ArticleFrontiers in endocrinology2024
Ferroptosis-related biomarkers for adamantinomatous craniopharyngioma treatment: conclusions from machine learning techniques.
Article in Frontiers in endocrinology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- The role of methylation in pituitary neuroendocrine tumors current insights and emerging perspectives.Molecular biology reports · 2026Review
- Identification and tissue-level validation of ferroptosis-related genes in small intestinal neuroendocrine neoplasms based on machine learning.BMC gastroenterology · 2025Article
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7 authors.
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Abstract
Introduction: Adamantinomatous craniopharyngioma (ACP) is difficult to cure completely and prone to recurrence after surgery. Ferroptosis as an iron-dependent programmed cell death, may be a critical process in ACP. The study aimed to screen diagnostic markers related to ferroptosis in ACP to improve diagnostic accuracy. Methods: Gene expression profiles of ACP were obtained from the gene expression omnibus (GEO) database. Limma package was used to analyze the differently expressed genes (DEGs). The intersection of DEGs and ferroptosis-related factors was obtained as differently expressed ferroptosis-related genes (DEFRGs). Enrichment analysis was processed, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), disease ontology (DO), gene set enrichment analysis (GSEA), and Gene Set Variation Analysis (GSVA) analysis. Machine learning algorithms were undertaken for screening diagnostic markers associated with ferroptosis in ACP. The levels of DEFRGs were verified in ACP patients. A nomogram was drawn to predict the relationship between key DEFRG expression and risk of disease. The disease groups were then clustered by consensus clustering analysis. Results: DEGs were screened between ACP and normal samples. Ferroptosis-related factors were obtained from the FerrDb V2 and GeneCard databases. The correlation between DEFRGs and ferroptosis markers was also confirmed. A total of 6 overlapped DEFRGs were obtained. Based on the results of the nomogram, CASP8, KRT16, KRT19, and TP63 were the protective factors of the risk of disease, while GOT1 and TFAP2C were the risk factors. According to screened DEFRGs, the consensus clustering matrix was differentiated, and the number of clusters was stable. CASP8, KRT16, KRT19, and TP63, were upregulated in ACP patients, while GOT1 was downregulated. CASP8, KRT16, KRT19, TP63, CASP8, and GOT1 affect multiple ferroptosis marker genes. The combination of these genes might be the biomarker for ACP diagnosis via participating ferroptosis process. Discussion: Ferroptosis-related genes, including CASP8, KRT16, KRT19, TP63, and GOT1 were the potential markers for ACP, which lays the theoretical foundation for ACP diagnosis.
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