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. 2025 Aug 15;104(33):e43156. doi: 10.1097/MD.0000000000043156

Diagnostic value of the iron apoptosis-related gene in recurrent miscarriage

Wenfei Zheng a,*, Min Wen a, Chandana Vayakkali Poochali a, Wei Xie a, Hongyuan Song a
PMCID: PMC12367016  PMID: 40826718

Abstract

Background:

The early diagnosis and treatment of recurrent miscarriage are essential for preventing adverse pregnancy outcomes. This study explores the value of iron apoptosis-related gene in the diagnosis of recurrent pregnancy loss (RPL).

Methods:

We obtained ferroptosis-related differentially expressed genes (DEGs) associated with RPL from the GEO database and the FerrDb database. Ferroptosis-related DEGs were subjected to functional analysis and protein–protein interaction analysis to obtain hub genes. A functional analysis of ferroptosis-related DEGs was performed. A protein–protein interaction network was used to identify the hub genes. In vitro experiments were used to verify their diagnostic value for RPL in our hospital. In addition, the online software Network Analyst was used to obtain miRNAs associated with core genes, and the immune landscape was explored in RPL patients and controls.

Results:

In total, 2245 DEGs were obtained from the GSE26787 dataset, of which 51 were ferroptosis-related DEGs. The top 4 genes are KRAS, SRC, EGFR, and MDM2. In the training set GSE26787, only SRC was significantly upregulated in the RSA group compared with the control group, with an area under the curve of 0.92. In the validation set GSE165004, 3 genes (EGFR, KRAS, SRC) were differentially expressed, with areas under the curve of 0.776, 0.964, and 0.814, respectively. Our serum samples revealed a statistically significant difference in KRAS expression in the RPL group only, with an area under the curve of 0.77. miR-1-3p and miR-155-5p interact with all 4 ferroptosis-related DEGs.

Conclusion:

The iron apoptosis-related genes such as EGFR, KRAS, SRC, and MDM2 are valuable for the early diagnosis of recurrent miscarriage. They affect the hypoxia of the endometrium during the implantation period through the FoxO signaling pathway, leading to a reduction in the number and volume of microvessels, which in turn causes recurrent miscarriage.

Keywords: bioinformatics, endometrium, ferroptosis, RPL

1. Introduction

Recurrent pregnancy loss (RPL) is a prevalent obstetric issue during pregnancy caused by various factors, such as genetic defects, structural abnormalities of the genitals, immune disorders,[1] and other unidentified etiologic factors, which present a challenge to the scientific and clinical community.[2] Studies have shown that the endometrium offers feedback on the quality of implanted embryos.[3] Research indicates that the differentiation of endometrial stromal cells into decidual cells involves varying responses on the basis of the quality of the embryo, and these cells are sensitive to embryonic signals.[2]

Iron-induced apoptosis is a recently discovered programmed cell death mechanism that is linked to various obstetric conditions.[4] Research has demonstrated that placental and uterine iron prolapse contribute to oxidative stress-related fetal loss in pregnant rats with polycystic ovary syndrome-like characteristics.[5] Iron prolapse inhibitors have been shown to reverse embryonic death in both LPS-induced abortion and spontaneous abortion models, suggesting that they could be potential treatment targets for patients with RPL.[6]

Downregulation of YAP1 has been shown to induce iron apoptosis and disrupt the trophoblast invasion process, thereby disrupting communication at the maternal–fetal interface and inducing recurrent miscarriage.

However, the role and impact of iron prolapse in the decidua, as well as its effects on embryo implantation, remain unclear. As a result, further investigation of ferroptosis-related genes (FRGs) associated with recurrent miscarriage is crucial for developing new strategies and approaches for treating recurrent miscarriage.

In this research, we gathered information on recurrent miscarriage from the Gene Expression Omnibus (GEO) database and conducted bioinformatics analyses to assess the significance of iron death-related genes in the early detection of recurrent miscarriage. Furthermore, our results were confirmed through the examination of samples obtained from patients with recurrent miscarriage at our hospital.

2. Materials and methods

2.1. Data collection

The Gene Expression Omnibus database is the source of the relevant datasets. GSE26787 consists of 5 patients with RPL and 5 patients with normal endometrial tissue. GSE165004 was utilized as a validation dataset, containing 24 transcriptomic profiles of mid secretory phase endometria of patients with RPLs and 24 normal controls. The FerrDb database contains 483 FRGs.[7]

2.2. Identification of differentially expressed genes (DEGs)

The GEO2R online analysis software tool was used to analyze the datasets to identify DEGs. To determine the differential expression of FRGs, we used an online Venn diagram to determine the intersection of DEGs and FRGs.

2.3. Analysis of functional enrichment of DEGs and analysis of the protein–protein interaction network

Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses of ferroptosis-related DEGs were conducted via the “cluster Profiler” package[8] of R. Analysis of the protein–protein interaction (PPI) network of the differential expression FRGs was conducted via the STRING database (https://string-db.org/). Cytoscape (version 3.9.1) was used to visualize the output of STRING, and the hub plugin was used to access the core genes.

2.4. Key gene-related miRNAs and hub gene validation

The identified hub genes were utilized to identify miRNA–gene interactions via the online software Network Analyst (version 3.0, https://www.networkanalyst.ca/). The diagnostic accuracy of the hub genes in the GSE26787 and GSE165004 datasets was analyzed via receiver operating characteristic (ROC) curve analysis.

Serum samples were obtained from 20 patients who were diagnosed with recurrent miscarriage and 20 patients who had induced abortion at our hospital during the same time period. Our hospital Ethics Committee approved this work. The levels of serum KRAS, MDM2, SRC, and EGFR (Jin Shao Yuan Biotechnology Co., Ltd., Shanghai, China) via human enzyme-linked immunosorbent assay (ELISA, Supplemental Digital Content, https://links.lww.com/MD/P328) kits.

2.5. Immune cell infiltration analysis using CIBERSORTx

To evaluate the role of the immune microenvironment in RSA, CIBERSORTx was used to calculate the ratios of 22 immune cell types and the associations between RPL and each immune cell type.

2.6. Statistical analysis

R software and Prism 9 were used to perform the statistical analysis. Continuous variables were compared via either Student t test or the Kruskal–Wallis H test, with a P value < .05 (two-sided) considered statistically significant.

3. Results

3.1. DEGs and ferroptosis-related DEGs

A total of 2245 DEGs from 5 patients with RPL and 5 controls were examined via the “limma” package, where |log FC| > 1 and P < .05 were identified as DEGs. Among these 2245 genes, 598 were upregulated, and 1647 were downregulated, as illustrated in the volcano plot in Figure 1A. Additionally, from the FerrDb database, 483 FRGs were collected. A total of 51 differentially expressed FRGs linked to RPL were identified by analyzing the intersection (Fig. 1B), which was chosen for further analysis. The standardized expression of ferroptosis-related DEGs is depicted in the heatmap (Fig. 2).

Figure 1.

Figure 1.

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(A) Differential gene volcano map (B) Intersection of DEGs in the GSE26787 and ferroptosis genes. DEGs = differentially expressed genes.

Figure 2.

Figure 2.

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Heatmap of 51 ferroptosis-related DEGs. DEGs = differentially expressed genes.

3.2. Analysis of ferroptosis-related DEGs enrichment and PPI network analysis

Enrichment analyses were conducted on the 51 genes via both GO and KEGG methods. GO analysis revealed 20 biological function (BP) terms, 11 cellular component terms and 10 molecular function terms, and the threshold value was P < .05. The main BP terms included inhibiting the regulation of the apoptotic process, the cellular response to hypoxia, and positive regulation of transcription from the RNA polymerase II promoter. The cellular component terms included nucleus, nucleoplasm, and membrane, whereas the molecular function terms included RNA polymerase II core promoter proximal region sequence-specific DNA binding, ATP binding, and chromatin binding. The top 10 catalogs were selected for scatter plots and trilinear chart (Fig. 3).

Figure 3.

Figure 3.

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Enrichment analysis of ferroptosis-related DEGs. (A) Top 10 bubble charts. (B) Top 10 histograms. DEGs = differentially expressed genes.

To gain a better understanding of the mechanism of FRGs in RPL, 66 signaling pathways were identified through enrichment analysis of the KEGG pathway (P < .05), which includes autophagy – animal, chemical carcinogenesis – reactive oxygen species, and the FoxO signaling pathway. Scatter plots were created on the basis of the top 10 catalogs (Fig. 4).

Figure 4.

Figure 4.

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Top 10 KEGG pathways. KEGG = Kyoto Encyclopedia of Genes and Genomes.

The STRING database (version 11.5, http://cn.string-db.org/) was used to examine the PPI network of the 51 ferroptosis-related DEGs. The outcome was visualized via Cytoscape software (3.9.1). A total of 10 hub genes were identified via the CytoHubba plugin of the software Cytoscape via the degree algorithm. Next, on the basis of the rank score, 4 first-level hub genes, including KRAS, SRC, EGFR, and MDM2, were identified with red nodes, and these genes were selected for further analysis (Fig. 5).

Figure 5.

Figure 5.

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PPI network of ferroptosis DEGs (red represents a higher degree, and blue represents a lower degree). DEGs = differentially expressed genes, PPI = protein–protein interaction.

3.3. Estimation of target miRNAs and establishment of the miRNA–target network

A potential miRNA-hub gene network was constructed to study the molecular mechanism responsible for the 4 ferroptosis-related DEGs accurately. Using the database Network Analyst, we predicted miRNAs that target 4 hub genes. Notably, both miR-1-3p and miR-155-5p interact with all 4 ferroptosis-related DEGs (Fig. 6).

Figure 6.

Figure 6.

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miRNA–gene interaction networks based on the 4 hub genes (red circles represent key genes; blue squares represent miRNAs related to key FDEGs). FDEGs = ferroptosis differential expression genes.

3.4. Database validation and in vitro validation of key genes

As shown in Figure 7A, the expression of 4 hub genes in the training set (GSE26787) was analyzed for their diagnostic value in RPL, and only SRC was significantly upregulated in the RPL group compared with the control group (P < .05). There was no statistically significant difference between the 2 groups in terms of EGFR, KRAS, or MDM2, possibly due to the limited sample size. The areas under the ROC curves of EGFR, KRAS, SRC, and MDM2 (Fig. 7B) were 0.8, 0.68, 0.92, and 0.72, respectively.

Figure 7.

Figure 7.

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Value of the hub genes in the diagnosis of RPL in the training and validation sets. (A) Expression of the hub genes in the 2 groups in GSE26787; (B) ROC curve of the 4 hub genes in the RPL diagnosis set (GSE26787); (C) Expression of the core genes in the 2 groups in GSE165004; (D) ROC curve of the 4 hub genes in the RPL diagnosis dataset (GSE165004). ROC = receiver operating characteristic, RPL = recurrent pregnancy loss.

GSE165004 served as the validation set, and R was utilized to obtain the ROC of each index. With the exception of MDM2, 3 hub genes were highly effective in RSA diagnosis (P < .001). As shown in Figure 7C, the areas under the ROC curves of EGFR, KRAS, SRC, and MDM2 (Fig. 7D) were 0.776, 0.964, 0.814, and 0.611, respectively.

During the period July–October 2024, the sera of 20 patients with RSA and control subjects were collected at our hospital, with a median age of 32.9 years (IQR 28–38). The concentrations of the 4 proteins in the serum were confirmed via ELISA. As shown in Figure 8A, only KRAS (median 1.722 vs 7.161 ng/mL, P < .001) was expressed at significantly greater levels in the RSA group than in the control group. MDM2 (P = .20), SRC (P = .70) and EGFR (P = .63) were not significantly different. The AUC value of 0.77 suggested that KRAS may have some diagnostic value for recurrent miscarriage (Fig. 8B).

Figure 8.

Figure 8.

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Serological validation of 2 groups of patients in our hospital (A) Serum ELISA results (B) ROC curve of the KRAS diagnosis of RPL. ELISA = enzyme-linked immunosorbent assay, ROC = receiver operating characteristic, RPL = recurrent pregnancy loss.

3.5. Immune cell infiltration analysis by CIBERSORTx

CIBERSORT analysis revealed 22 different types of immune cells that are involved in the development of RPL (Fig. 9A). There was no notable difference in immune cell infiltration between the experimental groups and the control group (Fig. 9B).

Figure 9.

Figure 9.

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Results of immune infiltration analysis via CIBERSORTx. (A) Composition of 22 kinds of immune cells in GSE26787. (B) Box plot showing the differences in the immune infiltration of 22 kinds of immune cells.

4. Discussion

Our study identified the molecular signatures of ferroptosis related to the endometrium through the analysis of DEGs between RPL patients and normal controls. A total of 51 differentially expressed FRGs related to RSA were identified by taking the intersection. GO and KEGG enrichment analyses were subsequently carried out on the 51 genes. Using GO term analysis, we observed that the ferroptosis-related DEGs were involved primarily in apoptosis and hypoxia, which aligns with previous findings indicating that a hypoxic environment in the peri-implantation endometrium of women with recurrent miscarriage can lead to reduced microvessel number and volume, contributing to recurrent miscarriage.[9] Furthermore, according to the KEGG enrichment analysis, these genes were significantly enriched in the FoxO signaling pathway and chemical carcinogenesis – reactive oxygen species.

The FoxO family regulates various cellular functions, such as cell cycle arrest, cell apoptosis, and cell differentiation.[10] Recent research suggest that these genes could represent novel treatment targets for various disorders, such as aging, diabetes, cancer, and infertility.[11] Kuscu demonstrated that FOXO proteins regulate apoptosis and cell cycle-related proteins in mouse preimplantation embryos. FOXO transcription factors might play a role in the development of preimplantation mouse embryos.[12] The question of whether these genes play important roles in human preimplantation embryos and infertility warrants further investigation. Our research could enhance the understanding of the mechanisms underlying FOXO and RPL.

Ferroptosis and lipid peroxidation play important roles in various obstetric disorders, such as preeclampsia, spontaneous preterm labor, and miscarriage. Unstable iron is extremely active and toxic, causing oxidative stress, such as iron-associated autophagy and iron apoptosis, which contribute to the development of these diseases.[13]

Next, 4 first-level hub genes, namely, KRAS, SRC, EGFR, and MDM2, were identified via the cytoHubba program in Cytoscape. Among these genes, SRC is classified as a repressor gene, whereas the other 3 genes are categorized as driver genes. KRAS is particularly crucial for homeostasis and embryonic development. Mice lacking KRAS exhibit significant growth abnormalities and die in utero at approximately E15.5.[14] Additionally, research suggests that KRAS could impact the embryo implantation procedure through the regulation of stromal cell proliferation and differentiation.[15] SRC-2 has been demonstrated to be a crucial factor in the establishment of early pregnancy in mice.[16] Decidualization of endometrial stromal cells is essential for establishing the maternofetal interface, with SRC playing a crucial role as a progesterone-dependent accelerator of glycolytic flux in ESCs prior to their decidualization. This function of the SRC is vital for the early formation of the conceptus–endometrial interface.[17] The EGFR pathway is essential for placental development, as it aids in the differentiation and proliferation of placental trophoblast cells. In addition, EGFR plays an important role in maintaining pregnancy, ensuring the successful implantation of fertilized eggs and optimal fetal growth.[18] On the other hand, MDM2 functions as an E3 ligase that polyubiquitinates and monoubiquitinates its targets. It is regulated by P53 and fox, and its action results in ubiquitin- and proteasome-dependent P53 degradation,[19] invasion, and the promotion of cellular DNA damage and metastasis. MDM2 is also implicated in various diseases, such as cancer,[20] neurodegeneration, inflammatory and autoimmune disease, kidney disease, cardiovascular disease, and diabetes.[21] However, no studies have been conducted on the connection between MDM2 and recurrent miscarriage.

The AUROC of the 4 genes yielded varying results in the initial diagnosis of recurrent miscarriage in both the validation sets and the training sets. In the training set (GSE26787), only SRC was significantly overexpressed in the RSA group compared with the control group (P < .05). Conversely, in the validation set (GSE165004), 3 hub genes, with the exception of MDM2, demonstrated excellent diagnostic potential for RSA (P < .001).

In vitro analysis revealed that only KRAS was highly expressed in the RSA group, indicating a statistically significant difference. Nevertheless, in contrast to the database results, KRAS was expressed at low levels in the RSA dataset GSE165004. Research on KRAS and RSA is limited, and their correlation necessitates additional validation through extensive sample studies and experiments. Moreover, how these genes affect trophoblast proliferation, apoptosis, and embryo implantation through the regulation of iron death requires additional investigation.

The relationship between RSA and miRNAs has been documented, with various miRNAs being linked to the pathogenesis of the disease.[22]

The Network Analyst database subsequently predicted miR-155-5p and miR-1-3p as the target miRNAs of the 4 hub genes. In a study involving 280 RPL patients and 280 controls, miR-155-5p rs767649 was significantly linked to a higher prevalence of RPL in patients.[23] Upregulated miR-155-5p triggered trophoblast centrosome expansion, resulting in the upregulation of cleaved-poly (ADP ribose) polymerase and apoptotic cleaved-caspase 3, thereby inhibiting trophoblast growth. Additionally, miR-155-5p activates autophagy and induces apoptosis.[24] However, studies on mir-1-3p and recurrent miscarriage are limited, and some studies have suggested that mir-1-3p affects embryo development by targeting and regulating YTHDF2.[25] Nevertheless, further investigation is needed to fully understand their role in iron death in recurrent abortions.

Maternal–fetal immune tolerance has been linked to eclampsia, recurrent miscarriage, pregnancy, and other obstetric conditions.[26] Decidual stromal cells promote immune tolerance and induce Th2 cytokine production, whereas iron prolapse has the opposite effect. Stimulation with lipopolysaccharide induces decidual stromal cells apoptosis and increases the release of Th1 inflammatory factors, resulting in an inflammatory reaction. Consequently, we utilized CIBERSORTx to examine the infiltration of immune cells in RSA endothelial tissue. Regrettably, there were no significant differences in immune cells between RSA endothelial tissue and normal endothelial tissue. Upon analysis, this may be attributed to the small size, necessitating the expansion of the sample for additional study.

Our study also has certain limitations that need to be acknowledged. First, while we confirmed the diagnostic value of the 4 genes mentioned above using peripheral blood from patients with recurrent miscarriage in our facility, there were some variations between the validation set, the training set, and our findings, and there were no signaling pathway-related mechanisms for additional validation. Second, a limited sample size was used for the study, which may have resulted in bias. Finally, the immune cell ratio was estimated via CIBERSORT rather than by directly measuring the number of immune cells in the peripheral blood, and due to the small sample size, there might be discrepancies with the actual results.

Acknowledgments

The authors are grateful to all the members of the Department of Gynecology and Obstetrics, Yichang Central People’s Hospital, and the public database for providing meaningful datasets for our research.

Author contributions

Conceptualization: Wenfei Zheng.

Investigation and data curation: Min Wen.

Software: Wei Xie.

Visualization: Hongyuan Song.

Writing – original draft: Wenfei Zheng.

Writing – review & editing: Chandana Vayakkali Poochali.

Supplementary Material

medi-104-e43156-s001.xlsx (11.3KB, xlsx)

Abbreviations:

BP
biological function
DEGs
differentially expressed genes
ELISA
enzyme-linked immunosorbent assay
FRGs
ferroptosis-related genes
GEO
Gene Expression Omnibus
GO
Gene Ontology
KEGG
Kyoto Encyclopedia of Genes and Genomes
PPI
protein–protein interaction
ROC
receiver operating characteristic
RPL
recurrent pregnancy loss

This study was supported by the Joint Fund for Innovation and Development of the Natural Science Foundation of Hubei Province (2024AFD177).

The studies involving human participants were reviewed and approved by the Ethics Committee of Yichang Central People’s Hospital (2024-214-01).

The authors have no conflicts of interest to disclose.

All data generated or analyzed during this study are included in this published article [and its supplementary information files].

Supplemental Digital Content is available for this article.

How to cite this article: Zheng W, Wen M, Poochali CV, Xie W, Song H. Diagnostic value of the iron apoptosis-related gene in recurrent miscarriage. Medicine 2025;104:33(e43156).

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Supplementary Materials

medi-104-e43156-s001.xlsx (11.3KB, xlsx)

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