Combination of Radiosensitivity Gene Signature and PD-L1 Status Predicts Clinical Outcome of Patients With Locally Advanced Head and Neck Squamous Cell Carcinoma: A Study Based on The Cancer Genome Atlas Dataset

Abstract

Aim: The aim of our study was to investigate the potential predictive value of the combination of radiosensitivity gene signature and PD-L1 expression for the prognosis of locally advanced head and neck squamous cell carcinoma (HNSCC).Methods: The cohort was selected from The Cancer Genome Atlas (TCGA) and classified into the radiosensitive (RS) group and radioresistant (RR) group by a radiosensitivity-related gene signature. The cohort was also grouped as PD-L1-high or PD-L1-low based on PD-L1 mRNA expression. The least absolute shrinkage and selection operator (lasso)-based Cox model was used to select hub survival genes. An independent validation cohort was obtained from the Gene Expression Omnibus (GEO) database.Results: We selected 288 locally advanced HNSCC patients from TCGA. The Kaplan–Meier method found that the RR and PD-L1-high group had a worse survival than others (p = 0.033). The differentially expressed gene (DEG) analysis identified 553 upregulated genes and 486 downregulated genes (p < 0.05, fold change >2) between the RR and PD-L1-high group and others. The univariate Cox analysis of each DEG and subsequent lasso-based Cox model revealed five hub survival genes (POU4F1, IL34, HLF, CBS, and RNF165). A further hub survival gene-based risk score model was constructed, which was validated by an external cohort. We observed that a higher risk score predicted a worse prognosis (p = 0.0013). The area under the receiver operating characteristic curve (AUC) plots showed that this risk score model had good prediction value (1-year AUC = 0.684, 2-year AUC = 0.702, and 3-year AUC = 0.688). Five different deconvolution methods all showed that the B cells were lower in the RR and PD-L1-high group (p < 0.05). Finally, connectivity mapping analysis showed that the histone deacetylase (HDAC) inhibitor trichostatin A might have the potential to reverse the phenotype of RR and PD-L1-high in locally advanced HNSCC (p < 0.05, false discovery rate <0.1).Conclusion: The combination of 31-gene signature and the PD-L1 mRNA expression had a potential predictive value for the prognosis of locally advanced HNSCC who had RT. The B cells were lower in the RR and PD-L1-high group. The identified risk gene signature of locally advanced HNSCC and the potential therapeutic drug trichostatin A for the RR and PD-L1-high group are worth being further studied in a prospective homogenous cohort.

What nature has to offer: Opportunities for immuno-oncology

“…Additionally, using TSA in conjunction with immune treatment and radiation therapy seems to enhance the prognosis for head and neck cancer, according to the cancer genome dataset. These pre-clinical study findings support the significance of TSA to assist in the tumor removal in cancer treatment, despite the lack of TSA use in the clinical experiments yet [ 44 ].…”

LINC00963-FOSB-mediated transcription activation of UBE3C enhances radioresistance of breast cancer cells by inducing ubiquitination-dependent protein degradation of TP73

“…GEO datasets have been increasingly used as advanced and convenient tools for the quick screening of candidate genes related to specific biological processes including radioresistance in cancer [ 29 – 31 ]. In this study, by analyzing GSE31863 and GSE101920 datasets and querying TCGA-BRCA database, we obtained UBE3C as an upregulated gene in BRCA tissue samples following radioresistance and its elevation was linked to poor radioresponse.…”

A Four-Gene Signature Associated with Radioresistance in Head and Neck Squamous Cell Carcinoma Identified by Text Mining and Data Analysis

“…Previous studies on radiosensitivity in head and neck cancer have identified some related genes. Some are based on the gene expression levels of cell lines [ 50 , 51 ], and some are based on gene expression levels in tumors and patient survival data [ 52 , 53 ]. Foy et al analyzed the data from the public database by data mining.…”