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Compound-protein interaction prediction based on heterogeneous network reveals potential antihepatoma agents.

ABSTRACT: Despite the development of an increasing number of multi-kinase and immune checkpoint inhibitors for hepatocellular carcinoma (HCC), improvement in cancer survival remains limited due to their similar structures and targets. Natural products (NPs) maintain diverse structures and activities and are important sources of drug discovery. Currently, most of active NPs exhibit ambiguous binding targets and mechanisms. Herein, we proposed the CIPHEN (compound-protein interactions prediction based on the heterogeneous network) to predict potential antihepatoma NPs and their targets. The evaluation of canonical compound-protein interactions (CPIs) databases and independent test demonstrated the good ability of CPIHN to reveal known and unreported CPIs. Both prediction and experiment results indicated that CIPHEN could unveil relationships between actively antihepatoma sesquiterpenoid dimers (SDs) and anti-HCC targets. In conclusion, the CIPHEN provides a promising choice to investigate the mode of action of compounds, which will help to accelerate the process of drug research and development against HCC.

SUBMITTER: Wang YC

PROVIDER: S-EPMC11301071 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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Compound-protein interaction prediction based on heterogeneous network reveals potential antihepatoma agents.

Wang Yong-Cui YC Li Tian-Ze TZ Chen Ji-Jun JJ

iScience 20240629 8

Despite the development of an increasing number of multi-kinase and immune checkpoint inhibitors for hepatocellular carcinoma (HCC), improvement in cancer survival remains limited due to their similar structures and targets. Natural products (NPs) maintain diverse structures and activities and are important sources of drug discovery. Currently, most of active NPs exhibit ambiguous binding targets and mechanisms. Herein, we proposed the CIPHEN (compound-protein interactions p ...[more]

PMID: 39108729

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Project description:Characterizing the biomolecular systems' properties underpinning prognosis signatures derived from gene expression profiles remains a key clinical and biological challenge. In breast cancer, while different "poor-prognosis" sets of genes have predicted patient survival outcome equally well in independent cohorts, these prognostic signatures have surprisingly little genetic overlap. We examine 10 such published expression-based signatures that are predictors or distinct breast cancer phenotypes, uncover their mechanistic interconnectivity through a protein-protein interaction network, and introduce a novel cross-"gene expression signature" analysis method using (i) domain knowledge to constrain multiple comparisons in a mechanistically relevant single-gene network interactions and (ii) scale-free permutation re-sampling to statistically control for hubness (SPAN - Single Protein Analysis of Network with constant node degree per protein). At adjusted p-values<5%, 54-genes thus identified have a significantly greater connectivity than those through meticulous permutation re-sampling of the context-constrained network. More importantly, eight of 10 genetically non-overlapping signatures are connected through well-established mechanisms of breast cancer oncogenesis and progression. Gene Ontology enrichment studies demonstrate common markers of cell cycle regulation. Kaplan-Meier analysis of three independent historical gene expression sets confirms this network-signature's inherent ability to identify "poor outcome" in ER(+) patients without the requirement of machine learning. We provide a novel demonstration that genetically distinct prognosis signatures, developed from independent clinical datasets, occupy overlapping prognostic space of breast cancer via shared mechanisms that are mediated by genetically different yet mechanistically comparable interactions among proteins of differentially expressed genes in the signatures. This is the first study employing a networks' approach to aggregate established gene expression signatures in order to develop a phenotype/pathway-based cancer roadmap with the potential for (i) novel drug development applications and for (ii) facilitating the clinical deployment of prognostic gene signatures with improved mechanistic understanding of biological processes and functions associated with gene expression changes. http://www.lussierlab.org/publication/networksignature/.

Dataset Information

Compound-protein interaction prediction based on heterogeneous network reveals potential antihepatoma agents.

Publications

Compound-protein interaction prediction based on heterogeneous network reveals potential antihepatoma agents.

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