ArticleOncology letters2016
Systematic tracking of disrupted modules identifies significant genes and pathways in hepatocellular carcinoma.
Article in Oncology letters, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- CommPath: An R package for inference and analysis of pathway-mediated cell-cell communication chain from single-cell transcriptomics.Computational and structural biotechnology journal · 2022Article
- Novel prognostic biomarkers of gastric cancer based on gene expression microarray: COL12A1, GSTA3, FGA and FGG.Molecular medicine reports · 2018Article
- Overexpression of Cullin7 is associated with hepatocellular carcinoma progression and pathogenesis.BMC cancer · 2017Article
- Exploring the key genes and pathways of osteosarcoma with pulmonary metastasis using a gene expression microarray.Molecular medicine reports · 2017Article
- A transcriptome-based protein network that identifies new therapeutic targets in colorectal cancer.BMC genomics · 2017Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
The objective of the present study is to identify significant genes and pathways associated with hepatocellular carcinoma (HCC) by systematically tracking the dysregulated modules of re-weighted protein-protein interaction (PPI) networks. Firstly, normal and HCC PPI networks were inferred and re-weighted based on Pearson correlation coefficient. Next, modules in the PPI networks were explored by a clique-merging algorithm, and disrupted modules were identified utilizing a maximum weight bipartite matching in non-increasing order. Then, the gene compositions of the disrupted modules were studied and compared with differentially expressed (DE) genes, and pathway enrichment analysis for these genes was performed based on Expression Analysis Systematic Explorer. Finally, validations of significant genes in HCC were conducted using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis. The present study evaluated 394 disrupted module pairs, which comprised 236 dysregulated genes. When the dysregulated genes were compared with 211 DE genes, a total of 26 common genes [including
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