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Comprehensive Profiling Reveals Distinct Microenvironment and Metabolism Characterization of Lung Adenocarcinoma.

ABSTRACT: Lung adenocarcinoma has entered into an era of immunotherapy with the development of immune checkpoint inhibitors (ICIs). The identification of immune subtype is crucial to prolonging survival in patients. The tumor microenvironment (TME) and metabolism have a profound impact on prognosis and therapy. The majority of previous studies focused on only one aspect, while both of them are essential to the understanding of tumorigenesis and development. We hypothesized that lung adenocarcinoma can be stratified into immune subgroups with alterations in the TME infiltration. We aimed to explore the "TME-Metabolism-Risk" patterns in each subtypes and the mechanism behind. Glycolysis and cholesterol were selected for the analysis of metabolic states based on the first half of the study. Bioinformatic analysis was performed to investigate the transcriptomic and clinical data integrated by three lung adenocarcinoma cohorts (GSE30219, GSE31210, GSE37745, N = 415). The results were validated in an independent cohort (GSE50081, N = 127). In total, 415 lung adenocarcinoma samples were integrated and analyzed. Four major immune subtypes were indentified using bioinformatic analysis. Subtype NC1, characterized by a high level of glycolysis, with extremely low microenvironment cell infiltration. Subtype NC2, characterized by the "Silence" and "Cholesterol biosynthesis Predominant" metabolic states, with a middle degree infiltration of microenvironment cell. Subtype NC3, characterized by the lack of "Cholesterol biosynthesis Predominant" metabolic state, with abundant microenvironment cell infiltration. Subtype NC4, characterized by "Mixed" metabolic state, with a relatively low microenvironment cell infiltration. Least absolute shrinkage and selection operator (LASSO) regression and multivariate analyses were performed to calculate the risk of each sample, and we attempted to find out the potential immune escape mechanism in different subtypes. The result revealed that the lack of immune cells infiltration might contribute to the immune escape in subtypes NC1 and NC4. NC3 was characterized by the high expression of immune checkpoint molecules and fibroblasts. NC2 had defects in activation of innate immune cells. There existed an obviously survival advantage in subtype NC2. Gene set enrichment analysis (GSEA) and Gene Ontology analysis indicated that the PI3K-AKT-mTOR, TGF-β, MYC-related pathways might be correlated with this phenomenon. In addition, some differentially expressed genes (DEGs) were indentified in subtype NC3, which might be potential targets for survival phenotype transformation.

SUBMITTER: Li C

PROVIDER: S-EPMC8193848 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Rationale: As the transcriptional products of active enhancers, enhancer RNAs (eRNAs) are essential for the initiation of tumorigenesis. However, the landscape and functional characteristics of eRNAs in Chinese lung adenocarcinoma, and the clinical utility of eRNA-based molecular subtypes remain largely unknown. Methods: A genome-wide profiling of eRNAs was performed in 80 Chinese lung adenocarcinoma patients with RNA-seq data. Functional eRNAs and associated genes were identified between paired adenocarcinoma and adjacent samples. Unsupervised clustering of functional eRNAs was conducted and the associations with molecular characteristics and clinical outcomes were accessed by integrating whole-genome sequencing data and clinical data. Additionally, 481 lung adenocarcinoma patients were used for the validation based on The Cancer Genome Atlas (TCGA) dataset. Results: A total of 3297 eRNAs with sufficient expression were identified, which were globally upregulated in adenocarcinoma samples compared to matched-adjacent pairs (P = 7.61×10-3). Further analyses indicated that these upregulated eRNAs were correlated with copy number amplification (CNA) status (Cor = 0.22, P = 0.045), and eRNA-correlated genes were primarily involved in cell cycle and immune system-related pathways. Based on the co-expression analysis of eRNAs with protein-coding genes, we defined 188 functional eRNAs and their correlated genes were overrepresented in cancer driver genes (ER = 1.98, P = 5.95×10-12) and clinically-actionable genes (ER = 2.19, P = 3.44×10-4). The eRNA-based consensus clustering further identified a novel molecular subtype with immune deficiency and a high-level of genomic alterations, which was associated with poor clinical outcomes of lung adenocarcinoma patients (OS: HR = 1.91, P = 0.015; PFI: HR = 1.64, P = 0.034). Conclusions: The genome-wide identification and characterization of eRNAs reveal novel regulators for the development of lung cancer, which provides a new biological dimension for the understanding of eRNAs during lung carcinogenesis and emphasize the clinical utility of eRNA-based molecular subtypes in the treatment of lung adenocarcinoma.

Project description:Lung adenocarcinoma (LUAD) is the most common malignancy, leading to more than 1 million related deaths each year. Due to low long-term survival rates, the exploration of molecular mechanisms underlying LUAD progression and novel prognostic predictors is urgently needed to improve LUAD treatment. In our study, cancer-specific differentially expressed genes (DEGs) were identified using the robust rank aggregation (RRA) method between tumor and normal tissues from six Gene Expression Omnibus databases (GSE43458, GSE62949, GSE68465, GSE115002, GSE116959, and GSE118370), followed by a selection of prognostic modules using weighted gene co-expression network analysis. Univariate Cox regression, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were applied to identify nine hub genes (CBFA2T3, CR2, SEL1L3, TM6SF1, TSPAN32, ITGA6, MAPK11, RASA3, and TLR6) that constructed a prognostic risk model. The RNA expressions of nine hub genes were validated in tumor and normal tissues by RNA-sequencing and single-cell RNA-sequencing, while immunohistochemistry staining from the Human Protein Atlas database showed consistent results in the protein levels. The risk model revealed that high-risk patients were associated with poor prognoses, including advanced stages and low survival rates. Furthermore, a multivariate Cox regression analysis suggested that the prognostic risk model could be an independent prognostic factor for LUAD patients. A nomogram that incorporated the signature and clinical features was additionally built for prognostic prediction. Moreover, the levels of hub genes were related to immune cell infiltration in LUAD microenvironments. A CMap analysis identified 13 small molecule drugs as potential agents based on the risk model for LUAD treatment. Thus, we identified a prognostic risk model including CBFA2T3, CR2, SEL1L3, TM6SF1, TSPAN32, ITGA6, MAPK11, RASA3, and TLR6 as novel biomarkers and validated their prognostic and predicted values for LUAD.

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Comprehensive Profiling Reveals Distinct Microenvironment and Metabolism Characterization of Lung Adenocarcinoma.

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