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Background: Lung adenocarcinoma (LUAD) patients with different American Joint Committee on Cancer stages have different overall 5-year survival rates. The tumor microenvironment (TME) and intra-tumor heterogeneity (ITH) have been shown to play a crucial role in the occurrence and development of tumors. However, the TME and ITH in different lesions of LUAD have not been extensively explored., Methods: We present a 204,157-cell catalog of the TME transcriptome in 29 lung samples to systematically explore the TME and ITH in the different stages of LUAD. Traditional RNA sequencing data and complete clinical information were downloaded from publicly available databases., Results: Based on these high-quality cells, we constructed a single-cell network underlying cellular and molecular features of normal lung, early LUAD, and advanced LUAD cells. In contrast with early malignant cells, we noticed that advanced malignant cells had a remarkably more complex TME and higher ITH level. We also found that compared with other immune cells, more differences in CD8+/CTL T cells, regulatory T cells, and follicular B cells were evident between early and advanced LUAD. Additionally, cell-cell communication analyses, revealed great diversity between different lesions of LUAD at the single-cell level. Flow cytometry and qRT-PCR were used to validate our results., Conclusion: Our results revealed the cellular diversity and molecular complexity of cell lineages in different stages of LUAD. We believe our research, which serves as a basic framework and valuable resource, can facilitate exploration of the pathogenesis of LUAD and identify novel therapeutic targets in the future., (© 2021 The Authors. Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics.)

The tumor microenvironment (TME) plays an essential role in the occurrence and progression of malignancy. The potential prognostic TME-related biomarkers of lung adenocarcinoma (LUAD) remained unclear, which were investigated in this research. The RNA-sequencing profiles and corresponding clinical parameters were extracted from TCGA and GEO databases, based on which the stromal and immune scores were calculated through the ESTIMATE algorithm. Overlapping differentially expressed genes between stromal and immune score group were analyzed by the LASSO and Random Forrest algorithms and validated in cases from our center. And a prognostic 8-gene signature was constructed using Cox regression. The infiltration of 22 hematopoietic cell phenotypes was assessed by the CIBERSORT algorithms. We found that female, elder patients, and solid predominant subtype had obviously higher stromal and immune scores. And patients with early stage LUAD received a prominently higher immune score. A high stromal or immune score meant a good prognosis. Subsequently, eight TME-related prognostic genes (ATAD5, CYP4F3, CYP4F12, ESPNL, FXYD2, GPX2, NLGN4Y, and SERPINC1) were identified by both LASSO regression and Radom Forest algorithms. High 8-gene signature group exhibited worse overall survival. Furthermore, B cell naïve, plasma cells, T cell follicular helper, and macrophages M1 were prominently more in high signature group. Nevertheless, fewer T cells CD4 memory resting, monocytes, and dendritic cell resting were identified in the high signature group. The composition of the tumor microenvironment significantly affected the prognosis of LUAD patients. We provided a new strategy for the exploration of prognostic TME-related biomarkers and immunotherapy.

• LUSC patients could be divided into 2 TME clusters based on the relative abundance of 24 immune cells. • The immune-related genes, cytokines, and miRNA were higher expressed in higher immune cells infiltration cluster. • The number of somatic mutations may play an important role in immune infiltration. • The TME cluster was a valuable predictive factor in LUSC patients. To compare the landscape of tumor microenvironment (TME) of lung squamous carcinoma (LUSC) in different immune pattern and explore potential factors on immune therapy and prognosis. We have obtained the LUSC data from TCGA, GEO, and our department and classified them into 2 TME clusters by random forest model based on the infiltration pattern of 24 immune cell populations. We systemically compared the genomic significance, clinical characteristics, and immune infiltration pattern in 2 TME clusters. Samples were divided into 2 TME clusters based on the relative abundance of 24 immune cells, and a random forest classifier model was constructed. TME cluster B was a higher immune infiltration group with lower mutation load, richer co-infiltrate immune cells, upregulated immune-related cytokines, immune checkpoint molecules, and higher active immune cells. TME cluster was also an independent predictor in prognosis (B vs. A, p < 0.05) in patients from TCGA, GEO, and our department. Our study has described the microenvironment landscape of LUSC in different immune infiltration patterns and systemically analyzed genomic and clinical characteristics with distinct immunophenotypes, thus partly revealed the interaction between tumors and the immune microenvironment, which may guide a more precise and personalized immune therapeutic strategy for LUSC patients. [ABSTRACT FROM AUTHOR]