ArticleFrontiers in immunology2023
Elaboration and validation of a prognostic signature associated with disulfidoptosis in lung adenocarcinoma, consolidated with integration of single-cell RNA sequencing and bulk RNA sequencing techniques.
Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 20 citations in OpenAlex.
- The emerging roles of disulfidptosis in cancer.Apoptosis : an international journal on programmed cell death · 2026Review
- Single-cell and machine learning reveal ROS-associated heterogeneity in hepatocellular carcinoma for precision medicine.BMC cancer · 2026Article
- SMARCA4 activation engages FOSL1 to drive enhancer reprogramming and tumorigenic phenotypes in SMARCA4-deficient LUAD cells.Cell death discovery · 2026Article
- IGSF9 drives malignant transformation and predicts early-stage lung adenocarcinoma in integrated transcriptomic analyses.Discover oncology · 2026Article
- Silencing ZIC5 suppresses glycolysis and promotes disulfidptosis in lung adenocarcinoma cells.Cancer biology & therapy · 2025Article
- Construction and validation of an EGFR-related risk signature identifiedTranslational cancer research · 2025Article
- A lactate related signature for predicting prognosis and tumor microenvironment in lung adenocarcinoma.Discover oncology · 2025Article
- Prognostic model of lung adenocarcinoma based on disulfidptosis-related genes and analysis of in vitro cell experiments for PPP1R14B in the model.Biology direct · 2025Article
- Disulfidptosis in tumor progression.Cell death discovery · 2025Review
- Development and validation of M2 macrophage-related genes in a prognostic model of lung adenocarcinoma based on bulk RNA and ScRNA datasets.Discover oncology · 2025Article
- Identification of PANoptosis Subtypes to Assess the Prognosis and Immune Microenvironment of Lung Adenocarcinoma Patients: A Bioinformatics Combined Machine Learning Study.Current cancer drug targets · 2025Article
- Bioinformatics analysis of genes associated with disulfidptosis in spinal cord injury.PloS one · 2025Article
- Tumor heterogeneity assessment using single-cell RNA sequencing (scRNA-seq): applications in lung cancer for diagnosis and treatment.Frontiers in immunology · 2025Review
- Identification of a disulfidptosis-related genes signature for diagnostic and immune infiltration characteristics in endometriosis.Scientific reports · 2024Article
- Exploring a new signature for lung adenocarcinoma: analyzing cuproptosis-related genes through Integrated single-cell and bulk RNA sequencing.Discover oncology · 2024Article
- Machine learning-based integration of CD8 T cell-related gene signatures for comprehensive prognostic assessment in lung adenocarcinoma.Translational cancer research · 2024Article
- Development and evaluation of a disulfidoptosis-related lncRNA index for prognostication in clear cell renal cell carcinoma.Heliyon · 2024Article
- A disulfidptosis-related lncRNAs cluster to forecast the prognosis and immune landscapes of ovarian cancer.Frontiers in genetics · 2024Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Lung adenocarcinoma (LUAD), the predominant subtype of non-small cell lung cancer (NSCLC), remains a pervasive global public health concern. Disulfidoptosis, a nascent form of regulated cell death (RCD), presents an emerging field of inquiry. Currently, investigations into disulfidoptosis are in their initial stages. Our undertaking sought to integrate single-cell RNA sequencing (scRNA-seq) in conjunction with traditional bulk RNA sequencing (bulk RNA-seq) methodologies, with the objective of delineating genes associated with disulfidoptosis and subsequently prognosticating the clinical outcomes of LUAD patients. Methods: Initially, we conducted an in-depth examination of the cellular composition disparities existing between LUAD and normal samples using scRNA-seq data sourced from GSE149655. Simultaneously, we scrutinized the expression patterns of disulfidoptosis-associated gene sets across diverse cell types. Subsequently, leveraging the bulk RNA-seq data, we formulated disulfidoptosis-related prognostic risk signatures (DRPS) employing LASSO-Cox regression. This was accomplished by focusing on genes implicated in disulfidoptosis that exhibited differential expression within endothelial cells (ECs). Sequentially, the robustness and precision of the DRPS model were rigorously verified through both internal and external validation datasets. In parallel, we executed single-cell trajectory analysis to delve into the differentiation dynamics of ECs. Concluding our study, we undertook a comprehensive investigation encompassing various facets. These included comparative assessments of enrichment pathways, clinicopathological parameters, immune cell abundance, immune response-associated genes, impacts of immunotherapy, and drug predictions among distinct risk cohorts. Results: The scrutiny of scRNA-seq data underscored discernible disparities in cellular composition between LUAD and normal samples. Furthermore, disulfidoptosis-associated genes exhibited marked discrepancies within endothelial cells (ECs). Consequently, we formulated the Disulfidoptosis-Related Prognostic Signature (DRPS) to facilitate prognostic prediction. The prognostic nomogram based on the risk score effectively demonstrated DRPS's robust capacity to prognosticate survival outcomes. This assertion was corroborated by rigorous assessments utilizing both internal and external validation sets, thus affirming the commendable predictive accuracy and enduring stability of DRPS. Functional enrichment analysis shed light on the significant correlation of DRPS with pathways intrinsic to the cell cycle. Subsequent analysis unveiled correlations between DRPS and gene mutations characteristic of LUAD, as well as indications of an immunosuppressive status. Through drug prediction, we explored potential therapeutic agents for low-risk patients. Concluding our investigation, qRT-PCR experiments confirmed the heightened expression levels of EPHX1, LDHA, SHC1, MYO6, and TLE1 in lung cancer cell lines.
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