Abstract：
Lung adenocarcinoma (LUAD), the most common subtype of lung cancer globally, has seen improved prognosis with advancements in diagnostic, surgical, radiotherapy, and molecular therapy techniques, while its 5-year survival rate remains low. Molecular biomarkers provide prognostic value. Oxidative stress factors, such as reactive nitrogen species and ROS, are crucial in various stages of tumor progression, influencing cell transformation, proliferation, angiogenesis, and metastasis. ROS demonstrate dual roles, affecting tumor cells, hypoxia sensitivity, and the microenvironment. Comprehensive analysis of oxidative stress in LUAD has not been conducted to date. Therefore, we systematically investigated the regulatory patterns of oxidative stress in LUAD based on oxidative stress-related genes and correlated these patterns with cellular infiltration characteristics of the tumor immune microenvironment. The model utilizes single-factor Cox analysis to screen key differential genes with prognostic value and employs least absolute shrinkage and selection operator (LASSO) penalized Cox regression analysis to construct a prognostic-related prediction model. Ten candidate genes were selected based on this model. The risk score was constructed using the coefficients and expression levels of these ten genes. Furthermore, the impact of this risk score on overall survival (OS) was determined. Two genes with the most significant differential expression, SFTPB and S100P, were selected through qRT-PCR. Cell experiments including CCK-8, Edu, transwell assays confirmed their effects on lung cancer cells growth, consistent with the results of bioinformatics analysis. These findings suggested that this model held potential clinical value for evaluating the prognosis of lung adenocarcinoma.
摘要：
肺腺癌(LUAD),全球最常见的肺癌亚型，随着诊断的进步，预后有所改善，外科,放射治疗,和分子治疗技术，而其5年生存率仍然很低。分子生物标志物提供预后价值。氧化应激因素，如活性氮物种和ROS，在肿瘤进展的不同阶段至关重要，影响细胞转化，扩散,血管生成,和转移。ROS表现出双重角色，影响肿瘤细胞，缺氧敏感性，和微环境。迄今为止，尚未对LUAD中的氧化应激进行综合分析。因此,我们基于氧化应激相关基因系统研究了LUAD中氧化应激的调控模式,并将这些模式与肿瘤免疫微环境的细胞浸润特征相关联.该模型利用单因素Cox分析来筛选具有预后价值的关键差异基因，并采用最小绝对收缩和选择算子（LASSO）惩罚Cox回归分析来构建与预后相关的预测模型。基于该模型选择10个候选基因。使用这十个基因的系数和表达水平构建风险评分。此外，本研究确定了该风险评分对总生存期(OS)的影响.差异表达最显著的两个基因，SFTPB和S100P,通过qRT-PCR选择。细胞实验包括CCK-8，Edu，transwell分析证实了它们对肺癌细胞生长的影响，与生物信息学分析结果一致。这些发现表明该模型对评估肺腺癌的预后具有潜在的临床价值。
