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. 2023 Jan 26:14:992765.
doi: 10.3389/fimmu.2023.992765. eCollection 2023.

Identification of potential biomarkers and immune infiltration characteristics in recurrent implantation failure using bioinformatics analysis

Zhen-Zhen Lai  1   2 , Jie Zhang  3 , Wen-Jie Zhou  4 , Jia-Wei Shi  2 , Hui-Li Yang  2 , Shao-Liang Yang  2 , Jiang-Nan Wu  5 , Feng Xie  6 , Tao Zhang  7 , Ming-Qing Li  1   2   8
Affiliations

Identification of potential biomarkers and immune infiltration characteristics in recurrent implantation failure using bioinformatics analysis

Zhen-Zhen Lai et al. Front Immunol. .

Abstract

Introduction: Recurrent implantation failure (RIF) is a frustrating challenge because the cause is unknown. The current study aims to identify differentially expressed genes (DEGs) in the endometrium on the basis of immune cell infiltration characteristics between RIF patients and healthy controls, as well as to investigate potential prognostic markers in RIF.

Methods: GSE103465, and GSE111974 datasets from the Gene Expression Omnibus database were obtained to screen DEGs between RIF and control groups. Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes Pathway analysis, Gene Set Enrichment Analysis, and Protein-protein interactions analysis were performed to investigate potential biological functions and signaling pathways. CIBERSORT was used to describe the level of immune infiltration in RIF, and flow cytometry was used to confirm the top two most abundant immune cells detected.

Results: 122 downregulated and 66 upregulated DEGs were obtained between RIF and control groups. Six immune-related hub genes were discovered, which were involved in Wnt/-catenin signaling and Notch signaling as a result of our research. The ROC curves revealed that three of the six identified genes (AKT1, PSMB8, and PSMD10) had potential diagnostic values for RIF. Finally, we used cMap analysis to identify potential therapeutic or induced compounds for RIF, among which fulvestrant (estrogen receptor antagonist), bisindolylmaleimide-ix (CDK and PKC inhibitor), and JNK-9L (JNK inhibitor) were thought to influence the pathogenic process of RIF. Furthermore, our findings revealed the level of immune infiltration in RIF by highlighting three signaling pathways (Wnt/-catenin signaling, Notch signaling, and immune response) and three potential diagnostic DEGs (AKT1, PSMB8, and PSMD10).

Conclusion: Importantly, our findings may contribute to the scientific basis for several potential therapeutic agents to improve endometrial receptivity.

Keywords: Wnt/β-catenin signaling; bioinformatics; biomarkers; immune infiltration; notch signaling; recurrent implantation failure.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be constructed as a potential conflict of interest. The reviewer JZ declared a shared parent affiliation with the author WJZ to the handling editor at the time of the review.

Figures

Figure 1
Figure 1
Data Preprocessing and Heat map of top 50 genes in RIF and Ctrl groups. (A, B) PCA analysis of GSE103465, and GSE111974 expression profiles before (A) and after (B) normalization. (C) Expression of top 50 genes in RIF and Ctrl groups was presented by heatmap.
Figure 2
Figure 2
Identification, PPI Network and Module Analysis of DEGs. (A) The 188 DEGs were visualized by volcano map. (B) PPI network constructed with the DEGs from GSE103465, and GSE111974 datasets. (C) The significant module identified from the PPI network using the molecular complex detection (MCODE) method with a score of ≥ 5.0. Different nodes’ color present different functions.
Figure 3
Figure 3
Functional Enrichment Analysis of DEGs. (A, B) GO analysis, KEGG pathway analysis (A), and GSEA enrichment analysis (B) of DEGs. (C) Up or down gene numbers related to cell cycle, signaling by Wnt/β-catenin, signaling by NOTCH, immune system regulation, ATP metabolic process, cellular protein catabolic process, lipid biosynthetic process, and thyroid hormone signaling pathway in RIF group from validation dataset (GSE183837) were presented by rose diagram.
Figure 4
Figure 4
Identification and Functional Enrichment Analysis of Hub Genes. (A) The top 20 hub genes were discovered by Cytoscape software (version 3.7.2, cytoHubba plug-ins). (B) GO analysis, KEGG pathway analysis and REAC analysis of hub genes were performed by g:Profiler (https://biit.cs.ut.ee/gprofiler/gost).
Figure 5
Figure 5
The landscape of immune infiltration between RIF and control samples. (A) The relative percentage of 22 types of immune cells. (B) The difference of immune infiltration between RIF and controls. The control group was marked as blue color and RIF group was marked as red color. (C) Stacked area plot showed the relative percentage of 7 types of immune cells from validation dataset (GSE183837). Data are presented as the median and the interquartile range. Statistical significance (Kruskal–Wallis test with Dunn’s multiple-comparison test): *p < 0.05.
Figure 6
Figure 6
The Proportions of Macrophage and NK Cells. (A) The gating strategy for identifying macrophage and NK cells from endometrial leukocytes. (B) The quantitative analysis of the proportions of macrophage, NK cells, CD16+ macrophage and CD16+ NK cells (n=10) in endometrium from control and RIF patients. Data are presented as the mean ± standard error of the mean. Statistical significance (Student’s t-test): **p < 0.01, ***p < 0.001.
Figure 7
Figure 7
Expression Levels and Diagnose Significance of Hub Genes. (A) Venn diagram of common hub genes identified among DEGs, Wnt/β-catenin signaling pathway, NOTCH signaling pathway, immune response, and hub genes. (B) The expression levels of 6 hub genes in GSE103465, and GSE111974 datasets. (C) The GSE103465, and GSE111974 datasets were used to train the diagnostic effectiveness of the biomarkers for RIF by ROC analysis. (D) The GSE58144 dataset was used to validate the diagnostic effectiveness of the biomarkers (AKT1, PSMB8, and PSMD10) for RIF by ROC analysis. Data are presented as the median and the interquartile range. Statistical significance (Kruskal–Wallis test with Dunn’s multiple-comparison test): NS, no significant difference, **p < 0.01, ***p < 0.001.
Figure 8
Figure 8
Drug-Target genes Interaction Network Analysis. From the left to right of alluvial diagram: the potential pharmaceuticals, target genes and enriched pathways by GO and KEGG analysis.
Figure 9
Figure 9
Diagram of the study design. Discover datasets, GSE103465, and GSE111974, contained 18301 genes with 27 RIF and 27 Ctrl samples. In total, 188 DEGs were identified: 122 genes were downregulated and 66 genes were upregulated. Ten important pathways related to RIF were enriched, and were validated by validation dataset (GSE183837). Top 20 hub genes were identified by Cytoscape through PPI network, and three of them may have potential predictive values for RIF in the validation set (GSE58144). Endometrial macrophages’ infiltration level was increased in patients with RIF, compared to the control group, which were performed by CIBERSORT algorithm, and validated by FCM analysis. Bisindolylmaleimide-ix and JNK-9L may be potential therapeutic compounds for RIF, related to the result of cMap analysis.
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