Screening Hub Genes as Prognostic Biomarkers of Hepatocellular Carcinoma by Bioinformatics Analysis

Zhou, Zengyuan; Li, Yuzheng; Hao, Haiyue; Wang, Yuanyuan; Zhou, Zihao; Wang, Zhipeng; Chu, Xia

doi:10.1177/0963689719893950

Cited by 92 publications

(83 citation statements)

References 51 publications

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“…Research found that RRM2 was a novel downstream target activated by HPV E7, and promoted angiogenesis by producing ROS, activating the ERK1/2 signaling pathway and inducing expression of HIF-1a and VEGF, leading to cervical angiogenesis and carcinogenesis [6]. At present, evidence of the relationship between RRM2 and CDKN3 in cervical cancer is limited, but their relationship can be found in other cancers, such as hepatocellular carcinoma, breast cancer, renal carcinoma and lung cancer [51][52][53][54]. Qi et al have found RRM2 and CDKN3 may become biomarkers for diagnosis and poor prognosis of breast cancer [51].…”

Section: Discussionmentioning

confidence: 99%

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Potential mechanism of RRM2 for promoting Cervical Cancer based on weighted gene co-expression network analysis

Wang

Chen

et al. 2020

Int. J. Med. Sci.

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Cervical cancer is the most common gynecologic malignant tumor, with a high incidence in 50-55-year-olds. This study aims to investigate the potential molecular mechanism of RRM2 for promoting the development of cervical cancer based on The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). RRM2 was found to be significant upregulated in cervical tissue (P<0.05) by extracting the expression of RRM2 from TCGA, GSE63514, GSE7410, GSE7803 and GSE9750. Survival analysis indicated that the overall survival was significantly worse in the patients with high-expression of RRM2 (P<0.05). The top 1000 positively/negatively correlated genes with RRM2 by Pearson Correlation test were extracted. The gene co-expression network by Weighted Gene Co-Expression Network Analysis (WGCNA) with these genes and the clinical characteristics (lymphocyte infiltration, monocyte infiltration, necrosis, neutrophil infiltration, the number of normal/stromal/tumor cells and the number of tumor nuclei) was constructed. By screening the hub nodes from the co-expression network, results suggested that RRM2 may co-express with relevant genes to regulate the number of stromal/tumor cells and the process of lymphocyte infiltration to promote the progression of cervical cancer. RRM2 is likely to become a novel potential diagnostic and prognostic biomarker of cervical cancer and provide evidence to support the study of mechanisms for cervical cancer.

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Section: Discussionmentioning

confidence: 99%

“…Qi et al have found RRM2 and CDKN3 may become biomarkers for diagnosis and poor prognosis of breast cancer [51]. Zhou et al have also found there is a correlation between RRM2 and CDKN3 in hepatocellular carcinoma [52]. Wei et al found RRM2 and CDKN3 associated with the metastasis of Clear cell renal cell carcinoma [53].…”

Section: Discussionmentioning

confidence: 99%

Potential mechanism of RRM2 for promoting Cervical Cancer based on weighted gene co-expression network analysis

Wang

Chen

et al. 2020

Int. J. Med. Sci.

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“…The regulatory network of circRNA-miRNAs-mRNAs and hub genes was constructed using Cytoscape software 3.6.0 [11,12]. The CytoHubba application, a Cytoscape plugin, was used to select hub genes [13].…”

Section: Target Gene Prediction and Regulatory Network Constructionmentioning

confidence: 99%

Identification of a novel circRNA, hsa_circ_0065898, that regulates tumor growth in cervical squamous cell carcinoma

Liu

Deng³

2020

Preprint

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Background: Circular RNAs (circRNAs) have been reported to play an important role in regulating tumor pathogenesis and progression. The molecular mechanism of circRNAs in cervical squamous cell carcinoma (CSCC) remains poorly understood. We aimed to identify the circRNAs differentially expressed, and to investigate the role of a novel circRNA, hsa_circ_0065898, in regulating proliferation, migration, and invasion in CSCC.Methods: The online Kaplan-Meier Plotter was used to analyze the relationship between miRNA expression and overall survival. Bioinformatics tools, such as R, Cytoscape, and Perl, were used to analyze the Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, protein-protein interaction (PPI) network, and regulatory network. The expression level of hsa_circ_0065898 in CSCC cell lines was evaluated using quantitative polymerase chain reaction in vitro. The cell counting kit-8 (CCK-8) and transwell assays were used to assess cell proliferation, migration, and invasion. Results: circRNA expression data (GSE102686) was downloaded from the Gene Expression Omnibus database, and this included data from 5 CSCC patients and 5 normal tissues. Thirteen differentially expressed circRNAs were identified, which included 9 upregulated circRNAs and 4 downregulated circRNAs. GO enrichment analysis showed that the target genes of miRNAs associated with hsa_circ_0065898 were enriched in ubiquitin-protein transferase activity, ubiquitin-like protein transferase activity, core promoter sequence-specific DNA binding, mRNA 3′-UTR AU-rich region binding, core promoter binding, AU-rich element binding, ubiquitin-like protein ligase activity, and transcription corepressor activity. KEGG results showed that the Hippo and p53 signaling pathways played significant role in the pathway network. Hsa_circ_0065898 was significantly overexpressed in the CSCC cell lines. Hsa_circ_0065898 facilitated cell proliferation, migration, and invasion in CSCC.Conclusions: This study identified differentially expressed circRNAs and constructed the regulatory network of hsa_circ_0065898 targeting microRNAs and mRNAs. We demonstrated that hsa_circ_0065898 promoted CSCC cell proliferation, migration, and invasion. Hence, hsa_circ_0065898 might be useful as a biomarker for CSCC diagnosis and targeted therapy.

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“…In the past, studies focusing on analysis of HCC-related genes usually included patients with HCC caused by various factors such as chronic hepatitis B or C virus infection and alcoholic liver disease [18,19,34]. But there is limited research that only discusses HCV-related liver cancer on the background of HCV-cirrhosis, so we re-analyzed the data in GSE6764.…”

Section: Disscusionmentioning

confidence: 99%

“…Now, there are rich public repositories of RNA-seq and microarray datasets, such as Gene Expression Omnibus (GEO) database [16] and The Cancer Genome Atlas (TCGA) [17]. Further analysis of those datasets combined with computational approach such as prediction model can provide the opportunity to establish a novel biomarker for disease diagnosis with high accuracy [18,19].…”

Section: Introductionmentioning

confidence: 99%

Identification of Key Genes in HCV-induced HCC at Early Stage on the Background of HCV-cirrhosis

nie

et al. 2020

Preprint

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Background: Hepatocellular carcinoma (HCC) is one of the most common and deadly malignant tumors worldwide. Hepatitis C virus (HCV)-induced cirrhosis (HCV-cirrhosis) is one of the leading causes of HCC occurrence. Due to the lack of effective biomarkers, most patients with HCV-induced HCC (HCV-HCC) are at advanced stages when they are first diagnosed. Our study aims to employ transcriptomic profiling dataset to identify potential biomarkers that can predict HCV-HCC at early stage on the background of HCV-cirrhosis. Methods: The dataset incorporating HCV-cirrhosis and HCV-HCC subjects at different stages from Gene Expression Omnibus was analyzed to identify gene signature-related to HCV-HCC on the background of HCV-cirrhosis. Multiple-genes based risk score-related prediction model was established to predict HCV-HCC samples at different stages, and receiver-operating characteristic (ROC) curve analysis was used to select the best cutoff value of risk scores for prediction and to evaluate the prediction model. Samples with risk scores higher than cutoff value were defined as high score group.Results: Highly clustered 20 genes were identified and were all gradually increased as HCV-HCC progressed from HCV-cirrhosis to very advanced HCC. Compared with HCV-cirrhosis, the prevalence of HCV-HCC at any of the stages in high score group was 100%. The risk score-related prediction model based on the 20 genes-biomarker got the accuracy of over 95.2 % with area under ROC (AUC) over 0.94. To implement the biomarker easily in real life and make it economical, we tried to limit the gene numbers. Interestingly, CDKN3 and FAM83D were significantly decreased in HCV-cirrhosis tissues as compared to normal tissues and then increased as HCV-HCC progressed. The results were attractive that the prediction model based on this 2 genes-biomarker indicated that prevalence of HCV-HCC in high score group was still 100% and got the predictive accuracy of over 95.2 % with AUC over 0.96, revealing even a better performance.Conclusion: Our study indicated a 2-genes-based biomarker that could identify HCV-HCC at earlier stages on the background of HCV-cirrhosis, which might be a promising biomarker for early diagnosis of HCV-HCC and potential novel treatment target.

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Screening Hub Genes as Prognostic Biomarkers of Hepatocellular Carcinoma by Bioinformatics Analysis

Cited by 92 publications

References 51 publications

Potential mechanism of RRM2 for promoting Cervical Cancer based on weighted gene co-expression network analysis

Potential mechanism of RRM2 for promoting Cervical Cancer based on weighted gene co-expression network analysis

Identification of a novel circRNA, hsa_circ_0065898, that regulates tumor growth in cervical squamous cell carcinoma

Identification of Key Genes in HCV-induced HCC at Early Stage on the Background of HCV-cirrhosis

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