ArticleCancers2022
A Gene Co-Expression Network-Based Drug Repositioning Approach Identifies Candidates for Treatment of Hepatocellular Carcinoma.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed, 23 citations in OpenAlex.
- Adrenocortical carcinoma survival gene HMMR was identified as being targeted by fluorouracil and epirubicin using a gene coexpression network-based drug repositioning strategy.Scientific reports · 2025Article
- From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies.Molecular biotechnology · 2025Review
- Article
- Network-based drug repurposing identifies small molecule drugs as immune checkpoint inhibitors for endometrial cancer.Molecular diversity · 2024Article
- Discovery of a Therapeutic Agent for Glioblastoma Using a Systems Biology-Based Drug Repositioning Approach.International journal of molecular sciences · 2024Article
- Identification ofFrontiers in immunology · 2024Article
- Systematic transcriptional analysis of human cell lines for gene expression landscape and tumor representation.Nature communications · 2023Article
- Drug repositioning targeting glutaminase reveals drug candidates for the treatment of Alzheimer's disease patients.Journal of translational medicine · 2023Article
- Non-canonical NLRC4 inflammasomes in astrocytes contribute to glioma malignancy.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2023Article
- Current trends and future prospects of drug repositioning in gastrointestinal oncology.Frontiers in pharmacology · 2023Review
- Investigations of nitazoxanide molecular targets and pathways for the treatment of hepatocellular carcinoma using network pharmacology and molecular docking.Frontiers in pharmacology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 4 institutions in 5 countries.
Funding
Abstract
Hepatocellular carcinoma (HCC) is a malignant liver cancer that continues to increase deaths worldwide owing to limited therapies and treatments. Computational drug repurposing is a promising strategy to discover potential indications of existing drugs. In this study, we present a systematic drug repositioning method based on comprehensive integration of molecular signatures in liver cancer tissue and cell lines. First, we identify robust prognostic genes and two gene co-expression modules enriched in unfavorable prognostic genes based on two independent HCC cohorts, which showed great consistency in functional and network topology. Then, we screen 10 genes as potential target genes for HCC on the bias of network topology analysis in these two modules. Further, we perform a drug repositioning method by integrating the shRNA and drug perturbation of liver cancer cell lines and identifying potential drugs for every target gene. Finally, we evaluate the effects of the candidate drugs through an in vitro model and observe that two identified drugs inhibited the protein levels of their corresponding target genes and cell migration, also showing great binding affinity in protein docking analysis. Our study demonstrates the usefulness and efficiency of network-based drug repositioning approach to discover potential drugs for cancer treatment and precision medicine approach.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.