Metabolic reprogramming is one of the cancer hallmarks, important for the survival of malignant cells. We investigated the prognostic value of genes associated with metabolism in thyroid carcinoma (THCA). A prognostic risk model of metabolism-related genes (MRGs) was built and tested based on datasets in The Cancer Genome Atlas (TCGA), with univariate Cox regression analysis, LASSO, and multivariate Cox regression analysis. We used Kaplan-Meier (KM) curves, time-dependent receiver operating characteristic curves (ROC), a nomogram, concordance index (C-index) and restricted mean survival (RMS) to assess the performance of the risk model, indicating the splendid predictive performance. We established a three-gene risk model related to metabolism, consisting of PAPSS2, ITPKA, and CYP1A1. The correlation analysis in patients with different risk statuses involved immune infiltration, mutation and therapeutic reaction. We also performed pan-cancer analyses of model genes to predict the mutational value in various cancers. Our metabolism-related risk model had a powerful predictive capability in the prognosis of THCA. This research will provide the fundamental data for further development of prognostic markers and individualized therapy in THCA.
Background Recently, increasing attention has been drawn to the impact of the tumor microenvironment (TME) on the occurrence and progression of malignant tumors. A variety of 3D culture techniques have been used to simulate TME in vitro. The purpose of this study was to reveal the differences in transcriptional and metabolic levels between osteosarcoma (OS) 2D cells, 3D cells, 3D cell-printed tissue, isolated tissue, and transplanted tumor tissue in vivo. Methods We cultured the OS Saos-2 cell line under different culture methods as 2D cells, 3D cells, 3D cell-printed tissue and isolated tissue for 14 days and transplanted tumors in vivo as a control group. Through transcriptomic and metabonomic analyses, we determined the changes in gene expression and metabolites in OS tissues under different culture methods. Results At the transcriptional level, 166 differentially expressed genes were found, including the SMAD family, ID family, BMP family and other related genes, and they were enriched in the TGF-β signaling pathway, complement and coagulation cascades, signaling pathways regulating pluripotency of stem cells, Hippo signaling pathway, ferroptosis, cGMP-PKG signaling pathway and other pathways. At the metabolic level, 362 metabolites were significantly changed and enriched in metabolic pathways such as the Fc Epsilon RI signaling pathway, histidine metabolism, primary bile acid biosynthesis, steroid biosynthesis, protein digestion and absorption, ferroptosis, and arachidonic acid metabolism. After integrating the transcriptome and metabolomics data, it was found that 44 metabolic pathways were changed, and the significantly enriched pathways were ferroptosis and pyrimidine metabolism. Conclusion Different culture methods affect the gene expression and metabolite generation of OS Saos-2 cells. Moreover, the cell and tissue culture method in vitro cannot completely simulate TME in vivo, and the ferroptosis and pyrimidine metabolism pathways mediate the functional changes of OS Saos-2 cells in different microenvironments.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.