Open AccessA novel algorithm for lung adenocarcinoma based on N6 methyladenosine‐related immune long noncoding RNAs as a reliable biomarker for predicting survival outcomes and selecting sensitive anti‐tumor therapies
Author(s) -
Yan Qiuwen,
Hu Bingchuan,
Chen Hang,
Zhu Linwen,
Lyu Yao,
Qian Dingding,
Shao Guofeng
Publication year - 2022
Publication title -
journal of clinical laboratory analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.536
H-Index - 50
eISSN - 1098-2825
pISSN - 0887-8013
DOI - 10.1002/jcla.24636
Subject(s) - lung cancer , docetaxel , immunotherapy , biomarker , adenocarcinoma , oncology , cancer , tumor microenvironment , medicine , immune system , cancer research , immunology , biology , biochemistry
Background Lung cancer is a highly heterogeneous malignant tumor with high incidence and mortality. Recently, increasing evidence has demonstrated that N6‐methyladenosine (m6A) methylation and the tumor microenvironment (TME) play important roles in the occurrence and development of lung adenocarcinoma (LUAD). Methods In this study, we constructed a novel and reliable algorithm based on m6A‐related immune lncRNAs (mrilncRNAs), consisting of molecular subtypes and a prognostic signature. Results According to the analyses of molecular subtypes, patients in cluster 1 were in a more advanced stage, showed poor prognosis, were sensitive to immunotherapy (anti‐programmed cell death 1 Ligand 1 (PD‐L1) and anti‐lymphocyte activating 3 (LAG‐3)), and had a highest tumor mutational burden (TMB), while anti‐cytotoxic T‐lymphocyte‐associated protein 4 (CTLA‐4) therapy seemed to be a good choice for patients in cluster 3. Subsequently, the results of the risk assessment model indicated that the low‐risk patients exhibited a survival advantage, had an earlier stage, and showed a higher response to common anti‐cancer drugs, including chemotherapy (Docetaxel, Paclitaxel), molecular targeted therapy (Erlotinib), and immunotherapy (anti‐CTLA‐4 therapy), while Gefitinib could be a good choice for patients with high‐risk scores. Conclusion In conclusion, the constructed algorithm exhibits promising practical prospects, and allows the selection of suitable and sensitive anti‐cancer drugs, which could provide theoretical support to predict the survival outcomes of patients with LUAD.