Transcriptome-Based Six-Gene Fatty Acid Metabolism Signature for Prognosis and Predicted Immunotherapy Response in Lung Adenocarcinoma: Cross-Population Validation

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

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

A robust prognostic model based on four RhoGTPase-related prognostic genes was established, effectively stratifying LUAD patients and provides valuable insights into the heterogeneity of LUAD and has the potential to inform personalized therapeutic strategies.

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.

Sodium Overload-Related Molecular Subtypes and a Four-Gene Prognostic Signature Predict Survival, Immune Landscape, and Therapeutic Response in Acute Myeloid Leukemia.

Sodium overload has recently emerged as a critical metabolic stressor involved in cancer progression; however, its molecular characteristics and clinical relevance in acute myeloid leukemia (AML) remain unexplored. RNA-seq data sets, clinical annotations, and mutational profiles of AML patients were annotations from The Cancer Genome Atlas and integrated with Genotype-Tissue Expression normal samples. Sodium overload-related genes (SORGs) were obtained from GeneCards. Differentially expressed SORGs (DESORGs) screened by applying the limma statistical model, followed by univariate Cox proportional hazards regression, consensus clustering, functional enrichment, immune infiltration analysis, and pathway evaluation. A prognostic signature was developed through least absolute shrinkage and selection operator regression followed by multivariate Cox modeling. The model's performance was further verified in two external GEO data sets (GSE71014 and GSE37642). Nomogram construction, subgroup analysis, tumor mutational burden (TMB) assessment, drug sensitivity prediction, transcription factor (TF) analysis, and competing endogenous RNA (ceRNA) network analyses were also performed. A total of 57 DESORGs were identified, and 2 sodium overload-related molecular subtypes exhibited distinct survival, immune infiltration, and inflammatory pathway activation. A robust four-gene signature (DOCK1, GABRE, HTR7, ACSM1) stratified patients into high- and low-risk categories with significantly different survival across training and validation cohorts. High-risk patients displayed increased immune infiltration, higher TMB, reduced sensitivity to multiple chemotherapeutic drugs, and inferior predicted response to PD-L1 blockade. TF and ceRNA networks revealed multilayered transcriptional and post-transcriptional regulation of the signature genes. This study identifies sodium overload-related molecular heterogeneity in AML and establishes a validated four-gene prognostic signature that integrates genomic, immunologic, and therapeutic features, offering potential utility for personalized risk assessment and treatment optimization.

Construction and Validation of a Ubiquitination-Related Gene Signature Based on WGCNA and Single-cell RNA Sequencing in Bladder Cancer.

BACKGROUND The potential pathogenesis and reliable biomarkers of Bladder cancer (BC) remain to be explored. Ubiquitination plays a crucial role in BC progression, warranting the necessity to develop prognostic gene signatures. METHODS By integrating weighted gene co-expression network analysis (WGCNA) and single-cell RNA sequencing (scRNA-seq), we identified the genes related to the single-- cell ubiquitination activity. A prognostic gene signature was established using univariate Cox analysis, LASSO analysis, and multivariate Cox analysis. BC patients were stratified into high-risk and low-risk groups according to the median risk score. The nomogram was constructed and validated. Potential biological processes of common ubiquitination-related genes anisk groups were explored. The immune infiltration, immunotherapy response, and drug sensitivity were evaluated. RESULTS A six-gene signature comprising CAPG, EMP3, GMFG, HLA-B, ID1, and TNFRSF12A stratified patients into high-risk and low-risk groups with significantly different overall survival. In the training group, the area under the curve was 0.695 at 1 year, 0.725 at 3 years, and 0.739 at 5 years. The high-risk group generally showed worse overall survival compared to the low-risk group. The nomogram model showed satisfactory predictive performance. Significant differences in immune cell proportions were observed in different risk groups. The low-risk group exhibited higher Immune scores and Stromal scores and showed better survival outcomes following PD-L1 immunotherapy. Doxorubicin, Gemcitabine, Methotrexate, and Vinblastine exhibited significantly lower IC50 values in the low-risk group. CONCLUSION 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.

The role of mitochondrial energy metabolism in drug resistance and prognosis of lung adenocarcinoma: a multi-omics and machine learning strategy for predictive and personalized therapy

Background As the leading histological form of lung cancer, lung adenocarcinoma (LUAD) displays considerable intratumoral heterogeneity, frequent therapeutic resistance, and an unfavorable clinical outcome. Although rewiring of mitochondrial energy metabolism is known to drive tumor progression and treatment failure, a comprehensive understanding of its dual role in LUAD drug resistance and prognosis has yet to be established. Here, we built a robust predictive signature that integrates mitochondrial metabolism with drug resistance through multi-omics integration and machine learning frameworks. Methods We explored single-cell RNA sequencing profiles together with TCGA-LUAD transcriptomic data. Weighted gene co-expression network analysis (WGCNA) was applied to extract gene modules linked to mitochondrial-related genes (MRGs) and drug resistance-related genes (DRGs). From these, a five-gene (KLF4, KLF10, CAT, ALDOA, HLA-DRA) prognostic classifier, designated MDrisk, was formulated using LASSO-Cox regression and 101 combinations of 10 machine learning algorithms. Results The MDrisk model demonstrated reliable and precise prognostic capacity across training, internal test, and external GEO cohorts, serving as an independent risk factor. Elevated MDrisk scores correlated with an immunosuppressive microenvironment, higher tumor mutational burden, distinct copy-number alteration profiles, and decreased drug sensitivity in computational predictions. In vitro experiments further validated that silencing ALDOA—a central component of the signature—suppressed the proliferation, migration, and invasive capacity of LUAD cells. Conclusion The MDrisk signature derived from mitochondrial energy metabolism and drug resistance may be useful for distinguishing prognosis, immune contexture, and computationally inferred drug susceptibility in LUAD. It may offer a tool for further exploration of individualized therapy and sheds light on the interplay between metabolic dysregulation and antitumor immunity.