scholarly journals Modularity of the metabolic gene network as a prognostic biomarker for hepatocellular carcinoma

2017 ◽  
Author(s):  
Fengdan Ye ◽  
Dongya Jia ◽  
Mingyang Lu ◽  
Herbert Levine ◽  
Michael W Deem
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Biomarker ◽  
Malignant Tumors ◽  
Aerobic Glycolysis ◽  
Expression Patterns ◽  
Cancer Recurrence ◽  
Metabolic Reprogramming ◽  
The Cancer Genome Atlas ◽  
Driver Genes ◽  
Tumor Stages

AbstractAbnormal metabolism is an emerging hallmark of cancer. Cancer cells utilize both aerobic glycolysis and oxidative phosphorylation (OXPHOS) for energy production and biomass synthesis. Understanding the metabolic reprogramming in cancer can help design therapies to target metabolism and thereby to improve prognosis. We have previously argued that more malignant tumors are usually characterized by a more modular expression pattern of cancer-associated genes. In this work, we analyzed the expression patterns of metabolism genes in terms of modularity for 371 hepatocellular carcinoma (HCC) samples from the Cancer Genome Atlas (TCGA). We found that higher modularity significantly correlated with glycolytic phenotype, later tumor stages, higher metastatic potential, and cancer recurrence, all of which contributed to poorer prognosis. Among patients with recurred tumor, we found the correlation of higher modularity with worse prognosis during early to mid-progression. Furthermore, we developed metrics to calculate individual modularity, which was shown to be predictive of cancer recurrence and patients’ survival and therefore may serve as a prognostic biomarker. Our overall conclusion is that more aggressive HCC tumors, as judged by decreased host survival probability, had more modular expression patterns of metabolic genes. These results may be used to identify cancer driver genes and for drug design.

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

BMC Cancer ◽  
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.

scholarly journals Epithelial–Mesenchymal Transition (EMT) Induced by TGF-β in Hepatocellular Carcinoma Cells Reprograms Lipid Metabolism

2021 ◽  
Vol 22 (11) ◽  
pp. 5543
Author(s):  
Jitka Soukupova ◽  
Andrea Malfettone ◽  
Esther Bertran ◽  
María Isabel Hernández-Alvarez ◽  
Irene Peñuelas-Haro ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Lipid Metabolism ◽  
Transforming Growth Factor ◽  
Epithelial Mesenchymal Transition ◽  
Aerobic Glycolysis ◽  
Metabolic Reprogramming ◽  
Migration And Invasion ◽  
Metabolic Switch ◽  
Mesenchymal Transition ◽  
Hcc Cells

(1) Background: The transforming growth factor (TGF)-β plays a dual role in liver carcinogenesis. At early stages, it inhibits cell growth and induces apoptosis. However, TGF-β expression is high in advanced stages of hepatocellular carcinoma (HCC) and cells become resistant to TGF-β induced suppressor effects, responding to this cytokine undergoing epithelial–mesenchymal transition (EMT), which contributes to cell migration and invasion. Metabolic reprogramming has been established as a key hallmark of cancer. However, to consider metabolism as a therapeutic target in HCC, it is necessary to obtain a better understanding of how reprogramming occurs, which are the factors that regulate it, and how to identify the situation in a patient. Accordingly, in this work we aimed to analyze whether a process of full EMT induced by TGF-β in HCC cells induces metabolic reprogramming. (2) Methods: In vitro analysis in HCC cell lines, metabolomics and transcriptomics. (3) Results: Our findings indicate a differential metabolic switch in response to TGF-β when the HCC cells undergo a full EMT, which would favor lipolysis, increased transport and utilization of free fatty acids (FFA), decreased aerobic glycolysis and an increase in mitochondrial oxidative metabolism. (4) Conclusions: EMT induced by TGF-β in HCC cells reprograms lipid metabolism to facilitate the utilization of FFA and the entry of acetyl-CoA into the TCA cycle, to sustain the elevated requirements of energy linked to this process.

scholarly journals A lipid metabolism-related genes prognosis biomarker associated with the tumor immune microenvironment in colorectal carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Chao Yang ◽  
Shuoyang Huang ◽  
Fengyu Cao ◽  
Yongbin Zheng
Keyword(s):  
Lipid Metabolism ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Expression Profiles ◽  
Risk Groups ◽  
Clinical Parameter ◽  
Gene Expression Omnibus ◽  
Metabolic Reprogramming ◽  
The Cancer Genome Atlas ◽  
Immune Microenvironment

Abstract Background and aim Lipid metabolic reprogramming is considered to be a new hallmark of malignant tumors. The purpose of this study was to explore the expression profiles of lipid metabolism-related genes (LMRG) in colorectal cancer (CRC). Methods The lipid metabolism statuses of 500 CRC patients from the Cancer Genome Atlas (TCGA) and 523 from the Gene Expression Omnibus (GEO GSE39582) database were analyzed. The risk signature was constructed by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) Cox regression. Results A novel four-LMRG signature (PROCA1, CCKBR, CPT2, and FDFT1) was constructed to predict clinical outcomes in CRC patients. The risk signature was shown to be an independent prognostic factor for CRC and was associated with tumour malignancy. Principal components analysis demonstrated that the risk signature could distinguish between low- and high-risk patients. There were significantly differences in abundances of tumor-infiltrating immune cells and mutational landscape between the two risk groups. Patients in the low-risk group were more likely to have higher tumor mutational burden, stem cell characteristics, and higher PD-L1 expression levels. Furthermore, a genomic-clinicopathologic nomogram was established and shown to be a more effective risk stratification tool than any clinical parameter alone. Conclusions This study demonstrated the prognostic value of LMRG and showed that they may be partially involved in the suppressive immune microenvironment formation.

scholarly journals Mining TCGA Database for Tumor Microenvironment-Related Genes of Prognostic Value in Hepatocellular Carcinoma

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Zhenfeng Deng ◽  
Jilong Wang ◽  
Banghao Xu ◽  
Zongrui Jin ◽  
Guolin Wu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Tumor Microenvironment ◽  
Immune Cells ◽  
Malignant Tumors ◽  
Public Access ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Protein Protein Interaction ◽  
Potential Association ◽  
Poor Outcomes

Hepatocellular carcinoma (HCC) is one of the most common and lethal malignancies. Recent studies reveal that tumor microenvironment (TME) components significantly affect HCC growth and progression, particularly the infiltrating stromal and immune cells. Thus, mining of TME-related biomarkers is crucial to improve the survival of patients with HCC. Public access of The Cancer Genome Atlas (TCGA) database allows convenient performance of gene expression-based analysis of big data, which contributes to the exploration of potential association between genes and prognosis of a variety of malignancies, including HCC. The “Estimation of STromal and Immune cells in MAlignant Tumors using Expression data” algorithm renders the quantification of the stromal and immune components in TME possible by calculating the stromal and immune scores. Differentially expressed genes (DEGs) were screened by dividing the HCC cohort of TCGA database into high- and low-score groups according to stromal and immune scores. Further analyses of functional enrichment and protein-protein interaction networks show that the DEGs are mainly involved in immune response, cell adhesion, and extracellular matrix. Finally, seven DEGs have significant association with HCC poor outcomes. These genes contain FABP3, GALNT5, GPR84, ITGB6, MYEOV, PLEKHS1, and STRA6 and may be candidate biomarkers for HCC prognosis.

scholarly journals Stiffer Matrix Accelerates Migration of Hepatocellular Carcinoma Cells through Enhanced Aerobic Glycolysis Via the MAPK-YAP Signaling

Cancers ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 490 ◽  
Author(s):  
Qiu-Ping Liu ◽  
Qing Luo ◽  
Bin Deng ◽  
Yang Ju ◽  
Guan-Bin Song
Keyword(s):  
Hepatocellular Carcinoma ◽  
Extracellular Matrix ◽  
Cell Migration ◽  
Aerobic Glycolysis ◽  
Mapk Signaling ◽  
Carcinoma Cells ◽  
Metabolic Reprogramming ◽  
Signaling Cascade ◽  
And Migration ◽  
Hcc Cells

Increased extracellular matrix (ECM) stiffness and metabolic reprogramming of cancer cells are two fundamental mediators of tumor progression, including hepatocellular carcinoma (HCC). Yet, the correlation between ECM stiffness and excessive aerobic glycolysis in promoting the development of HCC remains unknown. Here, we demonstrated that stiffer ECM promotes HCC cell migration depending on their accelerated aerobic glycolysis. Our results also indicated that stiffer ECM-induced YAP activation plays a major role in promoting aerobic glycolysis of HCC cells. Moreover, we showed that JNK and p38 MAPK signaling are critical for mediating YAP activation in HCC cells. Together, our findings established that the MAPK-YAP signaling cascade that act as a mechanotransduction pathway is essential for promoting HCC cell aerobic glycolysis and migration in response to ECM stiffness.

scholarly journals Development of an Aerobic Glycolysis Index for Predicting the Sorafenib Sensitivity and Prognosis of Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Yu Pan ◽  
Geng-yuan Hu ◽  
Shi Jiang ◽  
Shun-jie Xia ◽  
Hendi Maher ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Aerobic Glycolysis ◽  
Disease Free Survival ◽  
The Cancer Genome Atlas ◽  
Free Survival ◽  
Sorafenib Therapy ◽  
Tumor Tissues ◽  
Cancer Genome Atlas ◽  
Advanced Stages ◽  
Disease Free

Hepatocellular carcinoma (HCC) is a deadly tumor with high heterogeneity. Aerobic glycolysis is a common indicator of tumor growth and plays a key role in tumorigenesis. Heterogeneity in distinct metabolic pathways can be used to stratify HCC into clinically relevant subgroups, but these have not yet been well-established. In this study, we constructed a model called aerobic glycolysis index (AGI) as a marker of aerobic glycolysis using genomic data of hepatocellular carcinoma from The Cancer Genome Atlas (TCGA) project. Our results showed that this parameter inferred enhanced aerobic glycolysis activity in tumor tissues. Furthermore, high AGI is associated with poor tumor differentiation and advanced stages and could predict poor prognosis including reduced overall survival and disease-free survival. More importantly, the AGI could accurately predict tumor sensitivity to Sorafenib therapy. Therefore, the AGI may be a promising biomarker that can accurately stratify patients and improve their treatment efficacy.

scholarly journals LNCAROD enhances hepatocellular carcinoma malignancy by activating glycolysis through induction of pyruvate kinase isoform PKM2

2021 ◽  
Vol 40 (1) ◽  
Author(s):  
Guizhi Jia ◽  
Yan Wang ◽  
Chengjie Lin ◽  
Shihui Lai ◽  
Hongliang Dai ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cell Proliferation ◽  
Pyruvate Kinase ◽  
Cancer Cell ◽  
Malignant Tumors ◽  
Aerobic Glycolysis ◽  
Cell Counting ◽  
Loss Of Function ◽  
Rna Immunoprecipitation

Abstract Background Mounting evidence has suggested the essential role of long non-coding RNAs (lncRNAs) in a plethora of malignant tumors, including hepatocellular carcinoma. However, the underlyling mechanisms of lncRNAs remain unidentified in HCC. The present work was aimed to explore the regulatory functions and mechanisms of LncRNA LNCAROD in HCC progression and chemotherapeutic response. Methods The expression of LNCAROD in HCC tissues and cell lines were detected by quantitative reverse transcription PCR (qPCR). Cancer cell proliferation, migration, invasion, and chemoresistance were evaluated by cell counting kit 8 (CCK8), colony formation, transwell, and chemosensitivity assays. Methylated RNA immunoprecipitation qRCR (MeRIP-qPCR) was used to determine N6-methyladenosine (m6A) modification level. RNA immunoprecipitation (RIP) and RNA pull down were applied to identify the molecular sponge role of LNCAROD for modulation of miR-145-5p via the competing endogenous RNA (ceRNA) mechanism, as well as the interaction between LNCAROD and serine-and arginine-rich splicing factor 3 (SRSF3). The interaction between insulin-like growth factor 2 mRNA-binding protein 1 (IGF2BP1) and LNCAROD was also identified by RIP assay. Gain- or-loss-of-function assays were used to identify the function and underlying mechanisms of LNCAROD in HCC. Results We found that LNCAROD was significantly upregulated and predicted a poorer prognosis in HCC patients. LNCAROD upregulation was maintained by increased m6A methylation-mediated RNA stability. LNCAROD significantly promoted HCC cell proliferation, migration, invasion, and chemoresistance both in vitro and in vivo. Furthermore, mechanistic studies revealed that pyruvate kinase isoform M2 (PKM2)-mediated glycolysis enhancement is critical for the role of LNACROD in HCC. According to bioinformatics prediction and our experimental data, LNCAROD directly binds to SRSF3 to induce PKM switching towards PKM2 and maintains PKM2 levels in HCC by acting as a ceRNA against miR-145-5p. The oncogenic effects of LNCAROD in HCC were more prominent under hypoxia than normoxia due to the upregulation of hypoxia-triggered hypoxia-inducible factor 1α. Conclusions In summary, our present study suggests that LNCAROD induces PKM2 upregulation via simultaneously enhancing SRSF3-mediated PKM switching to PKM2 and sponging miR-145-5p to increase PKM2 level, eventually increasing cancer cell aerobic glycolysis to participate in tumor malignancy and chemoresistance, especially under hypoxic microenvironment. This study provides a promising diagnostic marker and therapeutic target for HCC patients.

scholarly journals Identifying Differentially Expressed MicroRNAs Between Cirrhotic and Non-Cirrhotic Hepatocellular Carcinoma and Exploring Their Functions Using Bioinformatic Analysis

2018 ◽  
Vol 48 (4) ◽  
pp. 1443-1456 ◽  
Author(s):  
Ying Mei ◽  
Yu You ◽  
Jin Xia ◽  
Jian-Ping Gong ◽  
Yun-Bing Wang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Bioinformatic Analysis ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Online Tools ◽  
Tumor Stages ◽  
Differentially Expressed Mirnas ◽  
Cancer Genome Atlas ◽  
Cirrhotic Patients ◽  
Independent Risk Factors

Background/Aims: Few studies have been designed to directly investigate the exact mechanisms underlying the different phenotypes between cirrhotic and non-cirrhotic hepatocellular carcinoma (HCC). This study aimed to illuminate the incidence and developmental mechanisms for both types of HCC through differentially expressed microRNAs (miRNAs) using bioinformatic analysis. Methods: The miRNA-seq data and clinical data of patients (from The Cancer Genome Atlas (TCGA) database) were utilized to determine differentially expressed miRNAs between cirrhotic and non-cirrhotic HCC. Afterwards, the function of the selected miRNAs was predicted with online tools. Furthermore, the miRNA expression and clinical significance were validated by external datasets and our experiment. Results: The present study included 135 non-cirrhotic and 80 cirrhotic patients to find differentially expressed miRNAs. Among them, four miRNAs (mir-1296, mir-23c, mir-149, and mir-95) were finally selected for further validation and analysis. Correlation analysis found that two miRNAs are greatly associated with the non-cirrhotic HCC patients’ clinical traits, such as the T, M, and N tumor stages. However, only mir-23c was associated with the cirrhotic HCC patients’ tumor T and N stages. Furthermore, survival analysis revealed that increased mir-149 in non-cirrhotic HCC, patients’ age and the existence of vessels in the tumor in cirrhotic HCC were independent risk factors for the patients’ postoperative overall survival. Functional and interaction analyses also supported the notion that these miRNAs function through some pathways that influence the behavior of the HCC. These results are strongly supported by analysis of extra datasets and our experiment. Conclusions: The cirrhotic and non-cirrhotic HCC types involve differentially expressed miRNAs, which are correlated with distinctive pathological traits. To some extent, non-cirrhotic HCC seems more dependent on miRNA network regulation, but additional studies are needed to confirm this conclusion.

scholarly journals Hub Gene Identification and Prognostic Model Establishment for Patients with HBV-related Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Lianmei Wang ◽  
Jing Liu ◽  
Zhong Xian ◽  
Jingzhuo Tian ◽  
Chunying Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Prognostic Model ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Cyclin B1 ◽  
Mitotic Checkpoint ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Hub Genes

Abstract Hepatocellular carcinoma (HCC) is associated with poor 5-year survival. Chronic infection with hepatitis B virus (HBV) contributes to ~ 50% of HCC cases. Establishment of a prognostic model is pivotal for clinical therapy of HBV-related HCC (HBV–HCC). We downloaded gene-expression profiles from Gene expression omnibus (GEO) datasets with HBV-HCC patients and the corresponding controls. Integration of these differentially expressed genes (DEGs) was achieved with the Robustrankaggreg (RRA) method. DEGs functional analyses and pathway analyses was performed using the Gene ontology (GO) database, and the Kyoto encyclopedia of genes and genomes (KEGG) database respectively. DNA topoisomerase II alpha (TOP2A), Disks large-associated protein 5 (DLGAP5), RAD51 associated protein 1 (RAD51AP1), ZW10 interactor (ZWINT), BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B), Cyclin B1 (CCNB1), Forkhead box M1 (FOXM1), Cyclin B2 (CCNB2), Aurora kinase A (AURKA), and Cyclin-dependent kinase 1 (CDK1) were identified as the top-ten hub genes. These hub-genes were verified by the Liver cancer-riken, JP project from international cancer genome consortium (ICGC-LIRI-JP) project, The Cancer genome atlas (TCGA) HCC cohort, and Human protein profiles dataset. FOXM1 and CDK1 were found to be prognostic-related molecules for HBV-HCC patients. The expression patterns of FOXM1 and CDK1were consistently in human and mouse. Furthermore, a nomogram model based on histology grade, pathology stage, sex and, expression of FOXM1 and CDK1 was built to predict the prognosis for HBV–HCC patients. The nomogram model could be used to predict the prognosis of HBV-HCC cases.

scholarly journals PGM5: a novel diagnostic and prognostic biomarker for liver cancer

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7070 ◽  
Author(s):  
Yan Jiao ◽  
Yanqing Li ◽  
Peiqiang Jiang ◽  
Wei Han ◽  
Yahui Liu
Keyword(s):  
Liver Cancer ◽  
Prognostic Biomarker ◽  
Area Under The Curve ◽  
Public Health Problem ◽  
Roc Curves ◽  
Normal Liver ◽  
Diagnostic Value ◽  
Metabolic Reprogramming ◽  
The Cancer Genome Atlas ◽  
Metabolic Enzyme

Background Liver cancer is a common malignancy and a significant public health problem worldwide, but diagnosis and prognostic evaluation remain challenging for clinicians. Metabolic reprogramming is a hallmark of cancer, and we therefore examined the diagnostic and prognostic value of a metabolic enzyme, phosphoglucomutase-like protein 5 (PGM5), in liver cancer. Methods All data were from The Cancer Genome Atlas database. R and related statistical packages were used for data analysis. Hepatic PGM5 expression was determined in different groups, and the chi-squared test and Fisher’s exact test were used to determine the significance of differences. The pROC package was used to determine receiver operating characteristic (ROC) curves, the survival package was used to for survival analysis and development of a Cox multivariable model, and the ggplot2 package was used for data visualization. Results PGM5 expression was significantly lower in cancerous than adjacent normal liver tissues, and had modest diagnostic value based on ROC analysis and calculations of area under the curve (AUC). Hepatic PGM5 expression had positive associations with male sex and survival, but negative associations with advanced histologic type, advanced histologic grade, advanced stage, and advanced T classification. Patents with low PGM5 levels had poorer overall survival and relapse-free survival. PGM5 was independently associated with patient prognosis. Conclusion PGM5 has potential use as a diagnostic and prognostic biomarker for liver cancer.

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