Identification and validation of a glutamine metabolism-related gene signature as a diagnostic and therapeutic target in osteoarthritis through integrated multi-omics analysis
Ontology highlight
ABSTRACT: Identification and validation of a glutamine metabolism-related gene signature as a diagnostic and therapeutic target in osteoarthritis through integrated multi-omics analysis
INSTRUMENT(S): Liquid Chromatography MS - - - Mass spectrometry - - - Thermo Scientific Q Exactive HF
PROVIDER: MTBLS13455 | MetaboLights | 2025-12-13
REPOSITORIES: MetaboLights
Dataset's files
Items per page: 5 1 - 5 of 6 |
Similar Datasets
Project description:Cholangiocarcinoma (CCA) is a highly heterogeneous and aggressive malignancy of the bile ducts with a poor prognosis and high mortality rate. Effective targeted therapy and accurate prognostic biomarkers are still lacking. Ferroptosis is a form of regulated cell death implicated in cancer progression and has emerged as a potential therapeutic target in various cancers. However, a comprehensive analysis of ferroptosis-related genes (FRGs) for predicting CCA prognosis and therapeutic targets and determining the role of ferroptosis in CCA remain to be performed. Here, we developed a prognostic FRG signature using a least absolute shrinkage and selection operator Cox regression analysis in a training cohort. We then validated it using four independent public datasets. The six-FRG signature was developed to predict CCA patient survival, stratifying them into low-risk and high-risk groups based on survival time. Significantly, the high-risk CCA patients had shorter overall survival. A receiver operating characteristic curve analysis further confirmed the prognostic FRG signature's strong predictive ability, indicating that it was an independent prognostic indicator for CCA patients. Furthermore, the high-risk group was associated with fluke infection and high clinical stages. Cancer-associated fibroblast (CAF) score and CAF markers were significantly higher in the high-risk group than the low-risk group. Moreover, our FRG signature could predict immune checkpoint markers for immunotherapy and drug sensitivity. The mRNA expression levels of the six-FRG signature was validated in 10 CCA cell lines and dividing them into low-risk and high-risk groups using the FRG signature. We further showed that high-risk CCA cell lines were more resistant to ferroptosis inducers, including erastin and RSL3, than the low-risk CCA cell lines. Our study constructed a novel FRG signature model to predict CCA prognoses which might provide prognostic biomarkers and potential therapeutic targets for CCA patients. Ferroptosis sensitivity in high-risk and low-risk CCA cell lines suggests that ferroptosis resistance is associated with high-risk group CCA. Therefore, ferroptosis could be a promising therapeutic target for precision therapy in CCA patients.
| S-EPMC9887182 | biostudies-literature
Project description:Ischaemic cardiomyopathy (IC) predominantly arises from prolonged deprivation of oxygen in the coronary arteries, resulting in compromised cardiac contractility or relaxation. This study investigates the role of disulfidptosis-associated genes (DiGs) in IC. Through the analysis of datasets GSE5406 and GSE57338, we explored the association between DiGs and immune characteristics to identify crucial genes contributing to IC development. The support vector machine model emerged as the most effective, identifying key genes such as MYH9, NUBPL, MYL6, MYH10 and NCKAP1. Validation with independent datasets GSE57345, GSE48166 and single-cell GSE145154 further supported these findings, demonstrating high predictive accuracy. Experimental validation in an IC mouse model, using Western blot, immunohistochemistry and RT-qPCR, confirmed the altered expression of these core genes in myocardial ischaemic regions. This research not only elucidates the significance of DiGs in IC but also underscores the diagnostic potential of identified core genes.
| S-EPMC11897062 | biostudies-literature
Project description:Osteoarthritis is a prevalent joint disease in the aging population. The hallmark of osteoarthritis is the degeneration of the joint cartilage, characterized by changes in chondrocytes including mitochondrial dysfunction. However, the precise mechanisms of how this affects chondrocyte homeostasis and whether such processes can be explored as therapeutic targets for osteoarthritis remain unclear. Here, we show that impaired mitochondrial function and disrupted cartilage matrix metabolism due to loss of mitofusin-2 (MFN2) expression in chondrocytes leads to the development of osteoarthritis. Sirtuin-3 (SIRT3), a key regulator of mitochondrial function, plays a critical role in modulating MFN2 to restore mitochondrial dynamics, reduce fragmentation, and preserve mitochondrial function in chondrocytes. Specifically, SIRT3 directly deacetylates and indirectly deubiquitinates MFN2, preventing its degradation. MFN2-mediated mitochondrial–endoplasmic reticulum (ER) junctions support cellular homeostasis, alleviate ER stress, and maintain mitochondrial calcium ion balance, which collectively mitigate chondrocyte senescence. Extracellular vesicles engineered with MFN2 mRNA effectively prevented cartilage degeneration and restored mobility in osteoarthritic mice. These findings suggest that targeting MFN2 is a promising strategy to prevent cartilage degeneration and alleviate progression of osteoarthritis.
2025-08-31 | GSE305514 | GEO
Project description:Cartilage destruction in osteoarthritis (OA) results from disturbed chondrocyte metabolism. Here, we used microarrays to show that TGF alpha and CCL2 are simultaneously upregulated in a rat model of OA and cooperate to drive cartilage degradation. The goals of the experiments included here were to a) characterize gene expression in knee joint articular chondrocytes at various stages of development of OA (2 and 8 weeks after surgical induction of OA), and b) to establish trends in gene expression among groups of genes related to the TGF alpha-EGFR axis, over time, in OA. The model chosen to study these results has been previously validated (Appleton, CT et al, 2007, Arthritis Rheum) and used to describe similar gene expression results at a different time point (4 weeks) after induction of OA. The rat model of OA involves surgical destabilization of the knee joint, followed by forced low-intensity mobilization over several weeks; a sham surgery is used as the control (representing a healthy non-OA knee joint) wherein a surgical incision is made but not structural (i.e. ligamentous) modification is made to the joint. Altogether, our data indicate that TGF and CCL2 cooperate to drive cartilage degradation in osteoarthritis. A total of 12 samples were analyzed. 3 replicates were used per condition: OA surgery at 2 weeks, OA surgery at 8 weeks, Sham (control) surgery at 2 weeks and Sham surgery at 8 weeks. Expression of OA samples was assessed relaitve to Sham (control) expression levels.
2012-11-15 | E-GEOD-42295 | biostudies-arrayexpress
Project description:IntroductionEndometrial cancer (EC) is the most common female reproductive system cancer in developed countries with growing incidence and associated mortality, which may be due to the growing prevalence of obesity. Metabolism reprogramming including glucose, amino acid, and lipid remodeling is a hallmark of tumors. Glutamine metabolism has been reported to participate in tumor proliferation and development. This study aimed to develop a glutamine metabolism-related prognostic model for EC and explore potential targets for cancer treatment.MethodTranscriptomic data and survival outcome of EC were retrieved from The Cancer Genome Atlas (TCGA). Differentially expressed genes related to glutamine metabolism were recognized and utilized to build a prognostic model by univariate and multivariate Cox regressions. The model was confirmed in the training, testing, and the entire cohort. A nomogram combing prognostic model and clinicopathologic features was established and tested. Moreover, we explored the effect of a key metabolic enzyme, PHGDH, on the biological behavior of EC cell lines and xenograft model.ResultsFive glutamine metabolism-related genes, including PHGDH, OTC, ASRGL1, ASNS, and NR1H4, were involved in prognostic model construction. Kaplan-Meier curve suggested that patients recognized as high risk underwent inferior outcomes. The receiver operating characteristic (ROC) curve showed the model was sufficient to predict survival. Enrichment analysis recognized DNA replication and repair dysfunction in high-risk patients whereas immune relevance analysis revealed low immune scores in the high-risk group. Finally, a nomogram integrating the prognostic model and clinical factors was created and verified. Further, knockdown of PHGDH showed cell growth inhibition, increasing apoptosis, and reduced migration. Promisingly, NCT-503, a PHGDH inhibitor, significantly repressed tumor growth in vivo (p = 0.0002).ConclusionOur work established and validated a glutamine metabolism-related prognostic model that favorably evaluates the prognosis of EC patients. DNA replication and repair may be the crucial point that linked glutamine metabolism, amino acid metabolism, and EC progression. High-risk patients stratified by the model may not be sufficient for immune therapy. PHGDH might be a crucial target that links serine metabolism, glutamine metabolism as well as EC progression.
| S-EPMC10469729 | biostudies-literature
Project description:BackgroundLactate is gradually proved as the essential regulator in intercellular signal transduction, energy metabolism reprogramming, and histone modification. This study aims to clarify the diagnosis value of lactate metabolism-related genes in osteoarthritis (OA).MethodsLactate metabolism-related genes were retrieved from the MSigDB. GSE51588 was downloaded from the Gene Expression Omnibus (GEO) as the training dataset. GSE114007, GSE117999, and GSE82107 datasets were adopted for external validation. Genomic difference detection, protein-protein interaction network analysis, LASSO, SVM-RFE, Boruta, and univariate logistic regression (LR) analyses were used for feature selection. Multivariate LR, Random Forest (RF), Support Vector Machine (SVM), and XGBoost (XGB) were used to develop the multiple-gene diagnosis models. 12 control and 12 OA samples were collected from the local hospital for re-verification. The transfection assays were conducted to explore the regulatory ability of the gene to the apoptosis and vitality of chondrocytes.ResultsThrough the bioinformatical analyses and machine learning algorithms, SLC2A1 and NDUFB9 of the 273 lactate metabolism-related genes were identified as the significant diagnosis biomarkers. The LR, RF, SVM, and XGB models performed impressively in the cohorts (AUC > 0.7). The local clinical samples indicated that SLC2A1 and NDUFB9 were both down-regulated in the OA samples (both P < 0.05). The knockdown of NDUFB9 inhibited the viability and promoted the apoptosis of the CHON-001 cells treated with IL-1beta (both P < 0.05).ConclusionsA lactate metabolism-related gene signature was constructed to diagnose OA, which was validated in multiple independent cohorts, local clinical samples, and cellular functional experiments.
| S-EPMC11552637 | biostudies-literature
Project description:Osteoarthritis (OA) is a chronic, degenerative whole-joint disorder that interferes with quality of life in older individuals. Here, we report a high expression of ZDHHC11 in articular chondrocytes, which is downregulated in the degenerated cartilage of aged mice and OA patients. ZDHHC11 prevents chondrocyte senescence and fosters cartilage anabolism, culminating in an improved OA phenotype. Mice with Zdhhc11 deletion (Zdhhc11fl/fl) exacerbate OA progression in a destabilized medial meniscus (DMM) model. Specifically, we identify ZDHHC11 as a key palmitoyltransferase, the depletion of which leads to GNB2-dependent E3 ubiquitin ligase-mediated proteasomal degradation of APOD.
2025-08-12 | PXD063840 | Pride
Project description:PurposeMetabolic reprogramming, as one of the hallmarks of cancer, shows promising translational potential for cancer diagnosis, treatment and prognostic prediction. This study aims to construct and validate a prognostic prediction model for osteosarcoma based on glutamine metabolism-related genes.Materials and methodsA group of glutamine metabolism-related genes was identified from a public database and intersected with a list of osteosarcoma survival-related genes, and a risk score model based on sixteen glutamine metabolism-related genes was developed by using LASSO penalized Cox regression analysis.ResultsThe prognosis of patients in the high-risk group was significantly worse than that of patients in the low-risk group in the training dataset (high- vs low-risk, 5-year overall survival: 11% vs 88%, p < 0.0001) and in two other external validation cohorts (high- vs low-risk, 5-year overall survival: 39% vs 81%, p = 0.015; 50% vs 94%, p = 0.011).In addition, a novel nomogram was constructed by integrating the risk score and clinical characteristics, including age, sex, metastasis status and chemotherapy response. This nomogram had superior predictive power compared with a nomogram composed of only conventional factors. Gene set enrichment analysis indicated that several well-known malignancy-associated gene sets, including MYC targets V1, DNA repair, and unfolded protein response, were enriched in the high-risk subgroup.ConclusionA novel glutamine metabolism-related prognostic prediction model and nomogram for osteosarcoma was developed and validated in the present study, which could predict the survival of patients with osteosarcoma and may facilitate individualized clinical decision-making for patients.
| S-EPMC8817953 | biostudies-literature
Project description:Cartilage destruction in osteoarthritis (OA) results from disturbed chondrocyte metabolism. Here, we used microarrays to show that TGF alpha and CCL2 are simultaneously upregulated in a rat model of OA and cooperate to drive cartilage degradation. The goals of the experiments included here were to a) characterize gene expression in knee joint articular chondrocytes at various stages of development of OA (2 and 8 weeks after surgical induction of OA), and b) to establish trends in gene expression among groups of genes related to the TGF alpha-EGFR axis, over time, in OA. The model chosen to study these results has been previously validated (Appleton, CT et al, 2007, Arthritis Rheum) and used to describe similar gene expression results at a different time point (4 weeks) after induction of OA. The rat model of OA involves surgical destabilization of the knee joint, followed by forced low-intensity mobilization over several weeks; a sham surgery is used as the control (representing a healthy non-OA knee joint) wherein a surgical incision is made but not structural (i.e. ligamentous) modification is made to the joint. Altogether, our data indicate that TGF and CCL2 cooperate to drive cartilage degradation in osteoarthritis.
2012-11-15 | GSE42295 | GEO
Project description:BackgroundGastric cancer (GC) is one of the most prevalent cancer types that reduce human life expectancy. The current tumor-node-metastasis (TNM) staging system is inadequate in identifying higher or lower risk of GC patients because of tumor heterogeneity. Research shows that complement plays a dual role in the tumor development and progression of GC.MethodsWe downloaded GC data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). A complement-related risk signature was constructed through bioinformatics analysis. Subsequently, the predictive ability of this signature was validated with GSE84437 dataset, and a nomogram integrating risk score and common clinical factors was established. Besides, we evaluated the association of risk score with the immune and stromal cell infiltration in TCGA. Furthermore, immunotherapy response prediction and drug susceptibility analysis were conducted to access the ability of the risk signature in predicting the therapeutic effect.ResultsA complement-related gene (CRG) signature, based on six genes (SPLG, C9, ITIH1, ZFPM2, CD36, and SERPINE1), was established. In both the training and validation sets, the overall survival of GC patients in the high-risk group was lower than that of the low-risk group, and the nomogram to predict the 1-, 2-, and 3-year survival rates of GC patients was developed. In addition, CIBERSORT algorithm showed the high-risk patients had higher levels of immune cell infiltration than low-risk patients, and the ESTIMATE results implied that the high-risk group had more stromal component in tumor microenvironment. Besides, compared to the low-risk group, there were higher expressions of most immune checkpoint genes and HLA genes in the high-risk group, and the high-risk patients showed higher sensitivity to the chemotherapy and targeted drugs (axitinib, dasatinib, pazopanib, saracatinib, sunitinib and temsirolimus).ConclusionsThe novel CRG signature may act as a reliable, efficient tool for prognostic prediction and treatment guidance in future clinical practice.
| S-EPMC10774048 | biostudies-literature