Department of Thoracic Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, 3 East Qing Chun Road, 310000 Hangzhou, Zhejiang, China.
Department of Neurosurgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, 3 East Qing Chun Road, 310000 Hangzhou, Zhejiang, China.
Biomed Res Int. 2020 Nov 7;2020:9124792. doi: 10.1155/2020/9124792. eCollection 2020.
After curative surgical resection, about 30-75% lung adenocarcinoma (LUAD) patients suffer from recurrence with dismal survival outcomes. Identification of patients with high risk of recurrence to impose intense therapy is urgently needed.
Gene expression data of LUAD were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Differentially expressed genes (DEGs) were calculated by comparing the recurrent and primary tissues. Prognostic genes associated with the recurrence-free survival (RFS) of LUAD patients were identified using univariate analysis. LASSO Cox regression and multivariate Cox analysis were applied to extract key genes and establish the prediction model.
We detected 37 DEGs between primary and recurrent LUAD tumors. Using univariate analysis, 31 DEGs were found to be significantly associated with RFS. We established the RFS prediction model including thirteen genes using the LASSO Cox regression. In the training cohort, we classified patients into high- and low-risk groups and found that patients in the high-risk group suffered from worse RFS compared to those in the low-risk group ( < 0.01). Concordant results were confirmed in the internal and external validation cohort. The efficiency of the prediction model was also confirmed under different clinical subgroups. The high-risk group was significantly identified as the risk factor of recurrence in LUAD by the multivariate Cox analysis (HR = 13.37, = 0.01). Compared to clinicopathological features, our prediction model possessed higher accuracy to identify patients with high risk of recurrence (AUC = 96.3%). Finally, we found that the G2M checkpoint pathway was enriched both in recurrent tumors and primary tumors of high-risk patients.
Our recurrence-specific gene-based prognostic prediction model provides extra information about the risk of recurrence in LUAD, which is conducive for clinicians to conduct individualized therapy in clinic.
经过根治性手术切除后，约 30-75%的肺腺癌 (LUAD) 患者会复发，且生存预后较差。因此，迫切需要识别出具有高复发风险的患者，以便对其进行强化治疗。
从癌症基因组图谱 (TCGA) 和基因表达综合 (GEO) 数据库中获取 LUAD 的基因表达数据。通过比较复发组织和原发组织，计算差异表达基因 (DEGs)。使用单因素分析识别与 LUAD 患者无复发生存 (RFS) 相关的预后基因。应用 LASSO Cox 回归和多因素 Cox 分析提取关键基因并建立预测模型。
我们在原发性和复发性 LUAD 肿瘤之间检测到 37 个 DEGs。通过单因素分析，发现 31 个 DEGs 与 RFS 显著相关。我们使用 LASSO Cox 回归建立了包括 13 个基因的 RFS 预测模型。在训练队列中，我们将患者分为高风险组和低风险组，发现高风险组的患者 RFS 较差，与低风险组相比 (<0.01)。在内部和外部验证队列中也得到了一致的结果。该预测模型在不同的临床亚组中也得到了验证。多因素 Cox 分析也证实了高风险组是 LUAD 复发的危险因素 (HR=13.37, =0.01)。与临床病理特征相比，我们的预测模型在识别具有高复发风险的患者方面具有更高的准确性 (AUC=96.3%)。最后，我们发现高危患者的 G2M 检查点途径在复发肿瘤和高危患者的原发肿瘤中均有富集。
我们基于复发特异性基因的预后预测模型为 LUAD 的复发风险提供了额外的信息，有助于临床医生在临床实践中进行个体化治疗。
