Comparative analysis of viral protein interaction networks in Hepatitis B virus and Hepatitis C virus infected HCC

Association analysis of hepatocellular carcinoma-related hub proteins and hub genes

PURPOSE

Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide. The occurrence and development of HCC are closely related to epigenetic modifications. Epigenetic modifications can regulate gene expression and related functions through DNA methylation. This paper presents an association analysis method of HCC-related hub proteins and hub genes.

EXPERIMENTAL DESIGN

Bioinformatics analysis of HCC-related DNA methylation data is carried out to clarify the molecular mechanism of HCC-related genes and to find hub genes (genes with more connections in the network) by constructing in the gene interaction network. This paper proposes an accurate prediction method of protein-protein interaction (PPI) based on deep learning model DeepSG2PPI. The trained DeepSG2PPI model predicts the interaction relationship between the synthetic proteins regulated by HCC-related genes.

RESULTS

This paper finds that four genes are the intersection of hub genes and hub proteins. The four genes are: FBL, CCNB2, ALDH18A1, and RPLP0. The association of RPLP0 gene with HCC is a new finding of this study. RPLP0 is expected to become a new biomarker for the treatment, diagnosis, and prognosis of HCC. The four proteins corresponding to the four genes are: ENSP00000221801, ENSP00000288207, ENSP00000360268, and ENSP00000449328.

CONCLUSIONS AND CLINICAL RELEVANCE

The association between the hub genes with the hub proteins is analyzed. The mutual verification of the hub genes and the hub proteins can obtain more credible HCC-related genes and proteins, which is helpful for the diagnosis, treatment, and drug development of HCC.

The emerging importance role of m6A modification in liver disease

N6-methyladenosine (m6A) modification, as one of the most common types of inner RNA modification in eukaryotes, plays a multifunctional role in normal and abnormal biological processes. This type of modification is modulated by m6A writer, eraser and reader, which in turn impact various processes of RNA metabolism, such as RNA processing, translation, nuclear export, localization and decay. The current academic view holds that m6A modification exerts a crucial role in the post-transcriptional modulation of gene expression, and is involved in multiple cellular functions, developmental and disease processes. However, the potential molecular mechanism and specific role of m6A modification in the development of liver disease have not been fully elucidated. In our review, we summarized the latest research progress on m6A modification in liver disease, and explored how these novel findings reshape our knowledge of m6A modulation of RNA metabolism. In addition, we also illustrated the effect of m6A on liver development and regeneration to prompt further exploration of the mechanism and role of m6A modification in liver physiology and pathology, providing new insights and references for the search of potential therapeutic targets for liver disease.

Differentially expressed discriminative genes and significant meta-hub genes based key genes identification for hepatocellular carcinoma using statistical machine learning

Hepatocellular carcinoma (HCC) is the most common lethal malignancy of the liver worldwide. Thus, it is important to dig the key genes for uncovering the molecular mechanisms and to improve diagnostic and therapeutic options for HCC. This study aimed to encompass a set of statistical and machine learning computational approaches for identifying the key candidate genes for HCC. Three microarray datasets were used in this work, which were downloaded from the Gene Expression Omnibus Database. At first, normalization and differentially expressed genes (DEGs) identification were performed using limma for each dataset. Then, support vector machine (SVM) was implemented to determine the differentially expressed discriminative genes (DEDGs) from DEGs of each dataset and select overlapping DEDGs genes among identified three sets of DEDGs. Enrichment analysis was performed on common DEDGs using DAVID. A protein-protein interaction (PPI) network was constructed using STRING and the central hub genes were identified depending on the degree, maximum neighborhood component (MNC), maximal clique centrality (MCC), centralities of closeness, and betweenness criteria using CytoHubba. Simultaneously, significant modules were selected using MCODE scores and identified their associated genes from the PPI networks. Moreover, metadata were created by listing all hub genes from previous studies and identified significant meta-hub genes whose occurrence frequency was greater than 3 among previous studies. Finally, six key candidate genes (TOP2A, CDC20, ASPM, PRC1, NUSAP1, and UBE2C) were determined by intersecting shared genes among central hub genes, hub module genes, and significant meta-hub genes. Two independent test datasets (GSE76427 and TCGA-LIHC) were utilized to validate these key candidate genes using the area under the curve. Moreover, the prognostic potential of these six key candidate genes was also evaluated on the TCGA-LIHC cohort using survival analysis.

A comprehensive genome-wide profiling comparison between HBV and HCV infected hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide, especially in East Asia. Even with the progress in therapy, 5-year survival rates remain unsatisfied. Chronic infection with the hepatitis B virus (HBV) or hepatitis C virus (HCV) has been epidemiologically associated with HCC and is the major etiology in the East Asian population. The detailed mechanism, especially the changes of DNA methylation and gene expression between the two types of virus-related HCC, and their contributions to the HCC development, metastasis, and recurrence remain largely unknown.

METHODS

In this integrated analysis, we characterized genome-scale profiles of HBV and HCV infected HCC by comparing their gene expression pattern, methylation profiles, and copy number variations from the publicly accessible data of The Cancer Genome Atlas Program (TCGA).

RESULTS

The HLA-A, STAT1, and OAS2 genes were highly enriched and up-regulated discovered in the HCV-infected HCC. Hypomethylation but not copy number variations might be the major factor for the up-regulation of these immune-related genes in HCV-infected HCC.

CONCLUSIONS

The results indicated the different epigenetic changes of HBV/HCV related hepatocarcinogenesis. The top up-regulated genes in HCV group were significantly clustered in the immune-related and defense response pathways. These findings will help us to understand the pathogenesis of HBV/HCV associated hepatocellular carcinoma.

iTRAQ-Based Proteomics Identification of Serum Biomarkers of Two Chronic Hepatitis B Subtypes Diagnosed by Traditional Chinese Medicine

Background. Chronic infection with hepatitis B virus (HBV) is a leading cause of cirrhosis and hepatocellular carcinoma. By traditional Chinese medicine (TCM) pattern classification, damp heat stasis in the middle-jiao (DHSM) and liver Qi stagnation and spleen deficiency (LSSD) are two most common subtypes of CHB. Results. In this study, we employed iTRAQ proteomics technology to identify potential serum protein biomarkers in 30 LSSD-CHB and 30 DHSM-CHB patients. Of the total 842 detected proteins, 273 and 345 were differentially expressed in LSSD-CHB and DHSM-CHB patients compared to healthy controls, respectively. LSSD-CHB and DHSM-CHB shared 142 upregulated and 84 downregulated proteins, of which several proteins have been reported to be candidate biomarkers, including immunoglobulin (Ig) related proteins, complement components, apolipoproteins, heat shock proteins, insulin-like growth factor binding protein, and alpha-2-macroglobulin. In addition, we identified that proteins might be potential biomarkers to distinguish LSSD-CHB from DHSM-CHB, such as A0A0A0MS51_HUMAN (gelsolin), PON3_HUMAN, Q96K68_HUMAN, and TRPM8_HUMAN that were differentially expressed exclusively in LSSD-CHB patients and A0A087WT59_HUMAN (transthyretin), ITIH1_HUMAN, TSP1_HUMAN, CO5_HUMAN, and ALBU_HUMAN that were differentially expressed specifically in DHSM-CHB patients. Conclusion. This is the first time to report serum proteins in CHB subtype patients. Our findings provide potential biomarkers can be used for LSSD-CHB and DHSM-CHB.

Neutrophil-to-lymphocyte ratio predicts recurrence after radiofrequency ablation in hepatitis B virus infection

BACKGROUND AND AIM

Radiofrequency ablation (RFA) is an established treatment for small hepatocellular carcinoma (HCC) wherein non-recurrence is essential for long-term survival. Recently, neutrophil-to-lymphocyte ratio (NLR), a marker of systemic inflammation that is associated with tumor-associated macrophages (TAMs), was suggested to be a prognostic marker of HCC treated with RFA. Therefore, we evaluated predictive factors, including NLR, associated with recurrence after curative RFA.

METHODS

A total of 163 patients initially diagnosed with HCC and treated with RFA were enrolled. We retrospectively analyzed factors associated with recurrence and survival after RFA. Furthermore, TAMs were evaluated using surgically resected specimens.

RESULTS

Hepatitis C virus (HCV) infection was the most frequent cause of HCC in this population (111 cases, 68.1%), whereas hepatitis B virus (HBV) infection accounted for 26 cases (16.0%). Recurrence, mostly intrahepatic distant recurrence, was found in 101 cases (61.9%). Recurrence and posttreatment NLR were independent prognostic factors related to survival, and male sex, HCV infection, serum des-γ-carboxy prothrombin >  40 AU/L, and posttreatment NLR were associated with recurrence. Pretreatment NLR showed no association with recurrence, whereas posttreatment NLR showed prognostic value. Interestingly, pretreatment NLR >  2.5 was significantly associated with recurrence in HBV-HCC patients (odds ratio 3.439, P = 0.037) not but HCV-HCC (odds ratio 1.430, P = 0.17). Furthermore, TAMs were increased in the peripheral area of HCCs with HBV infection compared with those with HCV.

CONCLUSIONS

Recurrence of HCC after RFA was strongly associated with survival. NLR is useful as a predictive marker of recurrence, especially in HBV-HCC patients.

Hepatitis B virus X protein and endoplasmic reticulum stress

Persistent hepatitis B virus (HBV) infection is closely related to chronic hepatitis, cirrhosis and liver cancer. China is a country with a high prevalence of HBV infection, where the infection rate is up to 60%-70%, bringing great threat and harm to public health. What's worse is that HBV infection is the main etiology factor of primary hepatocellular carcinoma (HCC). However, the underlying mechanisms of virus-induced tumor formation remain controversial. Numerous studies indicate that HBV X protein (HBx) plays a prominent role in HBV-induced liver cell damage, hepatitis, liver fibrosis and malignant transformation, and is related to liver cancer induced by environmental exposure factors. As a multifunctional regulatory protein, HBx regulates a variety of cell signal transduction pathways, including the endoplasmic reticulum (ER) stress response. ER stress refers to the dysfunction of the ER, and misfolded or unfolded proteins gather in the ER. It is noteworthy that the expression of HBx can induce or effect ER stress, although the molecular mechanism remains unclear. This review summarizes the role of HBx in ER stress pathways, providing clues for the liver injury induced by HBV infection.

Analysis of protein interaction networks for the detection of candidate hepatitis B and C biomarkers

Hepatitis B virus (HBV) and hepatitis C virus (HCV) infection are the major causes of chronic liver disease, cirrhosis and hepatocellular carcinoma (HCC). The resolution or chronicity of acute infection is dependent on a complex interplay between virus and innate/adaptive immunity. The mechanisms that lead a significant proportion of patients to more severe liver disease are not clearly defined and involve virus induced host gene/protein alterations. The utilization of protein interaction networks (PINs) is expected to identify novel aspects of the disease concerning the patients' immune response to virus as well as the main pathways that are involved in the development of fibrosis and HCC. In this study, we designed several PINs for HBV and HCV and employed topological, modular, and functional analysis techniques in order to determine significant network nodes that correspond to prominent candidate biomarkers. The networks were built using data from various interaction databases. When the overall PINs of HBV and HCV were compared, 48 nodes were found in common. The implementation of a statistical ranking procedure indicated that three of them are of higher importance.

Integrative analysis of transcriptional regulatory network and copy number variation in intrahepatic cholangiocarcinoma

Background

Transcriptional regulatory network (TRN) is used to study conditional regulatory relationships between transcriptional factors and genes. However few studies have tried to integrate genomic variation information such as copy number variation (CNV) with TRN to find causal disturbances in a network. Intrahepatic cholangiocarcinoma (ICC) is the second most common hepatic carcinoma with high malignancy and poor prognosis. Research about ICC is relatively limited comparing to hepatocellular carcinoma, and there are no approved gene therapeutic targets yet.

Method

We first constructed TRN of ICC (ICC-TRN) using forward-and-reverse combined engineering method, and then integrated copy number variation information with ICC-TRN to select CNV-related modules and constructed CNV-ICC-TRN. We also integrated CNV-ICC-TRN with KEGG signaling pathways to investigate how CNV genes disturb signaling pathways. At last, unsupervised clustering method was applied to classify samples into distinct classes.

Result

We obtained CNV-ICC-TRN containing 33 modules which were enriched in ICC-related signaling pathways. Integrated analysis of the regulatory network and signaling pathways illustrated that CNV might interrupt signaling through locating on either genomic sites of nodes or regulators of nodes in a signaling pathway. In the end, expression profiles of nodes in CNV-ICC-TRN were used to cluster the ICC patients into two robust groups with distinct biological function features.

Conclusion

Our work represents a primary effort to construct TRN in ICC, also a primary effort to try to identify key transcriptional modules based on their involvement of genetic variations shown by gene copy number variations (CNV). This kind of approach may bring the traditional studies of TRN based only on expression data one step further to genetic disturbance. Such kind of approach can easily be extended to other disease samples with appropriate data.

Role of hepatitis B virus X protein in hepatocarcinogenesis

Hepatocellular carcinoma (HCC) is the fifth most common cancer in the world and the third most common cause of cancer-related death. Chronic hepatitis B virus (HBV) infection has been identified as a major risk factor for HCC. Evidence suggests that the HBV X protein (HBx) plays a crucial role in the carcinogenesis of HCC. HBx is a multifunctional regulator that plays a key role in the occurrence, development, invasion and metastasis of cancers. Due to its important roles in the development of HCC, the research on the HBx protein has become a hot topic in recent years. This review describes the latest advances in understanding the role of the HBx protein in hepatocarcinogenesis.