Background: Zinc finger proteins (ZNFs), functioning as pervasive transcriptional modulators, serve as pivotal mediators of tumorigenesis and malignant advancement. However, the mechanistic contributions of these epigenetic orchestrators to lung adenocarcinoma pathogenesis remain incompletely characterized.Methods: To elucidate zinc finger proteins’ biological significance in lung adenocarcinoma (LUAD) pathogenesis, we first extracted relevant transcriptional data from TCGA. After preliminary screening with univariate Cox regression, a LASSO algorithm was applied to optimize the risk score model, incorporating key zinc finger protein markers. For independent validation, we accessed GEO dataset GSE68465, applying identical analytical protocols to confirm model generalizability. We performed multivariable Cox regression to identify independent predictors of clinical outcomes after adjusting for confounding variables. Cell-based validation included (1) comparative analysis of zinc finger protein expression across LUAD/normal cell models and (2) technical verification using standardized qRT-PCR protocols.Results: Following rigorous bioinformatics screening comprising differential expression and survival analysis, the final 21-zinc finger protein cohort was selected for risk score algorithm development aimed at clinical outcome prediction. Stratification based on computed risk scores revealed markedly superior survival outcomes in the low-risk cohort compared to high-risk patients. Comparative analysis revealed overall concordance in the transcriptional profiles of eight ZNFs (|coef| > 0.1) across experimental cell systems and TCGA datasets.Conclusions: Collectively, the prognostic framework incorporating zinc finger proteins demonstrates biomarker utility in lung adenocarcinoma survival prediction, while offering novel avenues for molecular target discovery in therapeutic strategies against this malignancy.
背景：锌指蛋白作为普遍存在的转录调节因子，在肿瘤发生和恶性进展中发挥着关键介导作用。然而，这些表观遗传调控因子在肺腺癌发病机制中的具体作用机制尚未完全阐明。 方法：为阐明锌指蛋白在肺腺癌发病中的生物学意义，我们首先从TCGA数据库中提取相关转录组数据。通过单变量Cox回归初步筛选后，采用LASSO算法优化风险评分模型，整合关键锌指蛋白标志物。为独立验证模型，我们使用GEO数据集GSE68465，采用相同分析流程验证模型的普适性。通过多变量Cox回归在调整混杂变量后确定临床结局的独立预测因子。细胞实验验证包括：（1）比较肺腺癌细胞模型与正常细胞模型中锌指蛋白的表达差异；（2）采用标准化qRT-PCR技术进行验证。 结果：经过差异表达分析和生存分析等严格生物信息学筛选，最终选定21个锌指蛋白构建临床预后预测的风险评分算法。基于计算风险评分进行分层分析显示，低风险组患者的生存结局显著优于高风险组。比较分析发现，八个锌指蛋白（|系数| > 0.1）在实验细胞系统和TCGA数据集中的转录谱呈现整体一致性。 结论：综合而言，整合锌指蛋白的预后预测框架在肺腺癌生存预测中展现出生物标志物的应用价值，同时为该恶性肿瘤治疗策略的分子靶点发现提供了新途径。
Zinc Finger Protein-Based Prognostic Signature Predicts Survival in Lung Adenocarcinoma
肿瘤(癌症)患者之家