ArticleBMC cancer2019
Novel significant stage-specific differentially expressed genes in hepatocellular carcinoma.
Article in BMC cancer, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
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Who cites it
51 citing papers in PubMed, 77 citations in OpenAlex.
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- Plasma-derived extracellular matrix for xenofree and cost-effective organoid modeling for hepatocellular carcinoma.Journal of translational medicine · 2024Article
- A pan-cancer analysis uncovering the function of CRHBP in tumor immunity, prognosis and drug response: especially its function in LIHC.Scientific reports · 2024Article
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- Transcriptional regulation of NDUFA4L2 by NFIB induces sorafenib resistance by decreasing reactive oxygen species in hepatocellular carcinoma.Cancer science · 2023Article
- Gamma-Aminobutyric Acid Type A Receptor Subunit Delta (Iranian journal of public health · 2023Article
- Identification of monotonically expressed long non-coding RNA signatures for breast cancer using variational autoencoders.PloS one · 2023Article
- Single-cell RNA sequencing reveals cell subpopulations in the tumor microenvironment contributing to hepatocellular carcinoma.Frontiers in cell and developmental biology · 2023Article
- Age-dependent expression of ion channel genes in rat.The Korean journal of physiology & pharmacology : official journal of the Korean Physiological Society and the Korean Society of Pharmacology · 2023Article
- CESCProg: a compact prognostic model and nomogram for cervical cancer based on miRNA biomarkers.PeerJ · 2023Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
backgroundLiver cancer is among top deadly cancers worldwide with a very poor prognosis, and the liver is a vulnerable site for metastases of other cancers. Early diagnosis is crucial for treatment of the predominant liver cancers, namely hepatocellular carcinoma (HCC). Here we developed a novel computational framework for the stage-specific analysis of HCC.
methodsUsing publicly available clinical and RNA-Seq data of cancer samples and controls and the AJCC staging system, we performed a linear modelling analysis of gene expression across all stages and found significant genome-wide changes in the log fold-change of gene expression in cancer samples relative to control. To identify genes that were stage-specific controlling for confounding differential expression in other stages, we developed a set of six pairwise contrasts between the stages and enforced a p-value threshold (< 0.05) for each such contrast. Genes were specific for a stage if they passed all the significance filters for that stage. The monotonicity of gene expression with cancer progression was analyzed with a linear model using the cancer stage as a numeric variable.
resultsOur analysis yielded two stage-I specific genes (CA9, WNT7B), two stage-II specific genes (APOBEC3B, FAM186A), ten stage-III specific genes including DLG5, PARI, NCAPG2, GNMT and XRCC2, and 35 stage-IV specific genes including GABRD, PGAM2, PECAM1 and CXCR2P1. Overexpression of DLG5 was found to be tumor-promoting contrary to the cancer literature on this gene. Further, GABRD was found to be signifincantly monotonically upregulated across stages. Our work has revealed 1977 genes with significant monotonic patterns of expression across cancer stages. NDUFA4L2, CRHBP and PIGU were top genes with monotonic changes of expression across cancer stages that could represent promising targets for therapy. Comparison with gene signatures from the BCLC staging system identified two genes, HSP90AB1 and ARHGAP42. Gene set enrichment analysis indicated overrepresented pathways specific to each stage, notably viral infection pathways in HCC initiation.
conclusionsOur study identified novel significant stage-specific differentially expressed genes which could enhance our understanding of the molecular determinants of hepatocellular carcinoma progression. Our findings could serve as biomarkers that potentially underpin diagnosis as well as pinpoint therapeutic targets.
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