Skip to main content

Inference of Dynamic Growth Regulatory Network in Cancer Using High-Throughput Transcriptomic Data

  • Protocol
  • First Online:
Reverse Engineering of Regulatory Networks

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2719))

Abstract

Growth is regulated by gene expression variation at different developmental stages of biological processes such as cell differentiation, disease progression, or drug response. In cancer, a stage-specific regulatory model constructed to infer the dynamic expression changes in genes contributing to tissue growth or proliferation is referred as a dynamic growth regulatory network (dGRN). Over the past decade, gene expression data has been widely used for reconstructing dGRN by computing correlations between the differentially expressed genes (DEGs). A wide variety of pipelines are available to construct the GRNs using DEGs and the choice of a particular method or tool depends on the nature of the study. In this protocol, we have outlined a step-by-step guide for the analysis of DEGs using RNA-Seq data, beginning from data acquisition, pre-processing, mapping to reference genome, and construction of a correlation-based co-expression network to further downstream analysis. We have also outlined the steps for the inclusion of publicly available interaction/regulation information into the dGRN followed by relevant topological inferences. This tutorial has been designed in a way that early researchers can refer to for an easy and comprehensive glimpse of methodologies used in the inference of dGRN using transcriptomics data.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Baute J, Herman D, Coppens F, De Block J, Slabbinck B, Dell’Acqua M, Pè ME, Maere S, Nelissen H, Inzé D (2016) Combined large-scale phenotyping and transcriptomics in maize reveals a robust growth regulatory network. Plant Physiol 170(3):1848–1867

    Article  Google Scholar 

  2. Baena-Lopez LA, Nojima H, Vincent JP (2012) Integration of morphogen signalling within the growth regulatory network. Curr Opin Cell Biol 24(2):166–172

    Article  Google Scholar 

  3. Claeys H, De Bodt S, Inzé D (2014) Gibberellins and DELLAs: central nodes in growth regulatory networks. Trends Plant Sci 19(4):231–239

    Article  Google Scholar 

  4. Carey M, Ramírez JC, Wu S, Wu H (2018) A big data pipeline: Identifying dynamic growth regulatory networks from time-course Gene Expression Omnibus data with applications to influenza infection. Stat Methods Med Res 27(7):1930–1955

    Article  MathSciNet  Google Scholar 

  5. Hurd PJ, Nelson CJ (2009) Advantages of next-generation sequencing versus the microarray in epigenetic research. Brief Funct Genom Proteom 8(3):174–183

    Article  Google Scholar 

  6. Contreras-López O, Moyano TC, Soto DC, Gutiérrez RA (2018) Step-by-step construction of gene co-expression networks from high-throughput Arabidopsis RNA sequencing data. Methods and Protocols, Root Development, pp 275–301

    Google Scholar 

  7. Hecker M, Lambeck S, Toepfer S, Van Someren E, Guthke R (2009) Growth regulatory network inference: data integration in dynamic models-a review. Bio Systems 96(1):86–103

    Article  Google Scholar 

  8. Stark R, Grzelak M, Hadfield J (2019) RNA sequencing: the teenage years. Nat Rev Genet 20(11):631–656

    Article  Google Scholar 

  9. de Jong H (2002) Modeling and simulation of genetic regulatory systems: a literature review. J Comput Biol 9(1):67–103

    Article  MathSciNet  Google Scholar 

  10. Jänes J, Hu F, Lewin A, Turro E (2015) A comparative study of RNA-seq analysis strategies. Brief Bioinform 16(6):932–940

    Article  Google Scholar 

  11. Costa-Silva J, Domingues DS, Menotti D, Hungria M, Lopes FM (2022) Temporal progress of gene expression analysis with RNA-Seq data: a review on the relationship between computational methods. Comput Struct Biotechnol J 21:86–98

    Article  Google Scholar 

  12. Ding J, Bar-Joseph Z (2020) Analysis of time-series regulatory networks. Curr Opin Syst Biol 21:16–24

    Article  Google Scholar 

  13. Steuer R, Kurths J, Daub CO, Weise J, Selbig J (2002) The mutual information: detecting and evaluating dependencies between variables. Bioinformatics 18(suppl_2):S231–S240

    Article  Google Scholar 

  14. Thomas R (1973) Boolean formalization of genetic control circuits. J Theor Biol 42(3):563–585

    Article  Google Scholar 

  15. Stuart JM, Segal E, Koller D, Kim SK (2003) A gene-coexpression network for global discovery of conserved genetic modules. Science 302(5643):249–255

    Article  Google Scholar 

  16. Shmulevich I, Dougherty ER, Kim S, Zhang W (2002) Probabilistic Boolean Networks: a rule-based uncertainty model for growth regulatory networks. Bioinformatics 18(2):261–274

    Article  Google Scholar 

  17. Friedman N, Linial M, Nachman I, Pe’er D (2000) Using Bayesian networks to analyze expression data. J Comput Biol 7(3–4):601–620

    Article  Google Scholar 

  18. Perrin BE, Ralaivola L, Mazurie A, Bottani S, Mallet J, d’Alche–Buc F (2003) Gene networks inference using dynamic Bayesian networks. Bioinformatics 19(Suppl_2):ii138–ii148

    Article  Google Scholar 

  19. Bar-Joseph Z, Gitter A, Simon I (2012) Studying and modelling dynamic biological processes using time-series gene expression data. Nat Rev Genet 13(8):552–564

    Article  Google Scholar 

  20. Spies D, Ciaudo C (2015) Dynamics in transcriptomics: advancements in RNA-seq time course and downstream analysis. Comput Struct Biotechnol J 13:469–477

    Article  Google Scholar 

  21. Oh S, Song S, Grabowski G, Zhao H, Noonan JP (2013) Time series expression analyses using RNA-seq: a statistical approach. BioMedResearch Int 2013:203681

    Google Scholar 

  22. Van Dam S, Vosa U, van der Graaf A, Franke L, de Magalhaes JP (2018) Gene co-expression analysis for functional classification and gene–disease predictions. Brief Bioinform 19(4):575–592

    Google Scholar 

  23. Singh R, Som A (2020) Role of network biology in cancer research. Recent trends in ‘Computational Omics’: concepts and methodology. Nova Science Publishers, New York

    Google Scholar 

  24. Morton ML, Bai X, Merry CR, Linden PA, Khalil AM, Leidner RS, Thompson CL (2014) Identification of mRNAs and lincRNAs associated with lung cancer progression using next-generation RNA sequencing from laser micro-dissected archival FFPE tissue specimens. Lung Cancer 85(1):31–39

    Article  Google Scholar 

  25. Andrews S (2010) Babraham bioinformatics – FastQC a quality control tool for high throughput sequence data. https://www.bioinformatics.babraham.ac.uk/projects/fastqc/

  26. Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30:2114–2120

    Article  Google Scholar 

  27. Kim D, Langmead B, Salzberg SL (2015) HISAT: a fast spliced aligner with low memory requirements. Nat Methods 12:357–360

    Article  Google Scholar 

  28. Thorvaldsdóttir H, Robinson JT, Mesirov JP (2013) Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief Bioinform 14(2):178–192

    Article  Google Scholar 

  29. Liao Y, Smyth GK, Shi W (2014) featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30:923–930

    Article  Google Scholar 

  30. Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15(12):1–21

    Article  Google Scholar 

  31. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504

    Article  Google Scholar 

  32. Sherman BT, Hao M, Qiu J, Jiao X, Baseler MW, Lane HC, Imamichi T, Chang W (2022) DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res 50(W1):W216–W221

    Article  Google Scholar 

  33. Lai Y (2010) Diferential expression analysis of digital gene expression data: RNA-tag filtering, comparison of t-type tests and their genome-wide co-expression-based adjustments. Int J Bioinforma Res Appl 6(4):353–365

    Article  Google Scholar 

  34. Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A, McPherson A, Szcześniak MW, Gaffney DJ, Elo LL, Zhang X, Mortazavi A (2016) A survey of best practices for RNA-seq data analysis. Genome Biol 17(1):1–9

    Google Scholar 

  35. Ghosh A, Som A (2022) Transcriptomic analysis of human naïve and primed pluripotent stem cells. Human Naïve Pluripotent Stem Cells 2022:213–237

    Article  Google Scholar 

  36. Wang L, Wang S, Li W (2012) RSeQC: quality control of RNA-seq experiments. Bioinformatics 28(16):2184–2185

    Article  Google Scholar 

  37. Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, Pimentel H, Salzberg SL, Rinn JL, Pachter L (2012) Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc 7(3):562–578

    Article  Google Scholar 

  38. Li B, Dewey CN (2011) RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinf 12:1–6

    Article  Google Scholar 

  39. Roberts A, Feng H, Pachter L (2013) Fragment assignment in the cloud with eXpress-D. BMC Bioinf 14(1):1–9

    Article  Google Scholar 

  40. Li D, Zand MS, Dye TD, Goniewicz ML, Rahman I, Xie Z (2022) An evaluation of RNA-seq differential analysis methods. PLoS One 17(9):e0264246

    Article  Google Scholar 

  41. Ghosh A, Som A (2021) Decoding molecular markers and transcriptional circuitry of naive and primed states of human pluripotency. Stem Cell Res 53:102334

    Article  Google Scholar 

  42. Joehanes R (2018) Network analysis of gene expression. Methods Mol Biol (Clifton, NJ) 1783:325–341

    Article  Google Scholar 

  43. Albert R, Barabási AL (2002) Statistical mechanics of complex networks. Rev Mod Phys 74(1):47

    Article  MathSciNet  MATH  Google Scholar 

  44. Ghosh A, Som A (2020) RNA-Seq analysis reveals pluripotency-associated genes and their interaction networks in human embryonic stem cells. Comput Biol Chem 85:107239

    Article  Google Scholar 

  45. Chaturvedi A, Som A (2022) The LCNetWork: an electronic representation of the mRNA-lncRNA-miRNA regulatory network underlying mechanisms of non-small cell lung cancer in humans, and its explorative analysis. Comput Biol Chem 101:107781

    Article  Google Scholar 

  46. Singh R, Som A (2020) Identification of common candidate genes and pathways for progression of ovarian, cervical and endometrial cancers. Meta Gene 23:100634

    Article  Google Scholar 

  47. Hu Z, Mellor J, Wu J, Yamada T, Holloway D, DeLisi C (2005) VisANT: data-integrating visual framework for biological networks and modules. Nucleic Acids Res 33(suppl_2):W352–W357

    Article  Google Scholar 

  48. Nikitin A, Egorov S, Daraselia N, Mazo I (2003) Pathway studio – the analysis and navigation of molecular networks. Bioinformatics 19(16):2155–2157

    Article  Google Scholar 

  49. Suderman M, Hallett M (2007) Tools for visually exploring biological networks. Bioinformatics 23(20):2651–2659

    Article  Google Scholar 

  50. Assenov Y, Ramírez F, Schelhorn SE, Lengauer T, Albrecht M (2008) Computing topological parameters of biological networks. Bioinformatics 24(2):282–284

    Article  Google Scholar 

  51. Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY (2014) cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol 8(4):1–7

    Google Scholar 

  52. Morris JH, Apeltsin L, Newman AM, Baumbach J, Wittkop T, Su G, Bader GD, Ferrin TE (2011) clusterMaker: a multi-algorithm clustering plugin for Cytoscape. BMC Bioinf 12(1):1–4

    Article  Google Scholar 

Download references

Acknowledgments

The authors thank Arindam Ghosh for reviewing the earlier version of the manuscript and for his valuable comments. AC is grateful to the University Grants Commission (India) for providing financial assistance to carry out the research work.

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Chaturvedi, A., Som, A. (2024). Inference of Dynamic Growth Regulatory Network in Cancer Using High-Throughput Transcriptomic Data. In: Mandal, S. (eds) Reverse Engineering of Regulatory Networks. Methods in Molecular Biology, vol 2719. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3461-5_4

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-3461-5_4

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3460-8

  • Online ISBN: 978-1-0716-3461-5

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics