scholarly journals Novel Biomarker NEDD1 and Its Analysis in Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Xiaopeng Ding ◽  
Jiahao Yu ◽  
Xin Shi ◽  
Kangwei Li ◽  
Shuoyi Ma ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cell Cycle ◽  
External Validation ◽  
Enrichment Analysis ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Related Data ◽  
Significant Difference

Abstract Background: NEDD1 (NEDD1 Gamma-Tubulin Ring Complex Targeting Factor) plays a crucial impact in regulating cell cycle and the development of scirrhous gastric cancer. However, the role of NEDD1 hasn’t been reported in hepatocellular carcinoma (HCC) so far. The aim of this research is to explore the role of NEDD1 on the development and prognosis of HCC. Methods: HCC-related data were download from The Cancer Genome Atlas (TCGA) database. Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and gene set enrichment analysis (GSEA) were conducted by the LinkedOmics database. Results: The expression of NEDD1 has significant difference between tumor and adjacent normal tissues in HCC (P＜0.01). We also found that NEDD1 was an independent risk factor in HCC patients (HR 1.643, 95%CI 1.125–2.398; P = 0.01). The study also demonstrated that NEDD1 expression was significantly relevant to the expression of several immune checkpoint genes, including CTLA-4, PD-L1 and PD-1. GSEA revealed that Cell cycle, MicroRNAs in cancer and Ribosome pathways were significantly enriched in NEDD1 overexpression phenotype. By integrating NEDD1 with other relevant factors, we constructed the prognostic nomogram to help the improvement of the prognosis for patients with HCC. The data from the International Cancer Genome Consortium (ICGC) database were used as an independent external validation of our prognostic model. Conclusion: The expression level of NEDD1 was negatively correlated to the prognosis of HCC patients and it may be a promising therapeutic target of HCC, which probably be able to predict the efficacy of immunotherapy for HCC patients.

scholarly journals HAMP Downregulation Contributes to Aggressive Hepatocellular Carcinoma via Mechanism Mediated by Cyclin4-Dependent Kinase-1/STAT3 Pathway

Diagnostics ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 48 ◽  
Author(s):  
Ying Shen ◽  
Xin Li ◽  
Yanwei Su ◽  
Shaikh Atik Badshah ◽  
Bin Zhang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cell Cycle ◽  
Cellular Proliferation ◽  
Flow Cytometric Analysis ◽  
Enrichment Analysis ◽  
Cell Cycle Checkpoints ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cell Cycle Gene

Background: Hepcidin encoded by HAMP is vital to regulating proliferation, metastasis, and migration. Hepcidin is secreted specifically by the liver. This study sought to examine the functional role of hepcidin in hepatocellular carcinoma (HCC). Methods: Data in the Cancer Genome Atlas database was used to analyze HAMP expression as it relates to HCC prognosis. We then used the 5-ethynyl-20-deoxyuridine (EdU) incorporation assay, transwell assay, and flow cytometric analysis, respectively, to assess proliferation, migration, and the cell cycle. Gene set enrichment analysis (GSEA) was used to find pathways affected by HAMP. Results: HAMP expression was lower in hepatocellular carcinoma samples compared with adjacent normal tissue controls. Low HAMP expression was linked with a higher rate of metastasis and poor disease-free status. Downregulation of HAMP induced SMMC-7721 and HepG-2 cell proliferation and promoted their migration. HAMP could affect the cell cycle pathway and Western blotting, confirming that reduced HAMP levels activated cyclin-dependent kinase-1/stat 3 pathway. Conclusion: Our findings indicate that HAMP functions as a tumor suppressor gene. The role of HAMP in cellular proliferation and metastasis is related to cell cycle checkpoints. HAMP could be considered as a diagnostic biomarker and targeted therapy in HCC.

scholarly journals Identification of the HMMR Gene as a Diagnostic and Prognostic Biomarker in Hepatocellular Carcinoma Based on Integrated Bioinformatics Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Honglan Guo ◽  
Qinqiao Fan
Keyword(s):  
Hepatocellular Carcinoma ◽  
Roc Curve ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Roc Curve Analysis ◽  
Cox Regression Analysis ◽  
Normal Tissues

Background. We aimed to investigate the expression of the hyaluronan-mediated motility receptor (HMMR) gene in hepatocellular carcinoma (HCC) and nonneoplastic tissues and to investigate the diagnostic and prognostic value of HMMR. Method. With the reuse of the publicly available The Cancer Genome Atlas (TCGA) data, 374 HCC patients and 50 nonneoplastic tissues were used to investigate the diagnostic and prognostic values of HMMR genes by receiver operating characteristic (ROC) curve analysis and survival analysis. All patients were divided into low- and high-expression groups based on the median value of HMMR expression level. Univariate and multivariate Cox regression analysis were used to identify prognostic factors. Gene set enrichment analysis (GSEA) was performed to explore the potential mechanism of the HMMR genes involved in HCC. The diagnostic and prognostic values were further validated in an external cohort from the International Cancer Genome Consortium (ICGC). Results. HMMR mRNA expression was significantly elevated in HCC tissues compared with that in normal tissues from both TCGA and the ICGC cohorts (all P values <0.001). Increased HMMR expression was significantly associated with histologic grade, pathological stage, and survival status (all P values <0.05). The area under the ROC curve for HMMR expression in HCC and normal tissues was 0.969 (95% CI: 0.948–0.983) in the TCGA cohort and 0.956 (95% CI: 0.932–0.973) in the ICGC cohort. Patients with high HMMR expression had a poor prognosis than patients with low expression group in both cohorts (all P < 0.001 ). Univariate and multivariate analysis also showed that HMMR is an independent predictor factor associated with overall survival in both cohorts (all P values <0.001). GSEA showed that genes upregulated in the high-HMMR HCC subgroup were mainly significantly enriched in the cell cycle pathway, pathways in cancer, and P53 signaling pathway. Conclusion. HMMR is expressed at high levels in HCC. HMMR overexpression may be an unfavorable prognostic factor for HCC.

scholarly journals A hypoxia-related signature for clinically predicting diagnosis, prognosis and immune microenvironment of hepatocellular carcinoma patients.

2020 ◽  
Author(s):  
Baohui Zhang ◽  
Bufu Tang ◽  
Jianyao Gao ◽  
Jiatong Li ◽  
Lingming Kong ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Immune Cell ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Diagnostic Model ◽  
Potential Biomarker

Abstract Background Hypoxia plays an indispensable role in the development of hepatocellular carcinoma (HCC). However, there are few studies on the application of hypoxia molecules in the prognosis predicting of HCC. We aimed to identify the hypoxia-related genes in HCC and construct reliable models for diagnosis, prognosis and recurrence of HCC patients as well as exploring the potential mechanism.Methods Differentially expressed genes (DEGs) analysis was performed using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database and four clusters were determined by a consistent clustering analysis. Three DEGs closely related to overall survival（OS）were identified using Cox regression and LASSO analysis and the hypoxia-related signature was developed and validated in TCGA and International Cancer Genome Consortium (ICGC) database. Then the Gene Set Enrichment Analysis (GSEA) was performed to explore signaling pathways regulated by the signature and the CIBERSORT was used for estimating the fractions of immune cell types.Results A total of 397 hypoxia-related DEGs were detected and three genes (PDSS1, CDCA8 and SLC7A11) were selected to construct a prognosis, recurrence and diagnosis model. Then patients were divided into high- and low-risk groups. Our hypoxia-related signature was significantly associated with worse prognosis and higher recurrence rate. The diagnostic model also accurately distinguished HCC from normal samples and nodules. Furthermore, the hypoxia-related signature could positively regulate immune response and the high-risk group had higher fractions of macrophages, B memory cells and follicle-helper T cells, and exhibited higher expression of immunocheckpoints such as PD1and PDL1.Conclusions Altogether, our study showed that hypoxia-related signature is a potential biomarker for diagnosis, prognosis and recurrence of HCC, and it provided an immunological perspective for developing personalized therapies.

scholarly journals Up-regulated Expression and Related Gene Regulatory Networks of ANLN Predict Poor Prognosis and Correlate with Immune Infiltration in Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Bo Hu ◽  
Xiao-Bo Yang ◽  
Xinting Sang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cell Cycle ◽  
Regulatory Networks ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
Immune Infiltration ◽  
Dismal Prognosis ◽  
Mrna Binding

Abstract Background: The aberrant Anillin (ANLN) expression is reported to be associated with carcinogenesis. In this study, sequencing data collected from the Cancer Genome Atlas database were utilized to analyze ANLN expression in hepatocellular carcinoma (HCC).Methods: The relationships of clinicopathological features with ANLN were investigated, and gene set enrichment analysis (GSEA) was performed to reveal the ANLN-related functions. LinkedOmics was employed to identify the co-expressed genes of ANLN and to examine the target networks of kinases, microRNAs (miRNAs) and transcription factors (TFs). Besides, the correlation of ANLN expression with cancer immune infiltrates was analyzed by TIMER. Results: ANLN over-expression predicted dismal prognosis, and GESA results revealed several functions that were related to cell cycle and mRNA binding. Moreover, functional network analysis indicated that, ANLN might regulate DNA replication and cell cycle signaling through pathways that involved several cancer-related kinases, miRNAs and E2F1. Additionally, ANLN was suggested to be associated with the infiltration of several immune cells, which was proved to be upregulated in both HCC cells and tissues. Conclusion: Those efficiently mined data reveal information regarding ANLN expression, the potential regulatory networks and the relationship with immune infiltration in HCC, which lay a foundation for further study on the role of ANLN in carcinogenesis.

scholarly journals Gene signatures and prognostic values of m1A-related regulatory genes in hepatocellular carcinoma

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Qingmiao Shi ◽  
Chen Xue ◽  
Xin Yuan ◽  
Yuting He ◽  
Zujiang Yu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Cancer Genome ◽  
Regulatory Genes ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Clinical Prognosis ◽  
Cox Regression Analysis ◽  
Genes Expression

Abstract Hepatocellular carcinoma (HCC) ranks fourth in cancer-related mortality worldwide. N1-methyladenosine (m1A), a methylation modification on RNA, is gaining attention for its role across diverse biological processes. However, m1A-related regulatory genes expression, its relationship with clinical prognosis, and its role in HCC remain unclear. In this study, we utilized The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) database to investigate alterations within 10 m1A-related regulatory genes and observed a high mutation frequency (23/363). Cox regression analysis and least absolute shrinkage and selection operator were used to explore the association between m1A-related regulatory genes expression and HCC patient survival and identified four regulators that were remarkably associated with HCC patient prognosis. Additionally, an independent cohort from International Cancer Genome Consortium was studied to validate our discoveries and found to be consistent with those in the TCGA dataset. In terms of mechanism, gene set enrichment analysis linked these four genes with various physiological roles in cell division, the MYC pathway, protein metabolism, and mitosis. Kyoto Encyclopedia of Genes and Genomes analysis revealed that PI3K/Akt signaling pathway had potential relevance to m1A-related regulatory genes in HCC. These findings indicate that m1A-related regulatory genes may play crucial roles in regulating HCC progression and be exploited for diagnostic and prognostic purposes.

Identification of prognostic and immune-related lncRNAs in hepatocellular carcinoma based on the cancer genome atlas data mining

2020 ◽  
Vol 10 (8) ◽  
pp. 1189-1196
Author(s):  
Kaikai Ren ◽  
Jiakang Ma ◽  
Bo Zhou ◽  
Xiaoyan Lin ◽  
Mingyu Hou ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
High Rate ◽  
Prognostic Indicators ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Hepatocellular carcinoma (HCC) is a malignancy originating from hepatocytes with a high rate of distant metastasis and recurrence. HCC prognosis remains poorly understood, although its diagnosis and treatment have improved globally. Therefore, it is necessary to identify reliable predictive and prognostic indicators of HCC. HCC gene expression profiles and corresponding clinical data were downloaded from The Cancer Genome Atlas. Seven lncRNAs (C10orf91, AC011352.3, AC015722.2, AC006372.1, PICSAR, AC110285.3, and AP001972.4) associated with immune and clinicopathological features were identified as biomarker candidates for HCC prognosis based on single-sample gene set enrichment analysis, the ESTIMATE algorithm, and Cox PHR analyses. Altogether, the findings revealed that the seven immune-related lncRNAs may provide a reference for improving HCC prognosis.

scholarly journals Upregulation of PTGFRN in Hepatocellular Carcinoma Predicts Poor Prognosis: A Study Based on the TCGA Database

2021 ◽  
Author(s):  
Yanlin Tan ◽  
Fan Wu ◽  
Wenzhen Lin ◽  
Yuqin Wei ◽  
Yitong Lin ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Enrichment Analysis ◽  
Cox Proportional Hazards Model ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Rank Test ◽  
Expression Data ◽  
Clinical Prognosis ◽  
Mesenchymal Transition

Abstract Background. The importance of prostaglandin F2 receptor inhibitors (PTGFRN) in the progression of a variety of malignant tumors has been recognized in recent years. So far, no role of PTGFRN in hepatocellular carcinoma (HCC) has been reported. In this study, we focused on the possible mechanisms of PTGFRN in HCC based on the Cancer Genome Atlas (TCGA) data. Methods. The mRNA gene expression data of PTGFRN were downloaded from TCGA database to analyze the expression level of PTGFRN in HCC. According to the human protein atlas database, the expression difference of PTGFRN protein between HCC and adjacent tissues was verified. Wilcoxon signed-rank test and logistic regression were used to analyze the relationship between PTGFRN and clinicopathological characteristics. Kaplan Meier method and Cox proportional hazards model were used to explore the prognostic role of PTGFRN in HCC. The ROC curve was used to evaluate the diagnostic value of PTGFRN in HCC. Gene Set Enrichment Analysis (GSEA) was used to investigate the function of PTGFRN related Gene sets. Finally, obtain the co-expressed genes of PTGFRN through the cBioPortal database, and use the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) function enrichment analysis to further explore the role of PTGFRN in HCC regulated related pathways.Results. Analysis of mRNA expression data of 377 HCC patients showed that the expression of PTGFRN was up-regulated in HCC, which was confirmed by immunohistochemistry. The overexpression of PTGFRN was significantly correlated with clinical stage (P = 0.028) and histological grade (P = 0.027). High expression of PTGFRN was associated with poorer overall survival.. Meanwhile, multivariate Cox analysis showed that PTGFRN may be a potential independent risk factor for HCC. GSEA enrichment results showed that the up-regulated PTGFRN phenotype was concentrated in "endocytosis", "oocyte meiosis" and "ERBB signaling pathway". In addition, through the analysis of KEGG and GO pathways, we found that PTGFRN co-expressed genes are mainly involved in extracellular matrix tissue, epithelial-mesenchymal transition, cell adhesion and cell cycle, and PI3K-Akt/NF-kB signaling pathways.Conclusions. PTGFRN is highly expressed in HCC and can be used as an independent predictor of the clinical prognosis of HCC.

scholarly journals A hypoxia-related signature for clinically predicting diagnosis, prognosis and immune microenvironment of hepatocellular carcinoma patients.

2020 ◽  
Author(s):  
Baohui Zhang ◽  
Bufu Tang ◽  
Jianyao Gao ◽  
Jiatong Li ◽  
Lingming Kong ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Immune Cell ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Diagnostic Model ◽  
Potential Biomarker

Abstract BackgroundHypoxia plays an indispensable role in the development of hepatocellular carcinoma (HCC). However, there are few studies on the application of hypoxia molecules in the prognosis predicting of HCC. We aim to identify the hypoxia-related genes in HCC and construct reliable models for diagnosis, prognosis and recurrence of HCC patients as well as exploring the potential mechanism.MethodsDifferentially expressed genes (DEGs) analysis was performed using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database and four clusters were determined by a consistent clustering analysis. Three DEGs closely related to overall survival（OS）were identified using Cox regression and LASSO analysis. Then the hypoxia-related signature was developed and validated in TCGA and International Cancer Genome Consortium (ICGC) database. The Gene Set Enrichment Analysis (GSEA) was performed to explore signaling pathways regulated by the signature. CIBERSORT was used for estimating the fractions of immune cell types.ResultsA total of 397 hypoxia-related DEGs in HCC were detected and three genes (PDSS1, CDCA8 and SLC7A11) among them were selected to construct a prognosis, recurrence and diagnosis model. Then patients were divided into high- and low-risk groups. Our hypoxia-related signature was significantly associated with worse prognosis and higher recurrence rate. The diagnostic model also accurately distinguished HCC from normal samples and nodules. Furthermore, the hypoxia-related signature could positively regulate immune response. Meanwhile, the high-risk group had higher fractions of macrophages, B memory cells and follicle-helper T cells, and exhibited higher expression of immunocheckpoints such as PD1and PDL1.ConclusionsAltogether, our study showed that hypoxia-related signature is a potential biomarker for diagnosis, prognosis and recurrence of HCC, and it provided an immunological perspective for developing personalized therapies.

scholarly journals A hypoxia-related signature for clinically predicting diagnosis, prognosis and immune microenvironment of hepatocellular carcinoma patients.

2020 ◽  
Author(s):  
Baohui Zhang ◽  
Bufu Tang ◽  
Jianyao Gao ◽  
Jiatong Li ◽  
Lingming Kong ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Immune Cell ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Diagnostic Model ◽  
Potential Biomarker

Abstract Background Hypoxia plays an indispensable role in the development of hepatocellular carcinoma (HCC). However, there are few studies on the application of hypoxia molecules in the prognosis predicting of HCC. We aimed to identify the hypoxia-related genes in HCC and construct reliable models for diagnosis, prognosis and recurrence of HCC patients as well as exploring the potential mechanism. Methods Differentially expressed genes (DEGs) analysis was performed using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database and four clusters were determined by a consistent clustering analysis. Three DEGs closely related to overall survival（OS）were identified using Cox regression and LASSO analysis and the hypoxia-related signature was developed and validated in TCGA and International Cancer Genome Consortium (ICGC) database. Then the Gene Set Enrichment Analysis (GSEA) was performed to explore signaling pathways regulated by the signature and the CIBERSORT was used for estimating the fractions of immune cell types. Results A total of 397 hypoxia-related DEGs were detected and three genes (PDSS1, CDCA8 and SLC7A11) were selected to construct a prognosis, recurrence and diagnosis model. Then patients were divided into high- and low-risk groups. Our hypoxia-related signature was significantly associated with worse prognosis and higher recurrence rate. The diagnostic model also accurately distinguished HCC from normal samples and nodules. Furthermore, the hypoxia-related signature could positively regulate immune response and the high-risk group had higher fractions of macrophages, B memory cells and follicle-helper T cells, and exhibited higher expression of immunocheckpoints such as PD1and PDL1. Conclusions Altogether, our study showed that hypoxia-related signature is a potential biomarker for diagnosis, prognosis and recurrence of HCC, and it provided an immunological perspective for developing personalized therapies.

scholarly journals CSIG-03. RECONCILING TUMOR HETEROGENEITY IN GLIOBLASTOMA USING A PATHWAY-BASED APPROACH

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii28-ii28
Author(s):  
Alvaro Alvarado ◽  
Kaleab Tessema ◽  
Kunal Patel ◽  
Riki Kawaguchi ◽  
Richard Everson ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Patient Survival ◽  
Molecular Subtype ◽  
Gene List ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Molecular Architecture ◽  
Survival Pathways ◽  
Cancer Genome Atlas

Abstract Despite efforts to gain a deeper understanding of its molecular architecture, glioblastoma (GBM) remains uniformly fatal. While genome-based molecular subtyping has revealed that GBMs may be parsed into several molecularly distinct categories, this insight has yielded little progress towards extending patient survival. In particular, the great phenotypic heterogeneity of GBM – both inter and intratumorally – has hindered therapeutic efforts. To this end, we interrogated tumor samples using a pathway-based approach to resolve tumoral heterogeneity. Gene set enrichment analysis (GSEA) was applied to gene expression data and used to provide an overview of each sample that can be compared to other samples by generating sample clusters based on overall patterns of enrichment. The Cancer Genome Atlas (TCGA) samples were clustered using the canonical and oncogenic signatures and in both cases the clustering was distinct from the molecular subtype previously reported and clusters were informative of patient survival. We also analyzed single cell RNA sequencing datasets and uniformly found two clusters of cells enriched for cell cycle regulation and survival pathways. We have validated our approach by generating gene lists from common elements found in the top contributing genesets for a particular cluster and testing the top targets in appropriate gliomasphere patient-derived lines. Samples enriched for cell cycle related genesets showed a decrease in sphere formation capacity when E2F1, out top target, was silenced and when treated with fulvestrant and calcitriol, which were identified as potential drugs targeting this genelist. Conversely, no changes were observed in samples not enriched for this gene list. Finally, we interrogated spatial heterogeneity and found higher enrichment of the proliferative signature in contrast enhancing compared with non-enhancing regions. Our studies relate inter- and intratumoral heterogeneity to critical cellular pathways dysregulated in GBM, with the ultimate goal of establishing a pipeline for patient- and tumor-specific precision medicine.

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