Identification of metabolism-associated molecular classification for effect and prognosis in lung adenocarcinoma based on multidatabases including the cancer genome atlas and gene expression omnibus - Lilin Que; Zhibing Liu; Yinghui Wu; Lan Luo; Leifeng Liang (SAGE Open Medicine)
Background: Lung adenocarcinoma is a highly heterogeneous group of diseases with distinct molecular genetic features, pathological characteristics, metabolic profiles, and clinical behaviors. However, the clinical relevance of metabolic characteristics of lung adenocarcinoma remains unclear. This study aimed to describe the molecular characteristics of lung adenocarcinoma. Methods: The gene expression profiles of 1037 lung adenocarcinoma samples were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. This study is based on sample data from 2006 to 2020. The long-time span and sufficient sample size ensure the robustness of the research findings. Using unsupervised transcriptome analysis, we identified three distinct subtypes (C1, C2, and C3). We then compared the prognostic traits, transcriptome characteristics, metabolic signatures, immune infiltration, clinical features, and drug sensitivity of the lung adenocarcinoma subclasses. A classifier was generated to determine lung adenocarcinoma classification, and we verified the clinical value of this classifier in other tumors. Results: Our results indicated that C1 possessed the most abundant metabolic pathways. Compared with C2 and C3, C1 possessed 35 metabolic pathways that exhibited significant differences. The immune score, matrix score, and immune infiltration for subtype C1 were significantly lower than those for subtypes C2 and C3, suggesting that C1 is a metabolically active subtype. Five metabolic pathways were observed in C2. Subtype C2 was associated with the best prognosis and exhibited the lowest tumor mutation burden and copy number variation. Subtype C3 comprised five metabolic pathways. Immune checkpoint analysis revealed that C3 cells may potentially benefit from immunotherapy. Conclusions: Our study deepens the understanding of the metabolic characteristics of lung adenocarcinoma and may provide valuable information for immunotherapy.