Li Yanqi, Lu Xiao, Zhang Jiao, Liu Quanxing, Zhou Dong, Deng Xufeng, Qiu Yuan, Chen Qian, Li Manyuan, Yang Guixue, Zheng Hong, Dai Jigang
Department of Thoracic Surgery, Xinqiao Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
Department of General Surgery, Xinqiao Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
Front Mol Biosci. 2021 Sep 20;8:735263. doi: 10.3389/fmolb.2021.735263. eCollection 2021.
Epidemiological investigations have shown that patients with Parkinson's disease (PD) have a lower probability of developing lung cancer. Subsequent research revealed that PD and lung cancer share specific genetic alterations. Therefore, the utilisation of PD biomarkers and therapeutic targets may improve lung adenocarcinoma (LUAD) diagnosis and treatment. We aimed to identify a gene-based signature from 25 Parkinson family genes for LUAD prognosis and treatment choice. We analysed Parkinson family gene expression and protein levels in LUAD, utilising multiple databases. Least absolute shrinkage and selection operator (LASSO) regression was used to construct a prognostic model based on the TCGA-LUAD cohort. We validated the model in external GEO cohorts. Immune cell infiltration was compared between risk groups, and GEO data was used to explore the model's predictive ability for LUAD treatment response. Nearly all Parkinson family genes exhibited significant differential expression between LUAD and normal tissues. LASSO regression confirmed that our seven Parkinson family gene-based signature had excellent prognostic performance for LUAD, as validated in three GEO cohorts. The high-risk group was clearly associated with low tumour immune cell infiltration, suggesting that immunotherapy may not be an optimal treatment choice. This is the first Parkinson family gene-based model for the prediction of LUAD prognosis and treatment outcome. The association of these genes with poor prognosis and low immune infiltration requires further investigation.
流行病学调查表明，帕金森病（PD）患者患肺癌的概率较低。随后的研究发现，PD和肺癌存在特定的基因改变。因此，利用PD生物标志物和治疗靶点可能会改善肺腺癌（LUAD）的诊断和治疗。我们旨在从25个帕金森家族基因中确定一个基于基因的特征，用于LUAD的预后评估和治疗选择。我们利用多个数据库分析了LUAD中帕金森家族基因的表达和蛋白质水平。使用最小绝对收缩和选择算子（LASSO）回归基于TCGA-LUAD队列构建了一个预后模型。我们在外部GEO队列中验证了该模型。比较了风险组之间的免疫细胞浸润情况，并利用GEO数据探索该模型对LUAD治疗反应的预测能力。几乎所有帕金森家族基因在LUAD和正常组织之间都表现出显著的差异表达。LASSO回归证实，我们基于七个帕金森家族基因的特征对LUAD具有出色的预后性能，这在三个GEO队列中得到了验证。高风险组明显与低肿瘤免疫细胞浸润相关，这表明免疫疗法可能不是最佳的治疗选择。这是第一个基于帕金森家族基因的预测LUAD预后和治疗结果的模型。这些基因与不良预后和低免疫浸润之间的关联需要进一步研究。
