Identification of Differentially Expressed Genes Induced by Aberrant Methylation in Oral Squamous Cell Carcinomas Using Integrated Bioinformatic Analysis

Abstract

Oral squamous cell carcinoma (OSCC) is a malignant disease. Methylation plays a key role in the etiology and pathogenesis of OSCC. The goal of this study was to identify aberrantly methylated differentially expressed genes (DEGs) in OSCCs, and to explore the underlying mechanisms of tumorigenesis by using integrated bioinformatic analysis. Gene expression profiles (GSE30784 and GSE38532) were analyzed using the R software to obtain aberrantly methylated DEGs. Functional enrichment analysis of screened genes was performed using the DAVID software. Protein–protein interaction (PPI) networks were constructed using the STRING database. The cBioPortal software was used to exhibit the alterations of genes. Lastly, we validated the results with the Cancer Genome Atlas (TCGA) data. Twenty-eight upregulated hypomethylated genes and 24 downregulated hypermethylated genes were identified. These genes were enriched in the biological process of regulation in immune response, and were mainly involved in the PI3K-AKT and EMT pathways. Additionally, three upregulated hypomethylated oncogenes and four downregulated hypermethylated tumor suppressor genes (TSGs) were identified. In conclusion, our study indicated possible aberrantly methylated DEGs and pathways in OSCCs, which could improve the understanding of the underlying molecular mechanisms. Aberrantly methylated oncogenes and TSGs may also serve as biomarkers and therapeutic targets for the precise diagnosis and treatment of OSCCs in the future.

Expression scoring of a small‐nucleolar‐RNA signature identified by machine learning serves as a prognostic predictor for head and neck cancer

“…The prognostic prediction would be very useful for clinicians to aid in choosing the magnitude and type of therapeutic approach (surgery, chemotherapy, radiotherapy, or a combination of these) on the basis of the molecular profile of HNSCC (Troiano et al, 2018). In the past few years, multiple molecular biomarkers have been proven to predict the clinical prognosis in different kinds of cancers (Quan et al, 2018; Zhang, Feng, Li, Liu et al, 2018; Zhu et al, 2016). In addition, combining several biomarkers achieved higher sensitivity and specificity compared to individual markers (Guo et al, 2018; Zhao, Sun, Zeng, & Cui, 2018).…”

Identification of hub methylated‐CpG sites and associated genes in oral squamous cell carcinoma

“…The prognostic model based on 5 aberrant differential methylation genes ( PAX9 , STK33 , GPR150 , INSM1 , and EPHX3 ) can be used as independent prognostic biomarkers for predicting the prognosis of patients with head and neck cancer . Using gene expression profiles of OSCC (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE30784 and http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE38532), 28 upregulated hypomethylated genes and 24 downregulated hypermethylated genes were identified, which were mainly enriched in the biological process of regulation in immune response, PI3K‐AKT and EMT pathways . WGCNA was used to directly screen out 5 methylation‐associated genes, CENPV , SYTL2 , OCLN , CASD1 , and TUB , which may be predictors of survival in patients with OSCC .…”

Bioinformatics analysis and verification of gene targets for benign tracheal stenosis

“…What is more, Zhang found that CCNB1 was highly expressed in pancreatic cancer tissues by quantitative detection. CCNB1 might affect pancreatic cancer cell cycle, proliferation and apoptosis through TP53 pathway (Zhang, Feng, et al, 2018; Zhang, Zhang, et al, 2018). In addition, Gu found that inhibition of CCNB1 expression could significantly inhibit the proliferation, migration, and invasion of cells (Gu, Liu, Li, & He, 2019).…”

“…Bioinformatics methods are widely used to find molecular changes in the occurrence and development of diseases and are effective ways to explore the pathogenesis of diseases. Zhang found the genes related to the pathogenesis of oralsquarous cell carcinoma (OSCC) utilizing bioinformatics analysis, and further verified these molecules might lead to OSCC through the PI3K‐AKT pathway, suggesting the relevant molecules may be a molecular target for specific diagnosis and therapy (Zhang, Feng, et al, 2018; Zhang, Zhang, et al, 2018). In addition, Liu found abnormally expressed molecules in multiple scar tissues by bioinformatics analysis and further verified that SFRP1(OMIM:604156) might take part in the occurrence of scar by regulating Wnt/β‐catenin (Liu et al, 2018).…”