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A Six-Gene-Based Prognostic Model Predicts Survival in Head and Neck Squamous Cell Carcinoma Patients

  • Shrikant PawarEmail author
  • Aditya Stanam
Short communication
  • 14 Downloads

Abstract

Background and Objective

Head and neck cancer is a malignant tumor that begins in the head and neck region, and has the sixth highest incidence worldwide. Previous studies have indicated several prognostic markers for head and neck squamous cell carcinoma (HNSCC), but due to poor accuracy and sensitivity of these clinical characteristic markers attention has been gradually switched to molecular biomarkers. This study aimed to sort out the mRNAs correlated with patient survival time to establish an mRNA combination prognostic biomarker model for HNSCC patient risk stratification, providing optimal therapeutic regimens and improving patient prognosis.

Methods

Clinical data and transcriptome sequencing data of HNSCC were retrieved from TCGA database and were allocated into training and validation datasets. The prognostic model was established using the mRNAs, which were sorted out from training dataset by a significant correlation with survival time. Eventually, the prediction property of the model was evaluated by Kaplan–Meier survival analysis and receiver operating characteristic (ROC) curve.

Results

An optimal prognostic model by the combination of six mRNAs was established. Kaplan–Meier survival analysis revealed effective risk stratification by this model for patients in the two datasets. The area under ROC curve (AUC) was > 0.65 for training and validation datasets, indicating good sensitivity and specificity of this model. Moreover, prominent superiority of this model to investigate prognostic biomarkers was demonstrated.

Conclusion

Our model provided effective prognostication in terms of death risk stratification and evaluation in HNSCC patients. Combination of this prognostic model with current treatment measures is expected to greatly improve the patients’ prognosis.

Keywords

Biomarkers Survival time Cox regression Kaplan–Meier survival analysis 

Notes

Author Contributions

AS and SP contributed to the conception and design, Cox proportional hazard regression analysis, Kaplan–Meier analysis and ROC analysis of the data, as well as the drafting of the manuscript. All authors read and approved the final paper.

Compliance with Ethical Standards

Conflict of interest

The author reports no conflicts of interest in this work.

Supplementary material

12663_2019_1187_MOESM1_ESM.xlsx (486.8 mb)
RNA sequencing gene expression and associated clinical data from TCGA (XLSX 498443 kb)

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Copyright information

© The Association of Oral and Maxillofacial Surgeons of India 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceGeorgia State UniversityAtlantaUSA
  2. 2.Department of BiologyGeorgia State UniversityAtlantaUSA
  3. 3.Department of PathologyThe University of IowaIowa CityUSA

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