A network medicine approach for identifying diagnostic and prognostic biomarkers and exploring drug repurposing in human cancer

Zhang, Le; Vera, Julio; Lai, Xin

doi:10.1016/j.csbj.2022.11.037

Cited by 9 publications

(6 citation statements)

References 89 publications

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“…Second, novel and more thorough CpG and CGI definitions are needed for understanding the relatedness of data to be mapped and their references. Since artificial intelligence (AI) has been gradually used in genome-wide methylation-associated studies [27,28], determining how to use AI techniques [29][30][31][32] such as neural networks to investigate the relationship between sequence features, CGI, methylation, and expression specificity of the human genome has become a future research direction. Finally, genome-wide CGI prediction and methylation analysis are not only data intensive but also timeconsuming.…”

Section: Discussionmentioning

confidence: 99%

CpG Island Definition and Methylation Mapping of the T2T-YAO Genome

Xiao,

Wei,

et al. 2023

Preprint

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Precise definition of every primary methylation sites and their secondary structural feature – CpG islands – are pivotal for subsequent genome-wide epigenetic studies of Chinese populations under different physiological and pathological conditions while the reference genome T2T-YAO is ready for use timely. We first align and examine the two high-quality genome references T2T-YAO and T2T-CHM13 from different ethnic backgrounds in a base-by-base fashion, computing their density-defined and position-defined CGIs. Second, we not only map some representative genome-wide methylation data of selective organs onto the T2T-YAO and T2T-CHM13 genomes but also scrutinize the difference of between these epigenetic parameters. Our major findings are: (1) There is about 4.7% (4.665%–5.751%) sequence difference between the two genomes, and the divergent sequences and related genes are closely correlated to neurological functions. (2) CGIs associated with the divergent sequences are statistically significantly different with respect to CpG density and O/E values. (3) Not only whole-genome-bisulfite-sequencing or WGBS data for T2T-YAO have greater coverage of CpG sites as compared to those of T2T-CHM13, but also T2T-YAO shows more hyper-methylated CpG sites than T2T-CHM13 does.

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Section: Discussionmentioning

confidence: 99%

CpG Island Definition and Methylation Mapping of the T2T-YAO Genome

Xiao,

Wei,

et al. 2023

Preprint

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“…An excellent review of these tools can be found in [100] . More and more often, these tools are applied not only to define regulatory network structure, but also to make viable clinical conclusions about prognostic biomarkers and molecular drug repurposing [137] , [139] .…”

Section: Modeling Approaches and Methodsmentioning

confidence: 99%

Mathematical modeling of regulatory networks of intracellular processes – Aims and selected methods

Kardyńska

Kogut

Pacholczyk

et al. 2023

Computational and Structural Biotechnology Journal

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“…Cancer proteins are not isolated but belong to large gene and protein networks. Thus, one can combine -omics data and network biology algorithms to select protein targets for drug repurposing [12,13]. Here, we present an integrative computational workflow that combines transcriptomic data and network-driven selection of proteins as molecular targets with pharmacophore modelling of an FDA-approved drug library to repurpose drugs for them.…”

Section: Computational Drug Repurposingmentioning

confidence: 99%

The integration of network biology and pharmacophore modeling suggests repurposing Clindamycin as an inhibitor of pyroptosis via Caspase-1 blockage in tumor-associated macrophages

Weich,

Flamann,

Berges

et al. 2024

Preprint

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Background: Uveal melanoma (UM) is a highly malignant intraocular tumor with a poor prognosis and response to therapy, including immune checkpoint inhibitors (ICIs), after the onset of liver metastasis. The metastatic microenvironment contains high levels of tumor-associated macrophages (TAMs) that correlate positively with a worse patient prognosis. We hypothesized that one could increase the efficacy of ICIs in UM metastases by immunomodulating UM-associated macrophages. Methods: To identify potential targets for the immunomodulation, we created a network-based representation of the biology of TAMs and employed (bulk and single-cell) differential gene expression analysis to obtain a regulatory core of UM macrophages-associated genes. We utilized selected targets for pharmacophore-based virtual screening against a library of FDA-approved chemical compounds, followed by refined flexible docking analysis. Finally, we ranked the interactions and selected one novel drug-target combination for in vitro validation. Results: Based on the generated TAM-specific interaction network (3863 nodes, 9073 edges), we derived a UM macrophages-associated regulatory core (74 nodes, 286 edges). From the regulatory core genes, we selected eight potential targets for pharmacophore-based virtual screening (YBX1, GSTP1, NLRP3, ISG15, MYC, PTGS2, NFKB1, CASP1). Of 266 drug-target interactions screened, we identified the interaction between the antibiotic Clindamycin and Caspase-1 as a priority for experimental validation. Our in vitro validation experiments showed that Clindamycin specifically interferes with activated Caspase-1 and inhibits the secretion of IL-1β, IL-18, and lactate dehydrogenase (LDH) in macrophages after stimulation. Our results suggest that repurposed Clindamycin could reduce pyroptosis in TAMs, a pro-inflammatory form of programmed immune cell death favouring tumor progression. Conclusion: We were able to predict a novel Clindamycin-Caspase-1 interaction that effectively blocks Caspase-1-mediated inflammasome activity and pyroptosis in vitro in macrophages. This interaction is a promising clinical immunomodulator of the tumor microenvironment for improving ICI responsivenss. This work demonstrates the power of combining network-based transcriptomic analysis with pharmacophore-guided screening for de novo drug-target repurposing.

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A network medicine approach for identifying diagnostic and prognostic biomarkers and exploring drug repurposing in human cancer

Cited by 9 publications

References 89 publications

CpG Island Definition and Methylation Mapping of the T2T-YAO Genome

CpG Island Definition and Methylation Mapping of the T2T-YAO Genome

Mathematical modeling of regulatory networks of intracellular processes – Aims and selected methods

The integration of network biology and pharmacophore modeling suggests repurposing Clindamycin as an inhibitor of pyroptosis via Caspase-1 blockage in tumor-associated macrophages

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