PDF(Pubmed)
Abstract：
OBJECTIVE: 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.
目的: 研究R环结合蛋白对肺腺癌患者预后及抗肿瘤药物敏感性的影响，为R环在肿瘤生物学中的调控机制研究及临床决策提供科学依据。方法: 从R环结合蛋白质组学研究文献及相关数据库中获取R环结合基因，以癌症基因组图谱数据库中的403例肺腺癌患者的数据作为训练集，以基因表达综合数据库中GSE14814与GSE31210两个数据集的数据作为验证集，采用加权基因共表达网络分析（WGCNA）、最小绝对收缩和选择算子（LASSO）、多因素Cox回归分析逐步筛选具有独立预后预测作用的临床变量及R环特征基因，maftools分析R环特征基因的突变特征，构建基于R环特征基因的风险评分和列线图模型，验证该模型对高、低风险患者预后预测的能力及其对抗肿瘤药物治疗敏感性的影响。最后采用实验验证R环特征基因表达对抗肿瘤药物敏感性的影响。结果: 收集整理得到R环特征基因1551个，WGCNA筛选得到显著影响临床表型的R环基因78个，LASSO回归分析保留R环基因14个，多因素Cox回归分析筛选到3个与患者预后密切相关的R环特征基因（HEXIM1、GLI2、PLEC）和一个临床变量（肿瘤分级），根据各参数的回归系数构建预后模型和列线图模型。Kaplan-Meier生存分析显示，高风险组患者预后明显差于低风险组（P<0.01）。时间依赖受试者工作特征曲线表明，该模型在训练集和验证集列队中均具有较好的预测能力。抗肿瘤药物敏感性预测结果表明，高风险组患者对肺癌化疗和靶向治疗药物的敏感性更低。PLEC基因沉默实验表明抑制PLEC的表达能增强表皮生长因子受体野生型非小细胞肺腺癌细胞株对吉非替尼的敏感性。结论: R环结合蛋白是肺腺癌预后的风险因素，联合临床信息和R环特征基因可以有效预测肺腺癌患者的预后，靶向上述R环特征基因可能对提高患者存活率具有重要意义。.
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.
目的: 研究R环结合蛋白对肺腺癌患者预后及抗肿瘤药物敏感性的影响，为R环在肿瘤生物学中的调控机制研究及临床决策提供科学依据。方法: 从R环结合蛋白质组学研究文献及相关数据库中获取R环结合基因，以癌症基因组图谱数据库中的403例肺腺癌患者的数据作为训练集，以基因表达综合数据库中GSE14814与GSE31210两个数据集的数据作为验证集，采用加权基因共表达网络分析（WGCNA）、最小绝对收缩和选择算子（LASSO）、多因素Cox回归分析逐步筛选具有独立预后预测作用的临床变量及R环特征基因，maftools分析R环特征基因的突变特征，构建基于R环特征基因的风险评分和列线图模型，验证该模型对高、低风险患者预后预测的能力及其对抗肿瘤药物治疗敏感性的影响。最后采用实验验证R环特征基因表达对抗肿瘤药物敏感性的影响。结果: 收集整理得到R环特征基因1551个，WGCNA筛选得到显著影响临床表型的R环基因78个，LASSO回归分析保留R环基因14个，多因素Cox回归分析筛选到3个与患者预后密切相关的R环特征基因（HEXIM1、GLI2、PLEC）和一个临床变量（肿瘤分级），根据各参数的回归系数构建预后模型和列线图模型。Kaplan-Meier生存分析显示，高风险组患者预后明显差于低风险组（P<0.01）。时间依赖受试者工作特征曲线表明，该模型在训练集和验证集列队中均具有较好的预测能力。抗肿瘤药物敏感性预测结果表明，高风险组患者对肺癌化疗和靶向治疗药物的敏感性更低。PLEC基因沉默实验表明抑制PLEC的表达能增强表皮生长因子受体野生型非小细胞肺腺癌细胞株对吉非替尼的敏感性。结论: R环结合蛋白是肺腺癌预后的风险因素，联合临床信息和R环特征基因可以有效预测肺腺癌患者的预后，靶向上述R环特征基因可能对提高患者存活率具有重要意义。.
摘要：
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