What question did this study set out to answer?

The aim is to identify key genes and molecular mechanisms involved in oral squamous cell carcinoma using bioinformatics approaches.

March 14, 2026Open Access

Identification of potential key genes and molecular mechanisms of oral squamous cell carcinoma based on integrated bioinformatics approach

Key Points

The aim is to identify key genes and molecular mechanisms involved in oral squamous cell carcinoma using bioinformatics approaches.
Utilized Gene Expression Omnibus to gather gene-expression profiles
Employed GEO2R tool for differential gene analysis
Identified common differentially expressed genes (DEGs) from two datasets
Conducted bioinformatics analysis including gene ontology and pathway analysis
Constructed protein-protein interaction (PPI) networks and identified hub genes.
Identified 265 common DEGs, with 69 up-regulated and 196 down-regulated
Created a PPI network consisting of 92 nodes and 226 interactions
Discovered 9 top hub genes linked to cancer progression
Identified significant clusters within the PPI network
Notably, KIF23 and AURKA show potential as biomarkers for OSCC.

Abstract

Oral Squamous Cell Carcinoma (OSCC) is one of the most occurred cancer types with yearly 377,713 cases and 177,000 deaths. Traditional risk factors of OSCC include smoking, alcohol consumption, excessive sun exposure, family history of cancer, and human papillomavirus (HPV). Last few years, the prevalence of OSCC is growing big in numbers particularly among younger people for their lifestyle. From the Gene Expression Omnibus, 2 gene-expression profiles (GSE23558 and GSE146483) were identified based on some conditions. The GEO2R tool was used to analyze those datasets to extract all the genes. Statistical cut-off criteria were applied to find out DEGs from both datasets, and after that common DEGs were identified by comparing both datasets. Common DEGs were used to perform bioinformatics analysis such as gene ontology and pathway analysis, protein–protein interaction (PPI) network construction, and generating Transcription factor − miRNA network. 265 common DEGs were identified from the datasets including 69 up-regulated and 196 down-regulated DEGs. Using the STRING database and a strong combine score > 0.70, a PPI network is generated including 92 nodes and 226 interactions. Using 3 different hub DEGs seeking algorithm, we identified 9 top hub DEGs. The hub genes are Kinesin Family Member 23 (KIF23), Aurora Kinase A (AURKA), Centromere Protein F (CENPF), Cell Division Cycle 20 (CDC20), Discs Large Associated Protein 5 (DLGAP5), Centrosomal Protein 55 (CEP55), Anillin Actin Binding Protein (ANLN), Non-SMC Condensin I Complex Subunit G (NCAPG), and Kinesin Family Member 14 (KIF14). 3 significant clusters also identified from the PPI network. Previous study shows KIF23 takes part in raising Cell Proliferation in Hepatocellular carcinoma cells and AURKA shows notable overexpression in cancer tissues, which indicates that KIF23 and AURKA showed promising character to become possible biomarkers for OSCC. Further analysis needed to justify the statement.

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Cite This Study

Alam et al. (Sun,) studied this question.

synapsesocial.com/papers/69b4fb1bb39f7826a300baf2 https://doi.org/https://doi.org/10.1016/j.jgeb.2026.100668

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