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Machine learning approach to screen new diagnostic features of adamantinomatous craniopharyngioma and explore personalised treatment strategies
BACKGROUND: Adamantinoma craniopharyngioma (ACP) is a non-malignant tumour of unknown pathogenesis that frequently occurs in children and has malignant potential. The main treatment options are currently surgical resection and radiotherapy. These treatments can lead to serious complications that gre...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10248946/ https://www.ncbi.nlm.nih.gov/pubmed/37305719 http://dx.doi.org/10.21037/tp-23-152 |
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author | Wu, Ji Qin, Chengjian Fang, Guoxing Shen, Lei Li, Muhua Lu, Bimin Li, Yanghong Yao, Xiaomin Fang, Dalang |
author_facet | Wu, Ji Qin, Chengjian Fang, Guoxing Shen, Lei Li, Muhua Lu, Bimin Li, Yanghong Yao, Xiaomin Fang, Dalang |
author_sort | Wu, Ji |
collection | PubMed |
description | BACKGROUND: Adamantinoma craniopharyngioma (ACP) is a non-malignant tumour of unknown pathogenesis that frequently occurs in children and has malignant potential. The main treatment options are currently surgical resection and radiotherapy. These treatments can lead to serious complications that greatly affect the overall survival and quality of life of patients. It is therefore important to use bioinformatics to explore the mechanisms of ACP development and progression and to identify new molecules. METHODS: Sequencing data of ACP was downloaded from the comprehensive gene expression database for differentially expressed gene identification and visualized by Gene Ontology, Kyoto Gene, and gene set enrichment analyses (GSEAs). Weighted correlation network analysis was used to identify the genes most strongly associated with ACP. GSE94349 was used as the training set and five diagnostic markers were screened using machine learning algorithms to assess diagnostic accuracy using receiver operating characteristic (ROC) curves, while GSE68015 was used as the validation set for verification. RESULTS: Type I cytoskeletal 15 (KRT15), Follicular dendritic cell secreted peptide (FDCSP), Rho-related GTP-binding protein RhoC (RHOC), Modulates negatively TGFB1 signaling in keratinocytes (CD109), and type II cytoskeletal 6A (KRT6A) (area under their receiver operating characteristic curves is 1 for both the training and validation sets), Nomograms constructed using these five markers can predict progression of ACP patients. Whereas ACP tissues with activated T-cell surface glycoprotein CD4, Gamma delta T cells, eosinophils and regulatory T cells were expressed at higher levels than in normal tissues, which may contribute to the pathogenesis of ACP. According to the analysis of the CellMiner database (Tumor cell and drug related database tools), high CD109 levels showed significant drug sensitivity to Dexrazoxane, which has the potential to be a therapeutic agent for ACP. CONCLUSIONS: Our findings extend understandings of the molecular immune mechanisms of ACP and suggest possible biomarkers for the targeted and precise treatment of ACP. |
format | Online Article Text |
id | pubmed-10248946 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-102489462023-06-09 Machine learning approach to screen new diagnostic features of adamantinomatous craniopharyngioma and explore personalised treatment strategies Wu, Ji Qin, Chengjian Fang, Guoxing Shen, Lei Li, Muhua Lu, Bimin Li, Yanghong Yao, Xiaomin Fang, Dalang Transl Pediatr Original Article BACKGROUND: Adamantinoma craniopharyngioma (ACP) is a non-malignant tumour of unknown pathogenesis that frequently occurs in children and has malignant potential. The main treatment options are currently surgical resection and radiotherapy. These treatments can lead to serious complications that greatly affect the overall survival and quality of life of patients. It is therefore important to use bioinformatics to explore the mechanisms of ACP development and progression and to identify new molecules. METHODS: Sequencing data of ACP was downloaded from the comprehensive gene expression database for differentially expressed gene identification and visualized by Gene Ontology, Kyoto Gene, and gene set enrichment analyses (GSEAs). Weighted correlation network analysis was used to identify the genes most strongly associated with ACP. GSE94349 was used as the training set and five diagnostic markers were screened using machine learning algorithms to assess diagnostic accuracy using receiver operating characteristic (ROC) curves, while GSE68015 was used as the validation set for verification. RESULTS: Type I cytoskeletal 15 (KRT15), Follicular dendritic cell secreted peptide (FDCSP), Rho-related GTP-binding protein RhoC (RHOC), Modulates negatively TGFB1 signaling in keratinocytes (CD109), and type II cytoskeletal 6A (KRT6A) (area under their receiver operating characteristic curves is 1 for both the training and validation sets), Nomograms constructed using these five markers can predict progression of ACP patients. Whereas ACP tissues with activated T-cell surface glycoprotein CD4, Gamma delta T cells, eosinophils and regulatory T cells were expressed at higher levels than in normal tissues, which may contribute to the pathogenesis of ACP. According to the analysis of the CellMiner database (Tumor cell and drug related database tools), high CD109 levels showed significant drug sensitivity to Dexrazoxane, which has the potential to be a therapeutic agent for ACP. CONCLUSIONS: Our findings extend understandings of the molecular immune mechanisms of ACP and suggest possible biomarkers for the targeted and precise treatment of ACP. AME Publishing Company 2023-05-10 2023-05-30 /pmc/articles/PMC10248946/ /pubmed/37305719 http://dx.doi.org/10.21037/tp-23-152 Text en 2023 Translational Pediatrics. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Original Article Wu, Ji Qin, Chengjian Fang, Guoxing Shen, Lei Li, Muhua Lu, Bimin Li, Yanghong Yao, Xiaomin Fang, Dalang Machine learning approach to screen new diagnostic features of adamantinomatous craniopharyngioma and explore personalised treatment strategies |
title | Machine learning approach to screen new diagnostic features of adamantinomatous craniopharyngioma and explore personalised treatment strategies |
title_full | Machine learning approach to screen new diagnostic features of adamantinomatous craniopharyngioma and explore personalised treatment strategies |
title_fullStr | Machine learning approach to screen new diagnostic features of adamantinomatous craniopharyngioma and explore personalised treatment strategies |
title_full_unstemmed | Machine learning approach to screen new diagnostic features of adamantinomatous craniopharyngioma and explore personalised treatment strategies |
title_short | Machine learning approach to screen new diagnostic features of adamantinomatous craniopharyngioma and explore personalised treatment strategies |
title_sort | machine learning approach to screen new diagnostic features of adamantinomatous craniopharyngioma and explore personalised treatment strategies |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10248946/ https://www.ncbi.nlm.nih.gov/pubmed/37305719 http://dx.doi.org/10.21037/tp-23-152 |
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