Normal Lung Tissue Research Articles

DNA methylation and mRNA expression of ZNF577 as biomarkers for the detection and prognosis of lung adenocarcinoma.

Despite advances in science and technology, lung cancer remains a major public health issue. The discovery of early diagnostic and prognostic markers is still needed to reduce the mortality rate of lung cancer, which is the highest among all cancer types. Aberrations in the DNA methylation system have an important role in human cancer and are promising for the development of early diagnostic and prognostic markers. The present study focused on zinc finger protein (ZNF)577, whose encoding gene was indicated to exhibit promoter hypermethylation together with 9 other genes in lung adenocarcinoma (LADC) in a previous study by our group. ZN577 is a member of the ZNG family and its functional role has so far remained elusive. LADC tissue samples surgically resected at Tokushima University Hospital (Tokushima, Japan) between April 1999 and November 2013 were collected. A total of 73 tumors and 27 paired tumor-adjacent normal tissues were examined for DNA methylation and mRNA expression of ZNF577. A total of 149 LADC tissue samples were collected and evaluated by immunohistochemistry (IHC) for the tissue expression of ZNF577. High methylation (n=27, P<0.0001) and low mRNA expression levels (n=27, P<0.031) of ZNF577 were identified in LADC tissues, and it was demonstrated that methylation levels were inversely correlated with mRNA expression levels (P=0.0116, ρ=-0.2515). Among the LADC tissues, lepidic-patterned samples had lower methylation levels of ZNF577 than other pathological types. In addition, mRNA expression levels of ZNF577 were significantly higher in females, non-smokers and stage I samples. Overall survival [P<0.0001; area under curve (AUC)=0.8658] and disease-free survival (DFS; P<0.0004; AUC=0.7232) rates were significantly higher in the ZNF577 high mRNA expression group than in the ZNF577 low mRNA expression group. Among the 149 LADC samples examined by IHC, 105 were negative and 44 were positive for the tissue expression of ZNF577. Cox regression analysis showed poorer DFS (hazard ratio: 3.917; P=0.023) in patients with lower expression of ZNF577. In conclusion, higher methylation levels of ZNF577 were observed in LADC tissues than in normal lung tissue and low mRNA expression of ZNF577 was associated with unfavorable prognosis.

Identification of effective diagnostic genes and immune cell infiltration characteristics in small cell lung cancer by integrating bioinformatics analysis and machine learning algorithms.

To identify potential diagnostic markers for small cell lung cancer (SCLC) and investigate the correlation with immune cell infiltration. GSE149507 and GSE6044 were used as the training group, while GSE108055 served as validation group A and GSE73160 served as validation group B. Differentially expressed genes (DEGs) were identified and analyzed for functional enrichment. Machine learning (ML) was used to identify candidate diagnostic genes for SCLC. The area under the receiver operating characteristic curves was applied to assess diagnostic efficacy. Immune cell infiltration analyses were carried out. There were 181 DEGs identified. The gene ontology analysis showed that DEGs were enriched in 455 functional annotations, some of which were associated with immunity. The kyoto encyclopedia of genes and genomes analysis revealed that there were 9 signaling pathways enriched. The disease ontology analysis indicated that DEGs were related to 116 diseases. The gene set enrichment analysis results displayed multiple items closely related to immunity. ZWINT and NRCAM were screened using ML and further validated as diagnostic genes. Significant differences were observed in SCLC with normal lung tissue samples among immune cell infiltration characteristics. Strong associations were found between the diagnostic genes and immune cell infiltration. This study identified 2 diagnostic genes, ZWINT and NRCAM, that were related to immune cell infiltration by integrating bioinformatics analysis and ML algorithms. These genes could serve as potential diagnostic biomarkers and provide possible molecular targets for immunotherapy in SCLC.

Interleukin-11 Is Involved in Hyperoxia-induced Bronchopulmonary Dysplasia in Newborn Mice by Mediating Epithelium-Fibroblast Cross-talk.

Bronchopulmonary dysplasia (BPD) is a chronic lung disorder predominantly affecting preterm infants. Oxygen therapy, a common treatment for BPD, often leads to hyperoxia-induced pulmonary damage, particularly targeting alveolar epithelial cells (AECs). Crucially, disrupted lung epithelium-fibroblast interactions significantly contribute to BPD's pathogenesis. Previous studies on interleukin-11 (IL-11) in lung diseases have yielded conflicting results. Recent research, however, highlights IL-11 as a key regulator of fibrosis, stromal inflammation, and epithelial dysfunction. Despite this, the specific role of IL-11 in BPD remains underexplored. Our transcriptome analysis of normal and hyperoxia-exposed murine lung tissues revealed an increased expression of IL-11 RNA. This study aimed to investigate IL-11's role in modulating the disrupted interactions between AECs and fibroblasts in BPD. BPD was modeled in vivo by exposing C57BL/6J neonatal mice to hyperoxia. Histopathological changes in lung tissue were evaluated with hematoxylin-eosin staining, while lung fibrosis was assessed using Masson staining and immunohistochemistry (IHC). To investigate IL-11's role in pulmonary injury contributing to BPD, IL-11 levels were reduced through intraperitoneal administration of IL-11RαFc in hyperoxia-exposed mice. Additionally, MLE-12 cells subjected to 95% oxygen were collected and co-cultured with mouse pulmonary fibroblasts (MPFs) to measure α-SMA and Collagen I expression levels. IL-11 levels in the supernatants were quantified using an enzyme-linked immunosorbent assay (ELISA). Both IHC and Masson staining revealed that inhibiting IL-11 expression alleviated pulmonary fibrosis in neonatal mice induced by hyperoxia, along with reducing the expression of fibrosis markers α-SMA and collagen I in lung tissue. In vitro analysis showed a significant increase in IL-11 levels in the supernatant of MLE-12 cells treated with hyperoxia. Silencing IL-11 expression in MLE-12 cells reduced α-SMA and collagen I concentrations in MPFs co-cultured with the supernatant of hyperoxia-treated MLE-12 cells. Additionally, ERK inhibitors decreased α-SMA and collagen I levels in MPFs co-cultured with the supernatant of hyperoxia-treated MLE-12 cells. Clinical studies found increased IL-11 levels in tracheal aspirates (TA) of infants with BPD. This research reveals that hyperoxia induces IL-11 secretion in lung epithelium. Additionally, IL-11 derived from lung epithelium emerged as a crucial mediator in myofibroblast differentiation via the ERK signaling pathway, highlighting its potential therapeutic value in BPD treatment.

Development of Preladenant-Based Radiotracers for Imaging A2AR in Tumors.

Activation of the adenosine 2A receptor (A2AR) can lead to tumor immunosuppression, which results in poor prognosis of immunotherapy. The aim of this study was to design novel 18F-labeled probes ([18F]F-PFP2 and [18F]F-PFP4) to visualize A2AR in the tumor. The uptake of radioprobes in A2AR-negative 4T1 breast tumor was lower than that of A2AR-positive B16F10 melanoma at 1 h p.i. (1.22 ± 0.36% ID/g vs 2.80 ± 0.72% ID/g), 2 h p.i. (1.09 ± 0.20% ID/g vs 2.93 ± 0.76% ID/g) and 3 h p.i. (0.89 ± 0.27% ID/g vs 2.73 ± 0.58% ID/g), respectively. B16F10 lung metastasis models were employed to expand the application scenarios, observing significantly higher uptake of [18F]F-PFP2 in metastatic lesions compared to normal lung tissue (5.55 ± 2.18% ID/g vs 1.89 ± 0.65% ID/g, tumor/lung ratio ∼3). It is given that [18F]F-PFP2 might lay the foundation for establishing an A2AR-targeted imaging evaluation system for tumors, which will provide more precise guidance for personalized treatment.

Family with sequence similarity 83, member A (FAM83A) inhibits ferroptosis via the Wnt/β-catenin pathway in lung squamous cell cancer

The function of Family With Sequence Similarity 83, Member A (FAM83A) in lung squamous cell carcinoma (LUSC) is largely unknown. Here, we detected its prognostic and regulation roles in LUSC. Bioinformatics methods were applied initially to predict the expression level and prognostic value of FAM83A mRNA in LUSC. In vitro experiments, such as western blot, colony formation and cell viability assay, lipid Reactive oxygen species (ROS), malondialdehyde (MDA), reduced glutathione (GSH)/oxidized glutathione disulfide (GSSG), and 4-hydroxy-2-nonenal (4-HNE) assay, were used to investigate its mechanism. In vivo experiments were further conducted to validate the mechanism. Results from TCGA and Oncomine databases revealed significantly higher FAM83A mRNA expression levels in LUSC than in normal lung tissue. TCGA and GEO databases and our database revealed that FAM83A expression level was an independent prognostic factor for both overall survival and progression-free survival. Besides, FAM83A was significantly associated with a higher ability of growth and clonogenicity. Mechanistically, in vitro and in vivo experiments revealed that FAM83A could promote LUSC cell growth by inhibiting ferroptosis via activating the Wnt/β-catenin signaling pathway. The rescue experiment demonstrated that inhibition of the Wnt/β-catenin pathway counteracted the function of FAM83A. FAM83A is overexpressed in LUSC and could serve as a prognosis prediction biomarker for LUSC. FAM83A promotes LUSC cell growth by inhibiting ferroptosis via activating the Wnt/β-catenin signaling pathway, which provides a new potential therapeutic target for LUSC treatment.

Plasma proteometabolome in lung cancer: exploring biomarkers through bidirectional Mendelian randomization and colocalization analysis.

Unlike other cancers with widespread screening (breast, colorectal, cervical, prostate, and skin), lung nodule biopsies for positive screenings have higher morbidity with clinical complications. Development of non-invasive diagnostic biomarkers could thereby significantly enhance lung cancer management for at-risk patients. Here, we leverage Mendelian Randomization (MR) to investigate the plasma proteome and metabolome for potential biomarkers relevant to lung cancer. Utilizing bidirectional MR and co-localization analyses, we identify novel associations, highlighting inverse relationships between plasma proteins SFTPB and KDELC2 in lung adenocarcinoma (LUAD) and positive associations of TCL1A with lung squamous cell carcinoma (LUSC) and CNTN1 with small cell lung cancer (SCLC). Additionally, our work reveals significant negative correlations between metabolites such as theobromine and paraxanthine, along with paraxanthine-related ratios, in both LUAD and LUSC. Conversely, positive correlations are found in caffeine/paraxanthine and arachidonate (20:4n6)/paraxanthine ratios with these cancer types. Through single-cell sequencing data of normal lung tissue, we further explore the role of lung tissue-specific protein SFTPB in carcinogenesis. These findings offer new insights into lung cancer etiology, potentially guiding the development of diagnostic biomarkers and therapeutic approaches.

Novel AT2 Cell Subpopulations and Diagnostic Biomarkers in IPF: Integrating Machine Learning with Single-Cell Analysis.

Idiopathic pulmonary fibrosis (IPF) is a long-term condition with an unidentified cause, and currently there are no specific treatment options available. Alveolar epithelial type II cells (AT2) constitute a heterogeneous population crucial for secreting and regenerative functions in the alveolus, essential for maintaining lung homeostasis. However, a comprehensive investigation into their cellular diversity, molecular features, and clinical implications is currently lacking. In this study, we conducted a comprehensive examination of single-cell RNA sequencing data from both normal and fibrotic lung tissues. We analyzed alterations in cellular composition between IPF and normal tissue and investigated differentially expressed genes across each cell population. This analysis revealed the presence of two distinct subpopulations of IPF-related alveolar epithelial type II cells (IR_AT2). Subsequently, three unique gene co-expression modules associated with the IR_AT2 subtype were identified through the use of hdWGCNA. Furthermore, we refined and identified IPF-related AT2-related gene (IARG) signatures using various machine learning algorithms. Our analysis demonstrated a significant association between high IARG scores in IPF patients and shorter survival times (p-value < 0.01). Additionally, we observed a negative correlation between the percent predicted diffusing capacity for lung carbon monoxide (% DLCO) and increased IARG scores (cor = -0.44, p-value < 0.05). The cross-validation findings demonstrated a high level of accuracy (AUC > 0.85, p-value < 0.01) in the prognostication of patients with IPF utilizing the identified IARG signatures. Our study has identified distinct molecular and biological features among AT2 subpopulations, specifically highlighting the unique characteristics of IPF-related AT2 cells. Importantly, our findings underscore the prognostic relevance of specific genes associated with IPF-related AT2 cells, offering valuable insights into the advancement of IPF.

Integrative metagenomic, transcriptomic, and proteomic analysis reveal the microbiota-host interplay in early-stage lung adenocarcinoma among non-smokers

BackgroundThe incidence of early-stage lung adenocarcinoma (ES-LUAD) is steadily increasing among non-smokers. Previous research has identified dysbiosis in the gut microbiota of patients with lung cancer. However, the local microbial profile of non-smokers with ES-LUAD remains largely unknown. In this study, we systematically characterized the local microbial community and its associated features to enable early intervention.MethodsA prospective collection of ES-LUAD samples (46 cases) and their corresponding normal tissues adjacent to the tumor (41 cases), along with normal lung tissue samples adjacent to pulmonary bullae in patients with spontaneous pneumothorax (42 cases), were subjected to ultra-deep metagenomic sequencing, host transcriptomic sequencing, and proteomic sequencing. The obtained omics data were subjected to both individual and integrated analysis using Spearman correlation coefficients.ResultsWe concurrently detected the presence of bacteria, fungi, and viruses in the lung tissues. The microbial profile of ES-LUAD exhibited similarities to NAT but demonstrated significant differences from the healthy controls (HCs), characterized by an overall reduction in species diversity. Patients with ES-LUAD exhibited local microbial dysbiosis, suggesting the potential pathogenicity of certain microbial species. Through multi-omics correlations, intricate local crosstalk between the host and local microbial communities was observed. Additionally, we identified a significant positive correlation (rho > 0.6) between Methyloversatilis discipulorum and GOLM1 at both the transcriptional and protein levels using multi-omics data. This correlated axis may be associated with prognosis. Finally, a diagnostic model composed of six bacterial markers successfully achieved precise differentiation between patients with ES-LUAD and HCs.ConclusionsOur study depicts the microbial spectrum in patients with ES-LUAD and provides evidence of alterations in lung microbiota and their interplay with the host, enhancing comprehension of the pathogenic mechanisms that underlie ES-LUAD. The specific model incorporating lung microbiota can serve as a potential diagnostic tool for distinguishing between ES-LUAD and HCs.

Single-Cell Transcriptome Analysis Reveals 2 Subtypes of Tumor Cells of Sclerosing Pneumocytoma With Distinct Molecular Features and Clinical Implications

Pulmonary sclerosing pneumocytoma (PSP) is a rare, distinctive benign lung adenoma of pneumocyte origin. Despite its rarity, the tumor’s unique cellular morphology has sparked ongoing debates regarding the origin of its constituent cells. This study aimed to elucidate the molecular features of PSP tumor cells and enhance our understanding of the cellular processes contributing to PSP formation and biological behavior. Tissue samples from PSP and corresponding normal lung tissues (n = 4) were collected. We employed single-cell RNA sequencing and microarray-based spatial transcriptomic analyses to identify cell types and investigate their transcriptomes, with a focus on transcription factors, enriched gene expression, and single-cell trajectory evaluations. Our analysis identified 2 types of tumor cells: mesenchymal-epithelial dual-phenotype (MEDP) cells and a distinct subpopulation of type II alveolar epithelial cells exhibiting characteristics slightly reminiscent of type I alveolar epithelial cells (AT2Cs) corresponding to histologic round stromal cells and surface cuboidal cells, respectively. MEDP cells displayed weak alveolar epithelial differentiation but strong collagen production capabilities, as indicated by the expression of both TTF-1 and vimentin. These cells played a pivotal role in forming the solid and sclerotic areas of PSP. Moreover, MEDP cells exhibited a pronounced propensity for epithelial-mesenchymal transition, suggesting a greater potential for metastasis compared with AT2Cs. The capillary endothelial cells of PSP displayed notable diversity. Overall, this study provides, for the first time, a comprehensive mapping of the single-cell transcriptome profile of PSP. Our findings delineate 2 distinct subtypes of tumor cells, MEDP cells and AT2Cs, each with its own biological characteristics and spatial distribution. A deeper understanding of these cell types promises insights into the histology and biological behaviors of this rare tumor.

Raman spectroscopy in lung cancer diagnostics: Can an in vivo setup compete with ex vivo applications?

Lung carcinoma remains the leading cause of cancer death worldwide. The tactic to change this unfortunate rate may be a timely and rapid diagnostic, which may in many cases improve patient prognosis.In our study, we focus on the comparison of two novel methods of rapid lung carcinoma diagnostics, label-free in vivo and ex vivo Raman spectroscopy of the epithelial tissue, and assess their feasibility in clinical practice. As these techniques are sensitive not only to the basic molecular composition of the analyzed sample but also to the secondary structure of large biomolecules, such as tissue proteins, they represent suitable candidate methods for epithelial cancer diagnostics.During routine bronchoscopy, we collected 78 in vivo Raman spectra of normal and cancerous lung tissue and 37 samples of endobronchial pathologies, which were subsequently analyzed ex vivo. Using machine learning techniques, namely principal component analysis (PCA) and support vector machines (SVM), we were able to reach 87.2% (95% CI, 79.8–94.6%) and 100.0% (95% CI, 92.1–100.0%) of diagnostic accuracy for in vivo and ex vivo setup, respectively. Although the ex vivo approach provided superior results, the rapidity of in vivo Raman spectroscopy might become unmatchable in the acceleration of the diagnostic process.

SCARF2 is a target for chronic obstructive pulmonary disease: Evidence from multi-omics research and cohort validation.

Age-related chronic inflammatory lung diseases impose a threat on public health, including idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD). However, their etiology and potential targets have not been clarified. We performed genome-wide meta-analysis for IPF with the largest sample size (2883 cases and 741,929 controls) and leveraged the summary statistics of COPD (17,547 cases and 617,598 controls). Transcriptome-wide and proteome-wide Mendelian randomization (MR) designs, together with genetic colocalization, were implemented to find robust targets. The mediation effect was assessed using leukocyte telomere length (LTL). The single-cell transcriptome analysis was performed to link targets with cell types. Individual-level data from UK Biobank (UKB) were used to validate our findings. Sixteen genetically predicted plasma proteins were causally associated with the risk of IPF and 6 proteins were causally associated with COPD. Therein, genetically-elevated plasma level of SCARF2 protein should reduce the risk of both IPF (odds ratio, OR = 0.9974 [0.9970, 0.9978]) and COPD (OR = 0.7431 [0.6253, 0.8831]) and such effects were not mediated by LTL. Genetic colocalization further corroborated these MR results of SCARF2. The transcriptome-wide MR confirmed that higher expression level of SCARF2 was associated with a reduced risk of both. However, the single-cell RNA analysis indicated that SCARF2 expression level was only relatively lower in epithelial cells of COPD lung tissue compared to normal lung tissue. UKB data implicated an inverse association of serum SCARF2 protein with COPD (hazard ratio, HR = 1.215 [1.106, 1.335]). The SCARF2 gene should be a novel target for COP.

An agent-based modelling framework to study growth mechanisms in EGFR-L858R mutant cell alveolar type II cells.

Mutations in the epidermal growth factor receptor (EGFR) are common in non-small cell lung cancer (NSCLC), particularly in never-smoker patients. However, these mutations are not always carcinogenic, and have recently been reported in histologically normal lung tissue from patients with and without lung cancer. To investigate the outcome of EGFR mutation in healthy lung stem cells, we grow murine alveolar type II organoids monoclonally in a three-dimensional Matrigel. Our experiments show that the EGFR-L858R mutation induces a change in organoid structure: mutated organoids display more 'budding', in comparison with non-mutant controls, which are nearly spherical. We perform on-lattice computational simulations, which suggest that this can be explained by the concentration of division among a small number of cells on the surface of the mutated organoids. We are currently unable to distinguish the cell-based mechanisms that lead to this spatial heterogeneity in growth, but suggest a number of future experiments which could be used to do so. We suggest that the likelihood of L858R-fuelled tumorigenesis is affected by whether the mutation arises in a spatial environment that allows the development of these surface protrusions. These data may have implications for cancer prevention strategies and for understanding NSCLC progression.

The prognostic value of immune escape-related genes in lung adenocarcinoma.

Lung cancer is one of the most common cancers in humans, and lung adenocarcinoma (LUAD) has become the most common histological type of lung cancer. Immune escape promotes progression of LUAD from the early to metastatic late stages and is one of the main obstacles to improving clinical outcomes for immunotherapy targeting immune detection points. Our study aims to explore the immune escape related genes that are abnormally expressed in lung adenocarcinoma, providing assistance in predicting the prognosis of lung adenocarcinoma and targeted. RNA data and related clinical details of patients with LUAD were obtained from The Cancer Genome Atlas (TCGA) database. Through weighted gene coexpression network analysis (WGCNA), 3112 key genes were screened and intersected with 182 immune escape genes obtained from a previous study to identify the immune escape-related genes (IERGs). The role of IERGs in LUAD was systematically explored through gene ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) analyses, which were used to enrich the relevant pathways of IERGs. The least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression analysis were used to identify the key prognostic genes, and a prognostic risk model was constructed. Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data (ESTIMATE) and microenvironment cell populations (MCP) counter methods (which can accurately assess the amount of eight immune cell populations and two stromal cell groups) were used to analyze the tumor immune status of the high and low risk subgroups. The protein expression level of the differentially expressed genes in lung cancer samples was determined by using the Human Protein Atlas (HPA) database. A nomogram was constructed, and the prognostic risk model was verified via the Gene Expression Omnibus (GEO) datasets GSE72094 and GSE30219. Twenty differentially expressed IERGs were obtained. GO analysis of these 20 IERGs revealed that they were mainly associated with the regulation of immune system processes, immune responses, and interferon-γ enrichment in mediating signaling pathways and apoptotic signaling pathways; meanwhile, KEGG analysis revealed that IERGs were associated with necroptosis, antigen processing and presentation, programmed cell death ligand 1 (PD-L1) expression and programmed cell death 1 (PD-1) pathway in tumors, cytokine-cytokine receptor interactions, T helper cell 1 (Th1) and Th2 differentiation, and tumor necrosis factor signaling pathways. Using LASSO and Cox regression analysis, we constructed a four-gene model that could predict the prognosis of patients with LUAD, and the model was validated with a validation cohort. The immunohistochemical results of the HPA database showed that AHSA1 and CEP55 had low expression in normal lung tissue but high expression in lung cancer tissue. We constructed an IERG-based model for predicting the prognosis of LUAD. Among the genes identified, CEP55 and AHSA1 may be potential prognostic and therapeutic targets, and reducing their expression may represent a novel approach in the treatment of LUAD.

Small Cell Lung Carcinoma Cells Depend on KIF11 for Survival.

Few efficacious treatment options are available for patients with small cell lung carcinoma (SCLC), indicating the need to develop novel therapeutic approaches. In this study, we explored kinesin family member 11 (KIF11), a potential therapeutic target in SCLC. An analysis of publicly available data suggested that KIF11 mRNA expression levels are significantly higher in SCLC tissues than in normal lung tissues. When KIF11 was targeted by RNA interference or a small-molecule inhibitor (SB743921) in two SCLC cell lines, Lu-135 and NCI-H69, cell cycle progression was arrested at the G2/M phase with complete growth suppression. Further work suggested that the two cell lines were more significantly affected when both KIF11 and BCL2L1, an anti-apoptotic BCL2 family member, were inhibited. This dual inhibition resulted in markedly decreased cell viability. These findings collectively indicate that SCLC cells are critically dependent on KIF11 activity for survival and/or proliferation, as well as that KIF11 inhibition could be a new strategy for SCLC treatment.

In Vivo PET Detection of Lung Micrometastasis in Mice by Targeting Endothelial VCAM-1 Using a Dual-Contrast PET/MRI Probe.

Current clinical diagnostic imaging methods for lung metastases are sensitive only to large tumours (1-2 mm cross-sectional diameter), and early detection can dramatically improve treatment. We have previously demonstrated that an antibody-targeted MRI contrast agent based on microparticles of iron oxide (MPIO; 1 μm diameter) enables the imaging of endothelial vascular cell adhesion molecule-1 (VCAM-1). Using a mouse model of lung metastasis, upregulation of endothelial VCAM-1 expression was demonstrated in micrometastasis-associated vessels but not in normal lung tissue, and binding of VCAM-MPIO to these vessels was evident histologically. Owing to the lack of proton MRI signals in the lungs, we modified the VCAM-MPIO to include zirconium-89 (89Zr, t1/2 = 78.4 h) in order to allow the in vivo detection of lung metastases by positron emission tomography (PET). Using this new agent (89Zr-DFO-VCAM-MPIO), it was possible to detect the presence of micrometastases within the lung in vivo from ca. 140 μm in diameter. Histological analysis combined with autoradiography confirmed the specific binding of the agent to the VCAM-1 expressing vasculature at the sites of pulmonary micrometastases. By retaining the original VCAM-MPIO as the basis for this new molecular contrast agent, we have created a dual-modality (PET/MRI) agent for the concurrent detection of lung and brain micrometastases.

The predictive value of E2F7 in immunotherapy efficacy for lung adenocarcinoma: An observational study.

Lung adenocarcinoma (LUAD) is the most common pathological type of lung cancer. In recent years, immunotherapy has greatly changed the treatment pattern of advanced LUAD. However, only a small proportion of LUAD patients benefitted from immune checkpoint inhibitor therapy. There is an urgent need to develop a biomarker to predict immune therapy response. E2F7 has been shown to be closely related to immune cell infiltration and immune checkpoint expression in tumors. However, it is unclear whether the E2F7 expression is related to the immunotherapy efficacy in LUAD. Therefore, we conducted this study to investigate the clinical characteristics, function, and immunotherapy responsiveness of E2F7 expression, and to explore the potential of E2F7 as an immunotherapy response biomarker in LUAD. We analyzed the clinical characteristics and biological function of E2F7 expression based on data from the Cancer Genome Atlas and Gene Expression Omnibus database. In addition, we used single-cell sequencing data to analyze the immune regulatory effects of E2F7 in LUAD. Furthermore, we analyzed the immunotherapy response prediction ability of E2F7 expression based on the immunotherapy database. Compared to normal lung tissue, E2F7 was specifically overexpressed in LUAD, and its expression was associated with higher malignancy and poor efficacy. E2F7 high expression was an independent risk factor affecting the prognosis of LUAD. E2F7 was enriched in cell division and cell cycle functions. In addition, the expressions of immune checkpoints were correlated with the E2F7 expression. E2F7 was highly expressed in myeloid cells, and E2F7 highly expressed myeloid cells were associated with immune and inflammatory responses. Moreover, the expression level of E2F7 can effectively distinguish different immune therapy responses in LUAD patients. E2F7 was upregulated in LUAD, and high expression of E2F7 was associated with higher malignancy and poor efficacy. E2F7 high expression was an independent risk factor affecting the prognosis of LUAD. Moreover, E2F7 may exert its immunosuppressive effect by affecting the function of myeloid cells. These results indicated the potential role of E2F7 as a biomarker for predicting LUAD immunotherapy responses.

Enhanced Detection of Early Pulmonary Fibrosis Disease Using 68Ga-FAPI-LM3 PET.

Early detection of pulmonary fibrosis is a critical yet insufficiently met clinical necessity. This study evaluated the effectiveness of FAPI-LM3, a 68Ga-radiolabeled heterobivalent molecular probe that targets fibroblast activating protein (FAP) and somatostatin receptor 2 (SSTR2), in the early detection of pulmonary fibrosis, leveraging its potential for early disease identification. A bleomycin-induced early pulmonary fibrosis model was established in C57BL/6 mice for 7 days. FAP and SSTR2 expression levels were quantitatively assessed in human idiopathic pulmonary fibrosis lung tissue samples and bleomycin-treated mouse lung tissues by using western blotting, real-time quantitative PCR (RT-qPCR), and immunofluorescence techniques. The diagnostic performance of FAPI-LM3 was investigated by synthesizing monomeric radiotracers 68Ga-FAPI-46 and 68Ga-DOTA-LM3 alongside the heterobivalent probe 68Ga-FAPI-LM3. These imaging radiopharmaceuticals were used in small-animal PET to compare their uptake in fibrotic and normal lung tissues. Results indicated significant upregulation of FAP and SSTR2 at both RNA and protein levels in fibrotic lung tissues compared with that in normal controls. PET imaging demonstrated significantly enhanced uptake of the 68Ga-FAPI-LM3 probe in fibrotic lung tissues, with superior visual effects compared to monomeric tracers. At 60 min postinjection, early stage fibrotic tissues (day 7) demonstrated low-to-medium uptake of monomeric probes, including 68Ga-DOTA-LM3 (0.45 ± 0.04% ID/g) and 68Ga-FAPI-46 (0.78 ± 0.09% ID/g), whereas the uptake of the heterobivalent probe 68Ga-FAPI-LM3 (1.90 ± 0.10% ID/g) was significantly higher in fibrotic lesions than in normal lung tissue. Blockade experiments confirmed the specificity of 68Ga-FAPI-LM3 uptake, which was attributed to synergistic targeting of FAP and SSTR2. This study demonstrates the potential of 68Ga-FAPI-LM3 for early pulmonary fibrosis detection via molecular imaging, offering significant benefits over monomeric tracers 68Ga-FAPI-46 and 68Ga-DOTA-LM3. This strategy offers new possibilities for noninvasive and precise early detection of pulmonary fibrosis.

Non-Small Cell Lung Cancer Imaging Using a Phospholipase A2 Activatable Fluorophore.

Lung cancer, the most common cause of cancer-related death in the United States, requires advanced intraoperative detection methods to improve evaluation of surgical margins. In this study we employed DDAO-arachidonate (DDAO-A), a phospholipase A2 (PLA2) activatable fluorophore, designed for the specific optical identification of lung cancers in real-time during surgery. The in vitro fluorescence activation of DDAO-A by porcine sPLA2 was tested in various liposomal formulations, with 100 nm extruded EggPC showing the best overall characteristics. Extruded EggPC liposomes containing DDAO-A were tested for their stability under various storage conditions, demonstrating excellent stability for up to 4 weeks when stored at -20 °C or below. Cell studies using KLN 205 and LLC1 lung cancer cell lines showed DDAO-A activation was proportional to cell number. DDAO-A showed preferential activation by human recombinant cPLA2, an isoform highly specific to arachidonic acid-containing lipids, when compared to a control probe, DDAO palmitate (DDAO-P). In vivo studies with DBA/2 mice bearing KLN 205 lung tumors recapitulated these results, with preferential activation of DDAO-A relative to DDAO-P following intratumoral injection. Topical application of DDAO-A-containing liposomes to human (n = 10) and canine (n = 3) lung cancers ex vivo demonstrated the preferential activation of DDAO-A in tumor tissue relative to adjacent normal lung tissue, with fluorescent tumor-to-normal ratios (TNR) of up to 5.2:1. The combined results highlight DDAO-A as a promising candidate for clinical applications, showcasing its potential utility in intraoperative and back-table imaging and topical administration during lung cancer surgeries. By addressing the challenge of residual microscopic disease at resection margins and offering stability in liposomal formulations, DDAO-A emerges as a potentially valuable tool for advancing precision lung cancer surgery and improving curative resection rates.

Characteristics of mast cell infiltration in lung adenocarcinoma and its impact on prognosis

BackgroundThe role of mast cells in malignancies remains unclear, and there is no clear correlation between mast cells and tumor microvessels, tumor growth, or lung adenocarcinoma (LUAD) prognosis. This study aims to explore the association between mast cell density (MCD) and intratumoral microvessel density (MVD), clinicopathological parameters, and prognosis in LUAD, by evaluating mast cell infiltration characteristics and their prognostic significance.MethodsThis retrospective investigation involved 238 patients with LUAD undergoing complete resection. Tumor and normal lung tissue sections outside the tumor were immunohistochemically stained for MCD in the intratumoral and outside regions, respectively. CD34 polyclonal antibody was used to measure intratumoral MVD.ResultsIntratumoral regions of LUAD had a higher MCD (P < 0.001) than normal lung tissue. In the intratumoral region, MCD and CD34-MVD were positively correlated (r = 0.411, P < 0.001). Intratumoral MCD correlated with sex, smoking history, tumor differentiation, pathological subtype, and tumor size. Female sex (P = 0.012), no smoking history (P = 0.002), acinar predominant type (P = 0.012), and tumor size ≤ 3 cm (P = 0.009) were associated with a higher MCD, whereas poorly differentiated (P = 0.039) and solid/micropapillary predominant types (P = 0.001) were associated with a lower MCD. Higher intratumoral MCD exhibited a marginally improved overall survival, and individuals with higher MCD infiltration ratios (intratumoral MCD/outside the MCD) had higher disease-free and overall survival rates (log-rank P < 0.001). A high MCD infiltration ratio was associated with decreased risk of tumor progression and death following complete resection.ConclusionThe tumor microenvironment controls mast cell infiltration in LUAD, and patients with increased intratumoral mast cell infiltration have better prognosis.

New vision of HookEfficientNet deep neural network: Intelligent histopathological recognition system of non-small cell lung cancer

BackgroundEfficient and precise diagnosis of non-small cell lung cancer (NSCLC) is quite critical for subsequent targeted therapy and immunotherapy. Since the advent of whole slide images (WSIs), the transition from traditional histopathology to digital pathology has aroused the application of convolutional neural networks (CNNs) in histopathological recognition and diagnosis. HookNet can make full use of macroscopic and microscopic information for pathological diagnosis, but it cannot integrate other excellent CNN structures. The new version of HookEfficientNet is based on a combination of HookNet structure and EfficientNet that performs well in the recognition of general objects. Here, a high-precision artificial intelligence-guided histopathological recognition system was established by HookEfficientNet to provide a basis for the intelligent differential diagnosis of NSCLC. MethodsA total of 216 WSIs of lung adenocarcinoma (LUAD) and 192 WSIs of lung squamous cell carcinoma (LUSC) were recruited from the First Affiliated Hospital of Zhengzhou University. Deep learning methods based on HookEfficientNet, HookNet and EfficientNet B4–B6 were developed and compared with each other using area under the curve (AUC) and the Youden index. Temperature scaling was used to calibrate the heatmap and highlight the cancer region of interest. Four pathologists of different levels blindly reviewed 108 WSIs of LUAD and LUSC, and the diagnostic results were compared with the various deep learning models. ResultsThe HookEfficientNet model outperformed HookNet and EfficientNet B4–B6. After temperature scaling, the HookEfficientNet model achieved AUCs of 0.973, 0.980, and 0.989 and Youden index values of 0.863, 0.899, and 0.922 for LUAD, LUSC and normal lung tissue, respectively, in the testing set. The accuracy of the model was better than the average accuracy from experienced pathologists, and the model was superior to pathologists in the diagnosis of LUSC. ConclusionsHookEfficientNet can effectively recognize LUAD and LUSC with performance superior to that of senior pathologists, especially for LUSC. The model has great potential to facilitate the application of deep learning-assisted histopathological diagnosis for LUAD and LUSC in the future.