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Abstract 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.

BackgroundCell 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.MethodsA 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.ResultsAmong 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.ConclusionNETRS can serves as a valuable prognostic indicator for patients with NSCLC, providing insights into the tumor microenvironment and predicting the response to cancer therapy.

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.