Aug 2026· Cancer Informatics· Vol 25· 0 citations· 17 references
Medicine
TL;DR
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.
Abstract
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.
Background and Aims Dysregulated lipid metabolism contributes to hepatocellular carcinoma (HCC) progression, but the prognostic value and mechanistic roles of lipid metabolism-related long noncoding RNAs (LRLs) remain insufficiently characterized. This study aimed to construct and validate an LRL-based prognostic model and to investigate the biological function and metabolic mechanism of AC026412.3 in HCC. Methods Transcriptomic and clinical data from the The Cancer Genome Atlas Liver Hepatocellular Carcinoma cohort were analyzed to identify LRLs based on their correlation with curated lipid metabolism genes. Differential expression, univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox analyses were performed to construct a prognostic signature, which was evaluated using Kaplan–Meier survival and time-dependent receiver operating characteristic (ROC) analyses. Functional enrichment analyses Gene Ontology [GO], Kyoto Encyclopedia of Genes and Genomes [KEGG] and gene set enrichment analysis [GSEA], mutation profiling, tumor mutational burden, immune infiltration estimation, and consensus clustering were applied to characterize associated features. A key LRL was identified through integrated bioinformatic screening and prioritization. Its biological role was assessed by quantitative reverse transcription polymerase chain reactionq (RT-PCR), western blotting, BODIPY staining, colony formation, Transwell assays, and xenograft models. RNA sequencing followed by pathway enrichment analysis was conducted to explore underlying mechanisms. Results A three-LRL signature (AL031985.3, NRAV, and AC026412.3) stratified HCC patients into distinct risk groups with significantly different survival outcomes and demonstrated independent prognostic value. AC026412.3 was markedly upregulated in HCC and associated with poor prognosis. Functional assays demonstrated that AC026412.3 promoted proliferation, invasion, and tumor growth while reducing lipid accumulation. Mechanistically, AC026412.3 upregulated solute carrier family 22 member 5 (SLC22A5), enhanced fatty acid β-oxidation, and increased adenosine triphosphate (ATP) production, thereby driving metabolic reprogramming. Conclusions This study establishes a robust LRL-based prognostic model and identifies AC026412.3 as a key regulator of lipid metabolic reprogramming via the SLC22A5–fatty acid β-oxidation axis, highlighting its potential as a biomarker and therapeutic target in HCC.
Li-Xin Liu, Yu-Hao Fan, Hao Zou et al.· Journal of Clinical and Tran...· 0 citations
Background Cervical cancer (CC) remains one of the most prevalent malignancies in the female reproductive system. Methionine metabolism (MM) plays a pivotal role in various biological processes and has been implicated in cancer progression. However, its mechanisms in CC remain unclear. Methods Transcriptomic data from 305 patients in The Cancer Genome Atlas (TCGA) (training cohort) and 299 patients from the Gene Expression Omnibus (GEO) (GSE44001) (external validation cohort) were analyzed for differentially expressed MM-related genes (MM-RGs). Prognostic MM-RGs were identified using Cox regression, proportional hazards testing, and Least Absolute Shrinkage and Selection Operator (LASSO) regression. A risk model was constructed and validated. Functional enrichment (Gene Set Enrichment Analysis/Gene Set Variation Analysis (GSEA/GSVA)), in-silico immune infiltration (CIBERSORT) and drug sensitivity (oncoPredict) analyses were performed. Five genes were validated by Quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR) on clinical samples. Results Eight MM-RGs (MTHFD1, SMYD2, MSRB3, MTR, ENOPH1, DNMT3B, SLC38A7, PEMT) were identified as prognostic genes. A robust risk score model was developed, stratifying patients into high- and low-risk groups with significant differences in survival outcomes. Functional enrichment revealed pathways such as ECM–receptor interaction and focal adhesion. Immune analysis indicated altered infiltration of Tregs and mast cells. In-silico drug sensitivity analysis predicted 57 agents with significantly different IC50 values between the high- and low-risk groups (p < 0.05). Notably, agents such as Cediranib and BI-2536 exhibited markedly lower IC50 values in the high-risk cohort, suggesting their potential efficacy for advanced-stage treatment. qRT-PCR preliminarily indicated the overexpression of four genes in CC tissues within a small clinical cohort. Conclusion This study establishes a novel MM-based prognostic model for CC and based on in-silico predictions suggests potential therapeutic targets through comprehensive transcriptomic and experimental validation.
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.
Li-Nen Li, Hui-Ling Zhu, Guangjun Gao et al.· Cancer Informatics· 0 citations
A robust four-gene signature stratified patients into high- and low-risk categories with significantly different survival across training and validation cohorts, offering potential utility for personalized risk assessment and treatment optimization.
Yuan Wang, Yi Yang· Molecular Carcinogenesis· 0 citations
Colorectal cancer (CRC) exhibits marked molecular and microenvironmental heterogeneity, and the TNM staging system alone does not fully explain differences in prognosis or treatment response. Neutrophil extracellular traps (NETs) and oxidative stress participate in inflammatory remodeling, immune regulation, and tumor progression in CRC. However, transcriptomic studies integrating NET-related programs with oxidative stress-associated signaling for molecular stratification and prognostic modeling remain limited. Transcriptome and clinical data from TCGA-COADREAD (training set, 449 tumor and 39 normal samples) and GSE39582 (validation set, 566 tumor and 19 non-tumor samples) were analyzed. Candidate NET-related and oxidative stress-associated genes were collected from GeneCards, Gene Ontology, and published NETosis signatures, and were further restricted by differential expression analysis (|log2FC| > 0.585, adjusted P < .05) and univariate Cox analysis (P < .05). Consensus clustering, survival analysis, immune infiltration analysis, mutation analysis, and oncoPredict-based drug sensitivity modeling were performed. A multigene prognostic model was constructed using LASSO-Cox regression and evaluated in both cohorts. Two molecular subtypes were identified. C1 showed a better prognosis, higher tumor mutational burden, higher immune checkpoint and immunogenicity scores, and an immune-active phenotype. C2 showed poorer survival, enrichment of NET formation and inflammatory pathways, lower relative neutrophil infiltration by CIBERSORT, and predicted sensitivity to JAK, Wnt/β-catenin, proteasome, and cell-cycle checkpoint inhibitors. The prognostic model separated high- and low-risk patients in both the training and validation cohorts, with 1-, 3-, and 5-year area under the curves of 0.698, 0.711, and 0.645 in TCGA-COADREAD and 0.542, 0.578, and 0.569 in GSE39582. This study proposes NET-related CRC molecular subtypes with oxidative stress-associated biological context and develops a transcriptomic risk model with external validation. The findings suggest subtype-specific immune and therapeutic features, while prospective clinical and functional validation is required before clinical translation.
Yi Wei, Weijian Chu, Chunhui Rao et al.· Medicine· 0 citations
Findings indicate telomere maintenance-related gene signature could serve as a preliminary auxiliary risk stratification tool for postoperative CRC patients, and PDE1B may also serve as a potential epithelial tumor-suppressor target for future preclinical studies.
Feng-Chan Huang, Jung-Yin Fong, C. Ng et al.· Frontiers in Molecular Biosc...· 0 citations
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