Bioinformatics identification of crucial genes and pathways associated with hepatocellular carcinoma

Gao, Xueren; Wang, Xixi; Zhang, Shulong

doi:10.1042/bsr20181441

Cited by 49 publications

(36 citation statements)

References 25 publications

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“…UALCAN is a user-friendly, interactive web resource allowing researchers to analyse cancer transcriptome data from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/) rapidly and easily (21). UALCAN is a vital portal for facilitating tumour gene expression and survival analyses using TCGA level 3 RNA-sequencing and clinical data from 31 types of cancer (22). UALCAN is designed to provide: i) Easy access to cancer transcriptome data available to the public; ii) high-quality graphs depicting gene expression and patient survival information based on gene expression; and iii) additional information regarding the selected genes/targets using links to databases, including GeneCards (https://www.genecards.org/), PubMed (https://www.ncbi.nlm.nih.gov/pubmed), TargetScan (https://www.targetscan.org/) and DrugBank (https://www.…”

Section: Gene Ontology (Go) and Pathway Enrichment Analysis Of Degs mentioning

confidence: 99%

Identification of crucial genes and pathways associated with colorectal cancer by bioinformatics analysis

Liu

Qiao

et al. 2020

Oncol Lett

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Colorectal cancer (CRC) is a prevalent malignant tumour type arising from the colon and rectum. The present study aimed to explore the molecular mechanisms of the development and progression of CRC. Initially, differentially expressed genes (DEGs) between CRC tissues and corresponding non-cancerous tissues were obtained by analysing the GSE15781 microarray dataset. The Database for Annotation, Visualization and Integrated Discovery was then utilized for functional and pathway enrichment analysis of the DEGs. Subsequently, a protein-protein interaction (PPI) network was created using the Search Tool for the Retrieval of Interacting Genes and Proteins database and visualized by Cytoscape software. Furthermore, CytoNCA, a Cytoscape plugin, was used for centrality analysis of the PPI network to identify crucial genes. Finally, UALCAN was employed to validate the expression of the crucial genes and to estimate their effect on the survival of patients with colon cancer by Kaplan-Meier curves and log-rank tests. A total of 1,085 DEGs, including 496 upregulated and 589 downregulated genes, were screened out. The DEGs identified were enriched in various pathways, including 'metabolic pathway', 'cell cycle', 'DNA replication', 'nitrogen metabolism', 'p53 signalling' and 'fatty acid degradation'. PPI network analysis suggested that interleukin-6, MYC, NOTCH1, inhibin subunit βA (INHBA), CDK1, cyclin (CCN)B1 and CCNA2 were crucial genes, and their expression levels were markedly upregulated. Survival analysis suggested that upregulated INHBA significantly decreased the survival probability of patients with CRC. Conversely, upregulation of CCNB1 and CCNA2 expression levels were associated with increased survival probabalities. The identified DEGs, particularly the crucial genes, may enhance the current understanding of the genesis and progression of CRC, and certain genes, including INHBA, CCNB1 and CCNA2, may be candidate diagnostic and prognostic markers, as well as targets for the treatment of CRC.

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Section: Gene Ontology (Go) and Pathway Enrichment Analysis Of Degs mentioning

confidence: 99%

Identification of crucial genes and pathways associated with colorectal cancer by bioinformatics analysis

Liu

Qiao

et al. 2020

Oncol Lett

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“…The measurements and intersection of degree and betweenness were calculated with cytoNCA (http://apps. cytoscape.org/apps/cytonca) in this study (19,20). Then, the high degrees and betweenness of the DN-related ceRNA network were obtained.…”

Section: Centrality Analysis and Functional Annotation Of The Dn-relamentioning

confidence: 99%

The topological key lncRNA H2k2 from the ceRNA network promotes mesangial cell proliferation in diabetic nephropathyviathe miR‐449a/b/Trim11/Mek signaling pathway

et al. 2019

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Evidence has shown that long noncoding RNAs (IncRNAs) in the competing endogenous RNA (ceRNA) network are involved in various diseases. However, there is a lack of studies of the ceRNA network in diabetic nephropathy (DN). In this study, we investigated the effect of IncRNAs on mesangial cell (MC) proliferation in DN‐related ceRNA networks. Differences in IncRNA and mRNA expression between DN and normal mouse kidney tissues were detected with RNA‐seq, and DN‐related IncRNA/mRNA/microRNA (miRNA) ceRNA networks were constructed by R3.4.3. Computational analysis was performed, and expression and interactions between the topological RNAs were detected by bioinformatics methods, real‐time quantitative PCR (qPCR), and luciferase assay. Cell proliferation ability was measured by 5‐ethynyl‐2′‐deoxyuridine (EdU) in MCs cultured under high‐ or low‐glucose conditions. Moreover, the effect of the topological key IncRNA histocompatibility 2 K region locus 2 (H2k2) H2k2 on MC proliferation via the miRNA (miR)‐449a/b/triplet motif 11 (Trim11)/Mek signaling pathway was examined by EdU, flow cytometry analysis, and Western blot. In total, 153 IncRNAs, 428 mRNAs, and 2242 interactions were included in the constructed DN‐related ceRNA network. There were 15 RNAs in the top 5% of degree and betweenness. The expression of IncRNA H2k2 and mRNA Trim11 in MCs was increased in DN, which is consistent with the results of RNA‐seq and real‐time qPCR in vivo and in vitro. miR‐449a and miR‐449b, which were down‐regulated in MCs cultured with high glucose, were selected for further analysis. The results of real‐time qPCR and luciferase assay revealed the IncRNA H2k2‐miR‐449a/b‐Trim11 interaction in MCs. In addition, the data showed that H2k2 regulates MC proliferation via the miR‐449ab/Trim11/Mek signaling pathway. Taken together, these results provide new insight into the association between the topological key IncRNA H2k2 in the DN‐related ceRNA network and the miR‐449a/b/Trim11/Mek signaling pathway during MC proliferation in DN.—Chen, W., Peng, R., Sun, Y., Liu, H., Zhang, L., Peng, H., Zhang, Z. The topological key IncRNA H2k2 from the ceRNA network promotes mesangial cell proliferation in diabetic nephropathy via the miR‐449a/b/Trim11/Mek signaling pathway. FASEB J. 33, 11492–11506 (2019). http://www.fasebj.org

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“…Meanwhile, a large number of cancer-related data are stored in publicly available databases, such as The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). A reanalysis of these data could facilitate the discovery of a vast range of valuable biological information (Gao et al, 2018(Gao et al, , 2019Liu et al, 2019;Zhou et al, 2019). For instance, several novel genes associated with poor prognosis of patients with pancreatic ductal adenocarcinoma (PDAC) were identified by analyzing the array data from GEO.…”

Section: Introductionmentioning

confidence: 99%

Identification of Genes Related to Clinicopathological Characteristics and Prognosis of Patients with Colorectal Cancer

Gao

Yang

2020

DNA and Cell Biology

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The aim of this study was to identify genes with clinical significance in colorectal cancer (CRC). Gene expression profiles of 585 CRC tissues and 61 normal colorectal tissues from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were used to identify differentially expressed genes (DEGs) between CRC and normal colorectal tissues. DAVID and KOBAS tools were used to explore Gene Ontology (GO) and KEGG pathways enriched by DEGs, respectively. In addition, TCGA data sets were also used to identify prognostic factors and develop a prognostic prediction model for CRC. A total of 353 DEGs including 117 upregulated and 236 downregulated genes in CRC were identified based on GSE32323 data set. These DEGs were significantly enriched in the biological process related to the regulation of cell proliferation and 50 signaling pathways, such as ''TGF-beta signaling pathway,'' ''Wnt signaling pathway,'' and ''Jak-STAT signaling pathway.'' GCG, ADH1B, SLC4A4, ZG16, and CLCA4 were the top five downregulated in CRC. FOXQ1, LGR5, CLDN1, KRT23, and DPEP1 were the top five upregulated in CRC. KRT23 expression could affect tumor stage and regional lymph node metastasis in CRC patients. FOXQ1 expression could affect tumor distant metastasis in CRC patients. Survival analysis indicated that SLC4A4 expression was associated with the prognosis of CRC patients. Prognostic prediction model developed based on age, tumor stage, and SLC4A4 expression exhibited an efficient performance in predicting 1-, 3-, and 5-year overall survival of CRC patients. In conclusion, the current study identified several genes and pathways related to CRC, which provided new insight in understanding molecular mechanism of tumorigenesis and development of CRC.

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Bioinformatics identification of crucial genes and pathways associated with hepatocellular carcinoma

Cited by 49 publications

References 25 publications

Identification of crucial genes and pathways associated with colorectal cancer by bioinformatics analysis

Identification of crucial genes and pathways associated with colorectal cancer by bioinformatics analysis

The topological key lncRNA H2k2 from the ceRNA network promotes mesangial cell proliferation in diabetic nephropathyviathe miR‐449a/b/Trim11/Mek signaling pathway

Identification of Genes Related to Clinicopathological Characteristics and Prognosis of Patients with Colorectal Cancer

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