Dataset Information

Screening of significant biomarkers with poor prognosis in hepatocellular carcinoma via bioinformatics analysis.

ABSTRACT: Hepatocellular carcinoma (HCC) is a malignant tumor with unsatisfactory prognosis. The abnormal genes expression is significantly associated with initiation and poor prognosis of HCC. The aim of the present study was to identify molecular biomarkers related to the initiation and development of HCC via bioinformatics analysis, so as to provide a certain molecular mechanism for individualized treatment of hepatocellular carcinoma.Three datasets (GSE101685, GSE112790, and GSE121248) from the GEO database were used for the bioinformatics analysis. Differentially expressed genes (DEGs) of HCC and normal liver samples were obtained using GEO2R online tools. Gene ontology term and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis were conducted via the Database for Annotation, Visualization, and Integrated Discovery online bioinformatics tool. The protein-protein interaction (PPI) network was constructed by the Search Tool for the Retrieval of Interacting Genes database and hub genes were visualized by Cytoscape. Survival analysis and RNA sequencing expression were conducted by UALCAN and Gene Expression Profiling Interactive Analysis.A total of 115 shared DEGs were identified, including 30 upregulated genes and 85 downregulated genes in HCC samples. P53 signaling pathway and cell cycle were the major enriched pathways for the upregulated DEGs whereas metabolism-related pathways were the major enriched pathways for the downregulated DEGs. The PPI network was established with 105 nodes and 249 edges and 3 significant modules were identified via molecular complex detection. Additionally, 17 candidate genes from these 3 modules were significantly correlated with HCC patient survival and 15 of 17 genes exhibited high expression level in HCC samples. Moreover, 4 hub genes (CCNB1, CDK1, RRM2, BUB1B) were identified for further reanalysis of KEGG pathway, and enriched in 2 pathways, the P53 signaling pathway and cell cycle pathway.Overexpression of CCNB1, CDK1, RRM2, and BUB1B in HCC samples was correlated with poor survival in HCC patients, which could be potential therapeutic targets for HCC.

SUBMITTER: Sun Q

PROVIDER: S-EPMC7593045 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Screening of significant biomarkers with poor prognosis in hepatocellular carcinoma via bioinformatics analysis.

Sun Quanquan Q Liu Peng P Long Bin B Zhu Yuan Y Liu Tongxin T

Medicine 20200801 32

Hepatocellular carcinoma (HCC) is a malignant tumor with unsatisfactory prognosis. The abnormal genes expression is significantly associated with initiation and poor prognosis of HCC. The aim of the present study was to identify molecular biomarkers related to the initiation and development of HCC via bioinformatics analysis, so as to provide a certain molecular mechanism for individualized treatment of hepatocellular carcinoma.Three datasets (GSE101685, GSE112790, and GSE121248) from the GEO da ...[more]

PMID: 32769939

Similar Datasets

Project description:BackgroundAbnormal miRNA and mRNA expression and dysregulated immune microenvironment have been found to frequently induce the progression of hepatocellular carcinoma (HCC) in recent reports. In particular, the immune-related competing endogenous RNAs (ceRNA) mechanism plays a crucial role in HCC progression. However, the underlying mechanisms remain unclear.MethodsDifferentially expressed immune-related genes were obtained from the Immport, GEO, and TCGA databases. The mRNA and protein expression levels in HCC tissues and adjacent normal tissues were confirmed, and we further investigated the methylation levels of these biomarkers to explore their function. Then, the TIMER and TISCH databases were used to assess the relationship between immune infiltration and hub genes. Survival analysis and univariate and multivariate Cox models were used to evaluate the association between hub genes and HCC diagnosis. Hub gene expression was experimentally validated in six HCC cell lines and 15 HCC samples using qRT-PCR and immunohistochemistry. The hub genes were uploaded to DSigDB for drug prediction enrichment analysis.ResultsWe identified that patients with abnormal miRNAs (hsa-miR-125b-5p and hsa-miR-21-5p) and their targeted genes (NTF3, PSMD14, CD320, and SORT1) had a worse prognosis. Methylation analysis of miRNA-targeted genes suggested that alteration of methylation levels is also a factor in the induction of tumorigenesis. We also found that the development of HCC progression caused by miRNA-mRNA interactions may be closely correlated with the infiltration of immunocytes. Moreover, the GSEA, GO, and KEGG analysis suggested that several common immune-related biological processes and pathways were related to miRNA-targeted genes. The results of qRT-PCR, immunohistochemistry, and western blotting were consistent with our bioinformatics results, suggesting that abnormal miRNAs and their targeted genes may affect HCC progression.ConclusionsBriefly, our study systematically describes the mechanisms of miRNA-mRNA interactions in HCC and predicts promising biomarkers that are associated with immune filtration for HCC progression.

Project description:Background - Hepatocellular carcinomas (HCCs) are heterogeneous tumors with respect to etiology, cell of origin and biology. The course of the disease is unpredictable and is in part dependent on the tumor microenvironment. One of the microenvironmental factors is hypoxia, which is known to promote aggressiveness in other malignant tumors. We hypothesized that certain regions in HCC exist with chronic hypoxia and a characteristic gene expression pattern. Moreover, during the development of HCC there is an important contribution of this chronic hypoxia on prognosis via this gene expression. Until now, most research has been performed in acute hypoxic models (< 24 hours). Methods – Human hepatoblastoma cells HepG2 were cultured in either normoxic (20% O2) or hypoxic (2% O2) conditions for 72 hrs, the time it takes to adapt to chronic hypoxia. After 3 days the cells were harvested and analyzed by microarray technology. The highly significant differentially expressed genes were selected and used to assess the clinical value of our in vitro chronic hypoxia gene signature in four published patient studies. Three of these independent microarray studies on HCC patients were used as training sets to determine a minimal prognostic gene set and one study was used for validation. Gene expression analysis and correlation with clinical outcome was assessed with the bioinfomatic method of Goeman et al (). Results – In the HepG2 cells, 3592 genes were differentially expressed in cells cultured at 2% oxygen for 72 hrs. Out of these, 265 showed a high significant change (2-fold change and p=0.0001). The level of gene expression after 72 hrs was different from the acute hypoxic response (during the first 24 hours) and represented chronicity. Using computational methods we identified 7 out of the 265 highly significant genes that showed correlation with prognosis in all three different training sets and this was independently validated in a 4th dataset. With our approach we could include the largest number of HCC patients in one single study. Conclusion – We identified a 7-gene signature, which is associated with chronic hypoxia and predicts prognosis in patients with HCC. In the future this signature could be used as a diagnostic tool. In addition, chronic hypoxia gene expression information can be used in the search for new therapeutic targets. Two conditions were compared and each sample has a biological replicate. Samples are hybridized in dye-swap, resulting in 4 hybridizations.

Project description:Hepatocellular carcinoma (HCC) is a malignant tumor with high morbidity and mortality, which makes the prognostic prediction challenging. Angiogenesis appears to be of critical importance in the progression and metastasis of HCC. Some of the angiogenesis-related genes promote this process, while other anti-angiogenesis genes suppress tumor growth and metastasis. Therefore, the comprehensive prognostic value of multiple angiogenesis-related genes in HCC needs to be further clarified. In this study, the mRNA expression profile of HCC patients and the corresponding clinical data were acquired from multiple public databases. Univariate Cox regression analysis was utilized to screen out differentially expressed angiogenesis-related genes with prognostic value. A multigene signature was established with the least absolute shrinkage and selection operator Cox regression in the Cancer Genome Atlas cohort, and validated through an independent cohort. The results suggested that a total of 16 differentially expressed genes (DEGs) were associated with overall survival (OS) and a 7-gene signature was constructed. The risk score of each patient was calculated using this signature, the median value of which was used to divide these patients into a high-risk group and a low-risk group. Compared with the low-risk group, the patients in the high-risk group had a poor prognosis. The risk score was an independent predictor for OS through multivariate Cox regression analysis. Then, unsupervised learning was used to verify the validity of this 7-gene signature. A nomogram by further integrating clinical information and the prognostic signature was utilized to predict prognostic risk and individual OS. Functional enrichment analyses demonstrated that these DEGs were enriched in the pathways of cell proliferation and mitosis, and the immune cell infiltration was significantly different between the two risk groups. In summary, a novel angiogenesis-related genes signature could be used to predict the prognosis of HCC and for targeted therapy.

Project description:ObjectiveWhether neutrophil-lymphocyte ratio (NLR) is an applicative predictor of poor prognosis in patients with hepatocellular carcinoma (HCC) remains controversial. In response to the current conflicting data, this meta-analysis was conducted to gain a comprehensive and systematic understanding of prognostic value of NLR in HCC.MethodsSeveral English databases, including PubMed, EMBASE, and the Cochrane Library, with an update date of February 25, 2023, were systematically searched. We set the inclusion criteria to include randomized controlled trial (RCT) studies that reported the prognostic value of serum NLR levels in patients with HCC receiving treatment. Both the combined ratio (OR) and the diagnosis ratio (DOR) were used to assess the prognostic performance of NLR. Additionally, we completed the risk of bias assessment by Cochrane Risk of Bias Assessment Tool.ResultsThis meta-analysis ultimately included 16 studies with a total of 4654 patients with HCC. The results showed that high baseline NLR was significantly associated with poor prognosis or recurrence of HCC. The sensitivity of 0.67 (95% confidence interval [CI]. 0.59-0.73); specificity of 0.723 (95% CI: 0.64-0.78) and DOR of 5.0 (95% CI: 4.0-7.0) were pooled estimated from patient-based analyses. Subsequently, the combined positive likelihood ratio (PLR) and negative likelihood ratio (NLHR) were calculated with the results of 2.4 (95% CI: 1.9-3.0) and 0.46 (95% CI: 0.39-0.56), respectively. In addition, area under the curve (AUC) of the summary receiver operating characteristic (SROC) reflecting prognostic accuracy was calculated to be 0.75 (95% CI: 0.71-0.78). The results of subgroup analysis suggested that high NLR was an effective predictive factor of poor prognosis in HCC in mainland China as well as in the northern region.ConclusionOur findings suggest that high baseline NLR is an excellent predictor of poor prognosis or relapse in patients with HCC, especially those from high-incidence East Asian populations.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/#recordDetails, identifier CRD42023440640.

Dataset Information

Screening of significant biomarkers with poor prognosis in hepatocellular carcinoma via bioinformatics analysis.

Publications

Screening of significant biomarkers with poor prognosis in hepatocellular carcinoma via bioinformatics analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets