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Updated: Oct 20, 2025
Author Spotlight: Advancements in Molecular Biomarker Testing for Non-Squamous Non-Small Cell Lung Cancer
Published on: September 8, 2023
Identification and Prognostic Value Exploration of Radiotherapy Sensitivity-Associated Genes in Non-Small-Cell Lung
Qing Ma1, Kai Geng2, Ping Xiao1
1Department Oncology, Tianjin Medical University General Hospital, Tianjin 300052, China.
Background:
Non-small-cell lung cancer (NSCLC) is a prevalent malignancy with high mortality and poor prognosis. The radiotherapy is one of the most common treatments of NSCLC, and the radiotherapy sensitivity of patients could affect the individual prognosis of NSCLC. However, the prognostic signatures related to radiotherapy response still remain limited. Here, we explored the radiosensitivity-associated genes and constructed the prognostically predictive model of NSCLC cases.
Methods:
The NSCLC samples with radiotherapy records were obtained from The Cancer Genome Atlas database, and the mRNA expression profiles of NSCLC patients from the GSE30219 and GSE31210 datasets were obtained from the Gene Expression Omnibus database. The Weighted Gene Coexpression Network Analysis (WGCNA), univariate, least absolute shrinkage and selection operator (LASSO), multivariate Cox regression analysis, and nomogram were conducted to identify and validate the radiotherapy sensitivity-related signature.
Results:
WGCNA revealed that 365 genes were significantly correlated with radiotherapy response. LASSO Cox regression analysis identified 8 genes, including FOLR3, SLC6A11, ALPP, IGFN1, KCNJ12, RPS4XP22, HIST1H2BH, and BLACAT1. The overall survival (OS) of the low-risk group was better than that of the high-risk group separated by the Risk Score based on these 8 genes for the NSCLC patients. Furthermore, the immune infiltration analysis showed that monocytes and activated memory CD4 T cells had different relative proportions in the low-risk group compared with the high-risk group. The Risk Score was correlated with immune checkpoints, including CTLA4, PDL1, LAG3, and TIGIT.
Conclusion:
We identified 365 genes potentially correlated with the radiotherapy response of NSCLC patients. The Risk Score model based on the identified 8 genes can predict the prognosis of NSCLC patients.
