A Rho GTPase-related gene signature predicts prognosis and reveals an immunosuppressive microenvironment in lung adenocarcinoma: an integrated analysis of bulk and single-cell RNA sequencing data

New insights into lysosomal ferroptosis-related prognostic signatures in prostate cancer: evidence from bulk and single-cell transcriptomic analysis

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.

Integrative analysis of bulk and single-cell RNA sequencing reveals PHLDA3 as a key regulator of Hypoxic TAMs and prognostic biomarker in HNSC.

BACKGROUND Head and neck squamous cell carcinoma (HNSC) is a highly aggressive malignancy characterized by a complex tumor microenvironment (TME). Despite advances in immunotherapy, the immunosuppressive microenvironment driven by metabolic reprogramming remains a critical factor contributing to treatment failure. The prognostic value of PHLDA3, a target gene of the p53 family, in HNSC and its specific mechanisms in shaping TME heterogeneity remain ill-defined. METHODS This study integrated large-scale transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, alongside single-cell RNA sequencing (scRNA-seq) data. A prognostic nomogram incorporating PHLDA3 and clinical characteristics was constructed using multivariate Cox regression analysis. Biological functions were assessed utilizing Weighted Gene Co-expression Network Analysis (WGCNA), Gene Set Variation Analysis (GSVA), and the drug sensitivity prediction algorithm, oncoPredict. At the single-cell resolution, algorithms including Seurat, CellChat, scMetabolism, and Monocle 2 were employed to comprehensively characterize cell type annotation, intercellular communication, metabolic activity scoring, and pseudotime differentiation trajectories. RESULTS PHLDA3 was significantly upregulated in HNSC tissues and strongly correlated with poor overall survival (OS). The PHLDA3-based nomogram demonstrated superior calibration and discrimination capabilities in both the training and validation cohorts. Genomic analysis revealed that high PHLDA3 expression was associated with a lower Tumor Mutation Burden (TMB) but an increased sensitivity to the PLK1 inhibitor, BI-2536. Single-cell analysis further unveiled that PHLDA3 expression was not ubiquitous; rather, it was specifically enriched in tumor-associated macrophage (TAM) subsets characterized by hypoxic and lipid metabolic signatures (Hypoxic TAMs and Lipid TAMs). These subpopulations exhibited transcriptomic signatures indicative of enhanced glycolysis and orchestrated immunosuppressive communication networks within the TME, predominantly via the SPP1-CD44 and MIF-CD74 signaling axes. Ultimately, through its specific enrichment in these metabolically reprogrammed TAM subsets, PHLDA3 intimately participates in remodeling an immunosuppressive TME, thereby facilitating tumor progression and immune evasion. CONCLUSIONS PHLDA3 serves as a robust prognostic biomarker for HNSC. Its elevated expression is indicative not only of a low TMB status but also of a critical association with metabolically reprogrammed SPP1+ TAM subpopulations. By potentially sustaining the survival and function of these specific TAMs under hypoxic conditions, PHLDA3 drives the formation of an immunosuppressive microenvironment, highlighting a promising therapeutic target for combined metabolic-immune strategies in HNSC.

Integrated Single-Cell and Bulk RNA-Sequencing Analysis Identifies an Aging-Related Gene Signature for Prognosis in Breast Cancer

Background: Cellular senescence exerts a complex influence on BRCA progression and TME remodeling. However, the specific roles of ASIGs in regulating the TME and determining patient outcomes remain unclear. Methods: Using TCGA (training), METABRIC (validation), and single-cell RNA-seq datasets, we systematically characterized ASIGs in BRCA. Prognostic ASIGs were identified to define molecular subtypes and construct a 17-gene LASSO-Cox risk model, which was integrated with clinical factors to develop a prognostic nomogram. Microenvironmental features and cell–cell communication networks were deconstructed using computational deconvolution and single-cell algorithms (SCISSOR and CellChat). Results: We established a robust 17-gene ASIG-based prognostic signature that effectively stratified BRCA patients into high- and low-risk groups and served as an independent prognostic predictor (HR = 3.94, p < 0.001). The nomogram accurately predicted 1-, 3-, and 5-year overall survival. Notably, the two risk groups exhibited strikingly distinct TME landscapes. The low-risk group was characterized by a coordinated, B cell-centric immune network, whereas the high-risk group displayed T cell exhaustion and immunosuppressive myeloid infiltration. Conclusions: The ASIG-based prognostic risk model is independent of traditional clinicopathological factors, providing a robust tool for patient risk stratification and offering biological insights into senescence-driven microenvironmental remodeling.

A neutrophil heterogeneity related gene signature predicts prognosis and reveals regulatory mechanism of tumor immune microenvironment in colorectal cancer

This study aims to construct a prognostic model based on neutrophil heterogeneity by integrating single-cell and bulk transcriptomic data. Additionally, it seeks to explore the features of the immune microenvironment across different risk subgroups to facilitate precise prognostic assessment and individualized immunotherapy for colorectal cancer. Colorectal cancer transcriptomic datasets from TCGA and GEO were utilized. Single-cell data underwent quality control, clustering, and cell annotation. Cell communication analysis characterized neutrophil signaling interactions. Candidate genes were identified through differential expression analysis and WGCNA, leading to the establishment and validation of a CCL15/CCDC154 dual-gene prognostic model. Differences in immune infiltration, immune checkpoint expression, and immune escape between high- and low-risk groups were analyzed, followed by molecular subtype clustering. Significant remodeling of the tumor microenvironment was observed, with neutrophils extensively participating in multicellular signaling networks. The dual-gene model demonstrated strong predictive performance and functioned as an independent prognostic indicator. The high-risk group exhibited a pronounced immunosuppressive phenotype, characterized by significant immune escape and upregulated inhibitory immune checkpoints. Notably, distinct immune and prognostic differences were identified among molecular subtypes. The dual-gene model based on neutrophil heterogeneity facilitates accurate prognostic stratification. The neutrophil-mediated immune microenvironment is closely linked to clinical outcomes, providing a theoretical basis for personalized treatment strategies in colorectal cancer.

An inflammation-associated five-gene expression signature stratifies survival and immune states in lung adenocarcinoma: an integrative public-cohort analysis

Integrative Bioinformatics and Experimental Validation Reveal that METTL9 Drives Lung Adenocarcinoma Progression via TGF-β/Smad-Mediated EMT Activation.

BACKGROUND Methyltransferase-like 9 (METTL9) has been implicated in tumor progression, yet its clinical significance, functional roles, and immunoregulatory functions in lung adenocarcinoma (LUAD) remain unclear. This study aimed to integrate multi-omics datasets and experimental evidence to systematically evaluate the expression profile, prognostic value, molecular mechanisms, and tumor microenvironment (TME)-related functions of METTL9 in LUAD. METHODS Transcriptome and clinical data for LUAD and METTL9 protein expression were obtained from public databases. Survival analysis was performed using Kaplan-Meier analysis and Cox proportional hazards models. METTL9-related differentially expressed genes (DEGs) were screened, followed by functional annotation and pathway enrichment analysis. A protein-protein interaction (PPI) network associated with METTL9 was constructed using STRING. METTL9 gene mutations and promoter methylation levels were analyzed via cBioPortal and UALCAN. Immune infiltration was evaluated through gene set variation analysis (GSVA) and the TIMER platform. The role of METTL9 depletion in regulating transforming growth factor-β (TGF-β)/Smad signaling, epithelial-mesenchymal transition (EMT), as well as the behavior of A549 and H1975 cells was investigated. RESULTS METTL9 was markedly elevated in LUAD, which was confirmed in external datasets and immunohistochemistry. Increased METTL9 expression showed a strong association with advanced TNM stage, unfavorable clinicopathological parameters, and poor survival outcomes in univariate analysis. Receiver operating characteristic (ROC) analysis indicated that METTL9 showed moderate diagnostic utility for LUAD, with an Area Under the Curve (AUC) of 0.704 (95% Confidence Interval [CI]: 0.650-0.758) in the The Cancer Genome Atlas (TCGA) dataset and 0.815 (95% CI: 0.758-0.872) in the GSE31210 dataset. Moreover, high METTL9 expression was significantly correlated with poor survival outcomes (Hazard Ratio [HR] = 1.349, 95% CI: 1.010-1.802, p = 0.043). METTL9 interacted with multiple oncogenic proteins in the PPI network and showed significant correlations with cuproptosis-related genes. Immune infiltration analysis further demonstrated that METTL9 expression showed significantly associated with immune cell infiltration and the tumor immune microenvironment in LUAD. METTL9 knockdown suppressed LUAD cell proliferation, migration, invasion, and EMT, while promoting apoptosis and reducing the expression of B-cell lymphoma 2 (Bcl-2), phosphorylated Smad2 (p-smad2), and phosphorylated Smad3 (p-smad3). CONCLUSION METTL9 is upregulated in LUAD and correlates with an unfavorable prognosis. Our results indicate that METTL9 may contribute to LUAD progression and EMT, potentially in relation to the TGF-β/Smad signaling pathway.