背景:缺氧与肿瘤发生发展的相关性已得到广泛认可。同时,受缺氧影响的最重要的细胞器是线粒体。这项研究旨在确定它们是否具有肺腺癌(LUAD)的预后特征。为此,进行了生物信息学分析,以评估缺氧和线粒体评分相关基因,从而成功建立了预后模型。方法:采用单样本基因集富集分析算法,计算缺氧和线粒体评分。差异表达分析和加权相关网络分析用于鉴定与缺氧和线粒体评分相关的基因。通过单变量Cox回归获得预后相关基因,其次采用最小绝对收缩和选择算子Cox回归建立预后模型。使用两个独立的验证数据集来验证使用接收器工作特性和校准曲线的预后模型的准确性。此外,列线图用于说明本研究的临床意义。结果:318个与缺氧和线粒体评分相关的差异表达基因被鉴定用于预后模型的构建。基于16个基因的预后模型,包括PKM,S100A16,RRAS,TUBA4A,PKP3,KCTD12,LPGAT1,ITPRID2,MZT2A,LIFR,PTPRM,LATS2,PDIK1L,GORAB,PCDH7和CPED1对LUAD预后具有良好的预测准确性。此外,肿瘤微环境分析和药物敏感性分析表明风险评分与某些免疫细胞之间存在关联,和较高的风险评分表明化疗疗效改善。结论:本研究建立了由16个基因组成的预后模型,并绘制列线图以准确预测LUAD患者的预后。这些发现可能有助于指导LUAD患者的临床决策和治疗选择。最终改善治疗结果。
Background: The correlation between hypoxia and tumor development is widely acknowledged. Meanwhile, the foremost organelle affected by hypoxia is mitochondria. This study aims to determine whether they possess prognostic characteristics in lung adenocarcinoma (LUAD). For this purpose, a bioinformatics analysis was conducted to assess hypoxia and
mitochondrial scores related genes, resulting in the successful establishment of a prognostic model. Methods: Using the single sample Gene Set Enrichment Analysis algorithm, the hypoxia and
mitochondrial scores were computed. Differential expression analysis and weighted correlation network analysis were employed to identify genes associated with hypoxia and
mitochondrial scores. Prognosis-related genes were obtained through univariate Cox regression, followed by the establishment of a prognostic model using least absolute shrinkage and selection operator Cox regression. Two independent validation datasets were utilized to verify the accuracy of the prognostic model using receiver operating characteristic and calibration curves. Additionally, a nomogram was employed to illustrate the clinical significance of this study. Results: 318 differentially expressed genes associated with hypoxia and
mitochondrial scores were identified for the construction of a prognostic model. The prognostic model based on 16 genes, including PKM, S100A16, RRAS, TUBA4A, PKP3, KCTD12, LPGAT1, ITPRID2, MZT2A, LIFR, PTPRM, LATS2, PDIK1L, GORAB, PCDH7, and CPED1, demonstrates good predictive accuracy for LUAD prognosis. Furthermore, tumor microenvironments analysis and drug sensitivity analysis indicate an association between risk scores and certain immune cells, and a higher risk scores suggesting improved chemotherapy efficacy. Conclusion: The research established a prognostic model consisting of 16 genes, and a nomogram was developed to accurately predict the prognosis of LUAD patients. These findings may contribute to guiding clinical decision-making and treatment selection for patients with LUAD, ultimately leading to improved treatment outcomes.