Deciphering the endometrial immune landscape of RIF during the window of implantation from cellular senescence by integrated bioinformatics analysis and machine learning

Zhao, Xiaoyan; Zhao, Yang; Jiang, Yuepeng; Zhang, Qin

doi:10.3389/fimmu.2022.952708

Cited by 6 publications

(4 citation statements)

References 93 publications

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“…The shared DEGs were performed further analyzed by R package “WGCNA” to explore the gene modules significantly related to disease ( 17 ). In a word, outlier samples were excluded by the hierarchical clustering analysis at first.…”

Section: Methodsmentioning

confidence: 99%

Integrated bioinformatics to identify potential key biomarkers for COVID-19-related chronic urticaria

Zhang

Feng²,

Zou³

et al. 2022

Front. Immunol.

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BackgroundA lot of studies have revealed that chronic urticaria (CU) is closely linked with COVID-19. However, there is a lack of further study at the gene level. This research is aimed to investigate the molecular mechanism of COVID-19-related CU via bioinformatic ways.MethodsThe RNA expression profile datasets of CU (GSE72540) and COVID-19 (GSE164805) were used for the training data and GSE57178 for the verification data. After recognizing the shared differently expressed genes (DEGs) of COVID-19 and CU, genes enrichment, WGCNA, PPI network, and immune infiltration analyses were performed. In addition, machine learning LASSO regression was employed to identify key genes from hub genes. Finally, the networks, gene-TF-miRNA-lncRNA, and drug-gene, of key genes were constructed, and RNA expression analysis was utilized for verification.ResultsWe recognized 322 shared DEGs, and the functional analyses displayed that they mainly participated in immunomodulation of COVID-19-related CU. 9 hub genes (CD86, FCGR3A, AIF1, CD163, CCL4, TNF, CYBB, MMP9, and CCL3) were explored through the WGCNA and PPI network. Moreover, FCGR3A, TNF, and CCL3 were further identified as key genes via LASSO regression analysis, and the ROC curves confirmed the dependability of their diagnostic value. Furthermore, our results showed that the key genes were significantly associated with the primary infiltration cells of CU and COVID-19, such as mast cells and macrophages M0. In addition, the key gene-TF-miRNA-lncRNA network was constructed, which contained 46 regulation axes. And most lncRNAs of the network were proved to be a significant expression in CU. Finally, the key gene-drug interaction network, including 84 possible therapeutical medicines, was developed, and their protein-protein docking might make this prediction more feasible.ConclusionsTo sum up, FCGR3A, TNF, and CCL3 might be potential biomarkers for COVID-19-related CU, and the common pathways and related molecules we explored in this study might provide new ideas for further mechanistic research.

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Section: Methodsmentioning

confidence: 99%

Integrated bioinformatics to identify potential key biomarkers for COVID-19-related chronic urticaria

Zhang

Feng²,

Zou³

et al. 2022

Front. Immunol.

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“…The development of biomedicine has been tremendously aided by breakthrough advancements in microarray technology and bioinformatics. Public databases have a lot of high-throughput data, which greatly helps in revealing disease pathogenesis and identifying potential targets for drug designing ( 12 ). However, the applications of these techniques are very adequate to combat schizophrenia.…”

Section: Introductionmentioning

confidence: 99%

Schizophrenia and cell senescence candidate genes screening, machine learning, diagnostic models, and drug prediction

Feng

Shen

et al. 2023

Front. Psychiatry

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BackgroundSchizophrenia (SC) is one of the most common psychiatric diseases. Its potential pathogenic genes and effective treatment methods are still unclear. Cell senescence has been confirmed in mental diseases. A link exists between cellular senescence and immunity, and immune-related problems affect suicide rates in individuals suffering from schizophrenia. Therefore, the aims of this study were to identify candidate genes based on cell senescence that can affect the diagnosis and treatment of schizophrenia.MethodsTwo data sets of schizophrenia were provided by the Gene Expression Omnibus (GEO) database, one was taken as training and the other as a validation group. The genes related to cell senescence were obtained from the CellAge database. DEGs were identified using the Limma package and weighted gene co-expression network analysis (WGCNA). The function enrichment analysis was conducted, followed by machine learning-based identification for least absolute shrinking and selection operators (LASSO) regression. Random Forest were used to identify candidate immune-related central genes and establish artificial neural networks for verification of the candidate genes. The receiver operating characteristic curve (ROC curve) was used for the diagnosis of schizophrenia. Immune cell infiltrates were constructed to study immune cell dysregulation in schizophrenia, and relevant drugs with candidate genes were collected from the DrugBank database.ResultsThirteen co-expression modules were screened for schizophrenia, of which 124 were the most relevant genes.There were 23 intersected genes of schizophrenia (including DEGs and the cellular senescence-related genes), and through machine learning six candidate genes were finally screened out. The diagnostic value was evaluated using the ROC curve data. Based on these results it was confirmed that these candidate genes have high diagnostic value.Two drugs related to candidate genes, Fostamatinib and Ritodine, were collected from the DrugBanks database.ConclusionSix potential candidate genes (SFN, KDM5B, MYLK, IRF3, IRF7, and ID1) had been identified, all of which had diagnostic significance. Fostamatinib might be a drug choice for patients with schizophrenia to develop immune thrombocytopenic purpura (ITP) after treatment, providing effective evidence for the pathogenesis and drug treatment of schizophrenia.

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“…Approximately 1%−5% of couples are affected by recurrent miscarriage (RM), 1 while approximately 15% of patients seeking in vitro fertilization (IVF) are affected by recurrent implantation failure (RIF) 2 . However, the cause for up to 50% of these cases remains unexplained 3 .…”

Section: Introductionmentioning

confidence: 99%

Association of human leukocyte antigen‐G and ‐F with recurrent miscarriage and implantation failure: A systematic review and meta‐analysis

Han,

Xia,

Zhu

et al. 2023

American J Rep Immunol

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ProblemThe immune system plays an essential role in embryonic implantation and pregnancy, but the molecular details remain controversial. In the past four decades, human leukocyte antigen (HLA)‐G and ‐F have garnered significant attention.Method of StudyMEDLINE, EMBASE, Web of Science, and the Cochrane Trials Registry were searched from their inception dates until December 2022. Studies were selected following PRISMA guidelines. Meta‐analyses were used to assess the relationship of soluble HLA‐G (sHLA‐G) and HLA‐G 3′‐untranslated region polymorphisms with recurrent miscarriage (RM) and recurrent implantation failure (RIF). Narrative synthesis was conducted to determine the association of RM with other single nucleotide polymorphisms (SNPs) and HLA‐G protein in tissues and of RIF with HLA‐F. Risk‐of‐bias was assessed using ROBINS‐I. Publication bias was assessed using Egger's and Begg's tests.ResultsFinally, 42 articles were eligible for inclusion in the systematic review (32 in the meta‐analysis; 13 in narrative synthesis). We found a significant association between the 14‐bp ins/del HLA‐G polymorphism and RM risk, but no definitive association with RIF risk. Women with RM had lower blood concentrations of sHLA‐G during pregnancy and non‐pregnancy than did controls. For women in the RIF group, no significant difference was found.ConclusionHLA‐G protein and gene expression levels may be closely related to RM. The relevance of HLA‐G to RIF is still being determined. A narrative synthesis of current studies has shown that HLA‐F is likely associated with RIF.

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Deciphering the endometrial immune landscape of RIF during the window of implantation from cellular senescence by integrated bioinformatics analysis and machine learning

Cited by 6 publications

References 93 publications

Integrated bioinformatics to identify potential key biomarkers for COVID-19-related chronic urticaria

Integrated bioinformatics to identify potential key biomarkers for COVID-19-related chronic urticaria

Schizophrenia and cell senescence candidate genes screening, machine learning, diagnostic models, and drug prediction

Association of human leukocyte antigen‐G and ‐F with recurrent miscarriage and implantation failure: A systematic review and meta‐analysis

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