4.8 Article

Identification and validation of a signature based on macrophage cell marker genes to predict recurrent miscarriage by integrated analysis of single-cell and bulk RNA-sequencing

期刊

FRONTIERS IN IMMUNOLOGY
卷 13, 期 -, 页码 -

出版社

FRONTIERS MEDIA SA
DOI: 10.3389/fimmu.2022.1053819

关键词

recurrent miscarriage; single-cell RNA-sequencing; bulk RNA-sequencing; macrophage; maternal fetal interface

资金

  1. Natural Science Foundation of Guangxi Zhuang Autonomous Region
  2. National Natural Science Foundation of China
  3. Guangxi Medical University Training Program for Distinguished Young Scholars
  4. Special Fund of the Female Fertility Preservation Innovation Team of the First Affiliated Hospital of Guangxi Medical University
  5. [2019GXNSFFA245013]
  6. [2018GXNSFDA050017]
  7. [81871172]

向作者/读者索取更多资源

The study identified 1293 macrophage marker genes through single-cell RNA sequencing analysis, and identified seven core genes closely related to recurrent miscarriage (RM) using various algorithms. A nomogram constructed from the expression levels of these genes showed high diagnostic value for RM, suggesting potential targets for preventative treatments.
Recurrent miscarriage (RM) is a chronic, heterogeneous autoimmune disease that has serious social and personal consequences. No valid and reliable diagnostic markers or therapeutic targets for RM have been identified. Macrophages impact the innate immune system and can be used as diagnostic and prognostic markers for many diseases. We first collected 16 decidua and villi tissue samples from 5 normal patients and 3 RM patients for single-cell RNA sequencing data analysis and identified 1293 macrophage marker genes. We then screened a recurrent miscarriage cohort (GSE165004) for 186 macrophage-associated marker genes that were significantly differentially expressed between RM patients and the normal pregnancy endometrial tissues, and performed a functional enrichment analysis of differentially expressed genes. We then identified seven core genes (ACTR2, CD2AP, MBNL2, NCSTN, PUM1, RPN2, and TBC1D12) from the above differentially expressed gene group that are closely related to RM using the LASSO, Random Forest and SVM-RFE algorithms. We also used GSE26787 and our own collection of clinical specimens to further evaluate the diagnostic value of the target genes. A nomogram was constructed of the expression levels of these seven target genes to predict RM, and the ROC and calibration curves showed that our nomogram had a high diagnostic value for RM. These results suggest that ACTR2 and NCSTN may be potential targets for preventative RM treatments.

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