Association of R-loop binding proteins with prognosis and anti-tumor drug sensitivity in lung adenocarcinoma: a bioinfor-matic study.

Abstract

Objectives: To investigate the association of R-loop binding proteins with prognosis and chemotherapy efficacy in lung adenocarcinoma.

Methods: The data related to R-loop regulatory genes were obtained from literature of R-loop proteomics and relevant databases. We used 403 cases of lung adenocarcinoma in the Cancer Genome Atlas as training set, and two datasets GSE14814 and GSE31210 in Gene Expression Omnibus as validation sets. The weighted gene co-expression network analysis (WGCNA) was employed to identify R-loop genes with a significant impact on the clinical phenotype of lung adenocarcinoma. Least absolute shrinkage and selection operator (LASSO) regression analysis was utilized to eliminate genes exhibiting multicollinearity. A multivariate Cox regression analysis was employed to scrutinize clinical variables and R-loop characteristic genes that exert independent prognostic effects on patient survival. Subsequently, a risk score model was constructed. The predictive capacity of this model for the prognosis of patients was analyzed and validated. Additionally, the performance of risk model on the anti-tumor drug sensitivity was assessed. The mutations of R-loop genes were analyzed by maftools. The effect of PLEC expression on anti-tumor drug sensitivity was tested on non-small cell lung adenocarcinoma H1299 and A549 cells in vitro.

Results: A collection of 1551 R-loop genes were obtained, and 78 genes exhibited significant effects on the clinical phenotype shown on WGCNA. The LASSO regression analysis retained fourteen R-loop genes. A multivariate Cox regression analysis further identified three R-loop genes (HEXIM1, GLI2, PLEC) and a clinical variable (tumor grading) that were associated with patient prognosis. Risk prediction model was established according to the regression coefficients of each parameter. Kaplan-Meier survival analysis showed that the prognosis of high-risk group was significantly worse than that of low-risk group (P<0.01). The time-dependent ROC curve showed that the risk model had good predictive ability in both training and validation sets. Predictive analyses of anti-neoplastic drug sensitivity indicated a diminished responsiveness to both chemotherapy and targeted treatment drugs among high-risk patients. The expression of PLEC was strongly correlated with sensitivity to gefitinib, a classical EGFR inhibitor.

Conclusions: R-loop binding proteins have been identified as significant determinants in the prognosis and therapeutic strategies for lung adenocarcinoma, which indicates that therapeutic interventions targeting these specific R-loop binding proteins might contribute to a better survival of the patients.