Identification of key candidate genes and pathways in hepatocellular carcinoma by integrated bioinformatical analysis

Xing, Tonghai; Tao, Yan; Zhou, Qiang

doi:10.3892/etm.2018.6075

Cited by 16 publications

(17 citation statements)

References 44 publications

(47 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yin et al showed that the genes involved in the G2/M checkpoint may act as biomarkers at the early HCC (Yin, Chang & Xu, 2017). In addition, another study found that hub genes TOP2A, PCNA and AURKA with low mutation frequency involved in HCC by bioinformatics analysis (Xing, Yan & Zhou, 2018). However, their study only analyzed a profile, and only identified the gene with a high node degree using the module analysis.…”

Section: Discussionmentioning

confidence: 99%

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

Liu

Zhang

Yong

et al. 2019

PeerJ

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

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

Liu

Zhang

Yong

et al. 2019

PeerJ

View full text Add to dashboard Cite

show abstract

“…Till now, there are several studies about integrated analysis of gene expression profiles in HCC. [2728293031] But the integration strategies are different. In a study by Shiraishi et al .…”

Section: Discussionmentioning

confidence: 99%

“…the authors chose four datasets, because they thought these datasets represented different racial populations. [31] In our study, gene expression profiles have been obtained by repurposing five GEO datasets. Although each of the five datasets used Affymetrix Human Genome U133 Plus 2.0 Array to analyze gene expression patterns, the interlab reproducibility of the results is often problematic due to the small sample size and other factors (such as pathologic staging and surgical outcome).…”

Section: Discussionmentioning

confidence: 99%

Cytochrome P450 family members are associated with fast-growing hepatocellular carcinoma and patient survival: An integrated analysis of gene expression profiles

Liu¹,

Yan²,

Dong³

et al. 2019

Saudi J Gastroenterol

View full text Add to dashboard Cite

Background/Aims: The biological heterogeneity of hepatocellular carcinoma (HCC) makes prognosis difficult. Although many molecular tools have been developed to assist in stratification and prediction of patients by using microarray analysis, the classification and prediction are still improvable because the high-through microarray contains a large amount of information. Meanwhile, gene expression patterns and their prognostic value for HCC have not been systematically investigated. In order to explore new molecular diagnostic and prognostic biomarkers, the gene expression profiles between HCCs and adjacent nontumor tissues were systematically analyzed in the present study. Materials and Methods: In this study, gene expression profiles were obtained by repurposing five Gene Expression Omnibus databases. Differentially expressed genes were identified by using robust rank aggregation method. Three datasets (GSE14520, GSE36376, and GSE54236) were used to validate the associations between cytochrome P450 (CYP) family genes and HCC. GSE14520 was used as the training set. GSE36376 and GSE54236 were considered as the testing sets. Results: From the training set, a four-CYP gene signature was constructed to discriminate between HCC and nontumor tissues with an area under curve (AUC) of 0.991. Accuracy of this four-gene signature was validated in two testing sets (AUCs for them were 0.973 and 0.852, respectively). Moreover, this gene signature had a good performance to make a distinction between fast-growing HCC and slow-growing HCC (AUC = 0.898), especially for its high sensitivity of 95%. At last, CYP2C8 was identified as an independent risk factor of recurrence-free survival (hazard ratio [HR] =0.865, 95% confidence interval [CI], 0.754–0.992, P = 0.038) and overall survival (HR = 0.849; 95% CI, 0.716–0.995, P = 0.033). Conclusions: In summary, our results confirmed for the first time that a four-CYP gene ( CYP1A2 , CYP2E1 , CYP2A7 , and PTGIS ) signature is associated with fast-growing HCC, and CYP2C8 is associated with patient survival. Our findings could help to identify HCC patients at high risk of rapid growth and recurrence.

show abstract

“…Development of bigdata has attracted attention in recent years (13)(14)(15)(16). It provided new insights toward novel strategies for early testing and diagnosis of cancer (17)(18)(19)(20). However, bioinformatics study of OS has not been catalogued.…”

Section: Introductionmentioning

confidence: 99%

Differentially expressed genes SNRPC and PRPF38A are potential biomarkers candidates for osteosarcoma

Chang¹,

Cao²

2020

Preprint

View full text Add to dashboard Cite

Background: Osteosarcoma (osteogenic sarcoma, OS) is a primary cause of morbidity and mortality and is associated with poor prognosis in the field of orthopedic. Globally, rates of OS are highest among 15 to 25-year-old adolescent. However, the mechanism of gene regulation and signaling pathway is unknown. Material and Methods: GSE9508, including 34 OS samples and 5 non-malignant bone samples, was gained from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were picked out by GEO2R online R soft tool. Furthermore, the protein-protein interaction (PPI) network between the DEGs was molded utilizing STRING online software. Afterward, PPI network of DEGs was constructed. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were carried out on DAVID online tool and visualized via cytoscape software. Subsequently, module analysis of PPI was performed by using MCODE app. What’s more, prognosis-related genes were screened by using online databases including GEPIA, UALCAN and cBioPortal databases. Results: Totally, 671 DEGs were picked out, including 501 up-regulated genes and 170 down-regulated genes. Moreover, 22 hub genes were identified to be significantly expressed in PPI network (16 up-regulated and 6 down-regulated). We found that spliceosome signaling pathway may provide a potential target in OS. Furthermore, on the basis of common crucial pathway, PRPF38A and SNRPC were closely associated with spliceosome. Conclusion: This study showed that SNRPC and PRPF38A are potential biomarkers candidates for osteosarcoma.

show abstract

Identification of key candidate genes and pathways in hepatocellular carcinoma by integrated bioinformatical analysis

Cited by 16 publications

References 44 publications

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

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

Cytochrome P450 family members are associated with fast-growing hepatocellular carcinoma and patient survival: An integrated analysis of gene expression profiles

Differentially expressed genes SNRPC and PRPF38A are potential biomarkers candidates for osteosarcoma

Contact Info

Product

Resources

About