Multi-omics analysis of complex RNA-seq and DNA methylation data

to reveal hidden disease mechanisms and uncovered novel drug targets for ovarian cancer

Summary:

This case study reveals how the Genome Enhancer platform transforms multi-omics data into actionable drug targets for ovarian neoplasms—highlighting PDGFRA and CDK1–Cyclin B1 as potential therapeutic keys.

Using integrated transcriptomics and DNA methylation data, Genome Enhancer identifies:

  • Key transcriptional regulators (EP300, NFYA, RXRA, RELA, HSF1)
  • Master regulators and druggable signaling nodes, including:
  • PDGFRA
  • VRK1
  • CDK1–Cyclin B1 complex

The analysis proceeds with a druggability assessment using cheminformatics and literature mining, revealing top candidates for drug repurposing:

Pazopanib

Fimepinostat

Paclitaxel

Etoposide

This report illustrates how AI-enhanced regulatory network modeling pinpoints molecular vulnerabilities—offering a novel, mechanism-based blueprint for cancer therapy development, driven by multi-layered omics data.

Genome Enhancer concept

Recording “-omics” data to measure gene activity, protein expression, or metabolic states has become a standard approach for characterizing the pathological state of tissues and organisms. Increasingly, these methods are combined into multi-omics strategies, generating large datasets that offer a broad view of molecular dysfunction. However, a major challenge remains: how to extract causal insights and reveal the underlying disease mechanisms from such data.

Pathological changes are often driven by rewiring of cellular regulatory networks, caused by genetic or epigenetic alterations that affect gene activity. Reconstructing these disease-specific regulatory networks can help identify master regulators—key molecules that control the cascade of downstream dysregulation. Targeting these regulators offers a promising path toward interrupting disease progression at its source.Traditional statistical approaches to -omics data typically identify correlation, but provide limited understanding of causality. In contrast, the “upstream analysis” method [1–4] used in this study is specifically designed to extract causal relationships from multi-omics data.

This method consists of three key steps:

  1. Promoter and enhancer analysis to identify transcription factors (TFs) that regulate differentially expressed genes. This step uses the TRANSFAC® database [6] and algorithms like MATCH™ [7] and Composite Module Analyst (CMA) [8].
  2. Pathway reconstruction to trace upstream signaling pathways that activate these TFs. This step leverages the TRANSPATH® signaling network database [9] and graph search algorithms [10–11], implemented within the Genome Enhancer software platform.
  3. Drug target discovery by identifying compounds capable of modulating the master regulators. This step integrates data from the HumanPSD™ database [5], including druggability assessments and compound-target associations.

Additionally, Genome Enhancer predicts potential drug candidates—both approved and investigational—based on a precomputed PASS database of biological activity spectra. These predictions are derived using a (Q)SAR-based modeling approach [12–14], offering a data-driven foundation for drug development and repurposing.

Together, this pipeline not only uncovers the regulatory mechanisms driving disease but also proposes actionable strategies to target them.

References

  1. Kel A, Voss N, Jauregui R, Kel-Margoulis O, Wingender E. Beyond microarrays: Finding key transcription factors controlling signal transduction pathways. BMC Bioinformatics. 2006;7(Suppl 2):S13. doi:10.1186/1471-2105-7-s2-s13
  2. Stegmaier P, Voss N, Meier T, Kel A, Wingender E, Borlak J. Advanced computational biology methods identify molecular switches for malignancy in an EGF mouse model of liver cancer. PLoS ONE. 2011;6(3):e17738. doi:10.1371/journal.pone.0017738
  3. Koschmann J, Bhar A, Stegmaier P, Kel A, Wingender E. “Upstream Analysis”: An integrated promoter-pathway analysis approach to causal interpretation of microarray data. Microarrays. 2015;4(2):270–286. doi:10.3390/microarrays4020270
  4. Kel A, Stegmaier P, Valeev T, Koschmann J, Poroikov V, Kel-Margoulis OV, Wingender E. Multi-omics “upstream analysis” of regulatory genomic regions helps identify targets against methotrexate resistance in colon cancer. EuPA Open Proteomics. 2016;13:1–13. doi:10.1016/j.euprot.2016.09.002
  5. Michael H, Hogan J, Kel A, et al. Building a knowledge base for systems pathology. Brief Bioinformatics. 2008;9(6):518–531. doi:10.1093/bib/bbn038
  6. Matys V, Kel-Margoulis OV, Fricke E, et al. TRANSFAC and its module TRANSCompel: Transcriptional gene regulation in eukaryotes. Nucleic Acids Res. 2006;34(Database issue):D108–D110. doi:10.1093/nar/gkj143
  7. Kel AE, Gössling E, Reuter I, Cheremushkin E, Kel-Margoulis OV, Wingender E. MATCH: A tool for searching transcription factor binding sites in DNA sequences. Nucleic Acids Res. 2003;31(13):3576–3579. doi:10.1093/nar/gkg585
  8. Waleev T, Shtokalo D, Konovalova T, et al. Composite Module Analyst: Identification of transcription factor binding site combinations using genetic algorithm. Nucleic Acids Res. 2006;34(Web Server issue):W541–W545.
  9. Krull M, Pistor S, Voss N, et al. TRANSPATH: An information resource for storing and visualizing signaling pathways and their pathological aberrations. Nucleic Acids Res. 2006;34(Database issue):D546–D551. doi:10.1093/nar/gkj107
  10. Boyarskikh U, Pintus S, Mandrik N, et al. Computational master-regulator search reveals mTOR and PI3K pathways responsible for low sensitivity of NCI-H292 and A427 lung cancer cell lines to cytotoxic action of p53 activator Nutlin-3. BMC Med Genomics. 2018;11(1):12. doi:10.1186/s1471-2105-7-s2-s13
  11. Kel A, Boyarskikh U, Stegmaier P, et al. Walking pathways with positive feedback loops reveal DNA methylation biomarkers of colorectal cancer. BMC Bioinformatics. 2019;20(Suppl 4):119. doi:10.1186/s12859-019-2687-7
  12. Filimonov D, Poroikov V. Probabilistic approaches in activity prediction. In: Varnek A, Tropsha A, eds. Cheminformatics Approaches to Virtual Screening. Cambridge (UK): RSC Publishing; 2008:182–216.
  13. Filimonov DA, Poroikov VV. Prognosis of specters of biological activity of organic molecules. Russian Chemical Journal. 2006;50(2):66–75. [In Russian]
  14. Filimonov D, Poroikov V, Borodina Y, Gloriozova T. Chemical similarity assessment through multilevel neighborhoods of atoms: Definition and comparison with other descriptors. ChemInform. 1999;39(4):666–670. doi:10.1002/chin.199940210