Molecular subtype classification and corresponding markers of oral squamous cell carcinoma

This study was carried out to identify the metabolic and prognostic molecular subtypes and corresponding molecular markers of oral squamous cell carcinoma (OSCC) based on the OSCC data on The Cancer Genome Atlas (TCGA). UCSC Xena and NCBI GEO database were used to obtain RNA-seq data. The single-factor Cox regression analysis and unsupervised consensus cluster analysis were employed to screen metabolic genes and identify clusters. Survival analysis and clinicopathological analysis were used to analyze the differences of clusters. The limma package was used to obtain featured genes. Database for Annotation, Visualization and Integrated Discovery (DAVID) software was utilized to analyze the Gene Ontology Biological Processes (GO-BP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway of each featured gene. STRING database, Cytoscape and CytoNCA were applied to Protein-protein interaction (PPI) network construction and module analysis. GSE42743 data set was applied to genes and cluster validation. A total of 54 genes were obtained as metabolic and prognosis-related genes and 4 clusters were divided. Survival rate in cluster4 and cluster1 is significantly higher than cluster3 and cluster2, and the formation of these clusters is only related to metabolic molecules. Different numbers of featured genes were obtained in clusters and a large number of functional signaling pathways were enriched. PPI network showed a total of 338 relationship pairs and 128 genes corresponding to proteins. and then 8 hub genes were identified. The feasibilities of 6 screened molecular markers of cluster1 were successfully verified by GSE42743 data set. OSCC was divided into 4 clusters through bioinformatics analysis. There are survival differences between these four clusters. A total of 6 molecular markers were identified in cluster1 for cluster identification of OSCC.

Automated summary

This study aimed to identify metabolic and prognostic molecular subtypes and corresponding molecular markers of OSCC based on TCGA data. Through various analysis methods, metabolic genes and clusters were identified, resulting in the discovery of metabolic and prognosis-related genes, divided into different clusters, and successfully verified the feasibility of some molecular markers.