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Identification of cholesterol metabolism-related subtypes in nonfunctioning pituitary neuroendocrine tumors and analysis of immune infiltration

OBJECTIVE: This study aimed to investigate the role of cholesterol metabolism-related genes in nonfunctioning pituitary neuroendocrine tumors (NF-PitNETs) invading the cavernous sinus and analyze the differences in immune cell infiltration between invasive and noninvasive NF-PitNETs. METHODS: First,...

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Autores principales: Feng, Tianshun, Hou, Pengwei, Mu, Shuwen, Fang, Yi, Li, Xinxiong, Li, Ziqi, Wang, Di, Chen, Li, Lu, Lingling, Lin, Kunzhe, Wang, Shousen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10413501/
https://www.ncbi.nlm.nih.gov/pubmed/37563740
http://dx.doi.org/10.1186/s12944-023-01883-3
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author Feng, Tianshun
Hou, Pengwei
Mu, Shuwen
Fang, Yi
Li, Xinxiong
Li, Ziqi
Wang, Di
Chen, Li
Lu, Lingling
Lin, Kunzhe
Wang, Shousen
author_facet Feng, Tianshun
Hou, Pengwei
Mu, Shuwen
Fang, Yi
Li, Xinxiong
Li, Ziqi
Wang, Di
Chen, Li
Lu, Lingling
Lin, Kunzhe
Wang, Shousen
author_sort Feng, Tianshun
collection PubMed
description OBJECTIVE: This study aimed to investigate the role of cholesterol metabolism-related genes in nonfunctioning pituitary neuroendocrine tumors (NF-PitNETs) invading the cavernous sinus and analyze the differences in immune cell infiltration between invasive and noninvasive NF-PitNETs. METHODS: First, a retrospective analysis of single-center clinical data was performed. Second, the immune cell infiltration between invasive and noninvasive NF-PitNETs in the GSE169498 dataset was further analyzed, and statistically different cholesterol metabolism-related gene expression matrices were obtained from the dataset. The hub cholesterol metabolism-related genes in NF-PitNETs were screened by constructing machine learning models. In accordance with the hub gene, 73 cases of NF-PitNETs were clustered into two subtypes, and the functional differences and immune cell infiltration between the two subtypes were further analyzed. RESULTS: The clinical data of 146 NF-PitNETs were evaluated, and the results showed that the cholesterol (P = 0.034) between invasive and noninvasive NF-PitNETs significantly differed. After binary logistic analysis, cholesterol was found to be an independent risk factor for cavernous sinus invasion (CSI) in NF-PitNETs. Bioinformatics analysis found three immune cells between invasive and noninvasive NF-PitNETs were statistically significant in the GSE169498 dataset, and 34 cholesterol metabolism-related genes with differences between the two groups were obtained 12 hub genes were selected by crossing the two machine learning algorithm results. Subsequently, cholesterol metabolism-related subgroups, A and B, were obtained by unsupervised hierarchical clustering analysis. The results showed that 12 immune cells infiltrated differentially between the two subgroups. The chi-square test revealed that the two subgroups had statistically significance in the invasive and noninvasive samples (P = 0.001). KEGG enrichment analysis showed that the differentially expressed genes were mainly enriched in the neural ligand–receptor pathway. GSVA analysis showed that the mTORC signaling pathway was upregulated and played an important role in the two-cluster comparison. CONCLUSION: By clinical data and bioinformatics analysis, cholesterol metabolism-related genes may promote the infiltration abundance of immune cells in NF-PitNETs and the invasion of cavernous sinuses by NF-PitNETs through the mTOR signaling pathway. This study provides a new perspective to explore the pathogenesis of cavernous sinus invasion by NF-PitNETs and determine potential therapeutic targets for this disease. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12944-023-01883-3.
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spelling pubmed-104135012023-08-11 Identification of cholesterol metabolism-related subtypes in nonfunctioning pituitary neuroendocrine tumors and analysis of immune infiltration Feng, Tianshun Hou, Pengwei Mu, Shuwen Fang, Yi Li, Xinxiong Li, Ziqi Wang, Di Chen, Li Lu, Lingling Lin, Kunzhe Wang, Shousen Lipids Health Dis Research OBJECTIVE: This study aimed to investigate the role of cholesterol metabolism-related genes in nonfunctioning pituitary neuroendocrine tumors (NF-PitNETs) invading the cavernous sinus and analyze the differences in immune cell infiltration between invasive and noninvasive NF-PitNETs. METHODS: First, a retrospective analysis of single-center clinical data was performed. Second, the immune cell infiltration between invasive and noninvasive NF-PitNETs in the GSE169498 dataset was further analyzed, and statistically different cholesterol metabolism-related gene expression matrices were obtained from the dataset. The hub cholesterol metabolism-related genes in NF-PitNETs were screened by constructing machine learning models. In accordance with the hub gene, 73 cases of NF-PitNETs were clustered into two subtypes, and the functional differences and immune cell infiltration between the two subtypes were further analyzed. RESULTS: The clinical data of 146 NF-PitNETs were evaluated, and the results showed that the cholesterol (P = 0.034) between invasive and noninvasive NF-PitNETs significantly differed. After binary logistic analysis, cholesterol was found to be an independent risk factor for cavernous sinus invasion (CSI) in NF-PitNETs. Bioinformatics analysis found three immune cells between invasive and noninvasive NF-PitNETs were statistically significant in the GSE169498 dataset, and 34 cholesterol metabolism-related genes with differences between the two groups were obtained 12 hub genes were selected by crossing the two machine learning algorithm results. Subsequently, cholesterol metabolism-related subgroups, A and B, were obtained by unsupervised hierarchical clustering analysis. The results showed that 12 immune cells infiltrated differentially between the two subgroups. The chi-square test revealed that the two subgroups had statistically significance in the invasive and noninvasive samples (P = 0.001). KEGG enrichment analysis showed that the differentially expressed genes were mainly enriched in the neural ligand–receptor pathway. GSVA analysis showed that the mTORC signaling pathway was upregulated and played an important role in the two-cluster comparison. CONCLUSION: By clinical data and bioinformatics analysis, cholesterol metabolism-related genes may promote the infiltration abundance of immune cells in NF-PitNETs and the invasion of cavernous sinuses by NF-PitNETs through the mTOR signaling pathway. This study provides a new perspective to explore the pathogenesis of cavernous sinus invasion by NF-PitNETs and determine potential therapeutic targets for this disease. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12944-023-01883-3. BioMed Central 2023-08-10 /pmc/articles/PMC10413501/ /pubmed/37563740 http://dx.doi.org/10.1186/s12944-023-01883-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Feng, Tianshun
Hou, Pengwei
Mu, Shuwen
Fang, Yi
Li, Xinxiong
Li, Ziqi
Wang, Di
Chen, Li
Lu, Lingling
Lin, Kunzhe
Wang, Shousen
Identification of cholesterol metabolism-related subtypes in nonfunctioning pituitary neuroendocrine tumors and analysis of immune infiltration
title Identification of cholesterol metabolism-related subtypes in nonfunctioning pituitary neuroendocrine tumors and analysis of immune infiltration
title_full Identification of cholesterol metabolism-related subtypes in nonfunctioning pituitary neuroendocrine tumors and analysis of immune infiltration
title_fullStr Identification of cholesterol metabolism-related subtypes in nonfunctioning pituitary neuroendocrine tumors and analysis of immune infiltration
title_full_unstemmed Identification of cholesterol metabolism-related subtypes in nonfunctioning pituitary neuroendocrine tumors and analysis of immune infiltration
title_short Identification of cholesterol metabolism-related subtypes in nonfunctioning pituitary neuroendocrine tumors and analysis of immune infiltration
title_sort identification of cholesterol metabolism-related subtypes in nonfunctioning pituitary neuroendocrine tumors and analysis of immune infiltration
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10413501/
https://www.ncbi.nlm.nih.gov/pubmed/37563740
http://dx.doi.org/10.1186/s12944-023-01883-3
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