouh N. Walter C. Tembely D. Morla L. Escher G. Vogt B. Crambert G. Planelles G. Doucet A. ANP-stimulated Na + secretion in the collecting duct prevents Na + retention in the renal adaptation to acid load. Am. J. Physiol. Renal Physiol. 2019 317 2 F435 F443 10.1152/ajprenal.00059.2019 31188029
    [Google Scholar]
  55. Zhu Q. Xiao L. Cheng G. He J. Yin C. Wang L. Wang Q. Li L. Wei B. Weng Y. Geng F. Shen X.Z. Shi P. Self-maintaining macrophages within the kidney contribute to salt and water balance by modulating kidney sympathetic nerve activity. Kidney Int. 2023 104 2 324 333 10.1016/j.kint.2023.04.023 37224917
    [Google Scholar]
  56. Wang Q. Ishizawa K. Li J. Fujii W. Nemoto Y. Yamazaki O. Tamura Y. Miura Y. Nie X. Abe R. Segawa H. Kuro-O M. Shibata S. Urinary phosphate-containing nanoparticle contributes to inflammation and kidney injury in a salt-sensitive hypertension rat model. Commun. Biol. 2020 3 1 575 10.1038/s42003‑020‑01298‑1 33060834
    [Google Scholar]
  57. Raizada V. Thakore K. Luo W. Mcguire P.G. Cardiac chamber-specific alterations of ANP and BNP expression with advancing age and with systemic hypertension. Mol. Cell. Biochem. 2001 216 1/2 137 140 10.1023/A:1011027231702 11216858
    [Google Scholar]
  58. Cheng H. Ton S. Tan J. Abdul Kadir K. The ameliorative effects of a tocotrienol-rich fraction on the AGE-RAGE axis and hypertension in high-fat-diet-fed rats with metabolic syndrome. Nutrients 2017 9 9 984 10.3390/nu9090984 28880217
    [Google Scholar]
  59. Cao X. Xia Y. Zeng M. Wang W. He Y. Liu J. Caffeic acid inhibits the formation of advanced glycation end products (AGES) and mitigates the ages‐induced oxidative stress and inflammation reaction in human umbilical vein endothelial cells (HUVECs). Chem. Biodivers. 2019 16 10 e1900174 10.1002/cbdv.201900174 31419039
    [Google Scholar]
  60. van Dongen K.C.W. Linkens A.M.A. Wetzels S.M.W. Wouters K. Vanmierlo T. van de Waarenburg M.P.H. Dietary advanced glycation endproducts (AGEs) increase their concentration in plasma and tissues, result in inflammation and modulate gut microbial composition in mice; evidence for reversibility. Food Res. Int. 2021 147 110547 10.1016/j.foodres.2021.110547 34399524
    [Google Scholar]
  61. Walke P.B. Bansode S.B. More N.P. Chaurasiya A.H. Joshi R.S. Kulkarni M.J. Molecular investigation of glycated insulin-induced insulin resistance via insulin signaling and AGE-RAGE axis. Biochim. Biophys. Acta Mol. Basis Dis. 2021 1867 2 166029 10.1016/j.bbadis.2020.166029 33248275
    [Google Scholar]
  62. Wasim R. Mahmood T. Siddiqui M.H. Ahsan F. Shamim A. Singh A. Shariq M. Parveen S. Aftermath of AGE-RAGE Cascade in the pathophysiology of cardiovascular ailments. Life Sci. 2022 307 120860 10.1016/j.lfs.2022.120860 35940220
    [Google Scholar]
  63. Chang H. Johnson E. Khoo C. Wang W. Gu L. Cranberry juice polyphenols inhibited the formation of advanced glycation end products in collagens, inhibited advanced glycation end product-induced collagen crosslinking, and cleaved the formed crosslinks. J. Agric. Food Chem. 2022 70 49 15560 15569 10.1021/acs.jafc.2c06502 36455288
    [Google Scholar]
  64. Vasan S. Foiles P. Founds H. Therapeutic potential of breakers of advanced glycation end product–protein crosslinks. Arch. Biochem. Biophys. 2003 419 1 89 96 10.1016/j.abb.2003.08.016 14568012
    [Google Scholar]
  65. Foiles P.G. Founds H.W. Vasan S. Therapeutic potential of AGE inhibitors and breakers of AGE protein cross-links. Expert Opin. Investig. Drugs 2001 10 11 1977 1987 10.1517/13543784.10.11.1977 11772301
    [Google Scholar]
  66. Duan Z. Wang Y. Lu Z. Tian L. Xia Z.Q. Wang K. Chen T. Wang R. Feng Z. Shi G. Xu X. Bu F. Ding Y. Jiang F. Zhou J. Wang Q. Chen Y. Wumei Wan attenuates angiogenesis and inflammation by modulating RAGE signaling pathway in IBD: Network pharmacology analysis and experimental evidence. Phytomedicine 2023 111 154658 10.1016/j.phymed.2023.154658 36706698
    [Google Scholar]
  67. Jahan H. Choudhary M.I. Gliclazide alters macrophages polarization state in diabetic atherosclerosis in vitro via blocking AGE-RAGE/TLR4-reactive oxygen species-activated NF-kβ nexus. Eur. J. Pharmacol. 2021 894 173874 10.1016/j.ejphar.2021.173874 33460615
    [Google Scholar]
  68. Ciurba A Antonoaea P Todoran N Rédai E Vlad RA Tătaru A Polymeric films containing tenoxicam as prospective transdermal drug delivery systems: design and characterization. Processes 2021 9 1 136 10.3390/pr9010136
    [Google Scholar]
  69. Omura T. Sasaoka M. Hashimoto G. Imai S. Yamamoto J. Sato Y. Nakagawa S. Yonezawa A. Nakagawa T. Yano I. Tasaki Y. Matsubara K. Oxicam-derived non-steroidal anti-inflammatory drugs suppress 1-methyl-4-phenyl pyridinium-induced cell death via repression of endoplasmic reticulum stress response and mitochondrial dysfunction in SH-SY5Y cells. Biochem. Biophys. Res. Commun. 2018 503 4 2963 2969 10.1016/j.bbrc.2018.08.078 30107908
    [Google Scholar]
  70. Fan M. Liu J. Zhao B. Wu X. Li X. Gu J. Indirect comparison of NSAIDs for ankylosing spondylitis: Network meta‑analysis of randomized, double‑blinded, controlled trials. Exp. Ther. Med. 2020 19 4 3031 3041 10.3892/etm.2020.8564 32256790
    [Google Scholar]
  71. Plantema F. Worldwide studies comparing piroxicam and naproxen. Acta Obstet. Gynecol. Scand. 1986 65 S138 15 17 10.3109/00016348509157061 3548206
    [Google Scholar]
  72. Guo X Wang X Li Q. Experience in treating dilated cardiomyopathy. J. Basic Chin. Medi. 2021 27 3 513 534 10.1097/MD.0000000000020777
    [Google Scholar]
  73. Li C. Liu B. Wang Z. Xie F. Qiao W. Cheng J. Kuang J. Wang Y. Zhang M. Liu D. Salvianolic acid B improves myocardial function in diabetic cardiomyopathy by suppressing IGFBP3. J. Mol. Cell. Cardiol. 2020 139 98 112 10.1016/j.yjmcc.2020.01.009 31982427
    [Google Scholar]
  74. Meng T. Li X. Li C. Liu J. Chang H. Jiang N. Li J. Zhou Y. Liu Z. Natural products of traditional Chinese medicine treat atherosclerosis by regulating inflammatory and oxidative stress pathways. Front. Pharmacol. 2022 13 997598 10.3389/fphar.2022.997598 36249778
    [Google Scholar]
  75. Ho J. Hong C.Y. Salvianolic acids: small compounds with multiple mechanisms for cardiovascular protection. J. Biomed. Sci. 2011 18 1 30 10.1186/1423‑0127‑18‑30 21569331
    [Google Scholar]
  76. He G. Chen G. Liu W. Ye D. Liu X. Liang X. Song J. Salvianolic acid B: a review of pharmacological effects, safety, combination therapy, new dosage forms, and novel drug delivery routes. Pharmaceutics 2023 15 9 2235 10.3390/pharmaceutics15092235 37765204
    [Google Scholar]
/content/journals/cpb/10.2174/0113892010335576241202061139
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Identifying Novel Therapeutic Opportunities for Dilated Cardiomyopathy: A Bioinformatics Approach to Drug Repositioning and Herbal Medicine Prediction
Bentham Science Publishers ; https://doi.org/10.2174/0113892010335576241202061139
/content/journals/cpb/10.2174/0113892010335576241202061139
/content/journals/cpb/10.2174/0113892010335576241202061139
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  • Article Type:     Research Article
Keywords: Bioinformatics ; dilated cardiomyopathy ; drug repositioning ; chinese herbal medicine prediction ; candidate compounds

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