Identification of gene expression models for laryngeal squamous cell carcinoma using co-expression network analysis.
Ontology highlight
ABSTRACT:
SUBMITTER: Yang CW
PROVIDER: S-EPMC5839854 | biostudies-literature | 2018 Feb
REPOSITORIES: biostudies-literature
Publications
Identification of gene expression models for laryngeal squamous cell carcinoma using co-expression network analysis.
Yang Chun-Wei CW Wang Shu-Fang SF Yang Xiang-Li XL Wang Lin L Niu Lin L Liu Ji-Xiang JX
Medicine 20180201 7
One of the most common head and neck cancers is laryngeal squamous cell carcinoma (LSCC). LSCC exhibits high mortality rates and has a poor prognosis. The molecular mechanisms leading to the development and progression of LSCC are not entirely clear despite genetic and therapeutic advances and increased survival rates. In this study, a total of 116 differentially expressed genes (DEGs), including 11 upregulated genes and 105 downregulated genes, were screened from LSCC samples and compared with ...[more]
Similar Datasets
Project description:Laryngeal squamous cell carcinoma (LSCC) is the most common malignant tumor in the head and neck, and can seriously affect the daily life of patients. To study the mechanisms of LSCC, the microarray of GSE51958 was analyzed in the present study. GSE51958 was downloaded from Gene Expression Omnibus, and included a collection of LSCC tissue samples and matched adjacent non-cancerous tissue samples from 10 patients. Differentially-expressed genes (DEGs) were identified using limma package. Next, a weighted co-expression network was constructed for the DEGs by WGCNA package in R. Modules of the weighted co-expression network were obtained through constructing a hierarchical clustering tree using the hybrid dynamic shear tree method. Using the clusterProfiler package, the potential functions of DEGs in the modules correlated with LSCC were predicted by pathway enrichment analysis. In total, 959 DEGs were screened from the LSCC samples compared with the adjacent non-cancerous samples, including 553 upregulated and 406 downregulated genes. The appointed black, brown, gray, pink and yellow modules were screened for the DEGs in the weighted co-expression network. For the DEGs in the brown and yellow modules, the enriched pathways were cytokine-cytokine receptor interaction and metabolic pathways, respectively. The DEGs in the pink module were involved in the majority of pathways. With high connectivity degrees in the pink module, TPX2, microtubule-associated (TPX2; degree, 25), minichromosome maintenance complex component 2 (MCM2; degree, 25), ubiquitin-like with PHD and ring finger domains 1 (UHRF1; degree, 22), cyclin-dependent kinase 2 (CDK2; degree, 20) and protein regulator of cytokinesis 1 (PRC1; degree, 20) may be involved in LSCC. Overall, In conclusion, from the integrated bioinformatics analysis of genes that may be associated with LSCC, 959 DEGs were obtained from LSCC samples compared with adjacent non-cancerous samples, and TPX2, MCM2, UHRF1, CDK2 and PRC1 were found to hold a possible association with the disease.
| S-EPMC4840875 | biostudies-literature
Project description:Laryngeal squamous cell carcinoma (LSCC) is one of the most common malignancies of the head and neck tumors Zhang et al., 2013 [1]). Previous studies have associated its occurrence with social activities, such as tobacco and alcohol consumption (Hashibe et al., 2007a [2]; Hashibe et al., 2007b [3]; Shangina et al., 2006 [4]). Here, we performed a genome-wide gene expression profiling in thirty-one patients positively diagnosed for LSCC, in order to investigate new targets involved in tumorigenesis.
| S-EPMC4583618 | biostudies-literature
Project description:Laryngeal squamous cell carcinoma (LSCC) is one of the most common types of head and neck squamous cell carcinomas (HNSCC) and is the second most prevalent malignancy occurring in the head and neck or respiratory tract, with a high incidence and mortality rate. Survival is limited for patients with LSCC. To identify more biomarkers associated with the prognosis of patients with LSCC, using bioinformatics analysis, this study used The Cancer Genome Atlas (TCGA) LSCC dataset and gene expression profiles of GSE59102 from the Gene Expression Omnibus (GEO). Eighty-one differentially co-expressed genes were identified by weighted gene co-expression network analysis (WGCNA). Next, 10 hub genes (PPL, KRT78, CRNN, PTK7, SCEL, AGRN, SPINK5, AIF1L, EMP1, and PPP1R3C) were screened from a protein-protein interaction (PPI) network. Based on survival analysis, SPINK5 was significantly correlated with survival time in LSCC patients. After verification in the TCGA and HPA databases, SPINK5 was selected as a prognostic biomarker. Finally, the GSEA results showed that downregulation of SPINK5 gene expression may promote tumorigenesis and the development of cancers by the "BASAL CELL CARCINOMA" pathway, and it has been implicated in disrupting DNA damage and repair pathways. Collectively, SPINK5 may serve as a potential prognostic biomarker in LSCC.
| S-EPMC8819746 | biostudies-literature
Project description:Hepatocellular carcinoma (HCC) is a common cancer with poor prognosis. Due to the lack of effective biomarkers and its complex immune microenvironment, the effects of current HCC therapies are not ideal. In this study, we used the GSE57957 microarray data from Gene Expression Omnibus database to construct a co-expression network. The weighted gene co-expression network analysis and CIBERSORT algorithm, which quantifies cellular composition of immune cells, were used to identify modules related to immune cells. Four hub genes (EFTUD2, GAPDH, NOP56, PA2G4) were identified by co-expression network and protein-protein interactions network analysis. We examined these genes in TCGA database, and found that the four hub genes were highly expressed in tumor tissues in multiple HCC groups, and the expression levels were significantly correlated with patient survival time, pathological stage and tumor progression. On the other hand, methylation analysis showed that the up-regulation of EFTUD2, GAPDH, NOP56 might be due to the hypomethylation status of their promoters. Next, we investigated the correlations between the expression levels of four hub genes and tumor immune infiltration using Tumor Immune Estimation Resource (TIMER). Gene set variation analysis suggested that the four hub genes were associated with numerous pathways that affect tumor progression or immune microenvironment. Overall, our results showed that the four hub genes were closely related to tumor prognosis, and may serve as targets for treatment and diagnosis of HCC. In addition, the associations between these genes and immune infiltration enhanced our understanding of tumor immune environment and provided new directions for the development of drugs and the monitoring of tumor immune status.
| S-EPMC8262220 | biostudies-literature
Project description:BackgroundTongue squamous cell carcinoma (TSCC) is one of the most common malignant tumors in head and neck, but its molecular mechanism is not clear.MethodsWeighted gene co-expression network analysis (WGCNA) combining with gene differential expression analysis, survival analysis to screen key modules and hub genes related to the progress of TSCC. Gene Set Enrichment Analysis (GSEA) was used to identify biological pathways that might be involved.ResultsWeighted gene co-expression network was constructed based on dataset GSE34105. The blue module and turquoise module most related to the progress of TSCC were identified by the network. Gene Ontology (GO) enrichment analysis showed that 2 key modules were significantly enriched in apoptosis and immunity related biological processes and pathway. Network topology analysis, gene difference analysis and survival analysis were used to screen 9 hub genes (NOC2L, AIMP2, ANXA2, DIABLO, H2AFZ, MANBAL, PRDX6, SNX14, TIMM23). The expression of hub genes was significantly correlated with the prognosis of TSCC. GSEA showed that the high expression group of hub genes was mainly enriched in olfactory transduction, neuroactive ligand receptor interaction, nicotinate and nicotinamide metabolism, and the low expression group was mainly enriched in base excision repair, cysteine and methionine metabolism, oxidative phosphorylation.ConclusionTwo key modules and 9 hub genes screened by WGCNA were closely related to the occurrence and prognosis of TSCC. Hub genes can be used as biomarkers and potential therapeutic targets for the accurate diagnosis and treatment of TSCC in the future.
| S-EPMC6750333 | biostudies-literature
Project description:lncRNA-mRNA co-expression pairs and prognostic markers related to the development of laryngeal squamous cell carcinoma (LSCC) were investigated. The lncRNA and mRNA expression data of LSCC in GSE84957 and RNA-seq data of 112 LSCC samples from TCGA database were used. Differentially expressed genes (DEGs) and lncRNAs (DE-lncRNAs) between LSCC and para-cancer tissues were identified. Co-expression analysis of DEGs and DE-lncRNA was conducted. Protein-protein interaction network for co-expressed DEGs of top 25 DE-lncRNA was constructed, followed by survival analysis for key nodes in co-expression network. Finally, expressions of several DE-lncRNAs and DEGs were verified using qRT-PCR. The lncRNA-mRNA network showed that ANKRD20A5P, C21orf15, CYP4F35P, LOC_I2_011146, XLOC_006053, XLOC_I2_003881, and LOC100506027 were highlighted in network. Some DEGs, including FUT7, PADI1, PPL, ARHGAP40, MUC21, and CEACAM1, were co-expressed with above lncRNAs. Survival analysis showed that PLOD1, GLT25D1, and KIF22 were significantly associated with prognosis. qRT-PCR results showed that the expressions of MUC21, CEACAM1, FUT7, PADI1, PPL, ARHGAP40, ANKRD20A5P, C21orf15, CYP4F35P, XLOC_I2_003881, LOC_I2_011146, and XLOC_006053 were downregulated, whereas the expression of LOC100506027 was upregulated in LSCC tissues. PLOD1, GLT25D1, and KIF22 may be potential prognostic markers in the development of LSCC. C21orf15-MUC21/CEACAM1/FUT7/PADI1/PPL/ARHGAP40 are potential lncRNA-mRNA pairs that play significant roles in the development of LSCC.
| S-EPMC8174121 | biostudies-literature
Project description:The present study aimed to identify key genes and relevant microRNAs (miRNAs) involved in laryngeal squamous cell carcinoma (LSCC). The gene expression profiles of LSCC tissue samples were analyzed with various bioinformatics tools. A gene expression data set (GSE51985), including ten laryngeal squamous cell carcinoma (LSCC) tissue samples and ten adjacent non-neoplastic tissue samples, was downloaded from the Gene Expression Omnibus. Differential analysis was performed using software package limma of R. Functional enrichment analysis was applied to the differentially expressed genes (DEGs) using the Database for Annotation, Visualization and Integrated Discovery. Protein-protein interaction (PPI) networks were constructed for the protein products using information from the Search Tool for the Retrieval of Interacting Genes/Proteins. Module analysis was performed using ClusterONE (a software plugin from Cytoscape). MicroRNAs (miRNAs) regulating the DEGs were predicted using WebGestalt. A total of 461 DEGs were identified in LSCC, 297 of which were upregulated and 164 of which were downregulated. Cell cycle, proteasome and DNA replication were significantly over-represented in the upregulated genes, while the ribosome was significantly over-represented in the downregulated genes. Two PPI networks were constructed for the up- and downregulated genes. One module from the upregulated gene network was associated with protein kinase. Numerous miRNAs associated with LSCC were predicted, including miRNA (miR)-25, miR-32, miR-92 and miR-29. In conclusion, numerous key genes and pathways involved in LSCC were revealed, which may aid the advancement of current knowledge regarding the pathogenesis of LSCC. In addition, relevant miRNAs were also identified, which may represent potential biomarkers for use in the diagnosis or treatment of the disease.
| S-EPMC4464462 | biostudies-literature
Project description:BackgroundCutaneous squamous cell carcinoma (cSCC) often follows actinic keratosis (AK) and is the second most common skin cancer worldwide. To reduce metastasis risk, it is important to diagnose and treat cSCC early. This study aimed to identify hub genes associated with cSCC and AK.MethodsThis study used three datasets GSE45216, GSE98774, and GSE108008. We combined samples from the GSE45216 and GSE98774 datasets into the new dataset GSE45216-98774. We applied a weighted gene co-expression network analysis (WGCNA) to investigate key modules and hub genes associated with cSCC and AK. We considered the hub genes found in both the GSE45216-98774 and GSE108008 datasets as validated hub genes. We tested whether the expression of hub genes could predict patient survival outcomes in other cancers using TCGA pan-cancer data.ResultsWe identified modules most relevant to cSCC and AK. Additionally, we identified and validated seven hub genes of cSCC: GATM, ARHGEF26, PTHLH, MMP1, POU2F3, MMP10 and GATA3. We did not find validated hub genes for AK. Each hub gene was significantly associated with the survival of various cancer types. Only GATA3 was significantly associated with melanoma survival.ConclusionsWe applied WGCNA to find seven hub genes that play important roles in cSCC tumorigenesis. These results provide new insights that help explain the pathogenesis of cSCC. These hub genes may become biomarkers or therapeutic targets for accurate diagnosis and treatment of cSCC in the future.
| S-EPMC8306372 | biostudies-literature
Project description:BackgroundLaryngeal squamous cell carcinoma (LSCC) is a leading malignant cancer of the head and neck. Patients with LSCC, in which the cancer has infiltrated and metastasized, have a poor prognosis. Therefore, there is an urgent need to identify more potential targets for drugs and biomarkers for early diagnosis.MethodsRNA sequence data from LSCC and patients' clinical traits were obtained from the Gene Expression Omnibus (GEO) (GSE142083) and The Cancer Genome Atlas (TCGA) database. Differentially expressed gene (DEG) analysis and weighted gene co-expression network analysis (WGCNA) were performed to identify hub genes. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, prognostic value analysis, receiver operating characteristic (ROC) curve analysis, gene mutation analysis, tumor-infiltrating immune cell abundance profile estimation, gene set variation analysis (GSVA), and gene set enrichment analysis (GSEA) were performed. Single-gene RNA sequencing data were obtained from the GSE150321 dataset. Cell proliferation and viability were confirmed by the CCK-8 assay and real-time PCR.ResultsA total of 701 DEGs, including 329 upregulated and 372 downregulated genes, were screened in the GSE142083 dataset. Using WGCNA, three modules were identified to be closely related to LSCC. After intersecting the DEGs and performing univariate and multivariate Cox analyses, a novel prognostic model based on three genes (SLC35C1, HOXB7, and TEDC2) for LSCC was established. Interfering TEDC2 expression inhibited tumor cell proliferation and migration.ConclusionsOur results show that SLC35C1, HOXB7, and TEDC2 have the potential to become new therapeutic targets and prognostic biomarkers for LSCC.
| S-EPMC8787159 | biostudies-literature
Project description:To explore the gene modules and key genes of head and neck squamous cell carcinoma (HNSCC), a bioinformatics algorithm based on the gene co-expression network analysis was proposed in this study.Firstly, differentially expressed genes (DEGs) were identified and a gene co-expression network (i-GCN) was constructed with Pearson correlation analysis. Then, the gene modules were identified with 5 different community detection algorithms, and the correlation analysis between gene modules and clinical indicators was performed. Gene Ontology (GO) analysis was used to annotate the biological pathways of the gene modules. Then, the key genes were identified with 2 methods, gene significance (GS) and PageRank algorithm. Moreover, we used the Disgenet database to search the related diseases of the key genes. Lastly, the online software onclnc was used to perform the survival analysis on the key genes and draw survival curves.There were 2600 up-regulated and 1547 down-regulated genes identified in HNSCC. An i-GCN was constructed with Pearson correlation analysis. Then, the i-GCN was divided into 9 gene modules. The result of association analysis showed that, sex was mainly related to mitosis and meiosis processes, event was mainly related to responding to interferons, viruses and T cell differentiation processes, T stage was mainly related to muscle development and contraction, regulation of protein transport activity processes, N stage was mainly related to mitosis and meiosis processes, while M stage was mainly related to responding to interferons and immune response processes. Lastly, 34 key genes were identified, such as CDKN2A, HOXA1, CDC7, PPL, EVPL, PXN, PDGFRB, CALD1, and NUSAP1. Among them, HOXA1, PXN, and NUSAP1 were negatively correlated with the survival prognosis.HOXA1, PXN, and NUSAP1 might play important roles in the progression of HNSCC and severed as potential biomarkers for future diagnosis.
| S-EPMC7717835 | biostudies-literature