Demystifying the landscape of endometrial immune microenvironment in luteal-phase from cuprotosis : Implications for the mechanism and treatment of RPL
Copyright © 2024. Published by Elsevier B.V..
BACKGROUND: Adaptive changes in the endometrial immune microenvironment during the luteal phase are essential for pregnancy, and their abnormalities are associated with recurrent pregnancy loss (RPL). Nevertheless, the specific mechanism is still unknown. Cuprotosis, an innovatively discovered type of programmed cell death, provides us with a pioneering perspective to decipher the landscape of luteal-phase endometrial immune microenvironment in RPL. This study aimed to analyze the immune landscape of luteal-phase endometrial microenvironment in RPL and explore the association of cuprotosis with it through integrative bioinformatics analysis.
METHODS: The microarrays involving the luteal phase endometrial tissue of RPL were obtained from the GEO database. Differentially expressed genes (DEGs) of RPL were screened and key modules were detected by WGCNA. GO, KEGG, and GSEA immune enrichment analyses were performed on the DEGs in the most relevant modules to RPL. Then, the endometrial immune microenvironment landscape of RPL was analyzed, including immune infiltration analysis and correlation analysis between immune cells or immune functions. The interaction of cuprotosis-related genes (CRGs), the expression level between groups, the immune localization and their correlation with immune cells and immune function were analyzed. LASSO regression and Nomogram evaluated the diagnostic value of immune-related CRGS in RPL. Functional enrichment analysis was performed on the RPL signature CRGs. And RPL samples were grouped according to the expression of 7 RPL signature CRGs through unsupervised clustering analysis. After that, we analyzed the expression level of CRGs and immune infiltration, as well as performed immune function enrichment analysis in subtypes. In addition, we also screened potential drugs that might act on CRGs to improve the pathological mechanism of RPL.
RESULTS: In this study, we uncovered that DEGs and genes in key modules derived from weighted gene co-expression network analysis (WGCNA) were involved in immune regulation. And the immune infiltration landscape of RPL was significantly different from healthy controls. Furthermore, six hub genes were screened from CRGs based on Cytohubba, and their expression profilings were verified in RPL and normal mouse samples. Besides, seven CRGs closely associated with the immune regulation of RPL were identified by Spearman correlation analysis, including SLC31A1, LIAS, DLD, DLAT, DBT, ATP7B, and ATP7A, named as immune-related CRGs. Furthermore, three subgroups clustered according to these seven genes showed significant differences in immune landscape, suggesting a remarkable effect of CRGs on immune regulation. Last but not least, we analyzed the regulation network of transcription factors, miRNAs, and CRGs, and screened potential compounds for the treatment of RPL by targeting CRGs.
CONCLUSIONS: The abnormal endometrial immune microenvironment in the luteal phase was associated with the pathomechanism of RPL, and cuprotosis was closely involved in the immune microenvironment in the luteal phase endometrium of RPL. Collectively, this study revealed the potential contribution of CRGs to the pathogenesis of RPL, providing a novel breakthroughs in insights into the pathogenesis, diagnosis, and treatment of RPL.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:903 |
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| Enthalten in: |
Gene - 903(2024) vom: 20. Feb., Seite 148191 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zhao, Xiaoxuan [VerfasserIn] |
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| Links: |
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| Themen: |
Cuprotosis |
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| Anmerkungen: |
Date Completed 14.02.2024 Date Revised 14.02.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.gene.2024.148191 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM367437813 |
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| 100 | 1 | |a Zhao, Xiaoxuan |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Demystifying the landscape of endometrial immune microenvironment in luteal-phase from cuprotosis |b Implications for the mechanism and treatment of RPL |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
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| 500 | |a Date Revised 14.02.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2024. Published by Elsevier B.V. | ||
| 520 | |a BACKGROUND: Adaptive changes in the endometrial immune microenvironment during the luteal phase are essential for pregnancy, and their abnormalities are associated with recurrent pregnancy loss (RPL). Nevertheless, the specific mechanism is still unknown. Cuprotosis, an innovatively discovered type of programmed cell death, provides us with a pioneering perspective to decipher the landscape of luteal-phase endometrial immune microenvironment in RPL. This study aimed to analyze the immune landscape of luteal-phase endometrial microenvironment in RPL and explore the association of cuprotosis with it through integrative bioinformatics analysis | ||
| 520 | |a METHODS: The microarrays involving the luteal phase endometrial tissue of RPL were obtained from the GEO database. Differentially expressed genes (DEGs) of RPL were screened and key modules were detected by WGCNA. GO, KEGG, and GSEA immune enrichment analyses were performed on the DEGs in the most relevant modules to RPL. Then, the endometrial immune microenvironment landscape of RPL was analyzed, including immune infiltration analysis and correlation analysis between immune cells or immune functions. The interaction of cuprotosis-related genes (CRGs), the expression level between groups, the immune localization and their correlation with immune cells and immune function were analyzed. LASSO regression and Nomogram evaluated the diagnostic value of immune-related CRGS in RPL. Functional enrichment analysis was performed on the RPL signature CRGs. And RPL samples were grouped according to the expression of 7 RPL signature CRGs through unsupervised clustering analysis. After that, we analyzed the expression level of CRGs and immune infiltration, as well as performed immune function enrichment analysis in subtypes. In addition, we also screened potential drugs that might act on CRGs to improve the pathological mechanism of RPL | ||
| 520 | |a RESULTS: In this study, we uncovered that DEGs and genes in key modules derived from weighted gene co-expression network analysis (WGCNA) were involved in immune regulation. And the immune infiltration landscape of RPL was significantly different from healthy controls. Furthermore, six hub genes were screened from CRGs based on Cytohubba, and their expression profilings were verified in RPL and normal mouse samples. Besides, seven CRGs closely associated with the immune regulation of RPL were identified by Spearman correlation analysis, including SLC31A1, LIAS, DLD, DLAT, DBT, ATP7B, and ATP7A, named as immune-related CRGs. Furthermore, three subgroups clustered according to these seven genes showed significant differences in immune landscape, suggesting a remarkable effect of CRGs on immune regulation. Last but not least, we analyzed the regulation network of transcription factors, miRNAs, and CRGs, and screened potential compounds for the treatment of RPL by targeting CRGs | ||
| 520 | |a CONCLUSIONS: The abnormal endometrial immune microenvironment in the luteal phase was associated with the pathomechanism of RPL, and cuprotosis was closely involved in the immune microenvironment in the luteal phase endometrium of RPL. Collectively, this study revealed the potential contribution of CRGs to the pathogenesis of RPL, providing a novel breakthroughs in insights into the pathogenesis, diagnosis, and treatment of RPL | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Cuprotosis | |
| 650 | 4 | |a Endometrium | |
| 650 | 4 | |a Immune microenvironment | |
| 650 | 4 | |a Luteal phase | |
| 650 | 4 | |a Recurrent pregnancy loss | |
| 700 | 1 | |a Wang, Sihui |e verfasserin |4 aut | |
| 700 | 1 | |a Du, Tingting |e verfasserin |4 aut | |
| 700 | 1 | |a Jiang, Yuepeng |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Yang |e verfasserin |4 aut | |
| 700 | 1 | |a Ma, Yiming |e verfasserin |4 aut | |
| 700 | 1 | |a Shen, Dan |e verfasserin |4 aut | |
| 700 | 1 | |a Shen, Yi |e verfasserin |4 aut | |
| 700 | 1 | |a Ma, Jing |e verfasserin |4 aut | |
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