Aug 2026· Frontiers in Cell and Developmental Biology· Vol 14· 0 citations· 44 references
Medicine
TL;DR
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.
Abstract
Background Prostate adenocarcinoma (PRAD) is a leading reason of cancer-related death in men worldwide, yet reliable biomarkers for accurate risk stratification are lacking. This study sought to build and test a lysosomal ferroptosis-related prognostic risk model for PRAD. Methods This study merged single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing datasets. Differentially expressed genes (DEGs) from the TCGA-PRAD cohort were intersected with 39 lysosomal ferroptosis-related genes (LFRGs). Univariate Cox regression and random survival forest (RSF) algorithms were applied to build a prognostic risk model, which was validated in two separate cohorts (GSE70768; GSE70769). Immune infiltration, drug sensitivity, and single-cell transcriptomic analyses were subsequently performed. Finally, the expression and potential mechanism of hub genes were investigated in experimental samples. Results Seven candidate genes were identified, from which MMD and FTH1 were chosen to create a prognostic risk model. The model achieved AUC values of 0.90, 0.89, and 0.87 at 1-, 2-, and 3-year timepoints in TCGA-PRAD, with consistent performance across both validation cohorts. High-risk patients displayed an immunosuppressive microenvironment noted for enhanced myeloid-derived suppressor cells, regulatory T cells, upregulation of 23 immune checkpoint genes, higher TIDE scores, and increased tumor mutational burden (TMB). Drug sensitivity analysis identified differential responses to 3 agents after FDR correction. Single-cell analysis revealed myeloid-predominant expression of MMD and FTH1, with divergent pseudotime kinetics and enhanced KRAS, IL2-STAT5, and mTORC1 signaling, with MIF–CD74 as a key intercellular communication axis. The hub genes were validated in experimental samples and these findings suggest a potential therapeutic strategy combining FTH1 degradation with PD-L1 blockade, although the immunological mechanisms require further validation in immunocompetent models. Conclusion This study presented a novel and potentially useful lysosomal ferroptosis-related prognostic risk model that effectively stratified PRAD patients by survival outcome and therapeutic response, providing a valuable framework for personalized clinical decision-making.
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
Glioblastoma (GBM) is the most common primary intracranial malignancy in adults, characterized by poor survival and high mortality. Emerging evidence suggests that macrophage-associated programmed cell death (MacPCD) plays a critical role in GBM pathogenesis. However, the underlying mechanisms remain poorly understood. This study aimed to identify MacPCD-related prognostic genes in GBM and explore their functional roles.
Transcriptomic data from the GSE68848 dataset were integrated with Macrophage-associated programmed cell death-related genes (MacPCD-RGs) to identify differentially expressed genes (DEGs). Univariate Cox and LASSO regression analyses were performed using the TCGA-GBM training set to construct a prognostic risk model. Beyond prognostic stratification, we conducted a comprehensive multi-omic landscape analysis, including gene set enrichment analysis (GSEA), tumor microenvironment (TME) characterization, tumor mutational burden (TMB) assessment, immunotherapy response prediction, and drug sensitivity prediction. Finally, single-cell RNA sequencing (scRNA-seq) was employed to resolve microenvironmental heterogeneity, identify key cell types and elucidate intercellular communication and developmental trajectories.
Analysis of GSE68848 identified 902 DEGs, of which five intersected with MacPCD-RGs.
FN1
and
TIMP1
were subsequently identified as core prognostic markers. The risk model demonstrated superior predictive performance across the CGGA-325 and GSE83300 validation cohorts. Functional analysis linked the risk score to specific signaling pathways,
PTEN
mutations, infiltration of immune cell subsets (e.g. NKT cells) and sensitivity to Trametinib. Tumor-associated macrophages (TAMs) were identified as the key cell type, exhibiting intense interaction with pericytes and enrichment in fructose/mannose metabolism. Furthermore, pseudotime analysis revealed that
FN1
and
TIMP1
expression peaked during the initial stages of TAM differentiation.
This study identified
FN1
and
TIMP1
as pivotal MacPCD-related prognostic genes in GBM. The risk model based on these markers exhibits moderate predictive performance, offering a reliable tool for clinical prognosis and paving the way for personalized immunotherapy strategies.
Polyamine metabolism (PM) is linked to the progression and prognosis of several cancers, but the specific mechanism of Polyamine metabolism-related genes (PMRGs) in Prostate carcinoma (PCa) is not fully understood. This study aims to construct and validate a PMRG-based prognostic risk model for PCa via bioinformatics.
In this study, PCa-related datasets (TCGA-PRAD and GSE70769) were used. Candidate genes were identified by intersecting DEGs from differential expression evaluation in TCGA-PRAD dataset with PMRGs. Subsequently, selected genes underwent univariate Cox and LASSO analyses to identify prognostic markers, which was utilized to develop a risk model. This model was validated using GSE70769 dataset. Furthermore, GSEA, tumor microenvironment, drug sensitivity and single-cell RNA sequencing (scRNA-seq) analysis were performed.
Nine candidate genes were obtained by intersecting 6,668 DEGs and 59 PMRGs. Then, SRM, SAT1, ODC1, SMOX, PAOX, and OAZ3 were identified as prognostic genes, which were markedly up-regulated in the PCa samples compared to control samples. The risk model had a moderate predictive accuracy for the risk of developing PCa. Meanwhile, we also found that prognostic genes were associated with multiple immune factors. The differential immune cells showing significant positive correlations, and all prognostic genes except SAT1 being negatively correlated with most differential immune cells (such as immature dendritic cells). The response to various drugs was significantly different between the two risk cohorts, such as Lapatinib, Bleomycin, Pyrimethamine. Helper T cells and epithelial cells were identified as key cell types, the occurrence of PCa was found to induce alterations in their communication function, and trajectory analysis of these cells revealed that prognostic gene expression changed with differentiation.
The present study identified six prognostic genes (SRM, SAT1, ODC1, SMOX, PAOX, and OAZ3) related to polyamine metabolism in PCa, which may offer novel insights for the clinical management of patients with PCa.
F. Luo, Ya-Shen Wang, Zhi-Hua Zhang et al.· Discover Oncology· 0 citations
Clear cell renal cell carcinoma (ccRCC) exhibits pronounced molecular heterogeneity and variable clinical outcomes, complicating prognosis and treatment. While many prognostic models exist, signatures centered on ribosomal RNA (rRNA) processing remain underexplored. This study aimed to establish a prognostic signature associated with rRNA processing and to investigate the functional role of DDX47 in ccRCC. rRNA processing-related genes were curated from Gene Ontology. Prognostic candidates were identified via differential expression and Cox regression in the TCGA-KIRC cohort. A total of 101 machine-learning model combinations were evaluated, and the optimal approach was used to develop the RPPS, with its predictive performance further confirmed in the E-MTAB-1980 and GSE167573 datasets. Prognostic accuracy was evaluated by Kaplan-Meier, ROC, Cox regression, calibration, and decision curve analyses. Immune features, drug sensitivity, and immune escape potential were assessed. Single-cell datasets were integrated to compute an rRNA processing activity score (RPAS), followed by cell–cell communication and pseudotime analyses. DDX47 was knocked out in B cells via scTenifoldKnk, and the resulting perturbed pathways were investigated. DDX47 function was examined in 786-O and A498 cells via siRNA knockdown, Western blotting, EdU, and Transwell assays. The eight-gene RPPS effectively stratified patients into high- and low-risk groups, yielding significantly distinct survival outcomes across all cohorts. The Riskscore independently predicted overall survival and enhanced a nomogram incorporating Age and Stage. High-risk patients showed increased immune infiltration, higher TIDE scores, distinct mutation patterns, and lower predicted IC50 values for selected TKIs. RPAS-low tumor cells displayed enhanced SPP1-mediated communication. Given its high B-cell expression, virtual knockout of DDX47 via scTenifoldKnk revealed enrichment of perturbed genes in immune-related processes, suggesting that DDX47 may be involved in immune-related regulatory programs. High DDX47 expression predicted poor prognosis, and its knockdown suppressed ccRCC cell proliferation, migration, and invasion. The RPPS provides a potential tool for individualized prognostic assessment and molecular stratification in ccRCC. RPAS-defined tumor-cell states may contribute to ccRCC progression through distinct mechanisms, while DDX47 may serve as a candidate prognostic biomarker and warrants further mechanistic investigation in ccRCC.
Jian-Lin Liu, Heng-Xing Tan, Jia-Qing Yang et al.· BMC Cancer· 0 citations
The six-gene ubiquitination-related signature may provide a valuable reference for potential therapeutic targets and prognosis of BC, however, prospective and experimental validation is still required.
Zhi-Biao Li, Ting Yan, Chuxiang Hu et al.· Current Medicinal Chemistry· 0 citations
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