Background: The tumor microenvironment (TME) plays a pivotal role in the progression and metastasis of lung adenocarcinoma (LUAD). However, the detailed characteristics of LUAD and its associated microenvironment are yet to be extensively explored. This study aims to delineate a comprehensive profile of the immune cells within the LUAD microenvironment, including CD8+ T cells, CD4+ T cells, and myeloid cells. Subsequently, based on marker genes of exhausted CD8+ T cells, we aim to establish a prognostic model for LUAD.Method: Utilizing the Seurat and Scanpy packages, we successfully constructed an immune microenvironment atlas for LUAD. The Monocle3 and PAGA algorithms were employed for pseudotime analysis, pySCENIC for transcription factor analysis, and CellChat for analyzing intercellular communication. Following this, a prognostic model for LUAD was developed, based on the marker genes of exhausted CD8+ T cells, enabling effective risk stratification in LUAD patients. Our study included a thorough analysis to identify differences in TME, mutation landscape, and enrichment across varying risk groups. Moreover, by integrating risk scores with clinical features, we developed a new nomogram. The expression of model genes was validated via RT-PCR, and a series of cellular experiments were conducted, elucidating the potential oncogenic mechanisms of GALNT2.Results: Our study developed a single-cell atlas for LUAD from scRNA-seq data of 19 patients, examining crucial immune cells in LUAD’s microenvironment. We underscored pDCs’ role in antigen processing and established a Cox regression model based on CD8_Tex-LAYN genes for risk assessment. Additionally, we contrasted prognosis and tumor environments across risk groups, constructed a new nomogram integrating clinical features, validated the expression of model genes via RT-PCR, and confirmed GALNT2’s function in LUAD through cellular experiments, thereby enhancing our understanding and approach to LUAD treatment.Conclusion: The creation of a LUAD single-cell atlas in our study offered new insights into its tumor microenvironment and immune cell interactions, highlighting the importance of key genes associated with exhausted CD8+ T cells. These discoveries have enabled the development of an effective prognostic model for LUAD and identified GALNT2 as a potential therapeutic target, significantly contributing to the improvement of LUAD diagnosis and treatment strategies.

Background: Globally, esophageal squamous cell carcinoma (ESCC) stands out as a common cancer type, characterized by its notably high rates of occurrence and mortality. Recent advancements in treatment methods, including immunotherapy, have shown promise, yet the prognosis remains poor. In the context of tumor development and treatment outcomes, the tumor microenvironment (TME), especially the function of dendritic cells (DCs), is significantly influential. Our study aims to delve deeper into the heterogeneity of DCs in ESCC using single-cell RNA sequencing (scRNA-seq) and bulk RNA analysis.Methods: In the scRNA-seq analysis, we utilized the SCP package for result visualization and functional enrichment analysis of cell subpopulations. CellChat was employed to identify potential oncogenic mechanisms in DCs, while Monocle 2 traced the evolutionary trajectory of the three DC subtypes. CopyKAT assessed the benign or malignant nature of cells, and SCENIC conducted transcription factor regulatory network analysis, offering a preliminary exploration of DC heterogeneity. In Bulk-RNA analysis, we constructed a prognostic model for ESCC prognosis and immunotherapy response, based on DC marker genes. This model was validated through quantitative PCR (qPCR) and immunohistochemistry (IHC), confirming the gene expression levels.Results: In this study, through intercellular communication analysis, we identified GALECTIN and MHC-I signaling pathways as potential oncogenic mechanisms within dendritic cells. We categorized DCs into three subtypes: plasmacytoid (pDC), conventional (cDC), and tolerogenic (tDC). Our findings revealed that pDCs exhibited an increased proportion of cells in the G2/M and S phases, indicating enhanced cellular activity. Pseudotime trajectory analysis demonstrated that cDCs were in early stages of differentiation, whereas tDCs were in more advanced stages, with pDCs distributed across both early and late differentiation phases. Prognostic analysis highlighted a significant correlation between pDCs and tDCs with the prognosis of ESCC (P< 0.05), while no significant correlation was observed between cDCs and ESCC prognosis (P = 0.31). The analysis of cell malignancy showed the lowest proportion of malignant cells in cDCs (17%), followed by pDCs (29%), and the highest in tDCs (48%), with these results being statistically significant (P< 0.05). We developed a robust ESCC prognostic model based on marker genes of pDCs and tDCs in the GSE53624 cohort (n = 119), which was validated in the TCGA-ESCC cohort (n = 139) and the IMvigor210 immunotherapy cohort (n = 298) (P< 0.05). Additionally, we supplemented the study with a novel nomogram that integrates clinical features and risk assessments. Finally, the expression levels of genes involved in the model were validated using qPCR (n = 8) and IHC (n = 16), thereby confirming the accuracy of our analysis.Conclusion: This study enhances the understanding of dendritic cell heterogeneity in ESCC and its impact on patient prognosis. The insights gained from scRNA-seq and Bulk-RNA analysis contribute to the development of novel biomarkers and therapeutic targets. Our prognostic models based on DC-related gene signatures hold promise for improving ESCC patient stratification and guiding treatment decisions.

Immunogenic cell death (ICD) triggers antitumor immune responses and plays a critical role in shaping the tumor microenvironment (TME). However, its specific contribution to lung adenocarcinoma (LUAD) progression and immunotherapy response remains insufficiently explored. We integrated single-cell RNA sequencing with machine learning to characterize ICD-related transcriptional features in LUAD. ICD activity was quantified across cell types using five scoring algorithms. To develop a robust prognostic model, we evaluated over 100 machine learning algorithm combinations and selected the CoxBoost + SuperPC approach based on the highest concordance index (C-index). The resulting ICD-related gene signature (ICDRS) was validated in six external cohorts. Downstream analyses included immune infiltration, mutation profiling, drug sensitivity, and immunotherapy response. SLC2A1 was selected for functional validation using qRT-PCR, CCK-8, Transwell, colony formation, and xenograft assays. Single-cell analysis revealed that macrophages exhibited the highest ICD activity and contributed significantly to intercellular communication. Based on ICD-associated genes, the ICDRS model consisting of 11 core genes was constructed and showed superior prognostic performance over 112 published LUAD signatures across multiple cohorts. The ICDRS stratified patients into distinct risk groups with significant differences in overall survival, immune infiltration patterns, and immunotherapy benefit. Low-risk patients exhibited higher levels of CD8⁺ T cells, dendritic cells, and immune function scores, along with greater sensitivity to standard chemotherapeutics and immune checkpoint inhibitors. Functional experiments confirmed that SLC2A1 was upregulated in LUAD tissues and cell lines. Silencing SLC2A1 suppressed proliferation and invasion in vitro and inhibited tumor growth in xenograft models, supporting its oncogenic role. This study highlights the crucial role of ICD in LUAD immune regulation and prognosis. The ICDRS serves as a robust biomarker for risk stratification and immunotherapy guidance, while SLC2A1 emerges as a potential therapeutic target to augment immunotherapeutic efficacy.

Background: Macrophages play a dual role in the tumor microenvironment(TME), capable of secreting pro-inflammatory factors to combat tumors while also promoting tumor growth through angiogenesis and immune suppression. This study aims to explore the characteristics of macrophages in lung adenocarcinoma (LUAD) and establish a prognostic model based on macrophage-related genes.Method: We performed scRNA-seq analysis to investigate macrophage heterogeneity and their potential pseudotime evolutionary processes. Specifically, we used scRNA-seq data processing, intercellular communication analysis, pseudotime trajectory analysis, and transcription factor regulatory analysis to reveal the complexity of macrophage subpopulations. Data from The Cancer Genome Atlas (TCGA) was used to assess the impact of various macrophage subtypes on LUAD prognosis. Univariate Cox regression was applied to select prognostic-related genes from macrophage markers. We constructed a prognostic model using Lasso regression and multivariate Cox regression, categorizing LUAD patients into high and low-risk groups based on the median risk score. The model’s performance was validated across multiple external datasets. We also examined differences between high and low-risk groups in terms of pathway enrichment, mutation information, tumor microenvironment(TME), and immunotherapy efficacy. Finally, RT-PCR confirmed the expression of model genes in LUAD, and cellular experiments explored the carcinogenic mechanism of COL5A1.Results: We found that signals such as SPP1 and MIF were more active in tumor tissues, indicating potential oncogenic roles of macrophages. Using macrophage marker genes, we developed a robust prognostic model for LUAD that effectively predicts prognosis and immunotherapy efficacy. A nomogram was constructed to predict LUAD prognosis based on the model’s risk score and other clinical features. Differences between high and low-risk groups in terms of TME, enrichment analysis, mutational landscape, and immunotherapy efficacy were systematically analyzed. RT-PCR and cellular experiments supported the oncogenic role of COL5A1.Conclusion: Our study identified potential oncogenic mechanisms of macrophages and their impact on LUAD prognosis. We developed a prognostic model based on macrophage marker genes, demonstrating strong performance in predicting prognosis and immunotherapy efficacy. Finally, cellular experiments suggested COL5A1 as a potential therapeutic target for LUAD.

Objective: To develop and validate a prediction model for early recurrence of stage I lung adenocarcinoma (LUAD) that combines radiomics features based on preoperative CT with tumour spread through air spaces (STAS). Materials and methods: The most recent preoperative thin-section chest CT scans and postoperative pathological haematoxylin and eosin-stained sections were retrospectively collected from patients with a postoperative pathological diagnosis of stage I LUAD. Regions of interest were manually segmented, and radiomics features were extracted from the tumour and peritumoral regions extended by 3 voxel units, 6 voxel units, and 12 voxel units, and 2D and 3D deep learning image features were extracted by convolutional neural networks. Then, the RAdiomics Integrated with STAS model (RAISm) was constructed. The performance of RAISm was then evaluated in a development cohort and validation cohort. Results: A total of 226 patients from two medical centres from January 2015 to December 2018 were retrospectively included as the development cohort for the model and were randomly split into a training set (72.6%, n = 164) and a test set (27.4%, n = 62). From June 2019 to December 2019, 51 patients were included in the validation cohort. RAISm had excellent discrimination in predicting the early recurrence of stage I LUAD in the training cohort (AUC = 0.847, 95% CI 0.762–0.932) and validation cohort (AUC = 0.817, 95% CI 0.625–1.000). RAISm outperformed single modality signatures and other combinations of signatures in terms of discrimination and clinical net benefits. Conclusion: We pioneered combining preoperative CT-based radiomics with STAS to predict stage I LUAD recurrence postoperatively and confirmed the superior effect of the model in validation cohorts, showing its potential to assist in postoperative treatment strategies.

The heterogeneity of epithelial cells and their interaction with the immune microenvironment play crucial roles in tumor progression, but the underlying mechanisms remain unclear. We analyzed single-cell transcriptomic data from normal and tumor tissues to characterize epithelial cells and their microenvironment. Key genes were identified and used, via survival analysis and multiple machine learning methods, to construct a prognostic model termed the Epithelial Signature (EpiSig). We further validated, through a series of experiments, the critical immunological roles of the key genes incorporated into the EpiSig model. Tumor tissues showed a marked increase in epithelial cells, a reduction in natural killer (NK)/T cells, and cell co-occurrence patterns distinct from normal tissues. We identified differentially expressed genes in tumor epithelial cells and integrated multiple machine-learning algorithms to construct the EpiSig model. This model effectively stratified patient prognosis, with the high-EpiSig group exhibiting significantly worse survival; receiver operator characteristic curve (ROC) and principal component analysis (PCA) analyses further supported its accuracy and robustness. Immune analyses indicated lower immune cell infiltration, decreased human leukocyte antigen (HLA) expression, and elevated programmed cell death ligand 1/programmed cell death protein 1 (PD-L1/PD-1) in the high-EpiSig group, reflecting a more pronounced immunosuppressive microenvironment. The core gene KRT18 correlated strongly with EpiSig scores (R=0.65) and promoted lung adenocarcinoma (LUAD) cell proliferation in functional assays. Further, low KRT18 tumors showed higher CD4+/CD8+ T cell and CD20+ B cell infiltration; in a mouse LLC subcutaneous tumor model, both Krt18 knockdown and PD-1 blockade suppressed tumor growth, with greater efficacy in combination. Mechanistically, KRT18 activated NF-kB/p65 to upregulate PD-L1, promoting immune exclusion and impairing T-cell effector function. This study highlights the close relationship between epithelial cell heterogeneity and immune microenvironment alterations in tumors, and presents the EpiSig as a robust tool for prognostic prediction. KRT18 may serve as a promising therapeutic target in LUAD.