OSlihc: An Online Prognostic Biomarker Analysis Tool for Hepatocellular Carcinoma

An, Yang; Wang, Qiang; Zhang, Guosen; Sun, Fengjie; Zhang, Lu; Li, Haojie; Li, Yingkun; Peng, Yanyu; Zhu, Wan; Ji, Shaoping; Guo, Xiangqian

doi:10.3389/fphar.2020.00875

Cited by 15 publications

(10 citation statements)

References 29 publications

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“…An adjusted p-value < 0.05 was considered statistically significant. Additionally, survival analysis of the hub genes was also performed by using OSlihc, an online tool, as previously described [31].…”

Section: Survival Analysismentioning

confidence: 99%

Identification of Five Hub Genes as Key Prognostic Biomarkers in Liver Cancer via Integrated Bioinformatics Analysis

Nguyen

Ngoc

Nguyễn-Thanh

et al. 2021

Biology

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Liver cancer is one of the most common cancers and the top leading cause of cancer death globally. However, the molecular mechanisms of liver tumorigenesis and progression remain unclear. In the current study, we investigated the hub genes and the potential molecular pathways through which these genes contribute to liver cancer onset and development. The weighted gene co-expression network analysis (WCGNA) was performed on the main data attained from the GEO (Gene Expression Omnibus) database. The Cancer Genome Atlas (TCGA) dataset was used to evaluate the association between prognosis and these hub genes. The expression of genes from the black module was found to be significantly related to liver cancer. Based on the results of protein–protein interaction, gene co-expression network, and survival analyses, DNA topoisomerase II alpha (TOP2A), ribonucleotide reductase regulatory subunit M2 (RRM2), never in mitosis-related kinase 2 (NEK2), cyclin-dependent kinase 1 (CDK1), and cyclin B1 (CCNB1) were identified as the hub genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses showed that the differentially expressed genes (DEGs) were enriched in the immune-associated pathways. These hub genes were further screened and validated using statistical and functional analyses. Additionally, the TOP2A, RRM2, NEK2, CDK1, and CCNB1 proteins were overexpressed in tumor liver tissues as compared to normal liver tissues according to the Human Protein Atlas database and previous studies. Our results suggest the potential use of TOP2A, RRM2, NEK2, CDK1, and CCNB1 as prognostic biomarkers in liver cancer.

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Section: Survival Analysismentioning

confidence: 99%

Identification of Five Hub Genes as Key Prognostic Biomarkers in Liver Cancer via Integrated Bioinformatics Analysis

Nguyen

Ngoc

Nguyễn-Thanh

et al. 2021

Biology

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“…To ensure that our results were consistent with those of other studies, we searched for the expression and overall survival (OS) of IER3 in HCC tissues from data on The Human Protein Atlas ( https://www.proteinatlas.org/ ). The present study also explored the effect of IER3 expression on OS rates of HCC patients using the GEPIA ( http://gepia.cancer-pku.cn/ ), the Kaplan–Meier Plotter ( https://kmplot.com/analysis/index.php?p=service ), SurvExpress ( Aguirre-Gamboa et al, 2013 ), and the Biomedical Informatics Institute ( An et al, 2020) . The hazard ratios (HRs) were merged using STATA v.14.0 software ( Ioannou, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Expression of IER3 in hepatocellular carcinoma: clinicopathology, prognosis, and potential regulatory pathways

Chen

et al. 2022

PeerJ

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Background Immediate early response 3 (IER3) is correlated to the prognosis of several cancers, but the precise mechanisms underlying the regulation by IER3 of the occurrence and development of hepatocellular carcinoma (HCC) remain unknown. Methods The expression level of IER3 was examined by using in-house immunohistochemistry (IHC), public gene chip, and public RNA-sequencing (RNA-seq). The standardized mean difference (SMD) was calculated to compare the expression levels of IER3 between HCC patients and controls. The summary receiver operating characteristics (sROC) was plotted to comprehensively understand the discriminatory capability of IER3 between HCC and non-HCC group. The Kaplan–Meier curves and the combined hazard ratios (HRs) were used to determine the prognostic value of IER3 in HCC. Moreover, differentially expressed genes (DEGs) and co-expression genes (CEGs) were used to explored the molecular mechanisms of IER3 underlying HCC. hTFtarget was used to predict the transcription factors (TFs) of IER3. The binding site of TFs and the IER3 promoter region was forecasted using the JASPAR website. The relevant ChIP-seq data were used to determine whether TF peaks were present in the IER3 transcription initiation. Results A significantly increased expression of IER3 protein was found in HCC tissue relative to non-HCC tissue as detected by IHC (p < 0.001). Compared to 1,263 cases of non-HCC tissues, IER3 in 1483 cases of HCC tissues was upregulated (SMD = 0.42, 95% confidence interval [CI] [0.09–0.76]). The sROC showed that IER3 had a certain ability at differentiating HCC tissues (area under the curve (AUC) = 0.65, 95% CI [0.61–0.69]). Comprehensive analysis of the effect of IER3 on the prognosis of patients with HCC demonstrated that higher IER3 expression was associated with poor prognosis in HCC (HRs = 1.30, 95% CI [1.03–1.64]). Pathway enrichment analysis revealed that IER3-related genes were mostly enriched in the PI3K-Akt signaling pathway, cancer-related signaling pathways, the p53 signaling pathway, and other signaling pathways. Regulatory factor X5 (RFX5) was identified as a possible regulator of IER3-related TF. Conclusion IER3 may be a potential prognostic marker for HCC. The molecular mechanisms of IER3 in HCC warrant further study.

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“…Prognostic values of the DEGs on OS and PFS of HCC patients with distinct clinical parameters were also assessed integrating KM Plotter and another prognosis analysis tool, Online consensus Survival for liver hepatocellular carcinoma (OSlihc, http://bioinfo.henu.edu.cn/ ) [ 58 ]. All cases were split into two groups by the median value of a gene’s expression level and univariant analysis was conducted.…”

Section: Methodsmentioning

confidence: 99%

Identification of prognostic biomarkers associated with the occurrence of portal vein tumor thrombus in hepatocellular carcinoma

Lin

Zheng

Mai

et al. 2021

Aging

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The occurrence of portal vein tumor thrombus (PVTT) is strongly correlated to the staging and poor prognosis of hepatocellular carcinoma (HCC) patients. However, the mechanisms of PVTT formation remain unclear. This study aimed to investigate differentially expressed genes (DEGs) between primary tumor (PT) and PVTT tissues and comprehensively explored the underlying mechanisms of PVTT formation. The DEGs between PT and paired PVTT tissues were analyzed using transcriptional data from the Gene Expression Omnibus (GEO) database. The expression, clinical relevance, prognostic significance, genetic alternations, DNA methylation, correlations with immune infiltration, co-expression correlations, and functional enrichment analysis of the DEGs were explored using multiple databases. As result, 12 DEGs were commonly down-expressed in PVTT compared with PT tissues among three datasets. The expression of DCN , CCL21 , IGJ , CXCL14 , FCN3 , LAMA2 , and NPY1R was progressively decreased from normal liver, PT, to PVTT tissues, whose up-expression associated with favorable survivals of HCC patients. The genetic alternations and DNA methylation of the DEGs frequently occurred, and several methylated CpG sites of the DEGs significantly correlated with outcomes of HCC patients. The immune infiltration in the tumor microenvironment of HCC was correlated with the expression level of the DEGs. Besides, the DEGs and their co-expressive genes participated in the biological processes of extracellular matrix (ECM) organization and focal adhesion. In summary, this study indicated the dysregulation of ECM and focal adhesion might contribute to the formation of PVTT. And the above seven genes might serve as potential biomarkers of PVTT occurrence and prognosis of HCC patients.

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OSlihc: An Online Prognostic Biomarker Analysis Tool for Hepatocellular Carcinoma

Cited by 15 publications

References 29 publications

Identification of Five Hub Genes as Key Prognostic Biomarkers in Liver Cancer via Integrated Bioinformatics Analysis

Identification of Five Hub Genes as Key Prognostic Biomarkers in Liver Cancer via Integrated Bioinformatics Analysis

Expression of IER3 in hepatocellular carcinoma: clinicopathology, prognosis, and potential regulatory pathways

Identification of prognostic biomarkers associated with the occurrence of portal vein tumor thrombus in hepatocellular carcinoma

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