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Identification of hub genes and pathways associated with bladder cancer based on co-expression network analysis.

ABSTRACT: The aim of the present study was to identify hub genes and signaling pathways associated with bladder cancer (BC) utilizing centrality analysis and pathway enrichment analysis. The differentially expressed genes (DEGs) were screened from the ArrayExpress database between normal subjects and BC patients. Co-expression networks of BC were constructed using differentially co-expressed genes and links, and hub genes were investigated by degree centrality analysis of co-expression networks in BC. The enriched signaling pathways were investigated by Kyoto Encyclopedia of Genes and Genomes database analysis based on the DEGs. The hub gene expression in BC tissues was validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and western blotting. A total of 329 DEGs were screened, including 147 upregulated and 182 downregulated genes. The co-expression network constructed between BC and normal controls consisted of 182 nodes and 434 edges, and the two genes in each gene pair were differentially co-expressed genes. Centrality analysis of co-expression networks suggested that the top 5 hub genes with high degree included lectin, galactoside-binding, soluble, 4 (LGALS4), protein tyrosine phosphatase, receptor type N2 (PTPRN2), transmembrane protease, serine 11E (TMPRSS11E), tripartite motif containing 31 (TRIM31) and potassium voltage-gated channel subfamily D member 3 (KCND3). Pathway analysis revealed that the 329 DEGs were significantly enriched in 5 terms (cell cycle, DNA replication, oocyte meiosis, p53 signaling pathway and peroxisome proliferator-activated receptor signaling pathway). According to RT-qPCR and western blot analysis, 4/5 hub genes were significantly expressed, including LGALS4, PTPRN2, TMPRSS11E, TRIM31; however, KCND3 was not significantly expressed. In the present study, 5 hub genes were successfully identified (LGALS4, PTPRN2, TMPRSS11E, TRIM31 and KCND3) and 5 biological pathways that may be underlying biomarkers for early diagnosis and treatment associated with bladder cancer were revealed.

SUBMITTER: Zhang DQ

PROVIDER: S-EPMC5494668 | biostudies-other | 2017 Jul

REPOSITORIES: biostudies-other

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Project description:BackgroundMuscular invasive bladder cancer (MIBC) is a common malignant tumor in the world. Because of their heterogeneity in prognosis and response to treatment, biomarkers that can predict survival or help make treatment decisions in patients with MIBC are essential for individualized treatment.AimWe aimed to integrate bioinformatics research methods to identify a set of effective biomarkers capable of predicting, diagnosing, and treating MIBC. To provide a new theoretical basis for the diagnosis and treatment of bladder cancer.Methods and resultsGene expression profiles and clinical data of MIBC were obtained by downloading from the Cancer Genome Atlas database. A dataset of 129 MIBC cases and controls was included. 2084 up-regulated genes and 2961 down-regulated genes were identified by differentially expressed gene (DEG) analysis. Then, gene ontology analysis was performed to explore the biological functions of DEGs, respectively. The up-regulated DEGs are mainly enriched in epidermal cell differentiation, mitotic nuclear division, and so forth. They are also involved in the cell cycle, p53 signaling pathway, PPAR signaling pathway, and so forth. The weighted gene co-expression network analysis yielded five modules related to pathological stages and grading, of which blue and turquoise were the most relevant modules for MIBC. Next, Using Kaplan-Meier survival analysis to identify further hub genes, the screening criteria at p ≤ .05, we found CNKSR1, HIP1R, CFL2, TPM1, CSRP1, SYNM, POPDC2, PJA2, and RBBP8NL genes associated with the progression and prognosis of MIBC patients. Finally, immunohistochemistry experiments further confirmed that CNKSR1 plays a vital role in the tumorigenic context of MIBC.ConclusionThe research suggests that CNKSR1, POPDC2, and PJA2 may be novel biomarkers as therapeutic targets for MIBC, especially we used immunohistochemical further to validate CNKSR1 as a therapeutic target for MIBC which may help to improve the prognosis for MIBC.

Project description:BackgroundHuman aortic valve stenosis (AS) and insufficiency (AI) are common diseases in aging population. Identifying the molecular regulatory networks of AS and AI is expected to offer novel perspectives for AS and AI treatment.MethodsHighly correlated modules with the progression of AS and AI were identified by weighted genes co-expression network analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by the clusterProfiler program package. Differentially expressed genes (DEGs) were identified by the DESeqDataSetFromMatrix function of the DESeq2 program package. The protein-protein interaction (PPI) network analyses were implemented using the STRING online tool and visualized with Cytoscape software. The DEGs in AS and AI groups were overlapped with the top 30 genes with highest connectivity to screen out ten hub genes. The ten hub genes were verified by analyzing the data in high throughput RNA-sequencing dataset and real-time PCR assay using AS and AI aortic valve samples.ResultsBy WGCNA algorithm, 302 highly correlated genes with the degree of AS, degree of AI, and heart failure were identified from highly correlated modules. GO analyses showed that highly correlated genes had close relationship with collagen fibril organization, extracellular matrix organization and extracellular structure organization. KEGG analyses also manifested that protein digestion and absorption, and glutathione metabolism were probably involved in AS and AI pathological courses. Moreover, DEGs were picked out for 302 highly correlated genes in AS and AI groups relative to the normal control group. The PPI network analyses indicated the connectivity among these highly correlated genes. Finally, ten hub genes (CD74, COL1A1, TXNRD1, CCND1, COL5A1, SERPINH1, BCL6, ITGA10, FOS, and JUNB) in AS and AI were found out and verified.ConclusionOur study may provide the underlying molecular targets for the mechanism research, diagnosis, and treatment of AS and AI in the future.

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Identification of hub genes and pathways associated with bladder cancer based on co-expression network analysis.

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