Genomics & Informatics

  • 제21권3호
  • /
  • Pages.38.1-38.8
  • /
  • 2023
  • /
  • 1598-866X(pISSN)
  • /
  • 2234-0742(eISSN)
한국유전체학회 (Korea Genome Organization)
권호 표지
DOI QR코드

DOI QR Code

Identification of novel potential drugs and miRNAs biomarkers in lung cancer based on gene co-expression network analysis

  • Sara Hajipour (Biology Department, Science and Research Branch, Islamic Azad University) ;
  • Sayed Mostafa Hosseini (Chemical Injuries Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences) ;
  • Shiva Irani (Biology Department, Science and Research Branch, Islamic Azad University) ;
  • Mahmood Tavallaie (Human Genetic Research Center, Baqiyatallah University of Medical Sciences)
  • 투고 : 2023.05.11
  • 심사 : 2023.08.02
  • 발행 : 2023.09.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Non-small cell lung cancer (NSCLC) is an important cause of cancer-associated deaths worldwide. Therefore, the exact molecular mechanisms of NSCLC are unidentified. The present investigation aims to identify the miRNAs with predictive value in NSCLC. The two datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed miRNAs (DEmiRNA) and mRNAs (DEmRNA) were selected from the normalized data. Next, miRNA-mRNA interactions were determined. Then, co-expression network analysis was completed using the WGCNA package in R software. The co-expression network between DEmiRNAs and DEmRNAs was calculated to prioritize the miRNAs. Next, the enrichment analysis was performed for DEmiRNA and DEmRNA. Finally, the drug-gene interaction network was constructed by importing the gene list to dgidb database. A total of 3,033 differentially expressed genes and 58 DEmiRNA were recognized from two datasets. The co-expression network analysis was utilized to build a gene co- expression network. Next, four modules were selected based on the Zsummary score. In the next step, a bipartite miRNA-gene network was constructed and hub miRNAs (let-7a-2-3p, let-7d-5p, let-7b-5p, let-7a-5p, and let-7b-3p) were selected. Finally, a drug-gene network was constructed while SUNITINIB, MEDROXYPROGESTERONE ACETATE, DOFETILIDE, HALOPERIDOL, and CALCITRIOL drugs were recognized as a beneficial drug in NSCLC. The hub miRNAs and repurposed drugs may act a vital role in NSCLC progression and treatment, respectively; however, these results must validate in further clinical and experimental assessments.

키워드

참고문헌

  1. Xu F, Xu P, Cui W, Gong W, Wei Y, Liu B, et al. Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios may aid in identifying patients with non-small cell lung cancer and predicting Tumor-Node-Metastasis stages. Oncol Lett 2018;16:483-490. https://doi.org/10.3892/ol.2018.8644
  2. Chen D, Zhang LQ, Huang JF, Liu K, Chuai ZR, Yang Z, et al. BRAF mutations in patients with non-small cell lung cancer: a systematic review and meta-analysis. PLoS One 2014;9: e101354.
  3. Li RZ, Fan XX, Duan FG, Jiang ZB, Pan HD, Luo LX, et al. Proscillaridin A induces apoptosis and suppresses non-small-cell lung cancer tumor growth via calcium-induced DR4 upregulation. Cell Death Dis 2018;9:696.
  4. Riveiro ME, Kwee I, Astorgues-Xerri L, Bekradda M, Vazquez R, Rinaldi A, et al. Gene expression profile of OTX015, a BET bromodomain inhibitor, in preclinical models of non-small-cell lung cancer (NSCLC) and small-cell lung cancer (SCLC) models. Cancer Res 2015;75(15 Suppl):3530.
  5. Li G, Gao Y, Cui Y, Zhang T, Cui R, Jiang Y, et al. Overexpression of CD44 is associated with the occurrence and migration of nonsmall cell lung cancer. Mol Med Rep 2016;14:3159-3167. https://doi.org/10.3892/mmr.2016.5636
  6. Altenberger C, Heller G, Ziegler B, Tomasich E, Marhold M, Topakian T, et al. SPAG6 and L1TD1 are transcriptionally regulated by DNA methylation in non-small cell lung cancers. Mol Cancer 2017;16:1.
  7. Morris S, Vachani A, Pass HI, Rom WN, Ryden K, Weiss GJ, et al. Whole blood FPR1 mRNA expression predicts both non-small cell and small cell lung cancer. Int J Cancer 2018;142:2355-2362.  https://doi.org/10.1002/ijc.31245
  8. Yu AM, Tian Y, Tu MJ, Ho PY, Jilek JL. MicroRNA pharmacoepigenetics: posttranscriptional regulation mechanisms behind variable drug disposition and strategy to develop more effective therapy. Drug Metab Dispos 2016;44:308-319. https://doi.org/10.1124/dmd.115.067470
  9. Hosseini SM, Soltani BM, Tavallaei M, Mowla SJ, Tafsiri E, Bagheri A, et al. Clinically significant dysregulation of hsa-miR-30d-5p and hsa-let-7b expression in patients with surgically resected nonsmall cell lung cancer. Avicenna J Med Biotechnol 2018;10:98-104.
  10. Yang F, Wei K, Qin Z, Liu W, Shao C, Wang C, et al. MiR-598 suppresses invasion and migration by negative regulation of derlin-1 and epithelial-mesenchymal transition in non-small cell lung cancer. Cell Physiol Biochem 2018;47:245-256. https://doi.org/10.1159/000489803
  11. Jin X, Guan Y, Zhang Z, Wang H. Microarray data analysis on gene and miRNA expression to identify biomarkers in non-small cell lung cancer. BMC Cancer 2020;20:329.
  12. Zavarzadeh PG, Bonyadi M, Abedi Z. Whole-exome sequencing analysis in a case of primary congenital glaucoma due to the partial uniparental isodisomy. Genomics Inform 2022;20:e28.
  13. Abedi Z, MotieGhader H, Maghsoudloo M, Sheikh Beig Goharrizi MA, Shojaei A, Masoudi-Nejad A. Novel potential drugs for therapy of age-related macular degeneration using protein-protein interaction network (PPI) analysis. J Ophthalmic Optom Sci 2019;3:11-23.
  14. Hosseini SS, Abedi Z, Maghsoudloo M, Sheikh Beig Goharrizi MA, Shojaei A. Investigation of genes associated with primary open-angle glaucoma (POAG) using expression profile analysis. J Ophthalmic Optom Sci 2019;3:37-54.
  15. Abedi Z, MotieGhader H, Hosseini SS, Sheikh Beig Goharrizi MA, Masoudi-Nejad A. mRNA-miRNA bipartite networks reconstruction in different tissues of bladder cancer based on gene co-expression network analysis. Sci Rep 2022;12:5885.
  16. Gurtan AM, Sharp PA. The role of miRNAs in regulating gene expression networks. J Mol Biol 2013;425:3582-3600. https://doi.org/10.1016/j.jmb.2013.03.007
  17. Davis S, Meltzer PS. GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics 2007;23:1846-1847. https://doi.org/10.1093/bioinformatics/btm254
  18. Zhao Y, Wong L, Goh WW. How to do quantile normalization correctly for gene expression data analyses. Sci Rep 2020;10: 15534.
  19. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 2015;43:e47.
  20. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 2008;9:559.
  21. Langfelder P, Luo R, Oldham MC, Horvath S. Is my network module preserved and reproducible? PLoS Comput Biol 2011;7:e1001057.
  22. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003;13:2498- 2504. https://doi.org/10.1101/gr.1239303
  23. Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2009;4:44-57. https://doi.org/10.1038/nprot.2008.211
  24. Fabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P, et al. The Reactome pathway knowledgebase. Nucleic Acids Res 2018;46:D649-D655. https://doi.org/10.1093/nar/gkx1132
  25. Li J, Han X, Wan Y, Zhang S, Zhao Y, Fan R, et al. TAM 2.0: tool for MicroRNA set analysis. Nucleic Acids Res 2018;46: W180-W185. https://doi.org/10.1093/nar/gky509
  26. Cotto KC, Wagner AH, Feng YY, Kiwala S, Coffman AC, Spies G, et al. DGIdb 3.0: a redesign and expansion of the drug-gene interaction database. Nucleic Acids Res 2018;46:D1068-D1073. https://doi.org/10.1093/nar/gkx1143
  27. Horvath S. Weighted Network Analysis: Applications in Genomics and Systems Biology. New York: Springer Science & Business Media, 2011.
  28. Zhao W, Hu JX, Hao RM, Zhang Q, Guo JQ, Li YJ, et al. Induction of microRNA-let-7a inhibits lung adenocarcinoma cell growth by regulating cyclin D1. Oncol Rep 2018;40:1843-1854. https://doi.org/10.3892/or.2018.6593
  29. Hong W, Xue M, Jiang J, Zhang Y, Gao X. Circular RNA circCPA4/ let-7 miRNA/PD-L1 axis regulates cell growth, stemness, drug resistance and immune evasion in non-small cell lung cancer (NSCLC). J Exp Clin Cancer Res 2020;39:149.
  30. Li Y, Dong R, Lu M, Cheng C, Feng Z, Zhao R, et al. Let-7b-3p inhibits tumor growth and metastasis by targeting the BRF2-mediated MAPK/ERK pathway in human lung adenocarcinoma. Transl Lung Cancer Res 2021;10:1841-1856. https://doi.org/10.21037/tlcr-21-299
  31. Zhu X, Kudo M, Huang X, Sui H, Tian H, Croce CM, et al. Frontiers of microRNA signature in non-small cell lung cancer. Front Cell Dev Biol 2021;9:643942.
  32. Dong X, Chang M, Song X, Ding S, Xie L, Song X. Plasma miR1247-5p, miR-301b-3p and miR-105-5p as potential biomarkers for early diagnosis of non-small cell lung cancer. Thorac Cancer 2021;12:539-548. https://doi.org/10.1111/1759-7714.13800
  33. Zhao B, Xue B. Consensus datasets of mouse miRNA-mRNA interactions from multiple online resources. Data Brief 2017;14:143-147. https://doi.org/10.1016/j.dib.2017.07.035
  34. Novello S, Camps C, Grossi F, Mazieres J, Abrey L, Vernejoux JM, et al. Phase II study of sunitinib in patients with non-small cell lung cancer and irradiated brain metastases. J Thorac Oncol 2011;6:1260-1266. https://doi.org/10.1097/JTO.0b013e318219a973
  35. Cerchietti LC, Navigante AH, Peluffo GD, Diament MJ, Stillitani I, Klein SA, et al. Effects of celecoxib, medroxyprogesterone, and dietary intervention on systemic syndromes in patients with advanced lung adenocarcinoma: a pilot study. J Pain Symptom Manage 2004;27:85-95. https://doi.org/10.1016/j.jpainsymman.2003.05.010
  36. MotieGhader H, Tabrizi-Nezhadi P, Deldar Abad Paskeh M, Baradaran B, Mokhtarzadeh A, Hashemi M, et al. Drug repositioning in non-small cell lung cancer (NSCLC) using gene co-expression and drug-gene interaction networks analysis. Sci Rep 2022;12:9417.