Gene expression profiling via bioinformatics analysis reveals biomarkers in laryngeal squamous cell carcinoma

Guan,Guo‑Fang; Zheng,Ying; Wen,Lian‑Ji; Zhang,De‑Jun; Yu,Duo‑Jiao; Lu,Yan‑Qing; Zhao,Yan; Zhang,Hui

doi:10.3892/mmr.2015.3701

Gene expression profiling via bioinformatics analysis reveals biomarkers in laryngeal squamous cell carcinoma

Authors:
- Guo‑Fang Guan
- Ying Zheng
- Lian‑Ji Wen
- De‑Jun Zhang
- Duo‑Jiao Yu
- Yan‑Qing Lu
- Yan Zhao
- Hui Zhang
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Affiliations: Department of Otolaryngology, Head and Neck Surgery, The Second Hospital of Jilin University, Changchun, Jilin 130041, P.R. China, Department of Otolaryngology, Head and Neck Surgery, Tumor Hospital of Jilin Province, Changchun, Jilin 130012, P.R. China
Published online on: April 29, 2015 https://doi.org/10.3892/mmr.2015.3701
Pages: 2457-2464
Copyright: © Guan et al. This is an open access article distributed under the terms of Creative Commons Attribution License [CC BY_NC 3.0].

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Abstract

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.

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