ABSTRACT
PURPOSE: Plenty of studies showed that the immune system was associated with cancer initiation and progression. This study aimed to explore the prognostic biomarkers from immune-related genes (IRGs) in oral squamous cell carcinoma (OSCC). MATERIALS AND METHODS: RNA-seq data were downloaded from The Cancer Genome Atlas (TCGA) and IRGs and transcription factors (TFs) were extracted. Then, the co-expression network between IRGs and TFs was constructed using the "WGCNA" package in R software. Furthermore, a gene expression signature according to IRGs was constructed to predict OSCC prognosis and its accuracy was validated by survival analysis. Subsequently, correlation analyses between risk-score and immune cells level and clinical parameters were performed. Finally, immune-related biomarkers were selected and further investigated using gain-of-function assays in vitro. RESULTS: A total of 32 normal cases and 317 OSCC cases were selected in our study. Differentially-expressed analysis indicated that there were 381 differentially-expressed IRGs and 62 TFs in OSCC. Among them, 25 TFs and 21 IRGs were enrolled in the co-expression network. Furthermore, we found that gene expression signature on the basis of 10 IRGs could predict the prognosis accurately and a high-risk score based on gene expression signature meant a high T classification, terminal clinical stage, and low immune cells level in OSCC. Finally, cathepsin G (CTSG) was identified as a potential immune-related biomarker and therapeutic target in OSCC. CONCLUSION: In conclusion, IRGs were directly involved in the development and progression of OSCC. Furthermore, CTSG was identified as a potential independent biomarker and might be an immunotherapeutic target in OSCC treatment.
ABSTRACT
Disease similarity study provides new insights into disease taxonomy, pathogenesis, which plays a guiding role in diagnosis and treatment. The early studies were limited to estimate disease similarities based on clinical manifestations, disease-related genes, medical vocabulary concepts or registry data, which were inevitably biased to well-studied diseases and offered small chance of discovering novel findings in disease relationships. In other words, genome-scale expression data give us another angle to address this problem since simultaneous measurement of the expression of thousands of genes allows for the exploration of gene transcriptional regulation, which is believed to be crucial to biological functions. Although differential expression analysis based methods have the potential to explore new disease relationships, it is difficult to unravel the upstream dysregulation mechanisms of diseases. We therefore estimated disease similarities based on gene expression data by using differential coexpression analysis, a recently emerging method, which has been proved to be more potential to capture dysfunctional regulation mechanisms than differential expression analysis. A total of 1,326 disease relationships among 108 diseases were identified, and the relevant information constituted the human disease network database (DNetDB). Benefiting from the use of differential coexpression analysis, the potential common dysfunctional regulation mechanisms shared by disease pairs (i.e. disease relationships) were extracted and presented. Statistical indicators, common disease-related genes and drugs shared by disease pairs were also included in DNetDB. In total, 1,326 disease relationships among 108 diseases, 5,598 pathways, 7,357 disease-related genes and 342 disease drugs are recorded in DNetDB, among which 3,762 genes and 148 drugs are shared by at least two diseases. DNetDB is the first database focusing on disease similarity from the viewpoint of gene regulation mechanism. It provides an easy-to-use web interface to search and browse the disease relationships and thus helps to systematically investigate etiology and pathogenesis, perform drug repositioning, and design novel therapeutic interventions.Database URL: http://app.scbit.org/DNetDB/ #.
