Bioinformatics identification and validation of maternal blood biomarkers and immune cell infiltration in preeclampsia: An observational study

Abstract

Preeclampsia (PE) is a pregnancy complication characterized by placental dysfunction. However, the relationship between maternal blood markers and PE is unclear. It is helpful to improve the diagnosis and treatment of PE using new biomarkers related to PE in the blood. Three PE-related microarray datasets were obtained from the Gene Expression Synthesis database. The limma software package was used to identify differentially expressed genes (DEGs) between PE and control groups. Least absolute shrinkage and selection operator regression, support vector machine, random forest, and multivariate logistic regression analyses were used to determine key diagnostic biomarkers, which were verified using clinical samples. Subsequently, functional enrichment analysis was performed. In addition, the datasets were combined for immune cell infiltration analysis and to determine their relationships with core diagnostic biomarkers. The diagnostic performance of key genes was evaluated using the receiver operating characteristic (ROC) curve, C-index, and GiViTi calibration band. Genes with potential clinical applications were evaluated using decision curve analysis (DCA). Seventeen DEGs were identified, and 6 key genes (FN1, MYADM, CA6, PADI4, SLC4A10, and PPP4R1L) were obtained using 3 types of machine learning methods and logistic regression. High diagnostic performance was found for PE through evaluation of the ROC, C-index, GiViti calibration band, and DCA. The 2 types of immune cells (M0 macrophages and activated mast cells) were significantly different between patients with PE and controls. All of these genes except SLC4A10 showed significant differences in expression levels between the 2 groups using quantitative reverse transcription-polymerase chain reaction. This model used 6 maternal blood markers to predict the occurrence of PE. The findings may stimulate ideas for the treatment and prevention of PE.

Figure 1.

Combination of datasets and screening of differentially expressed genes. (A) Before merging the principal component analysis datasets. (B) After the principal component analysis datasets were merged. (C) Differentially expressed genes between the preeclampsia (PE) and control groups. (D) Heatmap of differentially expressed genes between PE and control groups.

Figure 2.

Identification of intersecting genes through the use of 3 machine-learning methods in merged datasets. (A, B) Least absolute shrinkage and selection operator regression analysis results. (C, D) Random forest analysis results. (E) Support vector machine-recursive feature elimination analysis results. (F) The intersecting genes are machine-learning-based.

Figure 3.

Construction and evaluation of a nomogram model for preeclampsia patients. (A) Potential diagnostic markers for preeclampsia (PE) were identified using multivariate logistic regression. (B) Construction of nomogram model for PE prediction. (C) Receiver operating characteristic curve, (D) GiViTi calibration curve, € decision curve analysis, and (F) clinical impact plot for the PE prediction model.

Figure 4.

Functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of key genes. (A) Functional enrichment analysis. (B) KEGG signaling pathway analysis.

Figure 5.

Analysis of immune cell infiltration and its correlation with key gene expression levels. (A) Analysis of immune cell infiltration between the preecelampsia (PE) And control groups. (B) Correlation analysis between immune cell infiltration and key gene expression levels.

Figure 6.

Expression levels of key genes in the preeclampsia (PE) and control groups. (A) GSE149440, (B) GSE48424, (C) GSE166846. (D) Validation of clinical blood samples.