Based on Integrated Bioinformatics Analysis Identification of Biomarkers in Hepatocellular Carcinoma Patients from Different Regions

Accumulating statistics have shown that liver cancer causes the second highest mortality rate of cancer-related deaths worldwide, of which 80% is hepatocellular carcinoma (HCC). Given the underlying molecular mechanism of HCC pathology is not fully understood yet, identification of reliable predictive biomarkers is more applicable to improve patients’ outcomes. The results of principal component analysis (PCA) showed that the grouped data from 1557 samples in Gene Expression Omnibus (GEO) came from different populations, and the mean tumor purity of tumor tissues was 0.765 through the estimate package in R software. After integrating the differentially expressed genes (DEGs), we finally got 266 genes. Then, the protein-protein interaction (PPI) network was established based on these DEGs, which contained 240 nodes and 1747 edges. FOXM1 was the core gene in module 1 and highly associated with FOXM1 transcription factor network pathway, while FTCD was the core gene in module 2 and was enriched in the metabolism of amino acids and derivatives. The expression levels of hub genes were in line with The Cancer Genome Atlas (TCGA) database. Meanwhile, there were certain correlations among the top ten genes in the up- and downregulated DEGs. Finally, Kaplan–Meier curves and receiver operating characteristic (ROC) curves were plotted for the top five genes in PPI. Apart from CDKN3, the others were closely concerned with overall survival. In this study, we detected the potential biomarkers and their involved biological processes, which would provide a new train of thought for clinical diagnosis and treatment.

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High Squalene Epoxidase in Tumors Predicts Worse Survival in Patients With Hepatocellular Carcinoma: Integrated Bioinformatic Analysis on NAFLD and HCC

Cancer Control ◽

10.1177/1073274820914663 ◽

2020 ◽

Vol 27 (1) ◽

pp. 107327482091466

Author(s):

Tingting Shen ◽

Yunfei Lu ◽

Qin Zhang

Keyword(s):

Hepatocellular Carcinoma ◽

Messenger Rna ◽

Disease Free Survival ◽

Bioinformatic Analysis ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Squalene Epoxidase ◽

Nonalcoholic Fatty Liver ◽

Kaplan Meier ◽

High Level

This study aimed to identify candidate biomarkers for predicting outcomes in nonalcoholic fatty liver disease (NAFLD) and hepatocellular carcinoma (HCC). Using Gene Expression Omnibus and The Cancer Genome Atlas (TCGA) databases, we identified common upregulated differential expressed genes (DEGs) in patients with NAFLD/nonalcoholic steatohepatitis (NASH) and HCC and conducted survival analysis of these upregulated DEGs with HCC outcomes. Two common upregulated DEGs including squalene epoxidase (SQLE) and EPPK1 messenger RNA (mRNA) were significantly upregulated in NAFLD, NASH, and HCC tissues, both in GSE45436 ( P < .001) and TCGA profile ( P < .001). Both SQLE and EPPK1 mRNA were upregulated in 15.56% and 8.06% patients with HCC in TCGA profile. Overexpression of SQLE in tumors was significantly associated with worse overall survival (OS) and disease-free survival (DFS) in patients with HCC (log-rank P = .027 and log-rank P = .048, respectively), while no statistical significances of OS and DFS were found in EPPK1 groups (both log-rank P > .05). For validation, SQLE upregulation contributed to significantly worse OS in patients wih HCC using Kaplan-Meier plotter analysis (hazard ratio = 1.43, 95% confidence interval: 1.01-2.02, log-rank P = .043). In addition, high level of SQLE significantly associated with advanced neoplasm histologic grade, advanced AJCC stage, and α-fetoprotein elevation ( P = .036, .045, and .029, respectively). Squalene epoxidase is associated with OS and DFS and serves as a novel prognostic biomarker for patients with HCC.

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Transcriptomic identification of HBx-associated hub genes in hepatocellular carcinoma

PeerJ ◽

10.7717/peerj.12697 ◽

2021 ◽

Vol 9 ◽

pp. e12697

Author(s):

Zhengzhong Ni ◽

Jun Lu ◽

Weiyi Huang ◽

Hanif Khan ◽

Xuejun Wu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Hepg2 Cells ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Pathway Enrichment Analysis ◽

Hub Genes ◽

Protein Protein Interaction ◽

Transcription Profiles ◽

Kaplan Meier ◽

Primary Mouse

Background Hepatocellular carcinoma (HCC) is one of the most common malignancies around the world. Among the risk factors involved in liver carcinogenesis, hepatitis B virus (HBV) X protein (HBx) is considered to be a key regulator in hepatocarcinogenesis. Whether HBx promotes or protects against HCC remains controversial, therefore exploring new HBx-associated genes is still important. Methods HBx was overexpressed in HepG2, HepG2.2.15 and SMMC-7721 cell lines, primary mouse hepatocytes and livers of C57BL/6N mice. High-throughput RNA sequencing profiling of HepG2 cells with HBx overexpression and related differentially-expressed genes (DEGs), pathway enrichment analysis, protein-protein interaction networks (PPIs), overlapping analysis were conducted. In addition, Gene Expression Omnibus (GEO) and proteomic datasets of HBV-positive HCC datasets were used to verify the expression and prognosis of selected DEGs. Finally, we also evaluated the known oncogenic role of HBx by oncogenic array analysis. Results A total of 523 DEGs were obtained from HBx-overexpressing HepG2 cells. Twelve DEGs were identified and validated in cells transiently transfected with HBx and three datasets of HBV-positive HCC transcription profiles. In addition, using the Kaplan-Meier plotter database, the expression levels of the twelve different genes were further analyzed to predict patient outcomes. Conclusion Among the 12 identified HBx-associated hub genes, HBV-positive HCC patients expressing ARG1 and TAT showed a good overall survival (OS) and relapse-free survival (RFS). Thus, ARG1 and TAT expression could be potential prognostic markers.

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Identification of potential hub genes associated with the pathogenesis and prognosis of hepatocellular carcinoma via integrated bioinformatics analysis

Journal of International Medical Research ◽

10.1177/0300060520910019 ◽

2020 ◽

Vol 48 (7) ◽

pp. 030006052091001

Author(s):

Ziqi Meng ◽

Jiarui Wu ◽

Xinkui Liu ◽

Wei Zhou ◽

Mengwei Ni ◽

...

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Interaction Network ◽

Gene Expression Omnibus ◽

Molecular Therapy ◽

Integrated Analysis ◽

Hub Genes ◽

Protein Protein Interaction ◽

Interactive Analysis ◽

Kaplan Meier

Objective The objective was to identify potential hub genes associated with the pathogenesis and prognosis of hepatocellular carcinoma (HCC). Methods Gene expression profile datasets were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between HCC and normal samples were identified via an integrated analysis. A protein–protein interaction network was constructed and analyzed using the STRING database and Cytoscape software, and enrichment analyses were carried out through DAVID. Gene Expression Profiling Interactive Analysis and Kaplan–Meier plotter were used to determine expression and prognostic values of hub genes. Results We identified 11 hub genes ( CDK1, CCNB2, CDC20, CCNB1, TOP2A, CCNA2, MELK, PBK, TPX2, KIF20A, and AURKA) that might be closely related to the pathogenesis and prognosis of HCC. Enrichment analyses indicated that the DEGs were significantly enriched in metabolism-associated pathways, and hub genes and module 1 were highly associated with cell cycle pathway. Conclusions In this study, we identified key genes of HCC, which indicated directions for further research into diagnostic and prognostic biomarkers that could facilitate targeted molecular therapy for HCC.

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Identification of a Prognostic Signature of Malignant Melanoma Based on Tumor Immune Microenvironment Exploration

10.21203/rs.3.rs-94428/v1 ◽

2020 ◽

Author(s):

lingyan yuan ◽

Zhitong Bing ◽

Jianshu Wang ◽

Jing Li ◽

Xiaodong Jin ◽

...

Keyword(s):

Malignant Melanoma ◽

Cox Regression ◽

Predictive Biomarkers ◽

Roc Curves ◽

Risk Groups ◽

Gene Expression Omnibus ◽

Low Risk ◽

The Cancer Genome Atlas ◽

P Value ◽

Braf Mutations

Abstract Background: In contrast to identification of well-defined oncogenic alterations like BRAF mutations for malignant melanoma (MM) patient stratification, effective selection of predictive biomarkers remains a challenge in the era of checkpoint blockade.Methods: The differentially expressed genes (DEGs) related to the TME were identified using The Cancer Genome Atlas (TCGA) dataset by Wilcoxon rank sum test. The prognostic effects of immune-related genes (IRGs) were analyzed using univariate Cox regression. Next, the prognostic model was constructed by step multivariate Cox regression and risk score of each sample was calculated. Then, survival and Receiver Operating Characteristic (ROC) analyses were conducted to validate the model using TCGA and the Gene Expression Omnibus (GEO) datasets, respectively. Finally, the overall immune status, tumor purity of high- and low-risk groups was further analyzed to reveal the potential mechanisms of prognostic effects of the model.Results: Twenty eight IRGs were identified, the univariate cox analysis indicated the hazard ratio ranged from 0.796 to 2.621 (p-value < 0.05). 6 genes (SLPI, S100A7, LYZ, CCL19, CXCR4 and CD79A) were screened out by step multivariate cox regression and a 6-IRGs, which can be used as an independent prognostic factor, was constructed. The MM patients in both training (TCGA) and testing (GEO) datasets can be well stratified as high-risk and low-risk groups with the 6-IRGs signature, and the 3-year and 5-year area under curve (AUC) of ROC curves of GEO set were 0.681 and 0.678 (GSE19234). Conclusions: In sum, we identified and constructed a 6-IRGs , which can be used to predict the prognosis of metastasis in MM patients.

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Prognostic Values of Stabilin-2 in Hepatocellular Carcinoma

10.21203/rs.3.rs-27280/v1 ◽

2020 ◽

Author(s):

Zhicheng Du ◽

Pengfei Zhu ◽

Long Yu ◽

Kunlun Chen ◽

Janwen Ye ◽

...

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Survival Time ◽

Cox Regression ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Primary Malignancy ◽

Diagnosis And Prognosis ◽

Kaplan Meier ◽

Potential Biomarker

Abstract Background: Hepatocellular carcinoma (HCC) is the primary malignancy of the liver. However, biomarkers for early HCC diagnosis are not available. Stabilin (STAB) proteins are scavenger receptors involved in apoptosis and clearance of hyaluronic acid .The role of STAB in HCC has not been previously explored; therefore, the aim of this study was to assess whether STAB gene expression can be used as a novel HCC biomarker.Materials and Methods: Data on 370 HCC patients in the Cancer Genome Atlas database and 221 patients in the Gene Expression Comprehensive Database were retrieved and analyzed. Kaplan–Meier analysis and Cox regression model were used to calculate median survival time using hazard ratio (HR) and 95% confidence interval (CI). Results: The Gene Expression Omnibus dataset showed that high Stabilin-2（STAB2） expression implies longer overall survival (HR after correction = 0.541; 95% CI, 0.339–0.865; p = 0.0182, after correction p = 0.010) and longer recurrence-free survival time (adjusted HR = 0.554; 95% CI, 0.376-0.816; p = 0.0085, adjusted p = 0.003). Conclusions: STAB2 is a potential biomarker for the diagnosis and prognosis of HCC.

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Identification of lncRNA prognostic signature and analysis of their functions for HCV-related hepatocellular carcinoma

10.21203/rs.3.rs-50007/v1 ◽

2020 ◽

Author(s):

Kun Wang ◽

Wenxin Li ◽

Yefu Liu ◽

Zhiqiang Hao ◽

Xiangdong Hua ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Risk Score ◽

Cox Regression ◽

Roc Curves ◽

The Cancer Genome Atlas ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Clinical Variables ◽

Kaplan Meier ◽

Underlying Mechanisms

Abstract Background Hepatitis C virus (HCV) infection is a main contribution to the increase in hepatocellular carcinoma (HCC) incidence and patients’ death recently, but prognostic biomarkers for HCV-related HCC remain rarely reported. This study was to identify an lncRNA prognostic signature for HCV-HCC patients and explore their underlying function mechanisms. Methods In total, 102 HCV-HCC samples and 50 normal control samples were obtained from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate Cox regression analysis were conducted to screen an lncRNA signature that could predict overall survival (OS) and then, the risk score was calculated using this signature. The prognostic potential of this risk score was evaluated by drawing Kaplan-Meier, receiver operating characteristic (ROC) curves and performing multivariate Cox regression analyses with clinical variables. Furthermore, a co-expression and competing endogenous RNA (ceRNA) networks were constructed to explore the functional mechanisms of lncRNAs. Results Multivariate Cox regression showed six lncRNAs (SLC16A1-AS1, ZFPM2-AS1, JARID2-AS1, LINC01426, USP3-AS1 and LYPLAL1-AS1) were significantly associated with OS of HCV-HCC patients. These six lncRNAs were used to establish a risk score model, which displayed a higher prognosis prediction accuracy [area under the ROC curve (AUC) = 0.95 for training set; AUC = 0.885 for testing; AUC = 0.907 for entire set]. Also, this was independent of various clinical variables. The crucial co-expression (LINC01426/SLC16A1-AS1-AURKA/SFN/CCNB1, ZFPM2-AS1/LYPLAL1-AS1/JARID2-AS1-TSSK6) or ceRNA (USP3-AS1-hsa-miR-383-SFN) interaction axes were identified. Conclusion Our study identified a novel six-lncRNA prognosis signature for HCV-HCC patients and indicated their underlying mechanisms for HCC progression.

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Identification and Validation of TYMS as a Potential Biomarker for Risk of Metastasis Development in Hepatocellular Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.762821 ◽

2021 ◽

Vol 11 ◽

Author(s):

Shuai Li ◽

Jingyuan Zhao ◽

Linlin Lv ◽

Deshi Dong

Keyword(s):

Hepatocellular Carcinoma ◽

Interaction Network ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Protein Protein Interaction ◽

Potential Biomarker ◽

Metastasis Development ◽

Cancer Genome Atlas ◽

Ecm Degradation

Metastasis is the major cause of hepatocellular carcinoma (HCC) mortality. Unfortunately, there are few reports on effective biomarkers for HCC metastasis. This study aimed to discover potential key genes of HCC, which could provide new insights for HCC metastasis. GEO (Gene Expression Omnibus) microarray and TCGA (The Cancer Genome Atlas) datasets were integrated to screen for candidate genes involved in HCC metastasis. Differentially expressed genes (DEGs) were screened, and then we performed enrichment analysis of Gene Ontology (GO), together with Kyoto Encyclopedia of Genes and Genomes (KEGG). A protein-protein interaction network was then built and analyzed utilizing STRING and Cytoscape, followed by the identification of 10 hub genes by cytoHubba. Four genes were associated with survival, their prognostic value was verified by prognostic signature analysis. Thymidylate synthase (TYMS) gene was identified as significant HCC metastasis-associated genes after mRNA expression validation and IHC analysis. TYMS silencing in HCC cells remarkedly inhibited growth and invasion. Finally, we found TYMS silencing dramatically decrease DNA synthesis and extracellular matrix (ECM) degradation, resulting in the inhibition of HCC metastasis, indicating TYMS had close associations with HCC development. These findings provided new insights into HCC metastasis and identified candidate gene prognosis signatures for HCC metastasis.

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Expression and clinical significance of miR-3615 in hepatocellular carcinoma

Journal of International Medical Research ◽

10.1177/0300060520981547 ◽

2021 ◽

Vol 49 (1) ◽

pp. 030006052098154

Author(s):

Xin Yuan ◽

Yize Zhang ◽

Zujiang Yu

Keyword(s):

Hepatocellular Carcinoma ◽

Survival Curve ◽

Clinicopathological Characteristics ◽

The Cancer Genome Atlas ◽

Prognostic Information ◽

Ki 67 ◽

Tissue Samples ◽

Expression Levels ◽

Kaplan Meier ◽

Serum Alpha Fetoprotein

Objective To investigate the association between microRNA-3615 (miR-3615) expression and the prognosis and clinicopathological features in patients with hepatocellular carcinoma (HCC). Methods We obtained clinicopathological and genomic data and prognostic information on HCC patients from The Cancer Genome Atlas (TCGA) database. We then analyzed differences in miR-3615 expression levels between HCC and adjacent tissues using SPSS software, and examined the relationships between miR-3615 expression levels and clinicopathological characteristics. We also explored the influence of miR-3615 expression levels on the prognosis of HCC patients using Kaplan–Meier survival curve analysis. Results Based on data for 345 HCC and 50 adjacent normal tissue samples, expression levels of miR-3615 were significantly higher in HCC tissues compared with adjacent tissues. MiR-3615 expression levels in HCC patients were negatively correlated with overall survival time and positively correlated with high TNM stage, serum Ki-67 expression level, and serum alpha-fetoprotein level. There were no significant correlations between miR-3615 expression and age, sex, and pathological grade. Conclusion MiR-3615 may be a promising new biomarker and prognostic factor for HCC.

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Comprehensive analyses of competing endogenous RNA networks reveal potential biomarkers for predicting hepatocellular carcinoma recurrence

BMC Cancer ◽

10.1186/s12885-021-08173-0 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Ping Yan ◽

Zuotian Huang ◽

Tong Mou ◽

Yunhai Luo ◽

Yanyao Liu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Malignant Tumors ◽

Expression Profiles ◽

Clinical Information ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

High Rate ◽

Competing Endogenous Rna ◽

Tissue Samples ◽

Potential Biomarkers

Abstract Background Hepatocellular carcinoma (HCC) is one of the most common and deadly malignant tumors, with a high rate of recurrence worldwide. This study aimed to investigate the mechanism underlying the progression of HCC and to identify recurrence-related biomarkers. Methods We first analyzed 132 HCC patients with paired tumor and adjacent normal tissue samples from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs). The expression profiles and clinical information of 372 HCC patients from The Cancer Genome Atlas (TCGA) database were next analyzed to further validate the DEGs, construct competing endogenous RNA (ceRNA) networks and discover the prognostic genes associated with recurrence. Finally, several recurrence-related genes were evaluated in two external cohorts, consisting of fifty-two and forty-nine HCC patients, respectively. Results With the comprehensive strategies of data mining, two potential interactive ceRNA networks were constructed based on the competitive relationships of the ceRNA hypothesis. The ‘upregulated’ ceRNA network consists of 6 upregulated lncRNAs, 3 downregulated miRNAs and 5 upregulated mRNAs, and the ‘downregulated’ network includes 4 downregulated lncRNAs, 12 upregulated miRNAs and 67 downregulated mRNAs. Survival analysis of the genes in the ceRNA networks demonstrated that 20 mRNAs were significantly associated with recurrence-free survival (RFS). Based on the prognostic mRNAs, a four-gene signature (ADH4, DNASE1L3, HGFAC and MELK) was established with the least absolute shrinkage and selection operator (LASSO) algorithm to predict the RFS of HCC patients, the performance of which was evaluated by receiver operating characteristic curves. The signature was also validated in two external cohort and displayed effective discrimination and prediction for the RFS of HCC patients. Conclusions In conclusion, the present study elucidated the underlying mechanisms of tumorigenesis and progression, provided two visualized ceRNA networks and successfully identified several potential biomarkers for HCC recurrence prediction and targeted therapies.

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Inhibition of Kynurenine Metabolism and Its Effect in Mitochondrial Function in Hepatocellular Carcinoma

Innovation in Aging ◽

10.1093/geroni/igaa057.410 ◽

2020 ◽

Vol 4 (Supplement_1) ◽

pp. 124-125

Author(s):

Raul Castro-Portuguez ◽

Samuel Freitas ◽

George Sutphin

Keyword(s):

Hepatocellular Carcinoma ◽

Mitochondrial Function ◽

De Novo ◽

Principal Component ◽

Kynurenine Pathway ◽

The Cancer Genome Atlas ◽

Wilcoxon Test ◽

Nad Synthesis ◽

Significant Difference ◽

Kynurenine Metabolism

Abstract Hepatocellular carcinoma (HCC) is the most prevalent cancer in the liver. The majority of ingested tryptophan is processed in the liver through the kynurenine pathway, the endpoint of which is de novo NAD+ biosynthesis. Dysregulation of tryptophan-kynurenine metabolism and NAD+ synthesis may promote mitochondrial malfunction, tumor reprogramming, and carcinogenesis. Using a publicly available gene expression dataset from liver hepatocellular carcinoma (LIHC) samples available through The Cancer Genome Atlas (TCGA; n = 371), we employed Principal Component Analysis (PCA), hierarchical clustering, gene-pattern expression profiling, and survival analysis to cluster patients and determine overall survival. Our analysis of genes encoding kynurenine pathway enzymes determined that patients with high QPRT expression had a poor prognosis with decreased median survival, with no effect on the maximum survival. There is a significant difference in the survival between patients with high QPRT expression relative to patients with high HAAO/AFMID expression (HR = 1.2, [95% CI 0.5-1.8] P = 0.0181, Gehan-Breslow-Wilcoxon Test). Patients with high QPRT expression have higher survival rates compared with low QPRT expression (HR = 1.4, [95% CI 0.9-2.2] P = 0.0344, Gehan-Breslow-Wilcoxon Test). To test the consequences of kynurenine-pathway inhibition in mitochondrial function and morphology we use 4-Cl-3HAA, an irreversible HAAO inhibitor, and observed a small increase in mitochondrial fragmentation in HepG2 cells after 24 hours of treatment. We conclude that kynurenine metabolism may be useful as a biomarker to predict patient prognosis among HCC patients. In ongoing work, we are testing QPRT inhibitors in cell culture as a potential adjuvant for chemotherapies.

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