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Identification and validation of core genes as promising diagnostic signature in hepatocellular carcinoma based on integrated bioinformatics approach.

ABSTRACT: The primary objective of this investigation was to determine the hub genes of hepatocellular carcinoma (HCC) through an in silico approach. In the current context of the increased incidence of liver cancers, this approach could be a useful prognostic biomarker and HCC prevention target. This study aimed to examine hub genes for immune cell infiltration and their good prognostic characteristics for HCC research. Human genes selected from databases (Gene Cards and DisGeNET) were used to identify the HCC markers. Further, classification of the hub genes from communicating genes was performed using data derived from the targets' protein-protein interaction (PPI) platform. The expression as well as survival studies of all these selected genes were validated by utilizing databases such as GEPIA2, HPA, and immune cell infiltration. Based on the studies, five hub genes (TP53, ESR1, AKT1, CASP3, and JUN) were identified, which have been linked to HCC. They may be an important prognostic biomarker and preventative target of HCC. In silico analysis revealed that out of five hub genes, the TP53 and ESR1 hub genes potentially act as key targets for HCC prevention and treatment.

SUBMITTER: Kumar P

PROVIDER: S-EPMC9646875 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Identification and validation of core genes as promising diagnostic signature in hepatocellular carcinoma based on integrated bioinformatics approach.

Kumar Pradeep P Singh Amit Kumar AK Tiwari Kavindra Nath KN Mishra Sunil Kumar SK Rajput Vishnu D VD Minkina Tatiana T Cavalu Simona S Pop Ovidiu O

Scientific reports 20221109 1

The primary objective of this investigation was to determine the hub genes of hepatocellular carcinoma (HCC) through an in silico approach. In the current context of the increased incidence of liver cancers, this approach could be a useful prognostic biomarker and HCC prevention target. This study aimed to examine hub genes for immune cell infiltration and their good prognostic characteristics for HCC research. Human genes selected from databases (Gene Cards and DisGeNET) were used to identify t ...[more]

PMID: 36351994

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Project description:BackgroundAbnormal miRNA and mRNA expression and dysregulated immune microenvironment have been found to frequently induce the progression of hepatocellular carcinoma (HCC) in recent reports. In particular, the immune-related competing endogenous RNAs (ceRNA) mechanism plays a crucial role in HCC progression. However, the underlying mechanisms remain unclear.MethodsDifferentially expressed immune-related genes were obtained from the Immport, GEO, and TCGA databases. The mRNA and protein expression levels in HCC tissues and adjacent normal tissues were confirmed, and we further investigated the methylation levels of these biomarkers to explore their function. Then, the TIMER and TISCH databases were used to assess the relationship between immune infiltration and hub genes. Survival analysis and univariate and multivariate Cox models were used to evaluate the association between hub genes and HCC diagnosis. Hub gene expression was experimentally validated in six HCC cell lines and 15 HCC samples using qRT-PCR and immunohistochemistry. The hub genes were uploaded to DSigDB for drug prediction enrichment analysis.ResultsWe identified that patients with abnormal miRNAs (hsa-miR-125b-5p and hsa-miR-21-5p) and their targeted genes (NTF3, PSMD14, CD320, and SORT1) had a worse prognosis. Methylation analysis of miRNA-targeted genes suggested that alteration of methylation levels is also a factor in the induction of tumorigenesis. We also found that the development of HCC progression caused by miRNA-mRNA interactions may be closely correlated with the infiltration of immunocytes. Moreover, the GSEA, GO, and KEGG analysis suggested that several common immune-related biological processes and pathways were related to miRNA-targeted genes. The results of qRT-PCR, immunohistochemistry, and western blotting were consistent with our bioinformatics results, suggesting that abnormal miRNAs and their targeted genes may affect HCC progression.ConclusionsBriefly, our study systematically describes the mechanisms of miRNA-mRNA interactions in HCC and predicts promising biomarkers that are associated with immune filtration for HCC progression.

Project description:BackgroudIt has been shown that aberrant expression of microRNAs (miRNAs) and transcriptional factors (TFs) is tightly associated with the development of HCC. Therefore, in order to further understand the pathogenesis of HCC, it is necessary to systematically study the relationship between the expression of miRNAs, TF and genes. In this study, we aim to identify the potential transcriptomic markers of HCC through analyzing common microarray datasets, and further establish the differential co-expression network of miRNAs-TF-mRNA to screen for key miRNAs as candidate diagnostic markers for HCC.MethodWe first downloaded the mRNA and miRNA expression profiles of liver cancer from the GEO database. After pretreatment, we used a linear model to screen for differentially expressed genes (DEGs) and miRNAs. Further, we used weighed gene co-expression network analysis (WGCNA) to construct the differential gene co-expression network for these DEGs. Next, we identified mRNA modules significantly related to tumorigenesis in this network, and evaluated the relationship between mRNAs and TFs by TFBtools. Finally, the key miRNA was screened out in the mRNA-TF-miRNA ternary network constructed based on the target TF of differentially expressed miRNAs, and was further verified with external data set.ResultsA total of 465 DEGs and 215 differentially expressed miRNAs were identified through differential genes expression analysis, and WGCNA was used to establish a co-expression network of DEGs. One module that closely related to tumorigenesis was obtained, including 33 genes. Next, a ternary network was constructed by selecting 256 pairs of mRNA-TF pairs and 100 pairs of miRNA-TF pairs. Network mining revealed that there were significant interactions between 18 mRNAs and 25 miRNAs. Finally, we used another independent data set to verify that miRNA hsa-mir-106b and hsa-mir-195 are good classifiers of HCC and might play key roles in the progression of HCC.ConclusionOur data indicated that two miRNAs-hsa-mir-106b and hsa-mir-195-are identified as good classifiers of HCC.

Project description:BackgroundLiver fibrosis and hepatocellular carcinoma (HCC) are common adverse consequences of chronic liver injury. The interaction of various risk factors may cause them to happen. Identification of specific biomarkers is of great significance for understanding the occurrence, development mechanisms, and determining the novel tools for diagnosis and treatment of both liver fibrosis and HCC.AimTo identify liver fibrosis-related core genes, we analyzed the differential expression pattern of core genes in liver fibrosis and HCC.MethodsGene expression profiles of three datasets, GSE14323, GSE36411, and GSE89377, obtained from the Gene Expression Omnibus (GEO) database, were analyzed, and differentially expressed genes (DEGs) between patients with liver cirrhosis and healthy controls were identified by screening via R software packages and online tool for Venn diagrams. The WebGestalt online tool was used to identify DEGs enriched in biological processes, molecular functions, cellular components, and Kyoto Encyclopedia of Genes and Genomes pathways. The protein-protein interactions of DEGs were visualized using Cytoscape with STRING. Next, the expression pattern of core genes was analyzed using Western blot and immunohistochemistry in a carbon tetrachloride (CCl4)-induced liver cirrhosis mouse model and in patient liver samples. Finally, Kaplan-Meier curves were constructed using the Kaplan-Meier plotter online server.ResultsForty-five DEGs (43 upregulated and 2 downregulated genes) associated with liver cirrhosis were identified from three GEO datasets. Ten hub genes were identified, which were upregulated in liver cirrhosis. Western blot and immunohistochemical analyses of the three core genes, decorin (DCN), dermatopontin (DPT), and SRY-box transcription factor 9 (SOX9), revealed that they were highly expressed in the CCl4-induced liver cirrhosis mouse model. The expression levels of DCN and SOX 9 were positively correlated with the degree of fibrosis, and SOX 9 level in HCC patients was significantly higher than that in fibrosis patients. However, high expression of DPT was observed only in patients with liver fibrosis, and its expression in HCC was low. The gene expression profiling interactive analysis server (GEPIA) showed that SOX9 was significantly upregulated whereas DCN and DPT were significantly downregulated in patients with HCC. In addition, the Kaplan-Meier curves showed that HCC patients with higher SOX9 expression had significantly lower 5-year survival rate, while patients with higher expression of DCN or DPT had significantly higher 5-year survival rates.ConclusionThe expression levels of DCN, DPT, and SOX9 were positively correlated with the degree of liver fibrosis but showed different correlations with the 5-year survival rates of HCC patients.

Project description:Hepatocellular carcinoma (HCC) is a common yet deadly form of malignant cancer. However, the specific mechanisms involved in HCC diagnosis have not yet fully elucidated. Herein, we screened four publically available Gene Expression Omnibus (GEO) expression profiles (GSE14520, GSE29721, GSE45267 and GSE60502), and used them to identify 409 differentially expressed genes (DEGs), including 142 and 267 up- and down-regulated genes, respectively. The DAVID database was used to look for functionally enriched pathways among DEGs, and the STRING database and Cytoscape platform were used to generate a protein-protein interaction (PPI) network for these DEGs. The cytoHubba plug-in was utilized to detect 185 hub genes, and three key clustering modules were constructed with the MCODE plug-in. Gene functional enrichment analyses of these three key clustering modules were further performed, and nine core genes including BIRC5, DLGAP5, DTL, FEN1, KIAA0101, KIF4A, MCM2, MKI67, and RFC4, were identified in the most critical cluster. Subsequently, the hierarchical clustering and expression of core genes in TCGA liver cancer tissues were analyzed using the UCSC Cancer Genomics Browser, and whether elevated core gene expression was linked to a poor prognosis in HCC patients was assessed using the GEPIA database. The PPI of the nine core genes revealed an interaction between FEN1, MCM2, RFC4, and BIRC5. Furthermore, the expression of FEN1 was positively correlated with that of three other core genes in TCGA liver cancer tissues. FEN1 expression in HCC and other tumor types was assessed with the FIREBROWSE and ONCOMINE databases, and results were verified in HCC samples and hepatoma cells. FEN1 levels were also positively correlated with tumor size, distant metastasis and vascular invasion. In conclusion, we identified nine core genes associated with HCC development, offering novel insight into HCC progression. In particular, the aberrantly elevated FEN1 may represent a potential biomarker for HCC diagnosis and treatment.

Dataset Information

Identification and validation of core genes as promising diagnostic signature in hepatocellular carcinoma based on integrated bioinformatics approach.

Publications

Identification and validation of core genes as promising diagnostic signature in hepatocellular carcinoma based on integrated bioinformatics approach.

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