Comparison of hepatocellular carcinoma (HCC), cholangiocarcinoma (CC), and combined HCC-CC (CHC) with each other based on microarray dataset

Prediction and analysis of weighted genes in hepatocellular carcinoma using bioinformatics analysis

The aim of the present study was to identify the differentially expressed genes (DEGs) between primary tumor tissue and adjacent non-tumor tissue of hepatocellular carcinoma (HCC) samples in order to investigate the mechanisms of HCC. The microarray data of the datasets GSE76427, GSE84005 and GSE57957 were downloaded from the Gene Expression Omnibus database. DEGs were identified using the limma package in the R programming language. Following the intersection of the DEGs screened from the three datasets, 218 genes were selected for further study. A protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes database. The construction and analysis of modules were performed using Cytoscape and the module with the highest score was selected for further analysis. Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were conducted for genes involved in the PPI network and the selected subnetwork. The network of the enriched pathways and their associated genes was constructed using Cytoscape. For the genes in the global PPI network, metabolism-associated pathways were significantly enriched; whereas, for the genes in the subnetwork, ‘cell cycle’, ‘oocyte meiosis’ and ‘DNA replication’ pathways were significantly enriched. To demonstrate the portability and repeatability of the prognostic value of the weighted genes, a validation cohort was obtained from datasets of The Cancer Genome Atlas and Kaplan-Meier survival analysis was conducted. Evidence is presented that the expression levels of aldehyde dehydrogenase 2 family member, cytochrome P450 family 2 subfamily C member 8, alcohol dehydrogenase 4 (class II), pi polypeptide, alcohol dehydrogenase 1B (class I), β polypeptide and cytochrome P450 family 2 subfamily C member 9 were associated with the overall survival of patients with HCC and that the expression levels of pituitary tumor-transforming 1, cell division cycle 20, DNA topoisomerase II α and cyclin B2 were negatively associated with the overall survival of patients with HCC. In conclusion, 9 weighted genes, involved in the development and progression of HCC, were identified using bioinformatics and survival analyses.

Subclassification and Detection of New Markers for the Discrimination of Primary Liver Tumors by Gene Expression Analysis Using Oligonucleotide Arrays

Background/Aims

The failure to correctly differentiate between intrahepatic cholangiocarcinoma (CC) and hepatocellular carcinoma (HCC) is a significant clinical problem, particularly in terms of the different treatment goals for both cancers. In this study a specific gene expression profile to discriminate these two subgroups of liver cancer was established and potential diagnostic markers for clinical use were analyzed.

Methods

To evaluate the gene expression profiles of HCC and intrahepatic CC, Oligonucleotide arrays (AffymetrixU133A) were used. Overexpressed genes were checked for their potential use as new markers for discrimination and their expression values were validated by reverse transcription polymerase chain reaction and immunohistochemistry analyses.

Results

695 genes/expressed sequence tags (ESTs) in HCC (245 up-/450 down-regulated) and 552 genes/ESTs in CC (221 up-/331 down-regulated) were significantly dysregulated (p＜0.05, fold change >2, ≥70%). Using a supervised learning method, and one-way analysis of variance a specific 270-gene expression profile that enabled rapid, reproducible differentiation between both tumors and nonmalignant liver tissues was established. A panel of 12 genes (e.g., HSP90β, ERG1, GPC3, TKT, ACLY, and NME1 for HCC; SPT2, T4S3, CNX43, TTD1, HBD01 for CC) were detected and partly described for the first time as potential discrimination markers.

Conclusions

A specific gene expression profile for discrimination of primary liver cancer was identified and potential marker genes with feasible clinical impact were described.

Identification of hub genes and pathways associated with hepatocellular carcinoma based on network strategy

The objective of this study was to identify hub genes and pathways associated with hepatocellular carcinoma (HCC) by centrality analysis of a co-expression network. A co-expression network based on differentially expressed (DE) genes of HCC was constructed using the Differentially Co-expressed Genes and Links (DCGL) package. Centrality analyses, for centrality of degree, clustering coefficient, closeness, stress and betweenness for the co-expression network were performed to identify hub genes, and the hub genes were combined together to overcome inconsistent results. Enrichment analyses were conducted using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. Finally, validation of hub genes was conducted utilizing reverse transcription-polymerase chain reaction (RT-PCR) analysis. In total, 260 DE genes between normal controls and HCC patients were obtained and a co-expression network with 154 nodes and 326 edges was constructed. From this, 13 hub genes were identified according to degree, clustering coefficient, closeness, stress and betweenness centrality analysis. It was found that reelin (RELN), potassium voltage-gated channel subfamily J member 10 (KCNJ10) and neural cell adhesion molecule 1 (NCAM1) were common hub genes across the five centralities, and the results of RT-PCR analysis for RELN, KCNJ10 and NCAM1 were consistent with the centrality analyses. Pathway enrichment analysis of DE genes showed that cell cycle, metabolism of xenobiotics by cytochrome P450 and p53 signaling pathway were the most significant pathways. This study may contribute to understanding the molecular pathogenesis of HCC and provide potential biomarkers for its early detection and effective therapies.

Elevated urinary urea by high-protein diet could be one of the inducements of bladder disorders

Background

Previous work found that urea accumulation in urothelial cells caused by urea transporter B knockout led to DNA damage and apoptosis that contributed to the carcinogenesis. The purpose of this study is to explore the potential connection between high urinary urea concentration and the bladder disorders.

Methods

A high protein diet rat model was conducted by feeding with 40 % protein diet. In-silico modeling and algorithm, based on the results of microarray and proteomics from the bladder urothelium, were used for the reconstruction of accurate cellular networks and the identification of novel master regulators in the high-protein diet rat model. Pathway and biological process enrichment analysis were used to characterize predicted targets of candidate mRNAs/proteins. The expression pattern of the most significant master regulators was evaluated by qPCR and immunohistochemistry.

Results

Based on the analysis of different expressed mRNAs/proteins, 15 significant ones (CRP, MCPT2, MCPT9, EPXH2, SERPING1, SRGN, CDKN1C, CDK6, CCNB1, PCNA, BAX, MAGEB16, SERPINE1, HSPA2, FOS) were highly identified and verified by qPCR and immunohistochemistry. They were involved in immune and inflammatory response, cell cycle arrest, apoptosis and pathways in cancer. These abnormally activated processes caused the bladder interstitial congestion and inflammatory infiltrates under the thinner urothelium, cell desquamation, cytoplasm vacuolization, nucleus swelling and malformation in the high-protein diet group.

Conclusions

We provided evidences that high urinary urea concentration caused by high-protein diet might be a potential carcinogenic factor in bladder.

Differentially expressed gene profiles of intrahepatic cholangiocarcinoma, hepatocellular carcinoma, and combined hepatocellular-cholangiocarcinoma by integrated microarray analysis

Intrahepatic cholangiocarcinoma (ICC) and hepatocellular carcinoma (HCC) are common primary liver cancers worldwide. However, the survival and prognosis of ICC are much poorer than those of HCC, indicating the different molecular characteristics and mechanisms between ICC and HCC. To identify differentially expressed (DE) genes between ICC and HCC or combined hepatocellular-cholangiocarcinoma (CHC), we performed integrated analysis of publicly available microarray Gene Expression Omnibus (GEO) datasets by MetaOmics. Three GEO datasets comprising 32 ICC biochips, 77 HCC biochips, and 34 CHC biochips were available for the data integration. We identified 7313 DE genes between ICC and HCC, including 3650 upregulated genes and 3663 downregulated genes. The S100 family members on chromosome 1q21 were extensively upregulated in ICC, and S100A11 had the greatest degree of upregulation in ICC. Based on the DE genes, combined gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis showed the enhanced pathways of local adhesion, ECM-receptor interaction, and regulation of action cytoskeleton, suggesting the enhanced communication between ICC and the microenvironment. Additionally, development-related genes and development-related pathways, including the Notch, Wnt, and TGF-β signaling pathways, were shown to be active prominently in ICC. Taken together, we identified the characteristically upregulated or downregulated DE genes and pathways in ICC compared with HCC or CHC. These DE genes and pathways supply new transcriptomics evidence for ICC and could help identify new therapeutic targets.

Identification of GRB2 and GAB1 coexpression as an unfavorable prognostic factor for hepatocellular carcinoma by a combination of expression profile and network analysis

Aim

To screen novel markers for hepatocellular carcinoma (HCC) by a combination of expression profile, interaction network analysis and clinical validation.

Methods

HCC significant molecules which are differentially expressed or had genetic variations in HCC tissues were obtained from five existing HCC related databases (OncoDB.HCC, HCC.net, dbHCCvar, EHCO and Liverome). Then, the protein-protein interaction (PPI) network of these molecules was constructed. Three topological features of the network ('Degree', 'Betweenness', and 'Closeness') and the k-core algorithm were used to screen candidate HCC markers which play crucial roles in tumorigenesis of HCC. Furthermore, the clinical significance of two candidate HCC markers growth factor receptor-bound 2 (GRB2) and GRB2-associated-binding protein 1 (GAB1) was validated.

Results

In total, 6179 HCC significant genes and 977 HCC significant proteins were collected from existing HCC related databases. After network analysis, 331 candidate HCC markers were identified. Especially, GAB1 has the highest k-coreness suggesting its central localization in HCC related network, and the interaction between GRB2 and GAB1 has the largest edge-betweenness implying it may be biologically important to the function of HCC related network. As the results of clinical validation, the expression levels of both GRB2 and GAB1 proteins were significantly higher in HCC tissues than those in their adjacent nonneoplastic tissues. More importantly, the combined GRB2 and GAB1 protein expression was significantly associated with aggressive tumor progression and poor prognosis in patients with HCC.

Conclusion

This study provided an integrative analysis by combining expression profile and interaction network analysis to identify a list of biologically significant HCC related markers and pathways. Further experimental validation indicated that the aberrant expression of GRB2 and GAB1 proteins may be strongly related to tumor progression and prognosis in patients with HCC. The overexpression of GRB2 in combination with upregulation of GAB1 may be an unfavorable prognostic factor for HCC.