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Using constraint-based metabolic modeling to elucidate drug-induced metabolic changes in a cancer cell line

ABSTRACT: Since the discovery of the Warburg effect, metabolism has become a crucial cancer hallmark, for cancer cells change the activity of metabolic pathways to satisfy the demand of biomolecules and energy required to sustain continuous growth. Specially, gastric cancer (GC) still remains a growing burden of society, and often require the development of targeted and personalized interventions, including the use of combinatorial drug therapies that allow for synergistic interactions. However, the mechanism underlying synergistic interactions between multiple drugs remain poorly understood. The objective of this study is to use genome-scale metabolic models (GEMs) and transcriptomic data to investigate potential synergistic mechanisms in the GC cell line AGS following treatment with three kinase inhibitors and their combinations. To explore the metabolic phenotype of AGS cells, we curated and used a set of descriptions of metabolic functions (metabolic tasks) based on the latest iteration of the Human-GEM, and we used the Task Inferred from Differential Expression (TIDE) framework to infer the metabolic changes in AGS cells. Additionally, we expanded and complemented the TIDE framework by integrating essential genes from metabolic tasks, allowing for a more robust analysis. The results revealed a complex metabolic response in AGS cells after kinase inhibitor treatments, particularly in amino acid and nucleotide metabolism, and characterized by the down-regulation of metabolic functions. Using two definitions of synergistic effects, we identified significant metabolic shifts in combinatorial treatments, including a combination-specific increase in ornithine levels, potentially linked to an antiproliferative effect on AGS cell growth. These findings highlight the potential of using transcriptomic data to infer the metabolic phenotype of AGS cells and the role of metabolic alterations in cancer progression. In addition, we developed a Python library for Metabolic Task Enrichment Analysis, making the TIDE frameworks accessible to other researchers.

ORGANISM(S): Homo sapiens

PROVIDER: GSE285616 | GEO | 2025/09/03

REPOSITORIES: GEO

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Project description:Molecular subtyping is expected to enable bladder cancer (BC) precise treatment. However, reliable subtyping strategies for clinical application remains defective and controversial. Given the significance of tumor immune dysfunction and exclusion (TIDE) in tumor immune escape and immunotherapy, we aimed to develop a novel TIDE-based subtyping method to facilitate personalized management. Transcriptome data of BC was used to evaluate the heterogeneity and the status of TIDE patterns. We identified 69 TIDE biomarker genes and classified BC samples into three subtypes using consensus clustering. Subtype I showed the lowest TIDE status and malignancy with the best prognosis and highest sensitivity to immune checkpoint blockade (ICB) treatment, which was enriched of metabolic related signaling pathways. Subtype III represented the highest TIDE status and malignancy with the poorest prognosis and resistance to ICB treatment, resulting from its inhibitory immune microenvironment and T cell terminal exhaustion. Subtype II was in a transitional state with intermediate TIDE level, malignancy, and prognosis. We further confirmed the existence and characteristics of our novel TIDE subtypes using real-world BC samples. This subtyping method was proved to be more efficient than previous known methods in identifying non-responders to immunotherapy. We also propose that combining our TIDE subtypes with known biomarkers can potentially improve the sensitivity and specificity of these biomarkers. Moreover, besides guiding ICB treatment, this classification approach can assist in selecting the frontline or recommended drugs. Finally, we confirmed that the TIDE subtypes are conserved across the pan-tumors. In conclusion, our novel TIDE-based subtyping method can serve as a powerful clinical tool for BC and pan-cancer patients, and potentially guiding personalized therapy decisions for selecting potential beneficiaries and excluding resistant patients of ICB therapy.

Project description:Background: Zinc finger and scan domain containing 18 (ZSCAN18) belongs to zinc finger transcription factor superfamily, which consists of the hundreds of members that play critical roles in all steps of tumorigenesis. Although hypermethylation of ZSCAN18 promoter and its consequent deficiency in a variety of malignancies has been reported recently, its functions and mechanisms in the initiation and progression of gastric cancer (GC) are not clear. This study aims to investigate the roles of ZSCAN18 in gastric carcinogenesis. Methods: Methylation of ZSCAN18 promoter in GC cell lines were analyzed via MassARRAY and treatment of 5-Aza-2'-deoxycytidine. Bioinformatics analysis, qRT-PCR, western blot, immunohistochemistry, CCK-8, colony formation, and in vivo xenograft tumor model were used to examine the expression and function of ZSCAN18. Survival analysis was performed to determine the correlation between ZSCAN18 expression and prognosis of patients with GC. Genes modulated by ZSCAN18 in NCI-N87 cells were identified through RNA next generation sequence screening and further verified by qRT-PCR in AGS and NCI-N87 cells. Results: ZSCAN18 expression was markedly reduced in GC tissues as compared with that in adjacent normal tissues due to hypermethylation in GC. Likewise, ZSCAN18 expression was also significantly reduced in a panel of GC cell lines as the result of densely methylated ZSCAN18 promoter. Forced expression of ZSCAN18 inhibited proliferation in AGS and NCI-N87 cells in vitro. Additionally, ZSCAN18 impaired growth of NCI-N87 cells in immunodeficient mice. Survival analysis showed that ZSCAN18 expression was positively associated with favorable outcome in patients with GC. RNA next generation sequence screening showed three representative genes (FGF20, CEACAM5 and TP53INP2) involved in downstream signaling pathways modulated by ZSCAN18. Conclusions: Collectively, this study unveils the anti-cancer role of ZSCAN18 in GC, providing a promising diagnostic and therapeutic target.

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Using constraint-based metabolic modeling to elucidate drug-induced metabolic changes in a cancer cell line

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