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ORIGINAL ARTICLE
Year : 2018  |  Volume : 14  |  Issue : 10  |  Page : 675-679

Identification of genes correlated with oral squamous cell carcinoma


1 The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) and Key Laboratory of Oral Biomedicine Ministry of Education (KLOBM), School & Hospital of Stomatology, Wuhan University, Wuhan 430079, Hubei; Key Laboratory of Oral Medicine, Guangzhou Institute of Oral Disease, Stomatology Hospital of Guangzhou Medical University, Guangzhou 510140, P.R. China
2 Key Laboratory of Oral Medicine, Guangzhou Institute of Oral Disease, Stomatology Hospital of Guangzhou Medical University, Guangzhou 510140, P.R. China
3 The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) and Key Laboratory of Oral Biomedicine Ministry of Education (KLOBM), School & Hospital of Stomatology, Wuhan University, Wuhan 430079, Hubei, P.R. China

Correspondence Address:
Hong He
Department of Orthodontics, School and Hospital of Stomatology, Wuhan University, No. 237, Luoyu Road, Hongshan District, Wuhan 430079
P.R. China
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0973-1482.202893

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Objectives: The objective was to study the mechanisms of oral squamous cell carcinoma (OSCC). Materials and Methods: We analyzed microarrays of GSE23558 and GSE25103. GSE23558 and GSE25103 were downloaded from Gene Expression Omnibus. GSE23558 included 27 OSCC samples, 4 independent and 1 pooled normal samples. GSE25103 included 112 OSCC samples and ten normal samples. The differentially expressed genes (DEGs) and the risk single nucleotide polymorphisms (SNPs) separately were obtained by limma package and plink software. Then, candidate disease genes were screened from the common genes of the genes carrying SNPs and the DEGs using Fisher's combination method. Using TargetMine online tool, potential functions of the candidate disease genes were analyzed by functional and pathway enrichment analyses. Besides, protein–protein interaction (PPI) network of these genes was constructed by STRING and Cytoscape software. Furthermore, modules of PPI network were screened by the ClusterONE. Results: We screened 2353 DEGs and 35635 risk SNPs in OSCC samples compared with normal samples. Moreover, CA9 was the most significant upregulated genes. There were 754 candidate disease genes, including 299 upregulated (e.g., VEGFC and FAT1) and 455 downregulated genes. For the candidate disease genes, the enriched functions were mainly in biological process categories. Importantly, FN1 (degree = 42) and CCNA2 (degree = 38) had high degrees in the PPI network. Furthermore, FN1 and CCNA2 were separately involved in module 1 and module 2 of the PPI network. FN1, CCNA2, CA9, VEGFC, and FAT1 might affect OSCC. Conclusion: In general, our study obtained important genes implicated in OSCC.


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