Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou 225009, Jiangsu Province, China.
Zhejiang Da Xue Xue Bao Yi Xue Ban. 2024 Aug 25;53(4):472-480. doi: 10.3724/zdxbyxb-2024-0032.
To investigate the association of R-loop binding proteins with prognosis and chemotherapy efficacy in lung adenocarcinoma.
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 .
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 (, , ) 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 (<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.
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.
探讨 R 环结合蛋白与肺腺癌患者预后和化疗疗效的关系。
从 R 环蛋白质组学文献和相关数据库中获取 R 环调节基因相关数据。我们将癌症基因组图谱中 403 例肺腺癌患者作为训练集，GSE14814 和 GSE31210 两个基因表达综合数据库作为验证集。采用加权基因共表达网络分析（WGCNA）鉴定对肺腺癌临床表型有显著影响的 R 环基因。采用最小绝对收缩和选择算子（LASSO）回归分析消除存在多重共线性的基因。采用多变量 Cox 回归分析筛选对患者生存具有独立预后影响的临床变量和 R 环特征基因。随后构建风险评分模型，分析和验证该模型对患者预后的预测能力。此外，还评估了风险模型在评估抗肿瘤药物敏感性方面的性能。利用 maftools 分析 R 环基因的突变。在非小细胞肺腺癌 H1299 和 A549 细胞中测试 PLEC 表达对抗肿瘤药物敏感性的影响。
得到了 1551 个 R 环基因，WGCNA 显示 78 个基因对临床表型有显著影响。LASSO 回归分析保留了 14 个 R 环基因。多变量 Cox 回归分析进一步确定了三个 R 环基因（、和）和一个临床变量（肿瘤分级）与患者预后相关。根据各参数回归系数建立风险预测模型。Kaplan-Meier 生存分析表明，高危组的预后明显差于低危组（<0.01）。时间依赖性 ROC 曲线表明，该风险模型在训练集和验证集均具有良好的预测能力。抗肿瘤药物敏感性预测分析表明，高危患者对化疗和靶向治疗药物的反应性降低。PLEC 的表达与经典 EGFR 抑制剂吉非替尼的敏感性呈强相关。
R 环结合蛋白已被确定为肺腺癌预后和治疗策略的重要决定因素，表明针对这些特定 R 环结合蛋白的治疗干预可能有助于提高患者的生存率。
