Exploring Autophagy-Associated Genes in the Endometrium of Recurrent Spontaneous Abortion and Examining their Relationship with Immune Infiltration

doi:10.21203/rs.3.rs-3028443/v1

Download PDF

Research Article

Exploring Autophagy-Associated Genes in the Endometrium of Recurrent Spontaneous Abortion and Examining their Relationship with Immune Infiltration

https://doi.org/10.21203/rs.3.rs-3028443/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Recurrent spontaneous abortion (RSA) is a common complication in pregnant women. Autophagy impacts the initiation and progression of various diseases,the specific role of autophagy in the process of endometrial decidualization in RSA paitents is still largely unknown. The purpose of this research was to examine the involvement of autophagy-related genes (ARGs) in decidualization associated with RSA using bioinformatics approaches.To identify differentially expressed genes (DEGs), the Gene Expression Omnibus database provided gene expression datasets GSE165004 and GSE26787. ARGs were retrieved from the Human Autophagy Database and the Human Autophagy Modulator Database, and their intersection with DEGs resulted in 109 differentially expressed ARGs which were significantly enriched in 14 GO terms and and 10 KEGG pathways. To assess the diagnostic capacity of the genes, an receiver operating characteristic curve was utilized. Simultaneously,The disparities in RSA immune microenvironments between low- and high- expression gene groups were analyzed using CIBERSORT, indicating that AKT2, BCL2L1, CTNNB1, GRB2, GSK3B, PTEN, and PTPN11 may be linked to the immune response during decidualization within the endometrial microenvironment. Among them, hub genes exhibited positive correlations with neutrophils, mast cells, and dendritic cells, and negative correlations with plasma, memory B and naive B cells. The results show that the 10 DEARGs (AKT2, RPS27A, PTPN11, PIK3CD, PTEN, CTNNB1, KRAS, GSK3B, BCL2L1, and GRB2) could act as potential biomarkers for the diagnosis of RSA. Furthermore, a noteworthy correlation was detected between DEARGs and the immune landscape of the endometrium in RSA patients.

RSA

Autophagy-Related gene

bioinformatics analysis

hub genes

immune infiltration

Recurrent spontaneous abortion (RSA) refers to the loss of 2 or more consecutive pregnancies before the 20th week of gestation, which affects 1–5% of pregnant women of childbearing age(1) (2). A noticeable increase was found in the prevalence of RSA, and the risk of recurrent abortion occurring again after pregnancy is as high as 70–80% for women who suffer from RSA(3). The etiological categories of RSA are complicated, including genetic chromosome abnormalities, endocrine, maternal reproductive tract abnormalities, immunological dysfunction, and thrombophilia. Some of these etiologies are still considered controversial. Much previous evidence indicates that the pathogenesis of RSA might be related to maternal fetal immune tolerance(4) (5) (6), however the exact mechanism is not completely clear. A recent study has shown that more than 40% of cases still have unexplained(1).

Autophagy represents an evolutionarily preserved mechanism of degradation in eukaryotic organisms. The maladjustment of autophagy could be involved in a variety of pathological processes that affect the occurrence and development of diseases, including cancers, degenerative disease of the nervous system, metabolic disease, aging, infection, and immune response(7) (8). Investigations have demonstrated that autophagy is fundamentally involved in both pathological and physiological processes pertaining to the endometrial tissue, such as decidualization of the endometrium in pregnancy, endometriosis, endometrial carcinoma, and infertility(9). At the same time, studies have also found that autophagy could affect pregnancy by influencing fertilization, embryo differentiation, decidualization, and immune crosstalk at the maternal-fetal interface(10) (11). Correlation studies about endometrial decidualization in patients with unexplained recurrent spontaneous abortion (URSA) indicate that decidualization is an essential condition for embryo implantation. In this process, abnormal angiogenesis, abnormal cytokine secretion, increased decidual cell apoptosis, decreased cell proliferation, and abnormal expression of decidualization related genes could all lead to embryo implantation failure(9). However, the specific role of autophagy in the process of endometrial decidualization in RSA patients is still largely unknown and needs further study.

In the present research, DEARGs correlated with the endometrium of RSA patients were ascertained through bioinformatic assessments, and the corresponding biological functions and associated pathways were examined.. We speculated that these genes might affect the endometrium of patients with recurrent abortion by regulating autophagy. Such information will offer novel insights into the potential pathogenesis of RSA, and determine more effective indicators for molecular pathological diagnosis and prognosis in RSA patients.

Study design(Fig. 1)

Acquisition and Management of Data

From the widely used Gene Expression Omnibus (GEO) database, we downloaded 2 datasets (GSE165004 and GSE26787) (12) from Homo sapiens. The gene expression dataset GSE165004 was reliant on the GPL16699 platform comprising information from 24 case subjects and an equal number of control subjects, while the gene expression dataset GSE26787 utilized the GPL570 platform encompassing data from 5 case subjects and an identical number of control subjects. Data were filtered (retained genes with mean expression greater than 0) and standardized processing through the limma package(13).

Acquisition of autophagy-related genes.

A cumulative total of 222 autophagy related genes (ARGs) were procured from the Human Autophagy Database, while 366 ARGs were garnered from the Human Autophagy Modulator Database. The top 1000 ARGs were extracted from GeneCard utilizing "autophagy" as the search criterion. Through the intersection of the above autophagy related genes, 1183 ARGs were reserved for subsequent analysis.

Identification of DEARGs

Utilizing the limma package in R software, DEGs within the GSE165004 and GSE26787 gene expression datasets were discerned. The threshold for differential gene-expression was set to P < 0.05. LogFC > 0 indicated an upregulation of differential genes in the case group, and LogFC < 0 indicated the downregulation of differential genes in the case group. The results of difference analyses were displayed using the R package pheatmap to depict heat maps and ggplot2 to draw volcano maps. Subsequently, the DEGs of the two data sets were intersected with ARGs to obtain DEARGs.

Functional Enrichment Analysis (GO/KEGG)

The clusterProfiler R package(15) was employed to carry out GO/KEGG(16) (17) enrichment analyses on DEARGs to elucidate processes and the biological functions associated with DEARGs in RSA. GO characterized cellular component (CC), biological process (BP), and molecular function (MF) for gene annotation. Subsequently, genes were visualized using the ggplot2 package, with P < 0.05 as significant differences.

Gene set enrichment analysis (GSEA)

GSEA assesses a given gene expression dataset by utilizing gene sets defined based on prior biological understanding to determine if a gene set correlates with the phenotypic class distinction(18). The "c2.kegg.v7.4.symbols" gene set in the MSigDB database was employed to execute GSEA on the two datasets independently. The clusterProfiler R package was utilized to perform GSEA and visualize the outcomes. Statistical significance is attributed to differences with a FDR > 0.25 and P-value < 0.05.

Construction of transcription factors regulating hub genes, protein-protein interaction (PPI), miRNAs regulating hub genes, and drug-hub gene interactions networks.

STRING is a biological database used for searching for protein interactions, which allows the prediction of potential protein-protein interactions. Interactions obtained from the STRING database, and the interactions of DEARGs with interactive scores more than 0.4, were displayed using the software Cytoscape(19).

Hub genes were identified using the "cytoHubba"(20) plugin, applying the maximum cross-correlation criterion algorithm. Following this, the Network Analyst Database(21) was employed for analysis, resulting in the establishment of the TF-hub gene network and the miRNA-hub gene interaction network. Furthermore, resources such as miRTarBase, TarBase, and the TF database ENCODE were utilized.

The Drug-Gene Interaction Database (DGIdb) (22) functions as a resource for examining drug-targeted and responsive genomes, as well as drug-hub gene interactions. We investigated the DGIdb to forecast potential pharmaceuticals or molecular compounds that interact with DEGs. Ultimately, we illustrated mRNA-TF, mRNA-miRNA and mRNA-drug regulatory networks employing Cytoscape 3.8.0.

Examination via ROC Curve

The ROC curve approach yields precise measures of accuracy and enables evaluation of diagnostic efficacy via key parameters including sensitivity, and specificity and area under the ROC (AUC). For each hub gene, the optimal threshold was determined by the apex value of the sum of specificity and sensitivity(23). The generation of ROC curves was facilitated through the utilization of the “pROC” package(24).

Investigation of Immune Cell Infiltration

CIBERSORT(25), an algorithm built on support vector regression, is a prevalent tool that allows meticulous scrutiny of immune cell constitution from datasets. The gene expression matrix, sourced from the GSE165004 dataset, was processed through CIBERSORT, with samples yielding a P-value less than 0.05 being eliminated, subsequently resulting in a matrix of infiltrating immune cells. The graphical representation of the distribution across 22 different types of immune cells was achieved using the ggplot package.

Statistical Approach

All computational and statistical operations were executed utilizing R software, version 4.0.2. The comparison of normally distributed continuous variables across different groups was performed using Student’s t-tests, whereas the Wilcoxon rank-sum tests were employed for continuous variables that did not follow a normal distribution. In our research, all calculated P-values were two-tailed, and a P < 0.05 indicate statistical significance.

Data preprocessing and DEARGs screening

The computational tool known as the "limma" package was leveraged to conduct standardized assessments (Fig. 2A-2D) and differential scrutiny of the given datasets. From the GSE165004 dataset, we extracted 6890 differentially expressed genes (DEGs), consisting of 3226 genes that displayed an elevation in expression and 3664 genes that exhibited a reduction (Fig. 3A). A visual representation of the top 30 DEGs was created using a heat map (Fig. 3C). In contrast, the GSE26787 dataset yielded a total of 9020 DEGs, with 3614 genes showing an increase and 5406 exhibiting a decrease in expression (Volcano plot, Fig. 3B); we used the first 30 DEGs to draw the heat map (Fig. 3D). In order to identify DEARGs, we first obtained the intersection DEGs between GSE165004 and GSE26787 (Fig. 4A). Next, we took the intersection between the DEGs and ARGs datasets to obtain 109 DEARGs(Fig. 4B)

Functional Enrichment Analysis

We executed enrichment explorations to examine the molecular operations, cellular constituents, and biological processes and pathways engaged by 109 DEARGs in the RSA. Both GO and KEGG enrichment investigations were performed on these gene clusters. The results derived from the KEGG pathway examination suggested that the 109 DEARGs were predominantly implicated in an array of pathways including but not limited to Autophagy-animal, Endometrial cancer, Apoptosis, and various signaling pathways such as T cell receptor, JAK-STAT, mTOR, B cell receptor, PI3K-Akt, p53, Toll-like receptor, and NF-kappa B (Fig. 5A-5C). In the context of GO analysis, the alterations in biological processes were primarily concentrated in autophagy, the modulation of autophagy, responses to endoplasmic reticulum distress, cellular reactions to insulin stimulation, and the regulation of apoptotic signaling pathways. The molecular function alterations were found to be significantly concentrated in regions like the cytosolic part, endosome membrane, presynapse, beta-catenin destruction complex, and the inflammasome complex. The changes in cellular components associated with the DEARGs were mainly enriched in activities such as myosin V binding, GTP binding, phosphatase binding and ubiquitin protein ligase binding (Fig. 6A-6C).

GSEA results

To assess the impact of variations in gene expression levels on RSA, we performed GSEA separately on the GSE165004 and GSE26787 datasets. The GSEA outcomes from the GSE165004 dataset indicated that these differential genes were predominantly concentrated in biological functionalities such as the chemokine signaling pathway, PI3K/Akt signaling pathway, as well as the MAPK and NF-kappa B signaling pathways that are suppressed by Yersinia YopJ (Fig. 7A-7C). On the other hand, the GSEA findings from the GSE26787 dataset revealed that these differential genes were primarily enriched in biological functionalities like the chemokine signaling pathway, JAK-STAT signaling pathway, and the pathway associated with inflammatory responses(Fig. 7D-7F).

PPI network analysis of DEARGs

The cohort of 109 DEARGs was subjected to analysis via the STRING online database for PPI network construction and further visualized using the Cytoscape Software (Fig. 8A). Subsequent to this, we proceeded to evaluate the constructed PPI network utilizing the Network Analyzer plugin integrated within Cytoscape (Fig. 8B). In the final phase of our analysis, with the intention of isolating key ARGs, we identified the top 10 central hub genes employing the Maximal Clique Centrality (MCC) method facilitated by the cytoHubba plugin (Fig. 8C). These cardinal genes were denoted as AKT2, RPS27A, PTPN11, PIK3CD, PTEN, CTNNB1, KRAS, GSK3B, BCL2L1, and GRB2.

Establishment of miRNA- and TF-Hub Gene Networks

Utilizing Network Analyst, we integrated miRNA databases, specifically the miRTarBase and TarBase databases, to create a miRNA-gene regulatory network focusing on the identified 10 hub genes (Fig. 9A). The initial three genes within the network of miRNA-hub genes were designated as PTEN, KRAS, and GSK3B, respectively. Subsequently, the TF-hub genes network was constructed employing the ENCODE database (Fig. 9B). BCL2L1, AKT2, and PTEN were the top three genes pinpointed in this TF-hub gene network.

Construction of Hub Gene-Drug Interaction Network

The DGIdb database was harnessed to pinpoint 268 potential medicinal substances may possibly interacting with 109 hub genes, and these interactive networks were visualized employing Cytoscape software (Fig. 10). The first 3 genes regulated by drugs in the hub gene drug network were KRAS, PTEN, and PIK3CD, respectively. Among them, KRAS was affected by 133 drugs, including R, S, and P. Pwas affected by 97 drugs, including REGORAFENIB, SELUMETINIB, and PELAREOREP. PTEN was affected by 97 drugs, including CETEXIMAB, RIGOSERTIB, and METFORMIN. PIK3CD was affected by 47 drugs, including DUVELISIB, TASELISIB, and COPANLISIB.

ROC Curve Analysis and Confirmation of Hub Gene Expression

Validation was performed on two sets of samples from the GSE165004 dataset (RSA and normal samples). The expression distribution of 10 hub genes in the GSE165004 and GSE26787 data are illustrated in Fig. 11A-D. The diagnostic precision of the top 10 hub genes within the GSE165004 and GSE26787 datasets were evaluated via ROC curve. For the GSE165004 dataset, AUCs were determined to be 0.778, 0.747, 0.899, 0.701, and 0.769 for AKT2, CTNNB1, KRAS, GRB2, and RPS27A, respectively, as shown in Fig. 11E;.AUCs for genes GSK3B, PTPN11, BCL2L1, PTEN, and PIK3CD were 0.742, 0.771, 0.656, 0.819, and 0.726, respectively, as represented in Fig. 11E. Within the GSE26787 dataset, AUCs were found to be 0.960, 0.56, 0.600, 0.840, and 0.800 for genes for AKT2, CTNNB1, KRAS, and PTPN11, respectively, as shown in Fig. 11H. AUCs for genes GSK3B, GRB2, BCL2L1, PTEN, and PIK3CD were 0.640, 0.840, 0.680, 0.560, and 0.960, respectively as shown in Figure Fig. 11G.

Results of Immune Cell Infiltration Analysis

Utilizing the CIBERSORT algorithm, the level of penetration for 22 different types of immune cells was assessed within the RSA tissues. The Wilcoxon Test was employed for algorithmic analysis, following which immune cells with a nil expression abundance were filtered out, leaving us with 18 different types of immune cells: activated dendritic/NK/memory CD4 + T/ mast cells, M2/M1/M0 macrophages, resting NK/ memory CD4 + T/ mast cells, gamma-delta T cells, regulatory T cells (Tregs), T follicular helper cells, CD8 + T cells, plasma cells, memory B cells, naive B cells, and Neutrophils. The corresponding Fig. 12A presents a panoramic view of these immune cells.

Subsequently, we scrutinized the correlation between these immune cells within the GSE165004 dataset (Fig. 12B). Based on the expression value of the hub genes, the samples were bifurcated into two groups: those with high expression and those with low expression. The differential infiltration level of immune cells between these two groups was then computed. As per the violin plot highlighting the differences in immune cell infiltration, there were observable disparities in CD8 + T cells (P = 0.016) in the high and low expression groups of the BCL2L1 gene. Similarly, for the GSK3B gene expression groups, significant variations in immune cell infiltration of gamma-delta T cells (P = 0.008) and monocytes (P = 0.02) were noted. For the PTPN11 gene expression groups, differences were identified in the immune cell infiltration of activated dendritic cells (P = 0.032)(Fig. 13A-13C).

Lastly, we investigated the correlation between the 10 hub genes and the immune cells. AKT2 was found to be notably associated with memory B cells (R=-0.76, P = 0.011), M0 macrophages (R=-0.7, P = 0.023), Activated NK cells (R = 0.64, P = 0.047), Plasma cells (R = 0.66, P = 0.04), and T follicular helper cells (R = 0.72, P = 0.02) (Fig. 14A-E). BCL2L1 showed a significant association with T follicular helper cells (R = 0.63, P = 0.05) (Fig. 14F). CTNNB1 exhibited a significant association with Activated NK cells (R=-0.66, P = 0.039), memory B cells (R = 0.84, P = 0.0022), M0 macrophages (R = 0.75, P = 0.013), and Activated memory CD4 + T cells (R = 0.76, P = 0.012) (Fig. 14G-J). GRB2 was notably linked to M1 macrophages (R = 0.8, P = 0.0053) (Fig. 14K). GSK3B was significantly associated with gamma-delta T cells (R = − 0.71, P = 0.022) (Fig. 14L). PTEN was significantly associated with memory B cells (R = 0.76, P = 0.011) and M0 macrophages (R = 0.73, P = 0.017) (Fig. 14M-N). PTPN11 was significantly associated with M2 macrophages (R = 0.67, P = 0.033) and Activated memory CD4⁺ T cells (R = − 0.75, P = 0.012) (Fig. 14O-P).

The aforementioned results suggest that the functional alterations in the specified immune cells may exert crucial functions in the immune microenvironment of RSA.

The etiology of RSA is complicated, as more than 40% of RSA cases do not have clear causes and are categorized as URSA(26). URSA arises from a malfunction or interruption in the maternal-fetal interface's immune tolerance mechanisms.. It depends on a complex network of interactions between multiple cells at the maternal-fetal interface. Decidualization, enrichment, and infiltration of decidual immune cells are crucial events for the successful establishment of pregnancy. Numerous studies in recent years have shed light on the role of autophagy in both the normal physiological and pathophysiological processes of the endometrium(9). Relevant research has pointed out that autophagy is significantly increased in the process of endometrial decidualization, and impaired autophagy might be related to an adverse pregnancy outcome(27). In other studies, autophagy deficient transgenic mice with autophagy related protein FIP200 and ATG16L mutation knockout were also observed to have abnormal decidualization and decreased embryo implantation(28) (29). From the above research, we speculated that autophagy is pivotal to the endometrium's decidualization. However, the physiological significance and molecular mechanism of changes in autophagy during decidualization of the endometrium are still unclear and need to be further explored (9) (30). Thus, the differences between autophagy markers or their expression levels that are associated with RSA also need exploration.

We undertook an analysis of gene expression profiling data, specifically GSE165004 and GSE26787, which were screened from the GEO database, and identified the 109 DEARGs between the endometrial of the RSA group and the control group. To investigate the biological processes of DEARGs involved in RSA, we performed enrichment analysis using GO and KEGG. DEARGs were primarily enriched in autophagy, regulation of autophagy, endosome membrane, GTP binding, autophagy-animal pathway, apoptosis pathway, JAK-STAT pathway, PI3K-Akt pathway, and others. Some of these pathways are involved in the process of RSA. The combination of GSEA results suggested that most of the genes were primarily enriched in the Chemokine, PI3K/AKT, and JAK/STAT signaling pathways.

The chemokine/cytokine signaling pathway participates as a mediator of communication between the mother and fetus to promote a healthy pregnancy. Chemokines and their receptors are critical factors in embryo implantation and placental angiogenesis(31), and cytokines are involved in regulating the development of endometrial stromal cells into specialized decidual cells(32). At the same time, a relevant study found that the level of chemokine CXL16 increased when researcher used autophagy inhibitors, and this demonstrated that autophagy could negatively regulate the chemokine CXL16(31). GSEA in the current study suggested that DEARGs might be negatively correlated with the chemokine/cytokine pathway in the endometrium of RSA patients; These results align with those from preceding research

PI3K has an indispensable biological function in a myriad of biological mechanisms such as cell growth, proliferation, angiogenesis, and autophagy(33). PI3K could be used to link the activity of receptor tyrosine kinase and mTOR kinase through a variety of cytokine receptors, regulating autophagy activity in cells(34). A previous study has revealed that activity of the PI3K/Akt pathway, and activity of Akt and Akt isoforms protein levels decreased after decidualization induction(35). Furthermore, during endometriosis, activity of the PI3K/Akt pathway increased and decidual markers (IGFBP1 and PRL) decreased(36). Our results hints that the PI3K/Akt pathway may potentially regulate endometrial autophagy activity in RSA. However, the mechanism of this pathway in regulating the autophagy of endometrial deciduous cells still needs further experimental verification.

The JAK/STAT signaling pathway transduces multiple developmental and homeostatic signals. It is one of the key pathways of decidualization(37), and it has been previously reported that the formation of endometrial receptivity is mediated by the JAK/STAT signal pathway through IFNT induction(38). In the implantation period of sheep, the mRNA level of key signal components of the JAK/STAT pathway in the endometrial lumen epithelium was low(39). The JAK-STAT pathway could also regulate autophagy, As shown in the study of Guo et al(40), when STAT3 is inhibited in lung adenocarcinoma epithelial cells, it will induce autophagy and cell cycle arrest. At the same time, the current study suggested that the JAK-STAT pathway might negatively regulate autophagy in RSA patients.

In the PPI network discerned from this investigation, ten DEARGs- namely AKT2, RPS27A, PTPN11, PIK3CD, PTEN, CTNNB1, KRAS, GSK3B, BCL2L1, and GRB2, were underscored as paramount hub genes. These genes are conjectured to have essential functions in the decidualization of the endometrium in RSA. The top trio targeted DEARGs within the microRNA-gene matrix and transcription factor-gene matrix were PTEN, KRAS, and GSK3B, as well as BCL2L1, AKT2, and PTEN, respectively. The AUC for these ten hub genes was scrutinized for their diagnostic relevance; the AUCs for genes KRAS, PTEN, AKT2, PTPN11, GRB2 ,and PIK3CD fell within 0.800–0.960. This implies that these genes maintain a moderate level of diagnostic precision and could be prospective diagnostic markers for RSA.

AKT2 is instrumental in cellular locomotion, invasive behavior, and metastasis(41). Moreover, it mitigates autophagy through constraining the operation of the transcription factor EB (TFEB), a regulator of various autophagy-associated genes, including LC3B(42). Furthermore, AKT2 serves as a critical catalyst in TTF-instigated autophagy in glioblastoma multiforme, whereby the introduction of an active form of AKT2 diminishes the conversion of LC3-II(43).The RPS27A gene encodes the 40s ribosome protein S27A, it could perform extra-ribosomal functions, and it is reported to be over-expressed in some benign and malignant tumors. However, the specific role of the RPS27A in RSA remains to be determined. PTPN11 is an atypical phosphatase, which could regulate the intracellular kinase-mediated signaling pathways(44). It could also increase or decrease P13K/AKT and JAK/STAT signaling activity(45) (46); the PTPN11-mediated signaling pathway was found to regulate cell proliferation, differentiation, migration, and adhesion(47). PTEN is a natural inhibitor of the PI3K/AKT pathway and could regulate protein synthesis, cell cycle progression, migration, and survival(48). PTEN could also induce autophagy through the PI3K/AKT pathway(49). Relevant data have previously shown that PTEN is involved in alborixin-induced autophagy in neuronal cells(48). KRAS is a GTPase, involved in cell growth, differentiation, and survival pathways(50). KRAS protein regulates multiple cellular functions by mediating KRAS -dependent downstream signaling pathways such as the the RAF-MEK-ERK(MAPK) and PI3K-PTEN-AKT pathways (51) (52). GSK3B represents an isoform of a persistently active serine/threonine kinase, participating in multiple signal transduction pathways that govern a diverse range of vital cellular functions. GSK3B has been shown to suppress autophagy through activating mTORC1 in breast cancer cells(53). Furthermore, inhibition of GSK3B activity could lead to a significant increase of AMP/ATP ratio, thus leading to autophagy induction(54). BCL2L1 involves in apoptosis. The current study demonstrated that BcL-X could interact with Beclin1 to suppress autophagy through dissociating the Beclin1 and PIK3C3 complex, which is an important initiation complex of autophagy. It is speculated that these 10 hub genes might participate in pathogenesis of the endometrium of RSA patients by regulating autophagy.

Successful decidualization of the endometrium promotes immune regulation on the maternal-fetal interface. In recent years, increasing evidence indicates that immune imbalance is involved in maternal-fetal immune tolerance. For example, the high proportion of CD₁₆⁺ CD₅₆⁺ NK cells suggests that NK cells in RSA patients have high cytotoxicity(55); and M2 macrophages participate in trophoblast invasion and vascular remodeling.

We utilized the CIBERSORT algorithm to delve deeper into the function of immune cell infiltration in the endometrium of RSA. Autophagy's role in modulating components of the immune system, principally encompassing NK cells, DCs, macrophages along with T and B lymphocytes, has been documented. Hence, we examined the relationship between hub genes and infiltrating immune cells, discovering that these DEARGs (AKT2, BCL2L1, CTNNB1, GRB2, GSK3B, PTEN, and PTPN11) exhibited substantial correlation with immune cells. This could suggest a potential interplay between autophagy and immune response in decidualization of the endometrial microenvironment of RSA patients. Among the immune cells, these DEARGs displayed a positive correlation with activated dendritic cells, resting mast cells, and neutrophils. Conversely, a negative correlation was seen with naive B cells, memory B cells, and plasma cells. However, the intricate molecular mechanisms underpinning these interactions remain elusive and warrant further investigation. Although our approach in the current study helped us to further understand the association between autophagy and abnormal decidualization of the endometrium in RSA, inevitably, Our study is not without limitations. Primarily, it was a retrospective analysis based on bioinformatic data. Future studies should strive to increase the sample size and conduct prospective investigations. Secondly, the volume of data available in public databases is finite. To elucidate autophagy's precise role in the progression of endometrium in RSA, and to circumvent potential inaccuracies and biases comprehensively, all clinical factors and uniform interventional measures should be taken into consideration. Finally, the understanding of DEARGs function is not sufficiently comprehensive; we could not illustrate the expression of DEARGs from protein level, and also could not clearly evaluate the specific pathophysiological mechanisms of DEARGs involved in the development of RSA, which needs further investigation.

The DEARGs: AKT2, RPS27A, PTPN11, PIK3CD, PTEN, CTNNB1, KRAS, GSK3B, BCL2L1, and GRB2 could be pivotal in the pathogenesis of decidualization of the endometrium in RSA patients and may serve as potential biomarkers for its diagnosis. Nonetheless, more comprehensive research and subsequent experimental validations are needed to manifest the biological repercussions of autophagy in the endometrium of RSA patients.

Author information

Authors and Affiliations

¹Department of Obstetrics and Gynecology, Gansu Provincial Hospital, Lanzhou, 730000, China

Ruzhen Shuai

²Department of Reproductive Medicine,Jinan Maternity and Child Care Hospital Affiliated to Shandong First Medical University, Jinan, 250000,China

Dandan Li

³Department of Gynecology,General Hospital of Ningxia Medical University, Ningxia 750000,China

Dan Liu

⁴Department of Obstetrics and Gynecology, Baiyin Third People's Hospital, Baiyin, 730700, China

Jing He

⁵Department of Obstetrics and Gynecology, Qingyang Maternal and Child Health Hospital, Qingyang, 745000, China

Qiong Wu

⁶Department of Endocrinolog, Shandong Public Health Clinical Center ,Jinan,250000,China

Yuan Zhou

* Corresponding Author: Dan Liu

* Address for correspondence: Dan Liu, MD, Department of Gynecology / Key Laboratory of Ministry of Education for Fertility Preservation and Maintenance , General Hospital of Ningxia Medical University ,No.1160, Shengli Street, Xingqing District, Yinchuan, Ningxia

E-mail: [email protected]

Tel: +86 13995285699

Ethical approval and consent to participate

This research does not contain any studies with human participants or animals performed by any of the authors. No personal information is involved, so informed consent is not required.

Availability of data and materials

Data are available in a public, open access repository. All data relevant to the study areincluded in the article or uploaded as supplemental information. This work used publicly available datasets, available at NCBI: GSE165004 and GSE26787 found in the GEO (https://www.ncbi.nlm.nih.gov/). The data is avaible at: https://www.jianguoyun.com/p/DanEjKcQqrzbCxig74sFIAA.

Consent to publish

Not applicable

Funding

This research received no external funding

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Author contribution

Conceived and designed the study: RZS; Collected and processed data: JH,QW; Analyzed data: YZ; Wrote the manuscript and performed the literature research for the manuscript:RZS,DDL; Revised the manuscript:RZS,DDL; Final edit of paper: RZS,DL.All the authors have read and approved the final manuscript.

Acknowledgment

We acknowledge and appreciate our colleagues for valuable efforts and comments on this paper. In addition, we thank all members of the GEO database for providing such a useful platform for researchers.

Akobeng AK. Understanding diagnostic tests 3: Receiver operating characteristic curves. Acta Paediatr. 2007;96(5):644–7.
Ander SE, Diamond MS, Coyne CB. 2019. Immune responses at the maternal-fetal interface. Sci Immunol. 4(31).
Arck PC, Hecher K. Fetomaternal immune cross-talk and its consequences for maternal and offspring's health. Nat Med. 2013;19(5):548–56.
Arico S, Petiot A, Bauvy C, Dubbelhuis PF, Meijer AJ, Codogno P, Ogier-Denis E. The tumor suppressor pten positively regulates macroautophagy by inhibiting the phosphatidylinositol 3-kinase/protein kinase b pathway. J Biol Chem. 2001;276(38):35243–6.
Awolumate OJ, Kang A, Khokale R, Cancarevic I. Role of low molecular weight heparin in the management of unexplained recurrent pregnancy loss: A review of literature. Cureus. 2020;12(10):e10956.
Basu A, Lambring CB. 2021. Akt isoforms: A family affair in breast cancer. Cancers (Basel) 13(14).
Bender Atik R, Christiansen OB, Elson J, Kolte AM, Lewis S, Middeldorp S, Nelen W, Peramo B, Quenby S, Vermeulen N et al. 2018. Eshre guideline: Recurrent pregnancy loss. Hum Reprod Open. 2018(2):hoy004.
Binelli M, Subramaniam P, Diaz T, Johnson GA, Hansen TR, Badinga L, Thatcher WW. Bovine interferon-tau stimulates the janus kinase-signal transducer and activator of transcription pathway in bovine endometrial epithelial cells. Biol Reprod. 2001;64(2):654–65.
Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY. Cytohubba: Identifying hub objects and sub-networks from complex interactome. BMC Syst Biol 8 Suppl. 2014;4(Suppl 4):11.
Choi Y, Johnson GA, Burghardt RC, Berghman LR, Joyce MM, Taylor KM, Stewart MD, Bazer FW, Spencer TE. Interferon regulatory factor-two restricts expression of interferon-stimulated genes to the endometrial stroma and glandular epithelium of the ovine uterus. Biol Reprod. 2001;65(4):1038–49.
Cotto KC, Wagner AH, Feng YY, Kiwala S, Coffman AC, Spies G, Wollam A, Spies NC, Griffith OL, Griffith M. Dgidb 3.0: A redesign and expansion of the drug-gene interaction database. Nucleic Acids Res. 2018;46(D1):D1068–d1073.
Dorard C, Vucak G, Baccarini M. Deciphering the ras/erk pathway in vivo. Biochem Soc Trans. 2017;45(1):27–36.
Fabi F, Grenier K, Parent S, Adam P, Tardif L, Leblanc V, Asselin E. Regulation of the pi3k/akt pathway during decidualization of endometrial stromal cells. PLoS ONE. 2017;12(5):e0177387.
Fu B, Tian Z, Wei H. Th17 cells in human recurrent pregnancy loss and pre-eclampsia. Cell Mol Immunol. 2014;11(6):564–70.
Gellersen B, Brosens JJ. Cyclic decidualization of the human endometrium in reproductive health and failure. Endocr Rev. 2014;35(6):851–905.
Gene ontology consortium. Going forward. Nucleic Acids Res. 2015;43(Database issue):D1049–1056.
Green DM, O'Donoghue K. A review of reproductive outcomes of women with two consecutive miscarriages and no living child. J Obstet Gynaecol. 2019;39(6):816–21.
Gu H, Li L, Du M, Xu H, Gao M, Liu X, Wei X, Zhong X. Key gene and functional pathways identified in unexplained recurrent spontaneous abortion using targeted rna sequencing and clinical analysis. Front Immunol. 2021;12:717832.
Guo J, Wu G, Bao J, Hao W, Lu J, Chen X. Cucurbitacin b induced atm-mediated DNA damage causes g2/m cell cycle arrest in a ros-dependent manner. PLoS ONE. 2014;9(2):e88140.
Hiraoka T, Hirota Y, Fukui Y, Gebril M, Kaku T, Aikawa S, Hirata T, Akaeda S, Matsuo M, Haraguchi H, et al. Differential roles of uterine epithelial and stromal stat3 coordinate uterine receptivity and embryo attachment. Sci Rep. 2020;10(1):15523.
Hviid TV. Hla-g in human reproduction: Aspects of genetics, function and pregnancy complications. Hum Reprod Update. 2006;12(3):209–32.
Jung S, Li C, Jeong D, Lee S, Ohk J, Park M, Han S, Duan J, Kim C, Yang Y, et al. Oncogenic function of p34sei-1 via nedd4–1–mediated pten ubiquitination/degradation and activation of the pi3k/akt pathway. Int J Oncol. 2013;43(5):1587–95.
Kanehisa M, Goto S. Kegg: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30.
Ke Y, Lesperance J, Zhang EE, Bard-Chapeau EA, Oshima RG, Muller WJ, Feng GS. Conditional deletion of shp2 in the mammary gland leads to impaired lobulo-alveolar outgrowth and attenuated stat5 activation. J Biol Chem. 2006;281(45):34374–80.
Kim EH, Jo Y, Sai S, Park MJ, Kim JY, Kim JS, Lee YJ, Cho JM, Kwak SY, Baek JH, et al. Tumor-treating fields induce autophagy by blocking the akt2/mir29b axis in glioblastoma cells. Oncogene. 2019;38(39):6630–46.
Lédée N, Munaut C, Aubert J, Sérazin V, Rahmati M, Chaouat G, Sandra O, Foidart JM. Specific and extensive endometrial deregulation is present before conception in ivf/icsi repeated implantation failures (if) or recurrent miscarriages. J Pathol. 2011;225(4):554–64.
Levy JMM, Towers CG, Thorburn A. Targeting autophagy in cancer. Nat Rev Cancer. 2017;17(9):528–42.
Lum JJ, Bauer DE, Kong M, Harris MH, Li C, Lindsten T, Thompson CB. Growth factor regulation of autophagy and cell survival in the absence of apoptosis. Cell. 2005;120(2):237–48.
Mancinelli R, Carpino G, Petrungaro S, Mammola CL, Tomaipitinca L, Filippini A, Facchiano A, Ziparo E, Giampietri C. 2017. Multifaceted roles of gsk-3 in cancer and autophagy-related diseases. Oxid Med Cell Longev. 2017:4629495.
Mendoza MC, Er EE, Blenis J. The ras-erk and pi3k-mtor pathways: Cross-talk and compensation. Trends Biochem Sci. 2011;36(6):320–8.
Merckaert T, Zwaenepoel O, Gevaert K, Gettemans J. An akt2-specific nanobody that targets the hydrophobic motif induces cell cycle arrest, autophagy and loss of focal adhesions in mda-mb-231 cells. Biomed Pharmacother. 2021;133:111055.
Nakashima A, Aoki A, Kusabiraki T, Shima T, Yoshino O, Cheng SB, Sharma S, Saito S. Role of autophagy in oocytogenesis, embryogenesis, implantation, and pathophysiology of pre-eclampsia. J Obstet Gynaecol Res. 2017;43(4):633–43.
Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, Hoang CD, Diehn M, Alizadeh AA. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015;12(5):453–7.
Oestreich AK, Chadchan SB, Medvedeva A, Lydon JP, Jungheim ES, Moley KH, Kommagani R. The autophagy protein, fip200 (rb1cc1) mediates progesterone responses governing uterine receptivity and decidualization†. Biol Reprod. 2020a;102(4):843–51.
Oestreich AK, Chadchan SB, Popli P, Medvedeva A, Rowen MN, Stephens CS, Xu R, Lydon JP, Demayo FJ, Jungheim ES et al. 2020b. The autophagy gene atg16l1 is necessary for endometrial decidualization. Endocrinology 161(1).
Ogawa F, Walters MS, Shafquat A, O'Beirne SL, Kaner RJ, Mezey JG, Zhang H, Leopold PL, Crystal RG. Role of kras in regulating normal human airway basal cell differentiation. Respir Res. 2019;20(1):181.
Puri P, Walker WH. The regulation of male fertility by the ptpn11 tyrosine phosphatase. Semin Cell Dev Biol. 2016;59:27–34.
R. G. 2011. The investigation and treatment of couples with recurrent first-trimester and second-trimester miscarriage. RCOG Green-top Guideline No 17 1–18.
Rastaldo R, Vitale E, Giachino C. Dual role of autophagy in regulation of mesenchymal stem cell senescence. Front Cell Dev Biol. 2020;8:276.
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. Limma powers differential expression analyses for rna-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47.
Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, Müller M. Proc: An open-source package for r and s + to analyze and compare roc curves. BMC Bioinformatics. 2011;12:77.
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504.
Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545–50.
Sun A, Li C, Chen R, Huang Y, Chen Q, Cui X, Liu H, Thrasher JB, Li B. Gsk-3β controls autophagy by modulating lkb1-ampk pathway in prostate cancer cells. Prostate. 2016;76(2):172–83.
Tajan M, de Rocca Serra A, Valet P, Edouard T, Yart A. Shp2 sails from physiology to pathology. Eur J Med Genet. 2015;58(10):509–25.
Wang NN, Dong J, Zhang L, Ouyang D, Cheng Y, Chen AF, Lu AP, Cao DS. Hamdb: A database of human autophagy modulators with specific pathway and disease information. J Cheminform. 2018;10(1):34.
Wang P, Pan J, Tian X, Dong X, Ju W, Wang Y, Zhong N. Transcriptomics-determined chemokine-cytokine pathway presents a common pathogenic mechanism in pregnancy loss and spontaneous preterm birth. Am J Reprod Immunol. 2021;86(1):e13398.
Wani A, Gupta M, Ahmad M, Shah AM, Ahsan AU, Qazi PH, Malik F, Singh G, Sharma PR, Kaddoumi A, et al. Alborixin clears amyloid-β by inducing autophagy through pten-mediated inhibition of the akt pathway. Autophagy. 2019;15(10):1810–28.
Xia J, Gill EE, Hancock RE. Networkanalyst for statistical, visual and network-based meta-analysis of gene expression data. Nat Protoc. 2015;10(6):823–44.
Xu L, Li Y, Sang Y, Li DJ, Du M. Crosstalk between trophoblasts and decidual immune cells: The cornerstone of maternal-fetal immunotolerance. Front Immunol. 2021;12:642392.
Yang D, Jiang T, Liu J, Zhang B, Lin P, Chen H, Zhou D, Tang K, Wang A, Jin Y. Creb3 regulatory factor -mtor-autophagy regulates goat endometrial function during early pregnancy. Biol Reprod. 2018;98(5):713–21.
Yang S, Wang H, Li D, Li M. Role of endometrial autophagy in physiological and pathophysiological processes. J Cancer. 2019;10(15):3459–71.
Yin X, Pavone ME, Lu Z, Wei J, Kim JJ. Increased activation of the pi3k/akt pathway compromises decidualization of stromal cells from endometriosis. J Clin Endocrinol Metab. 2012;97(1):E35–43.
Yu G, Wang LG, Han Y, He QY. Clusterprofiler: An r package for comparing biological themes among gene clusters. Omics. 2012;16(5):284–7.
Zhang SQ, Tsiaras WG, Araki T, Wen G, Minichiello L, Klein R, Neel BG. Receptor-specific regulation of phosphatidylinositol 3'-kinase activation by the protein tyrosine phosphatase shp2. Mol Cell Biol. 2002;22(12):4062–72.

No competing interests reported.

supplementalinformation.docx

Download PDF

Version 1

posted

You are reading this latest preprint version

Exploring Autophagy-Associated Genes in the Endometrium of Recurrent Spontaneous Abortion and Examining their Relationship with Immune Infiltration

Status:

Version 1

Abstract

Figures

Introduction

Materials and Methods

Acquisition and Management of Data

Identification of DEARGs

Functional Enrichment Analysis (GO/KEGG)

Gene set enrichment analysis (GSEA)

Examination via ROC Curve

Investigation of Immune Cell Infiltration

Statistical Approach

Results

Data preprocessing and DEARGs screening

Functional Enrichment Analysis

GSEA results

PPI network analysis of DEARGs

Establishment of miRNA- and TF-Hub Gene Networks

Construction of Hub Gene-Drug Interaction Network

ROC Curve Analysis and Confirmation of Hub Gene Expression

Results of Immune Cell Infiltration Analysis

Discussion

Conclusion

Declarations

Author information

Ethical approval and consent to participate

Conflict of Interest

Author contribution

Acknowledgment

References

Additional Declarations

Supplementary Files

Status:

Version 1