Lipids in health and disease · 2023
Identification of cholesterol metabolism-related subtypes in nonfunctioning pituitary neuroendocrine tumors and analysis of immune infiltration.
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Feng Tianshun, Hou Pengwei, Mu Shuwen, Fang Yi, Li Xinxiong, Li Ziqi, Wang Di, Chen Li, Lu Lingling, Lin Kunzhe, Wang Shousen
Аннотация
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.
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.