Machine-learning developed an iron, copper, and sulfur-metabolism associated signature predicts lung adenocarcinoma prognosis and therapy response.

BACKGROUND: Previous studies have largely neglected the role of sulfur metabolism in LUAD, and no study has combine iron, copper, and sulfur-metabolism associated genes together to create prognostic signatures. METHODS: This study encompasses 1564 LUAD patients, 1249 NSCLC patients, and over 10,000 patients with various cancer types from diverse cohorts. We employed the R package ConsensusClusterPlus to separate patients into different ICSM (Iron, Copper, and Sulfur-Metabolism) subtypes. Various machine-learning methods were utilized to develop the ICSMI. Enrichment analyses were conducted using ClusterProfiler and GSVA, while IOBR quantified immune cell infiltration. GISTIC2.0 and maftools were utilized for CNV and SNV data analysis. The Oncopredict package predicted drug information based on GDSC1. TIDE algorithm and cohorts GSE91061 and IMvigor210 evaluated patient response to immunotherapy. Single-cell data was processed using the Seurat package, AUCell package calculated cells geneset activity scores, and the Scissor algorithm identified ICSMI-associated cells. In vitro experiments was conducted to explore the role of ICSMRGs in LUAD. RESULTS: Unsupervised clustering identified two distinct ICSM subtypes of LUAD, each with unique clinical characteristics. The ICSMI, comprising 10 genes, was constructed using integrated machine-learning methods. Its prognostic power was validated in 10 independent datasets, revealing that LUAD patients with higher ICSMI levels had poorer prognoses. Furthermore, ICSMI demonstrated superior predictive abilities compared to 102 previously published signatures. A nomogram incorporating ICSMI and clinical features exhibited high predictive performance. ICSMI positively correlated with patients gene mutations, and integrated analysis of bulk and single-cell transcriptome data revealed its association with TME modulators. Cells representing the high-ICSMI phenotype exhibited more malignant features. LUAD patients with high ICSMI levels exhibited sensitivity to chemotherapy and targeted therapy but displayed resistance to immunotherapy. In a comprehensive analysis across various cancers, ICSMI retained significant prognostic value and emerged as a risk factor for the majority of cancer patients. CONCLUSIONS: ICSMI provides critical prognostic insights for LUAD patients, offering valuable insights into the tumor microenvironment and predicting treatment responsiveness.

Identification of a novel ADCC-related gene signature for predicting the prognosis and therapy response in lung adenocarcinoma.

BACKGROUND: Previous studies have largely neglected the role of ADCC in LUAD, and no study has systematically compiled ADCC-associated genes to create prognostic signatures. METHODS: In this study, 1564 LUAD patients, 2057 NSCLC patients, and more than 5000 patients with various cancer types from diverse cohorts were included. R package ConsensusClusterPlus was utilized to classify patients into different subtypes. A number of machine-learning algorithms were used to construct the ADCCRS. GSVA and ClusterProfiler were used for enrichment analyses, and IOBR was used to quantify immune cell infiltration level. GISTIC2.0 and maftools were used to analyze the CNV and SNV data. The Oncopredict package was used to predict drug information based on the GDSC1. Three immunotherapy cohorts were used to evaluate patient response to immunotherapy. The Seurat package was used to process single-cell data, the AUCell package was used to calculate cells' geneset activity scores, and the Scissor algorithm was used to identify ADCCRS-associated cells. RESULTS: Through unsupervised clustering, two distinct subtypes of LUAD were identified, each exhibiting distinct clinical characteristics. The ADCCRS, consisted of 16 genes, was constructed by integrated machine-learning methods. The prognostic power of ADCCRS was validated in 28 independent datasets. Further, ADCCRS shows better predictive abilities than 102 previously published signatures in predicting LUAD patients' survival. A nomogram incorporating ADCCRS and clinical features was constructed, demonstrating high predictive performance. ADCCRS positively correlates with patients' gene mutation, and integrated analysis of bulk and single-cell transcriptome data revealed the association of ADCCRS with TME modulators. Cells representing high-ADCCRS phenotype exhibited more malignant features. LUAD patients with high ADCCRS levels exhibited sensitivity to chemotherapy and targeted therapy, while displaying resistance to immunotherapy. In pan-cancer analysis, ADCCRS still exhibited significant prognostic value and was found to be a risk factor for most cancer patients. CONCLUSIONS: ADCCRS offers a critical prognostic insight for patients with LUAD, shedding light on the tumor microenvironment and forecasting treatment responsiveness.

Lung-specific exosomes for doxorubicin delivery in lung adenocarcinoma therapy.

Doxorubicin (DOX) could be utilized to treat lung adenocarcinoma (LUAD), while dose-limiting cardiotoxicity limits its clinical utilization. MDA-MB-231 cell-derived exosomes show lung-specific organotropism features. In this study, we aimed to explore the potential of MDA-MB-231 cell-derived exosomes in DOX specific delivery to the lung. MDA-MB-231 cell-derived exosomes were coincubated with to construct for the doxorubicin delivery system (D-EXO). Exosomes labeled with fluorescein isothiocyanate were incubated with A549 cells or 293T cells, and the engulf and the mean intensity of the fluorescence were detected with immunofluorescence and flow cytometry assay. Cell viability was detected with cell counting kit-8 (CCK-8), and cell migration was determined by scratch test. The protein expression was detected by Western blot assay. A549 cell line-derived xenograft mouse model was constructed to examine the treatment effect of D-EXO. MDA-MB-231 cell-derived exosomes could be specially taken up by A549 cells with diminished cell viability but not engulfed by 293T cells. D-EXO inhibited A549 cell migration, and upregulated the protein expression of caspase 3 and cleaved caspase 3 expression, while did not show any inhibition on 293T cells. In vivo orthotopic xenotransplantation model indicated that D-EXO inhibited tumor growth characterized by diminished tumor weight and improved survival rate. No significant change in body weight was observed after the D-EXO treatment. In conclusion, D-EXO proposed in this study could be utilized to treat LUAD with lung-specific delivery effects to improve the survival rate.

In-depth single-cell and bulk-RNA sequencing developed a NETosis-related gene signature affects non-small-cell lung cancer prognosis and tumor microenvironment: results from over 3,000 patients.

Background: Cell death caused by neutrophil extracellular traps (NETs) is known as NETosis. Despite the increasing importance of NETosis in cancer diagnosis and treatment, its role in Non-Small-Cell Lung Cancer (NSCLC) remains unclear. Methods: A total of 3298 NSCLC patients from different cohorts were included. The AUCell method was used to compute cells' NETosis scores from single-cell RNA-sequencing data. DEGs in sc-RNA dataset were obtained by the Seurat's "FindAllMarkers" function, and DEGs in bulk-RNA dataset were acquired by the DESeq2 package. ConsensusClusterPlus package was used to group patients into different NETosis subtypes, and the Enet algorithm was used to construct the NETosis-Related Riskscore (NETRS). Enrichment analyses were conducted using the GSVA and ClusterProfiler packages. Six distinct algorithms were utilized to evaluate patients' immune cell infiltration level. Patients' SNV and CNV data were analyzed by maftools and GISTIC2.0, respectively. Drug information was obtained from the GDSC1, and predicted by the Oncopredict package. Patient response to immunotherapy was evaluated by the TIDE algorithm in conjunction with the phs000452 immunotherapy cohort. Six NRGs' differential expression was verified using qRT-PCR and immunohistochemistry. Results: Among all cell types, neutrophils had the highest AUCell score. By Intersecting the DEGs between high and low NETosis classes, DEGs between normal and LUAD tissues, and prognostic related genes, 61 prognostic related NRGs were identified. Based on the 61 NRGs, all LUAD patients can be divided into two clusters, showing different prognostic and TME characteristics. Enet regression identified the NETRS composed of 18 NRGs. NETRS significantly associated with LUAD patients' clinical characteristics, and patients at different NETRS groups showed significant differences on prognosis, TME characteristics, immune-related molecules' expression levels, gene mutation frequencies, response to immunotherapy, and drug sensitivity. Besides, NETRS was more powerful than 20 published gene signatures in predicting LUAD patients' survival. Nine independent cohorts confirmed that NETRS is also valuable in predicting the prognosis of all NSCLC patients. Finally, six NRGs' expression was confirmed using three independent datasets, qRT-PCR and immunohistochemistry. Conclusion: NETRS can serves as a valuable prognostic indicator for patients with NSCLC, providing insights into the tumor microenvironment and predicting the response to cancer therapy.

Machine-learning and combined analysis of single-cell and bulk-RNA sequencing identified a DC gene signature to predict prognosis and immunotherapy response for patients with lung adenocarcinoma.

BACKGROUND: Innate immune effectors, dendritic cells (DCs), influence cancer prognosis and immunotherapy significantly. As such, dendritic cells are important in killing tumors and influencing tumor microenvironment, whereas their roles in lung adenocarcinoma (LUAD) are largely unknown. METHODS: In this study, 1658 LUAD patients from different cohorts were included. In addition, 724 cancer patients who received immunotherapy were also included. To identify DC marker genes in LUAD, we used single-cell RNAsequencing data for analysis and determined 83 genes as DC marker genes. Following that, integrative machine learning procedure was developed to construct a signature for DC marker genes. RESULTS: Using TCGA bulk-RNA sequencing data as the training set, we developed a signature consisting of seven genes and classified patients by their risk status. Another six independent cohorts demonstrated the signature' s prognostic power, and multivariate analysis demonstrated it was an independent prognostic factor. LUAD patients in the high-risk group displayed more advanced features, discriminatory immune-cell infiltrations and immunosuppressive states. Cell-cell communication analysis indicates that tumor cells with lower risk scores communicate more actively with the tumor microenvironment. Eight independent immunotherapy cohorts revealed that patients with low-risk had better immunotherapy responses. Drug sensitivity analysis indicated that targeted therapy agents exhibited greater sensitivity to low-risk patients, while chemotherapy agents displayed greater sensitivity to high-risk patients. In vitro experiments confirmed that CTSH is a novel protective factor for LUAD. CONCLUSIONS: An unique signature based on DC marker genes that is highly predictive of LUAD patients' prognosis and response to immunotherapy. CTSH is a new biomarker for LUAD.

SiO2/hyaluronic acid nanoparticles carry CaO2, DOX and p53 plasmid to effectively achieve ion interference/chemical/gene multimodal therapy of lung cancer.

Monotherapy of lung cancer shows limited therapeutic effects due to its poorly targeted enrichment and low bioavailability. Using nanomaterials as carriers to form drug delivery systems has become a popular method to improve the targeting of anticancer drug therapy and patients' safety. However, the uniformity of the loaded drugs and the unsatisfactory effects are still the bottleneck in this field up to now. This study aims to construct a novel nanocomposite carrying 3 different types of anticancer drugs to enhance treatment efficacy. Herein, mesoporous silica (MSN) with high loading rate was constructed by dilute sulfuric acid thermal etching as the framework. Hyaluronic acid (HA) was loaded with CaO2, p53 and DOX to construct nanoparticle complexes-SiO2@CaO2@DOX@P53-HA. First, MSN was proved to be a porous sorbent with a mesoporous structure through BET analysis. The images obtained from the uptake experiment clearly show the gradual enrichment of the DOX and Ca2+ within the target cell. For in vitro experiments, the pro-apoptotic effects of SiO2@CaO2@DOX@P53-HA significantly increased compared to that of the single-agent group at different time points. Furthermore, in the tumor-bearing mouse experiment, the tumor volume was remarkably inhibited in the SiO2@CaO2@DOX@P53-HA group compared to that in the single-agent group. By observing the pathological sections of the euthanized mice, it is obvious that the tissues of the mice treated with the nanoparticles were more intact. Based on these beneficial results, it is believed that multimodal therapy is a meaningful treatment strategy for lung cancer.

Protein Tyrosine Phosphatase Receptor Type R (PTPRR) Reduces AChR Clustering by Dephosphorylating MuSK.

Neuromuscular junction (NMJ) formation and maintenance depend on the proper localization and concentration of various molecules at synaptic contact sites. Acetylcholine receptor (AChR) clustering on the postsynaptic membrane is a cardinal event in NMJ formation. Muscle-specific tyrosine kinase (MuSK), which functions depending on its phosphorylation, plays an essential role in AChR clustering. In the present study, we used plasmid-based biochemical screening and determined that protein tyrosine phosphatase receptor type R (PTPRR) is responsible for dephosphorylating MuSK on tyrosine residue 754. Furthermore, we showed that PTPRR significantly reduced MuSK-dependent AChR clustering in C2C12 myotubes. Collectively, these data illustrate a negative regulation function of PTPRR in AChR clustering.

Immune-Related Molecular Profiling of Thymoma With Myasthenia Gravis.

Background: Approximately 50% of thymoma patients also show myasthenia gravis (MG), which is an autoimmune disease; however, the pathogenesis of MG-associated thymoma remains elusive. Our aim was to investigate immune-related lncRNA profiles of a set of candidate genes for better understanding of the molecular mechanism underlying the pathogenesis of thymoma with or without MG. Methods: Molecular profiles of thymoma with or without MG were downloaded from The Cancer Genome Atlas, and Pearson's correlation analysis was performed to identify immune-related lncRNAs. T test was used to examine the differential expression and differential methylation between thymoma patients with or without MG. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed to predict the function of target genes of immune-related lncRNAs. Results: Analyses of the 87 thymoma samples with complete MG information revealed that 205 mRNAs and 56 lncRNAs showed up-regulated expression in thymoma with MG patients, while 458 mRNAs and 84 lncRNAs showed down-regulated expression. The methylation level of three immune-related lncRNAs (AP000787.1, AC004943.1, WT1-AS, FOXG1-AS1) was significantly decreased in thymoma tissues, and the methylation level of these immune-related lncRNAs (WT1-AS: Cor = 0.368, p < 0.001; FOXG1-AS1: Cor = 0.288, p < 0.01; AC004943.1: Cor = -0.236, p < 0.05) correlated with their expression. GO and KEGG pathway analysis revealed that targets of the immune-related lncRNA FOXG1-AS1 were enriched in small GTPase binding and herpes simplex virus 1 infection. Transcription coregulator activity and cell cycle were the most enriched pathways for targets of lncRNA AC004943.1. LncRNA WT1-AS targets were most enriched in actin binding and axon guidance. Conclusion: Our results revealed the immune-related molecular profiling of thymoma with MG and without MG and identified key pathways involved in the underlying molecular mechanism of thymoma-related MG. These findings provide insights for further research of potential markers for thymoma-related MG.