Dysregulated signaling hubs of liver lipid metabolism reveal hepatocellular carcinoma pathogenesis

Hepatitis B-related hepatocellular carcinoma: classification and prognostic model based on programmed cell death genes

Instruction

Hepatitis B virus (HBV) infection is a major risk factor for hepatocellular carcinoma (HCC). Programmed cell death (PCD) is a critical process in suppressing tumor growth, and alterations in PCD-related genes may contribute to the progression of HBV-HCC. This study aims to develop a prognostic model that incorporates genomic and clinical information based on PCD-related genes, providing novel insights into the molecular heterogeneity of HBV-HCC through bioinformatics analysis and experimental validation.

Methods

In this study, we analyzed 139 HBV-HCC samples from The Cancer Genome Atlas (TCGA) and validated them with 30 samples from the Gene Expression Omnibus (GEO) database. Various bioinformatics tools, including differential expression analysis, gene set variation analysis, and machine learning algorithms were used for comprehensive analysis of RNA sequencing data from HBV-HCC patients. Furthermore, among the PCD-related genes, we ultimately chose DLAT for further research on tissue chips and patient cohorts. Besides, immunohistochemistry, qRT-PCR and Western blot analysis were conducted.

Results

The cluster analysis identified three distinct subgroups of HBV-HCC patients. Among them, Cluster 2 demonstrated significant activation in DNA replication-related pathways and tumor-related processes. Analysis of copy number variations (CNVs) of PCD-related genes also revealed distinct patterns in the three subgroups, which may be associated with differences in pathway activation and survival outcomes. DLAT in tumor tissues of HBV-HCC patients is upregulated.

Discussion

Based on the PCD-related genes, we developed a prognostic model that incorporates genomic and clinical information and provided novel insights into the molecular heterogeneity of HBV-HCC. In our study, we emphasized the significance of PCD-related genes, particularly DLAT, which was examined in vitro to explore its potential clinical implications.

Polyphenols as potential metabolism mechanisms regulators in liver protection and liver cancer prevention

BACKGROUND

Liver cancer is one of the common malignancies. The dysregulation of metabolism is a driver of accelerated tumourigenesis. Metabolic changes are well documented to maintain tumour growth, proliferation and survival. Recently, a variety of polyphenols have been shown to have a crucial role both in liver disease prevention and metabolism regulation.

METHODS

We conducted a literature search and combined recent data with systematic analysis to comprehensively describe the molecular mechanisms that link polyphenols to metabolic regulation and their contribution in liver protection and liver cancer prevention.

RESULTS

Targeting metabolic dysregulation in organisms prevents and resists the development of liver cancer, which has important implications for identifying new therapeutic strategies for the management and treatment of cancer. Polyphenols are a class of complex compounds composed of multiple phenolic hydroxyl groups and are the main active ingredients of many natural plants. They mediate a broad spectrum of biological and pharmacological functions containing complex lipid metabolism, glucose metabolism, iron metabolism, intestinal flora imbalance, as well as the direct interaction of their metabolites with key cell-signalling proteins. A large number of studies have found that polyphenols affect the metabolism of organisms by interfering with a variety of intracellular signals, thereby protecting the liver and reducing the risk of liver cancer.

CONCLUSION

This review systematically illustrates that various polyphenols, including resveratrol, chlorogenic acid, caffeic acid, dihydromyricetin, quercetin, catechins, curcumin, etc., improve metabolic disorders through direct or indirect pathways to protect the liver and fight liver cancer.

Role of follistatin-like protein 1 in liver diseases

Liver diseases, including viral hepatitis, fatty liver, metabolic-associated fatty liver disease, liver cirrhosis, alcoholic liver disease, and liver neoplasms, are major global health challenges. Despite the continued development of new drugs and technologies, the prognosis of end-stage liver diseases, including advanced liver cirrhosis and liver neoplasms, remains poor. Follistatin-like protein 1 (FSTL1), an extracellular glycoprotein, is secreted by various cell types. It is a glycoprotein that belongs to the family of secreted proteins acidic and rich in cysteine (SPARC). It is also known as transforming growth factor-beta inducible TSC-36 and follistatin-related protein (FRP). FSTL1 plays a key role in cell survival, proliferation, differentiation, and migration, as well as the regulation of inflammation and immunity. Studies have demonstrated that FSTL1 significantly affects the occurrence and development of liver diseases. This article reviews the role and mechanism of FSLT1 in liver diseases.

LRP1B mutation associates with increased tumor mutation burden and inferior prognosis in liver hepatocellular carcinoma

BACKGROUND

Liver hepatocellular carcinoma (LIHC) is the most common primary liver cancer and the main cause of death in patients with cirrhosis. LRP1B is found to involve in a variety of cancers, but the association of LRP1B mutation with tumor mutation burden (TMB) and prognosis of LIHC is rarely studied.

METHODS AND RESULTS

Herein, we analyzed the somatic mutation data of 364 LIHC patients from The Cancer Genome Atlas (TCGA) and found that LRP1B showed elevated mutation rate. Calculation of the TMB in LRP1B mutant and LRP1B wild-type groups showed that LRP1B mutant group had higher TMB compared with that in LRP1B wild-type group. Then survival analysis was performed and the survival curve showed that LRP1B mutation was associated with poor survival outcome, and this association remained to be significant after adjusting for multiple confounding factors including age, gender, tumor stage, mutations of BRCA1, BRCA2, and POLE.

CONCLUSION

Collectively, our results revealed that LRP1B mutation was related to high TMB value and poor prognosis in LIHC, indicating that LRP1B mutation is probably helpful for the selection of immunotherapy and prognosis prediction in LIHC.

Network medicine in ovarian cancer: topological properties to drug discovery

Network medicine provides network theoretical tools, methods and properties to study underlying laws governing human interactome to identify disease states and disease complexity leading to drug discovery. Within this framework, we investigated the topological properties of ovarian cancer network (OCN) and the roles of hubs to understand OCN organization to address disease states and complexity. The OCN constructed from the experimentally verified genes exhibits fractal nature in the topological properties with deeply rooted functional communities indicating self-organizing behavior. The network properties at all levels of organization obey one parameter scaling law which lacks centrality lethality rule. We showed that $\langle k\rangle $ can be taken as a scaling parameter, where, power law exponent can be estimated from the ratio of network diameters. The betweenness centrality $C_B$ shows two distinct behaviors one shown by high degree hubs and the other by segregated low degree nodes. The $C_B$ power law exponent is found to connect the exponents of distributions of high and low degree nodes. OCN showed the absence of rich-club formation which leads to the missing of a number of attractors in the network causing formation of weakly tied diverse functional modules to keep optimal network efficiency. In OCN, provincial and connector hubs, which includes identified key regulators, take major responsibility to keep the OCN integrity and organization. Further, most of the key regulators are found to be over expressed and positively correlated with immune infiltrates. Finally, few potential drugs are identified related to the key regulators.

Role of ferroptosis-related molecular patterns in hepatocellular carcinoma microenvironment

Heterogeneity and complexity of hepatocellular carcinoma (HCC) have been an impediment to effective diagnosis and treatment of HCC. Mounting evidence suggests that ferroptosis-related genes (FRGs) regulate the development of HCC by affecting the tumor microenvironment (TME). Herein, we explored the role of ferroptosis-related molecular patterns in the HCC microenvironment. The transcriptome and corresponding clinicopathological data of HCC patients were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database, respectively. Molecular patterns of ferroptosis were explored using consensus clustering analysis and ferroptosis-related molecular patterns in the individual patients were analyzed using principal component analysis (PCA). The ability of ferroptosis-related patterns to predict the biological status and survival outcomes of HCC patients was investigated. Based on the expression of FRGs, three molecular patterns related to ferroptosis were identified. Single sample gene set enrichment analysis (ssGSEA) showed that the molecular patterns associated with the worst prognosis were significantly correlated with high infiltration of immunosuppressive cells in the TME. Besides, we identified three ferroptosis gene clusters underlying the different biological features of the three ferroptosis patterns. Patients in the high-risk group had a worse biological status and survival outcomes than those in the low-risk group. This study demonstrates that ferroptosis-related molecular patterns lead to high heterogeneity in HCC. These molecular patterns can be used to assess the survival of HCC patients and guide the design of immunotherapy strategies for HCC patients.

Circ_0091581 Promotes the Progression of Hepatocellular Carcinoma Through Targeting miR-591/FOSL2 Axis

BACKGROUND

Circular RNAs (circRNAs) have shown crucial regulatory roles in cancer biology. We aimed to uncover the role and underlying mechanism of circ_0091581 in hepatocellular carcinoma (HCC) progression.

METHODS

The abundance of circ_0091581, microRNA-591 (miR-591) and FOS like 2, AP-1 transcription factor subunit (FOSL2) was measured by quantitative real-time polymerase chain reaction. Cell viability, colony formation ability, and invasion ability were assessed by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, colony formation assay, and transwell invasion assay. The migration ability was analyzed by transwell migration assay and wound healing assay. Flow cytometry was used to evaluate the cell cycle and apoptosis of HCC cells. The interaction between miR-591 and circ_0091581 or FOSL2 was predicted by Circular RNA Interactome database or TargetScan database and confirmed by dual-luciferase reporter assay and RNA immune co-precipitation assay. FOSL2 protein expression was measured by Western blot assay. Xenograft tumor assay was conducted to analyze the role of circ_0091581 in HCC tumor growth in vivo.

RESULTS

Circ_0091581 was highly expressed in HCC tissue samples and cell lines in contrast to that in adjacent normal tissue samples and THLE-2 cell line. Circ_0091581 accelerated the viability, colony formation, metastasis, and cell cycle, while it impeded the apoptosis of HCC cells. MiR-591 bound to circ_0091581, and circ_0091581 knockdown-mediated effects in HCC cells were largely overturned by miR-591 silencing. FOSL2 was a target of miR-591, and FOSL2 overexpression largely reversed miR-591 accumulation-induced influences in HCC cells. FOSL2 protein expression was down-regulated by circ_0091581 silencing, and the addition of miR-591 inhibitor partly recovered the expression of FOSL2 in HCC cells. Circ_0091581 interference notably suppressed HCC tumor growth in vivo.

CONCLUSION

Circ_0091581 acted as an oncogene to enhance the viability, colony formation, metastasis and cell cycle and inhibit the apoptosis of HCC cells through targeting miR-591/FOSL2 axis.

Bioinformatics analysis of different candidate genes involved in hepatocellular carcinoma induced by HepG2 cells or tumor cells of patients

OBJECTIVE

Hepatocellular carcinoma (HCC) is a common cancer with a high mortality rate; the molecular mechanism involved in HCC remain unclear. We aimed to provide insight into HCC induced with HepG2 cells and identify genes and pathways associated with HCC, as well as potential therapeutic targets.

METHODS

Dataset GSE72581 was downloaded from the Gene Expression Omnibus, including samples from mice injected in liver parenchyma with HepG2 cells, and from mice injected with cells from patient tumor explants. Differentially expressed genes (DEGs) between the two groups of mice were analyzed. Then, gene ontology and Kyoto Encyclopedia of Gene and Genomes pathway enrichment analyses were performed. The MCODE plug-in in Cytoscape was applied to create a protein-protein interaction (PPI) network of DEGs.

RESULTS

We identified 1,405 DEGs (479 upregulated and 926 downregulated genes), which were enriched in complement and coagulation cascades, peroxisome proliferator-activated receptor signaling pathway, and extracellular matrix-receptor interaction. The top 4 modules and top 20 hub genes were identified from the PPI network, and associations with overall survival were determined using Kaplan-Meier analysis.

CONCLUSION

This preclinical study provided data on molecular targets in HCC that could be useful in the clinical treatment of HCC.

Fumonisin B1 regulates LDL receptor and ABCA1 expression in an LXR dependent mechanism in liver (HepG2) cells

The metabolic toxicity of Fumonisin B₁ (FB₁) converges at the accumulation of sphingoid bases and reduced ceramide levels. Several studies have alluded to a hypercholesterolemic endpoint after FB₁ exposure, yet the molecular mechanisms remain elusive. Cell surface receptors are important regulators of cholesterol metabolism by regulating influx of lipids and efflux of cholesterol. Western blot analysis showed that FB₁ elevates the expression of ABCA1 (a cholesterol efflux promoter) in an LXR dependent mechanism. We further highlight the potential role of PCSK9 in the degradation of LDL receptor. These data provide important evidence for the mechanism underlying hypercholesterolemia in FB₁ treated models. The disruption of lipid homeostasis by FB₁ is beginning to shift away from canonical ceramide synthase inhibition, and this changed perspective may shed light on diseases caused by dysregulated cholesterol metabolism such as cancer initiation and promotion.

Identification of a prognostic signature of nine metabolism-related genes for hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is the fifth most common cancer. Since changes in liver metabolism contribute to liver disease development, it is necessary to build a metabolism-related prognostic model for HCC.

Methods

We constructed a metabolism-related-gene (MRG) signature comprising nine genes, which segregated HCC patients into high- and low-risk groups.

Results

The survival rate (overall survival: OS; relapse-free survival; and progression-free survival) of patients in the low-risk group of The Cancer Genome Atlas (TCGA) cohort was significantly higher than that of patients in the high-risk group. The OS prognostic signature was validated in the International Cancer Genome Consortium independent cohort. The corresponding receiver operating characteristic curves of the model indicated that the signature had good diagnostic efficiency, in terms of improving OS over 1, 3, and 5 years. Hierarchical analysis demonstrated that the MRG signature was significantly associated with better prognosis in male patients, patients aged ≤ 65 years, and patients carrying the wild-type TP53 or CTNNB1 genes. A nomogram was established, and good performance and clinical practicability were confirmed. Additionally, using the GSE109211 dataset from the Gene Expression Omnibus database, we were able to verify that the nine genes in this MRG signature had different responses to sorafenib, suggesting that some of these MRGs may act as therapeutic targets for HCC.

Conclusions

We believe that these findings will add value in terms of the diagnosis, treatment, and prognosis of HCC.

Network of clinically-relevant lncRNAs-mRNAs associated with prognosis of hepatocellular carcinoma patients

Long non-coding RNAs (lncRNAs) are often aberrantly expressed in Hepatocellular Carcinoma (HCC). We hypothesize that lncRNAs modulate HCC prognoses through differential deregulation of key lncRNAs affecting important gene network in key cancer pathways associated with pertinent clinical phenotype. Here, we present a novel approach integrating lncRNA-mRNA expression profiles with clinical characteristics to identify lncRNA signatures in clinically-relevant co-expression lncRNA-mRNA networks residing in pertinent cancer pathways. Notably one network, associated with poorer prognosis, comprises five up-regulated lncRNAs significantly correlated (|Pearson Correlation Coefficient|≥ 0.9) with 91 up-regulated genes in the cell-cycle and Rho-GTPase pathways. All 5 lncRNAs and 85/91 (93.4%) of the correlated genes were significantly associated with higher tumor-grade while 3/5 lncRNAs were also associated with no tumor capsule. Interestingly, 2/5 lncRNAs that are correlated with numerous genes in this oncogenic network were experimentally shown to up-regulate genes involved in cell-cycle and transcriptional regulation. Another network comprising 4 down-regulated lncRNAs and 8 down-regulated metallothionein-family genes are significantly associated with tumor invasion. The identification of these key lncRNAs signatures that deregulate important network of genes in key cancer pathways associated with pertinent clinical phenotype may facilitate the design of novel therapeutic strategies targeting these 'master' regulators for better patient outcome.

A systems biology approach for studying neurodegenerative diseases

Neurodegenerative diseases (NDDs), such as Alzheimer's (AD) and Parkinson's (PD), are among the leading causes of lost years of healthy life and exert a great strain on public healthcare systems. Despite being first described more than a century ago, no effective cure exists for AD or PD. Although extensively characterised at the molecular level, traditional neurodegeneration research remains marred by narrow-sense approaches surrounding amyloid β (Aβ), tau, and α-synuclein (α-syn). A systems biology approach enables the integration of multi-omics data and informs discovery of biomarkers, drug targets, and treatment strategies. Here, we present a comprehensive timeline of high-throughput data collection, and associated biotechnological advancements and computational analysis related to AD and PD. We hereby propose that a philosophical change in the definitions of AD and PD is now needed.

Improvement in the Current Therapies for Hepatocellular Carcinoma Using a Systems Medicine Approach

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death primarily due to the lack of effective targeted therapies. Despite the distinct morphological and phenotypic patterns of HCC, treatment strategies are restricted to relatively homogeneous therapies, including multitargeted tyrosine kinase inhibitors and immune checkpoint inhibitors. Therefore, more effective therapy options are needed to target dysregulated metabolic and molecular pathways in HCC. Integrative genomic profiling of HCC patients provides insight into the most frequently mutated genes and molecular targets, including telomerase reverse transcriptase, the TP53 gene, and the Wnt/β-catenin signaling pathway oncogene (CTNNB1). Moreover, emerging techniques, such as genome-scale metabolic models may elucidate the underlying cancer-specific metabolism, which allows for the discovery of potential drug targets and identification of biomarkers. De novo lipogenesis has been revealed as consistently upregulated since it is required for cell proliferation in all HCC patients. The metabolic network-driven stratification of HCC patients in terms of redox responses, utilization of metabolites, and subtype-specific pathways may have clinical implications to drive the development of personalized medicine. In this review, the current and emerging therapeutic targets in light of molecular approaches and metabolic network-based strategies are summarized, prompting effective treatment of HCC patients.

A Logistic Regression Model for Noninvasive Prediction of AFP-Negative Hepatocellular Carcinoma

α-Fetoprotein is commonly used in the diagnosis of hepatocellular carcinoma. However, the diagnostic significance of α-fetoprotein has been questioned because a number of patients with hepatocellular carcinoma are α-fetoprotein negative. It is therefore necessary to develop novel noninvasive techniques for the early diagnosis of hepatocellular carcinoma, particularly when α-fetoprotein level is low or negative. The current study aimed to evaluate the diagnostic efficiency of hematological parameters to determine which can act as surrogate markers in α-fetoprotein-negative hepatocellular carcinoma. Therefore, a retrospective study was conducted on a training set recruited from Zhongnan Hospital of Wuhan University-including 171 α-fetoprotein-negative patients with hepatocellular carcinoma and 102 healthy individuals. The results show that mean values of mean platelet volume, red blood cell distribution width, mean platelet volume-PC ratio, neutrophils-lymphocytes ratio, and platelet count-lymphocytes ratio were significantly higher in patients with hepatocellular carcinoma in comparison to the healthy individuals. Most of these parameters showed moderate area under the curve in α-fetoprotein-negative patients with hepatocellular carcinoma, but their sensitivities or specificities were not satisfactory enough. So, we built a logistic regression model combining multiple hematological parameters. This model presented better diagnostic efficiency with area under the curve of 0.922, sensitivity of 83.0%, and specificity of 93.1%. In addition, the 4 validation sets from different hospitals were used to validate the model. They all showed good area under the curve with satisfactory sensitivities or specificities. These data indicate that the logistic regression model combining multiple hematological parameters has better diagnostic efficiency, and they might be helpful for the early diagnosis for α-fetoprotein-negative hepatocellular carcinoma.

TCSBN: a database of tissue and cancer specific biological networks

Biological networks provide new opportunities for understanding the cellular biology in both health and disease states. We generated tissue specific integrated networks (INs) for liver, muscle and adipose tissues by integrating metabolic, regulatory and protein-protein interaction networks. We also generated human co-expression networks (CNs) for 46 normal tissues and 17 cancers to explore the functional relationships between genes as well as their relationships with biological functions, and investigate the overlap between functional and physical interactions provided by CNs and INs, respectively. These networks can be employed in the analysis of omics data, provide detailed insight into disease mechanisms by identifying the key biological components and eventually can be used in the development of efficient treatment strategies. Moreover, comparative analysis of the networks may allow for the identification of tissue-specific targets that can be used in the development of drugs with the minimum toxic effect to other human tissues. These context-specific INs and CNs are presented in an interactive website http://inetmodels.com without any limitation.

Urinary Metabolites Diagnostic and Prognostic of Intrahepatic Cholangiocarcinoma

BACKGROUND

Liver cancer is the second leading cause of cancer-related deaths worldwide. With a predicted 2.4-fold rise in liver cancer incidence by 2020, there is an urgent need for early, inexpensive diagnostic biomarkers to deploy in the clinic.

METHODS

We employed ultraperformance liquid chromatography tandem mass-spectrometry (UPLC/MS-MS) for the quantitation of four metabolites, creatine riboside (CR), N-acetylneuraminic acid (NANA), cortisol sulfate, and a lipid molecule designated as 561+, in urine samples from the NCI-MD cohort comprising 98 hepatocellular carcinoma (HCC) cases, 101 high-risk subjects, and 95 controls. Validation was carried out in the TIGER-LC cohort [n = 370 HCC and intrahepatic cholangiocarcinoma (ICC) cases, 471 high-risk subjects, 251 controls], where ICC, the second most common primary hepatic malignancy, is highly prevalent. Metabolite quantitation was also conducted in TIGER-LC tissue samples (n = 48 ICC; n = 51 HCC).

RESULTS

All profiled metabolites were significantly increased in liver cancer when compared with high-risk subjects and controls in the NCI-MD study. In the TIGER-LC cohort, the four-metabolite profile was superior at classifying ICC than a clinically utilized marker, CA19-9, and their combination led to a significantly improved model (AUC = 0.88, P = 4E-8). Metabolites CR and NANA were significantly elevated in ICC when compared with HCC cases in both urine and tissue samples. High levels of CR were associated with poorer prognosis in ICC.

CONCLUSIONS

Four metabolites are significantly increased in HCC and ICC and are robust at classifying ICC in combination with the clinically utilized marker CA19-9.

IMPACT

Noninvasive urinary metabolite biomarkers hold promise for diagnostic and prognostic evaluation of ICC.

Traditional Chinese medicine combined with hepatic targeted drug delivery systems: A new strategy for the treatment of liver diseases

Liver diseases are clinically common and present a substantial public health issue. Many of the currently available drugs for the treatment of liver diseases suffer from limitations that include low hepatic distribution, lack of target effects, poor in vivo stability and adverse effects on other organs. Consequently, conventional treatment of hepatic diseases is ineffective. TCM is commonly used in the treatment of liver diseases worldwide, particularly in China, and has advantages over conventional therapy. HTDDS can be designed to enhance clinical efficacy in the treatment of liver diseases. We have conducted an extensive review of 335 studies reported since 1964. These included about 166 references involving the treatment of liver diseases with TCM (covering active components of TCM, single TCM and Chinese medicine formulas), 169 reports on HTDDS and background studies on liver-related diseases. Here we review the long history of TCM in the treatment of liver diseases.We have also reviewed the status of studies on active components of TCM using nanotechnology-based targeted delivery systems to provide support for further research and development of TCM-based targeted preparations for the treatment of liver disease.

Pyruvate kinase L/R is a regulator of lipid metabolism and mitochondrial function

The pathogenesis of non-alcoholic fatty liver disease (NAFLD) and hepatocellular carcinoma (HCC) has been associated with altered expression of liver-specific genes including pyruvate kinase liver and red blood cell (PKLR), patatin-like phospholipase domain containing 3 (PNPLA3) and proprotein convertase subtilisin/kexin type 9 (PCSK9). Here, we inhibited and overexpressed the expression of these three genes in HepG2 cells, generated RNA-seq data before and after perturbation and revealed the altered global biological functions with the modulation of these genes using integrated network (IN) analysis. We found that modulation of these genes effects the total triglycerides levels within the cells and viability of the cells. Next, we generated IN for HepG2 cells, identified reporter transcription factors based on IN and found that the modulation of these genes affects key metabolic pathways associated with lipid metabolism (steroid biosynthesis, PPAR signalling pathway, fatty acid synthesis and oxidation) and cancer development (DNA replication, cell cycle and p53 signalling) involved in the progression of NAFLD and HCC. Finally, we observed that inhibition of PKLR lead to decreased glucose uptake and decreased mitochondrial activity in HepG2 cells. Hence, our systems level analysis indicated that PKLR can be targeted for development efficient treatment strategy for NAFLD and HCC.

Effect of serum triglyceride level on the prognosis of patients with hepatocellular carcinoma in the absence of cirrhosis

Background

The liver plays an important role in the metabolism of lipid and lipoprotein. Dyslipidemia has been demonstrated to be related with several cancers, but the association between serum lipid and hepatocellular carcinoma (HCC) in the absence of cirrhosis remains unclear.

Methods

A total of 2528 patients with HCC at the Beijing Ditan Hospital between February 2008 and December 2017 were retrospectively included in the study. We identified 200 patients with HCC without cirrhosis by histopathology, imaging, endoscopic findings, and laboratory tests. Multivariate regression analysis was performed to determine the independent characteristics associated with HCC without cirrhosis and its prognosis.

Results

In the logistics regression analysis, compared to patients with HCC with cirrhosis, patients with HCC without cirrhosis were more likely to have elevated triglyceride (TG) levels (OR = 2.66; 95% CI, 1.18–6.01; P = 0.019). The Kaplan-Meier analysis revealed that a lower TG level was a risk factor regardless of the presence of cirrhosis. The results of the Cox proportional hazard regression analysis showed that a decreased TG level was significantly related to a worse overall survival (HR = 0.51; 95% CI, 0.29–0.89; P = 0.017).

Conclusion

Serum TG level may be an independent factor to predict the prognosis of patients with HCC in the absence of cirrhosis.

Electronic supplementary material

The online version of this article (10.1186/s12944-018-0898-y) contains supplementary material, which is available to authorized users.

Immunometabolism: A novel perspective of liver cancer microenvironment and its influence on tumor progression

The initiation and progression of liver cancer, including hepatocellular carcinoma and intrahepatic cholangiocarcinoma, are dependent on its tumor microenvironment. Immune cells are key players in the liver cancer microenvironment and show complicated crosstalk with cancer cells. Emerging evidence has shown that the functions of immune cells are closely related to cell metabolism. However, the effects of metabolic changes of immune cells on liver cancer progression are largely undefined. In this review, we summarize the recent findings of immunometabolism and relate these findings to liver cancer progression. We also explore the translation of the understanding of immunometabolism for clinical use.

Systems biology in hepatology: approaches and applications

Detailed insights into the biological functions of the liver and an understanding of its crosstalk with other human tissues and the gut microbiota can be used to develop novel strategies for the prevention and treatment of liver-associated diseases, including fatty liver disease, cirrhosis, hepatocellular carcinoma and type 2 diabetes mellitus. Biological network models, including metabolic, transcriptional regulatory, protein-protein interaction, signalling and co-expression networks, can provide a scaffold for studying the biological pathways operating in the liver in connection with disease development in a systematic manner. Here, we review studies in which biological network models were used to integrate multiomics data to advance our understanding of the pathophysiological responses of complex liver diseases. We also discuss how this mechanistic approach can contribute to the discovery of potential biomarkers and novel drug targets, which might lead to the design of targeted and improved treatment strategies. Finally, we present a roadmap for the successful integration of models of the liver and other human tissues with the gut microbiota to simulate whole-body metabolic functions in health and disease.

A distinctively expressed long noncoding RNA, RP11-466I1.1, may serve as a prognostic biomarker in hepatocellular carcinoma

It is urgent to explore effective diagnostic and prognostic biomarkers for hepatocellular carcinoma (HCC). Now, both lncRNAs and lipid metabolism are involved in tumor pathogenesis. Long noncoding RNA, RP11‐466I1.1, could likely be linked to lipid metabolism according to our bioinformatics analysis, yet studies about RP11‐466I1.1 expression in tumors and its potential functions are still lacking. We aimed to explore the expression and correlations with clinical features of a long noncoding RNA, RP11‐466I1.1, and further analyze its diagnostic and prognostic values in hepatocellular carcinoma. Expression levels of RP11‐466I1.1 were detected by quantitative real‐time PCR (qRT‐PCR) in tissue and serum level, and expression differences were analyzed by independent 2‐tailed t tests. Clinical features were obtained, and their correlations with RP11‐466I1.1 were analyzed by chi‐squared test. Receiver operating characteristic (ROC) curve was performed to assess the diagnostic value. Kaplan‐Meier method and log‐rank test were used to evaluate the prognostic value of RP11‐466I1.1. Results showed that RP11‐466I1.1 was upregulated in HCC tissues (P < .01) and serum (P < .05). Significant upregulation of RP11‐466I1.1 in HCC tissues with poor histological grade (P < .01) and incomplete tumor capsule (P < .01) was found compared to that with better histological grade and complete tumor capsule, respectively. The diagnostic value of RP11‐466I1.1 was not supported by ROC curve analysis (AUROC=0.665, P = .079). Yet, the significant correlation of RP11‐466I1.1 with poor prognosis indicated its potential prognostic value in HCC. This study suggested that RP11‐466I1.1 is distinctively expressed in HCC and may serve as a promising novel prognostic biomarker. The concrete mechanisms of RP11‐466I1.1 playing roles in HCC pathogenesis need further study.

Selenium Supplementation Alters Hepatic Energy and Fatty Acid Metabolism in Mice

Background

Human and animal studies have raised concerns that supplemental selenium can increase the risk of metabolic disorders, but underlying mechanisms are unclear.

Objective

We used an integrated transcriptome and metabolome analysis of liver to test for functional pathway and network responses to supplemental selenium in mice.

Methods

Male mice (8-wk-old, C57BL/6J) fed a standard diet (0.41 ppm Se) were given selenium (Na2SeO4, 20 μmol/L) or vehicle (drinking water) for 16 wk. Livers were analyzed for selenium concentration, activity of selenoproteins, reduced glutathione (GSH) redox state, gene expression, and high-resolution metabolomics. Transcriptomic and nontargeted metabolomic data were analyzed with biostatistics, bioinformatics, pathway enrichment analysis, and combined transcriptome-metabolome-wide association study (TMWAS).

Results

Mice supplemented with selenium had greater body mass gain from baseline to 16 wk (55% ± 5%) compared with controls (40% ± 3%) (P < 0.05); however, no difference was observed in liver selenium content, selenoenzyme transcripts, or enzyme activity. Selenium was higher in the heart, kidney, and urine of mice supplemented with selenium. Gene enrichment analysis showed that supplemental selenium altered pathways of lipid and energy metabolism. Integrated transcriptome and metabolome network analysis showed 2 major gene-metabolite clusters, 1 centered on the transcript for the bidirectional glucose transporter 2 (Glut2) and the other centered on the transcripts for carnitine-palmitoyl transferase 2 (Cpt2) and acetyl-CoA acyltransferase (Acaa1). Pathway analysis showed that highly associated metabolites (P < 0.05) were enriched in fatty acid metabolism and bile acid biosynthesis, including acylcarnitines, triglycerides and glycerophospholipids, long-chain acyl-coenzyme As, phosphatidylcholines, and sterols. TMWAS of body weight gain confirmed changes in the same pathways.

Conclusions

Supplemental selenium in mice alters hepatic fatty acid and energy metabolism and causes increases in body mass. A lack of effect on hepatic selenium content suggests that signaling involves an extrahepatic mechanism.

A potentially functional variant of ARID1B interacts with physical activity in association with risk of hepatocellular carcinoma

The tumor suppressor role of AT-rich interactive domain containing protein 1B (ARID1B) has drawn much attention in area of cancer etiology. However, it had remained unknown whether or not genetic variants of ARID1B involved in development of hepatocellular carcinoma (HCC). In this study, three putatively functional variants in ARID1B (rs73013281C>T, rs167007A>G, and rs9397984C>T) were selected using bioinformatics tools, and a case-control study of 611 cases and 614 controls was conducted to investigate genetic associations with HCC risk in a Southern Chinese population. Two-dimensional gene-environment interactions were also explored using both multiplicative and additive scales. A dominant effect of the rs73013281 was found for HCC risk, with an adjusted odds ratio (OR) of 1.70 [95% confidence interval (CI) = 1.03-2.80] for the CT/TT genotypes compared to the CC genotype. In stratified analysis, the detrimental effect of the T allele on elevated HCC risk was attenuated by physical activity, with an adjusted OR of 2.75 (95% CI = 1.39-5.41) among inactive individuals against that of 0.89 (95% CI = 0.42-1.91) in those who exercised regularly. Expectably, the rs73013281 showed both multiplicative and additive interactions with physical activity (P = 0.037 and 0.006, respectively). In conclusion, these results highlighted the significant genetic contribution of the ARID1B variant, rs73013281, to susceptibility for HCC, especially in interaction with physical activity.

Dietary Supplement of Large Yellow Tea Ameliorates Metabolic Syndrome and Attenuates Hepatic Steatosis in db/db Mice

Yellow tea has been widely recognized for its health benefits. However, its effects and mechanism are largely unknown. The current study investigated the mechanism of dietary supplements of large yellow tea and its effects on metabolic syndrome and the hepatic steatosis in male db/db mice. Our data showed that dietary supplements of large yellow tea and water extract significantly reduced water intake and food consumption, lowered the serum total and low-density lipoprotein cholesterol and triglyceride levels, and significantly reduced blood glucose level and increased glucose tolerance in db/db mice when compared to untreated db/db mice. In addition, the dietary supplement of large yellow tea prevented the fatty liver formation and restored the normal hepatic structure of db/db mice. Furthermore, the dietary supplement of large yellow tea obviously reduced the lipid synthesis related to gene fatty acid synthase, the sterol regulatory element-binding transcription factor 1 and acetyl-CoA carboxylase α, as well as fatty acid synthase and sterol response element-binding protein 1 expression, while the lipid catabolic genes were not altered in the liver of db/db mice. This study substantiated that the dietary supplement of large yellow tea has potential as a food additive for ameliorating type 2 diabetes-associated symptoms.

A pathology atlas of the human cancer transcriptome

Cancer is one of the leading causes of death, and there is great interest in understanding the underlying molecular mechanisms involved in the pathogenesis and progression of individual tumors. We used systems-level approaches to analyze the genome-wide transcriptome of the protein-coding genes of 17 major cancer types with respect to clinical outcome. A general pattern emerged: Shorter patient survival was associated with up-regulation of genes involved in cell growth and with down-regulation of genes involved in cellular differentiation. Using genome-scale metabolic models, we show that cancer patients have widespread metabolic heterogeneity, highlighting the need for precise and personalized medicine for cancer treatment. All data are presented in an interactive open-access database (www.proteinatlas.org/pathology) to allow genome-wide exploration of the impact of individual proteins on clinical outcomes.

High MRPS23 expression contributes to hepatocellular carcinoma proliferation and indicates poor survival outcomes

Hepatocellular carcinoma is one of the most prevalent neoplasms and the leading cause of cancer-related mortality worldwide. Mitochondrial ribosomal protein S23 is encoded by a nuclear gene and participates in mitochondrial protein translation. Mitochondrial ribosomal protein S23 overexpression has been found in many types of cancer. In this study, we explored mitochondrial ribosomal protein S23 expression in primary hepatocellular carcinoma tissues compared with matched adjacent non-tumoral liver tissues using mitochondrial ribosomal protein S23 messenger RNA and protein levels collected from public databases and clinical samples. Immunohistochemistry was performed to analyze the relationship between mitochondrial ribosomal protein S23 and various clinicopathological features. The results indicated that mitochondrial ribosomal protein S23 was significantly overexpressed in hepatocellular carcinoma. High mitochondrial ribosomal protein S23 expression was correlated with the tumor size and tumor–metastasis–node stage. Moreover, patients with high mitochondrial ribosomal protein S23 expression levels presented poorer survival rates. Mitochondrial ribosomal protein S23 was an independent prognostic factor for survival, especially at the early stage of hepatocellular carcinoma. In addition, the downregulation of mitochondrial ribosomal protein S23 decreased the proliferation of hepatocellular carcinoma in vitro and in vivo. In conclusion, we verified for the first time that mitochondrial ribosomal protein S23 expression was upregulated in hepatocellular carcinoma. High mitochondrial ribosomal protein S23 levels can predict poor clinical outcomes in hepatocellular carcinoma, and this protein plays a key role in tumor proliferation. Therefore, mitochondrial ribosomal protein S23 may be a potential therapeutic target for hepatocellular carcinoma.

Computational modelling of genome-scale metabolic networks and its application to CHO cell cultures

Genome-scale metabolic models (GEMs) have become increasingly important in recent years. Currently, GEMs are the most accurate in silico representation of the genotype-phenotype link. They allow us to study complex networks from the systems perspective. Their application may drastically reduce the amount of experimental and clinical work, improve diagnostic tools and increase our understanding of complex biological phenomena. GEMs have also demonstrated high potential for the optimisation of bio-based production of recombinant proteins. Herein, we review the basic concepts, methods, resources and software tools used for the reconstruction and application of GEMs. We overview the evolution of the modelling efforts devoted to the metabolism of Chinese Hamster Ovary (CHO) cells. We present a case study on CHO cell metabolism under different amino acid depletions. This leads us to the identification of the most influential as well as essential amino acids in selected CHO cell lines.

Aspirin suppresses the abnormal lipid metabolism in liver cancer cells via disrupting an NFκB-ACSL1 signaling

Abnormal lipid metabolism is a hallmark of tumorigenesis. Hence, the alterations of metabolism enhance the development of hepatocellular carcinoma (HCC). Aspirin is able to inhibit the growth of cancers through targeting nuclear factor κB (NF-κB). However, the role of aspirin in disrupting abnormal lipid metabolism in HCC remains poorly understood. In this study, we report that aspirin can suppress the abnormal lipid metabolism of HCC cells through inhibiting acyl-CoA synthetase long-chain family member 1 (ACSL1), a lipid metabolism-related enzyme. Interestingly, oil red O staining showed that aspirin suppressed lipogenesis in HepG2 cells and Huh7 cells in a dose-dependent manner. In addition, aspirin attenuated the levels of triglyceride and cholesterol in the cells, respectively. Strikingly, we identified that aspirin was able to down-regulate ACSL1 at the levels of mRNA and protein. Moreover, we validated that aspirin decreased the nuclear levels of NF-κB in HepG2 cells. Mechanically, PDTC, an inhibitor of NF-κB, could down-regulate ACSL1 at the levels of mRNA and protein in the cells. Functionally, PDTC reduced the levels of lipid droplets, triglyceride and cholesterol in HepG2 cells. Thus, we conclude that aspirin suppresses the abnormal lipid metabolism in HCC cells via disrupting an NFκB-ACSL1 signaling. Our finding provides new insights into the mechanism by which aspirin inhibits abnormal lipid metabolism of HCC. Therapeutically, aspirin is potentially available for HCC through controlling abnormal lipid metabolism.

Personal model-assisted identification of NAD+ and glutathione metabolism as intervention target in NAFLD

To elucidate the molecular mechanisms underlying non‐alcoholic fatty liver disease (NAFLD), we recruited 86 subjects with varying degrees of hepatic steatosis (HS). We obtained experimental data on lipoprotein fluxes and used these individual measurements as personalized constraints of a hepatocyte genome‐scale metabolic model to investigate metabolic differences in liver, taking into account its interactions with other tissues. Our systems level analysis predicted an altered demand for NAD⁺ and glutathione (GSH) in subjects with high HS. Our analysis and metabolomic measurements showed that plasma levels of glycine, serine, and associated metabolites are negatively correlated with HS, suggesting that these GSH metabolism precursors might be limiting. Quantification of the hepatic expression levels of the associated enzymes further pointed to altered de novo GSH synthesis. To assess the effect of GSH and NAD⁺ repletion on the development of NAFLD, we added precursors for GSH and NAD⁺ biosynthesis to the Western diet and demonstrated that supplementation prevents HS in mice. In a proof‐of‐concept human study, we found improved liver function and decreased HS after supplementation with serine (a precursor to glycine) and hereby propose a strategy for NAFLD treatment.