Identification of necroptosis-related genes and immune infiltration analysis on the pathogenesis of recurrent implantation failure through integrated bioinformatics analysis

doi:10.21203/rs.3.rs-3215336/v1

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Identification of necroptosis-related genes and immune infiltration analysis on the pathogenesis of recurrent implantation failure through integrated bioinformatics analysis

https://doi.org/10.21203/rs.3.rs-3215336/v1

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Purpose

This study aimed to elucidate the role of necroptosis, a cell death process, in the pathogenesis of Recurrent Implantation Failure (RIF), a significant hurdle in Assisted Reproductive Technology.

Methods

Using the Gene Expression Omnibus database, we isolated RIF-related RNA-seq data and identified differentially expressed necroptosis-related genes (DENRGs). Functional enrichment analysis, Protein-Protein Interaction (PPI) networks, transcription factors (TFs) regulatory network, and Single sample gene set enrichment analysis (ssGSEA) were employed to understand the role of these genes in RIF.

Results

Twenty DENRGs were filtered, and six key genes (MLKL, FASLG, XIAP, CASP1, BIRC3, and TLR3) were identified at the hub of the PPI network. A predictive model for RIF, based on these genes, displayed good performance. A positive correlation of TLR3 with PER2, RORC, and FOXO1, and a negative correlation with UHRF1, BRCA2, and GLI1 was observed in the TFs network. The RIF group demonstrated significantly lower infiltration levels of 16 immune cells. Expression levels of key genes, except for XIAP, differed significantly between RIF and control groups. Potential therapeutic drugs modulating CASP1 were also predicted.

Conclusion

The investigation highlights the crucial role of necroptosis-related genes, especially MLKL, FASLG, XIAP, CASP1, BIRC3, and TLR3 in RIF pathogenesis. The identified genes can contribute to an effective RIF predictive model and suggest potential therapeutic targets for novel RIF treatments.

recurrent implantation failure

necroptosis

hub genes

immune cell infiltration

bioinformatics analysis

This study reveals the key role of necroptosis-related genes in the development of Recurrent Implantation Failure. These findings offer new ways for predicting and treating this condition.

Recurrent implantation failure (RIF), a notable challenge in assisted reproductive technology, instills significant emotional distress in couples yearning to conceive. RIF is generally defined as the failure to achieve a clinical pregnancy after transfer of at least four good-quality embryos in a minimum of three fresh or frozen cycles in a woman under the age of 40 [1][2]. Despite technological advancements and increased understanding, the etiology of RIF remains complex and elusive, embracing a myriad of factors such as endometrial receptivity, embryonic competency, and maternal systemic factors [1].

Recently, necroptosis, a distinct form of programmed cell death separate from classical apoptosis, has attracted significant attention in the disease pathogenesis field. Unlike apoptosis, which is a caspase-dependent and often immunologically silent process, necroptosis is caspase-independent and typically associated with inflammation due to the release of cellular contents [3][4]. This process is regulated by specific genes such as receptor-interacting protein kinase 1 (RIPK1), RIPK3, and mixed lineage kinase domain-like pseudokinase (MLKL) [4].

Emerging research has highlighted potential roles for necroptosis across a range of diseases including neurodegenerative diseases, cancer, and more recently, conditions related to female reproduction such as ovarian aging, follicular atresia, and endometriosis [5][6]. However, the potential involvement of necroptosis in the pathogenesis of RIF has not been thoroughly explored.

Given the pro-inflammatory nature of necroptosis, it is plausible to hypothesize that deregulated necroptotic pathways may disrupt the immune environment necessary for successful embryo implantation [7]. A balanced immune response, teetering between tolerance and activation, is vital to establish and maintain pregnancy. Disruptions to this balance, perhaps through necroptosis-induced inflammation, could impair endometrial receptivity, contributing to RIF.

In this study, we utilized an integrated bioinformatics approach to identify and analyze differentially expressed necroptosis-related genes (DENRGs) in RIF. We built protein-protein interaction networks, performed immune infiltration analysis, and constructed a diagnostic model for RIF based on these genes. Furthermore, we predicted potential therapeutic drugs for RIF, providing new insight into the molecular mechanisms underlying this condition.

Data collection

GSE111974 and GSE92324 datasets were acquired from the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo/). The RNA-seq data of endometrial tissue from 24 patients with RIF and 24 controls were extracted from the GSE111974 dataset and used as the training set. The GSE92324 dataset, which contained the RNA-seq data of endometrial tissue from 10 patients and eight controls, was regarded as the validation set. Then, 159 NRGs were obtained from prior reviews, and they are presented in Table S1[8].

Identification of differentially expressed NRGs (DENRGs)

The differentially expressed genes (DEGs) between RIF and the controls were screened using the limma (version 3.42.2) package in the GSE111974 dataset, and the thresholds were |log2FC| > 0.5 and P < 0.05[9, 10]. The DEGs were exhibited through volcano plot and heatmap. Furthermore, a Venn diagram was constructed with the DEGs and NRGs by jvenn online tools (http://jvenn.toulouse.inra.fr/app/index.html), and the DENRGs were retrieved. The pheatmap (version 0.7.7) package was used to draw the heatmap of the DENRGs between RIF and the controls [11]. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted using the Cluster Profiler (version 3.14.3) package (P < 0.05), and the top 10 enrichment results were drawn [12]. The GO consists of three categories: molecular function (MF), biological process (BP), and cellular component (CC).

Construction of protein − protein interaction (PPI) network

A PPI network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING, version 11.0) online database (confidence = 0.4, https://string-db.org) to explore the interaction between DENRGs [13]. The result was imported into Cytoscape (version 3.8.0) to visualize the PPI network [14]. The hub genes were identified using the maximal clique centrality (MCC) of CytoHubba in the Cytoscape software, and the top 10 genes were screened out in accordance with the MCC value. Meanwhile, the modular was analyzed using the Molecular Complex Detection (MCODE) plugin, and the key sub-module was identified. Then, by overlapping the hub genes and the sub-module genes, the key genes were identified utilizing a Venn diagram.

Construction of diagnostic model

A diagnostic model was constructed using the key genes screened by the PPI network through the STRING database (version 11.0, confidence = 0.4) [15]. Then, the receiver operating characteristic (ROC) curve was used to assess the specificity and sensitivity in the prediction of key genes by calculating the area under the curve (AUC) in the GSE111974 dataset [16]. External verification was performed in the GSE92324 dataset to further verify the diagnostic ability of the key genes.

Construction of transcription-factor (TF) regulatory network

The human transcription factors were downloaded from hTFtarget database (http://bioinfo.life.hust.edu.cn/hTFtarget#!/), RegNetwork database (https://regnetworkweb.org/home.jsp), and TRRUST database (version 2, https://www.grnpedia.org/trrust/). The expression levels of key genes and TFs were obtained from the GSE111974 dataset, and the Pearson’s correlation between them was calculated. Multiple testing correction was then performed using the Benjamini and Hochberg method, and the threshold was set to q < 0.05 and |r| > 0.7. Finally, the regulatory network was visualized by Cytoscape.

Single-sample gene-set enrichment analysis (ssGSEA) algorithm

The ssGSEA algorithm was used to evaluate the immune infiltration degrees on the basis of 28 immune cells in the GSE111974 dataset [17]. Then, ggplot2 (version 3.3.2) package was used to draw a boxplot via Wilcox test to screen the immune cells that were different between RIF and the controls (p < 0.05). Moreover, the correlation between key genes and different immune cells was calculated by Spearman method (p < 0.05 and |cor| > 0.3), and a heatmap was plotted.

Validation of the expression levels of key genes and prediction of potential treatment drugs

The expression levels of key genes were extracted from the GSE111974 training set and the GSE92324 validation set. They were investigated via T-test, and boxplots were drawn. The potential treatment drugs of ER were predicted by searching the Drug Gene Interaction Database (DGIdb). The interaction network with key genes and treatment drugs was visualized using Cytoscape.

Identification of 20 DENRGs

In total, 3289 DEGs were retrieved between RIF and the controls, of which were 1567 upregulated genes and 1722 were downregulated genes (Fig. 1A and B and Table S1). Then, 20 DENRGs were obtained by overlapping the 3289 DEGs and 159 NRGs (Fig. 1C and Table 1). The heatmap of DENRGs is exhibited in Fig. 1D. GO function enrichment and KEGG pathway analyses were performed to assess the function of 20 DENRGs. The DENRGs were enriched in 182 GO BPs, 21 GO CCs, and 33 GO MFs, including peptidase regulator activity, calcium-dependent phospholipid binding, cysteine-type endopeptidase regulator activity involved in apoptotic process, vacuolar membrane, rough endoplasmic reticulum, pore complex, programmed necrotic cell death, cellular response to external stimulus, and regulation of tumor necrosis factor-mediated signaling pathway (Fig. 1E and Table 2). In total, 50 enriched KEGG pathways were identified, and the top 10 KEGG pathways were shown, including necroptosis, lipid and atherosclerosis, NOD − like receptor signaling pathway, and apoptosis (Fig. 1F and Table 3).

PPI network

On the basis of the data from STRING database analysis, the PPI network of 20 DENRGs was constructed with 18 nodes and 34 edges (Fig. 2A). MLKL had the most maximum connection degree, which was interacted with eight proteins. XIAP, BIRC3, FASLG, and CASP1 interacted with seven proteins, among which XIAP and BIRC3 were upregulated DEGs, and FASLG and CASP1 were downregulated DEGs. The top 10 genes (BIRC3, MLKL, FASLG, CASP1, XIAP, TLR3, RNF31, TNFRSF10A, CYBB, and BAX) were selected as hub genes by MCC (Fig. 2B). Meanwhile, a key sub-module was identified, which included six module genes (MLKL, FASLG, XIAP, CASP1, BIRC3, and TLR3; Fig. 2C). Then, six key genes were identified (MLKL, FASLG, XIAP, CASP1, BIRC3, and TLR3) by overlapping 10 hub genes and six module genes (Fig. 2D).

Diagnostic model construction and validation

The ROC curve of the six key genes were plotted to check the diagnostic ability, and the results showed that the AUC of these genes were greater than 0.696, indicating that these genes had high diagnostic value (Fig. 3A). Subsequently, the six genes were used to construct a model, and the AUC of the model was 0.911, which confirmed its effectiveness and agility (Fig. 3B). In addition, the ROC curve was externally verified in the GSE92324 validation dataset, and the results showed that except for XIAP, the AUC values of five key genes were greater than 0.8, and the AUC of the model was 1 (Fig. 3C and D). This finding showed that the evaluation effect of the key genes was good.

TF regulatory network

A total of 1013 TFs were retrieved by merging three human TF databases. The regulatory network was then established with 185 nodes (six key genes and 179 TFs) and 232 edges (TF − key gene relationship pairs, Fig. 4). TLR3 was modulated with 93 TFs, BIRC3 was regulated with 52 TFs, and XIAP was modulated with two TFs. Among them, TLR3 was positively correlated with PER2, RORC, and FOXO1 and negatively correlated with UHRF1, BRCA2, and GLI1. BIRC3 was positively correlated with PAX2, TFCP2L1, and POU5F1 and negatively correlated with TLE3, MEF2C, and TWIST2.

Immune cell infiltration landscape

Immune cell infiltration analysis was performed, and the boxplot exhibited the infiltration level of 28 immune cells in RIF and the controls. Sixteen immune cells showed significant difference between RIF and the controls, and the infiltration levels of these 16 immune cells were significantly lower in the RIF group than in the control group (Fig. 5A). Then, the correlation between the six key genes and 16 immune cells was calculated, and 80 significantly correlated relationship pairs were screened. FASLG was markedly positively correlated with activated CD8 T cell, NK T cell, and T follicular helper cell. CASP1 was markedly positively correlated with T follicular helper cell, MDSC, and type 1 T helper cell. Meanwhile, BIRC3 was notably negatively correlated with immature DC, central memory CD8 T cell, and type 2 T helper cell. TLR3 was evidently negatively correlated with type 2 T helper cell and activated CD4 T cell. Moreover, except for TLR3, the key genes were closely related to NK T cell (Fig. 5B).

Validation of expression levels and potential treatment drugs

The expression levels of the key genes were significantly different between the RIF and controls in the GSE111974 dataset, and the expression levels of BIRC3, TLR3, and XIAP were significantly higher in the RIF group than in the control group. Meanwhile, the expression of XIAP did not significantly differ between the RIF and controls in the GSE92324 validation set, but its expression trend was consistent with that in the training set (Fig. 6A and B). The potential drugs were predicted using DGIdb, and a drug − gene interaction network was established, including five genes and 49 drugs (Fig. 6C). CASP1 predicted 23 therapeutic drugs, including nivocasan, emricasan, and pralnacasan. A total of 20 drugs, including birinapant, cisplatin, and emapalumab, interacted with XIAP. In addition, BIRC3 and TLR3 were regulated by four drugs. Cisplatin only interacted with asunercept.

Recurrent implantation failure (RIF) remains a significant challenge in the field of assisted reproductive technology (ART) [18]. A deep understanding of the underlying molecular mechanisms is paramount for the development of effective diagnostic and therapeutic strategies. In this study, we leveraged a comprehensive bioinformatics approach to unravel the potential role of necroptosis-related genes in RIF, contributing to the limited body of literature on the subject.

Necroptosis is a form of regulated necrotic cell death that has gained increasing attention for its roles in several disease processes, such as neuroinflammation [19, 20]. In this context, our research identified six key necroptosis-related genes (MLKL, FASLG, XIAP, CASP1, BIRC3, and TLR3) significantly associated with RIF, hinting at a possible involvement of necroptosis in the pathogenesis of this condition.Mixed lineage kinase domain-like protein (MLKL), a pivotal executor of necroptosis, has been extensively studied in the context of inflammatory diseases [21]. When activated, MLKL compromises the cellular membrane integrity, leading to necroptotic cell death [22]. Therefore, an increased expression of MLKL, as observed in our study, might result in elevated necroptosis in the endometrium, promoting an inflammatory milieu unfavorable for embryo implantation.The Fas Ligand (FASLG) is a transmembrane protein that initiates apoptosis upon binding to its receptor FAS. However, under certain circumstances, FASLG can also trigger necroptosis [23]. Overexpression of FASLG in our RIF samples could imply an altered balance between apoptosis and necroptosis within the endometrium, potentially perturbing the microenvironment required for successful implantation [24].X-linked inhibitor of apoptosis (XIAP) and Baculoviral IAP repeat-containing protein 3 (BIRC3) belong to the inhibitor of apoptosis (IAP) family, which is known to regulate apoptosis and necroptosis [25]. Disruptions in the expression of XIAP and BIRC3 could tip the balance between these processes, impacting the endometrial viability and potentially contributing to RIF [26, 27]. Caspase-1 (CASP1) primarily mediates the activation of pro-inflammatory cytokines, although it is also implicated in necroptosis induction [28]. Elevated expression of CASP1 could instigate a pro-inflammatory state in the endometrium, making it less receptive to implantation [29]. Lastly, Toll-like receptor 3 (TLR3) is an innate immune receptor that can trigger necroptosis and is involved in immune responses [30]. Dysregulation of TLR3, as suggested by our data, might lead to an intensified inflammatory response, thereby exacerbating RIF [31].

This study also provided valuable insights into the correlation between necroptosis and the transcription factors PER2, RORC, FOXO1, UHRF1, BRCA2, and GLI1, demonstrating the complexity of necroptosis regulation. For example, PER2, a circadian clock gene, has been shown to regulate apoptosis in certain contexts [32], suggesting a potential role in necroptosis. Similarly, RORC and FOXO1 have been implicated in various immune processes [33], hinting at their possible influence on immune interactions during implantation.

Moreover, the altered immune landscape, as demonstrated by the significant decrease in 16 types of immune cells in the RIF group, raises crucial questions about the role of immunity in RIF. There is a complex balance between the immune system's involvement in providing a receptive environment for implantation and avoiding an overly aggressive response that could harm the embryo [34]. This observed alteration in immune cell infiltration could suggest an imbalance in this delicate system, further complicating the pathogenesis of RIF.

Additionally, the identification of potential therapeutic drugs, particularly those modulating the activity of CASP1, was a notable outcome of the study. CASP1, a gene involved in inflammation and programmed cell death, could be a promising therapeutic target for treating RIF [35]. Given the significant role of inflammation in successful implantation and the ability of CASP1 to modulate inflammatory responses, drugs targeting CASP1 could potentially provide a novel treatment approach for RIF [36].

While this study's results offer a significant advancement in understanding the molecular mechanisms underlying RIF, they should be interpreted within the limitations of the research. Firstly, the study relied heavily on data extracted from the Gene Expression Omnibus database, which, although reliable, may contain potential bias inherent in any secondary data source. Therefore, the findings need validation through in vitro and in vivo experimental studies to confirm the involvement of the identified NRGs in RIF. Furthermore, while the bioinformatics approach used in this study provides robust initial findings, more extensive mechanistic studies are necessary to understand the role of these genes in RIF. Future research should also focus on the clinical application of these findings, testing potential therapeutic interventions in randomized controlled trials.

In conclusion, this study, through an integrated bioinformatics analysis, has elucidated a possible connection between necroptosis and RIF, with the identification of key genes and potential therapeutic targets. This represents a significant step towards understanding the intricate molecular landscape of RIF, ultimately opening avenues for novel treatment strategies.

Acknowledgements

We thanks the patients who participated in GEO database.

Funding

This work was supported by the Special Project for Maternal and Child Development of Haidian District Maternal and Child Health Hospital, Beijing, China (2021-02).

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Author Contributions

Xiuye Xing contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Xiuye Xing, Xiaoxiao Ni, Jiaojiao Wang, and Junmei Shi. The first draft of the manuscript was written by Xiuye Xing, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Ethics approval

This is a bioinformatics analysis and Ethics Committee has confirmed that no ethical approval is required.

Consent to participate

Not applicable.

Consent to publish

Not applicable.

Coughlan C, Ledger W, Wang Q, Liu F, Demirol A, Gurgan T et al (2014) Recurrent implantation failure: definition and management. Reprod Biomed Online 28(1):14–38
Polanski LT, Baumgarten MN, Quenby S, Brosens J, Campbell BK, Raine-Fenning NJ (2014) What exactly do we mean by 'recurrent implantation failure'? A systematic review and opinion. Reprod Biomed Online 28(4):409–423
Galluzzi L, Vitale I, Aaronson SA, Abrams JM, Adam D, Agostinis P et al (2018) Molecular mechanisms of cell death: recommendations of the Nomenclature Committee on Cell Death 2018. Cell Death Differ 25(3):486–541
Pasparakis M, Vandenabeele P (2015) Necroptosis and its role in inflammation. Nature 517(7534):311–320
Christofferson DE, Yuan J (2010) Necroptosis as an alternative form of programmed cell death. Curr Opin Cell Biol 22(2):263–268
Wang Y, Kim NS, Haince JF, Kang HC, David KK, Andrabi SA et al (2011) Poly(ADP-ribose) (PAR) binding to apoptosis-inducing factor is critical for PAR polymerase-1-dependent cell death (parthanatos). Sci Signal 4(167):ra20
Mor G, Aldo P, Alvero AB (2017) The unique immunological and microbial aspects of pregnancy. Nat Rev Immunol 17(8):469–482
Liu FQ, Wei T, Liu L, Hou FX, Xu CX, Guo H, Zhang W, Ma MJ, Zhang YL et al (2022) Role of Necroptosis and Immune Infiltration in Human Stanford Type A Aortic Dissection: Novel Insights from Bioinformatics Analyses. Oxidative Medicine and Cellular Longevity 6184802
Ritchie ME, Phipson B, Wu D, Hu Y, Law C, Shi W, Smyth GK (2015) limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43:e47
Liu X, Yin H, Li Y, Hao H, Liu Y, Zhao Q (2022) The Construction and Analysis of a ceRNA Network Related to Salt-Sensitivity Hypertensives. Biomed Res Int 8258351
Li G, Zhang C, Rui R, Sun B, Guo W (2018) Bioinformatics analysis of common differential genes of coronary artery disease and ischemic cardiomyopathy. Eur Rev Med Pharmacol Sci 22:3553–3569
Yu G, Wang L, Han Y, He Q (2012) clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16:284–287
Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P et al (2019) STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 47:D607–D613
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504
Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S et al (2019) STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 47:D607–D613
Hoo ZH, Candlish J, Teare D (2017) What is an ROC curve? Emerg Med J 34:357–359
Jin Y, Wang Z, He D, Zhu Y, Chen X, Cao K (2021) Identification of novel subtypes based on ssGSEA in immune-related prognostic signature for tongue squamous cell carcinoma. Cancer Med 10:8693–8707
Coughlan C, Ledger W, Wang Q et al (2014) Recurrent implantation failure: definition and management. Reprod Biomed Online 28(1):14–38
Vanden Berghe T, Linkermann A, Jouan-Lanhouet S, Walczak H, Vandenabeele P (2014) Regulated necrosis: the expanding network of non-apoptotic cell death pathways. Nat Rev Mol Cell Biol 15(2):135–147
Ito Y, Ofengeim D, Najafov A et al (2016) RIPK1 mediates axonal degeneration by promoting inflammation and necroptosis in ALS. Science 353(6299):603–608
Sun L, Wang H, Wang Z et al (2012) Mixed lineage kinase domain-like protein mediates necrosis signaling downstream of RIP3 kinase. Cell 148(1–2):213–227
Zhao J, Jitkaew S, Cai Z et al (2012) Mixed lineage kinase domain-like is a key receptor interacting protein 3 downstream component of TNF-induced necrosis. Proc Natl Acad Sci 109(14):5322–5327
Strasser A, Jost PJ, Nagata S (2009) The many roles of FAS receptor signaling in the immune system. Immunity 30(2):180–192
Peter ME, Krammer PH (2003) The CD95(APO-1/Fas) DISC and beyond. Cell Death Differ 10(1):26–35
Eckelman BP, Salvesen GS, Scott FL (2006) Human inhibitor of apoptosis proteins: why XIAP is the black sheep of the family. EMBO Rep 7(10):988–994
Gyrd-Hansen M, Meier P (2010) IAPs: from caspase inhibitors to modulators of NF-κB, inflammation and cancer. Nat Rev Cancer 10(8):561–574
Bertrand MJ, Milutinovic S, Dickson KM et al (2008) cIAP1 and cIAP2 facilitate cancer cell survival by functioning as E3 ligases that promote RIP1 ubiquitination. Mol Cell 30(6):689–700
Kayagaki N, Warming S, Lamkanfi M et al (2011) Non-canonical inflammasome activation targets caspase-11. Nature 479(7371):117–121
Dinarello CA (2011) A clinical perspective of IL-1β as the gatekeeper of inflammation. Eur J Immunol 41(5):1203–1217
Alexopoulou L, Holt AC, Medzhitov R, Flavell RA (2001) Recognition of double-stranded RNA and activation of NF-kappaB by Toll-like receptor 3. Nature 413(6857):732–738
Karikó K, Ni H, Capodici J, Lamphier M, Weissman D (2004) mRNA is an endogenous ligand for Toll-like receptor 3. J Biol Chem 279(13):12542–12550
Gery S, Koeffler HP (2010) Circadian rhythms and cancer. Cell Cycle 9(6):1097–1103
Eberl G, Littman DR (2004) Thymic origin of intestinal alphabeta T cells revealed by fate mapping of RORgammat + cells. Science 305(5681):248–251
Mor G, Aldo P, Alvero AB (2017) The unique immunological and microbial aspects of pregnancy. Nat Rev Immunol 17(8):469–482
Lamkanfi M, Dixit VM (2009) Inflammasomes: guardians of cytosolic sanctity. Immunol Rev 227(1):95–105
Nadeau-Vallée M, Obari D, Palacios J, Brien M, Duval C, Chemtob S, Girard S (2016) Sterile inflammation and pregnancy complications: a review. Reproduction 152(6):R277–R292

Tables 1 to 3 are available in the Supplementary Files section.

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Version 1

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You are reading this latest preprint version

Identification of necroptosis-related genes and immune infiltration analysis on the pathogenesis of recurrent implantation failure through integrated bioinformatics analysis

Status:

Version 1

Abstract

Purpose

Methods

Results

Conclusion

Figures

What does this study adds to the clinical work

Introduction

Methods

Data collection

Identification of differentially expressed NRGs (DENRGs)

Construction of protein − protein interaction (PPI) network

Construction of diagnostic model

Construction of transcription-factor (TF) regulatory network

Single-sample gene-set enrichment analysis (ssGSEA) algorithm

Validation of the expression levels of key genes and prediction of potential treatment drugs

Results

Identification of 20 DENRGs

PPI network

Diagnostic model construction and validation

TF regulatory network

Immune cell infiltration landscape

Validation of expression levels and potential treatment drugs

Discussion

Declarations

References

Tables

Supplementary Files

Status:

Version 1