4.5 Article

Novel prognostic model established for patients with head and neck squamous cell carcinoma based on pyroptosis-related genes

Journal

TRANSLATIONAL ONCOLOGY
Volume 14, Issue 12, Pages -

Publisher

ELSEVIER SCIENCE INC
DOI: 10.1016/j.tranon.2021.101233

Keywords

HNSCC; Prognosis; Pyroptosis; Carcinoma; Prediction

Categories

Funding

  1. National Natural Science Foundation of China [81871458]
  2. Health Clinical Research Project of Shanghai Municipal Health Commission [202040328]
  3. Fundamental research program funding of Ninth People's Hospital affiliated to Shanghai Jiao Tong university School of Medicine [JYZZ076]
  4. Clinical Research Program of Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine [JYLJ201801, JYLJ201911]
  5. China Postdoctoral Science Foundation [2017M611585]

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This study aimed to establish a risk-score model using pyroptosis-related genes to predict the prognosis of patients with head and neck squamous cell carcinoma (HNSCC). The risk-score model independently predicted the overall survival (OS) of HNSCC patients and effectively differentiated between high-risk and low-risk groups.
We aimed at establishing a risk - score model using pyroptosis-related genes to predict the prognosis of patients with head and neck squamous cell carcinoma (HNSCC). A total of 33 pyroptosis-related genes were selected. We then evaluated the data of 502 HNSCC patients and 44 normal patients from TCGA database. Gene expression was then profiled to detect differentially expressed genes (DEGs). Using the univariate, the least absolute shrinkage and selection operator (LASSO) Cox regression analyses, we generated a risk - score model. Tissue samples from neoplastic and normal sites of 44 HNSCC patients were collected. qRT-PCR were employed to analyze the mRNA level of the samples. Kaplan-Meier method was used to evaluate the overall survival rate (OS). Enrichment analysis was performed to elucidate the underlying mechanism of HNSCC patient's differentially survival status from the perspective of tumor immunology. 17 genes were categorized as DEGs. GSDME, IL-6, CASP8, CASP6, NLRP1 and NLRP6 were used to establish the risk - score model. Each patient's risk score in the TCGA cohort was calculated using the risk - score formula. The risk score was able to independently predict the OS of the HNSCC patients (P = 0.02). The OS analysis showed that the risk score model (P < 0.0001) was more reliable than single gene, a phenomenon verified by practical patient cohort. Additionally, enrichment analysis indicated more active immune activities in low-risk group than high-risk group. In conclusion, our risk - score model has provided novel strategy for the prediction of HNSCC patients' prognosis.

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