Journal of Ecology and Toxicology
Open Access

Gastric cancer; Metabolism; Immunal reprogramming; Codependencies; Microenviroment; tumor

Introduction

The TME of gastric cancer comprises a diverse array of cell types, including cancer cells, immune cells, fibroblasts, and endothelial cells, along with an intricate network of blood vessels and extracellular matrix components. Within this dynamic microenvironment, metabolic reprogramming occurs as cancer cells adapt to their surroundings, altering nutrient utilization and energy production to support their uncontrolled growth and survival. Several metabolic interactions within the TME of gastric cancer have been identified, involving both cancer cells and stromal components. One prominent feature is the Warburg effect, where cancer cells favor glycolysis, even in the presence of oxygen, to generate energy and metabolic intermediates for rapid proliferation. This metabolic switch leads to increased glucose consumption and lactate production, contributing to tumor acidosis and the formation of an immunosuppressive microenvironment [1].

Moreover, the TME of gastric cancer exhibits alterations in nutrient availability, such as decreased glucose and increased glutamine levels. Cancer-associated fibroblasts and immune cells, including tumorassociated macrophages, contribute to these metabolic changes. CAFs provide nutrients, including lactate and amino acids, to cancer cells, while TAMs promote the scavenging of extracellular nutrients and produce factors that support tumor growth and immune evasion [2]. Despite the growing recognition of metabolic interactions within the TME of gastric cancer, several barriers exist in fully deciphering these intricate processes. The heterogeneity of gastric cancer, both between patients and within individual tumors, poses a significant challenge in understanding the specific metabolic alterations that occur. Additionally, the lack of appropriate preclinical models that faithfully recapitulate the complex TME limits the translation of findings to clinical applications .Nevertheless, promising avenues have emerged to overcome these barriers and advance our understanding of metabolic interactions in gastric cancer TME. Recent advancements in metabolomics, proteomics, and single-cell sequencing technologies have allowed for more comprehensive profiling of metabolic alterations in patient samples. Integrated analyses combining genomic, transcriptomic, and metabolomics data offer a systems-level understanding of metabolic rewiring and potential therapeutic targets [3].

Metabolic reprogramming and gastric cancer

The immune metabolic phenotype is the result of a combination of intracellular and extracellular factors, including epigenetics, tissue physiological structure, and populations of commensal microorganisms. During the process of tumor progression, cancer cells use carbohydrate and lipid metabolism to maintain their rapid proliferation, migration, and other malignant biological behaviors [4]. Due to rapid increases in tumor size, angiogenesis is often poor in tumors, especially in the tumor center. This can lead to chronic nutritional deficiency and reduced oxygen concentration, which tumor cells respond to by adapting their metabolism. Cancer cells are incredibly efficient at capturing extracellular metabolites and maximizing enzymatic activity; Flexibility in cellular metabolic processes allow tumor cells to maintain homeostasis despite the constantly changing levels of nutrients in the microenvironments [5].

Relationship between metabolism and microenvironment

Tumor microenvironment is a special environment for tumor survival, which is characterized by hypoxia, acidity, nutrient deficiency, and immunosuppression. The environment consists of the vasculature, immune cells, extracellular matrix, and proteins or metabolic molecules. Furthermore, the development of advanced in vitro and in vivo models, such as patient-derived organoids and genetically engineered mouse models enables the study of metabolic interactions within a more clinically relevant context. These models facilitate the testing of metabolic inhibitors, immunotherapies, and combination therapies to target specific metabolic vulnerabilities and enhance treatment outcomes [6].

Lipid metabolism

Lipid metabolism is a complex process that involves multiple steps involving the dietary intake of lipids the production of lipids within the body to degradation or transformation into several lipidcontaining structures in the body. In addition to the classical role of lipid metabolites as energy sources, several lipid metabolites have other functions, including a role in signaling pathways and as structural components of cell membranes. These functions usually involve specific forms of fatty acids or lipid metabolites, other than fatty acids [7,8].

Fatty acid oxidation

Cancer cells use the unusual fatty acid sapienate for membrane biosynthesis, therefore bypassing the known process for desaturation of fatty acids that is dependent on stearoyl-CoA desaturase. Fatty acid synthase converts acetyl coenzyme A into palmitic acid or malonyl coenzyme A as redox equivalents in the presence of NADPH [9]. The expression of FASN is higher in many types of tumors, such as gastric cancer tissues which are highly dependent on ab initio synthesis. Overexpression of FASN promotes tumor cell proliferation and increases metastasis, and inhibition of FASN can selectively induce cancer cell apoptosis in vitro and in vivo, with minimal impact on normal cells [10].

Conclusion

Deciphering metabolic interactions in the TME of gastric cancer represents a challenging yet promising area of research. Understanding the metabolic rewiring and crosstalk between cancer cells, stromal components, and immune cells holds great potential for identifying novel therapeutic targets and developing personalized treatment strategies. Overcoming the barriers through advanced technologies and clinically relevant models will contribute to the advancement of precision medicine and improved patient outcomes in gastric cancer. Into acid metabolic pathways were also more active in this group, with the exception of glutamine, histidine, lysine, tyrosine, and L-phenylalanine, for which the activities of synthesis and transport were similar across risk groups.

References

1. Shafie S, Samari E, Jeyagurunathan A, Abdin E, Chang S et al. (2021) Gender difference in quality of life (QoL) among outpatients with schizophrenia in a tertiary care setting. BMC Psychiatry1:21.

Indexed at, Google Scholar, Crossref

2. defaru desalegn, shimelia girma T. Religiosity among patients with. Quality of life and its association with psychiatric symptoms and socio-demographic characteristics among people with schizophrenia: A hospital-based cross-sectional study.

Indexed at, Google Scholar, Crossref

3. Hsiao CY, Hsieh MH, Tseng CJ, Chien SH, Chang CC (2012) Quality of life of individuals with schizophrenia living in the community: relationship to socio-demographic, clinical and psychosocial characteristics. J Clin Nurs 21:2367-76.

Indexed at, Google Scholar, Crossref

4. Murwasuminar B, Munro I, Recoche K (2023) Mental health recovery for people with schizophrenia in Southeast Asia: A systematic review. J Psychiatr Ment Health Nurs 30:620-36.

Indexed at, Google Scholar, Crossref

5. Zafar SN, Gowani SA, Waqar S, Zubair A, Yousaf W et al. (2008) Perceptions about the cause of schizophrenia and the subsequent help seeking behavior in a Pakistani population - Results of a cross-sectional survey. BMC Psychiatry 17:8.

Indexed at, Google Scholar, Crossref

6. Duñó R, Oliva JC, Tobeña A, Palao D, Labad J (2020) Religiosity and Psychotic Ideation in Stable Schizophrenia: A Role for Empathic Perspective-Taking. Behavioral Sciences 10.

Indexed at, Google Scholar, Crossref

7. Roll P, Mahmood Z, Muhammad K, Feuchtenberger M, Dörner T et al. (2015) Long-term repopulation of peripheral B-cell subsets after single and repeated rituximab infusions in patients with rheumatoid arthritis. Clin Exp Rheumatol 33:347-53.

Indexed at, Google Scholar, Crossref

8. Magliulo D, Wade SD, Kyttaris VC (2022) Immunogenicity of SARS-CoV-2 vaccination in rituximab-treated patients: Effect of timing and immunologic parameters. Clin Immunol 234:108897.

Indexed at, Google Scholar, Crossref

9. Levin MJ, Ustianowski A, De Wit S, Launay O, Avila M  et al. Intramuscular AZD7442 (Tixagevimab-Cilgavimab) for Prevention of Covid-19. N Engl J Med 386:2188-2200.

Indexed at, Google Scholar, Crossref

10. Ocon AJ, Mustafa SS (2023) Real-World Experience of Tixagevimab and Cilgavimab (Evusheld) in Rheumatologic Patients on Rituximab. J Clin Rheumatol 29:109-111.

Indexed at, Google Scholar, Crossref