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COMBO: A Computational Framework to Analyze RNA-seq and Methylation Data Through Heterogeneous Multi-layer Networks

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Complex Networks and Their Applications XI (COMPLEX NETWORKS 2016 2022)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1077))

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Abstract

Multi-layer Complex networks are commonly used for modeling and analysing biological entities. This paper presents a new computational framework called COMBO (Combining Multi Bio Omics) for generating and analyzing heterogeneous multi-layer networks. Our model uses gene expression and DNA-methylation data. The power of COMBO relies on its ability to join different omics to study the complex interplay between various components in the disease. We tested the reliability and versatility of COMBO on colon and lung adenocarcinoma cancer data obtained from the TCGA database.

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References

  1. Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., Hwang, D.: Complex networks: structure and dynamics. Phys. Rep., 175–308 (2006)

    Google Scholar 

  2. De Domenico, M.: Multilayer network modeling of integrated biological systems: comment on “network science of biological systems at different scales: a review” by Gosak et al. Phys. Life Rev., 149–52 (2018)

    Google Scholar 

  3. Rai, A., Pradhan, P., Nagraj, J., Lohitesh, K., Chowdhury, R., Jalan, S.: Understanding cancer complexome using networks, spectral graph theory and multilayer framework. Sci. Rep. 7, 41676 (2017)

    Article  Google Scholar 

  4. Barabási, A.-L., Gulbahce, N., Loscalzo, J.: Network medicine: a network-based approach to human disease. Nature Rev. Genet., 56–68 (2011)

    Google Scholar 

  5. Goh, K.-I., Cusick, M.E., Valle, D., Childs, B., Vidal, M., Barabási, A.-L.: The human disease network [Internet]. In: Proceedings of the National Academy of Sciences, pp. 8685–90 (2007)

    Google Scholar 

  6. Lv, Y., Huang, S., Zhang, T., Gao, B.: Application of multilayer network models in bioinformatics. Front. Genet. 12, 664860 (2021)

    Article  Google Scholar 

  7. Zheng, W., Wang, D., Zou, X.: Control of multilayer biological networks and applied to target identification of complex diseases. BMC Bioinform. 20, 271 (2019)

    Article  Google Scholar 

  8. Boccaletti, S., Bianconi, G., Criado, R., del Genio, C.I., Gómez-Gardeñes, J., Romance, M., et al.: The structure and dynamics of multilayer networks [Internet]. Phys. Rep., 1–122 (2014)

    Google Scholar 

  9. Mangioni, G., Jurman, G., De Domenico, M.: Multilayer flows in molecular networks identify biological modules in the human proteome [Internet]. IEEE Trans. Netw. Sci. Eng., 411–20 (2020)

    Google Scholar 

  10. Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J.P., Moreno, Y., Porter, M.A.: Multilayer networks [Internet]. J. Compl. Netw., 203–71 (2014)

    Google Scholar 

  11. Kenett, D.Y., Perc, M., Boccaletti, S.: Networks of Networks—An Introduction [Internet]. Chaos, Solitons and Fractals, pp. 1–6 (2015)

    Google Scholar 

  12. Hammoud, Z., Kramer, F.: Multilayer networks: aspects, implementations, and application in biomedicine. Big Data Anal. (2020)

    Google Scholar 

  13. McGee, F., Ghoniem, M., Melançon, G., Otjacques, B., Pinaud, B.: The state of the art in multilayer network visualization. Comp. Graph. Forum. 125–49 (2019)

    Google Scholar 

  14. Bersanelli, M., Mosca, E., Remondini, D., Giampieri, E., Sala, C., Castellani, G., et al.: Methods for the integration of multi-omics data: mathematical aspects. BMC Bioinform. (2016)

    Google Scholar 

  15. Tordini, F., Aldinucci, M., Milanesi, L., Liò, P., Merelli, I.: The Genome Conformation As an Integrator of Multi-Omic Data: The Example of Damage Spreading in Cancer. Frontiers in Genetics (2016)

    Google Scholar 

  16. Zitnik, M., Leskovec, J.: Predicting multicellular function through multi-layer tissue networks. Bioinformatics., i190–8 (2017)

    Google Scholar 

  17. Gligorijević, V., Pržulj, N.: Methods for biological data integration: perspectives and challenges. J. Roy. Soc. Interface., 20150571 (2015)

    Google Scholar 

  18. Domenico, M.D., De Domenico, M., Porter, M.A., Arenas, A.: MuxViz: a tool for multilayer analysis and visualization of networks. J. Compl. Netw., 159–76 (2015)

    Google Scholar 

  19. De Bacco, C., Power, E.A., Larremore, D.B., Moore, C.: Community detection, link prediction, and layer interdependence in multilayer networks. Phys. Rev. E. 95, 042317 (2017)

    Article  Google Scholar 

  20. Škrlj, B., Kralj, J., Lavrač, N.: Py3plex toolkit for visualization and analysis of multilayer networks. Appl. Netw. Sci. (2019)

    Google Scholar 

  21. Hammoud, Z., Kramer, F.M.: An R Package to Create, Modify and Visualize Multilayered Graph. Genes, p. 519 (2018)

    Google Scholar 

  22. Sahoo, D., Dill, D.L., Tibshirani, R., Plevritis, S.K.: Extracting binary signals from microarray time-course data. Nucl. Acids Res., 3705–12 (2007)

    Google Scholar 

  23. Sahoo, D., Dill, D.L., Gentles, A.J., Tibshirani, R., Plevritis, S.K.: Boolean implication networks derived from large scale, whole genome microarray datasets. Genome Biol. 9, R157 (2008)

    Article  Google Scholar 

  24. Ritchie, M.E., Phipson, B., Wu, D., Hu, Y., Law, C.W., Shi, W., et al.: limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucl. Acids Res. 43, e47 (2015)

    Article  Google Scholar 

  25. Du, P., Zhang, X., Huang, C.-C., Jafari, N., Kibbe, W.A., Hou, L., et al.: Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinform. BioMed. Central 11, 1–9 (2010)

    Google Scholar 

  26. Sgariglia, D., Conforte, A.J., Pedreira, C.E., de Carvalho, L.A.V., Carneiro, F.R.G., Carels, N., et al.: Data-Driven Modeling of Breast Cancer Tumors Using Boolean Networks. Frontiers in Big Data [Internet]. Frontiers Media SA (2021)

    Google Scholar 

  27. Xu, X., Zhu, L., Yang, Y., Pan, Y., Feng, Z., Li, Y., et al.: Low tumour PPM1H indicates poor prognosis in colorectal cancer via activation of cancer-associated fibroblasts. Br J Cancer. Nature Publ. Group 120, 987–995 (2019)

    Google Scholar 

  28. Dabydeen, S.A., Desai, A., Sahoo, D.: Unbiased Boolean Analysis of Public Gene Expression Data for Cell Cycle Gene Identification. The American Society for Cell Biology, Mol Biol Cell (2019)

    Book  Google Scholar 

  29. Sahoo, D., Wei, W., Auman, H., Hurtado-Coll, A., Carroll, P.R., Fazli, L., et al.: Boolean analysis identifies CD38 as a biomarker of aggressive localized prostate cancer. Oncotarget. Impact J. 9, 6550–6561 (2018)

    Article  Google Scholar 

  30. da Mata, A.S., da Mata, A.S.: Complex networks: a mini-review [Internet]. Brazilian J. Phys. 658–72 (2020)

    Google Scholar 

  31. Kinsley, A.C., Rossi, G., Silk, M.J., VanderWaal, K.: Multilayer and multiplex networks: an introduction to their use in veterinary epidemiology. Front. Vet. Sci. 7, 596 (2020)

    Article  Google Scholar 

  32. Alaimo, S., Giugno, R., Acunzo, M., Veneziano, D., Ferro, A., Pulvirenti, A.: Post-transcriptional knowledge in pathway analysis increases the accuracy of phenotypes classification. Oncotarget 7, 54572–54582 (2016)

    Article  Google Scholar 

  33. Alaimo, S., Marceca, G.P., Ferro, A., Pulvirenti, A.: Detecting disease specific pathway substructures through an integrated systems biology approach. Noncoding RNA. 3 (2017)

    Google Scholar 

  34. Alaimo, S., Rapicavoli, R.V., Marceca, G.P., La Ferlita, A., Serebrennikova, O.B., Tsichlis, P.N., et al.: PHENSIM: phenotype simulator. PLoS Comput. Biol. 17, e1009069 (2021)

    Article  Google Scholar 

  35. Silva, T.C., Colaprico, A., Olsen, C., D’Angelo, F., Bontempi, G., Ceccarelli, M., et al.: TCGA workflow: analyze cancer genomics and epigenomics data using bioconductor packages. F1000 Res., 1542 (2016)

    Google Scholar 

  36. Lambert, S.A., Jolma, A., Campitelli, L.F., Das, P.K., Yin, Y., Albu, M., et al.: The human transcription factors. Cell 175, 598–599 (2018)

    Article  Google Scholar 

  37. Yu, G., He, Q.-Y.: ReactomePA: an R/Bioconductor package for reactome pathway analysis and visualization. Mol. Biosyst. 477–9 (2016)

    Google Scholar 

  38. Condorelli, D.F., Spampinato, G., Valenti, G., Musso, N., Castorina, S., Barresi, V.: Positive Caricature Transcriptomic Effects Associated with Broad Genomic Aberrations in Colorectal Cancer. Scientific Reports (2018)

    Google Scholar 

  39. Condorelli, D.F., Privitera, A.P., Barresi, V.: Chromosomal density of cancer up-regulated genes, aberrant enhancer activity and cancer fitness genes are associated with transcriptional cis-effects of broad copy number GAINs in colorectal cancer. Int. J. Mol. Sci. 20 (2019)

    Google Scholar 

  40. Sillars-Hardebol, A.H., Carvalho, B., Beliën, J.A.M., de Wit, M., Delis-van Diemen, P.M., Tijssen, M., et al.: BCL2L1has a functional role in colorectal cancer and its protein expression is associated with chromosome 20q GAIN. J. Pathol. 442–50 (2012)

    Google Scholar 

  41. Carvalho, B., Postma, C., Mongera, S., Hopmans, E., Diskin, S., van de Wiel, M.A., et al.: Multiple putative oncogenes at the chromosome 20q amplicon contribute to colorectal adenoma to carcinoma progression. Gut 58, 79–89 (2009)

    Article  Google Scholar 

  42. Sillars-Hardebol, A.H., Carvalho, B., Tijssen, M., Beliën, J.A.M., de Wit, M., Delis-van Diemen, P.M., et al.: TPX2 and AURKA promote 20q amplicon-driven colorectal adenoma to carcinoma progression. Gut 61, 1568–1575 (2012)

    Article  Google Scholar 

  43. Ptashkin, R.N., Pagan, C., Yaeger, R., Middha, S., Shia, J., O’Rourke, K.P., et al.: Chromosome 20q amplification defines a subtype of microsatellite stable, left-sided colon cancers with wild-type RAS/RAF and better overall survival. Mol. Cancer Res. (2017)

    Google Scholar 

  44. Voutsadakis, I.A.: Chromosome 20q11.21 amplifications in colorectal cancer. Cancer Genom. Proteom. 18, 487–96 (2021)

    Google Scholar 

  45. Bui, V.M.H., Mettling, C., Jou, J., Sun, H.S.: Genomic amplification of chromosome 20q13.33 is the early biomarker for the development of sporadic colorectal carcinoma. BMC Med. Genom. 13, 149 (2020)

    Google Scholar 

  46. Liu, Q., Guo, L., Qi, H., Lou, M., Wang, R., Hai, B., et al.: A MYBL2 complex for RRM2 transactivation and the synthetic effect of MYBL2 knockdown with WEE1 inhibition aGAINst colorectal cancer. Cell Death Dis. Nature Publ. Group 12, 1–11 (2021)

    Google Scholar 

  47. Song, S., Li, D., Yang, C., Yan, P., Bai, Y., Zhang, Y., et al.: Overexpression of NELFCD promotes colorectal cancer cells proliferation, migration, and invasion. Oncol. Targ. Ther. Dove Press 11, 8741 (2018)

    Google Scholar 

  48. Li, L., Li, P., Zhang, W., Zhou, H., Guo, E., Hu, G., et al.: FERMT1 contributes to the migration and invasion of nasopharyngeal carcinoma through epithelial–mesenchymal transition and cell cycle arrest. Cancer Cell Int. BioMed. Central 22, 1–14 (2022)

    Google Scholar 

  49. Yang, C., Li, D., Bai, Y., Song, S., Yan, P., Wu, R., et al.: DEAD-box helicase 27 plays a tumor-promoter role by regulating the stem cell-like activity of human colorectal cancer cells. Oncol. Targ. Ther. Dove Press 12, 233 (2019)

    Google Scholar 

  50. Wu, S., Zhang, W., Shen, D., Lu, J., Zhao, L.: PLCB4 upregulation is associated with unfavorable prognosis in pediatric acute myeloid leukemia. Oncol. Lett. Spandidos Publ. 18, 6057 (2019)

    Google Scholar 

  51. Belužić, L., Grbeša, I., Belužić, R., Park, J.H., Kong, H.K., Kopjar, N., et al.: Knock-down of AHCY and depletion of adenosine induces DNA damage and cell cycle arrest. Sci. Rep. Nature Publ. Group 8, 1–16 (2018)

    Google Scholar 

  52. Pimiento, J.M., Neill, K.G., Henderson-Jackson, E., Eschrich, S.A., Chen, D.T., Husain, K., et al.: Knockdown of CSE1L gene in colorectal cancer reduces tumorigenesis in vitro. Am. J. Pathol. (2016)

    Google Scholar 

  53. El Khoury, W., Nasr, Z.: Deregulation of ribosomal proteins in human cancers. Biosci Rep. 41 (2021)

    Google Scholar 

  54. Wang, Y., Pan, S., He, X., Wang, Y., Huang, H., Chen, J., et al.: CPNE1 Enhances Colorectal Cancer Cell Growth, Glycolysis, and Drug Resistance Through Regulating the AKT-GLUT1/HK2 Pathway, Vol. 14. Onco Targets Ther. Dove Press, p. 699 (2021)

    Google Scholar 

  55. Chen, J., Elfiky, A., Han, M., Chen, C., Saif, M.W.: The role of Src in colon cancer and its therapeutic implications. Clin. Colorectal Cancer. 13, 5–13 (2014)

    Article  Google Scholar 

  56. Jin, W.: Regulation of Src Family Kinases during Colorectal Cancer Development and Its Clinical Implications. Cancers, pp. 12 (2020)

    Google Scholar 

  57. Yao, C., Li, G., Cai, M., Qian, Y., Wang, L., Xiao, L., et al.: Prostate cancer downregulated SIRP-α modulates apoptosis and proliferation through p38-MAPK/NF-κB/COX-2 signaling. Oncol. Lett. 13, 4995–5001 (2017)

    Article  Google Scholar 

  58. Sanidas, I., Polytarchou, C., Hatziapostolou, M., Ezell, S.A., Kottakis, F., Hu, L., et al.: Phosphoproteomics screen reveals akt isoform-specific signals linking RNA processing to lung cancer. Mol. Cell. 53, 577–590 (2014)

    Article  Google Scholar 

  59. Paronetto, M.P., Passacantilli, I., Sette, C.: Alternative splicing and cell survival: from tissue homeostasis to disease. Cell Death Differ. 23, 1919–1929 (2016)

    Article  Google Scholar 

  60. Coomer, A.O., Black, F., Greystoke, A., Munkley, J., Elliott, D.J.: Alternative splicing in lung cancer. Biochim. Biophys. Acta Gene Regul. Mech. 1862, 194388 (2019)

    Article  Google Scholar 

  61. Yoshimoto, T., Matsubara, D., Soda, M., Ueno, T., Amano, Y., Kihara, A., et al.: Mucin 21 is a key molecule involved in the incohesive growth pattern in lung adenocarcinoma. Cancer Sci. 110, 3006–3011 (2019)

    Article  Google Scholar 

  62. Hou, L., Lin, T., Wang, Y., Liu, B., Wang, M.: Collagen type 1 alpha 1 chain is a novel predictive biomarker of poor progression-free survival and chemoresistance in metastatic lung cancer. J. Cancer. 12, 5723–5731 (2021)

    Article  Google Scholar 

  63. Ruan, J.S., Zhou, H., Yang, L., Wang, L., Jiang, Z.S., Wang, S.M.: CCNA2 facilitates epithelial-to-mesenchymal transition via the integrin αvβ3 signaling in NSCLC. Int. J. Clin. Exp. Pathol. 10, 8324–8333 (2017)

    Google Scholar 

  64. Serveaux-Dancer, M., Jabaudon, M., Creveaux, I., Belville, C., Blondonnet, R., Gross, C., et al.: Pathological implications of receptor for advanced glycation end-product gene polymorphism. Dis. Markers. 2019, 2067353 (2019)

    Article  Google Scholar 

  65. Zhang, W., Fan, J., Chen, Q., Lei, C., Qiao, B., Liu, Q.: SPP1 and AGER as potential prognostic biomarkers for lung adenocarcinoma. Oncol. Lett. 15, 7028–7036 (2018)

    Google Scholar 

  66. Yuan, L., et al.: SFTPA1 is a potential prognostic biomarker correlated with immune cell infiltration and response to immunotherapy in lung adenocarcinoma. Cancer Immunol. Immunother. 71(2), 399–415 (2021). https://doi.org/10.1007/s00262-021-02995-4

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Physics and Astronomy, University of Catania, 95124, Catania, Italy

    Ilaria Cosentini

  2. Department of Biomedical and Biotechnological Sciences, Section of Medical Biochemistry, University of Catania, 95123, Catania, Italy

    Vincenza Barresi & Daniele Filippo Condorelli

  3. Department of Clinical and Experimental Medicine, Bioinformatics Unit, University of Catania, 95123, Catania, Italy

    Alfredo Ferro, Alfredo Pulvirenti & Salvatore Alaimo

Authors
  1. Ilaria Cosentini
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  2. Vincenza Barresi
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  3. Daniele Filippo Condorelli
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  4. Alfredo Ferro
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  5. Alfredo Pulvirenti
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  6. Salvatore Alaimo
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Corresponding author

Correspondence to Salvatore Alaimo .

Editor information

Editors and Affiliations

  1. University of Burgundy, Dijon, France

    Hocine Cherifi

  2. Dipartimento di Fisica e Chimica Emilio Segrè, Università degli Studi Palermo, Palermo, Italy

    Rosario Nunzio Mantegna

  3. Thomas J. Watson College of Engineering and Applied Science, Binghamton University, Binghamton, NY, USA

    Luis M. Rocha

  4. IUT Lumière - Université Lyon 2, University of Lyon, Bron, France

    Chantal Cherifi

  5. Dipartimento di Fisica e Chimica Emilio Segrè, Università degli Studi Palermo, Palermo, Italy

    Salvatore Miccichè

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Cosentini, I., Barresi, V., Condorelli, D.F., Ferro, A., Pulvirenti, A., Alaimo, S. (2023). COMBO: A Computational Framework to Analyze RNA-seq and Methylation Data Through Heterogeneous Multi-layer Networks. In: Cherifi, H., Mantegna, R.N., Rocha, L.M., Cherifi, C., Miccichè, S. (eds) Complex Networks and Their Applications XI. COMPLEX NETWORKS 2016 2022. Studies in Computational Intelligence, vol 1077. Springer, Cham. https://doi.org/10.1007/978-3-031-21127-0_21

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  • DOI: https://doi.org/10.1007/978-3-031-21127-0_21

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