Background: Esophageal squamous cell carcinoma (ESCC) is among the most prevalent causes of cancerrelated death in adults. Tumor microenvironment (TME) has been associated with therapeutic failure and lethal outcomes for patients. However, published reports on the heterogeneity and TME in ESCC are scanty. Methods: Five tumor samples and five corresponding non-malignant samples were subjected to scRNA-seq analysis. Bulk RNA sequencing data were retrieved in publicly available databases. Findings: From the scRNA-seq data, a total of 128,688 cells were enrolled for subsequent analyses. Gene expression and CNV status exhibited high heterogeneity of tumor cells. We further identified a list of tumorspecific genes and four malignant signatures, which are potential new markers for ESCC. Metabolic analysis revealed that energy supply-related pathways are pivotal in cancer metabolic reprogramming. Moreover, significant differences were found in stromal and immune cells between the esophagus normal and tumor tissues, which promoted carcinogenesis at both cellular and molecular levels in ESCC. Immune checkpoints, regarded as potential targets for immunotherapy in ESCC were significantly highly expressed in ESCC, including LAG3 and HAVCR2. Eventually, we constructed a cell-to-cell communication atlas based on cancer cells and immune cells and performed the flow cytometry, qRT-PCR, immunofluorescence, and immunohistochemistry analyses to validate the results. Interpretation: This study demonstrates a widespread reprogramming across multiple cellular elements within the TME in ESCC, particularly in transcriptional states, cellular functions, and cell-to-cell interactions. The findings offer an insight into the exploration of TME and heterogeneity in the ESCC and provide new therapeutic targets for its clinical management in the future.

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.

Purpose: Lung adenocarcinoma (LUAD) is the leading cause of cancer-related deaths worldwide. Although tumor cell-T cell interactions are known to play a fundamental role in promoting tumor progression, these interactions have not been explored in LUAD. Methods: The 10x genomics single-cell RNA sequencing (scRNA-seq) and gene expression data of LUAD patients were obtained from ArrayExpress, TCGA, and GEO databases. scRNA-seq data were analyzed and infiltrating tumor cells, epithelial cells, and T cells were identified in the tumor microenvironment. Differentially expressed ligand-receptor pairs were identified in tumor cells/normal epithelial cells and tumor T cells/non-tumor T cells based on corresponding scRNA-seq and gene expression data, respectively. These important interactions inside/across cancer cells and T cells in LUAD were systematically analyzed. Furthermore, a valid prognostic machine-learning model based on ligand-receptor interactions was built to predict the prognosis of LUAD patients. Flow cytometry and qRT-PCR were performed to validate the significantly differently expressed ligand-receptor pairs. Results: Overall, 39,692 cells in scRNA-seq data were included in our study after quality filtering. A total of 65 ligand-receptor pairs (17 upregulated and 48 downregulated), including LAMB1-ITGB1, CD70-CD27, and HLA-B-LILRB2, and 96 ligand-receptor pairs (41 upregulated and 55 downregulated), including CCL5-CCR5, SELPLG-ITGB2, and CXCL13-CXCR5, were identified in LUAD cancer cells and T cells, respectively. To explore the crosstalk between cancer cells and T cells, 114 ligand-receptor pairs, including 11 ligand-receptor pair genes that could significantly affect survival outcomes, were identified in our research. A machine-learning model was established to accurately predict the prognosis of LUAD patients and ITGB4, CXCR5, and MET were found to play an important role in prognosis in our model. Flow cytometry and qRT-PCR analyses indicated the reliability of our study. Conclusion:Our study revealed functionally significant interactions within and between cancer cells and T cells. We believe these observations will improve our understanding of potential mechanisms of tumor microenvironment contributions to cancer progression and help identify potential targets for immunotherapy in the future.

Background: Lung adenocarcinoma (LUAD), the major subtype of lung cancer, is among the leading cause of cancer-related death worldwide. Energy-related metabolic reprogramming metabolism is a hallmark of cancer shared by numerous cancer types, including LUAD. Nevertheless, the functional pathways and molecular mechanism by which FAM83A-AS1 acts in metabolic reprogramming in lung adenocarcinoma have not been fully elucidated. Methods: We used transwell, wound-healing scratch assay, and metabolic assays to explore the effect of FAM83A-AS1 in LUAD cell lines. Western blotting, Co-IP assays, and ubiquitination assays were used to detect the effects of FAM83A-AS1 on HIF-1α expression, degradation, and its binding to VHL. Moreover, an in vivo subcutaneous tumor formation assay was used to detect the effect of FAM83A-AS1 on LUAD. Results: Herein, we identified FAM83A-AS1 as a metabolism-related lncRNA, which was highly correlated with glycolysis, hypoxia, and OXPHOS pathways in LUAD patients using bioinformatics analysis. In addition, we uncovered that FAM83A-AS1 could promote the migration and invasion of LUAD cells, as well as influence the stemness of LUAD cells in vivo and vitro. Moreover, FAM83A-AS1 was shown to promote glycolysis in LUAD cell lines in vitro and in vivo, and was found to influence the expression of genes related to glucose metabolism. Besides, we revealed that FAM83A-AS1 could affect glycolysis by regulating HIF-1α degradation. Finally, we found that FAM83A-AS1 knockdown could inhibit tumor growth and suppress the expression of HIF-1α and glycolysis-related genes in vivo. Conclusion:Our study demonstrates that FAM83A-AS1 contributes to LUAD proliferation and stemness via the HIF-1α/glycolysis axis, making it a potential biomarker and therapeutic target in LUAD patients.

Among all NSCLC patients, approximately 30-40% of Asian patients and 10-20% of Caucasian patients have epidermal growth factor receptor (EGFR) gene mutations, while approximately 10% of Asian patients and 30% of Caucasian patients have Kirsten rat sarcoma viral oncogene (KRAS) mutations, and 1-7% and 1.7% of patients have anaplastic lymphoma kinase (ALK) and ROS1 gene mutations, respectively. A small number of patients also have human epidermal growth factor receptor-2 (HER2) gene mutations (4).

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