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Background <p>Preeclampsia (PE) is a pregnancy disorder that occurs after 20&#xa0;weeks of pregnancy. The objective of this study was to identify potential immune-related biomarkers and molecular subtypes for the treatment of PE.</p> Methods <p>Three datasets of GSE10588, GSE25906 and GSE48424 were downloaded from the Gene Expression Omnibus (GEO) database. The names of immune-related genes were retrieved from the ImmPort immune database. To screen the differentially expressed immune-related genes, the “limma” R package was used. An analysis of logistic regression was used to identify the key genes and a nomogram was constructed using these key genes. These&#xa0;key gene&#xa0;expression profiles&#xa0;were&#xa0;further validated using&#xa0;qRT-PCR.&#xa0;In addition, the landscape of immune cell infiltration was investigated using the CIBERSORTX software. The potential molecular subtypes of PE were also investigated using the “ConsensusClusterPlus” R package.</p> Results <p>The 103 immune-related genes differentially expressed were identified, including 47 up-regulated genes and 56 down-regulated genes. Univariate and multivariate logistic regression analysis was used to screen five key genes, including <i>CCL24, ENG, LCP2, GNAI1</i> and <i>FLT3</i>. The key genes were strongly associated with immune cell infiltration. Two&#xa0;molecular subtypes (C1 and C2) were identified. Both exhibited distinct levels of immune cell infiltration and gene expression.</p> Conclusion <p>This study identified five key genes, as well as immune-related subtypes, that could provide potential therapeutic targets and aid in the design of more precise PE immunotherapy.</p>

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Identification of immune-related genes and molecular subtypes associated with preeclampsia via bioinformatics analysis and experimental validation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月29日

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  • Tingting Zhao,
  • Ying Peng

摘要

Background

Preeclampsia (PE) is a pregnancy disorder that occurs after 20 weeks of pregnancy. The objective of this study was to identify potential immune-related biomarkers and molecular subtypes for the treatment of PE.

Methods

Three datasets of GSE10588, GSE25906 and GSE48424 were downloaded from the Gene Expression Omnibus (GEO) database. The names of immune-related genes were retrieved from the ImmPort immune database. To screen the differentially expressed immune-related genes, the “limma” R package was used. An analysis of logistic regression was used to identify the key genes and a nomogram was constructed using these key genes. These key gene expression profiles were further validated using qRT-PCR. In addition, the landscape of immune cell infiltration was investigated using the CIBERSORTX software. The potential molecular subtypes of PE were also investigated using the “ConsensusClusterPlus” R package.

Results

The 103 immune-related genes differentially expressed were identified, including 47 up-regulated genes and 56 down-regulated genes. Univariate and multivariate logistic regression analysis was used to screen five key genes, including CCL24, ENG, LCP2, GNAI1 and FLT3. The key genes were strongly associated with immune cell infiltration. Two molecular subtypes (C1 and C2) were identified. Both exhibited distinct levels of immune cell infiltration and gene expression.

Conclusion

This study identified five key genes, as well as immune-related subtypes, that could provide potential therapeutic targets and aid in the design of more precise PE immunotherapy.