Construction of a novel radioresistance-related signature for prediction of prognosis, immune microenvironment and anti-tumour drug sensitivity in non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is a fatal disease, and radioresistance is an important factor leading to treatment failure and disease progression. The objective of this research was to detect radioresistance-related genes (RRRGs) with prognostic value in NSCLC. The weighted gene coexpression network analysis (WGCNA) and differentially expressed genes (DEGs) analysis were performed to identify RRRGs using expression profiles from TCGA and GEO databases. The least absolute shrinkage and selection operator (LASSO) regression and random survival forest (RSF) were used to screen for prognostically relevant RRRGs. Multivariate Cox regression was used to construct a risk score model. Then, Immune landscape and drug sensitivity were evaluated. The biological functions exerted by the key gene <i>LBH</i> were verified by <i>in vitro</i> experiments. Ninety-nine RRRGs were screened by intersecting the results of DEGs and WGCNA, then 11 hub RRRGs associated with survival were identified using machine learning algorithms (LASSO and RSF). Subsequently, an eight-gene (<i>APOBEC3B, DOCK4, IER5L, LBH, LY6K, RERG, RMDN2</i> and <i>TSPAN2</i>) risk score model was established and demonstrated to be an independent prognostic factor in NSCLC on the basis of Cox regression analysis. The immune landscape and sensitivity to anti-tumour drugs showed significant disparities between patients categorized into different risk score subgroups. <i>In vitro</i> experiments indicated that overexpression of <i>LBH</i> enhanced the radiosensitivity of A549 cells, and knockdown <i>LBH</i> reversed the cytotoxicity induced by X-rays. Our study developed an eight-gene risk score model with potential clinical value that can be adopted for choice of drug treatment and prognostic prediction. Its clinical routine use may assist clinicians in selecting more rational practices for individuals, which is important for improving the prognosis of NSCLC patients. These findings also provide references for the development of potential therapeutic targets. An eight-gene (<i>APOBEC3B, DOCK4, IER5L, LBH, LY6K, RERG, RMDN2,</i> and <i>TSPAN2</i>) risk score model that can predict the clinical prognosis of NSCLC patients was developed and validated.NSCLC patients in the low-risk score group had a higher degree of immune cell infiltration and were more sensitive to anti-tumor drugs.Overexpression of LBH inhibited the malignant phenotype of A549 cells and increased their sensitivity to X-rays. An eight-gene (<i>APOBEC3B, DOCK4, IER5L, LBH, LY6K, RERG, RMDN2,</i> and <i>TSPAN2</i>) risk score model that can predict the clinical prognosis of NSCLC patients was developed and validated. NSCLC patients in the low-risk score group had a higher degree of immune cell infiltration and were more sensitive to anti-tumor drugs. Overexpression of LBH inhibited the malignant phenotype of A549 cells and increased their sensitivity to X-rays.