Network Subgraph-based Method: Alignment-free Technique for Molecular Network Analysis


Cite item

Full Text

Abstract

Background:Comparing directed networks using the alignment-free technique offers the advantage of detecting topologically similar regions that are independent of the network size or node identity.

Objective:We propose a novel method to compare directed networks by decomposing the network into small modules, the so-called network subgraph approach, which is distinct from the network motif approach because it does not depend on null model assumptions.

Methods:We developed an alignment-free algorithm called the Subgraph Identification Algorithm (SIA), which could generate all subgraphs that have five connected nodes (5-node subgraph). There were 9,364 such modules. Then, we applied the SIA method to examine 17 cancer networks and measured the similarity between the two networks by gauging the similarity level using Jensen- Shannon entropy (HJS).

Results:We identified and examined the biological meaning of 5-node regulatory modules and pairs of cancer networks with the smallest HJS values. The two pairs of networks that show similar patterns are (i) endometrial cancer and hepatocellular carcinoma and (ii) breast cancer and pathways in cancer. Some studies have provided experimental data supporting the 5-node regulatory modules.

Conclusion:Our method is an alignment-free approach that measures the topological similarity of 5-node regulatory modules and aligns two directed networks based on their topology. These modules capture complex interactions among multiple genes that cannot be detected using existing methods that only consider single-gene relations. We analyzed the biological relevance of the regulatory modules and used the subgraph method to identify the modules that shared the same topology across 2 cancer networks out of 17 cancer networks. We validated our findings using evidence from the literature.

About the authors

Efendi Zaenudin

Department of Bioinformatics and Medical Engineering, Asia University

Email: info@benthamscience.net

Ezra Wijaya

Department of Bioinformatics and Medical Engineering, Asia University

Email: info@benthamscience.net

Venugopal Mekala

Department of Bioinformatics and Medical Engineering, Baylor College of MediAsia Universitycine

Email: info@benthamscience.net

Ka-Lok Ng

Department of Bioinformatics and Medical Engineering, Asia University

Author for correspondence.
Email: info@benthamscience.net

References

  1. Kelley BP. PathBLAST: A tool for alignment of protein interaction networks. Nucleic Acids Res 2004; 32: W83-8. doi: 10.1093/nar/gkh411
  2. Flannick J, Novak A, Do CB, Srinivasan BS, Batzoglou S. Automatic parameter learning for multiple local network alignment. J Comput Biol 2009; 16(8): 1001-22. doi: 10.1089/cmb.2009.0099 PMID: 19645599
  3. Yaveroğlu ÖN, Milenković T, Pržulj N. Proper evaluation of alignment-free network comparison methods. Bioinformatics 2015; 31(16): 2697-704. doi: 10.1093/bioinformatics/btv170 PMID: 25810431
  4. Tantardini M, Ieva F, Tajoli L, Piccardi C. Comparing methods for comparing networks. Sci Rep 2019; 9(1): 17557. doi: 10.1038/s41598-019-53708-y PMID: 31772246
  5. Pržulj N. Biological network comparison using graphlet degree distribution. Bioinformatics 2007; 23(2): e177-83. doi: 10.1093/bioinformatics/btl301 PMID: 17237089
  6. Kuchaiev O, Pržulj N. Integrative network alignment reveals large regions of global network similarity in yeast and human. Bioinformatics 2011; 27(10): 1390-6. doi: 10.1093/bioinformatics/btr127 PMID: 21414992
  7. Bagrow JP, Bollt EM. An information-theoretic, all-scales approach to comparing networks. Appl Netw Sci 2019; 4(1): 45. doi: 10.1007/s41109-019-0156-x
  8. Sarajlić A, Malod-Dognin N, Yaveroğlu ÖN, Pržulj N. Graphlet-based characterization of directed networks. Sci Rep 2016; 6(1): 35098. doi: 10.1038/srep35098 PMID: 27734973
  9. Zenil H, Kiani NA, Tegnér J. Quantifying loss of information in network-based dimensionality reduction techniques. J Complex Netw 2016; 4(3): 342-62. doi: 10.1093/comnet/cnv025
  10. Zenil H, Kiani NA, Tegnér J. Algorithmic complexity of motifs clusters superfamilies of networks. IEEE International Conference on Bioinformatics and Biomedicine. Shanghai, China. 2013; 18-21. Dec; doi: 10.1109/BIBM.2013.6732768
  11. Trpevski I, Dimitrova T, Boshkovski T, Stikov N, Kocarev L. Graphlet characteristics in directed networks. Sci Rep 2016; 6(1): 37057. doi: 10.1038/srep37057 PMID: 27830769
  12. Martin AJM, Dominguez C, Contreras-Riquelme S, Holmes DS, Perez-Acle T. Graphlet based metrics for the comparison of gene regulatory networks. PLoS One 2016; 11(10): e0163497. doi: 10.1371/journal.pone.0163497 PMID: 27695050
  13. Wernicke S, Rasche F. FANMOD: A tool for fast network motif detection. Bioinformatics 2006; 22(9): 1152-3. doi: 10.1093/bioinformatics/btl038 PMID: 16455747
  14. Martin AJ, Contreras-Riquelme S, Dominguez C, Perez-Acle T. LoTo: A graphlet based method for the comparison of local topology between gene regulatory networks. PeerJ 2017; 5: e3052. doi: 10.7717/peerj.3052 PMID: 28265516
  15. Meira LAA, Máximo VR, Fazenda AL, da Conceição AF. acc-Motif: Accelerated network motif detection. IEEE/ACM Trans Comput Biol Bioinformatics 2014; 11(5): 853-62. doi: 10.1109/TCBB.2014.2321150 PMID: 26356858
  16. Stivala A, Lomi A. Testing biological network motif significance with exponential random graph models. Appl Netw Sci 2021; 6(1): 91. doi: 10.1007/s41109-021-00434-y PMID: 34841042
  17. Huang CH, Zaenudin E, Tsai JJP, Kurubanjerdjit N, Dessie EY, Ng KL. Dissecting molecular network structures using a network subgraph approach. PeerJ 2020; 8: e9556-6. doi: 10.7717/peerj.9556 PMID: 33005483
  18. Huang CH, Zaenudin E, Tsai JJP, Kurubanjerdjit N, Ng KL. Network subgraph-based approach for analyzing and comparing molecular networks. PeerJ 2022; 10: e13137. doi: 10.7717/peerj.13137 PMID: 35529499
  19. Heymans M, Singh AK. Deriving phylogenetic trees from the similarity analysis of metabolic pathways. Bioinformatics 2003; 19: i138-46. doi: 10.1093/bioinformatics/btg1018 PMID: 12855450
  20. Zhu D, Qin ZS. Structural comparison of metabolic networks in selected single cell organisms. BMC Bioinformatics 2005; 6(1): 8. doi: 10.1186/1471-2105-6-8 PMID: 15649332
  21. Aparicio D, Ribeiro P, Silva F. Extending the applicability of graphlets to directed networks. IEEE/ACM Trans Comput Biol Bioinform 2017; 14(6): 1302-5. doi: 10.1109/TCBB.2016.2586046
  22. Milo R, Shen-Orr S, Itzkovitz S, Kashtan N, Chklovskii D, Alon U. Network motifs: Simple building blocks of complex networks. Science 2002; 298(5594): 824-7. doi: 10.1126/science.298.5594.824 PMID: 12399590
  23. Zhang Q, Bhattacharya S, Conolly RB, Clewell HJ III, Kaminski NE, Andersen ME. Molecular signaling network motifs provide a mechanistic basis for cellular threshold responses. Environ Health Perspect 2014; 122(12): 1261-70. doi: 10.1289/ehp.1408244 PMID: 25117432
  24. Widder S, Schicho J, Schuster P. Dynamic patterns of gene regulation I: Simple two-gene systems. J Theor Biol 2007; 246(3): 395-419. doi: 10.1016/j.jtbi.2007.01.004 PMID: 17337276
  25. Ahnert SE, Fink TMA. Form and function in gene regulatory networks: The structure of network motifs determines fundamental properties of their dynamical state space. J R Soc Interface 2016; 13(120): 20160179. doi: 10.1098/rsif.2016.0179 PMID: 27440255
  26. Burack WR, Shaw AS. Signal transduction: Hanging on a scaffold. Curr Opin Cell Biol 2000; 12(2): 211-6. doi: 10.1016/S0955-0674(99)00078-2 PMID: 10712921
  27. Efendi Z, Huang CH, Ng KL. Identifying network subgraph-associated essential genes in molecular networks using a network subgraph approach. Int J Math Comput Sci 2021; 15(5): 2021.
  28. Qi H, Pei D. The magic of four: Induction of pluripotent stem cells from somatic cells by Oct4, Sox2, Myc and Klf4. Cell Res 2007; 17(7): 578-80. doi: 10.1038/cr.2007.59 PMID: 17632550
  29. van Schaijik B, Davis PF, Wickremesekera AC, Tan ST, Itinteang T. Subcellular localisation of the stem cell markers OCT4, SOX2, NANOG, KLF4 and c-MYC in cancer: A review. J Clin Pathol 2018; 71(1): 88-91. doi: 10.1136/jclinpath-2017-204815 PMID: 29180509
  30. Villodre ES, Felipe KB, Oyama MZ, et al. Silencing of the transcription factors Oct4, Sox2, Klf4, c-Myc or Nanog has different effect on teratoma growth. Biochem Biophys Res Commun 2019; 517(2): 324-9. doi: 10.1016/j.bbrc.2019.07.064 PMID: 31353083
  31. Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M. KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res 2010; 38(Database issue): D355-60. doi: 10.1093/nar/gkp896 PMID: 19880382
  32. Efendi Zaenudin EBW. A parallel algorithm to generate connected network motifs. IAENG Int J Comput Sci 2019; 46(4): 518-23.
  33. Kugler KG, Mueller LAJ, Graber A, Dehmer M. Integrative network biology: Graph prototyping for co-expression cancer networks. PLoS One 2011; 6(7): e22843. doi: 10.1371/journal.pone.0022843 PMID: 21829532
  34. Lin J. Divergence measures based on the Shannon entropy. IEEE Trans Inf Theory 1991; 37(1): 145-51. doi: 10.1109/18.61115
  35. Stirewalt DL, Radich JP. The role of FLT3 in haematopoietic malignancies. Nat Rev Cancer 2003; 3(9): 650-65. doi: 10.1038/nrc1169 PMID: 12951584
  36. Takahashi S. Downstream molecular pathways of FLT3 in the pathogenesis of acute myeloid leukemia: Biology and therapeutic implications. J Hematol Oncol 2011; 4(1): 13. doi: 10.1186/1756-8722-4-13 PMID: 21453545
  37. Mizuki M, Schwäble J, Steur C, et al. Suppression of myeloid transcription factors and induction of STAT response genes by AML-specific Flt3 mutations. Blood 2003; 101(8): 3164-73. doi: 10.1182/blood-2002-06-1677 PMID: 12468433
  38. Chen YJ, Lee LY, Chao YK, et al. DSG3 facilitates cancer cell growth and invasion through the DSG3-plakoglobin-TCF/LEF-Myc/cyclin D1/MMP signaling pathway. PLoS One 2013; 8(5): e64088. doi: 10.1371/journal.pone.0064088 PMID: 23737966
  39. Yang J, Nie J, Ma X, Wei Y, Peng Y, Wei X. Targeting PI3K in cancer: Mechanisms and advances in clinical trials. Mol Cancer 2019; 18(1): 26. doi: 10.1186/s12943-019-0954-x PMID: 30782187
  40. Huang R, Dai Q, Yang R, et al. A Review: PI3K/AKT/mTOR signaling pathway and its regulated eukaryotic translation initiation factors may be a potential therapeutic target in esophageal squamous cell carcinoma. Front Oncol 2022; 12: 817916. doi: 10.3389/fonc.2022.817916 PMID: 35574327
  41. Liberzon A, Birger C, Thorvaldsdóttir H, Ghandi M, Mesirov JP, Tamayo P. The molecular signatures database (MSigDB) hallmark gene set collection. Cell Syst 2015; 1(6): 417-25. doi: 10.1016/j.cels.2015.12.004 PMID: 26771021
  42. Haupt S, Berger M, Goldberg Z, Haupt Y. Apoptosis - the p53 network. J Cell Sci 2003; 116(20): 4077-85. doi: 10.1242/jcs.00739 PMID: 12972501
  43. Wen J, Chen X, Liu X, et al. Small nucleolar RNA and C/D Box 15B regulate the TRIM25/P53 Complex to promote the development of endometrial cancer. J Oncol 2022; 2022: 1-13. doi: 10.1155/2022/7762708 PMID: 36199797
  44. Roh J, Kim J, Park N, et al. p53 and p21 genetic polymorphisms and susceptibility to endometrial cancer1. Gynecol Oncol 2004; 93(2): 499-505. doi: 10.1016/j.ygyno.2004.02.005 PMID: 15099969
  45. Costa BP, Nassr MT, Diz FM, et al. Methoxyeugenol regulates the p53/p21 pathway and suppresses human endometrial cancer cell proliferation. J Ethnopharmacol 2021; 267: 113645. doi: 10.1016/j.jep.2020.113645 PMID: 33271245
  46. Ou X, Lu Y, Liao L, et al. Nitidine chloride induces apoptosis in human hepatocellular carcinoma cells through a pathway involving p53, p21, Bax and Bcl-2. Oncol Rep 2015; 33(3): 1264-74. doi: 10.3892/or.2014.3688 PMID: 25530218
  47. Shi Y-Z, Hui A-M, Takayama T, Li X, Cui X, Makuuchi M. Reduced p21WAF1/CIP1 protein expression is predominantly related to altered p53 in hepatocellular carcinomas. Br J Cancer 2000; 83(1): 50-5. doi: 10.1054/bjoc.2000.1310 PMID: 10883667
  48. Lee T, Man K, Poon R, Lo CM, Ng I, Fan ST. Disruption of p53-p21/WAF1 cell cycle pathway contributes to progression and worse clinical outcome of hepatocellular carcinoma. Oncol Rep 2004; 12(1): 25-31. doi: 10.3892/or.12.1.25 PMID: 15201954
  49. Burotto M, Chiou VL, Lee JM, Kohn EC. The MAPK pathway across different malignancies: A new perspective. Cancer 2014; 120(22): 3446-56. doi: 10.1002/cncr.28864 PMID: 24948110
  50. Hu M, Zhang Y, Li X, et al. Alterations of endometrial epithelial–mesenchymal transition and MAPK signalling components in women with PCOS are partially modulated by metformin in vitro. Mol Hum Reprod 2020; 26(5): 312-26. doi: 10.1093/molehr/gaaa023 PMID: 32202622
  51. Zhang F, Ni ZJ, Ye L, et al. Asparanin A inhibits cell migration and invasion in human endometrial cancer via Ras/ERK/MAPK pathway. Food Chem Toxicol 2021; 150: 112036. doi: 10.1016/j.fct.2021.112036 PMID: 33561516
  52. Jiang J, Zhou N, Ying P, Zhang T, Liang R, Jiang X. Emodin promotes apoptosis of human endometrial cancer through regulating the MAPK and PI3K/AKT pathways. Open Life Sci 2019; 13(1): 489-96. doi: 10.1515/biol-2018-0058 PMID: 33817118
  53. Zhang W, Liu M, Ji Y, et al. Tanshinone IIA inhibits endometrial carcinoma growth through the MAPK/ERK/TRIB3 pathway. Arch Biochem Biophys 2023; 743: 109655. doi: 10.1016/j.abb.2023.109655 PMID: 37285895
  54. Moon H, Ro SW. MAPK/ERK signaling pathway in hepatocellular carcinoma. Cancers 2021; 13(12): 3026. doi: 10.3390/cancers13123026 PMID: 34204242
  55. Yang Z, Zhang H, Yin M, et al. Neurotrophin3 promotes hepatocellular carcinoma apoptosis through the JNK and P38 MAPK pathways. Int J Biol Sci 2022; 18(15): 5963-77. doi: 10.7150/ijbs.72982 PMID: 36263167
  56. Guo P, Hu Q, Wang J, Hai L, Nie X, Zhao Q. Butorphanol inhibits angiogenesis and migration of hepatocellular carcinoma and regulates MAPK pathway. J Antibiot 2022; 75(11): 626-34. doi: 10.1038/s41429-022-00565-z PMID: 36131028
  57. Tian D, Yu Y, Zhang L, Sun J, Jiang W. 23-hydroxybetulinic acid reduces tumorigenesis, metastasis and immunosuppression in a mouse model of hepatocellular carcinoma via disruption of the MAPK signaling pathway. Anticancer Drugs 2022; 33(9): 815-25. doi: 10.1097/CAD.0000000000001325 PMID: 36136986
  58. Yang S, Liu G. Targeting the Ras/Raf/MEK/ERK pathway in hepatocellular carcinoma. Oncol Lett 2017; 13(3): 1041-7. doi: 10.3892/ol.2017.5557 PMID: 28454211
  59. Muthukaruppan A, Lasham A, Woad KJ, et al. Multimodal assessment of estrogen receptor mRNA profiles to quantify estrogen pathway activity in breast tumors. Clin Breast Cancer 2017; 17(2): 139-53. doi: 10.1016/j.clbc.2016.09.001 PMID: 27756582
  60. Liu LC, Su CH, Wang HC, et al. Contribution of personalized Cyclin D1 genotype to triple negative breast cancer risk. Biomedicine 2014; 4(1): 3. doi: 10.7603/s40681-014-0003-4 PMID: 25520916
  61. Cui Y, Zhao M, Yang Y, et al. Reversal of epithelial-mesenchymal transition and inhibition of tumor stemness of breast cancer cells through advanced combined chemotherapy. Acta Biomater 2022; 152: 380-92. doi: 10.1016/j.actbio.2022.08.024 PMID: 36028199
  62. Pintor S, Lopez A, Flores D, et al. FOXO1 promotes the expression of canonical WNT target genes in examined basal‐like breast and glioblastoma multiforme cancer cells. FEBS Open Bio 2023; 13(11): 2108-23. doi: 10.1002/2211-5463.13696 PMID: 37584250
  63. Akbarzadeh M, Mihanfar A, Akbarzadeh S, Yousefi B, Majidinia M. Crosstalk between miRNA and PI3K/AKT/mTOR signaling pathway in cancer. Life Sci 2021; 285: 119984. doi: 10.1016/j.lfs.2021.119984 PMID: 34592229
  64. Zaidi NE, Shazali NAH, Leow TC, Osman MA, Ibrahim K, Rahman NMANA. Crosstalk between fatty acid metabolism and tumour-associated macrophages in cancer progression. Biomedicine 2022; 12(4): 9-19. doi: 10.37796/2211-8039.1381 PMID: 36816174
  65. Li S, Hu H, He Z, Liang D, Sun R, Lan K. fine-tuning of the kaposi’s sarcoma-associated herpesvirus life cycle in neighboring cells through the RTA-JAG1-notch pathway. PLoS Pathog 2016; 12(10): e1005900. doi: 10.1371/journal.ppat.1005900 PMID: 27760204
  66. Gu S, Liu F, Xie X, et al. β-Sitosterol blocks the LEF-1-mediated Wnt/β-catenin pathway to inhibit proliferation of human colon cancer cells. Cell Signal 2023; 104: 110585. doi: 10.1016/j.cellsig.2022.110585 PMID: 36603684

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Copyright (c) 2024 Bentham Science Publishers