A Six-Gene-Based Prognostic Model Predicts Survival in Head and Neck Squamous Cell Carcinoma Patients
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.
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.
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.
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.
KeywordsBiomarkers Survival time Cox regression Kaplan–Meier survival analysis
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.
- 5.Detector performance analysis using ROC curves—MATLAB & Simulink example. www.mathworks.com. Retrieved 11 Aug 2016
- 17.Pawar P, Donthamsetty S, Pannu P, Rida P, Ogden A, Bowen N, Osan R, Cantuaria G, Aneja R (2014) KIFCI, a novel putative prognostic biomarker for ovarian adenocarcinomas: delineating protein interaction networks and signaling circuitries. J Ovarian Res 7(1):53CrossRefPubMedPubMedCentralGoogle Scholar
- 18.Ashraf MI, Ong SK, Mujawar S, Pawar P, More P, Paul S, Lahiri C (2018) A side-effect free method for identifying cancer drug targets. Sci Rep 8(1):6669. https://doi.org/10.1038/s41598-018-25042-2
- 19.Pawar S, Ashraf MI, Mujawar S, Mishra R, Lahiri C (2018) In silico identification of the indispensable quorum sensing proteins of multidrug resistant proteus mirabilis. Front Cell Infect Microbiol 8:269. https://doi.org/10.3389/fcimb.2018.00269
- 22.Lahiri C, Shrikant P, Sabarinathan P, Ashraf MI, Chakravortty D (2012) Identifying indispensable proteins of the type III secretion systems of Salmonella enterica serovar Typhimurium strain LT2. BMC Bioinform 13 (S12)Google Scholar