Dataset Information

Enrichment and identification of differentially expressed genes in hepatocellular carcinoma stem-like cells.

ABSTRACT: Cancer stem cells are considered to be tumor-initiating cells. To explain the initiation or progression of hepatocellular carcinoma (HCC), we previously established a culture system that may enrich hepatic cancer stem-like cells (HCSCs). However, the regulatory mechanisms by which HCSCs acquire stem cell properties remain unclear. In the present study, three pairs of HCSCs and case-matched human HCC cells were analyzed by high-throughput screening, and novel biomarkers and pathways for the regulation of HCSCs were identified. The results led to the identification and stratification of 406 differentially expressed genes (DEGs), among which 73 GO terms were found to be significantly associated with DEGs in HCSCs, and only complement and coagulation cascade pathways were identified during the development of HCSCs. By combining the results of the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses, it was revealed that 7 genes were downregulated in the complement and coagulation cascade pathways, and 7 miRNAs were predicted to target several downregulated genes involved in these pathways. The results may contribute toward hepatic cancer stem cell studies and novel drug research for HCC treatment.

SUBMITTER: Li J

PROVIDER: S-EPMC7577079 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Enrichment and identification of differentially expressed genes in hepatocellular carcinoma stem-like cells.

Li Jiang J Liu Kai K Sheng Yuehong Y Zhang Qin Q Chen Lei L Qian Haihua H Wu Hongping H Su Changqing C

Oncology letters 20200928 6

Cancer stem cells are considered to be tumor-initiating cells. To explain the initiation or progression of hepatocellular carcinoma (HCC), we previously established a culture system that may enrich hepatic cancer stem-like cells (HCSCs). However, the regulatory mechanisms by which HCSCs acquire stem cell properties remain unclear. In the present study, three pairs of HCSCs and case-matched human HCC cells were analyzed by high-throughput screening, and novel biomarkers and pathways for the regul ...[more]

PMID: 33101493

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Project description:Background: Methylation plays a significant role in the etiology and pathogenesis of hepatocellular carcinoma (HCC). The aim of the present study is to identify aberrantly methylated-diferentially expressed genes (DEGs) and dysregulated pathways associated with the development of HCC through integrated analysis of gene expression and methylation microarray. Method: Aberrantly methylated-DEGs were identified from gene expression microarrays (GSE62232, GSE74656) and gene methylation microarrays (GSE44909, GSE57958). Functional enrichment and pathway enrichment analyses were performed through the database of DAVID. Protein-protein interaction (PPI) network was established by STRING and visualized in Cytoscape. Subsequently, overall survival (OS) analysis of hub genes was performed by OncoLnc. Finally, we validated the expression level of CDCA5 by quantitative real-time PCR (qRT-PCR) and western blotting, and performed Immunohistochemical experiments utilizing a tissue microarray. Cell growth assay and flow cytometry were behaved to explore the function of CDCA5. Results: Aberrantly methylated-DEGs were enriched in biological process, molecular function, cellular component and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. Among them, cell cycle was enriched most frequently, and some terms associated with cancer were enriched, such as p53 signaling pathway, pathways in cancers, PI3K-Akt signaling pathway and AMPK signaling pathway. After survival analysis and validation in TCGA database including methylation and gene expression status, 12 hub genes were identified. Furthermore, the expression level of new gene CDCA5 was validated in HCC cell lines and hepatic normal cell lines through qRT-PCR and western blotting. In additional, immunohistochemistry experiments revealed higher CDCA5 protein expression from HCC tumor tissues compared with paracancer tissues by tissue microarray. Finally, through loss of function, we demonstrated that CDCA5 promoted proliferation by regulating the cell cycle. Conclusions: In summary, the present study implied possible aberrantly methylated-differentially expressed genes and dysregulated pathways in HCC by bioinformatics analysis and experiments, which could be helpful in understanding the molecular mechanisms underlying the development and progression of HCC. Hub genes including CDC20, AURKB, BIRC5, RRM2, MCM2, PTTG1, CDKN2A, NEK2, CENPF, RACGAP1, GNA14 and especially the new gene CDCA5 may serve as biomarkers for diagnosis, treatment and prognosis of HCC.

Project description: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.

Dataset Information

Enrichment and identification of differentially expressed genes in hepatocellular carcinoma stem-like cells.

Publications

Enrichment and identification of differentially expressed genes in hepatocellular carcinoma stem-like cells.

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