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pmid:31410310

Identification of crucial genes based on expression profiles of hepatocellular carcinomas by bioinformatics analysis.

Ze-Kun Liu · Ren-Yu Zhang · Yu-Le Yong · Zhi-Yun Zhang · Can Li · Zhi-Nan Chen · Huijie Bian

PeerJ · 2019 doi:10.7717/peerj.7436

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title
Identification of crucial genes based on expression profiles of hepatocellular carcinomas by bioinformatics analysis.
doi
10.7717/peerj.7436
pmid
31410310
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Ze-Kun Liu
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Ren-Yu Zhang
[2]
Yu-Le Yong
[3]
Zhi-Yun Zhang
[4]
Can Li
[5]
Zhi-Nan Chen
[6]
Huijie Bian
journal
PeerJ
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2019
abstract
Hepatocellular carcinoma (HCC) is one of the most heterogeneous malignant cancers with no effective targets and treatments. However, the molecular pathogenesis of HCC remains largely uncertain. The aims of our study were to find crucial genes involved in HCC through multidimensional methods and revealed potential molecular mechanisms. Here, we reported the gene expression profile GSE121248 findings from 70 HCC and 37 adjacent normal tissues, all of which had chronic hepatitis B virus (HBV) infection, we were seeking to identify the dysregulated pathways, crucial genes and therapeutic targets implicated in HBV-associated HCC. We found 164 differentially expressed genes (DEGs) (92 downregulated genes and 72 upregulated genes). Gene ontology (GO) analysis of DEGs revealed significant functional enrichment of mitotic nuclear division, cell division, and the epoxygenase P450 pathway. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the DEGs were mainly enriched in metabolism, cell cycle regulation and the p53 signaling pathway. The Mcode plugin was calculated to construct a module complex of DEGs, and the module was mainly enriched in cell cycle checkpoints, RHO GTPase effectors and cytochrome P450. Considering a weak contribution of each gene, gene set enrichment analysis (GSEA) was performed, revealing results consistent with those described above. Six crucial proteins were selected based on the degree of centrality, including NDC80, ESR1, ZWINT, NCAPG, ENO3 and CENPF. Real-time quantitative PCR analysis validated the six crucial genes had the same expression trend as predicted. Furthermore, the methylation data of The Cancer Genome Atlas (TCGA) with HCC showed that mRNA expression of crucial genes was negatively correlated with methylation levels of their promoter region. The overall survival reflected that high expression of NDC80, CENPF, ZWINT, and NCAPG significantly predicted poor prognosis, whereas ESR1 high expression exhibited a favorable prognosis. The identification of the crucial genes and pathways would contribute to the development of novel molecular targets and biomarker-driven treatments for HCC.
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The t test methods of the limma package (version 3.38.2, http://bioconductor.org/packages/limma/) (Ritchie et al., 2015) were used to identify DEGs in HCC tissues compared to adjacent normal tissues.
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The t test methods of the limma package (version 3.38.2, http://bioconductor.org/packages/limma/) (Ritchie et al., 2015) were used to identify DEGs in HCC tissues compared to adjacent normal tissues.
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sections[33] "Data Availability Statement" paragraphs 1-3
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The following information was supplied regarding data availability: The raw measurements are available in the Supplemental Tables.
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GSM548538
sample_count
397
group_sizes
null
group_sizes_basis
null
platform
9733;10457;6793
taxon
Homo sapiens; Mus musculus
title
miR-122 as a Regulator of Mitochondrial Metabolic Gene Network in Hepatocellular Carcinoma
resolution_checked_at
2026-11-01T15:39:54Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
[3] object · 15 fields
id
data:geo:GSE25097
role
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uri
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reader_status
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identifier
GSE25097
version
null
note
The following information was supplied regarding data availability: The raw measurements are available in the Supplemental Tables.
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
The following information was supplied regarding data availability: The raw measurements are available in the Supplemental Tables.
access_statement_kind
data_availability_statement
sample_accessions array · 557 items
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[180]
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[209]
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[210]
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[227]
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[228]
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[229]
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[230]
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[231]
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[232]
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[233]
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[234]
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[235]
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[236]
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[237]
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[238]
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[239]
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[240]
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[241]
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[242]
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[243]
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[244]
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[245]
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[246]
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[247]
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[248]
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[249]
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[250]
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[251]
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[252]
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[253]
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[254]
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[255]
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[256]
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[257]
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[258]
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[259]
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[260]
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[261]
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[262]
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[263]
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[265]
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[266]
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[267]
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[268]
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[269]
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[270]
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[271]
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[272]
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[273]
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[274]
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[275]
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[276]
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[277]
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[278]
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[279]
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[280]
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[281]
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[282]
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[283]
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[284]
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[285]
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[286]
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[287]
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[288]
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[289]
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[290]
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[291]
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[292]
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[293]
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[294]
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[295]
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[296]
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[297]
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[298]
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[299]
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[300]
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[301]
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[302]
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[303]
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[304]
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[305]
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[306]
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[307]
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[309]
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[310]
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[311]
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[313]
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[314]
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[315]
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[316]
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[317]
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[318]
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[319]
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[320]
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[321]
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[322]
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[323]
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[324]
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[325]
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[327]
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[328]
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[329]
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[331]
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[332]
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[333]
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[334]
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[335]
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[336]
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[337]
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[338]
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[339]
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[340]
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[341]
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[342]
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[343]
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[344]
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[345]
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[346]
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[347]
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[348]
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[349]
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[350]
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[351]
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[352]
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[353]
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[354]
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[355]
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[356]
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[357]
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[358]
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[359]
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[360]
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[361]
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[362]
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[363]
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[364]
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[365]
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[366]
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[367]
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[368]
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[369]
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[370]
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[371]
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[372]
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[373]
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[374]
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[375]
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[376]
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[377]
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[378]
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[379]
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[380]
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[381]
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[388]
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[390]
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[391]
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[392]
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[393]
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[394]
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[395]
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[402]
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[403]
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[404]
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[406]
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[407]
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[408]
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[409]
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[410]
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[411]
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[412]
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[413]
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[414]
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[415]
GSM617430
[416]
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[417]
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[418]
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[419]
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[420]
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[421]
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[422]
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[423]
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[424]
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[425]
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[426]
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[427]
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[428]
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[429]
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[430]
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[431]
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[432]
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[433]
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[434]
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[435]
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[436]
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[437]
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[438]
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[439]
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[440]
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[441]
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[442]
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[443]
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[444]
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[445]
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[446]
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[447]
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[448]
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[449]
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[450]
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[451]
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[452]
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[453]
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[454]
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[455]
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[456]
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[457]
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[458]
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[459]
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[460]
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[461]
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[462]
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[463]
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[464]
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[465]
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[466]
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[467]
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[468]
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[469]
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[470]
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[471]
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[472]
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[473]
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[474]
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[475]
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[476]
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[477]
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[478]
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[479]
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[480]
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[481]
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[482]
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[483]
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[484]
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[485]
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[486]
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[487]
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[488]
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[489]
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[490]
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[491]
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[492]
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[493]
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[494]
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[495]
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[499]
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[500]
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[501]
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[502]
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[503]
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[504]
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[505]
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[506]
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[507]
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[509]
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[510]
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[512]
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[517]
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[523]
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[524]
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[526]
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[531]
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[533]
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[534]
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[536]
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[537]
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[538]
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[541]
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[544]
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[545]
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[546]
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[547]
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[548]
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[549]
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[550]
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[551]
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[552]
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[553]
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[554]
GSM617569
[555]
GSM617570
[556]
GSM617571
sample_count
557
group_sizes
null
group_sizes_basis
null
platform
10687
taxon
Homo sapiens
title
Study of gene expression of human liver Hepatocellular carcinoma
resolution_checked_at
2026-11-01T15:39:54Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
[4] object · 16 fields
id
asset:table:pmid:31410310:1
role
table
format
jats-table
media_type
application/vnd.jats.table+json
uri
null
sha256
sha256:840b388db0123ec9e7e961560d37a1047657567d86ded4146165ab9ea0cbf306
bytes
1459
access
open
state
verified
reader
read_table
reader_status
available
identifier
section[14]/table-wrap[1]#table-1
version
null
note
The top 10 proteins ranked based on the node centrality of the PPI network.
structure object · 5 fields
pmcid
PMC6689388
section_index
14
section_title
Protein–protein interaction network of DEGs
row_count
12
column_count
9
table object · 8 fields
label
Table 1
caption
The top 10 proteins ranked based on the node centrality of the PPI network.
rows array · 12 items
[0] array · 5 items
[0]
Rank
[1]
Degree centrality
[2]
Betweenness centrality
[3]
Closeness centrality
[4]
Eigenvector centrality
[1] array · 9 items
[0]
[1]
Gene symbol
[2]
Expression in HCC
[3]
Gene symbol
[4]
Expression in HCC
[5]
Gene symbol
[6]
Expression in HCC
[7]
Gene symbol
[8]
Expression in HCC
[2] array · 9 items
[0]
1
[1]
TOP2A
[2]
up-regulated
[3]
TOP2A
[4]
up-regulated
[5]
TOP2A
[6]
up-regulated
[7]
CCNB1
[8]
up-regulated
[3] array · 9 items
[0]
2
[1]
NDC80
[2]
up-regulated
[3]
ESR1
[4]
down-regulated
[5]
NDC80
[6]
up-regulated
[7]
CDK1
[8]
up-regulated
[4] array · 9 items
[0]
3
[1]
CCNB1
[2]
up-regulated
[3]
CYP3A4
[4]
down-regulated
[5]
ESR1
[6]
down-regulated
[7]
NDC80
[8]
up-regulated
[5] array · 9 items
[0]
4
[1]
CDK1
[2]
up-regulated
[3]
CYP2E1
[4]
down-regulated
[5]
CDKN3
[6]
up-regulated
[7]
ZWINT
[8]
up-regulated
[6] array · 9 items
[0]
5
[1]
CDKN3
[2]
up-regulated
[3]
NDC80
[4]
up-regulated
[5]
BIRC5
[6]
up-regulated
[7]
BUB1
[8]
up-regulated
[7] array · 9 items
[0]
6
[1]
ZWINT
[2]
up-regulated
[3]
IGF1
[4]
down-regulated
[5]
CENPF
[6]
up-regulated
[7]
TTK
[8]
up-regulated
[8] array · 9 items
[0]
7
[1]
TTK
[2]
up-regulated
[3]
CYP2B6
[4]
down-regulated
[5]
CCNB1
[6]
up-regulated
[7]
NCAPG
[8]
up-regulated
[9] array · 9 items
[0]
8
[1]
BUB1
[2]
up-regulated
[3]
COL1A2
[4]
up-regulated
[5]
CDK1
[6]
up-regulated
[7]
RACGAP1
[8]
up-regulated
[10] array · 9 items
[0]
9
[1]
CENPF
[2]
up-regulated
[3]
CENPF
[4]
up-regulated
[5]
EZH2
[6]
up-regulated
[7]
BUB1B
[8]
up-regulated
[11] array · 9 items
[0]
10
[1]
NCAPG
[2]
up-regulated
[3]
ENO3
[4]
down-regulated
[5]
NEK2
[6]
up-regulated
[7]
MAD2L1
[8]
up-regulated
locator
section[14]/table-wrap[1]#table-1
[5] object · 16 fields
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asset:table:pmid:31410310:2
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table
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jats-table
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application/vnd.jats.table+json
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null
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sha256:93bdb1d7a555c3bbcac7e618665231194274b3e868749be5a4680c77d2aaef58
bytes
1503
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open
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verified
reader
read_table
reader_status
available
identifier
section[16]/table-wrap[1]#table-2
version
null
note
Validation of the relationship between methylation and mRNA expression of crucial genes in TCGA.
structure object · 5 fields
pmcid
PMC6689388
section_index
16
section_title
Validation of mRNA expression of crucial genes
row_count
21
column_count
7
table object · 8 fields
label
Table 2
caption
Validation of the relationship between methylation and mRNA expression of crucial genes in TCGA.
rows array · 21 items
[0] array · 3 items
[0]
Crucial gene
[1]
mRNA expression in HCC
[2]
Methylation status in HCC
[1] array · 7 items
[0]
[1]
mRNA expression
[2]
P-value
[3]
Methylation
[4]
Position
[5]
Pearson correlation
[6]
P-value
[2] array · 7 items
[0]
NDC80
[1]
High expression
[2]
<2.2E-16
[3]
Hypomethylation
[4]
chr18:2571606
[5]
−0.150
[6]
1.64E-03
[3] array · 3 items
[0]
chr18:2571747
[1]
−0.282
[2]
<2.2E-16
[4] array · 7 items
[0]
ZWINT
[1]
High expression
[2]
<2.2E-16
[3]
Hypomethylation
[4]
chr10:58121299
[5]
−0.142
[6]
0.03
[5] array · 7 items
[0]
NCAPG
[1]
High expression
[2]
<2.2E-16
[3]
Hypomethylation
[4]
chr4:17812759
[5]
−0.236
[6]
2.26E-11
[6] array · 3 items
[0]
chr4:17813558
[1]
−0.544
[2]
<2.2E-16
[7] array · 7 items
[0]
CENPF
[1]
High expression
[2]
<2.2E-16
[3]
Hypomethylation
[4]
chr1:214776904
[5]
−0.243
[6]
1.48E-12
[8] array · 3 items
[0]
chr1:214777596
[1]
−0.304
[2]
1.60E-14
[9] array · 3 items
[0]
chr1:214780880
[1]
−0.174
[2]
6.27E-11
[10] array · 3 items
[0]
chr1:214781456
[1]
−0.154
[2]
1.38E-04
[11] array · 3 items
[0]
chr1:214781663
[1]
−0.217
[2]
1.21E-05
[12] array · 3 items
[0]
chr1:214788192
[1]
−0.150
[2]
7.40E-07
[13] array · 7 items
[0]
ESR1
[1]
Low expression
[2]
<2.2E-16
[3]
Hypermethylation
[4]
chr6:152002969
[5]
−0.139
[6]
2.58E-02
[14] array · 3 items
[0]
chr6:152003038
[1]
−0.142
[2]
5.60E-05
[15] array · 7 items
[0]
ENO3
[1]
Low expression
[2]
7.46E-14
[3]
Hypermethylation
[4]
chr17:4850943
[5]
−0.160
[6]
1.39E-03
[16] array · 3 items
[0]
chr17:4851808
[1]
−0.146
[2]
5.41E-12
[17] array · 3 items
[0]
chr17:4852013
[1]
−0.139
[2]
8.45E-04
[18] array · 3 items
[0]
chr17:4852349
[1]
−0.118
[2]
9.08E-12
[19] array · 3 items
[0]
chr17:4854754
[1]
−0.705
[2]
8.20E-05
[20] array · 3 items
[0]
chr17:4855149
[1]
−0.604
[2]
2.24E-06
locator
section[16]/table-wrap[1]#table-2
gaps array · 6 items
[0]
asset:data:geo:GSE121248 not downloaded
[1]
asset:data:geo:GSE121248 licence not read
[2]
asset:data:geo:GSE22058 not downloaded
[3]
asset:data:geo:GSE22058 licence not read
[4]
asset:data:geo:GSE25097 not downloaded
[5]
asset:data:geo:GSE25097 licence not read
analysis object · 4 fields
state
partial
methods array · 6 items
[0] object · 10 fields
id
method-1
family
differential_expression
specific
limma_voom
label
Differential expression analysis
dsl_operations array · 3 items
[0]
normalise_counts
[1]
differential_expression_fit
[2]
fdr_bh
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
count_matrix_with_condition
evidence object · 3 fields
score
1
band
verbatim
quote
The t test methods of the limma package (version 3.38.2, http://bioconductor.org/packages/limma/) (Ritchie et al., 2015) were used to identify DEGs in HCC tissues compared to adjacent normal tissues.
[1] object · 10 fields
id
method-2
family
survival_analysis
specific
cox_proportional_hazards
label
Survival analysis
dsl_operations array · 1 item
[0]
cox_ph
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
time_to_event_with_censoring
evidence object · 3 fields
score
0.8
band
high
quote
Cox regression
[2] object · 10 fields
id
method-3
family
survival_analysis
specific
log_rank_test
label
Survival analysis
dsl_operations array · 1 item
[0]
logrank_test
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
time_to_event_with_censoring
evidence object · 3 fields
score
0.8
band
high
quote
log-rank
[3] object · 10 fields
id
method-4
family
network_analysis
specific
ppi_network
label
Network / pathway analysis
dsl_operations array · 3 items
[0]
build_graph
[1]
centrality_scores
[2]
pathway_enrich
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
edge_list
evidence object · 3 fields
score
0.8
band
high
quote
PPI
[4] object · 10 fields
id
method-5
family
network_analysis
specific
centrality
label
Network / pathway analysis
dsl_operations array · 3 items
[0]
build_graph
[1]
centrality_scores
[2]
pathway_enrich
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
edge_list
evidence object · 3 fields
score
0.7
band
medium
quote
centrality
[5] object · 10 fields
id
method-6
family
contingency_analysis
specific
chi_square
label
Categorical (contingency table) analysis
dsl_operations array · 1 item
[0]
chi_square_test
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
categorical_cross_tabulation
evidence object · 3 fields
score
0.8
band
high
quote
χ2
targets array · 0 items
gaps array · 1 item
[0]
method.reader_differs_from_extractor
capability object · 3 fields
state
complete
requirements array · 1 item
[0] object · 6 fields
capability_id
capability:computational_analysis
label
Computational analysis platform
assay_families array · 9 items
[0]
meta_analysis
[1]
machine_learning
[2]
survival_analysis
[3]
statistical_regression
[4]
pca
[5]
molecular_docking
[6]
network_analysis
[7]
differential_expression
[8]
gwas
candidate
Automated Assays Laravel application and simulated adapters
gaps array · 0 items
readiness object · 8 fields
state
complete
tier
R1
blocked_on
nothing:replicable_now
axes object · 3 fields
method object · 5 fields
tier
R1
expressible
true
required_operations array · 3 items
[0]
normalise_counts
[1]
differential_expression_fit
[2]
fdr_bh
missing_operations array · 0 items
basis
The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: limma_voom.
capability object · 4 fields
tier
R1
capability_id
capability:computational_analysis
availability_state
owned
basis
The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here.
data object · 6 fields
tier
R1
reachable
true
accessions array · 3 items
[0]
GSE121248
[1]
GSE25097
[2]
GSE22058
resolved_accessions array · 3 items
[0]
GSE121248
[1]
GSE25097
[2]
GSE22058
statements array · 1 item
[0]
The following information was supplied regarding data availability: The raw measurements are available in the Supplemental Tables.
basis
The paper names GSE121248, GSE25097, GSE22058 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read.
can_run object · 3 fields
dsl_operations array · 3 items
[0]
normalise_counts
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differential_expression_fit
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fdr_bh
assets array · 4 items
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source:pmid:31410310
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data:geo:GSE121248
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asset:table:pmid:31410310:1
[3]
asset:table:pmid:31410310:2
steps array · 1 item
[0]
run_analysis
must_build object · 5 fields
dsl_operations array · 0 items
converters array · 0 items
readers array · 0 items
capabilities array · 0 items
data_gaps array · 1 item
[0]
data:geo:GSE121248
gaps array · 0 items
review object · 5 fields
state
partial
machine_critique object · 5 fields
kind
extraction_review
reader object · 5 fields
model
MiniMax-M3.1-Flash
provider
minimax
provider_family
minimax
read_at
2026-10-31T18:15:49Z
independence
single_reader
evidence_check object · 2 fields
method
verbatim
availability
verbatim
calls
3
usage object · 3 fields
prompt_tokens
40474
completion_tokens
2082
cached_tokens
776
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id
extraction
state
complete
reader
MiniMax-M3.1-Flash
provider
minimax
gaps array · 3 items
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review.independent_reader
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review.machine_critique
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review.human_critique
execution object · 5 fields
state
absent
status
not_run
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null
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gaps array · 1 item
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execution.not_run
provenance object · 7 fields
entities array · 3 items
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id
pmid:31410310
type
paper
label
pmid:31410310
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id
source:pmid:31410310
type
manuscript
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PMID 31410310 full text
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id
data:geo:GSE121248
type
dataset
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GEO GSE121248
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id
activity-reading-sci-2026-11-01
type
classification
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id
pmid:31410310
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paper
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pmid:31410310
[1] object · 3 fields
id
source:pmid:31410310
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manuscript
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PMID 31410310 full text
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id
data:geo:GSE121248
type
dataset
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GEO GSE121248
agent
agent:corpus-assay-engine
[1] object · 4 fields
id
activity-read-31410310
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extraction
used array · 1 item
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source:pmid:31410310
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agent:minimax-reader
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activity-resolve-GSE121248
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used array · 1 item
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data:geo:GSE121248
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agent:corpus-assay-engine
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Corpus assay engine
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agent:minimax-reader
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MiniMax literature reader
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343fb30756ba6ec3
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lineage object · 4 fields
parent_id
pmid:31410310
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sha256:5ed45ec4d64a28334ddf755a4b3cae35cc11e683e4c4bdf619761869e7eaae82
fork_of
null
gaps array · 11 items
[0]
asset:data:geo:GSE121248 licence not read
[1]
asset:data:geo:GSE121248 not downloaded
[2]
asset:data:geo:GSE22058 licence not read
[3]
asset:data:geo:GSE22058 not downloaded
[4]
asset:data:geo:GSE25097 licence not read
[5]
asset:data:geo:GSE25097 not downloaded
[6]
execution.not_run
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method.reader_differs_from_extractor
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review.human_critique
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review.independent_reader
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role
data_generation
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support_state
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assay_lexicon
99e1195c7d6f
confidence
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evidence array · 2 items
[0]
g NDC80, ESR1, ZWINT, NCAPG, ENO3 and CENPF. Real-time quantitative PCR analysis validated the six crucial genes had
[1]
II (TaKaRaBio, Otsu, Japan) and primers, and qPCR data were analyzed using △△Ct method with β-
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paper_text_order:1583
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99e1195c7d6f
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atlas.org) was used for protein detection by immunohistochemistry. We performed TCGA methylation data of MEXPR
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evidence array · 1 item
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The t test methods of the limma package (version 3.38.2, http://bioconductor.org/packages/limma/) (Ritchie et al., 2015) were used to identify DEGs in HCC tissues compared to adjacent normal tissues.
order_basis
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The aims of our study were to find crucial genes involved in HCC through multidimensional methods and revealed potential molecular mechanisms. Here, we reported the gene expression profile GSE121248 findings from 70 HCC and 37 adjacent normal tissues, all of which had chronic hepatitis B virus (HBV) infection, we were seeking to identify the dysregulated pathways, crucial genes and therapeutic targets implicated in HBV-associated HCC. We found 164 differentially expressed genes (DEGs) (92 downregulated genes and 72 upregulated genes). Gene ontology (GO) analysis of DEGs revealed significant functional enrichment of mitotic nuclear division, cell division, and the epoxygenase P450 pathway. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the DEGs were mainly enriched in metabolism, cell cycle regulation and the p53 signaling pathway. The Mcode plugin was calculated to construct a module complex of DEGs, and the module was mainly enriched in cell cycle checkpoints, RHO GTPase effectors and cytochrome P450. Considering a weak contribution of each gene, gene set enrichment analysis (GSEA) was performed, revealing results consistent with those described above. Six crucial proteins were selected based on the degree of centrality, including NDC80, ESR1, ZWINT, NCAPG, ENO3 and CENPF. Real-time quantitative PCR analysis validated the six crucial genes had the same expression trend as predicted. Furthermore, the methylation data of The Cancer Genome Atlas (TCGA) with HCC showed that mRNA expression of crucial genes was negatively correlated with methylation levels of their promoter region. The overall survival reflected that high expression of NDC80, CENPF, ZWINT, and NCAPG significantly predicted poor prognosis, whereas ESR1 high expression exhibited a favorable prognosis. 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