构建免疫相关 LncRNAs 分类器以预测胸腺上皮肿瘤预后和免疫治疗反应

The primary objective of this study was to construct an immune-related long noncoding RNAs (IRLs) classifier to precisely predict the prognosis and immunotherapy response of patients with thymic epithelial tumors (TET). Based on univariable Cox regression analysis and Lasso regression, six prognosis-related IRLs (AC004466. 3, AC138207. 2, AC148477. 2, AL450270. 1, HOXB-AS1 and SNHG8) were selected to build an IRL classifier.

Importantly, results of qRT-PCR validated that higher expression levels of AC138207. 2, AC148477. 2, AL450270. 1 and SNHG8 as well as lower expression levels of AC004466. 3, and HOXB-AS1 in TETs samples compared with normal controls. The IRL classifier could effectively classify patients into the low-risk and high-risk groups based on the different survival parameters. In terms of predictive ability and clinical utility, the IRL classifier was superior to Masaoka staging system.

Additionally, IRL classifier is significantly associated with immune cells infiltration (dendritic cells, activated CD4 memory T cells and tumor-infiltrating lymphocyte (TIL), T cell subsets in particular), immune microenvironment (immune score and immune checkpoint inhibitors) and immunogenicity (TMB) in TETs, which hints that IRL classifier is tightly correlated with immune characteristics and might guide more effective immunotherapy strategies for TETs patients.

Encouragingly, according to TIDE algorithm, there were more immunotherapy responders in the low-risk IRL subgroup and the IRL score was robustly negatively linked to the immunotherapeutic response. To sum up, the IRL classifier was established, which can be used to predict the prognosis, immune infiltration status, immunotherapy response in TETs patients, and may facilitate personalized counseling for immunotherapy.