scholarly journals A six-mRNA prognostic model to predict survival in head and neck squamous cell carcinoma

2018 ◽  
Vol Volume 11 ◽  
pp. 131-142 ◽  
Author(s):  
Saisai Tian ◽  
Guofeng Meng ◽  
Weidong Zhang
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Squamous Cell ◽  
Prognostic Model ◽  
Neck Squamous Cell Carcinoma

OC-0642 A radiomics based prognostic model for patients with head and neck squamous cell carcinoma

2021 ◽  
Vol 161 ◽  
pp. S509-S510
Author(s):  
S. Keek ◽  
F. Wesseling ◽  
H. Woodruff ◽  
J. van Timmeren ◽  
I. Nauta ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Squamous Cell ◽  
Prognostic Model ◽  
Neck Squamous Cell Carcinoma

scholarly journals Development and Validation of a Three-Gene-Based Prognostic Model for Predicting the Overall Survival of Head and Neck Squamous Cell Carcinoma Through Bioinformatics Analysis

2022 ◽  
Vol 12 ◽  
Author(s):  
Yiyuan Han ◽  
Xiaolin Cao ◽  
Xuemei Wang ◽  
Qing He
Keyword(s):  
Overall Survival ◽  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Squamous Cell ◽  
Prognostic Model ◽  
Neck Squamous Cell Carcinoma ◽  
Joint Analysis ◽  
Individual Treatment ◽  
Survival Prediction

Head and neck squamous cell carcinoma (HNSCC) is one of the most common cancer worldwide and seriously threats public health safety. Despite the improvement of diagnostic and treatment methods, the overall survival for advanced patients has not improved yet. This study aimed to sort out prognosis-related molecular biomarkers for HNSCC and establish a prognostic model to stratify the risk hazards and predicate the prognosis for these patients, providing a theoretical basis for the formulation of individual treatment plans. We firstly identified differentially expressed genes (DEGs) between HNSCC tissues and normal tissues via joint analysis based on GEO databases. Then a total of 11 hub genes were selected for single-gene prognostic analysis to identify the prognostic genes. Later, the clinical information and transcription information of HNSCC were downloaded from the TCGA database. With the application of least absolute shrinkage and selection operator (LASSO) algorithm analyses for the prognostic genes on the TCGA cohort, a prognostic model consisting of three genes (COL4A1, PLAU and ITGA5) was successfully established and the survival analyses showed that the prognostic model possessed a robust performance in the overall survival prediction. Afterward, the univariate and multivariate regression analysis indicated that the prognostic model could be an independent prognostic factor. Finally, the predicative efficiency of this model was well confirmed in an independent external HNSCC cohort.

scholarly journals Novel Prognostic Model Based on Immune Signature for Head and Neck Squamous Cell Carcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Yingying Wang ◽  
Yu Xu ◽  
Qingquan Hua ◽  
Yang Jiang ◽  
Peiqiang Liu ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Squamous Cell ◽  
Prognostic Model ◽  
Predictive Biomarkers ◽  
Neck Squamous Cell Carcinoma ◽  
Clinicopathological Parameters ◽  
Immune Signature ◽  
Model Based

Background. Deciphering the immune characteristics within tumors and identifying the immune signals related to the prognostic factor are helpful for the treatment and management of tumor patients. However, systematic analysis of immune signatures in head and neck squamous cell carcinoma (HNSCC) remains largely unstudied. Methods. A total of 718 immune-related genes were extracted from RNA sequencing data from 519 HNSCC patients in the TCGA database, and survival analysis with integrated bioinformatics analyses was performed to build the final predictive prognosis model. Results. The 178 survival-associated genes ( P < 0.05 ) participated in important immune functions, including immune cell activation and migration. Multivariate regression analysis using 93 genes ( P < 0.01 ), together with survival-associated clinicopathological parameters, identified 35 independent prognostic factors. The most significant 8 independent factors were CD3E, CD40LG, TNFRSF4, CD3G, CD5, ITGA2B, ABCB1, and TNFRSF13b. The final prognostic model achieved outstanding predictive efficiency with the highest AUC of 0.963. Conclusion. Our prognostic model based on the immune signature could effectively predict the prognosis of HNSCC patients, providing novel predictive biomarkers and potential therapeutic targets for HNSCC patients.

Integrating pretreatment diffusion weighted MRI into a multivariable prognostic model for head and neck squamous cell carcinoma

2014 ◽  
Vol 110 (3) ◽  
pp. 429-434 ◽  
Author(s):  
Maarten Lambrecht ◽  
Ben Van Calster ◽  
Vincent Vandecaveye ◽  
Frederik De Keyzer ◽  
Ilse Roebben ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Squamous Cell ◽  
Prognostic Model ◽  
Neck Squamous Cell Carcinoma ◽  
Diffusion Weighted Mri ◽  
Diffusion Weighted
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