Construction and multi-omics validation of a five-gene ferroptosis-based model for predicting prognosis and therapy response in pancreatic ductal adenocarcinoma with immune landscape analysis.
A concise and externally validated five-gene ferroptosis-related prognostic signature was developed, which effectively stratifies survival outcomes in PDAC patients and reflects ferroptosis-associated alterations in tumor proliferation, metabolic reprogramming, immune evasion, and drug response.
Abstract
Background
Ferroptosis is an iron-dependent programmed cell death, which plays a complex role in pancreatic ductal adenocarcinoma (PDAC), regulating both tumor development and immune interaction. However, the clinical significance and potential molecular mechanism of ferroptosis in PDAC have not been fully clarified, which limits its application in treatment.
Methods
In order to identify ferroptosis-related genes (FRGs), we integrated transcriptome data from TCGA-PDAC and GTEx databases, as well as supplementary data from three GEO datasets (GSE62452, GSE78229 and GSE183795). We constructed a prognostic risk model by sequential analysis, which included univariate Cox regression, LASSO regression and multivariate Cox regression. Through Kaplan-Meier survival curve, time-specific ROC curve analysis and correlation study with clinicopathological features, we strictly evaluated the prediction accuracy and clinical relevance of this model. Multi-omics analyses included GO/KEGG/GSEA/GSVA pathway enrichment, CIBERSORT immune deconvolution, ESTIMATE scores, TIDE/ICB response prediction, tumor mutational burden (TMB) profiling, oncoPredict drug sensitivity estimation, single-cell RNA-seq (GSE212966), spatial transcriptome (GSM8452850), pseudotime trajectory, and HPA immunohistochemistry validation.
Results
A robust five-gene ferroptosis-related prognostic signature (BCAR3, GSK3B, STAT1, MAGED2, MYEOV) was established. Patients categorized into the high-risk group demonstrated a markedly inferior overall survival compared to those in the low-risk group across both the TCGA cohort (5-year AUC = 0.87) and three external validation cohorts (5-year AUC 0.804-0.833). High-risk tumors showed enrichment in mitotic spindle, PI3K-AKT-mTOR, G2M checkpoint, glycolysis, and p53 pathways, markedly elevated KRAS/TP53 mutation rates, higher TMB, an "inflammatory yet immunosuppressive" microenvironment (increased resting NK cells/plasma cells, decreased activated NK cells, upregulated checkpoints including CD44/HHLA2/LGALS9), and differential chemotherapy sensitivity (greater sensitivity to gemcitabine, erlotinib, gefitinib, trametinib in high-risk group). Single-cell and spatial analyses confirmed predominant expression in malignant ductal cells, with dynamic pseudotime-dependent patterns (especially GSK3B upregulation in late-stage cells) and enhanced MIF signaling-mediated crosstalk in high-score subpopulations. Protein-level heterogeneity was verified by HPA-IHC.
Conclusion
In this study, a concise and externally validated five-gene ferroptosis-related prognostic signature was developed, which effectively stratifies survival outcomes in PDAC patients and reflects ferroptosis-associated alterations in tumor proliferation, metabolic reprogramming, immune evasion, and drug response. This model provides a framework for mechanism-informed precision treatment strategies, such as ferroptosis induction combined with immunotherapy or risk-adapted chemotherapy, and may offer new insights into improving outcomes in this highly lethal malignancy.
A ferroptosis- and lipid metabolism-related prognostic signature is developed that accurately predicts survival outcomes and immune characteristics in CRC and CRY2 was identified as a critical regulator of tumor growth.
Yu Guo, Yong-Bo Zou, Min Wang· Annals medicus· 0 citations
A novel and potentially useful lysosomal ferroptosis-related prognostic risk model that effectively stratified PRAD patients by survival outcome and therapeutic response is presented, providing a valuable framework for personalized clinical decision-making.
Xudong Zhu, Xi-Xi Ji, Hao Liu et al.· Frontiers in Cell and Develo...· 0 citations
Background: Diabetic cardiomyopathy (DCM) is a serious cardiovascular complication specific to diabetes mellitus, with rising global prevalence. Ferroptosis, an iron-dependent form of regulated cell death driven by lethal lipid peroxidation, has been implicated in the pathogenesis of DCM. However, the key regulatory genes remain poorly characterized. This study aimed to identify and validate ferroptosis-related signature genes in DCM. Methods: Three murine transcriptomic datasets (GSE123975, GSE155377, and GSE210611) were retrieved from GEO and merged after batch correction. Differentially expressed genes were intersected with weighted gene co-expression network analysis disease-associated module genes and FerrDb ferroptosis annotations to define the ferroptosis-related differentially expressed gene candidate pool. LASSO regression and random forest selection then prioritized hub genes, defined operationally as candidates consistently prioritized by both machine-learning algorithms rather than by network-topological centrality. Classification performance was evaluated by ROC analysis and validated in two independent cohorts (GSE161931 and GSE274500). mMCPcounter estimated immune and stromal infiltration. ScRNA-seq (GSE290095) and spatial transcriptomic (GSE290094) profiling characterized cellular distribution, predicted cardiomyocyte network perturbations and tissue-level expression patterns. High-fat diet/streptozotocin (HFD/STZ)-induced DCM rat models provided experimental validation. Results: Acot1 and Txnip were identified as hub genes, with strong discriminatory performance in the discovery cohort (AUC = 1.000 and 0.988; in-sample estimates, n = 26) and independent external validation (AUC = 0.951 and 0.988). Immune profiling linked both genes inversely with vessel scores, and Txnip was also linked with eosinophils. Single-cell analysis localized Acot1 enrichment to cardiomyocytes and endothelial cells, while Txnip was broadly expressed across multiple cell types, with elevated levels in DCM. In silico knockout analysis predicted distinct cardiomyocyte network perturbation profiles for Acot1 and Txnip, and spatial transcriptomics revealed modest but disease-specific spatial associations between hub gene expression and ferroptosis driver scores (Acot1: rho = 0.123; Txnip: rho = 0.154). Both genes were significantly upregulated at mRNA and protein levels in HFD/STZ-induced DCM rats, with concurrent GPX4 depletion, ACSL4 accumulation, and FTH1 reduction consistent with ferroptosis activation. Conclusions: This study identifies Acot1 and Txnip as ferroptosis-related molecular signatures in DCM and provides multistep prioritization and validation spanning bulk transcriptomics, single-cell and spatial transcriptomics, and in vivo experimental verification, offering potential targets for ferroptosis-targeted therapeutic intervention.
Feng Zhou, Jia-Bin Zhou, Ling Zhang et al.· Current Issues in Molecular...· 0 citations
Diffuse large B-cell lymphoma (DLBCL) is biologically heterogeneous and is associated with variable clinical outcomes. We aimed to develop a tumor-associated macrophage (TAM)-related ferroptosis–glycolysis prognostic signature and to explore selected signature genes in preclinical models. This retrospective multi-cohort computational prognostic biomarker-development study integrated public single-cell and bulk transcriptomic datasets. GSE10846 was used for feature selection, model fitting, and parameter tuning, whereas GSE32918, GSE69051, and TCGA-DLBC were used as model-selection validation cohorts. Exploratory immune, genomic, and computational drug-sensitivity analyses were performed. Signature-gene expression was assessed in 27 archived DLBCL tissues, and GCLC and SLC1A5 were further examined in TAM-related preclinical models in vitro and in vivo. An 11-gene TAM-related ferroptosis–glycolysis signature (TAMFGS) was developed. The time-dependent AUCs (95% CIs) were 0.978 (0.966–0.990), 0.986 (0.976–0.995), and 0.982 (0.966–0.999) at 1, 3, and 5 years, respectively, in GSE10846; 0.622 (0.524–0.720), 0.608 (0.515–0.702), and 0.619 (0.524–0.715) in GSE32918; 0.516 (0.286–0.747), 0.741 (0.548–0.934), and 0.754 (0.516–0.993) at 1, 2, and 3 years, respectively, in GSE69051; and 0.929 (0.850–1.000), 0.674 (0.382–0.966), and 0.677 (0.402–0.952) at 1, 3, and 5 years, respectively, in TCGA-DLBC. In GSE10846, the continuous TAMFGS score remained associated with overall survival after adjustment for available covariates (HR, 1.094; 95% CI, 1.082–1.106; p < 0.001). Exploratory analyses identified associations between TAMFGS and immune-related transcriptomic estimates and computationally predicted drug sensitivity. GCLC or SLC1A5 knockdown was associated with ferroptosis-associated molecular changes and an M1-like inflammatory shift in the THP-1-derived TAM model and was associated with reduced DLBCL growth in preclinical models. In retrospective transcriptomic DLBCL cohorts, TAMFGS was associated with overall survival. GCLC and SLC1A5 emerged as TAM-related candidate genes that warrant further mechanistic and prospective validation.
Yingjun Wang, Lai Wei, Jie-Ting Wang et al.· European Journal of Medical...· 0 citations
Background Clear cell renal cell carcinoma (ccRCC) is metabolically primed for ferroptosis, yet the prognostic relevance and mechanistic contribution of ferroptosis-related genes remain incompletely defined. This study aimed to identify ferroptosis-associated biomarkers with prognostic value and to clarify their functional relevance in ccRCC progression. Methods We integrated single-cell RNA sequencing, bulk RNA-seq, spatial transcriptomics, and machine-learning-based feature selection to identify ferroptosis-related prognostic genes in ccRCC. A four-gene risk model and an integrated nomogram were constructed and evaluated in independent cohorts. PANX2 was prioritized for experimental validation using stable knockdown models, RNA sequencing, lipid peroxidation and iron probes, redox assays, Western blot, luciferase reporter assays, xenografts, and an immunocompetent murine renal carcinoma model. Results A four-gene prognostic signature (CA9, PVT1, RRM2, and PANX2) was identified and used to construct a risk model with consistent predictive performance in the training and external validation cohorts. Among these genes, PANX2 was predominantly enriched in epithelial tumor compartments and had not been functionally characterized in ccRCC. PANX2 knockdown inhibited ccRCC cell proliferation, reduced antioxidant capacity, increased intracellular Fe2+ accumulation and lipid peroxidation, and sensitized cells to erastin-induced ferroptotic death. Mechanistically, PANX2 loss was associated with reduced Akt/mTOR pathway activity and diminished SLC7A11 expression; rescue with an Akt activator or SLC7A11 overexpression attenuated ferroptosis-associated phenotypes. In an immunocompetent murine renal carcinoma model, PANX2 knockdown was accompanied by increased infiltration of CD45+ leukocytes, CD3+ T cells, and CD8+ T cells, supporting a potential link between PANX2-dependent ferroptosis resistance and the tumor immune contexture. Conclusions This study identifies PANX2 as a ccRCC-relevant suppressor of ferroptosis and supports the involvement of a PANX2-Akt/mTOR-SLC7A11-associated signaling axis in redox homeostasis and tumor progression. The ferroptosis-related prognostic model and nomogram may support risk stratification, while PANX2 represents a candidate therapeutic vulnerability that warrants further mechanistic and translational validation.
Xing-Lin Li, Yiqi Xiong, Ji-Yin Wang et al.· Frontiers in Immunology· 0 citations
OBJECTIVE
To explore the potential biological significance and diagnostic value of ferroptosis-related differentially expressed genes (DEGs) in liver fibrosis (LF).
STUDY DESIGN
Bioinformatic analysis of a publicly available microarray dataset. Place and Duration of the Study: Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hangzhou First People's Hospital, West Lake University School of Medicine, Hangzhou, China, from June 2 to July 2, 2024.
METHODOLOGY
Gene expression data from GSE139602 and ferroptosis-related genes from the GeneCards database were analysed to identify ferroptosis-related DEGs in LF. Functional enrichment, gene set enrichment, immune infiltration, protein-protein interaction, and ROC analyses were performed to identify key genes and their potential roles in LF.
RESULTS
Forty-one DEGs were associated with ferroptosis, and LF were identified. Among these, CEBPA, MYC, SCD, and SREBF1 were highlighted as important genes with significant diagnostic ability, confirmed through the receiver operating characteristic curve. Notably, CEBPA and SCD were implicated in fibrosis progression via their roles in lipid metabolism and oxidative stress modulation, which are essential components of ferroptosis.
CONCLUSION
The bioinformatics analysis identified four key ferroptosis-related genes-CEBPA, MYC, SCD, and SREBF1-as diagnostic biomarkers for LF. This study found the ferroptosis-related pathways as potential therapeutic targets for LF.
KEY WORDS
Biomedicine, Liver fibrosis, Ferroptosis, Differentially expressed genes.
Chao Wang· Journal of the College of Ph...· 0 citations
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