Oral diseases · 2024
Metabolic Analysis of Tumor Cells Within Ameloblastoma at the Single-Cell Level.
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Li Rui-Fang, Zhao Yi, Man Qi-Wen
Аннотация
BACKGROUND: To meet their high energy needs, tumor cells undergo aberrant metabolic reprogramming. A tumor cell may expertly modify its metabolic pathways and the differential expression of the genes for metabolic enzymes. The physiological requirements of the host tissue and the tumor cell of origin mostly dictate metabolic adaptation. Ameloblastoma (AB) is a benign odontogenic tumor of epithelial origin. Due to its unrestricted growth potential, local aggressiveness, and high likelihood of recurrence, this condition poses a significant risk to the patient's health. This study aimed to characterize the metabolic heterogeneity at single-cell resolution of AB.
METHODS: Single-cell RNA sequencing (scRNA-seq) was performed on 17,284 cells from three AB donors. Bioinformatic analysis was used to examine differentially expressed genes, subtypes, and regulatory mechanisms when combined with odontogenic keratocyst scRNA-seq data. Based on metabolic pathway gene sets, the metabolic landscape of AB tumor cells was examined.
RESULTS: Using scRNA-seq, we discovered that AB tumor cells had substantial heterogeneity. The biggest contributor to tumor cell metabolic characteristics is determined to be variation in mitochondrial programming and glycolysis. Surprisingly, hypoxia corresponds with both oxidative phosphorylation and glycolysis activity in AB tumor cells at the single-cell level.
CONCLUSION: This study presents a computational framework for defining metabolism using single-cell expression data and identifies oxidative phosphorylation and glycolysis as critical components of metabolism for AB tumor cells.
METHODS: Single-cell RNA sequencing (scRNA-seq) was performed on 17,284 cells from three AB donors. Bioinformatic analysis was used to examine differentially expressed genes, subtypes, and regulatory mechanisms when combined with odontogenic keratocyst scRNA-seq data. Based on metabolic pathway gene sets, the metabolic landscape of AB tumor cells was examined.
RESULTS: Using scRNA-seq, we discovered that AB tumor cells had substantial heterogeneity. The biggest contributor to tumor cell metabolic characteristics is determined to be variation in mitochondrial programming and glycolysis. Surprisingly, hypoxia corresponds with both oxidative phosphorylation and glycolysis activity in AB tumor cells at the single-cell level.
CONCLUSION: This study presents a computational framework for defining metabolism using single-cell expression data and identifies oxidative phosphorylation and glycolysis as critical components of metabolism for AB tumor cells.