A panel of Transcription factors identified by data mining can predict the prognosis of head and neck squamous cell carcinoma.

Background: Transcription factors (TFs) are responsible for the regulation of various activities related to cancer like cell proliferation, invasion, and migration. It is thought that, the measurement of TFs levels could assist in developing strategies for diagnosis and prognosis of cancer detection. However, due to lack of effective genome-wide tests, this cannot be carried out in clinical settings.

Methods: A complete assessment of RNA-seq data in samples of a head and neck squamous cell carcinoma (HNSCC) cohort in The Cancer Genome Atlas (TCGA) database was carried out. From the expression data of six TFs, a risk score model was developed and further validated in the GSE41613 and GSE65858 series. Potential functional roles were identified for the six TFs via gene set enrichment analysis.

Results: Based on our multi-TF signature, patients are stratified into high- and low-risk groups with significant variations in overall survival (OS) (median survival 2.416 vs. 5.934 years, log-rank test P < 0.001). The sensitivity and specificity evaluation of our multi-TF for 3-year OS in TCGA, GSE41613 and GSE65858 was 0.707, 0.679 and 0.605, respectively, demonstrating good reproducibility and robustness for predicting overall survival of HNSCC patients. Through multivariate Cox regression analyses (MCRA) and stratified analyses, we confirmed that the predictive capability of this risk score (RS) was not dependent on any of other factors like clinicopathological parameters.

Conclusions: With the help of a RS obtained from a panel of TFs expression signatures, effective OS prediction and stratification of HNSCC patients can be carried out.