Pharmacogenomic characterization of a uric acid metabolism-related signature associated with prognosis and drug sensitivity in gastric cancer.

A Glutathione Metabolism-Related Transcriptomic Signature for Prognostic Assessment and Biological Characterization of Lung Adenocarcinoma

Background Glutathione metabolism plays an important role in redox homeostasis, oxidative stress responses, and metabolic adaptation in cancer. However, its prognostic significance in lung adenocarcinoma (LUAD) remains incompletely understood. This study aimed to develop a glutathione metabolism-related prognostic signature and investigate its associations with immune characteristics, genomic alterations, and biological pathways in LUAD. Methods Transcriptomic, clinical, and somatic mutation data from TCGA-LUAD were analyzed, and GSE50081 was used as an independent validation cohort. Glutathione metabolism-related genes were identified through differential expression and Cox regression analyses, followed by least absolute shrinkage and selection operator (LASSO) Cox regression to construct a prognostic signature. Survival analysis, time-dependent receiver operating characteristic (ROC) analysis, Cox regression, immune infiltration analysis, Gene Set Enrichment Analysis (GSEA), mutation profiling, tumor mutation burden (TMB) analysis, and drug-sensitivity prediction were subsequently performed. Results A glutathione metabolism-related signature stratified patients into high- and low-risk groups with significantly different overall survival in both the TCGA training cohort and the GSE50081 validation cohort. The risk score remained an independent prognostic factor in multivariable Cox regression analysis. High-risk tumors exhibited reduced B-cell and dendritic-cell infiltration, enrichment of cell-cycle- and metabolism-related pathways, higher frequencies of TP53 and KEAP1 mutations, and elevated tumor mutation burden. Computational drug-sensitivity analysis identified differences in predicted responses to several therapeutic agents between risk groups. Conclusions The proposed glutathione metabolism-related signature demonstrated prognostic value in both training and validation cohorts and was associated with immune characteristics, pathway enrichment patterns, genomic alterations, and tumor mutation burden in LUAD. These findings provide additional insights into glutathione metabolism-related heterogeneity in LUAD and warrant further biological and clinical validation.

Integrative Analysis of Steroid Metabolism–Based Molecular Subtypes Reveals Prognostic Subtypes and Therapeutic Implications in Gastric Cancer

Objective This study aimed to identify steroid metabolism–related molecular subtypes, investigate the gene expression patterns of these subtypes, and construct a prognostic risk model as well as predict therapeutic response in gastric cancer. Methods We analyzed 410 TCGA-STAD and 483 GSE84437 gastric cancer samples. Unsupervised consensus clustering based on steroid metabolism–related genes identified molecular subtypes. Differential expression and functional enrichment analyses (GO, KEGG, GSEA) were performed. Prognostic genes were intersected with survival-associated genes, and a Lasso-Cox regression model was used to build a seven-gene risk score. Immune infiltration, tumor mutational burden (TMB), immune checkpoint expression, and drug sensitivity were evaluated. Results Two steroid metabolism–related gastric cancer subtypes were identified, with steroid-metabolism–poor prognosis subtype showing poorer overall survival. Differential expression analysis revealed 1,709 genes enriched in immune regulation, calcium signaling, cell adhesion, and extracellular matrix remodeling. Seven key genes (PRICKLE1, SERPINE1, APOD, RIMS1, GLP2R, CDH19, GRP) were used to construct a risk score, which correlated with advanced stage, steroid-metabolism–poor prognosis subtype subtype, and worse survival, and was an independent prognostic factor. High-risk and steroid-metabolism–poor prognosis subtype tumors displayed higher immune infiltration and immune scores, lower TMB, and upregulated immune checkpoint genes, indicating an immunosuppressive microenvironment. Drug sensitivity differed across subtypes and risk groups, suggesting potential implications for personalized therapy. Conclusions Steroid metabolism defines molecular heterogeneity, immune features, and prognosis in gastric cancer. The seven-gene risk model provides a reliable tool for survival prediction and may guide personalized therapeutic strategies.

Phosphoinositide metabolism-related prognostic genes and their prognostic value in cervical cancer: an investigation based on transcriptome, single-cell analyses and verification.

BACKGROUND Cervical cancer (CESC) is a major gynecological malignancy, and abnormalities in phosphoinositide metabolism (PPM) are closely linked to tumor progression. This study aimed to screen phosphoinositide metabolism-related genes (PPM-RGs) with prognostic value for CESC and explore their molecular mechanisms and clinical significance. METHODS CESC transcriptomic datasets and PPM-RGs were retrieved from public databases. Differentially expressed genes were intersected with PPM-RGs to obtain candidate genes. Prognostic genes were screened via univariate Cox regression and proportional hazards assumption test, and a risk model was constructed using random survival forest (RSF). Independent prognostic analysis and nomogram establishment were performed, followed by pathway enrichment, immune infiltration, somatic mutation and single-cell analyses to characterize the tumor microenvironment. Immunohistochemical staining was used to verify the protein expression of key genes in CESC tissues. RESULTS Twenty candidate genes were initially identified, and two protective prognostic genes VAC14 and INPP5K were finally determined. The risk model showed favorable predictive performance with area under the curve (AUC) over 0.7, and high-risk patients had poorer survival. The nomogram combining risk score and M stage presented high accuracy. The high-risk group exhibited distinct pathway enrichment, altered immune infiltration and low somatic mutation rates. Monocytes were identified as potential key cells with impaired communication and differentiation in CESC. Immunohistochemistry confirmed downregulated expression of the two genes in CESC epithelial cells. CONCLUSION VAC14 and INPP5K were PPM-related prognostic genes for CESC. Monocytes played critical roles in CESC progression. The risk model and nomogram aided prognosis prediction, providing new insights for CESC treatment.

Construction of a risk model based on folate metabolism-related genes to predict prognosis and immunological characteristics of stomach adenocarcinoma

Given the substantial disease burden of stomach adenocarcinoma (STAD)—the dominant and lethal subtype of gastric cancer—and the pivotal role of folate metabolism reprogramming in malignancy and treatment response, this study aimed to construct a prognostic model utilizing folate metabolism-related genes (FMRGs). Such a model is urgently required to supplement traditional TNM staging and to guide personalized precision medicine. Interrogation of TCGA and GEO transcriptomic profiles enabled the identification of differentially expressed FMRGs. These genes subsequently facilitated the construction of a prognostic signature via LASSO-Cox regression, which was then subjected to external validation. We further evaluated the clinical relevance of this risk signature by exploring its correlations with the tumor immune microenvironment, immunotherapy efficacy, and drug sensitivity, employing CIBERSORT and ssGSEA analytical frameworks. A five-gene prognostic model established from 220 FMRGs demonstrated moderate prognostic performance in identifying high-risk stomach adenocarcinoma patients with poorer survival. The high-risk group exhibited immunosuppressive microenvironments with stromal activation, while the low-risk group demonstrated “hot” tumor phenotypes characterized by higher immunogenicity and superior responses to immunotherapy and chemotherapy. Consequently, this model serves as a robust independent prognostic indicator. This five-gene folate metabolism signature shows moderate prognostic value in STAD and may help inform future investigations of the tumor immune microenvironment and therapeutic stratification. This study identified and validated a prognostic risk model consisting of five key genes, which can predict the survival outcomes of patients with STAD. The high-risk group exhibits an immunosuppressive and stroma-remodeling phenotype, while the low-risk group demonstrates an immunologically active state with stronger responsiveness to immunotherapy and chemotherapy. The model not only performs excellently in independent prognostic assessment but also predicts patient sensitivity to immunotherapy and various chemotherapeutic drugs. This study identified and validated a prognostic risk model consisting of five key genes, which can predict the survival outcomes of patients with STAD. The high-risk group exhibits an immunosuppressive and stroma-remodeling phenotype, while the low-risk group demonstrates an immunologically active state with stronger responsiveness to immunotherapy and chemotherapy. The model not only performs excellently in independent prognostic assessment but also predicts patient sensitivity to immunotherapy and various chemotherapeutic drugs.

A Prognostic Model Based on Adipokine-Related Genes for Hepatocellular Carcinoma: Assessment of Patient Outcomes and Tumor Microenvironment Characterization.

Bioinformatics-based identification of glycolysis-related signatures associated with drug resistance and prognosis in lung adenocarcinoma

It is suggested that glycolytic activity is closely associated with both drug resistance and clinical prognosis in LUAD, and the proposed integrative model holds promise for enhancing precision treatment decision-making through optimized risk assessment and rational selection of chemotherapeutic regimens.