Screening and identification of autophagy-related biomarkers for oral squamous cell carcinoma (OSCC) via integrated bioinformatics analysis.

Increasing evidences have showed that autophagy played a significant role in oral squamous cell carcinoma (OSCC). Purpose of our study was to explore the prognostic value of autophagy-related genes (ATGs) and screen autophagy-related biomarkers for OSCC. RNA-seq and clinical data were downloaded from The Cancer Genome Atlas (TCGA) database following extracting ATG expression profiles. Then, differentially expressed analysis was performed in R software and a risk score model according to ATGs was established. Moreover, comprehensive bioinformatics analyses were used to screen autophagy-related biomarkers which were later verified in OSCC tissues and cell lines. A total of 232 ATGs were extracted, and 37 genes were differentially expressed in OSCC. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis demonstrated that these genes were mainly located in autophagosome membrane and associated with autophagy. Furthermore, the risk score on basis of ATGs was identified as potential independent prognostic biomarker. Moreover, ATG12 and BID were identified as potential autophagy-related biomarkers of OSCC. This study successfully constructed a risk model, and the risk score could predict the prognosis of OSCC patients accurately. Moreover, ATG12 and BID were identified as two potential independent prognostic autophagy-related biomarkers and might provide new OSCC therapeutic targets.

Huang GZ ，Lu ZY ，Rao Y ，Gao H ，Lv XZ ... -  《-》

Identification of a Gene Prognostic Signature for Oral Squamous Cell Carcinoma by RNA Sequencing and Bioinformatics.

Oral squamous cell carcinoma (OSCC) is the most common oral cancer and has a poor prognosis. We aimed to identify new biomarkers or potential therapeutic targets for OSCC. Four pairs of tumor and adjacent normal tissues were collected from OSCC patients, and differentially expressed genes (DEGs) were screened via high-throughput RNA sequencing (RNA-seq). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were used to analyze the DEGs. A protein-protein interaction (PPI) network was established with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database and Cytoscape, and two significant clusters were found. Candidate genes were screened by analyzing head and neck squamous cell carcinoma (HNSCC) data from The Cancer Genome Atlas (TCGA). A DEG-based risk model was established to predict the overall survival (OS) of OSCC patients via Kaplan-Meier analysis and the log-rank test. Furthermore, univariate Cox regression analysis was applied to assess associations between potential biomarkers and the overall survival rate. Of 720 total DEGs, fifty-two DEGs in the two subclusters of the PPI network analysis were selected. A risk model was established, and five candidate genes (SPRR2E, ICOS, CTLA4, HTR1D, and CCR4) were identified as biomarkers of OS in OSCC patients. We successfully constructed a prognostic signature to predict prognosis and identified five candidate genes associated with the OS of OSCC patients that are potential tumor biomarkers and targets in OSCC.

Zhang YY ，Mao MH ，Han ZX 《-》

Identification for Exploring Underlying Pathogenesis and Therapy Strategy of Oral Squamous Cell Carcinoma by Bioinformatics Analysis.

Xu Z ，Jiang P ，He S 《MEDICAL SCIENCE MONITOR》

A Five-mRNA Expression Signature to Predict Survival in Oral Squamous Cell Carcinoma by Integrated Bioinformatic Analyses.

Guo H ，Li C ，Su X ，Huang X ... -  《-》

Identification of Key Biomarkers and Potential Molecular Mechanisms in Oral Squamous Cell Carcinoma by Bioinformatics Analysis.

Yang B ，Dong K ，Guo P ，Guo P ，Jie G ，Zhang G ，Li T ... -  《-》