Fat mass and obesity-associated protein (FTO) regulates critical pathways in various diseases, including malignant tumours. However, the functional link between FTO and its target genes in epithelial ovarian cancer (EOC) development remains to be elucidated. In this study, the biological functions of FTO were verified in vitro and in vivo. The m6A modification and the binding sites of SNAI1 mRNA were confirmed by m6A RNA immunoprecipitation (MeRIP) and RIP experiments. The actinomycin D assay was used to test the stability of RNA. We found that FTO was downregulated with increased m6A levels in EOC. Reduced expression of FTO was associated with a higher FIGO stage in patients with EOC. Mechanistically, FTO decreased the m6A level and stability of SNAI1 mRNA, causing downregulation of SNAI1 and inhibiting epithelial–mesenchymal transition (EMT). Furthermore, FTO-mediated downregulation of SNAI1 expression depended on IGF2BP2, which acted as an m6A reader binding to the 3′ UTR region of SNAI1 mRNA to promote its stability. In conclusion, FTO inhibits SNAI1 expression to attenuate the growth and metastasis of EOC cells in an m6A-IGF2BP2-dependent manner. Our findings suggest that the FTO-IGF2BP2-SNAI1 axis is a potential therapeutic target in EOC.
Objective: Metabolic reprogramming is an important biomarker of cancer. Metabolic adaptation driven by oncogenes allows tumor cells to survive and grow in a complex tumor microenvironment. The heterogeneity of tumor metabolism is related to survival time, somatic cell-driven gene mutations, and tumor subtypes. Using the heterogeneity of different metabolic pathways for the classification of gynecological pan-cancer is of great significance for clinical decision-making and prognosis prediction.Methods: RNA sequencing data for patients with ovarian, cervical, and endometrial cancer were downloaded from The Cancer Genome Atlas database. Genes related to glycolysis and cholesterol were extracted and clustered coherently by using ConsensusClusterPlus. The mutations and copy number variations in different subtypes were compared, and the immune scores of the samples were evaluated. The limma R package was used to identify differentially expressed genes between subtypes, and the WebGestaltR package (V0.4.2) was used to conduct Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology functional enrichment analyses. A risk score model was constructed based on multivariate Cox analysis. Prognostic classification efficiency was analyzed by using timeROC, and internal and external cohorts were used to verify the robustness of the model.Results: Based on the expression of 11 glycolysis-related genes and seven cholesterol-related genes, 1,204 samples were divided into four metabolic subtypes (quiescent, glycolysis, cholesterol, and mixed). Immune infiltration scores showed significant differences among the four subtypes. Survival analysis showed that the prognosis of the cholesterol subtype was better than that of the quiescent subtype. A nine-gene signature was constructed based on differentially expressed genes between the cholesterol and quiescent subtypes, and it was validated by using an independent cohort of the International Cancer Genome Consortium. Compared with existing models, our nine-gene signature had good prediction performance.Conclusion: The metabolic classification of gynecological pan-cancer based on metabolic reprogramming may provide an important basis for clinicians to choose treatment options, predict treatment resistance, and predict patients' clinical outcomes.
Weaning stress commonly damages the intestinal barrier of mammals, resulting in gut microbiota dysbiosis, intestinal illness, and even severe diarrhea. Probiotics are utilized as a nutritional strategy to promote the...
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