Dataset Information

Screening and identification of key genes between liver hepatocellular carcinoma (LIHC) and cholangiocarcinoma (CHOL) by bioinformatic analysis.

ABSTRACT:

Background

Liver hepatocellular carcinoma (LIHC) and cholangiocarcinoma (CHOL) are common primary liver cancers worldwide. Liver stem cells have biopotential to differentiate into either hepatocytes and cholangiocytes, the phenotypic overlap between LIHC and CHOL has been acceptable as a continuous liver cancer spectrum. However, few studies directly investigated the underlying molecular mechanisms between LIHC and CHOL.

Method

To identify the candidate genes between LIHC and CHOL, three data series including GSE31370, GSE15765 and GSE40367 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and function enrichment analyses were performed. The protein-protein interaction network (PPI) was constructed and the module analysis was performed using STRING and Cytoscape.

Results

A total of 171 DEGs were identified, consisting of 49 downregulated genes and 122 upregulated genes. Compared with CHOL, the enriched functions of the DEGs mainly included steroid metabolic process, acute inflammatory response, coagulation. Meanwhile, the pathway of KEGG enrichment analyses showed that the upregulated gene(s) were mainly enriched complement and coagulation cascades, cholesterol metabolism and PPAR signaling pathway, while the downregulated gene(s) were mainly enriched in ECM-receptor interaction, focal adhesion, bile secretion. Similarly, the most significant module was identified and biological process analysis revealed that these genes were mainly enriched in regulation of blood coagulation, acute inflammatory response, complement and coagulation cascades. Finally, two (ITIH2 and APOA2) of 10 hub genes had been screened out to help differential diagnosis.

Conclusion

171 DEGs and two (ITIH2 and APOA2) of 10 hub genes identified in the present study help us understand the different molecular mechanisms between LIHC and CHOL, and provide candidate targets for differential diagnosis.

SUBMITTER: Kang X

PROVIDER: S-EPMC7738106 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Screening and identification of key genes between liver hepatocellular carcinoma (LIHC) and cholangiocarcinoma (CHOL) by bioinformatic analysis.

Kang Xindan X Bai Li L Qi Xiaoguang X Wang Jing J

Medicine 20201201 50

<h4>Background</h4>Liver hepatocellular carcinoma (LIHC) and cholangiocarcinoma (CHOL) are common primary liver cancers worldwide. Liver stem cells have biopotential to differentiate into either hepatocytes and cholangiocytes, the phenotypic overlap between LIHC and CHOL has been acceptable as a continuous liver cancer spectrum. However, few studies directly investigated the underlying molecular mechanisms between LIHC and CHOL.<h4>Method</h4>To identify the candidate genes between LIHC and CHOL ...[more]

PMID: 33327311

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Project description:Background:Alcohol-related hepatocellular carcinoma (HCC) was reported to be diagnosed at a later stage, but the mechanism was unknown. This study aimed to identify special key genes (SKGs) during alcohol-related HCC development and progression. Methods:The mRNA data of 369 HCC patients and the clinical information were downloaded from the Cancer Genome Atlas project (TCGA). The 310 patients with certain HCC-related risk factors were included for analysis and divided into seven groups according to the risk factors. Survival analyses were applied for the HCC patients of different groups. The patients with hepatitis B virus or hepatitis C virus infection only were combined into the HCC-V group for further analysis. The differentially expressed genes (DEGs) between the HCCs with alcohol consumption only (HCC-A) and HCC-V tumors were identified through limma package in R with cutoff criteria?log2 fold change (logFC)|>1.0 and p < 0.05. The DEGs between eight alcohol-related HCCs and their paired normal livers of GSE59259 from the Gene Expression Omnibus (GEO) were identified through GEO2R (a built-in tool in GEO database) with cutoff criteria |logFC|> 2.0 and adj.p < 0.05. The intersection of the two sets of DEGs was considered SKGs which were then investigated for their specificity through comparisons between HCC-A and other four HCC groups. The SKGs were analyzed for their correlations with HCC-A stage and grade and their prognostic power for HCC-A patients. The expressional differences of the SKGs in the HCCs in whole were also investigated through Gene Expression Profiling Interactive Analysis (GEPIA). The SKGs in HCC were validated through Oncomine database analysis. Results:Pathological stage is an independent prognostic factor for HCC patients. HCC-A patients were diagnosed later than HCC patients with other risk factors. Ten SKGs were identified and nine of them were confirmed for their differences in paired samples of HCC-A patients. Three (SLC22A10, CD5L, and UROC1) and four (SLC22A10, UROC1, CSAG3, and CSMD1) confirmed genes were correlated with HCC-A stage and grade, respectively. SPP2 had a lower trend in HCC-A tumors and was negatively correlated with HCC-A stage and grade. The SKGs each was differentially expressed between HCC-A and at least one of other HCC groups. CD5L was identified to be favorable prognostic factor for overall survival while CSMD1 unfavorable prognostic factor for disease-free survival for HCC-A patients and HCC patients in whole. Through Oncomine database, the dysregulations of the SKGs in HCC and their clinical significance were confirmed. Conclusion:The poor prognosis of HCC-A patients might be due to their later diagnosis. The SKGs, especially the four stage-correlated genes (CD5L, SLC22A10, UROC1, and SPP2) might play important roles in HCC development, especially alcohol-related HCC development and progression. CD5L might be useful for overall survival and CSMD1 for disease-free survival predication in HCC, especially alcohol-related HCC.

Project description:BackgroundRecent studies have reported a role of protein phosphatase 4 regulatory subunit 1 (PPP4R1) in cancer development. However, its expression, diagnostic significance, prognostic value and biological function in liver hepatocellular carcinoma (LIHC) are not known.MethodsThe expression level of PPP4R1 in pan-cancer was evaluated by analyzing publicly accessible data from the University of California Santa Cruz (UCSC) Xena database. The diagnostic value of PPP4R1 for tumors was assessed using receiver operating characteristic (ROC) curves, whereas the impact of PPP4R1 on tumor prognosis was determined using Kaplan-Meier survival curves, and a prognostic model for LIHC was established using cox regression analysis. In addition, analysis of the correlation between PPP4R1 and anti-cancer drugs using Spearman's correlation coefficient was carried out. Four databases, miRWalk (mRNA-miRNA interactions), MicroT-CDS (mRNA-miRNA interactions), LncBase (miRNA-lncRNA interactions) and Encyclopedia of RNA Interactomes (ENCORI), were used to predict the competitive endogenous RNA (ceRNA) regulatory network of PPP4R1. Finally, the expression of PPP4R1 protein levels was verified using experiments.ResultsThe findings indicated that the PPP4R1 expression level in cancerous tissues was notably greater than in adjacent tissues (P<0.05). PPP4R1 showed diagnostic significance for 14 tumors based on the ROC curves results area under the curve >0.7. Furthermore, the Kaplan-Meier survival plots demonstrated that PPP4R1 exhibited prognostic significance for all five tumors (P<0.05). According to the cox regression analysis, LIHC patients' prognosis was independently influenced by pathological stage, M stage, and PPP4R1 (P<0.05). The drug sensitivity analysis revealed a positive correlation between the expression level of PPP4R1 and the half maximal inhibitory concentration (IC50) of fludarabine. Additionally, the ceRNA network prediction indicated that the FGD5 antisense RNA 1 (FGD5-AS1)-hsa-miR-22-3p-PPP4R1 ceRNA network could potentially contribute to the progression of LIHC. The experimental results showed that the expression level of PPP4R1 protein was higher in cancer tissues than in paracancerous tissues.ConclusionsPPP4R1 has diagnostic value in most cancers, and high expression of PPP4R1 is associated with poor prognosis, drug resistance and natural killer cell-mediated toxicity, particularly in LIHC. Therefore, PPP4R1 may be a prognostic biomarker and a potential target for immunotherapy in LIHC.

Project description:BackgroundThe molecular mechanism by which hepatitis B virus (HBV) induces hepatocellular carcinoma (HCC) is still unknown. The genomic expression profile and bioinformatics methods were used to investigate the potential pathogenesis and therapeutic targets for HBV-associated HCC (HBV-HCC).MethodsThe microarray dataset GSE55092 was downloaded from the Gene Expression Omnibus (GEO) database. The data was analyzed by the bioinformatics software to find differentially expressed genes (DEGs). Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, ingenuity pathway analysis (IPA), and protein-protein interaction (PPI) network analysis were then performed on DEGs. The hub genes were identified using Centiscape2.2 and Molecular Complex Detection (MCODE) in the Cytoscape software (Cytoscape_v3.7.2). The survival data of these hub genes was downloaded from the Gene Expression Profiling Interactive Analysis (GEPIA).ResultsA total of 2264 mRNA transcripts were differentially expressed, including 764 upregulated and 1500 downregulated in tumor tissues. GO analysis revealed that these DEGs were related to the small-molecule metabolic process, xenobiotic metabolic process, and cellular nitrogen compound metabolic process. KEGG pathway analysis revealed that metabolic pathways, complement and coagulation cascades, and chemical carcinogenesis were involved. Diseases and biofunctions showed that DEGs were mainly associated with the following diseases or biological function abnormalities: cancer, organismal injury and abnormalities, gastrointestinal disease, and hepatic system disease. The top 10 upstream regulators were predicted to be activated or inhibited by Z-score and identified 25 networks. The 10 genes with the highest degree of connectivity were defined as the hub genes. Cox regression revealed that all the 10 genes (CDC20, BUB1B, KIF11, TTK, EZH2, ZWINT, NDC80, TPX2, MELK, and KIF20A) were related to the overall survival.ConclusionOur study provided a registry of genes that play important roles in regulating the development of HBV-HCC, assisting us in understanding the molecular mechanisms that underlie the carcinogenesis and progression of HCC.

Project description:BackgroundNon-alcoholic fatty liver disease (NAFLD) is a risk factor for hepatocellular carcinoma (HCC). However, its carcinogenic mechanism is still unclear, looking for both diseases' transcriptome levels, the same changes as we are looking for NAFLD may provide a potential mechanism of action of HCC. Thus, our study aimed to discover the coexisting pathogenic genes of NAFLD and HCC.MethodsWe performed a variance analysis with public data for both diseases. At the same time, weighted gene correlation network analysis (WGCNA) was used to find highly correlated gene modules in both diseases. The darkturquoise gene module was found to be highly correlated with both diseases. Based on the diagnosis related module genes and the differential genes of the two diseases, we constructed diagnostic and prognostic models by logistic regression, univariate Cox regression, and LASSO regression. Public datasets verified the results. Meanwhile, we built a competing endogenous RNA (ceRNA) network based on the model genes and explored the related pathways and immune correlation involved in the two diseases by using Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set enrichment analyses. Immunohistochemistry was used to verify the different expression of ABCC5 and TUBG1 among the normal liver, NAFLD, and HCC tissues. Sodium palmitate/sodium oleate was used to establish high-fat cell models, and Real Time Quantitative Polymerase Chain Reaction (RT-qPCR) was used to verify the messenger RNA (mRNA) expression of ABCC5 in lipidization cells.ResultsA total of 26 upregulated genes and 87 downregulated genes were found using limma package identification analysis. According to WGCNA, the darkturquoise gene module was highly correlated with the prognosis of both diseases. The coexisting genes acquired by the two groups were only three central genes, that is, ABCC5, DHODH and TUBG1. The results indicated that the diagnostic and prognostic models constructed by ABCC5 and TUBG1 genes had high accuracy in both diseases. The results of immunohistochemistry showed that ABCC5 and TUBG1 were significantly overexpressed in NAFLD and HCC tissues compared with normal liver tissues. The Oil Red O staining and triglyceride identified the successful construction of HepG2 and LO2 high-fat models using PA/OA. The results of RT-qPCR showed that the lipidization of LO2 and HepG2 increased the mRNA expression of ABCC5.ConclusionsThe gene model constructed by ABCC5 and TUBG1 has high sensibility and veracity in the diagnosis of NAFLD as well as the diagnosis and prognosis of HCC. ABCC5 and TUBG1 may play an important role in the development of NAFLD to HCC. In addition, lipidization could upregulate the mRNA expression of ABCC5 in HCC.

Dataset Information

Screening and identification of key genes between liver hepatocellular carcinoma (LIHC) and cholangiocarcinoma (CHOL) by bioinformatic analysis.

Background

Method

Results

Conclusion

Publications

Screening and identification of key genes between liver hepatocellular carcinoma (LIHC) and cholangiocarcinoma (CHOL) by bioinformatic analysis.

Similar Datasets

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