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.

As an early type of lung adenocarcinoma, ground glass nodule (GGN) has been detected increasingly and now accounts for most lung cancer outpatients. GGN has a satisfactory prognosis and its characteristics are quite different from solid adenocarcinoma (SADC). We compared the GGN adenocarcinoma (GGN-ADC) with SADC using the single-cell RNA sequencing (scRNA-seq) to fully understand GGNs. The tumor samples of five patients with lung GGN-ADCs and five with SADCs underwent surgery were digested to a single-cell suspension and analyzed using 10× Genomic scRNA-seq techniques. We obtained 60,459 cells and then classified them as eight cell types, including cancer cells, endothelial cells, fibroblasts, T cells, B cells, Nature killer cells, mast cells, and myeloid cells. We provided a comprehensive description of the cancer cells and stromal cells. We found that the signaling pathways related to cell proliferation were downregulated in GGN-ADC cancer cells, and stromal cells had different effects in GGN-ADC and SADC based on the analyses of scRNA-seq results. In GGN-ADC, the signaling pathways of angiogenesis were downregulated, fibroblasts expressed low levels of some collagens, and immune cells were more activated. Furthermore, we used flow cytometry to isolate the cancer cells and T cells in 12 GGN-ADC samples and in an equal number of SADC samples, including CD4+ T and CD8+ T cells, and validated the expression of key molecules by quantitative real-time polymerase chain reaction analyses. Through comprehensive analyses of cell phenotypes in GGNs, we provide deep insights into lung carcinogenesis that will be beneficial in lung cancer prevention and therapy.

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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.

Malignant tumor represents a major reason for death in the world and its incidence is growing rapidly. Developing the tools for early diagnosis is possibly a promising way to offer diverse therapeutic options and promote the survival chance. Secreted phosphoprotein 1 (SPP1), also called Osteopontin (OPN), has been demonstrated overexpressed in many cancers. However, the specific role of SPP1 in prognosis, gene mutations, and changes in gene and miRNA expression in human cancers is unclear. In this report, we found SPP1 expression was higher in most of the human cancers. Based on Kaplan-Meier plotter and the PrognoScan database, we found high SPP1 expression was significantly correlated with poor survival in various cancers. Using a large dataset of colon adenocarcinoma (COAD), head and neck cancer (HNSC), lung adenocarcinoma (LUAD), and lung squamous cell carcinoma (LUSC) patients from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, this study identified 22 common genes and 2 common miRNAs. GO, and KEGG paths analyses suggested that SPP1 correlated genes were mainly involved in positive regulation of immune cell activation and infiltration. SPP1-associated genes and miRNAs regulatory networks suggested that their interactions may play a role in the progression of four selected cancers. SPP1 showed significant positive correlation with the immunocyte and immune marker sets infiltrating degrees. All of these data provide strong evidence that SPP1 may promote tumor progress through interacting with carcinogenic genes and facilitating immune cells’ infiltration in COAD, HNSC, LUAD, and LUSC.

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Esophageal squamous cell carcinoma (ESCC) accounts for 90% of all cases of esophageal cancers worldwide. Although neoadjuvant chemotherapy (NACT-ESCC) improves the survival of ESCC patients, the five-year survival rate of these patients is dismal. The tumor microenvironment (TME) and tumor heterogeneity decrease the efficacy of ESCC therapy. In our study, 113,581 cells obtained from five ESCC patients who underwent surgery alone (SA-ESCC) and five patients who underwent preoperative paclitaxel plus platinum chemotherapy (NACT-ESCC), were used for scRNA-seq analysis to explore molecular and cellular reprogramming patterns. The results showed samples from NACT-ESCC patients exhibited the characteristics of malignant cells and TME unlike samples from SA-ESCC patients. Cancer cells from NACT-ESCC samples were mainly at the ‘intermediate transient stage’. Stromal cell dynamics showed molecular and functional shifts that formed the immune-activation microenvironment. APOE, APOC1, and SPP1 were highly expressed in tumor-associated macrophages resulting in anti-inflammatory macrophage phenotypes. Levels of CD8+ T cells between SA-ESCC and NACT-ESCC tissues were significantly different. Immune checkpoints analysis revealed that LAG3 is a potential immunotherapeutic target for both NACT-ESCC and SA-ESCC patients. Cell–cell interactions analysis showed the complex cell-cell communication networks in the TME. In summary, our findings elucidate on the molecular and cellular reprogramming of NACT-ESCC and ESCC patients. These findings provide information on the potential diagnostic and therapeutic targets for ESCC patients.

Background Most cancer cells have fundamentally different metabolic characteristics, particularly much higher glycolysis rates than normal tissues, which support the increased demand for biosynthesis and promote tumor progression. We found that transforming growth factor (TGF)-β plays a dual function in regulating glycolysis and cell proliferation in non-small cell lung cancer. Methods We used the PET/MRI imaging system to observe the glucose metabolism of subcutaneous tumors in nude mice. Energy metabolism of non-small cell lung cancer cell lines detected by the Seahorse XFe96 cell outflow analyzer. Co-immunoprecipitation assays were used to detect the binding of Smads and HIF-1α. Western blotting and qRT-PCR were used to detect the regulatory effects of TGF-β and HIF-1α on c-MYC, PKM1/2, and cell cycle-related genes. Results We discovered that TGF-β could inhibit glycolysis under normoxia while significantly promoting tumor cells’ glycolysis under hypoxia in vitro and in vivo. The binding of hypoxia-inducible factor (HIF)-1α to the MH2 domain of phosphorylated Smad3 switched TGF-β function to glycolysis by changing Smad partners under hypoxia. The Smad-p107-E2F4/5 complex that initially inhibited c-Myc expression was transformed into a Smad-HIF-1α complex that promoted the expression of c-Myc. The increased expression of c-Myc promoted alternative splicing of PKM to PKM2, resulting in the metabolic reprogramming of tumor cells. In addition, the TGF-β/Smad signal lost its effect on cell cycle regulatory protein p15/p21. Furthermore, high expression of c-Myc inhibited p15/p21 and promoted the proliferation of tumor cells under hypoxia. Conclusions Our results indicated that HIF-1α functions as a critical factor in the dual role of TGF-β in tumor cells, and may be used as a biomarker or therapeutic target for TGF-β mediated cancer progression.