Revealing the role of regulatory T cells in the tumor microenvironment of lung adenocarcinoma: a novel prognostic and immunotherapeutic signature

Identification of a deubiquitinating gene-related signature in ovarian cancer using integrated transcriptomic analysis and machine learning framework

BACKGROUND

Ovarian carcinoma represents an aggressive malignancy with poor prognosis and limited therapeutic efficacy. While deubiquitinating (DUB) genes are known to regulate crucial cellular processes and cancer progression, their specific roles in ovarian carcinoma remain poorly understood.

METHODS

We conducted an integrated analysis of single-cell RNA sequencing and bulk transcriptome data from public databases. DUB genes were identified through Genecard database. Using the Seurat package, we performed cell clustering and differential expression analysis. Cell-cell communications were analyzed using CellChat. A DUB-related risk signature (DRS) was developed using machine learning approaches through integration of GEO and TCGA datasets. The prognostic value and immune characteristics of the signature were systematically evaluated.

RESULTS

Our analysis revealed eight distinct cell subtypes in the tumor microenvironment, including epithelial, fibroblast, myeloid, and Treg cells. DUB-high cells were predominantly found in Treg and myeloid populations, exhibiting elevated expression of tumor-related pathways and enhanced cell-cell communication networks, particularly between fibroblasts and myeloid cells. Conversely, DUB-low cells were enriched in epithelial populations with reduced immune activity. The DRS model demonstrated robust prognostic value across multiple independent cohorts. High-risk patients, as classified by the DRS, showed significantly poorer survival outcomes and distinct immune infiltration patterns compared to low-risk patients.

CONCLUSION

This study provides comprehensive insights into DUB gene expression patterns across different cell populations in ovarian carcinoma. The established DRS model offers a promising tool for risk stratification and may guide personalized therapeutic strategies. Our findings highlight the potential role of DUB genes in modulating the tumor immune microenvironment and patient outcomes in ovarian carcinoma.

Integrative analysis of genetic variability and functional traits in lung adenocarcinoma epithelial cells via single-cell RNA sequencing, GWAS, bayesian deconvolution, and machine learning

BACKGROUND

Lung adenocarcinoma remains a leading cause of cancer-related mortality worldwide, characterized by high genetic and cellular heterogeneity, especially within the tumor microenvironment.

OBJECTIVE

This study integrates single-cell RNA sequencing (scRNA-seq) with genome-wide association studies (GWAS) using Bayesian deconvolution and machine learning techniques to unravel the genetic and functional complexity of lung adenocarcinoma epithelial cells.

METHODS

We performed scRNA-seq and GWAS analysis to identify critical cell populations affected by genetic variations. Bayesian deconvolution and machine learning techniques were applied to investigate tumor progression, prognosis, and immune-epithelial cell interactions, particularly focusing on immune checkpoint markers such as PD-L1 and CTLA-4.

RESULTS

Our analysis highlights the importance of genes like SLC2A1, which regulates glucose metabolism and correlates with tumor invasiveness and poor prognosis. Immune-epithelial interactions suggest a suppressive tumor microenvironment, potentially hindering immune responses. Additionally, machine learning models identify core prognostic genes such as F12, GOLM1, and S100P, which are significantly associated with patient survival.

CONCLUSIONS

This comprehensive approach provides novel insights into lung adenocarcinoma biology, emphasizing the role of genetic and immune factors in tumor progression. The findings support the development of personalized therapeutic strategies targeting both tumor cells and the immune microenvironment.

Multi-cohort validation based on a novel prognostic signature of anoikis for predicting prognosis and immunotherapy response of esophageal squamous cell carcinoma

Immunotherapy is recognized as an effective and promising treatment modality that offers a new approach to cancer treatment. However, identifying responsive patients remains challenging. Anoikis, a distinct form of programmed cell death, plays a crucial role in cancer progression and metastasis. Thus, we aimed to investigate prognostic biomarkers based on anoikis and their role in guiding immunotherapy decisions for esophageal squamous cell carcinoma (ESCC). By consensus clustering, the GSE53624 cohort of ESCC patients was divided into two subgroups based on prognostic anoikis-related genes (ARGs), with significant differences in survival outcomes between the two subgroups. Subsequently, we constructed an ARGs signature with four genes, and its reliability and accuracy were validated both internally and externally. Additional, different risk groups showed notable variances in terms of immunotherapy response, tumor infiltration, functional enrichment, immune function, and tumor mutation burden. Notably, the effectiveness of the signature in predicting immunotherapy response was confirmed across multiple cohorts, including GSE53624, GSE53625, TCGA-ESCC, and IMvigor210, highlighting its potential utility in predicting immunotherapy response. In conclusion, the ARGs signature has the potential to serve as an innovative and dependable prognostic biomarker for ESCC, facilitating personalized treatment strategies in this field, and may represent a valuable new tool for guiding ESCC immunotherapy decision-making.

Machine Learning-Based Glycolipid Metabolism Gene Signature Predicts Prognosis and Immune Landscape in Oesophageal Squamous Cell Carcinoma

Using machine learning approaches, we developed and validated a novel prognostic model for oesophageal squamous cell carcinoma (ESCC) based on glycolipid metabolism-related genes. Through integrated analysis of TCGA and GEO datasets, we established a robust 15-gene signature that effectively stratified patients into distinct risk groups. This signature demonstrated superior prognostic value and revealed significant associations with immune infiltration patterns. High-risk patients exhibited reduced immune cell infiltration, particularly in B cells and NK cells, alongside increased tumour purity. Single-cell RNA sequencing analysis uncovered unique cellular composition patterns and enhanced interaction intensities in the high-risk group, especially within epithelial and smooth muscle cells. Functional validation confirmed MECP2 as a promising therapeutic target, with its knockdown significantly inhibiting tumour progression both in vitro and in vivo. Drug sensitivity analysis identified specific therapeutic agents showing potential efficacy for high-risk patients. Our study provides both a practical prognostic tool and novel insights into the relationship between glycolipid metabolism and tumour immunity in ESCC, offering potential strategies for personalised treatment.

PANoptosis-Related Optimal Model (PROM): A Novel Prognostic Tool Unveiling Immune Dynamics in Lung Adenocarcinoma

Background: PANoptosis, a recently characterized inflammatory programmed cell death modality orchestrated by the PANoptosome complex, integrates molecular mechanisms of pyroptosis, apoptosis, and necroptosis. Although this pathway potentially mediates tumor progression, its role in lung adenocarcinoma (LUAD) remains largely unexplored. Methods: Through comprehensive single-cell transcriptomic profiling, we systematically identified critical PANoptosis-associated gene signatures. Prognostic molecular determinants were subsequently delineated via univariate Cox proportional hazards regression analysis. We constructed a PANoptosis-related optimal model (PROM) through the integration of 10 machine learning algorithms. The model was initially developed using The Cancer Genome Atlas (TCGA)-LUAD cohort and subsequently validated across six independent LUAD cohorts. Model performance was evaluated using mean concordance index. Furthermore, we conducted extensive multiomics analyses to delineate differential pathway activation patterns and immune cell infiltration profiles between PROM-stratified risk subgroups. Results: Cellular populations exhibiting elevated PANoptosis signatures demonstrated enhanced intercellular signaling networks. PROM demonstrated superior prognostic capability across multiple validation cohorts. Receiver operating characteristic curve analyses revealed area under the curve values exceeding 0.7 across all seven cohorts, with several achieving values above 0.8, indicating robust discriminative performance. The model score exhibited significant correlation with immunological parameters. Notably, high PROM scores were associated with attenuated immune responses, suggesting an immunosuppressive tumor microenvironment. Multiomics investigations revealed significant alterations in critical oncogenic pathways and immune landscape between PROM-stratified subgroups. Conclusion: This investigation establishes PROM as a clinically applicable prognostic tool for LUAD risk stratification. Beyond its predictive utility, PROM elucidates PANoptosis-associated immunological and biological mechanisms underlying LUAD progression. These findings provide novel mechanistic insights into LUAD pathogenesis and may inform the development of targeted therapeutic interventions and personalized treatment strategies to optimize patient outcomes.

Development of a novel prognostic signature based on cytotoxic T lymphocyte-evasion genes for hepatocellular carcinoma patient management

OBJECTIVES

Cytotoxic T lymphocytes (CTLs) are major actors in innate and adaptive antitumor response. We attempted to apply cancer cell-intrinsic CTL evasion genes (CCGs) to identify and verify a risk stratification signature in hepatocellular carcinoma (HCC) patients to assess the prognosis and benefits of immunotherapy, sorafenib treatment and transcatheter arterial chemoembolization (TACE) treatment.

METHODS

We developed a novel prognostic signature including six CCGs was developed by LASSO Cox regression. CIBERSORT, quanTIseq, and ssGSEA algorithms were used to investigated the correlation between the CCG signature and immune cell infiltration. We also assessed the performance of the CCG signature predicting immunotherapy, sorafenib treatment and TACE treatment with independent clinical mRNA sequencing data.

RESULTS

The area under the curve (AUC) of the CCG signature for predicting 1-, 3-, and 5-year OS was 0.77, 0.70 and 0.70 in the learning cohort, respectively. In the external verification cohort, the AUCs of the CCG signature were 0.71, 0.74 and 0.75. The CCG signature was significantly positively related to both TMB and MSI. In addition, responders had a significantly higher risk score than nonresponders when the signature was applied in urothelial cancer patients with immunotherapy, and the AUC of the CCG signature for predicting the response was 0.65. We further found that responders had a significantly lower risk score than nonresponders in the sorafenib and TACE treatment cohorts, and the AUCs of the CCG signature for predicting the response were 0.87 and 0.76, respectively. Finally, we identified four small molecule compounds negatively related to differentially expressed genes (DEGs) between the two categories of HCC patients, including monensin, etiocholanolone, naringenin, and Prestwick-1103.

CONCLUSIONS

The CCG signature has some clinical significance that may enhance HCC patient outcomes and even help develop novel strategies for HCC patient management.

Comprehensive Analysis of m6A-Related Programmed Cell Death Genes Unveils a Novel Prognostic Model for Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) involves complex dysregulated cellular processes, including programmed cell death (PCD), influenced by N6-methyladenosine (m6A) RNA modification. This study integrates bulk RNA and single-cell sequencing data to identify 43 prognostically valuable m6A-related PCD genes, forming the basis of a 13-gene risk model (m6A-related PCD signature [mPCDS]) developed using machine-learning algorithms, including CoxBoost and SuperPC. The mPCDS demonstrated significant predictive performance across multiple validation datasets. In addition to its prognostic accuracy, mPCDS revealed distinct genomic profiles, pathway activations, associations with the tumour microenvironment and potential for predicting drug sensitivity. Experimental validation identified RCN1 as a potential oncogene driving LUAD progression and a promising therapeutic target. The mPCDS offers a new approach for LUAD risk stratification and personalised treatment strategies.

Leveraging single-cell and multi-omics approaches to identify MTOR-centered deubiquitination signatures in esophageal cancer therapy

Background

Esophageal squamous cell carcinoma (ESCC) remains a significant challenge in oncology due to its aggressive nature and heterogeneity. As one of the deadliest malignancies, ESCC research lags behind other cancer types. The balance between ubiquitination and deubiquitination processes plays a crucial role in cellular functions, with its disruption linked to various diseases, including cancer.

Methods

Our study utilized diverse analytical approaches, encompassing Cox regression models, single-cell RNA sequencing, intercellular communication analysis, and Gene Ontology enrichment. We also conducted mutation profiling and explored potential immunotherapeutic agents. Furthermore, in vitro cellular experiments and in vivo mouse models were performed to validate findings. These methodologies aimed to establish deubiquitination-related gene signatures (DRGS) for predicting ESCC patient outcomes and response to immunotherapy.

Results

By integrating datasets from TCGA-ESCC and GSE53624, we developed a DRGS model based on 14 deubiquitination-related genes (DUBGs). This signature effectively forecasts ESCC prognosis, drug responsiveness, and immune cell infiltration patterns. It also influences the mutational landscape of patients. Those classified as high-risk exhibited reduced survival rates, increased genetic alterations, and more complex cellular interactions, potentially explaining their poor outcomes. Notably, in vitro and in vivo experiments identified MTOR, a key component of the signature, as a promising therapeutic target for ESCC treatment.

Conclusion

Our research highlights the significance of 14 DUBGs in ESCC progression. The risk score derived from this gene set enables clinical stratification of patients into distinct prognostic groups. Moreover, MTOR emerges as a potential target for personalized ESCC therapy, offering new avenues for treatment strategies.

Integrating necroptosis into pan-cancer immunotherapy: a new era of personalized treatment

Introduction

Necroptosis has emerged as a promising biomarker for predicting immunotherapy responses across various cancer types. Its role in modulating immune activation and therapeutic outcomes offers potential for precision oncology.

Methods

A comprehensive pan-cancer analysis was performed using bulk RNA sequencing data to develop a necroptosis-related gene signature, termed Necroptosis.Sig. Multi-omics approaches were employed to identify critical pathways and key regulators of necroptosis, including HMGB1. Functional validation experiments were conducted in A549 lung cancer cells to evaluate the effects of HMGB1 knockdown on tumor proliferation and malignancy.

Results

The Necroptosis.Sig gene signature effectively predicted responses to immune checkpoint inhibitors (ICIs). Multi-omics analyses highlighted HMGB1 as a key modulator of necroptosis, with potential to enhance immune activation and therapeutic efficacy. Functional experiments demonstrated that HMGB1 knockdown significantly suppressed tumor proliferation and malignancy, reinforcing the therapeutic potential of targeting necroptosis.

Discussion

These findings underscore the utility of necroptosis as a biomarker to guide personalized immunotherapy strategies. By advancing precision oncology, necroptosis provides a novel avenue for improving cancer treatment outcomes.

Comprehensive multi-omics analysis identifies chromatin regulator-related signatures and TFF1 as a therapeutic target in lung adenocarcinoma through a 429-combination machine learning approach

Introduction

Lung cancer is a leading cause of cancer-related deaths, with its incidence continuing to rise. Chromatin remodeling, a crucial process in gene expression regulation, plays a significant role in the development and progression of malignant tumors. However, the role of chromatin regulators (CRs) in lung adenocarcinoma (LUAD) remains underexplored.

Methods

This study developed a chromatin regulator-related signature (CRRS) using a 429-combination machine learning approach to predict survival outcomes in LUAD patients. The CRRS model was validated across multiple independent datasets. We also investigated the impact of CRRS on the immune microenvironment, focusing on immune cell infiltration. To identify potential therapeutic targets, TFF1, a chromatin regulator, was knocked down using siRNA in LUAD cells. We assessed its impact through apoptosis analysis, proliferation assays, and in vivo tumor growth studies. Additional validation was performed using Ki67 expression and TUNEL assays.

Results

The CRRS accurately predicted survival outcomes and was shown to modulate immune cell infiltration in the tumor microenvironment. High-risk patients demonstrated increased activity in cell cycle regulation and DNA repair pathways, along with distinct mutation profiles and immune responses compared to low-risk patients. TFF1 emerged as a key therapeutic target. Knockdown of TFF1 significantly inhibited LUAD cell proliferation, induced apoptosis, and suppressed in vivo tumor growth. Ki67 and TUNEL assays confirmed the role of TFF1 in regulating tumor growth and cell death.

Discussion

These findings highlight the potential of chromatin regulators in prognostic modeling and immune modulation in LUAD. TFF1 was identified as a promising therapeutic target, suggesting that targeting TFF1 could provide new treatment strategies. Further research is warranted to explore its full potential and therapeutic applicability.

Investigating cellular similarities and differences between upper tract urothelial carcinoma and bladder urothelial carcinoma using single-cell sequencing

Background

Upper tract urothelial carcinoma (UTUC) and bladder urothelial carcinoma (BLCA) both originate from uroepithelial tissue, sharing remarkably similar clinical manifestations and therapeutic modalities. However, emerging evidence suggests that identical treatment regimens may lead to less favorable outcomes in UTUC compared to BLCA. Therefore, it is imperative to explore molecular processes of UTUC and identify biological differences between UTUC and BLCA.

Methods

In this study, we performed a comprehensive analysis using single-cell RNA sequencing (scRNA-seq) on three UTUC cases and four normal ureteral tissues. These data were combined with publicly available datasets from previous BLCA studies and RNA sequencing (RNA-seq) data for both cancer types. This pooled analysis allowed us to delineate the transcriptional differences among distinct cell subsets within the microenvironment, thus identifying critical factors contributing to UTUC progression and phenotypic differences between UTUC and BLCA.

Results

scRNA-seq analysis revealed seemingly similar but transcriptionally distinct cellular identities within the UTUC and BLCA ecosystems. Notably, we observed striking differences in acquired immunological landscapes and varied cellular functional phenotypes between these two cancers. In addition, we uncovered the immunomodulatory functions of vein endothelial cells (ECs) in UTUC, and intercellular network analysis demonstrated that fibroblasts play important roles in the microenvironment. Further intersection analysis showed that MARCKS promote UTUC progression, and immunohistochemistry (IHC) staining revealed that the diverse expression patterns of MARCKS in UTUC, BLCA and normal ureter tissues.

Conclusion

This study expands our multidimensional understanding of the similarities and distinctions between UTUC and BLCA. Our findings lay the foundation for further investigations to develop diagnostic and therapeutic targets for UTUC.

MIF and CD74 as Emerging Biomarkers for Immune Checkpoint Blockade Therapy

Immune checkpoint blockade (ICB) therapy is used to treat a wide range of cancers; however, some patients are at risk of developing treatment resistance and/or immune-related adverse events (irAEs). Thus, there is a great need for the identification of reliable predictive biomarkers for response and toxicity. The cytokine MIF (macrophage migration inhibitory factor) and its cognate receptor CD74 are intimately connected with cancer progression and have previously been proposed as prognostic biomarkers for patient outcome in various cancers, including solid tumors such as malignant melanoma. Here, we assess their potential as predictive biomarkers for response to ICB therapy and irAE development. We provide a brief overview of their function and roles in the context of cancer and autoimmune disease. We also review the evidence showing that MIF and CD74 may be of use as predictive biomarkers of patient response to ICB therapy and irAE development. We also highlight that careful consideration is required when assessing the potential of serum MIF levels as a biomarker due to its reported circadian expression in human plasma. Finally, we suggest future directions for the establishment of MIF and CD74 as predictive biomarkers for ICB therapy and irAE development to guide further research in this field.

Influence of Breast Cancer Extracellular Vesicles on Immune Cell Activation: A Pilot Study

Breast cancer is the leading cause of cancer-related death in women worldwide. It is well known that breast cancer shows significant alterations in the tumor microenvironment (TME), which is composed of a variety of immune cells, including natural killer (NK) cells, that have a key role in tumor development or anti-tumor responses in breast cancer patients. Luminal B (BT474) and triple-negative breast cancer (HS578T) cell lines were cultured in 2D and 3D model systems. PMBCs from healthy donors were isolated and treated with extracellular vesicles (EVs) from monolayer and spheroids of BT474 and HS578T and analyzed using cytofluorimetric approaches. We observed that EVs can alter the activation and presence of CD335+/CD11b+ NK cells. EVs derived from BT474 and HS578T cells trigger the activation and, simultaneously, a reduction in the percentage of CD335+/CD11b+ NK cells. In addition, EVs derived from BT474 also significantly reduce CD39+ T-regulatory (T-reg) cells. Our preliminary data suggest that using EVs to treat tumors could potentially alter components of the immune system, which causes hyperactivation of specific cell types and can lead to aggressive growth. These data will guide the designing of new personalized diagnostic approaches based on in-depth study of the TME.

Low-density lipoprotein receptor promotes crosstalk between cell stemness and tumor immune microenvironment in breast cancer: a large data-based multi-omics study

BACKGROUND

Tumor cells with stemness in breast cancer might facilitate the immune microenvironment's suppression process and led to anti-tumor immune effects. The primary objective of this study was to identify potential targets to disrupt the communication between cancer cell stemness and the immune microenvironment.

METHODS

In this study, we initially isolated tumor cells with varying degrees of stemness using a spheroid formation assay. Subsequently, we employed RNA-seq and proteomic analyses to identify genes associated with stemness through gene trend analysis. These stemness-related genes were then subjected to pan-cancer analysis to elucidate their functional roles in a broader spectrum of cancer types. RNA-seq data of 3132 patients with breast cancer with clinical data were obtained from public databases. Using the identified stemness genes, we constructed two distinct stemness subtypes, denoted as C1 and C2. We subsequently conducted a comprehensive analysis of the differences between these subtypes using pathway enrichment methodology and immune infiltration algorithms. Furthermore, we identified key immune-related stemness genes by employing lasso regression analysis and a Cox survival regression model. We conducted in vitro experiments to ascertain the regulatory impact of the key gene on cell stemness. Additionally, we utilized immune infiltration analysis and pan-cancer analysis to delineate the functions attributed to this key gene. Lastly, single-cell RNA sequencing (scRNA-seq) was employed to conduct a more comprehensive examination of the key gene's role within the microenvironment.

RESULTS

In our study, we initially identified a set of 65 stemness-related genes in breast cancer cells displaying varying stemness capabilities. Subsequently, through survival analysis, we pinpointed 41 of these stemness genes that held prognostic significance. We observed that the C2 subtype exhibited a higher stemness capacity compared to the C1 subtype and displayed a more aggressive malignancy profile. Further analysis using Lasso-Cox algorithm identified LDLR as a pivotal immune-related stemness gene. It became evident that LDLR played a crucial role in shaping the immune microenvironment. In vitro experiments demonstrated that LDLR regulated the cell stemness of breast cancer. Immune infiltration analysis and pan-cancer analysis determined that LDLR inhibited the proliferation of immune cells and might promote tumor cell progression. Lastly, in our scRNA-seq analysis, we discovered that LDLR exhibited associations with stemness marker genes within breast cancer tissues. Moreover, LDLR demonstrated higher expression levels in tumor cells compared to immune cells, further emphasizing its relevance in the context of breast cancer.

CONCLUSION

LDLR is an important immune stemness gene that regulates cell stemness and enhances the crosstalk between breast cancer cancer cell stemness and tumor immune microenvironment.

Single-cell transcriptomics reveals heterogeneity in esophageal squamous epithelial cells and constructs models for predicting patient prognosis and immunotherapy

Background

Esophageal squamous cell carcinoma (ESCC), characterized by its high invasiveness and malignant potential, has long been a formidable challenge in terms of treatment.

Methods

A variety of advanced analytical techniques are employed, including single-cell RNA sequencing (scRNA-seq), cell trajectory inference, transcription factor regulatory network analysis, GSVA enrichment analysis, mutation profile construction, and the inference of potential immunotherapeutic drugs. The purpose is to conduct a more comprehensive exploration of the heterogeneity among malignant squamous epithelial cell subgroups within the ESCC microenvironment and establish a model for predicting the prognosis and immunotherapy outcomes of ESCC patients.

Results

An analysis was conducted through scRNA-seq, and three Cluster of malignant epithelial cells were identified using the infer CNV method. Cluster 0 was found to exhibit high invasiveness, whereas Cluster 1 displayed prominent characteristics associated with epithelial-mesenchymal transition. Confirmation of these findings was provided through cell trajectory analysis, which positioned Cluster 0 at the initiation stage of development and Cluster 1 at the final developmental stage. The abundance of Cluster 0-2 groups in TCGA-LUAD samples was assessed using ssGSEA and subsequently categorized into high and low-expression groups. Notably, it was observed that Cluster 0-1 had a significant impact on survival (p<0.05). Furthermore, GSVA enrichment analysis demonstrated heightened activity in hallmark pathways for Cluster 0, whereas Cluster 1 exhibited notable enrichment in pathways related to cell proliferation. It is noteworthy that a prognostic model was established utilizing feature genes from Cluster 0-1, employing the Lasso and stepwise regression methods. The results revealed that in TCGA and GSE53624 cohorts, the low-risk group demonstrated significantly higher overall survival and increased levels of immune infiltration. An examination of four external immunotherapy cohorts unveiled that the low-risk group exhibited improved immunotherapeutic efficacy. Additionally, more meaningful treatment options were identified for the low-risk group.

Conclusion

The findings revealed distinct interactions between malignant epithelial cells of ESCC and subgroups within the tumor microenvironment. Two cell clusters, strongly linked to survival, were pinpointed, and a signature was formulated. This signature is expected to play a crucial role in identifying and advancing precision medicine approaches for the treatment of ESCC.

Regulatory T cells in lung disease and transplantation

Pulmonary disease can refer to the disease of the lung itself or the pulmonary manifestations of systemic diseases, which are often connected to the malfunction of the immune system. Regulatory T (Treg) cells have been shown to be important in maintaining immune homeostasis and preventing inflammatory damage, including lung diseases. Given the increasing amount of evidence linking Treg cells to various pulmonary conditions, Treg cells might serve as a therapeutic strategy for the treatment of lung diseases and potentially promote lung transplant tolerance. The most potent and well-defined Treg cells are Foxp3-expressing CD4+ Treg cells, which contribute to the prevention of autoimmune lung diseases and the promotion of lung transplant rejection. The protective mechanisms of Treg cells in lung disease and transplantation involve multiple immune suppression mechanisms. This review summarizes the development, phenotype and function of CD4+Foxp3+ Treg cells. Then, we focus on the therapeutic potential of Treg cells in preventing lung disease and limiting lung transplant rejection. Furthermore, we discussed the possibility of Treg cell utilization in clinical applications. This will provide an overview of current research advances in Treg cells and their relevant application in clinics.