Abstract 6305: hGRN-ChEAR: human transcription factor regulatory network with a knowledge graph interactive user interface
Abstract Gene expression is controlled by transcription factors that selectively bind to DNA to regulate mRNA expression of all human genes. In cancer, gene expression regulation is misconfigured to accelerate uncontrolled cell proliferation. Transcription factors control the expression of other transcription factors, forming a complex gene regulatory network (GRN) with switches, feedback loops, and other regulatory motifs. Many experimental methods and computational tools have been developed to reconstruct GRNs in-silico. Here we present a novel approach to reconstruct the human GRN. By submitting thousands of gene sets from the RummaGEO resource for transcription factor enrichment analysis with ChEA3, we are able to distill a signed and directed edges that connect all human transcription factors to construct a novel human GRN. The GRN has 130, 793 signed and directed edges between 703 source and 1, 543 target nodes. The network is made accessible via an interactive web-based application called the Human Gene Regulatory Network based on ChEA3 and RummaGEO (hGRN-ChEAR). hGRN-ChEAR enables users to query the GRN by searching for single or pairs of transcription factors, as well as by submitting their gene sets to perform transcription factor enrichment analysis. To demonstrate the utility of hGRN-ChEAR, we systematically identified transcription factor subnetworks that regulate differentially expressed genes in tumors from ten cancer types, and 69 subtypes, profiled by the Clinical Proteomic Tumor Analysis Consortium (CPTAC). hGRN-ChEAR is freely available from: https://hgrn-chear.maayanlab.cloud/. Citation Format: Anna I. Byrd, John Erol Evangelista, Alexander Lachmann, Ho-Young Chung, Sherry L. Jenkins, Avi Ma'ayan. hGRN-ChEAR: human transcription factor regulatory network with a knowledge graph interactive user interface [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6305.
Authors
- John Erol Evangelista (ORCID: https://orcid.org/0000-0003-4836-0518)
- Alexander Lachmann (ORCID: https://orcid.org/0000-0002-1982-7652)
- Avi Ma’ayan (ORCID: https://orcid.org/0000-0002-6904-1017)
- Anna I Byrd
- Sherry L. Jenkins (ORCID: https://orcid.org/0000-0003-1730-0977)
- Ho Young Chung (ORCID: https://orcid.org/0000-0001-7264-0865)
Institutions
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- Cancer Research
- Published
- 2025-04-21
- DOI
- https://doi.org/10.1158/1538-7445.am2025-6305
- Primary Topic
- Advanced Graph Neural Networks
- Type
- article
- Field-Weighted Citation Impact
- 0.00