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.

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.

Background：The FGD family consists of six genes, namely FGD1/2/3/4/5/6. Their roles in lung adenocarcinoma have been unidentified. This research focused on determining the diagnostic efficacy, prognostic value, and immune-related functions of them in lung adenocarcinoma(LUAD). Methods：From the TCGA database, mRNA data for the FGD gene family and clinical data for the patients were obtained. Immunohistochemistry was performed to validate representative FGD gene’s expression. A relationship between the FGD genes and immune system molecules was examined using the TIMER and GEPIA databases, the ssGSEA and the MCPcounter methods. Clinical prognosis in LUAD were analyzed by searching for TCGA, KMplotter and GEPIA databases. The TIDE algorithm, TCIA, KMplotter, ROCplotter and ICBatlas databases were used to analyze the value of FGD2 in predicting the efficacy of immunotherapy. TIGER database was used to analyze single-cell RNA-sequencing data. The immune-related prognostic model was constructed using 3 machine learning algorithms: K-means clustering, LASSO regression, and WGCNA analysis. Results: All the six FGD genes’ protein and mRNA were aberrant expressed in the tissues of LUAD in contrast to healthy ones, and our external experiment confirmed FGD2’s expression pattern. Low expression of FGD2, 3, 5 resulted in a shorter OS time and were determined as independent prognostic factors via multivariate analyses. FGD2, 3, 5 were markedly linked to immune infiltration while FGD1, 4, 6 were not. Sc-RNA sequencing analysis indicating that FGD2, 3, 5 were mainly expressed in immunocytes. NSCLC patients with higher FGD2 may more responsive to ICB therapy. The functions of FGD2, 3, 5 and FGD1, 4, 6 in LUAD are heterogeneous, and patients can be separated in to two groups based on these six FGDs’ expression. A prognostic model constructed by immune-related DEGs between these two groups had good predictive value in one training set and 4 testing sets. Conclusions: FGD2, FGD3 and FGD5 can be used as diagnostic, prognostic, and immune-implicated biomarkesr for patients with LUAD and FGD2 may help to predict the ICB therapy efficacy. The immune-related prognostic model had satisfactory predictive value.

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.

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