AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Cancer Biology & Medicine Article
PDF (1.1 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Original Article | Open Access

Drug repurposing for cancer treatment through global propagation with a greedy algorithm in a multilayer network

Xi Cheng1,*, Wensi Zhao2,3,*, Mengdi Zhu2,3, Bo Wang1, Xuege Wang4, Xiaoyun Yang1, Yuqi Huang2,3, Minjia Tan2,3 ( ), Jing Li1 ( )
Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
The Chemical Proteomics Center and State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
University of Chinese Academy of Sciences, Beijing 100049, China
Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences (CAS), Shanghai 200031, China

*These authors contributed equally to this work.

Show Author Information

Abstract

Objective

Drug repurposing, the application of existing therapeutics to new indications, holds promise in achieving rapid clinical effects at a much lower cost than that of de novo drug development. The aim of our study was to perform a more comprehensive drug repurposing prediction of diseases, particularly cancers.

Methods

Here, by targeting 4,096 human diseases, including 384 cancers, we propose a greedy computational model based on a heterogeneous multilayer network for the repurposing of 1,419 existing drugs in DrugBank. We performed additional experimental validation for the dominant repurposed drugs in cancer.

Results

The overall performance of the model was well supported by cross-validation and literature mining. Focusing on the top-ranked repurposed drugs in cancers, we verified the anticancer effects of 5 repurposed drugs widely used clinically in drug sensitivity experiments. Because of the distinctive antitumor effects of nifedipine (an antihypertensive agent) and nortriptyline (an antidepressant drug) in prostate cancer, we further explored their underlying mechanisms by using quantitative proteomics. Our analysis revealed that both nifedipine and nortriptyline affected the cancer-related pathways of DNA replication, the cell cycle, and RNA transport. Moreover, in vivo experiments demonstrated that nifedipine and nortriptyline significantly inhibited the growth of prostate tumors in a xenograft model.

Conclusions

Our predicted results, which have been released in a public database named The Predictive Database for Drug Repurposing (PAD), provide an informative resource for discovering and ranking drugs that may potentially be repurposed for cancer treatment and determining new therapeutic effects of existing drugs.

Keywords

machine learningDatabaseproteomics analysisdrug repurposingnetwork random walk

Electronic Supplementary Material

Download File(s)
cbm-19-1-74_ESM.pdf (787.1 KB)

References

1

Corsello SM, Bittker JA, Liu Z, Gould J, McCarren P, Hirschman JE, et al. The Drug Repurposing Hub: a next-generation drug library and information resource. Nat Med. 2017; 23: 405-8.
Crossref Google Scholar
2

Wang K, Wu H, Li G, Huang Q. BCTD: a drug-target relation database for drug repositioning. HJCB. 2015; 5: 41-7.
Crossref Google Scholar
3

McCullough AR. Four-year review of sildenafil citrate. Rev Urol. 2002; 4(Suppl 3): S26-38.
Google Scholar
4

Ridker PM, Everett BM, Thuren T, MacFadyen JG, Chang WH, Ballantyne C, et al. Antiinflammatory therapy with canakinumab for atherosclerotic disease. N Engl J Med. 2017; 377: 1119-31.
Crossref Google Scholar
5

Haaland GS, Falk RS, Straume O, Lorens JB. Association of warfarin use with lower overall cancer incidence among patients older than 50 years. JAMA Intern Med. 2017; 177: 1774-80.
Crossref Google Scholar
6

Peska L, Buza K, Koller J. Drug-target interaction prediction: a Bayesian ranking approach. Comput Methods Programs Biomed. 2017; 152: 15-21.
Crossref Google Scholar
7

Zhao K, So HC. Drug repositioning for schizophrenia and depression/anxiety disorders: a machine learning approach leveraging expression data. IEEE J Biomed Health Inform. 2018; 23: 1304-15.
Crossref Google Scholar
8

Chen X, Liu MX, Yan GY. Drug-target interaction prediction by random walk on the heterogeneous network. Mol Biosyst. 2012; 8: 1970-8.
Crossref Google Scholar
9

Seal A, Ahn YY, Wild DJ. Optimizing drug-target interaction prediction based on random walk on heterogeneous networks. J Cheminform. 2015; 7: 40.
Crossref Google Scholar
10

Wu C, Schwartz JM, Nenadic G. Pathner: a tool for systematic identification of biological pathway mentions in the literature. BMC Syst Biol. 2013; 7(Suppl 3): S2.
Crossref Google Scholar
11

Martinez V, Navarro C, Cano C, Fajardo W, Blanco A. Drugnet: network-based drug-disease prioritization by integrating heterogeneous data. Artif Intell Med. 2015; 63: 41-9.
Crossref Google Scholar
12

Zhou X, Dai E, Song Q, Ma X, Meng Q, Jiang Y, et al. In silico drug repositioning based on drug-mirna associations. Brief Bioinform. 2019; 21: 498-510.
Crossref Google Scholar
13

Luo H, Wang J, Li M, Luo J, Ni P, Zhao K, et al. Computational drug repositioning with random walk on a heterogeneous network. IEEE/ACM Trans Comput Biol Bioinform. 2018; 16: 1890-900.
Crossref Google Scholar
14

Gayvert KM, Dardenne E, Cheung C, Boland MR, Lorberbaum T, Wanjala J, et al. A computational drug repositioning approach for targeting oncogenic transcription factors. Cell Rep. 2016; 15: 2348-56.
Crossref Google Scholar
15

Wan F, Hong L, Xiao A, Jiang T, Zeng J. Neodti: neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions. Bioinformatics. 2019; 35: 104-11.
Crossref Google Scholar
16

Davis AP, Grondin CJ, Johnson RJ, Sciaky D, King BL, McMorran R, et al. The comparative toxicogenomics database: update 2017. Nucleic Acids Res. 2017; 45: D972-78.
Crossref Google Scholar
17

Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, et al. Drugbank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 2006; 34: D668-72.
Crossref Google Scholar
18

Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015; 43: D447-52.
Crossref Google Scholar
19

O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. Open Babel: an open chemical toolbox. J Cheminform. 2011; 3: 33.
Crossref Google Scholar
20

Zhou J, Shui Y, Peng S, Li X, Mamitsuka H, Zhu S. Meshsim: an R/bioconductor package for measuring semantic similarity over mesh headings and MEDLINE documents. J Bioinform Comput Biol. 2015; 13: 1542002.
Crossref Google Scholar
21

Xu G, Wang J, Wu Z, Qian L, Dai L, Wan X, et al. SAHA regulates histone acetylation, Butyrylation, and protein expression in neuroblastoma. J Proteome Res. 2014; 13: 4211-9.
Crossref Google Scholar
22

Qian LL, Nie LT, Chen M, Liu P, Zhu J, Zhai LH, et al. Global profiling of protein lysine malonylation in Escherichia coli reveals its role in energy metabolism. J Proteome Res. 2016; 15: 2060-71.
Crossref Google Scholar
23

Pan LL, Wang XJ, Yang LH, Zhao L, Zhai LH, Xu JY, et al. Proteomic and phosphoproteomic maps of lung squamous cell carcinoma from chinese patients. Front Oncol. 2020; 10: 963.
Crossref Google Scholar
24

Huang X, Yan J, Zhang M, Wang YF, Chen Y, Fu XH, et al. Targeting epigenetic crosstalk as a therapeutic strategy for EZH2-aberrant solid tumors. Cell. 2018; 175: 186-199.e19.
Crossref Google Scholar
25

Hu H, Zhao WS, Zhu MD, Zhao L, Zhai LH, Xu JY, et al. Lysarginase and chemical derivatization based strategy for facilitating in-depth profiling of C-terminome. Anal Chem. 2019; 91: 14522-29.
Crossref Google Scholar
26

Cox J, Mann M. Maxquant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol. 2008; 26: 1367-72.
Crossref Google Scholar
27

Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 1999; 27: 29-34.
Crossref Google Scholar
28

DeAngelis CD, Drazen JM, Frizelle FA, Haug C, Hoey J, Horton R, et al. Clinical trial registration: a statement from the International Committee of Medical Journal Editors. J Am Med Assoc. 2004; 292: 1363-4.
Crossref Google Scholar
29

De Angelis C, Drazen JM, Frizelle FA, Haug C, Hoey J, Horton R, et al. Clinical trial registration: a statement from the International Committee of Medical Journal Editors. Lancet. 2004; 364: 911-2.
Crossref Google Scholar
30
World Health Organization. Who Model List of Essential Medicines, 19th list. 2015; 1-45.
31

Gillman PK. Tricyclic antidepressant pharmacology and therapeutic drug interactions updated. Br J Pharmacol. 2007; 151: 737-48.
Crossref Google Scholar
32

Zakikhani M, Dowling R, Fantus IG, Sonenberg N, Pollak M. Metformin is an AMP kinase-dependent growth inhibitor for breast cancer cells. Cancer Res. 2006; 66: 10269-73.
Crossref Google Scholar
33

Zakikhani M, Dowling RJ, Sonenberg N, Pollak MN. The effects of adiponectin and metformin on prostate and colon neoplasia involve activation of AMP-activated protein kinase. Cancer Prev Res (Phila). 2008; 1: 369-75.
Crossref Google Scholar
34

Kato K, Gong J, Iwama H, Kitanaka A, Tani J, Miyoshi H, et al. The antidiabetic drug metformin inhibits gastric cancer cell proliferation in vitro and in vivo. Mol Cancer Ther. 2012; 11: 549-60.
Crossref Google Scholar
35

Nam S, Buettner R, Turkson J, Kim D, Cheng JQ, Muehlbeyer S, et al. Indirubin derivatives inhibit Stat3 signaling and induce apoptosis in human cancer cells. Proc Natl Acad Sci U S A. 2005; 102: 5998-6003.
Crossref Google Scholar
36

Rivest P, Renaud M, Sanderson JT. Proliferative and androgenic effects of indirubin derivatives in LNCaP human prostate cancer cells at sub-apoptotic concentrations. Chem Biol Interact. 2011; 189: 177-85.
Crossref Google Scholar
37

Long Z, Chen B, Liu Q, Zhao J, Yang Z, Dong X, et al. The reverse-mode NCX1 activity inhibitor KB-R7943 promotes prostate cancer cell death by activating the JNK pathway and blocking autophagic flux. Oncotarget. 2016; 7: 42059-70.
Crossref Google Scholar
38

Wishart DS, Jewison T, Guo AC, Wilson M, Knox C, Liu Y, et al. HMDB 3.0--the human metabolome database in 2013. Nucleic Acids Res. 2013; 41: D801-7.
Crossref Google Scholar
39

Deshpande A, Sicinski P, Hinds PW. Cyclins and cdks in development and cancer: a perspective. Oncogene. 2005; 24: 2909-15.
Crossref Google Scholar
40

Coqueret O. Linking cyclins to transcriptional control. Gene. 2002; 299: 35-55.
Crossref Google Scholar
41

Harbour JW, Dean DC. The Rb/E2F pathway: expanding roles and emerging paradigms. Genes Dev. 2000; 14: 2393-409.
Crossref Google Scholar
42

O’Connor PM. Mammalian G1 and G2 phase checkpoints. Cancer Surv. 1997; 29: 151-82.
Google Scholar
43

Roskoski Jr R. Cyclin-dependent protein kinase inhibitors including palbociclib as anticancer drugs. Pharmacol Res. 2016; 107: 249-75.
Crossref Google Scholar
44

Shapiro GI. Cyclin-dependent kinase pathways as targets for cancer treatment. J Clin Oncol. 2006; 24: 1770-83.
Crossref Google Scholar
45

Archer KJ, Mas VR, David K, Maluf DG, Bornstein K, Fisher RA. Identifying genes for establishing a multigenic test for hepatocellular carcinoma surveillance in hepatitis C virus-positive cirrhotic patients. Cancer Epidemiol Biomarkers Prev. 2009; 18: 2929-32.
Crossref Google Scholar
46

Khammanivong A, Anandharaj A, Qian X, Song JM, Upadhyaya P, Balbo S, et al. Transcriptome profiling in oral cavity and esophagus tissues from (S)-N’-nitrosonornicotine-treated rats reveals candidate genes involved in human oral cavity and esophageal carcinogenesis. Mol Carcinog. 2016; 55: 2168-82.
Crossref Google Scholar
47

Zhang X, Sun Y, Wang P, Yang C, Li S. Exploration of the molecular mechanism of prostate cancer based on mRNA and miRNA expression profiles. Onco Targets Ther. 2017; 10: 3225-32.
Crossref Google Scholar
48

Liu D, Ma X, Yang F, Xiao D, Jia Y, Wang Y. Discovery and validation of methylated-differentially expressed genes in helicobacter pylori-induced gastric cancer. Cancer Gene Ther. 2020; 27: 473-85.
Crossref Google Scholar
49

Devis-Jauregui L, Eritja N, Davis ML, Matias-Guiu X, LlobetNavas D. Autophagy in the physiological endometrium and cancer. Autophagy. 2020; 1-19.
Crossref Google Scholar
50

Ashktorab H, Washington K, Zarnogi S, Shakoori A, Varma S, Lee E, et al. Determination of distinctive hypomethylated genes in African American colorectal neoplastic lesions. Therap Adv Gastroenterol. 2020; 13: 1756284820905482.
Crossref Google Scholar
Cancer Biology & Medicine
Volume 19 Issue 1,
January 2022
Pages 74-89
DOI: 10.20892/j.issn.2095-3941.2020.0218
Cite this article:
Cheng X, Zhao W, Zhu M, et al. Drug repurposing for cancer treatment through global propagation with a greedy algorithm in a multilayer network. Cancer Biology & Medicine, 2022, 19(1): 74-89. https://doi.org/10.20892/j.issn.2095-3941.2020.0218

127

Views

1

Downloads

5

Crossref

0

Web of Science

8

Scopus

Altmetrics
Received: 26 May 2020
Accepted: 07 December 2020
Published: 21 March 2022
©2022 Cancer Biology & Medicine.

Creative Commons Attribution-NonCommercial 4.0 International License
Return