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. 2023 Jul 3;13(1):10751.
doi: 10.1038/s41598-023-38046-4.

Identification of novel biomarkers and immune infiltration features of recurrent pregnancy loss by machine learning

Yujia Luo  1 , Yuanyuan Zhou  2
Affiliations

Identification of novel biomarkers and immune infiltration features of recurrent pregnancy loss by machine learning

Yujia Luo et al. Sci Rep. .

Abstract

Recurrent pregnancy loss (RPL) is a complex reproductive disorder. The incompletely understood pathophysiology of RPL makes early detection and exact treatment difficult. The purpose of this work was to discover optimally characterized genes (OFGs) of RPL and to investigate immune cell infiltration in RPL. It will aid in better understanding the etiology of RPL and in the early detection of RPL. The RPL-related datasets were obtained from the Gene Expression Omnibus (GEO), namely GSE165004 and GSE26787. We performed functional enrichment analysis on the screened differentially expressed genes (DEGs). Three machine learning techniques are used to generate the OFGs. A CIBERSORT analysis was conducted to examine the immune infiltration in RPL patients compared with normal controls and to investigate the correlation between OFGs and immune cells. Between the RPL and control groups, 42 DEGs were discovered. These DEGs were found to be involved in cell signal transduction, cytokine receptor interactions, and immunological response, according to the functional enrichment analysis. By integrating OFGs from the LASSO, SVM-REF, and RF algorithms (AUC > 0.880), we screened for three down-regulated genes: ZNF90, TPT1P8, FGF2, and an up-regulated FAM166B. Immune infiltration study revealed that RPL samples had more monocytes (P < 0.001) and fewer T cells (P = 0.005) than controls, which may contribute to RPL pathogenesis. Additionally, all OFGs linked with various invading immune cells to varying degrees. In conclusion, ZNF90, TPT1P8, FGF2, and FAM166B are potential RPL biomarkers, offering new avenues for research into the molecular mechanisms of RPL immune modulation and early detection.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
The workflow of the study.
Figure 2
Figure 2
Differentially expressed genes (DEGs) identified between RPL and control women. (A) Heatmap. (B) Volcano plot.
Figure 3
Figure 3
Functional enrichment analysis of DEGs. (A) GO analysis was executed to identify the potential functions of DEGs, containing CC, MF, and BP. (B) KEGG pathway was evaluated between RPL and control patients regarding DEGs. (C) DO analysis was used to evaluate the enrichment of DEG in the disease.
Figure 4
Figure 4
Screening underlying OFGs by machine learning. (A) Identifying biomarkers by LASSO algorithm. (B) Random Forest algorithm treated the top 5 genes in terms of MeanDecreaseGini score as OFGs. (C) SVM-RFE algorithm filters out 8 OFGs. (D) Venn diagram displaying four OFGs intersected by machine learning algorithms.
Figure 5
Figure 5
Validation of the OFGs. (A–D) Expression of ZNF90, TPT1P8, FGF2 and FAM166B in RPL patients compared to controls. (E–H) Diagnostic effectiveness of ZNF90, TPT1P8, FGF2 and FAM166B in ROC curves.
Figure 6
Figure 6
Proportion and association of immune cell infiltration. (A) The ratio of 22 immune cell subtypes between RPL and controls women. (B) Violin diagram showing differences in immune cells between RPL and controls women. (C) Correlation analysis among 22 immune cells.
Figure 7
Figure 7
Visualization of Spearman correlation between 4 OFGs and immune cells in RPL patients. (A) ZNF90. (B) TPT1P8. (C) FGF2. (D) FAM166B.
See this image and copyright information in PMC

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