A pyroptosis-related gene expression signature predicts immune microenvironment and prognosis in head and neck squamous cell carcinoma

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous and aggressive malignancy with a poor prognosis. Pyroptosis triggered by gasdermins family proteins is reported vital for tumor microenvironment and cancer progression. However, pyroptosis-related gene expression and its relationship with immune infiltration and prognosis of HNSCC have not been fully defined.

MATERIAL AND METHODS

RNA-sequencing data of HNSCC patients were acquired from The Cancer Genome Atlas (TCGA) database. A pyroptosis-related gene expression signature and infiltrated immune cells were analyzed. Univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression and nomogram analyses were used to construct a clinical-molecular risk model for survival prognosis.

RESULTS

HNSCC was classified into three different molecular subtypes based on the expression information of pyroptosis-related genes. Immune cell infiltration was demonstrated to be distinct between the three subtypes. The segregation of patients into the high-risk group and low-risk group, were carried out using the signature of differential expression genes (DEGs) signature among the three molecular subtypes. The precision of this signature was corroborated by Receiver operating characteristic curve (ROC) analysis with the 3-year area under time-dependent ROC curve (AUC) reaching 0.711. The risk model was validated in another dataset from the Gene Expression Omnibus (GEO) database. Subsequently we established a clinical-molecular nomogram which combined the risk score with age and stage. The calibration plots for predicting the overall survival rate of 1-, 3-, and 5-years indicated that the nomogram performs well.

CONCLUSION

The expression signature that encompasses pyroptosis-related genes could be used as molecular classification for HNSCC and pyroptosis might be a promising therapeutic target in HNSCC.

A novel and independent survival prognostic model for OSCC: the functions and prognostic values of RNA-binding proteins

BACKGROUND

Oral squamous cell carcinoma (OSCC), exhibiting high morbidity and malignancy, is the most common type of oral cancer. The abnormal expression of RNA-binding proteins (RBPs) plays important roles in the occurrence and progression of cancer. The objective of the present study was to establish a prognostic assessment model of RBPs and to evaluate the prognosis of OSCC patients.

METHODS

Gene expression data in The Cancer Genome Atlas (TCGA) were analyzed by univariate Cox regression analysis model that established a novel nine RBPs, which were used to build a prognostic risk model. A multivariate Cox proportional regression model and the survival analysis were used to evaluate the prognostic risk model. Moreover, the receive operator curve (ROC) analysis was tested further the efficiency of prognostic risk model based on data from TCGA database and Gene Expression Omnibus (GEO).

RESULTS

Nine RBPs' signatures (ACO1, G3BP1, NMD3, RNGTT, ZNF385A, SARS, CARS2, YARS and SMAD6) with prognostic value were identified in OSCC patients. Subsequently, the patients were further categorized into high-risk group and low-risk in the overall survival (OS) and disease-free survival (DFS), and external validation dataset. ROC analysis was significant for both the TCGA and GEO. Moreover, GSEA revealed that patients in the high-risk group significantly enriched in many critical pathways correlated with tumorigenesis than the low, including cell cycle, adheres junctions, oocyte meiosis, spliceosome, ERBB signaling pathway and ubiquitin-mediated proteolysis.

CONCLUSIONS

Collectively, we developed and validated a novel robust nine RBPs for OSCC prognosis prediction. The nine RBPs could serve as an independent and reliable prognostic biomarker and guiding clinical therapy for OSCC patients.