Background: The prevalence and cancer-specific death rate of lung cancer (LC) have risen in recent decades. A universally applicable prognostic signature for both adenocarcinoma LC (LUAD) and squamous cell carcinoma LC (LUSC) is still lacking.
Methods: A total of 453 patients from The Cancer Genome Atlas (TCGA)-LUAD cohort and 452 patients from TCGA-LUSC cohort were enrolled, and a prognostic model was constructed using least absolute shrinkage and selection operator (LASSO) regression analysis based on the consensus prognostic genes in both cohorts. The newly defined pan-lung cancer risk count (PLCRC) of each patient was calculated via the summation formula.
Results: A total of 23 genes were selected for the calculation of the PLCRC. The PLCRC showed a moderate prognostic value in the entire (p < 0.001, HR: 2.75, AUC: 0.643), LUAD (p < 0.001, HR: 2.51, AUC: 0.636) and LUSC (p < 0.001, HR: 2.89, AUC: 0.656) cohorts. The PLCRC was an independent prognostic factor after adjusting the clinical features. The PLCRC was also effective in nine external validation cohorts and in patients with different clinical features. Activation of extracellular matrix pathways and infiltration of immunocytes promoted the tumorigenesis and development of both LUAD and LUSC. We generated a universally applicable prognostic signature, the PLCRC, which could dichotomize patients with significantly different clinical outcomes and guide the clinical treatment of LC patients. Chemotherapy is more suitable for patients with a low PLCRC, while anti-cytotoxic T-lymphocyte-associated protein 4 immunotherapy is more suitable for patients with a high PLCRC.
Conclusion: We established and validated a newly defined prognostic signature, the PLCRC, for both LUAD and LUSC patients and provided clinical strategies for patients from different risk subgroups.



中文翻译：

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腺癌和鳞状细胞癌肺癌风险评估和个性化化疗/免疫治疗反应预测的新工具

背景：近几十年来，肺癌 (LC) 的患病率和癌症特异性死亡率呈上升趋势。仍然缺乏腺癌 LC (LUAD) 和鳞状细胞癌 LC (LUSC) 的普遍适用的预后特征。
方法：共纳入癌症基因组图谱（TCGA）-LUAD 队列的 453 名患者和 TCGA-LUSC 队列的 452 名患者，并基于最小绝对收缩和选择算子（LASSO）回归分析构建预后模型。两个队列中的共识预后基因。通过求和公式计算每个患者新定义的泛肺癌风险计数（PLCCRC）。
结果：总共选择了 23 个基因用于 PLCCRC 的计算。PLCCRC 在整体 (p < 0.001, HR: 2.75, AUC: 0.643)、LUAD (p < 0.001, HR: 2.51, AUC: 0.636) 和 LUSC (p < 0.001, HR: 2.89, AUC) 中显示出中等的预后价值: 0.656) 队列。在调整临床特征后，PLCCRC是一个独立的预后因素。PLCCRC 在九个外部验证队列和具有不同临床特征的患者中也有效。细胞外基质通路的激活和免疫细胞的浸润促进了 LUAD 和 LUSC 的肿瘤发生和发展。我们生成了一个普遍适用的预后特征，PLCCRC，它可以将具有显着不同临床结果的患者分开，并指导 LC 患者的临床治疗。
结论：我们为 LUAD 和 LUSC 患者建立并验证了新定义的预后特征 PLCCRC，并为来自不同风险亚组的患者提供了临床策略。