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Identification of a novel signature based on immune microenvironment to predict prognosis，immunotherapeutic efficacy and immunogenicity in lung adenocarcinoma

ABSTRACT: Identification of a novel signature based on immune microenvironment to predict prognosis，immunotherapeutic efficacy and immunogenicity in lung adenocarcinoma

PROVIDER: PRJNA1128059 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR29542841_1.fastq.gz	Fastqsanger.gz
	SRR29542841_2.fastq.gz	Fastqsanger.gz
	SRR29542842_1.fastq.gz	Fastqsanger.gz
	SRR29542842_2.fastq.gz	Fastqsanger.gz
	SRR29542843_1.fastq.gz	Fastqsanger.gz

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Project description:Abstract: Immunotherapy has emerged as a crucial treatment approach for lung adenocarcinoma (LUAD), yet a notable percentage of patients do not exhibit the anticipated prognosis and therapeutic response. In this study, we embarked on a comprehensive analysis of immune infiltration-related genes in LUAD by integrating transcriptome sequencing data and TCGA dataset. And an immune risk prognosis model was developed, which accurately forecasts the prognosis and predicts the immunotherapy efficacy in LUAD. Patients with LUAD were categorized into high-risk and low-risk groups based on the model's risk value. The high-risk group exhibited a poorer prognosis, higher tumor mutation burden (TMB), greater genomic heterogeneity and lower immune scores. In contrast, the low-risk group manifested characteristics conducive to immunotherapy, including a more favorable prognosis, heightened sensitivity to immunotherapy and reduced likelihood of immune escape. The robustness of the model's predictive capability was corroborated using the GEO dataset, Nomogram and an independent immunotherapy cohort. Furthermore, the model risk value was found to be significantly associated with the prognosis of LUAD patients, genomic heterogeneity, immune cell infiltration, immune regulation and immunotherapy outcomes. Additionally, we characterized the significance of the model's feature factors using single-cell clustering and pseudo-temporal analysis, revealing that the model's feature genes are predominantly located in tumor-infiltrating T cells, particularly in the exhausted and effector subtypes. TNFRSF11A emerged as a key gene in the model, promoting LUAD cell proliferation, migration, and apoptosis, and involvement in LUAD M6A methylation and PD-L1-mediated immune escape. TNFRSF11A was mainly expressed in T cell clusters and represented a potential immune system regulatory factor associated with T cell exhaustion, fostering malignant progression in LUAD cells. Our study offers new mechanistic insights and therapeutic targets for the biological behaviors and immune suppression in LUAD.

Project description:Glutamine (Gln) metabolism has been reported to play an essential role in cancer. However, a comprehensive analysis of its role in lung adenocarcinoma is still unavailable. This study established a novel system of quantification of Gln metabolism to predict the prognosis and immunotherapy efficacy in lung cancer. Further, the Gln metabolism in tumor microenvironment (TME) was characterized and the Gln metabolism-related genes were identified for targeted therapy. We comprehensively evaluated the patterns of Gln metabolism in 513 patients diagnosed with lung adenocarcinoma (LUAD) based on 73 Gln metabolism-related genes. Based on differentially expressed genes (DEGs), a risk model was constructed using Cox regression and Lasso regression analysis. The prognostic efficacy of the model was validated using an individual LUAD cohort form Shandong Provincial Hospital, an integrated LUAD cohort from GEO and pan-cancer cohorts from TCGA databases. Five independent immunotherapy cohorts were used to validate the model performance in predicting immunotherapy efficacy. Next, a series of single-cell sequencing analyses were used to characterize Gln metabolism in TME. Finally, single-cell sequencing analysis, transcriptome sequencing, and a series of in vitro experiments were used to explore the role of EPHB2 in LUAD. Patients with LUAD were eventually divided into low- and high-risk groups. Patients in low-risk group were characterized by low levels of Gln metabolism, survival advantage, “hot” immune phenotype and benefit from immunotherapy. Compared with other cells, tumor cells in TME exhibited the most active Gln metabolism. Among immune cells, tumor-infiltrating T cells exhibited the most active levels of Gln metabolism, especially CD8 T cell exhaustion and Treg suppression. EPHB2, a key gene in the model, was shown to promote LUAD cell proliferation, invasion and migration, and regulated the Gln metabolic pathway. Finally, we found that EPHB2 was highly expressed in macrophages, especially M2 macrophages. It may be involved in the M2 polarization of macrophages and mediate the negative regulation of M2 macrophages in NK cells. This study revealed that the Gln metabolism-based model played a significant role in predicting prognosis and immunotherapy efficacy in lung cancer. We further characterized the Gln metabolism of TME and investigated the Gln metabolism-related gene EPHB2 to provide a theoretical framework for anti-tumor strategy targeting Gln metabolism.

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Identification of a novel signature based on immune microenvironment to predict prognosis，immunotherapeutic efficacy and immunogenicity in lung adenocarcinoma

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