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Radiation-sensitive genetic prognostic model identifies individuals at risk for radiation resistance in head and neck squamous cell carcinoma

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Abstract

Background

The advantages of radiotherapy for head and neck squamous cell carcinoma (HNSCC) depend on the radiation sensitivity of the patient. Here, we established and verified radiological factor-related gene signature and built a prognostic risk model to predict whether radiotherapy would be beneficial.

Methods

Data from The Cancer Genome Atlas, Gene Expression Omnibus, and RadAtlas databases were subjected to LASSO regression, univariate COX regression, and multivariate COX regression analyses to integrate genomic and clinical information from patients with HNSCC. HNSCC radiation-related prognostic genes were identified, and patients classified into high- and low-risk groups, based on risk scores. Variations in radiation sensitivity according to immunological microenvironment, functional pathways, and immunotherapy response were investigated. Finally, the expression of HNSCC radiation-related genes was verified by qRT-PCR.

Results

We built a clinical risk prediction model comprising a 15-gene signature and used it to divide patients into two groups based on their susceptibility to radiation: radiation-sensitive and radiation-resistant. Overall survival was significantly greater in the radiation-sensitive than the radiation-resistant group. Further, our model was an independent predictor of radiotherapy response, outperforming other clinical parameters, and could be combined with tumor mutational burden, to identify the target population with good predictive value for prognosis at 1, 2, and 3 years. Additionally, the radiation-resistant group was more vulnerable to low levels of immune infiltration, which are significantly associated with DNA damage repair, hypoxia, and cell cycle regulation. Tumor Immune Dysfunction and Exclusion scores also suggested that the resistant group would respond less favorably to immunotherapy.

Conclusions

Our prognostic model based on a radiation-related gene signature has potential for application as a tool for risk stratification of radiation therapy for patients with HNSCC, helping to identify candidates for radiation therapy and overcome radiation resistance.

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Data availability

The RNA seq data and clinical information in this study are available in the TCGA database (https://portal.gdc.cancer.gov/) and GEO database (https://www.ncbi.nlm.nih.gov/geo/), access number: GSE65858, GSE41613, GSE42743, and the dataset related to radiation exposure can be found in RadAtlas (http://biokb.ncpsb.org.cn/RadAtlas/index.php). All results have been presented in the form of tables and supplementary tables. Details of the data used in this manuscript can be obtained from the corresponding author.

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Acknowledgements

We appreciate the data obtained from TCGA, GEO and RadAtlas. We sincerely thank all the people involved.

Funding

This study was funded by the Natural Science Foundation of Guangdong (Grant Number 2021A1515010838), Science and Technology Program of Guangzhou (Grant Number 201903010028), The Project Supported by Natural Science Foundation of Jiangxi (20192BAB205065), The Excellent Young Scientists Fund of Jiangxi Cancer Hospital (2021EYS04), Guangdong Provincial People’s Hospital Intermural Program (Grant Number KJ012019447), The Nation Cancer Center Climb Plan (NCC201914B05).

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Author notes

  1. Peimeng You and Shengbo Liu have contributed equally to this work and share first authorship.

Authors and Affiliations

  1. Nanchang University, Nanchang, China

    Peimeng You & Haiyu Zhou

  2. Second Clinical College of Medicine, Southern Medical University, Guangzhou, China

    Shengbo Liu

  3. Department of Thoracic Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China

    Shengbo Liu, Qiaxuan Li, Daipeng Xie, Lintong Yao, Hongrui Qiu & Haiyu Zhou

  4. Shantou University Medical College, Shantou, China

    Qiaxuan Li, Lintong Yao & Hongrui Qiu

  5. Guangdong Cardiovascular Institute, Guangzhou, China

    Daipeng Xie

  6. Department of Radiation Oncology, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China

    Chenguang Guo, Zefeng Guo & Ting Wang

  7. Jiangxi Lung Cancer Institute, Nanchang, China

    Haiyu Zhou

  8. Jiangxi Key Laboratory of Translational Cancer Research, Jiangxi Cancer Hospital, Nanchang, China

    Peimeng You, Yangzhong Guo & Junyu Li

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  1. Peimeng You
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Contributions

PY and SL, QL contributed to conceptualization, data curation, formal analysis, data curation, software and project administration and writing manuscript. HQ, YG contributed to software, visualization and manuscript editing. CG, ZG and TW contributed to the methodology and resources. DX and LY contributed to manuscript review. HZ and JL contributed to project administration and supervision.

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Correspondence to Junyu Li or Haiyu Zhou.

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Supplementary Information

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432_2023_5304_MOESM1_ESM.tif

Supplementary file1 Supplementary Figure 1. (A) Survival analysis and ROC curves of the complete TCGA cohort. (B)- (I) Survival analysis of different clinical subgroups (treatment mode, age, sex, TNM stage, stage, grade) (TIF 5841 KB)

432_2023_5304_MOESM2_ESM.tif

Supplementary file2 Supplementary Figure 2. (A) 15 genes biological function enrichment. (B) Immunotherapy response in radiosensitive and radioresistant groups. (C) Expression of hypoxia factors in HPV+ patients. (D) Risk Score and Immune Cell Correlation. (E)Top 10 genes with mutation frequency among 15 genes. (F) TMB score of radiation sensitive group and radiation resistant group. (G) The 15 genes of this model are expressed in HNSCC. (H) The 15 genes of this model are expressed in different HNSCC cell lines from CCLE database. (I) Results of qRT-PCR of five genes expressed in cell lines (TIF 10014 KB)

432_2023_5304_MOESM3_ESM.tif

Supplementary file3 Supplementary Figure 3. (A) The performance of each model on all validation datasets was calculated using machine learning combination algorithms. (B)GSE41613 validation cohort and (C) GSE42743 validation cohort KM survival curve, and 1-year, 2-year, 3-year ROC curve.(D) Manage test calculates risk scores, immune microenvironment scores, immune checkpoints(E)and signature correlation heatmaps (TIF 17616 KB)

Supplementary file4 (DOCX 39 KB)

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You, P., Liu, S., Li, Q. et al. Radiation-sensitive genetic prognostic model identifies individuals at risk for radiation resistance in head and neck squamous cell carcinoma. J Cancer Res Clin Oncol 149, 15623–15640 (2023). https://doi.org/10.1007/s00432-023-05304-x

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