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Integrated multiplex network based approach for hub gene identification in oral cancer.

ABSTRACT: Background: The incidence of Oral Cancer (OC) is high in Asian countries, which goes undetected at its early stage. The study of genetics, especially genetic networks holds great promise in this endeavor. Hub genes in a genetic network are prominent in regulating the whole network structure of genes. Thus identification of such genes related to specific cancer types can help in reducing the gap in OC prognosis. Methods: Traditional study of network biology is unable to decipher the inter-dependencies within and across diverse biological networks. Multiplex network provides a powerful representation of such systems and encodes much richer information than isolated networks. In this work, we focused on the entire multiplex structure of the genetic network integrating the gene expression profile and DNA methylation profile for OC. Further, hub genes were identified by considering their connectivity in the multiplex structure and the respective protein-protein interaction (PPI) network as well. Results: 46 hub genes were inferred in our approach with a high prediction accuracy (96%), outstanding Matthews coefficient correlation value (93%) and significant biological implications. Among them, genes PIK3CG, PIK3R5, MYH7, CDC20 and CCL4 were differentially expressed and predominantly enriched in molecular cascades specific to OC. Conclusions: The identified hub genes in this work carry ontological signatures specific to cancer, which may further facilitate improved understanding of the tumorigenesis process and the underlying molecular events. Result indicates the effectiveness of our integrated multiplex network approach for hub gene identification. This work puts an innovative research route for multi-omics biological data analysis.

SUBMITTER: Mahapatra S

PROVIDER: S-EPMC8258848 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description: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.

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:Colon adenocarcinoma (COAD) is a prevalent and aggressive form of cancer that necessitates the identification of robust biomarkers for early diagnosis and treatment. Therefore, this project was launched to identify a few key biomarkers from CC and CXC chemokine families for the accurate detection of COAD. Hub gene identification was performed using CytoHubba analysis. Clinical samples from COAD patients and normal individuals were collected and subjected to appropriate methods for DNA and RNA extraction. The expression levels of hub genes were analyzed using reverse transcription-quantitative polymerase chain reaction (RT-qPCR), while promoter methylation analysis was conducted using targeted bisulfite sequencing (bisulfite-seq). Additionally, The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were utilized to validate the findings based on clinical samples. CXCL10 (C-X-C motif chemokine ligand 10), CXCL12 (C-X-C motif chemokine ligand 12), CXCL16 (C-X-C motif chemokine ligand 16), and CCL25 (CC motif chemokine ligand 25) were denoted as the key hubs among CC and CXC chemokine families. Through RT-qPCR analysis, it was found that CXCL10, CXCL12, and CXCL16 were significantly up-regulated, while CCL25 was down-regulated in COAD patients compared to healthy controls. Later on, these findings were also validated using TCGA and GEO datasets consisting of COAD and normal control samples. Furthermore, we investigated the promoter methylation status of these chemokine genes in COAD patients. Our results revealed significant dysregulation of promoter methylation, suggesting an epigenetic mechanism underlying the altered expression of CXCL10, CXCL12, CXCL16, and CCL25 in COAD. In addition to this, various additional aspects of the CCL25, CXCL10, CXCL12, and CXCL16 have also been uncovered in COAD during the present study. This study highlights the dysregulation of CXCL10, CXCL12, CXCL16, and CCL25 chemokine members in COAD patients, emphasizing their significance as potential biomarkers and therapeutic targets in the management of this deadly disease. However, further investigations are warranted to elucidate the underlying molecular mechanisms and evaluate the clinical utility of these findings.

Project description:BackgroundIrradiation commonly causes long-term bone marrow injury charactertized by defective HSC self-renewal and a decrease in HSC reserve. However, the effect of high-dose IR on global gene expression during bone marrow recovery remains unknown.MethodologyMicroarray analysis was used to identify differentially expressed genes that are likely to be critical for bone marrow recovery. Multiple bioinformatics analyses were conducted to identify key hub genes, pathways and biological processes.Principal findings1) We identified 1302 differentially expressed genes in murine bone marrow at 3, 7, 11 and 21 days after irradiation. Eleven of these genes are known to be HSC self-renewal associated genes, including Adipoq, Ccl3, Ccnd1, Ccnd2, Cdkn1a, Cxcl12, Junb, Pten, Tal1, Thy1 and Tnf; 2) These 1302 differentially expressed genes function in multiple biological processes of immunity, including hematopoiesis and response to stimuli, and cellular processes including cell proliferation, differentiation, adhesion and signaling; 3) Dynamic Gene Network analysis identified a subgroup of 25 core genes that participate in immune response, regulation of transcription and nucleosome assembly; 4) A comparison of our data with known irradiation-related genes extracted from literature showed 42 genes that matched the results of our microarray analysis, thus demonstrated consistency between studies; 5) Protein-protein interaction network and pathway analyses indicated several essential protein-protein interactions and signaling pathways, including focal adhesion and several immune-related signaling pathways.ConclusionsComparisons to other gene array datasets indicate that global gene expression profiles of irradiation damaged bone marrow show significant differences between injury and recovery phases. Our data suggest that immune response (including hematopoiesis) can be considered as a critical biological process in bone marrow recovery. Several critical hub genes that are key members of significant pathways or gene networks were identified by our comprehensive analysis.

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Integrated multiplex network based approach for hub gene identification in oral cancer.

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