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Non-small cell lung cancer (NSCLC), half of which are lung adenocarcinoma (LUAD), is one of the most widely spread cancers in the world. Telomerase, which maintains telomere length and chromosomal integrity, enables cancer cells to avoid replicative senescence. When telomerase is inhibited, cancer cells' senescence began, preventing them from growing indefinitely. Cellular senescence and telomeres are intrinsically linked. As of yet, still laking a systematic study of the involvement of telomere-senescence related genes in lung adenocarcinoma development. In this study, myeloid cells were identified as the cell type which are most correlated with cellular senescence based on its highest telomere-related gene activity. GO, KEGG, GSEA and GSVA analyses were used to explore the biological function of telomere-senescence related genes in LUAD. The combined analysis of single-cell RNA-sequencing and bulk-RNA sequencing identified a gene signature composed of 14 genes which can accurately predict the prognosis of patients with LUAD. In one training and four validation sets, patients with higher Telomere Related Gene Signature (TRGS) had a worse prognosis than those with lower TRGS. Different TRGS patient groups showed varying degrees of immune cell infiltration, frequency of gene missense mutation, sensitivity to different drugs, and tumor mutation burden (TMB). Collectively, we developed a brand new signature composed of telomere-senescence related genes that can accurately predicts patients' prognosis in LUAD, which provides new insights for future research into the role of cellular senescence in LUAD. [ABSTRACT FROM AUTHOR]

Background: Lung adenocarcinoma (LUAD) represents a significant global health burden, necessitating advanced prognostic tools for improved patient management. RNA modifications (m6A, m1A, m5C, m7G), and disulfidptosis, a novel cell death mechanism, have emerged as promising biomarkers and therapeutic targets in cancer. Methods: We systematically compiled disulfidptosis-correlated genes and RNA modification-related genes from existing literature. A novel disulfidptosis-correlated m6A/m1A/m5C/m7G riskscore was computed using integrated machine-learning algorithms. Transcriptomic data from TCGA and GEO databases were downloaded analyzed. Single-cell RNA-sequencing data from the TISCH database was processed using the Seurat package. Genes' protein–protein interaction network was constructed using the String database. Functional phenotype analysis was performed using GSVA, ClusterProfiler, and IOBR packages. Consensus clustering divided patients into two distinct groups. Drug sensitivity predictions were obtained from the GDSC1 database and predicted using the Oncopredict package. Results: The disulfidptosis-correlated m6A/m1A/m5C/m7G risk score effectively stratified LUAD patients into prognostically distinct groups, demonstrating superior predictive accuracy compared to conventional clinical parameters. Patients in different risk groups exhibited significant molecular and clinical differences. Subsequent analyses identified two molecular subtypes associated with RNA modification and disulfidptosis, revealing differences in immune infiltration and prognosis. Functional enrichment analyses highlighted pathways involving RNA modification and disulfidptosis, underscoring their roles in LUAD pathogenesis. Single-cell analysis revealed distinct features between high- and low-risk status cells. Conclusion: This study introduces a novel disulfidptosis-correlated m6A/m1A/m5C/m7G risk score as a robust prognostic tool for LUAD, integrating insights from RNA modifications and cell death mechanisms. The risk score enhances prognostic stratification and identifies potential targets for personalized therapeutic strategies in LUAD. This comprehensive approach emphasizes the critical roles of RNA modifications and disulfidptosis in LUAD biology, paving the way for future research and clinical applications aimed at improving patient outcomes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this study, we report the use of transareolar uniportal video-assisted thoracoscopic surgery (VATS) in thoracic surgery for the treatment of male patients with peripheral pulmonary nodules. From February 2014 to September 2018, 46 male patients with small PPNs underwent VATS, of whom 41 underwent unilateral VATS, and five underwent bilateral VATS. All procedures were successfully performed. Patients received 1 year of outpatient follow-up postoperatively. The data indicated that the average operation times were 32.7 min (unilateral) and 58.6 min (bilateral). The mean length of the incision was 2.7 ± 0.3 cm, with a mean cosmetic score of 3.2 ± 0.7 at the time of discharge. All patients rapidly recovered consciousness postoperatively. The length of postoperative hospitalization (LOH) was 3.4 ± 1.4 days. There was no obvious surgical scarring on the chest wall, and no patients complained of postoperative pain at 6 months postoperatively. No recurrence or metastasis was found during the 1-year follow-up. Transareolar uniportal VATS for peripheral pulmonary nodules resulted in good cosmetic outcomes and high patient satisfaction. Transareolar uniportal VATS is a safe and effective therapeutic procedure for male patients with small PPNs and especially produces good cosmetic outcomes. [ABSTRACT FROM AUTHOR]