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BACKGROUND The aim of this study was to identify biomarkers closely related to the pathogenesis and prognosis of oral squamous cell carcinoma (OSCC) by using weighted gene co-expression network analysis (WGCNA) based on integrative transcriptome datasets. MATERIAL AND METHODS Gene expression profiles of OSCC were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained and we then performed with Gene ontology (GO) and pathway enrichment analysis as well as protein-protein interactions (PPI) network analysis. WGCNA was used to construct the co-expression network. Multipart results were intersected to acquire the candidate genes, and survival analysis was used to identify the hub genes. RESULTS A total of 568 DEGs, including 272 upregulated genes and 296 downregulated genes, were identified. GO and pathway analyses revealed that these DEGs were mainly enriched in extracellular matrix (ECM), ECM organization, structural constituent of muscle, and ECM-receptor interaction. The PPI network of DEGs was established, comprising 428 nodes and 1944 edges. In the co-expression network, pink module was the key module, in which 34 genes with high connectivity were identified. After the intersection of multipart results, 24 common genes were chosen as the candidate genes, among which 7 hub genes (PLAU, SERPINE1, LAMC2, ITGA5, TGFBI, FSCN1, and HLF) were identified using survival analysis. CONCLUSIONS Seven potential biomarkers were identified as being closely related with the initiation and prognosis of OSCC and might serve as potential targets for early diagnosis and personalized therapy of OSCC.

Weighted Gene Co-Expression Network Analysis Identifies Hub Genes Associated with Occurrence and Prognosis of Oral Squamous Cell Carcinoma