Dataset Information

Integrated Bioinformatics Analysis Reveals Key Candidate Genes and Pathways Associated With Clinical Outcome in Hepatocellular Carcinoma.

ABSTRACT: Hepatocellular carcinoma (HCC) accounts for approximately 85-90% of all liver cancer cases and has poor relapse-free survival. There are many gene expression studies that have been performed to elucidate the genetic landscape and driver pathways leading to HCC. However, existing studies have been limited by the sample size and thus the pathogenesis of HCC is still unclear. In this study, we performed an integrated characterization using four independent datasets including 320 HCC samples and 270 normal liver tissues to identify the candidate genes and pathways in the progression of HCC. A total of 89 consistent differentially expression genes (DEGs) were identified. Gene-set enrichment analysis revealed that these genes were significantly enriched for cellular response to zinc ion in biological process group, collagen trimer in the cellular component group, extracellular matrix (ECM) structural constituent conferring tensile strength in the molecular function group, protein digestion and absorption, mineral absorption and ECM-receptor interaction. Network system biology based on the protein-protein interaction (PPI) network was also performed to identify the most connected and important genes based on our DEGs. The top five hub genes including osteopontin (SPP1), Collagen alpha-2(I) chain (COL1A2), Insulin-like growth factor I (IGF1), lipoprotein A (LPA), and Galectin-3 (LGALS3) were identified. Western blot and immunohistochemistry analysis were employed to verify the differential protein expression of hub genes in HCC patients. More importantly, we identified that these five hub genes were significantly associated with poor disease-free survival and overall survival. In summary, we have identified a potential clinical significance of these genes as prognostic biomarkers for HCC patients who would benefit from experimental approaches to obtain optimal outcome.

SUBMITTER: Li Y

PROVIDER: S-EPMC7396661 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Integrated Bioinformatics Analysis Reveals Key Candidate Genes and Pathways Associated With Clinical Outcome in Hepatocellular Carcinoma.

Li Yubin Y Chen Runzhe R Yang Jian J Mo Shaowei S Quek Kelly K Kok Chung H CH Cheng Xiang-Dong XD Tian Saisai S Zhang Weidong W Qin Jiang-Jiang JJ

Frontiers in genetics 20200724

Hepatocellular carcinoma (HCC) accounts for approximately 85-90% of all liver cancer cases and has poor relapse-free survival. There are many gene expression studies that have been performed to elucidate the genetic landscape and driver pathways leading to HCC. However, existing studies have been limited by the sample size and thus the pathogenesis of HCC is still unclear. In this study, we performed an integrated characterization using four independent datasets including 320 HCC samples and 270 ...[more]

PMID: 32849813

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Project description:Breast cancer (BC) is the leading malignancy in women worldwide, yet relatively little is known about the genes and signaling pathways involved in BC tumorigenesis and progression. The present study aimed to elucidate potential key candidate genes and pathways in BC. Five gene expression profile data sets (GSE22035, GSE3744, GSE5764, GSE21422 and GSE26910) were downloaded from the Gene Expression Omnibus (GEO) database, which included data from 113 tumorous and 38 adjacent non?tumorous tissue samples. Differentially expressed genes (DEGs) were identified using t?tests in the limma R package. These DEGs were subsequently investigated by pathway enrichment analysis and a protein?protein interaction (PPI) network was constructed. The most significant module from the PPI network was selected for pathway enrichment analysis. In total, 227 DEGs were identified, of which 82 were upregulated and 145 were downregulated. Pathway enrichment analysis results revealed that the upregulated DEGs were mainly enriched in 'cell division', the 'proteinaceous extracellular matrix (ECM)', 'ECM structural constituents' and 'ECM?receptor interaction', whereas downregulated genes were mainly enriched in 'response to drugs', 'extracellular space', 'transcriptional activator activity' and the 'peroxisome proliferator?activated receptor signaling pathway'. The PPI network contained 174 nodes and 1,257 edges. DNA topoisomerase 2?a, baculoviral inhibitor of apoptosis repeat?containing protein 5, cyclin?dependent kinase 1, G2/mitotic?specific cyclin?B1 and kinetochore protein NDC80 homolog were identified as the top 5 hub genes. Furthermore, the genes in the most significant module were predominantly involved in 'mitotic nuclear division', 'mid?body', 'protein binding' and 'cell cycle'. In conclusion, the DEGs, relative pathways and hub genes identified in the present study may aid in understanding of the molecular mechanisms underlying BC progression and provide potential molecular targets and biomarkers for BC.

Project description:Hepatocellular carcinoma (HCC) is a typical inflammation-driven cancer. Chronically unresolved inflammation may remodel the immunosuppressive tumor microenvironment, which is rich in innate immune cells. The mechanisms via which HCC progresses through the evasion of the innate immune surveillance remain unclear. The present study thus aimed to identify key genes involved in HCC immunosuppression and to establish an innate immune risk signature, with the ultimate goal of obtaining new insight into effective immunotherapies. HCC and normal liver tissue mRNA expression and clinicopathological data were obtained from the Cancer Genome Atlas database. The immunosuppressive innate immune-related genes (IIRGs) in HCC were screened using integrated bioinformatics analyses. Gene expression was then validated using the Gene Expression Omnibus database and the Human Protein Atlas database, and tissues were obtained from patients with HCC who underwent surgery. In total, 3,676 genes were identified as differentially expressed mRNAs after comparing the HCC tissues with the normal liver tissues in TCGA. Gene Set Enrichment Analyses revealed 21 highly expressed IIRGs in HCC tissues. A survival analysis and Cox regression model were used to construct an innate immune risk signature, including three IIRGs: Collectin-12 (COLEC12), matrix metalloproteinase-12 (MMP12) and mucin-12 (MUC12) genes. Univariate and multivariate Cox analyses revealed that the signature of the three IIRGs was a robust independent risk factor in relation to the overall survival (OS) of patients with HCC. The expression of the three aforementioned IIRGs was confirmed through external validation. Moreover, COLEC12 and MMP12 expression significantly correlated with that of immune checkpoint molecules or immunosuppressive cytokines. The tumor immune dysfunction and exclusion tool predicted that the increased expression of the three IIRGs in patients with HCC was significantly associated with the efficacy of relatively poor immune checkpoint blockade therapy. Conclusively, a novel innate immune-related risk signature for patients with HCC was constructed and validated. This signature may be involved in immunosuppression, and may be used to predict a poor prognosis, functioning as a potential immunotherapeutic target for patients with HCC.

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Project description:BACKGROUND To provide a better understanding of anaplastic thyroid carcinoma (ATC) at the molecular level, this study aimed to identify the genes and key pathways associated with ATC by using integrated bioinformatics analysis. MATERIAL AND METHODS Based on the microarray data GSE9115, GSE65144, and GSE53072 derived from the Gene Expression Omnibus, the differentially expressed genes (DEGs) between ATC samples and normal controls were identified. With DEGs, we performed a series of functional enrichment analyses. Then, a protein-protein interaction (PPI) network was constructed and visualized, with which the hub gene nodes were screened out. Finally, modules analysis for the PPI network was performed to further investigate the potential relationships between DEGs and ATC. RESULTS A total of 537 common DEGs were screened out from all 3 datasets, among which 247 genes were upregulated and 275 genes were downregulated. GO analysis indicated that upregulated DEGs were mainly involved in cell division and mitotic nuclear division and the downregulated DEGs were significantly enriched in ventricular cardiac muscle cell action potential. KEGG pathway analysis showed that the upregulated DEGs were mainly enriched in cell cycle and ECM-receptor interaction and the downregulated DEGs were mainly enriched in thyroid hormone synthesis, insulin resistance, and pathways in cancer. The top 10 hub genes in the constructed PPI network were CDK1, CCNB1, TOP2A, AURKB, CCNA2, BUB1, AURKA, CDC20, MAD2L1, and BUB1B. The modules analysis showed that genes in the top 2 significant modules of PPI network were mainly associated with mitotic cell cycle and positive regulation of mitosis, respectively. CONCLUSIONS We identified a series of key genes along with the pathways that were most closely related with ATC initiation and progression. Our results provide a more detailed molecular mechanism for the development of ATC, shedding light on the potential biomarkers and therapeutic targets.

Dataset Information

Integrated Bioinformatics Analysis Reveals Key Candidate Genes and Pathways Associated With Clinical Outcome in Hepatocellular Carcinoma.

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Integrated Bioinformatics Analysis Reveals Key Candidate Genes and Pathways Associated With Clinical Outcome in Hepatocellular Carcinoma.

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