A novel recurrence-associated metabolic prognostic model for risk stratification and therapeutic response prediction in patients with stage Ⅰ lung adenocarcinoma

• 作者: Chengming Liu Sihui Wang Sufei Zheng Xinfeng Wang Jianbin Huang Yuanyuan Lei Shuangshuang Mao Xiaoli Feng Nan Sun Jie He
• 来源期刊: 癌症生物学与医学（英文版）
• 年，卷（期）: 2021,（3）
• 所属期刊栏目: ORIGINAL ARTICLE
• 页码范围: 734-749
• 页数: 16页
• 语种: 英文
• DOI: 10.20892/j.issn.2095-3941.2020.0397

摘要

Objective: The proportion of patients with stage Ⅰ lung adenocarcinoma (LUAD) has dramatically increased with the prevalence of low-dose computed tomography use for screening. Up to 30％ of patients with stage Ⅰ LUAD experience recurrence within 5 years after curative surgery. A robust risk stratification tool is urgently needed to identify patients who might benefit from adjuvant treatment. Methods: In this first investigation of the relationship between metabolic reprogramming and recurrence in stage Ⅰ LUAD, we developed a recurrence-associated metabolic signature (RAMS). This RAMS was based on metabolism-associated genes to predict cancer relapse and overall prognoses of patients with stage Ⅰ LUAD. The clinical significance and immune landscapes of the signature were comprehensively analyzed. Results: Based on a gene expression profile from the GSE31210 database, functional enrichment analysis revealed a significant difference in metabolic reprogramming that distinguished patients with stage Ⅰ LUAD with relapse from those without relapse. We then identified a metabolic signature (i.e., RAMS) represented by 2 genes (ACADM and RPS8) significantly related to recurrence-free survival and overall survival times of patients with stage Ⅰ LUAD using transcriptome data analysis of a training set. The training set was well validated in a test set. The discriminatory power of the 2 gene metabolic signature was further validated using protein values in an additional independent cohort. The results indicated a clear association between a high risk score and a very poor patient prognosis. Stratification analysis and multivariate Cox regression analysis showed that the RAMS was an independent prognostic factor. We also found that the risk score was positively correlated with inflammatory response, the antigen-presenting process, and the expression levels of many immunosuppressive checkpoint molecules (e.g., PD-L1, PD-L2, B7-H3, galectin-9, and FGL-1). These results suggested that high risk patients had immune response suppression. Further analysis revealed that anti-PD-1/PD-L1 immunotherapy did not have significant benefits for high risk patients. However, the patients could respond better to chemotherapy. Conclusions: This study is the first to highlight the relationship between metabolic reprogramming and recurrence in stage Ⅰ LUAD, and is the first to also develop a clinically feasible signature. This signature may be a powerful prognostic tool and help further optimize the cancer therapy paradigm.