A network biology approach to discover the molecular biomarker associated with hepatocellular carcinoma

Fibulin-2 Facilitates Malignant Progression of Hepatocellular Carcinoma

BACKGROUND

Identification of biomarkers to assist in the clinical management of hepatocellular carcinoma represents an urgent requirement. Fibulin-2 is known to contribute to the development and progression of various cancer types. This research investigated the role of fibulin-2 in hepatocellular carcinoma and explored the possible mechanisms.

METHODS

The expression of fibulin-2 in hepatocellular carcinoma was measured by bioinformatic analysis and confirmed by western blot and immunohistochemical staining in cell lines or patients' samples. The clinicopathologic features of hepatocellular carcinoma patients was analyzed. Cell viability assays were used to explore the role of fibulin-2 on proliferation in hepatocellular carcinoma. Western blot was conducted to uncover changes of protein expression of Ras-MEK-ERK1/2 pathway when Fibulin-2 was overexpressed or silenced. Flow cytometry analyses were used to determine the roles of fibulin-2 in the function of apoptosis and cell cycle. Subcutaneous xenograft mouse models showed the tumor growth pattern after fibulin-2 silence in vivo.

RESULTS

We reported the upregulation of fibulin-2 in most hepatocellular carcinoma tissues and cells lines. Fibulin-2 promoted the proliferation of hepatocellular carcinoma cells in vitro by regulating Ras-MEK-ERK1/2 signaling pathway, whereas knockdown of fibulin-2 incurred the opposite effect on proliferation. Consistently, knockdown of fibulin-2 resulted in increased apoptosis and induced growth arrest during the G0/G1 phase transition. In vivo xenograft assessment confirmed that knockdown of fibulin-2 inhibited hepatocellular carcinoma tumor growth.

CONCLUSIONS

Fibulin-2 exhibited tumor promotor activities in malignant progression of hepatocellular carcinoma. The results of the study highlighted the potential of fibulin-2 to be utilized as a promising biomarker and therapeutic target for hepatocellular carcinoma.

Deciphering Key Genes and miRNAs Associated With Hepatocellular Carcinoma via Network-Based Approach

Hepatocellular carcinoma (HCC)is a common type of liver cancer and has a high mortality world-widely. The diagnosis, prognoses, and therapeutics are very poor due to the unclear molecular mechanism of progression of the disease. To unveil the molecular mechanism of progression of HCC, we extract a large sample of mRNA expression levels from the GEO database where a total of 167 samples were used for study, and out of them, 115 samples were from HCC tumor tissue. This study aims to investigate the module of differentially expressed genes (DEGs)which are co-expressed only in HCC sample data but not in normal tissue samples. Thereafter, we identified the highly significant module of significant co-expressed genes and formed a PPI network for these genes. There were only six genes (namely, MSH3, DMC1, ALPP, IL10, ZNF223, and HSD17B7)obtained after analysis of the PPI network. Out of six only MSH3, DMC1, HSD17B7, and IL10 were found enriched in GO Term & Pathway enrichment analysis and these candidate genes were mainly involved in cellular process, metabolic and catalytic activity, which promote the development & progression of HCC. Lastly, the composite 3-node FFL reveals the driver miRNAs and TFs associated with our key genes.

CCNB2, CDC20, AURKA, TOP2A, MELK, NCAPG, KIF20A, UBE2C, PRC1, and ASPM May Be Potential Therapeutic Targets for Hepatocellular Carcinoma Using Integrated Bioinformatic Analysis

Background

Hepatocellular carcinoma (HCC) is a highly malignant, recurrent and drug-resistant tumor, and patients often lose the opportunity for surgery when they are diagnosed. Abnormal gene expression is closely related to the occurrence of HCC. The aim of the present study was to identify the differentially expressed genes (DEGs) between tumor tissue and non-tumor tissue of HCC samples in order to investigate the mechanisms of liver cancer.

Methods

The gene expression profile (GSE62232, GSE89377, and GSE112790) was downloaded from the Gene Expression Omnibus (GEO) and analyzed using the online tool GEO2R to identify differentially expressed genes (DEGs). Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the Database for Annotation, Visualization and Integrated Discovery. Protein–protein interaction (PPI) of these DEGs was analyzed based on the Search Tool for the Retrieval of Interacting Genes database and visualized by Cytoscape software. In addition, we used the online Kaplan–Meier plotter survival analysis tool to evaluate the prognostic value of hub genes expression. HPA database was used to reveal the differences in protein level of hub genes.

Results

A total of 50 upregulated DEGs and 122 downregulated DEGs were identified. Among them, ten hub genes with a high degree of connectivity were picked out. Overexpression of these hub genes was associated with unfavorable prognosis of HCC.

Conclusion

Our study suggests that CCNB2, CDC20, AURKA, TOP2A, MELK, NCAPG, KIF20A, UBE2C, PRC1, and ASPM were overexpressed in HCC compared with normal liver tissue. Overexpression of these genes was an unfavorable prognostic factor of HCC patients. Further study is needed to explore the value of them in the diagnosis and treatment of HCC.

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Integrated Proteomics and Bioinformatics to Identify Potential Prognostic Biomarkers in Hepatocellular Carcinoma

BACKGROUND

Liver hepatocellular carcinoma (HCC) is the third most common cause of death by cancer and has a high mortality world-widely. Approximately 75-85% of primary liver cancers are caused by HCC. Uncovering novel genes with prognostic significance would shed light on improving the HCC patient's outcome.

OBJECTIVE

In this research, we aim to identify novel prognostic biomarkers in hepatocellular carcinoma.

METHODS

Integrated proteomics and bioinformatics analysis were performed to investigate the expression landscape of prognostic biomarkers in 24 paired HCC patients.

RESULTS

As a result, eight key genes related to prognosis, including ACADS, HSD17B13, PON3, AMDHD1, CYP2C8, CYP4A11, SLC27A5, CYP2E1, were identified by comparing the weighted gene co-expression network analysis (WGCNA), proteomic differentially expressed genes (DEGs), proteomic turquoise module, The Cancer Genome Atlas (TCGA) cohort DEGs of HCC. Furthermore, we trained and validated eight pivotal genes integrating these independent clinical variables into a nomogram with superior accuracy in predicting progression events, and their lower expression was associated with a higher stage/risk score. The Gene Set Enrichment Analysis (GSEA) further revealed that these key genes showed enrichment in the HCC regulatory pathway.

CONCLUSION

All in all, we found that these eight genes might be the novel potential prognostic biomarkers for HCC and also provide promising insights into the pathogenesis of HCC at the molecular level.

Identifying potential drug targets in hepatocellular carcinoma based on network analysis and one-class support vector machine

Hepatocellular carcinoma (HCC) is one major cause of cancer-related death worldwide. But now, the systematic therapy for the advanced stages of HCC is rather limited. Thus, the discovery of novel drug targets and thereafter targeted drugs against HCC is continuously needed. In this study, we combined clinical association data, gene expression profiles and manually collected drug target genes with the human protein-protein interaction (PPI) network to establish an in-silico HCC drug target predictor. First, we found drug target genes (DTGs), disease-associated genes (DAGs), prognostic unfavorable genes (PUGs) and cancer up-regulated genes (URGs) have higher degree, betweenness, closeness centrality, while cancer down-regulated genes (DRGs), prognostic favorable genes (PFGs) have lower degrees, in comparison with background genes. Moreover, DTG nodes were shown to be closer to DAG, PUG and URG nodes, but farther away from PFG and DRG nodes. Compared to the background, PFGs and DRGs were shown to have relatively bigger genetic dependency scores, while PUGs and URGs have smaller genetic dependency scores. Finally, based on the observed features of DTGs, we constructed a drug target predictor using one-class support vector machine (one-class SVM). Performance evaluation results suggested our predictor could effectively identify putative drug target genes for further research.

Protein-Protein Interaction Network Analysis of Salivary Proteomic Data in Oral Cancer Cases

Background:

Oral cancer is a frequently encountered neoplasm of the head and neck region, being the eight most common type of human malignancy worldwide. Despite improvement in its control, morbidity and mortality rates have improved little in the past decades. Therefore, prevention and/or early detection are a high priority. Proteomics with network analysis have emerged as a powerful tool to identify important proteins associated with cancer development and progression that can be potential targets for early diagnosis. In the present study, network- based protein- protein interactions (PPI) for oral cancer were identified and then analyzed for use as key proteins/potential biomarkers.

Material and Methods:

Gene expression data in articles which focused on saliva proteomics of oral cancer were collected and 74 candidate genes or proteins were extracted. Related protein networks of differentially expressed proteins were explored and visualized using cytoscape software. Further PPI analysis was performed by Molecular Complex Detection (MCODE) and BiNGO methods.

Results:

Network analysis of genes/proteins related to oral cancer identified kininogen-1, angiotensinogen, annexin A1, IL-8, IgG heavy variable and constant chains, CRP, collagen alpha-1 and fibronectin as 9 hub-bottleneck proteins. In addition, based on clustering with the MCODE tool, vitronectin, collagen alpha-2, IL-8 and integrin alpha-v were established as 5 distinct seed proteins.

Conclusion:

A hub-bottleneck protein panel may offer a potential /candidate biomarker pattern for diagnosis and treatment of oral cancer disease. Further investigation and validation of these proteins are warranted.

Identification of a gene signature for discriminating metastatic from primary melanoma using a molecular interaction network approach

Understanding the biological factors that are characteristic of metastasis in melanoma remains a key approach to improving treatment. In this study, we seek to identify a gene signature of metastatic melanoma. We configured a new network-based computational pipeline, combined with a machine learning method, to mine publicly available transcriptomic data from melanoma patient samples. Our method is unbiased and scans a genome-wide protein-protein interaction network using a novel formulation for network scoring. Using this, we identify the most influential, differentially expressed nodes in metastatic as compared to primary melanoma. We evaluated the shortlisted genes by a machine learning method to rank them by their discriminatory capacities. From this, we identified a panel of 6 genes, ALDH1A1, HSP90AB1, KIT, KRT16, SPRR3 and TMEM45B whose expression values discriminated metastatic from primary melanoma (87% classification accuracy). In an independent transcriptomic data set derived from 703 primary melanomas, we showed that all six genes were significant in predicting melanoma specific survival (MSS) in a univariate analysis, which was also consistent with AJCC staging. Further, 3 of these genes, HSP90AB1, SPRR3 and KRT16 remained significant predictors of MSS in a joint analysis (HR = 2.3, P = 0.03) although, HSP90AB1 (HR = 1.9, P = 2 × 10-4) alone remained predictive after adjusting for clinical predictors.

Integrated Genomic and Network-Based Analyses of Complex Diseases and Human Disease Network

A disease phenotype generally reflects various pathobiological processes that interact in a complex network. The highly interconnected nature of the human protein interaction network (interactome) indicates that, at the molecular level, it is difficult to consider diseases as being independent of one another. Recently, genome-wide molecular measurements, data mining and bioinformatics approaches have provided the means to explore human diseases from a molecular basis. The exploration of diseases and a system of disease relationships based on the integration of genome-wide molecular data with the human interactome could offer a powerful perspective for understanding the molecular architecture of diseases. Recently, subnetwork markers have proven to be more robust and reliable than individual biomarker genes selected based on gene expression profiles alone, and achieve higher accuracy in disease classification. We have applied one of these methodologies to idiopathic dilated cardiomyopathy (IDCM) data that we have generated using a microarray and identified significant subnetworks associated with the disease. In this paper, we review the recent endeavours in this direction, and summarize the existing methodologies and computational tools for network-based analysis of complex diseases and molecular relationships among apparently different disorders and human disease network. We also discuss the future research trends and topics of this promising field.

Decreased expression and clinical significance of miR-148a in hepatocellular carcinoma tissues

Background

Aberrant expression of microRNA-148a (miR-148a) has been reported in several types of malignancies. However, its expression and clinicopathological significance in hepatocellular carcinoma (HCC) has not been entirely clarified. Our objective was to investigate the clinicopathological contribution of the miR-148a expression in HCC formalin-fixed paraffin-embedded (FFPE) tissues.

Methods

Eighty-nine HCC and their para-cancerous liver tissues were recruited. Total mRNA including miRNA was isolated and miR-148a expression was determined by using real time RT-qPCR. Furthermore, the relationship between the miR-148a level and clinicopathological features was explored.

Results

Significantly lower miR-148a expression in HCC tissues was observed than that in adjacent noncancerous hepatic tissues. miR-148a expression was also correlated to clinical TNM stage, metastasis, status of capsular infiltration and numbers of tumor nodes.

Conclusions

Underexpression of miR-148a might be associated with HCC tumorigenesis and deterioration of HCC. miR-148a might act as a suppressor miRNA of HCC and it therefore has a potential role in prognosis of HCC patients.