Prediction of Radiosensitivity in Head and Neck Squamous Cell Carcinoma Based on Multiple Omics Data.

Jie Liu,Chao Dong,Yannan Bin,Lijun Wu,Zhenyu Yue,Junfeng Xia,Pengbo Wen,Guoping Zhao,Mengmeng Han

doi:10.3389/fgene.2020.00960

Abstract

Head and neck squamous cell carcinoma (HNSCC) is a malignant tumor. Radiotherapy (RT) is an important treatment for HNSCC, but not all patients derive survival benefit from RT due to the individual differences on radiosensitivity. A prediction model of radiosensitivity based on multiple omics data might solve this problem. Compared with single omics data, multiple omics data can illuminate more systematical associations between complex molecular characteristics and cancer phenotypes. In this study, we obtained 122 differential expression genes by analyzing the gene expression data of HNSCC patients with RT (N = 287) and without RT (N = 189) downloaded from The Cancer Genome Atlas. Then, HNSCC patients with RT were randomly divided into a training set (N = 149) and a test set (N = 138). Finally, we combined multiple omics data of 122 differential genes with clinical outcomes on the training set to establish a 12-gene signature by two-stage regularization and multivariable Cox regression models. Using the median score of the 12-gene signature on the training set as the cutoff value, the patients were divided into the high- and low-score groups. The analysis revealed that patients in the low-score group had higher radiosensitivity and would benefit from RT. Furthermore, we developed a nomogram to predict the overall survival of HNSCC patients with RT. We compared the prognostic value of 12-gene signature with those of the gene signatures based on single omics data. It suggested that the 12-gene signature based on multiple omics data achieved the best ability for predicting radiosensitivity. In conclusion, the proposed 12-gene signature is a promising biomarker for estimating the RT options in HNSCC patients.

Highlights

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common malignancy in the world, and nearly 60% of newly diagnosed HNSCC is locally advanced disease (Alsahafi et al, 2019; van der Heijden et al, 2019; Wang et al, 2019)
In order to predict the radiosensitivity of HNSCC patients, we constructed a gene signature on the training set according to the expression levels of these 12 genes as follows: gene score = TDRD9 × 6.950E6 + CELF3 × 1.106E-2 + FGF19 × 1.937E-5 + KCNB2 × 5.388E3 + CLDN6 × 1.334E-4 − BEST2 × 6.053E4 − DDX25 × 1.802E-3 − TMPRSS15 × 4.378E4 − ALPI × 2.134E-3 − FABP7 × 1.754E-3 − IL17REL × 2.132E3 − RORB × 1.182E-3
On the training (149 HNSCC patients receiving RT) and test (139 patients receiving RT) sets, HNSCC patients with a gene score ≥-0.06338 were divided into the high-score group, while those with a gene score

Summary

Introduction

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common malignancy in the world, and nearly 60% of newly diagnosed HNSCC is locally advanced disease (Alsahafi et al, 2019; van der Heijden et al, 2019; Wang et al, 2019). Some gene expression-based signatures have been constructed to predict the survival rate of HNSCC patients with RT. Ma et al (2019) identified a 4-gene methylation signature to predict the survival rate of HNSCC patients with RT. These studies only used single omics data which could not draw more comprehensive associations between complex molecular characteristics and cancer phenotypes. Multiple omics data involve multidimensional studies of cancer cells, potentially revealing the molecular mechanisms behind different phenotypes of cancer, such as metastasis and recurrence (Chakraborty et al, 2018; Xi et al, 2018; Wang et al, 2020). A model based on multiple omics data could be an effective method for radiosensitivity prediction of HNSCC patients

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Results

Conclusion