Integrated analysis of diagnostic, prognostic value and potential drug treatment of GSDME in head and neck squamous cell carcinoma.

BACKGROUND: Head and neck squamous cell carcinoma (HNSC) poses a global health challenge. Effective biomarkers for early detection are necessary to improve the survival rate of HNSC patient. The purpose of this study was using integrated bioinformatic analysis to investigate the potential biological roles of GSDME in HNSC. METHODS: The Gene Expression Omnibus (GEO) and Cancer Gnome Atlas (TCGA) databases were used to analyze the expression of GSDME in different cancer types. The correlation between GSDME expression and immune cell infiltration or immune checkpoint genes was examined by Spearman correlation analysis. DNA methylation analysis of the GSDME gene was conducted using the MethSurv database. Kaplan-Meier (K-M) survival curves, diagnostic receiver operating characteristic (ROC) curves, nomogram model, and Cox regression analysis were chosen to evaluate the diagnostic and prognostic predictive value of GSDME. Connectivity Map (Cmap) online platform, Protein Data Bank (PDB) database and Chem3D, AutoDock Tool and PyMol software were used to predict and visualize potential molecular drugs aimed for GSDME. RESULTS: GSDME expression level in HNSC was significantly higher than in the controls (p < 0.001). Differentially expressed genes (DEGs) correlation with GSDME were enriched in the GO pathways, such as protein activation cascade, complement activation and classical pathway (p < 0.05). According to GSEA, GSDME-associated differentially expressed genes were significantly enriched in KRAS signaling pathway and cytokine signaling molecule (p < 0.05). There is a significant relation between GSDME expression and immune cell infiltration in HNSC tissues, as well as immune checkpoint genes expression (p < 0.001). DNA methylation status of cg17790129 CpG islands of GSDME gene is correlated with HNSC prognosis (p < 0.05). Based on Cox regression analysis of HNSC patients, GSDME as a potential risk gene has high correlation with overall survival (OS) and disease specific survival (DSS) (p < 0.05). In a ROC curve analysis, HNSC tissues were differentiated from adjacent peritumoral tissues based on GSDME expression levels (AUC = 0.928). Totally six potential drugs targeted for GSDME were screened and the molecular docking tests between GSDME protein and candidate drugs were conducted. CONCLUSIONS: GSDME is a promising therapeutic target as well as a potential clinical biomarker in HNSC patients.

Comprehensive analysis of ubiquitin-proteasome system genes related to prognosis and immunosuppression in head and neck squamous cell carcinoma.

The ubiquitin-proteasome system (UPS) with a capacity of degrading multiple intracellular proteins is an essential regulator in tumor immunosurveillance. Tumor cells that escape from recognition and destruction of immune system have been consistently characterized an important hallmark in the setting of tumor progression. Little know about the exact functions of UPS-related genes (UPSGs) and their relationships with antitumor immunity in head and neck squamous cell carcinoma (HNSCC) patients. In this study, for the first time, we comprehensively identified 114 differentially expressed UPSGs (DEUPSGs) and constructed a prognostic risk model based on the eight DEUPSGs (BRCA1, OSTM1, PCGF2, PSMD2, SOCS1, UCHL1, UHRF1, and USP54) in the TCGA-HNSCC database. This risk model was validated using multiple data sets (all P < 0.05). The high-risk score was found to be an independently prognostic factor in HNSCC patients and was significantly correlated with T cells suppression. Accordingly, our risk model can act as a prognostic signature and provide a novel concept for improving the precise immunotherapy for patients with HNSCC.

[Correlation between estrogen receptor ß and ABCC11 gene single nucleotide polymorphisms and axillary osmidrosis].

OBJECTIVE: To explore the correlation between single nucleotide polymorphisms (SNPs) of hormone receptor gene or other related genes and axillary osmidrosis (AO).
 Methods: Whole blood samples of 219 patients with AO and 159 normal people were collected, and their genomic DNA was extracted. SNPs of 49 selected gene loci were detected and analyzed by using matrix-assisted laser analysis and ionization time of flight mass spectrometry and other related technologies.
 Results: There were significant differences in SNPs at rs1256061 of estrogen receptor ß gene and rs17822931, rs16945916 and rs62058521 in ABCC11 gene between the AO patients and normal people (all P<0.01). 81.1% of patients with AO carried G allele at rs1256061, while only 63.2% of normal people carried G allele; 96.3% of patients with AO carried G allele at rs17822931, while only 4.4% of the normal people carried G allele; 28.6% of the patients with armpit odor carried the G allele of rs16945916, while only 0.6% of the normal people carried G allele; 28.0% of patients with AO carried G allele at rs62058521, while only 0.6% of the normal people carried G allele.
 Conclusion: SNPs of rs1256061 at the locus of estrogen receptor gene are correlated with the pathogenesis of AO, while SNPs at multiple loci (rs16945916, rs62058521 and rs17822931) in ABCC11 gene are correlated with the pathogenesis of AO.