Endoplasmic reticulum stress-related gene TRAF6 as a potential biomarker for diminished ovarian reserve

Endoplasmic reticulum stress-related gene TRAF6 as a potential biomarker for diminished ovarian reserve | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Endoplasmic reticulum stress-related gene TRAF6 as a potential biomarker for diminished ovarian reserve Lirong Wang, Jiajing He, Xinyue Zhou, Haofei Shen, Rui Zhang, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9228384/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background In this study, the role of endoplasmic reticulum stress-related differentially expressed genes (ER-StressRDEGs) in diminished ovarian reserve (DOR) is examined. Comprehensive bioinformatics analyses were performed to identify key genes and pathways associated with DOR, providing a foundation for future clinical applications. Methods Follicular fluid samples from six patients with DOR and six patients with normal ovarian reserve (NOR) were used for exosomal miRNA analysis. Gene expression data were retrieved from the Gene Expression Omnibus (GEO) database using the GEOquery package, and endoplasmic reticulum stress-related genes (ER-StressRGs) were retrieved from the GeneCards database. miRNA target genes were predicted using the MicroRNA Target Prediction Database and overlapped with ER-StressRDEGs to identify co-genes. Clinical data and biological samples (follicular fluid and granulosa cells) were collected from 42 patients with DOR and 42 patients with NOR for validation using reverse transcription quantitative polymerase chain reaction (RT-qPCR) and enzyme-linked immunosorbent assay (ELISA). After identifying the target gene, additional validation was conducted in vivo using experimental models. Female C57BL/6 J mice received single intraperitoneal injections of cyclophosphamide (CTX) at three dosage regimens. Ovarian tissue was then evaluated using histopathological analysis (hematoxylin and eosin staining), hormone level assessment, and protein expression analysis, including TUNEL assays, immunofluorescence staining, and Western blotting. Results Analysis of the GSE87201 dataset identified 1,389 differentially expressed genes, including 606 upregulated genes and 783 downregulated genes, with 115 classified as ER-StressRDEGs. A support vector machine model was constructed incorporating five key co-genes ( TRAF6, CD109, BAG2, USP25, EIF3H ). Validation experiments using RT-qPCR and ELISA showed that TRAF6 was highly expressed in the DOR group and was statistically significant compared to the control group. Furthermore, female mice induced by CTX 100 mg/kg exhibited characteristic features of DOR. Consistently, the expression level of the target protein TRAF6 was increased in ovarian tissues. Conclusions These findings indicate that TRAF6 is highly expressed in DOR and is associated with clinical indicators of ovarian reserve. These results suggest that TRAF6 may serve as a novel biomarker for diagnosing DOR. Exosomal microRNAs endoplasmic reticulum stress diminished ovarian reserve bioinformatics biomarker Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 1. Introduction A decline in oocyte quantity and quality with advancing age is a normal physiologic process known as diminished ovarian reserve (DOR) [ 1 ] . However, some women experience this decline much earlier than expected, leading to premature infertility; this condition is often referred to as pathologic DOR [ 2 ] . In recent years, the incidence of DOR has gradually increased, with a noticeable trend toward earlier onset in younger populations. The reported prevalence of DOR ranges from 10% to 35% [ 3 ] , making it a significant contributor to female infertility. Currently, there is no internationally unified consensus regarding the diagnosis or management of DOR [ 2 ] . Existing treatment strategies—such as hormonal therapies and assisted reproductive technologies [ 4 ] —often yield limited effectiveness and may involve substantial financial costs. Furthermore, most current treatment modalities focus primarily on symptomatic management rather than addressing the underlying pathophysiological mechanisms of the condition. This limitation highlights a critical gap in our understanding of DOR. Thus, identifying novel diagnostic biomarkers and potential therapeutic targets is imperative. Endoplasmic reticulum stress (ERS)-induced apoptosis is involved in the pathogenesis of various diseases, including neurodegenerative disorders, stroke, breast cancer, and diabetes [ 5 ] . Despite their apparent diversity, these conditions share a common feature: intracellular and/or extracellular factors that disrupt protein folding and lead to the accumulation of misfolded proteins in the endoplasmic reticulum (ER). Under normal physiological conditions, ERS plays a crucial role in the growth and maturation of ovarian follicles, follicular closure, corpus luteum formation, and implantation. However, disruption of ER homeostasis by ERS may negatively impact female fertility [ 6 ] . Harada M reported that oocyte growth and granulosa cell proliferation in ovarian tissue cause hypoxia, leading to ER dysfunction resulting in ERS [ 7 ] . Additionally, apoptosis of ovarian granulosa cells during early follicular atresia has been associated with ERS [ 8 ] . Previous studies have indicated a potential link between ERS and various reproductive disorders [ 9 ] . Nevertheless, the specific role of ER stress-related differentially expressed genes (ER-StressRDEGs) in DOR remains poorly elucidated. Therefore, this study seeks to explore the relationship between ER-StressRDEGs and DOR to uncover novel molecular mechanisms that may inform the development of more effective diagnostic and therapeutic strategies. Research has shown that exosomes can transport proteins and RNA from donor cells to recipient cells, playing a crucial role in cell communication, signal transduction, and the regulation of various physiological and pathological processes [ 10 ] . Among these, exosomal microRNAs (ex-miRNAs), encapsulated within extracellular vesicles, play a vital role in facilitating paracrine communication among granulosa cells (GCs), cumulus cells, and the oocyte inside follicular fluid (FF). Previous studies have primarily focused on the molecular functions and regulatory mechanisms of miRNAs carried by these exosomes. For example, exosomal miR-122-5p has been shown to promote apoptosis of ovarian GCs by targeting BCL9 [ 11 ] . Conversely, exosomal miR-644-5p derived from bone marrow mesenchymal stem cells [ 12 ] and miR-144-5p [ 13 ] have been reported to inhibit the apoptosis of damaged GCs and prevent follicular atresia after chemotherapy. Additional studies suggest that changes in ex-miRNA expression in FF may contribute to declines in ovarian reserve and oocyte quality in patients with DOR [ 14 ] . In this study, a comprehensive suite of bioinformatics tools was applied to identify the key genes and signaling pathways associated with ovarian reserve decline. Using these results, an excellent predictive diagnostic model was constructed. These findings not only deepen our understanding of the molecular mechanisms underlying this pathological process but also lay the foundation for the future development of novel diagnostic and therapeutic strategies. 2. Materials and Methods 2.1 Sample collection For the self-test ex-miRNA analysis, FF samples were collected from six patients with DOR and six cases of normal ovarian reserve (NOR). The raw count data were used for subsequent differential analyses. Patients undergoing in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI) were recruited from the Reproductive Center of the First Hospital of Lanzhou University between March 2024 and June 2024. The diagnostic criteria for patients with DOR were as follows: age ≤ 35 years; antral follicular count (AFC) < 5–7 follicles; anti-Müllerian hormone (AMH) < 1.1 ng/ml; and basic follicle-stimulating hormone (bFSH) at 10–25 mIU/mL. Patients with NOR included individuals whose infertility was mainly attributable to tubal or male factors. Their diagnostic criteria were as follows: age ≤ 35 years; unilateral AFC between 5 and 12; AMH > 2.0 ng/mL; bFSH < 10 mIU/mL; and basic luteinizing hormone (bLH) 35 years, the presence of female chromosomal abnormalities, previous ovarian or fallopian tube surgery, endometriosis, polycystic ovary syndrome, gonadal dysplasia, related endocrine disorders (e.g., thyroid dysfunction, hyperprolactinemia, diabetes), and autoimmune diseases (e.g., systemic lupus erythematosus). Medical histories and clinical characteristics were obtained from electronic medical records of patients with DOR (n = 42) and NOR (n = 42). All patients underwent controlled ovarian stimulation using either the antagonist protocol or the progesterone-primed ovarian stimulation protocol. FF and GCs were collected on the day of oocyte retrieval and stored separately at − 80°C for subsequent use in reverse transcription quantitative polymerase chain reaction (RT-qPCR) and enzyme-linked immunosorbent assay (ELISA) validation experiments. 2.2 Data download The DOR dataset GSE87201 [ 17 ] was downloaded from the Gene Expression Omnibus (GEO) database [ 16 ] ( https://www.ncbi.nlm.nih.gov/geo/ ) using the R package GEOquery [ 15 ] (version 2.70.0). Raw data were processed and standardized using the R package limma [ 22 ] (version 3.58.1), including normalization of probe annotations. All samples in GSE87201 were from Homo sapiens , and the tissue source was mature MII oocytes. The chip platform for GSE87201 was GPL17586; detailed information is provided in Table 1 . The dataset included 18 DOR samples and 17 NOR samples, all of which were included in this study. Table 1 GEO Microarray Chip Information Platform GSE87201 GPL17586 Experiment type Expression profiling by array Species Homo sapiens Tissue Mature MII oocytes Samples in DOR group 18 Samples in Control group 17 Reference 28586423 Abbreviations: GEO, Gene Expression Omnibus; DOR, diminished ovarian reserve. Table 2 Results of GO and KEGG Enrichment Analysis ONTOLOGY ID Description GeneRatio BgRatio pvalue p.adjust qvalue BP GO:0010639 negative regulation of organelle organization 5/19 365/18888 2.43904E-05 0.01746493 0.009634467 BP GO:0035666 TRIF-dependent toll-like receptor signaling pathway 2/19 11/18888 5.24438E-05 0.01746493 0.009634467 BP GO:1903050 regulation of proteolysis involved in protein catabolic process 4/19 228/18888 6.9514E-05 0.01746493 0.009634467 BP GO:0007250 activation of NF-kappaB-inducing kinase activity 2/19 14/18888 8.66145E-05 0.01746493 0.009634467 BP GO:0002756 MyD88-independent toll-like receptor signaling pathway 2/19 16/18888 0.00011408 0.01746493 0.009634467 CC GO:0140672 ATAC complex 2/19 14/19894 7.81044E-05 0.009294425 0.006083923 CC GO:0070461 SAGA-type complex 2/19 39/19894 0.000627003 0.037306688 0.024420123 CC GO:0000123 histone acetyltransferase complex 2/19 94/19894 0.00358467 0.092276356 0.060402037 CC GO:0031248 protein acetyltransferase complex 2/19 104/19894 0.004367593 0.092276356 0.060402037 CC GO:1902493 acetyltransferase complex 2/19 105/19894 0.004449873 0.092276356 0.060402037 MF GO:0034450 ubiquitin-ubiquitin ligase activity 2/19 14/18522 9.00587E-05 0.009906456 0.005687917 MF GO:0031625 ubiquitin protein ligase binding 3/19 305/18522 0.003523281 0.084733626 0.048650886 MF GO:0044389 ubiquitin-like protein ligase binding 3/19 324/18522 0.004174353 0.084733626 0.048650886 MF GO:0043130 ubiquitin binding 2/19 99/18522 0.004557884 0.084733626 0.048650886 MF GO:0101005 deubiquitinase activity 2/19 115/18522 0.006099126 0.084733626 0.048650886 KEGG hsa03250 Viral life cycle - HIV-1 3/17 64/8538 0.000253445 0.012250649 0.009861203 KEGG hsa04140 Autophagy - animal 4/17 169/8538 0.000288251 0.012250649 0.009861203 KEGG hsa04657 IL-17 signaling pathway 3/17 95/8538 0.000810439 0.022962436 0.018483695 KEGG hsa05145 Toxoplasmosis 3/17 112/8538 0.001306841 0.027770364 0.022353853 KEGG hsa05162 Measles 3/17 139/8538 0.002429144 0.041295442 0.033240913 Abbreviations: GO, Gene Ontology; BP, biological process; CC, cellular component; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genomes. Table 3 GSEA enrichment analysis of the Disease versus Control Groups ID setSize enrichmentScore NES pvalue p.adjust qvalue REACTOME_METALLOTHIONEINS_BIND_METALS 11 0.887774845 1.96187129 3.25E-05 0.016673964 0.016106786 ZHENG_RESPONSE_TO_ARSENITE_UP 14 0.864426208 2.033759036 1.22E-05 0.010295164 0.009944966 HEBERT_MATRISOME_TNBC_BONE_METASTASIS 11 0.854905334 1.889233784 0.000219442 0.046176892 0.044606149 REACTOME_RESPONSE_TO_METAL_IONS 14 0.854093035 2.009447899 2.43E-05 0.014324357 0.013837103 Table 4 Comparison of clinical baseline data between DOR group and NOR group. Age(year) NOR(n = 42) DOR(n = 42) t 95% p 29.74 ± 3.04 32.00 ± 2.49 -3.73 -3.47 -1.05 <0.001 Duration of infertility (y) 3.62 ± 1.96 4.26 ± 1.71 -1.6 -1.44 0.16 0.113 BMI (kg/m 2 ) 21.6 ± 2.21 21.81 ± 2.59 -0.41 -1.26 0.83 0.685 AMH (ng/ml) 4.24 ± 1.35 0.48 ± 0.31 17.573 3.33 4.18 <0.001 AFC 15.92 ± 3.10 3.76 ± 1.78 22.05 11.07 13.26 <0.001 FSH (mIU/ml) 6.80 ± 1.17 13.34 ± 3.78 -10.70 -7.75 -5.32 <0.001 LH (mIU/ml) 5.80 ± 1.81 5.22 ± 2.41 1.25 -0.34 1.51 0.214 E2 (pg/ml) 40.33 ± 13.71 41.50 ± 19.93 -0.31 -8.59 6.26 0.755 Abbreviations: AMH, anti-Müllerian hormone; BMI, Body Mass Index; AFC, antral follicular count; FSH, follicle-stimulating hormone; LH, luteinizing hormone; E2, estradiol Endoplasmic reticulum stress-related genes (ER-StressRGs) were obtained from the GeneCards database [ 18 ] ( https://www.genecards.org/ ), which provides comprehensive information on human genes. Using“Endoplasmic Reticulum Stress”as the search keyword and restricting the results to “protein-coding,” 2,414 ER-StressRGs were identified. Additional ER-StressRGs were collected from published literature indexed in PubMed ( https://pubmed.ncbi.nlm.nih.gov/ ) using “Endoplasmic Reticulum Stress” as the keyword [ 19 – 21 ] . After removing duplicates and irrelevant entries, 37 ER-StressRGs were identified. Merging genes from the two sources and deduplicating yielded a final set of 2,425 ER-StressRGs. Detailed information on these genes is provided in Table S1 . 2.3 Differentially expressed genes related to endoplasmic reticulum stress in diminished ovarian reserve Based on the miRNA dataset samples, the samples were grouped into DOR and NOR groups. Differential gene expression analysis between DOR and NOR was performed using the R package DESeq2 [ 23 ] (version 1.42.0). Differentially expressed genes (DEGs) were defined using a threshold of |logFC| > 0 and adj. p 0 and adj. p < 0.05 were considered upregulated, while those with logFC < 0 and adj. p < 0.05 were considered downregulated. Volcano plots illustrating the differential expression result were generated using the R package ggplot2 (version 3.4.4). Similarly, for the GSE87201 dataset, samples were divided into DOR and NOR groups. Differential expression analysis was conducted using the R package limma [ 22 ] (version 3.58.1). DEGs were identified using the threshold of |logFC| > 0 and p 0 and p < 0.05 were classified as upregulated DEGs, whereas genes with logFC < 0 and p < 0.05 were classified as downregulated DEGs. Volcano plots illustrating the differential expression results were generated using the R package ggplot2 (version 3.4.4). To identify ER-StressRDEGs associated with DOR, all DEGs from GSE87201 were intercrossed with the previously obtained ER-StressRGs. A Venn diagram was used to illustrate the overlap. The resulting ER-StressRDEGs were further visualized using a heatmap of the top 20 genes, generated with the R package pheatmap (version 1.0.12). Finally, the corresponding target genes of the differentially expressed miRNAs were predicted using the MicroRNA Target Prediction Database (miRDB; https://mirdb.org/ ). These predicted target genes were then intersected with the ER-StressRDEGs from GSE87201 to identify overlapping genes, referred to as co-genes. 2.4 Differential expression validation and receiver operating characteristic curve analysis of co-genes To further examine the expression levels of co-genes between the DOR and NOR groups in the GSE87201 dataset, a group comparison plot was generated using the expression profiles of the identified co-genes. Subsequently, receiver operating characteristic (ROC) curve analysis was performed to evaluate the potential diagnostic performance of the co-genes. The ROC curves were generated using the R package pROC [ 24 ] (version 1.18.5), and the corresponding area under the curve (AUC) values were calculated. AUC values were used to evaluate the diagnostic value of co-gene expression in the occurrence of DOR. Generally, AUC values range from 0.5 to 1. Values closer to 1 indicate stronger diagnostic performance, whereas values closer to 0.5 indicate limited diagnostic ability. AUC values between 0.5 and 0.7 were considered to indicate low accuracy, values between 0.7 and 0.9 were considered to indicate moderate accuracy, and values greater than 0.9 were considered to indicate high accuracy. 2.5 Gene Ontology and pathway enrichment analysis Gene Ontology (GO) analysis [ 25 ] is widely used for large-scale functional enrichment studies. It classifies gene functions into three independent domains: biological process (BP), cellular component (CC), and molecular function (MF). The Kyoto Encyclopedia of Genes and Genomes (KEGG) [ 26 ] is a widely used resource that integrates information on genomes, biological pathways, diseases, and drugs. The R package cluster-Profiler [ 27 ] (version 4.10.0) was used to perform GO functional enrichment analysis and KEGG pathway enrichment analysis for the co-genes. Enrichment results were considered statistically significant when the p -value was < 0.05 and the false discovery rate (FDR; q -value) was < 0.25. 2.6 Gene set enrichment analysis Gene set enrichment analysis (GSEA) [ 28 ] is used to evaluate the distribution of genes within a predefined gene set in a gene table ranked by correlation with the phenotype, thereby determining their contribution to the phenotype. In this study, genes from GSE87201 were first ranked according to their logFC values between the DOR and NOR groups. GSEA was then performed on the ranked gene list using the R package cluster-Profiler [ 27 ] (version 4.10.0). The analysis was conducted using the c2 gene set obtained from the Molecular Signatures Database (MSigDB), version v2023.2.Hs. The following parameters were applied: a random seed of 2020, a minimum gene set size of 10, and a maximum gene set size of 500. Enrichment significance was evaluated using adj. p < 0.05 and an FDR value ( q -value) < 0.25. P -value adjustment was performed using the BH method. 2.7 Gene set variation analysis Gene set variation analysis (GSVA) [ 29 ] is a nonparametric, unsupervised analytical method used to estimate variations in gene set enrichment across samples in transcriptomic datasets. Instead of evaluating differential expression at the level of individual genes, GSVA converts the gene expression matrix into pathway-level enrichment scores for each sample. This approach allows the identification of biological pathways that may be differentially enriched across samples. In this study, GSVA was performed using all genes from the GSE87201 dataset to evaluate functional enrichment differences between the DOR and NOR groups. The gene set used for the analysis was obtained from the MSigDB [ 30 ] , specifically the c2.cp.v2023.2.Hs. symbols gene set in GMT format. The analysis was conducted using the R package GSVA (version 1.50.0). Pathways with a p -value < 0.05 were considered statistically significant. 2.8 Establishment of a diagnostic model for diminished ovarian reserve To construct a diagnostic model for DOR using GSE87201, logistic regression analysis was first performed on the co-genes. Logistic regression is used to analyze the association between an independent variable and a binary dependent variable. In this analysis, the dependent variable represents the sample group classification (DOR or NOR). Co-genes with p -values < 0.05 were selected as the criterion for screening co-genes and constructing a logistic regression model. A forest plot was generated to visualize the group-wise expression of the co-genes included in the model. Subsequently, a model based on the support vector machine (SVM) [ 31 ] algorithm was constructed using the co-genes retained from the logistic regression analysis. The SVM model was used to further screen genes by evaluating model performance, with gene selection based on achieving the highest accuracy and the lowest error rate. Next, least absolute shrinkage and selection operator (LASSO) regression analysis was performed using the R package glmnet [ 32 ] (version 4.1-8). The analysis was conducted with the parameters set.seed (500) and family = "binomial," using the co-genes retained from the SVM model. LASSO regression analysis introduces a penalty term to the regression model to reduce overfitting and improve model generalizability by shrinking regression coefficients toward zero. The strength of this penalty is controlled by the regularization parameter (lambda × absolute value of slope). The diagnostic model plot and variable trajectory plot were used to visualize the results of the LASSO regression analysis. Genes retained in the final LASSO model were defined as key genes, and the resulting model was considered the diagnostic model for DOR. Finally, a LASSO-based risk score was computed using the risk coefficients obtained from the LASSO model. The risk score was calculated using the following formula: $$\:\text{r}\text{i}\text{s}\text{k}\:score\:=\:\sum\:_{i}Coefficient\:\left({gene}_{i}\right)\text{*}mRNA\:Expression\:\left({gene}_{i}\right)$$ 2.9 Validation of the diagnostic model for diminished ovarian reserve A nomogram [ 33 ] is a graphical tool that uses a cluster of disjoint line segments to represent the functional relationship between multiple independent variables in a rectangular coordinate system. The R package rms (version 6.7-1) was used to construct a nomogram based on the logistic regression results to visualize the relationship among key genes. A calibration curve was generated from the LASSO regression results to evaluate the accuracy and discrimination of the diagnostic model for DOR. The R package ggDCA (version 1.1) was used to generate the decision curve analysis (DCA) plot based on the key genes identified in the GSE87201 dataset [ 34 ] . DCA is a method used for evaluating clinical prediction models, diagnostic tests, or molecular markers. Next, the R package pROC [ 24 ] (version 1.18.5) was used to plot the ROC curve for the GSE87201 dataset and to calculate the corresponding AUC. This analysis evaluates the diagnostic performance of the LASSO-derived risk score in distinguishing samples with DOR from those with NOR. The semantic comparison of GO [ 25 ] annotation provides a quantitative method for calculating the similarity between genes and genomes and has become an important basis for many bioinformatics analysis methods. The R package GOSemSim [ 35 ] (version 2.28.0) was used to calculate the functional correlation of key genes, and the functional correlation between key genes was analyzed using functional similarity (Friends). 2.10 Construction of the regulatory network The ENCORI database (starBase v3.0) [ 36 ] ( https://starbase.sysu.edu.cn/ ) provides various visual tools for examining miRNA–target interactions. Furthermore, the miRDB database [ 37 ] is used for miRNA target gene prediction and functional annotation. In this study, the ENCORI and miRDB databases were used to predict miRNAs that interact with the identified key genes. The predicted interactions were intersected with the mRNA–miRNA interaction data in the ENCORI database to obtain the intersected mRNA–miRNA interaction network. Subsequently, the ENCORI database was used to predict long noncoding RNAs (lncRNAs) that interact with the identified miRNAs. Interactions with clipExpNum ≥ 30 were retained as the screening criterion to construct the miRNA–lncRNA interaction network. The mRNA–miRNA and miRNA–lncRNA interactions were then integrated to establish an mRNA–miRNA–lncRNA network. Additionally, transcription factors (TFs) regulate gene expression by interacting with key genes at the post-transcriptional stage. TF–gene regulatory relationships were retrieved from the ChIPBase database [ 38 ] ( http://rna.sysu.edu.cn/chipbase/ ). These data were used to analyze TF regulation of key genes, and the resulting miRNA–mRNA–TF regulatory network was visualized using Cytoscape [ 39 ] software. RNA-binding proteins (RBPs) [ 40 ] play a key role in the process of gene regulation, including RNA synthesis, alternative splicing, modification, transport, and translation. Based on interaction information from the ENCORI (starBase v3.0) database [ 36 ] ( https://starbase.sysu.edu.cn/ ), key genes of the target RBP were identified. The resulting miRNA–mRNA–RBP regulatory network was also visualized in Cytoscape. Finally, the Comparative Toxicogenomics Database [ 41 ] ( https://ctdbase.org/ ) was used to identify potential drugs that directly or indirectly target the key genes. These interactions were used to explore gene–drug associations. The resulting miRNA–mRNA–drug regulatory network was visualized in Cytoscape, thereby completing its construction. 2.11 Protein–protein interaction network The protein–protein interaction (PPI) network is composed of proteins that interact with one another and participate in a wide range of cellular processes, including biological signaling, regulation of gene expression, energy and substance metabolism, and cell cycle regulation. Systematic analysis of protein interactions in biological systems is important for understanding how proteins function within cellular networks, how biological signals and energy are transmitted, and how metabolic processes operate under specific physiological and pathological conditions. Such analyses may also help clarify the functional relationships among proteins. The STRING database [ 42 ] ( https://cn.string-db.org/ ) is a platform for identifying interactions among known and predicted proteins. In this study, a PPI network was constructed using the STRING database based on the identified key genes with a minimum required interaction score set to > 0.400, corresponding to medium confidence (0.400), which served as the threshold for network construction. Tightly connected local regions within the resulting PPI network may represent molecular complexes with specific biological functions. The GeneMANIA database [ 43 ] ( https://genemania.org/ ) is used to generate hypotheses about gene function, analyze gene lists, and prioritize genes for functional analysis. Given a list of query genes, GeneMANIA identifies genes with similar functional characteristics using a large set of genomics and proteomics data. In this framework, each functional genomic dataset is weighted according to its predicted relevance to the query. GeneMANIA can also be used to predict gene function by identifying genes that are likely to share functional properties with a query gene based on interaction patterns. The PPI network was constructed by predicting functionally similar genes to key genes using the GeneMANIA online website. 2.12 Immune infiltration analysis of diminished ovarian reserve CIBERSORT [ 44 ] applies linear support vector regression to deconvolute bulk transcriptome expression matrices and estimate the composition and abundance of immune cell types within mixed cell populations. Using the LM22 signature matrix as a reference, the CIBERSORT algorithm was applied to retain only samples with immune cell enrichment scores greater than zero. The immune cell infiltration matrix for the GSE87201 dataset was generated, and the relative proportions of immune cells were visualized using a bar chart. Pairwise correlations among immune cells were calculated using Spearman’s correlation, and the R package heatmap (version 1.0.12) was used to generate a correlation heatmap. Correlations between key genes and immune cell proportions were calculated using Spearman’s correlation, and the results showing statistically significant associations ( p < 0.05) were retained. The R package ggplot2 (version 3.4.4) was used to generate correlation bubble plots illustrating the correlations between the key genes and immune cells. 2.13 Statistical analysis All data processing and analyses were performed using R software (version 4.2.2). For comparisons of continuous variables between two groups, the statistical significance of normally distributed variables was estimated using independent Student’s t -tests, unless otherwise specified. The Mann–Whitney U Test (Wilcoxon rank-sum test) was used to analyze the differences between variables that did not follow a normal distribution. The Kruskal–Wallis test was used to compare three or more groups. Spearman correlation analysis was used to calculate correlation coefficients between different molecules. All p -values were two-sided unless otherwise specified, and a p -value < 0.05 was considered statistically significant. 2.14 Real‑time quantitative PCR The mRNA expression levels of five key co-genes ( TRAF6 , CD109 , BAG2 , USP25 , and EIF3H ) were measured using RT-qPCR. Total cellular RNA was extracted using Trizol reagent (Coolaber, RE600), and RNA concentration was measured using a Nanodrop One spectrophotometer (Thermo Fisher Scientific). The RNA specimen was subjected to reverse transcription to generate a single-stranded cDNA in a 20 µL reaction volume using FastKing gDNA Dispelling RT SuperMix (TIANGEN, KR118). Quantitative real-time PCR was performed using SuperReal PreMix Plus (SYBR Green) (TIANGEN, FP205). GAPDH primers were used to normalize the relative expression of target genes, which was calculated using the 2 −ΔΔCt method. All primer sequences were synthesized by Sangong Biotech Ltd. (Shanghai, China) and are listed in Table 5 . Table 5 The primer sequences used for qRT-PCR. Name Forward 5′ → 3′ primer Reverse 5′ → 3′ primer TRAF6 TTTGCTCTTATGGATTGTCCCC CATTGATGCAGCACAGTTGTC CD109 GAAGCCATCTCTCAACTTCACA TTCCACTGTTAGATCCGCTCC EIF3H CAGATGGAAATGATGCGGAGC AGTATGTGGACTGATACCAGCC BAG2 TGGGAAGAACTCTCACCGTTG CCTCATCAATAATCCTTGTGGCA USP25 GCACCAGCAGACGTTTTTGAA AGCATTCTTCGCAGTAAGGAAA GAPDH AGCGAGATCCCTCCAAAA AAATGAGCCCCAGCCTT 2.15 ELISA The concentration of TRAF6 in the FF was measured using an ELISA kit (JINGMEI, JM-5953H1). The levels of the reproductive hormones AMH (Solarbio, SEKM-0310), follicle-stimulating hormone (FSH; Elabscience, E-EL-M0511), and estradiol (E2; Elabscience, E-OSEL-M0008) were also determined using ELISA kits according to the manufacturer’s instructions. The concentrations of these proteins were determined by measuring absorbance at 450 nm using a spectrophotometer. 2.16 Animals and DOR model establishment Female wild-type C57BL/6J mice (aged 6–8 weeks) were obtained from the Animal Experimental Center of Lanzhou University and housed in the Specific Pathogen-Free facility at the Medical Experimental Center of Lanzhou University. Mice were maintained in a temperature-controlled environment (20–25°C) with 45%–55% humidity and a 12-h light/dark cycle. All experimental procedures were approved by the ethics committee of the First Hospital of Lanzhou University (Approval no. LDYYLL2025-799), and all methods were performed in accordance with relevant guidelines and regulations. After 1 week of adaptive feeding, mice were randomly assigned to four groups (n = 5 per group) and administered intraperitoneal injections of Cyclophosphamide (CTX, MedChem Express, HY-17420) at varying doses: Control group: 200 µL normal saline CTX 50 mg/kg group: 200 µL of CTX (50 mg/kg, single dose) CTX 75 mg/kg group: 200 µL of CTX (75 mg/kg, single dose) CTX 100 mg/kg group: 200 µL of CTX (100 mg/kg, single dose) Two weeks post-injection, mice were weighed and anesthetized through an intraperitoneal injection of 3% sodium pentobarbital. Blood samples were collected for hormone analysis. Ovarian tissues were either fixed in 4% paraformaldehyde (PFA) or snap-frozen and stored at − 80°C. 2.17 The estrous cycle Vaginal smears were collected daily between 9:00 and 10:00 AM for 14 days. After air-drying, the smears were stained using Giemsa Stain Solution (Solarbio, G1010) and examined under a light microscope. The murine estrus cycle lasts approximately 4–5 days and comprises four stages: Proestrus (P), Estrus (E), Metestrus (M), and Diestrus (D). 2.18 Histopathological analysis and follicle counting Ovarian tissues were fixed in 4% PFA, dehydrated in an ascending ethanol series, and embedded in paraffin. Serial sections of 5 µm thickness were prepared using a microtome, and every fifth section was stained with hematoxylin and eosin. Follicles were categorized into primordial, primary, secondary, antral, or atretic based on established criteria. 2.19 Immunofluorescence Following deparaffinization and rehydration, antigen retrieval was performed in a pressure cooker for 10 min. Sections were then blocked with 10% donkey serum to prevent nonspecific binding and incubated overnight at 4°C with the primary antibody anti-TRAF6 (Abclonal, A23385). Subsequently, the sections were washed with PBS and incubated with the appropriate secondary antibody at room temperature (RT) for 50 min. Finally, the sections were counterstained with DAPI and mounted for imaging. 2.20 Western blotting The expression levels of target proteins in mouse ovarian tissue were quantitatively analyzed by Western blotting. Ovarian tissues were lysed with RIPA lysis buffer (Coolaber, SL1020) and homogenized using a tissue crusher. Total protein was extracted using low-temperature ultracentrifugation, and protein concentration was determined using the BCA Protein Assay Kit (Coolabe, SK1070). Equal amounts of protein were separated in sodium dodecyl sulfate–polyacrylamide gel electrophoresis (Coolaber, SK6010) and transferred onto polyvinylidene fluoride (Millipore) membranes. Membranes were blocked with 5% skim milk for 2 h and incubated overnight at 4°C using the following primary antibodies: anti-TRAF6 (1:1000), GAPDH (1:5000), Bax (1:1000), Bcl-2 (1:500), and β-actin (1:3000). After washing with PBS, the membranes were incubated with the corresponding secondary antibodies for 1 h at RT. Protein bands were visualized using enhanced chemiluminescence, and the grayscale values of the stripes were analyzed using Image-J software. 2.21 TUNEL TUNEL staining was performed to assess the extent of apoptosis in ovarian tissues. Ovarian sections were first deparaffinized, rehydrated, and permeabilized using proteinase K for 30 min. Sections were then incubated with the TUNEL cell apoptosis detection kit (Servicebio, G1504) for 1 h in the dark. After washing, nuclei were then counterstained with DAPI for 10 min, and the sections were mounted using an antifade medium (Servicebio, G1401). Fluorescence microscopy was used to capture the images, and TUNEL-positive apoptotic cells were labeled green. 3. Results Abbreviations: DOR, diminished ovarian reserve; ER-StressRDEGs, endoplasmic reticulum stress-related differentially expressed genes; ROC, receiver operating characteristic; AUC, area under the curve; TPR, true positive rate; FPR, false positive rate. In the plots, yellow represents NOR samples and brown represent DOR samples. 3 .5 Gene Ontology and pathway enrichment analysis GO and KEGG enrichment analyses were performed to further explore the biological significance of the 19 co-genes in the context of DOR, focusing on the BP, CC, MF, and KEGG pathways. The detailed results are presented in Table 2. The enrichment analysis revealed that the 19 co-genes were mainly enriched in specific BPs related to DOR, including the negative regulation of organelle organization, TRIF-dependent toll-like receptor signaling pathway, regulation of proteolysis involved in protein catabolic process, activation of NF-κB–inducing kinase activity, and the MyD88-independent toll-like receptor signaling pathway. For CC, the co-genes were enriched in the ATAC complex, SAGA-type complex, histone acetyltransferase complex, protein acetyltransferase complex, and other acetyltransferase-related complexes. Regarding MF, the co-genes were enriched ubiquitin–ubiquitin ligase activity, ubiquitin protein ligase binding, ubiquitin-like protein ligase binding, ubiquitin binding, and deubiquitinase activity. KEGG pathway analysis indicated enrichment in Viral life cycle–HIV-1, Autophagy–animal, IL-17 signaling pathway, toxoplasmosis, measles, and additional relevant pathways. The overall GO and KEGG enrichment results were visualized using a bubble diagram (Fig. 5A). Furthermore, network maps were generated for BP, CC, MF, and KEGG based on the enrichment analyses (Fig. 5B–E). In these network diagrams, the lines represent the corresponding molecules and annotations of the corresponding entries, while node size corresponds to the number of molecules associated with each entry. 3 .6 Gene set enrichment analysis To assess the impact of gene expression on the onset of DOR, GSEA was performed using logFC values for all genes in the GSE87201 dataset between the DOR and NOR groups. GSEA was used to evaluate the association of gene expression with BP, CC, and MF, and the results were visualized using a mountain plot (Fig. 6A). Detailed results are provided in Table 3. The analysis revealed that all genes in GSE87201 were significantly enriched in several biologically relevant pathways and functions, including Reactome Metallothioneins Bind Metals (Fig. 6B), Zheng Response to Arsenite Up (Fig. 6C), Zheng Response to Arsenite Up (Fig. 6C), Hebert Matrisome Tnbc Bone Metastasis (Fig. 6D), and Reactome Response to Metal Ions (Fig. 6E). In the mountain plot, color indicates the adj. p : red represents smaller adj. p values, and blue represents larger adj. p values. The screening criteria for GSEA were adj. p < 0.05 and FDR ( q -value) < 0.25, with p -value correction performed using the BH method. 3 .7 Gene set variation analysis To explore pathway-level differences between the DOR and NOR groups in GSE87201, the c2.Cp.v2023.2.Hs.symbols.gmt gene set was used for GSVA. Detailed results are provided in Table S4. From the results, the top 20 pathways were selected based on p < 0.05 and descending absolute logFC values. The differential expression of these 20 pathways between the DOR and NOR groups was analyzed and visualized using a heat–map (Fig. 7A). Differences were further evaluated using the Mann–Whitney U test, and a group comparison plot was generated (Fig. 7B). The GSVA revealed that the following pathways showed statistically significant differences between the DOR and NOR groups ( p < 0.05): Signaling by PDGFR in Disease, LPA GNA12/13 RhoA signaling pathway, receptor-type tyrosine protein phosphatases, lectin pathway of the coagulation cascade (fibrinogen to fibrin), nitric oxide stimulates guanylate cyclase, depolymerization of the nuclear lamina, repression of WNT target genes, recycling of bile acids and salts, regulation of gene expression in late-stage branching morphogenesis, pancreatic bud precursor cells, glucocorticoid biosynthesis, elevation of cytosolic Ca 2+ levels, Biocarta HSP27 signaling pathway, sumoylation of immune response proteins, activated TAK1 mediates p38 MAPK activation, and Variant Mutation Inactivated Sigmar1 to Ca 2+ apoptosis pathway. 3 .8 Construction of a diagnostic model for diminished ovarian reserve To determine the diagnostic value of the 19 co-genes in DOR, a logistic regression analysis was first performed, and a logistic regression model was constructed using all 19 co-genes. The analysis identified 10 co-genes with statistically significant contributions ( p < 0.05): UBE4B , BAG2 , CD109 , EIF3H , SAMD8 , PPIF , UVRAG , YAP1 , TRAF6 , and USP25 . Subsequently, an SVM model was constructed using these 10 co-genes. The SVM algorithm was applied to identify the number of genes with the lowest error rate (Fig. 8A) and the highest accuracy rate (Fig. 8B). The results showed that the SVM model achieved the highest accuracy with five co-genes: TRAF6 , CD109 , BAG2 , USP25 , and EIF3H . Finally, a LASSO regression analysis was performed using these five co-genes to construct a diagnostic model for DOR. The resulting LASSO regression model and variable trajectory are visualized in Fig. 8C and Fig. 8D, respectively. The results showed that the five co-genes (key genes) included in the LASSO regression model were TRAF6 , CD109 , BAG2 , USP25 , and EIF3H . 3 .9 Validation of the diagnostic model for diminished ovarian reserve To further verify the diagnostic performance of the model for DOR, a nomogram was constructed using key genes to illustrate their contributions in the GSE87201 dataset (Fig. 9A). The results showed that TRAF6 contributed the most to the predictive power of the model, whereas EIF3H showed comparatively lower diagnostic utility. DCA was performed to evaluate the clinical usefulness of the model based on the key genes in GSE87201 (Fig. 9B). The results showed that the model consistently provided a higher net benefit than the “All positive” and “All negative” strategies across a certain range, indicating strong clinical applicability. Additionally, the R package pROC was used to generate the ROC curve based on the risk score in the GSE87201 dataset. The ROC curve (Fig. 9C) indicated high predictive accuracy with an AUC > 0.9. The risk score was calculated using the following formula: Functional similarity (Friends) analysis was conducted to identify genes that play critical roles in the biological processes underlying DOR (Fig. 9D). The results suggest that USP25 plays an important role in DOR pathophysiology. 3 .10 Construction of regulatory networks First, miRNAs related to the key genes were obtained from the StarBase database, and the mRNA–miRNA regulatory network was constructed and visualized using Cytoscape software (Fig. 10A). The resulting network consisted of four mRNAs and 26 miRNAs. Detailed information on these interactions is provided in Table S5. Next, the ENCORI database was used to predict the lncRNAs associated with the identified miRNAs. Finally, Cytoscape was used to map the mRNA–miRNA–lncRNA interaction network for visualization (Fig. 10B). This network included four mRNAs ( BAG2 , CD109 , TRAF6 , and USP25 ), 26 miRNAs, and 10 lncRNAs. Detailed information on the mRNA–miRNA–lncRNA interaction relationships is provided in Table S6. Potential drugs or molecular compounds associated with the key genes were identified using the CTD database. Based on these associations, an miRNA–mRNA–drug regulatory network was constructed and visualized using Cytoscape (Fig. 11A). Only miRNAs, mRNAs, and drugs or molecular compounds with documented interactions in the miRNA–mRNA–drug network are shown. This network included four mRNAs, 26 miRNAs, and two drugs or molecular compounds. Detailed information on these interactions is provided in Table S7. Next, RBPs associated with the key genes were predicted using the StarBase database. A miRNA–mRNA–RBP regulatory network was constructed and visualized using Cytoscape (Fig. 11B). Only miRNAs, mRNAs, and RBPs with documented interactions within the miRNA–mRNA–RBP network are shown. This network included four mRNAs, 26 miRNAs, and 17 RBPs. Detailed information on these interactions is provided in Table S8. Finally, TFs that bind to the key genes were obtained through the ChIPBase database. A miRNA–mRNA–TF regulatory network was constructed and visualized using Cytoscape (Fig. 11C). Only miRNAs, mRNAs, and TFs documented within the miRNA–mRNA–TF network are shown. This network included four mRNAs, 26 miRNAs, and 22 TFs. Detailed information on these interactions is provided in Table S9. 3 .11 Protein–protein interaction network First, a PPI analysis was performed. A PPI network for five key genes— EIF3H , USP25 , TRAF6 , IFIH1 , and UBE2I —was constructed using the STRING database (Fig. 12A). The results showed that three key genes ( EIF3H , USP25 , and TRAF6 ) exhibited interactions within the network. Second, an interaction network comprising the five key genes and their functionally similar genes was predicted and constructed using GeneMANIA (Fig. 12B). In this network, lines of different colors represent the coexpression between them and share information, such as protein domains. The resulting network contained five hub genes and 20 functionally similar proteins. 3 .12 Immune infiltration analysis of diminished ovarian reserve (CIBERSORT) GSE87201 was used to estimate the relative abundance of 22 immune cells using the CIBERSORT algorithm. First, based on the results of the immune infiltration analysis, a bar chart illustrating the proportions of immune cells in the dataset was generated (Fig. 13A). The results showed that 20 immune cells were enriched in the DOR samples, including the following: naïve B cells, plasma cells, CD 8+ T cells, naïve CD 4+ T cells, resting memory CD 4+ T cells, activated memory CD 4+ T cells, follicular helper T cells, regulatory T cells (Tregs), gamma–delta T cells, resting natural killer (NK) cells, activated NK cells, monocytes, M0 macrophages, M1 macrophages, M2 macrophages, resting dendritic cells, resting mast cells, activated mast cells, eosinophils, and neutrophils. Next, the correlations among the abundances of the 20 infiltrating immune cell types in the DOR samples were visualized using a correlation heatmap (Fig. 13B). The heatmap showed that many immune cell types showed strong correlations with one another. Among these, resting memory CD 4+ T cells and Tregs showed the strongest negative correlation ( r = −0.783, p < 0.05). Finally, the correlations between key genes and the abundance of immune cell infiltration were visualized using a correlation bubble plot (Fig. 13C). The results showed that most immune cells showed strong correlations. Among these, CD109 and resting NK cells showed the strongest negative correlation ( r = −0.452, p < 0.05). 3.13 Clinical sample validation and correlation study A total of 84 individuals who met the inclusion criteria were included in this study, with 42 participants in the DOR group and 42 in the NOR group. The clinical baseline data of the patients are summarized in Table 4. No statistically significant differences were observed between the DOR and NOR groups in terms of duration of infertility, body mass index (BMI), basal luteinizing hormone (LH), or E2 levels ( p > 0.05). However, age and basal FSH were higher in the DOR group than in the NOR group ( p < 0.05). Conversely, AMH levels and AFC were significantly lower in the DOR group than those in the NOR group ( p < 0.05). The mRNA expression of FRAF6 in the GCs of patients with DOR was significantly higher than that in the NOR group ( p ＜0.001), whereas the mRNA expression levels of CD109 , BAG2 , USP25 , and EIF3H did not differ significantly between the two groups (Fig.14A–E). Furthermore, the concentration of TRAF6 in FF was measured using ELISA. The FRAF6 protein level was significantly higher in the FF of patients with DOR (2591.88 ± 237.83 pg/mL vs. 2800.68 ± 307.77 pg/mL, p < 0.001; mean ± SD) (Fig. 14F). Correlation analysis further showed that TRAF6 levels were negatively correlated with AMH (Spearman r = −0.3205, p = 0.003) and AFC (Spearman r = −0.30, p = 0.0062), and positively correlated with FSH (Spearman r = 0.2239, p = 0.004) (Fig. 14 G–I). 3 .14 A nimals Twenty healthy female rats (6–8 weeks) were randomly divided into four groups and treated as described in Fig. 15A. The DOR model was established by a single administration of CTX at doses of 50, 75, and 100 mg/kg on the first day, whereas the Control group received saline. Vaginal smears were collected daily between 9:00 and 10:00 AM for 14 consecutive days. Giemsa staining revealed characteristic changes at each stage (Fig. 15B). Compared with the Control group, the CTX-treated groups exhibited irregular estrus cycles (Fig. 15C). During the treatment period, the mice’s body weight was recorded every other day. As shown in Fig. 15D, two weeks after CTX injection (day 15), rats in the CTX 75 mg/kg and CTX 100 mg/kg groups exhibited significantly higher body weights than the control groups. Serum AMH levels decreased significantly in the CTX-treated groups (50, 75, and 100 mg/kg) compared to the Control groups. FSH levels were significantly elevated in the CTX 100 mg/kg group, and serum E2 levels were significantly altered in the CTX 75 mg/kg and CTX 100 mg/kg groups compared with the controls (Fig. 15E). Histological results of the mice’s ovaries showed that the total number of follicles in the CTX-treated groups was significantly lower than in the control group. Primordial and primary follicles in the CTX 75 mg/kg and CTX 100 mg/kg groups were significantly reduced compared to the controls. Secondary and antral follicles in the four groups showed no significant differences. The number of atretic follicles was higher in the CTX 100 mg/kg group compared with the controls (Fig. 15F, G). As shown in Fig. 16A, Western blot analysis revealed that compared with the control group, TRAF6 expression in the CTX 100 mg/kg group was significantly increased. Immunofluorescence analysis also showed significantly increased TRAF6 expression in the CTX 75 mg/kg and CTX 100 mg/kg groups compared to the control group (Fig. 16C, D). Furthermore, Western blot analysis of apoptosis-related proteins (Bax/Bcl-2 ratio) and the TUNEL assay indicated that in the CTX 75 mg/kg and CTX 100 mg/kg groups, granule cell apoptosis significantly increased compared with the control group (Fig. 16B–E). Statistical significance is indicated as follows: * p < 0.05 , ** p < 0.01 , *** p < 0.001 , **** p < 0.0001. 4. Discussion DOR contributes significantly to female infertility, and its management remains one of the most challenging areas in reproductive medicine. The bioactive substances carried by exosomes reflect changes occurring within the parent cells and can indicate their physiological and pathological states [45] . The close relationship between the contents of exosomes and those of the parent cells has been confirmed in several studies [46] , and exosomes are suitable biomarkers [47] . Recent evidence also indicates that exosomes serve as crucial regulators of intercellular signaling in the ovary [48] . Therefore, we isolated exosomes from FF and performed miRNA sequencing to explore their regulatory roles. Apoptosis induced by ERS is involved in the occurrence of various diseases. Beyond tumors, these include neurodegenerative diseases, liver diseases, and chronic metabolic diseases [49] . Some studies have also found that under normal physiological conditions, ERS plays a crucial role in the growth and maturation of ovarian follicles, the closure of follicles, corpus luteum formation, and implantation. However, the disruption of ER homeostasis can lead to negative pathological conditions that affect female fertility [6−8] . The core design of this study is to identify key genes through cross-integration of multi-omics data. By cross-referencing miRNA target genes with ER-StressRDEGs, we identified 19 co-genes. The innovation of this approach lies in linking intercellular communication mediated by FF exosomes to the ERS status of oocytes, thereby identifying a set of candidate genes that are both subject to epigenetic regulation, have core functions, and are highly related to the pathology of DOR. Subsequent GO and KEGG enrichment analyses suggest that the GCs of patients with DOR may experience disrupted protein homeostasis and heightened ERS. This may eventually lead to the activation of apoptotic signaling pathways, resulting in a large number of GC deaths, accelerating follicular closure, and a more rapid decline in ovarian reserve function. These findings align with previous reports indicating that ERS is involved in GC apoptosis and early follicular closure [50] . In our GSVA analysis, two pathways highly correlated with ERS—Biocarta HSP27 Signaling Pathway [51] and Variant Mutation Inactivated Sigmar1 to Ca 2+ Apoptosis Pathway [52] —were found to be abnormally activated in DOR. Consistently, Lile Jiang’s study also revealed that this pathway is involved in DOR pathogenesis [53] . Validation of our diagnostic model, which incorporates five key co-genes ( TRAF6 , CD109 , BAG2 , USP25 , and EIF3H ), yielded an AUC exceeding 0.9, indicating excellent accuracy in distinguishing patients with DOR from healthy controls. Correlation analysis also indicated that TRAF6 was negatively correlated with ovarian reserve indicators, including AMH and AFC, and positively correlated with FSH. To validate these findings, RT‑qPCR and ELISA were performed on clinical samples, which confirmed that TRAF6 was highly expressed in the DOR group. Our in vivo experimental results indicate that increasing doses of CTX led to elevated expression of both the target protein TRAF6 and apoptosis-related proteins in mouse ovarian tissues, consistent with observations in human ovarian GCs. Additionally, H&E staining, serum hormone analysis, and follicle counting revealed that CTX 100 mg/kg may be the appropriate dose for establishing the DOR model. Tumor necrosis factor receptor-associated factor 6 (TRAF6), a member of the TRAF protein family, is broadly expressed in mammalian tissues and is conserved among species. Structurally, TRAF6 comprises an N-terminal RING finger domain, a series of four zinc finger motifs, a coiled-coil structure domain, and a C-terminal TRAF-C domain [54] . TRAF6 is a central hub for multiple intracellular signaling pathways, playing significant roles in regulating cell apoptosis, proliferation, and autophagy [55] . As a key hub of the signaling pathways of pattern recognition receptors, such as TLRs and IL-1R, TRAF6 undergoes K63-linked ubiquitination through its E3 ubiquitin ligase activity, which amplifies downstream signals such as NF-κB, MAPK, and ROS production, thereby driving inflammatory responses, oxidative stress, and cell death [55 – 58] . Therefore, activated TRAF6 may promote the production of substantial proinflammatory cytokines by GCs and ovarian macrophages through the NF-κB pathway, creating a chronic inflammatory ovarian microenvironment. Simultaneously, TRAF6 activation induced NOX2-mediated ROS bursts, triggering ERS and mitochondrial dysfunction, which directly impair GC function. Together, these processes accelerate follicular depletion. Despite its well-established roles in other systems, studies on TRAF6 in obstetrics and gynecology remain scarce. Therefore, we plan to examine TRAF6 function through both in vivo and in vitro experiments and explore its interacting proteins to elucidate the underlying mechanisms in DOR. These findings may provide potential therapeutic targets for preserving ovarian function and offer new insights to improve assisted reproductive technologies. This study has several limitations. First, although our core findings have been preliminarily verified using internal clinical samples, independent validation in larger prospective external cohorts is still required. Furthermore, this study primarily focuses on transcriptomic data and does not integrate proteomic or metabolomic analyses; thus, it is unable to comprehensively capture the dynamic changes in protein modifications and metabolic networks that may occur during the progression of DOR. Through bioinformatics analyses and clinical validation, we identified the E3 ubiquitin ligase TRAF6 as a key driver molecule for DOR. TRAF6 may participate in processes involving ERS, ubiquitination, and inflammatory signaling, suggesting a central role in the decline of ovarian reserve. These findings not only contribute to a deeper understanding of the molecular mechanisms underlying DOR but also provide new tools for early diagnosis and risk stratification of the disease. Furthermore, they may support the future exploration of therapeutic strategies targeting TRAF6 or related pathways to delay the decline in ovarian function and preserve female fertility. 5. Conclusions Through bioinformatics analysis, five co‑genes (TRAF6, CD109, BAG2, USP25, and EIF3H) were identified. Further validation in CTX‑induced DOR mouse models and clinical samples confirmed that TRAF6 expression was significantly upregulated in the DOR group compared with the control group. These results suggest that TRAF6 may serve as a novel biomarker for diagnosing DOR. Abbreviations Abbreviation Full form ER-StressRDEGs endoplasmic reticulum stress-related differentially expressed genes DOR diminished ovarian reserve NOR normal ovarian reserve GEO gene expression omnibus ER-StressRGs endoplasmic reticulum stress-related genes CTX cyclophosphamide ERS endoplasmic reticulum stress ER endoplasmic reticulum GCs granulosa cells FF follicular fluid IVF in vitro fertilization ICSI intracytoplasmic sperm injection AMH anti-Müllerian hormone AFC antral follicular count bFSH basic follicle-stimulating hormone ROC receiver operating characteristic AUC area under the curve GO gene ontology BP biological process CC cellular component MF molecular function KEGG kyoto encyclopedia of genes and genomes GSEA gene set enrichment analysis GSVA gene set variation analysis SVM support vector machine LASSO least absolute shrinkage and selection operator PPI protein–protein interaction TRAF6 tumor necrosis factor receptor-associated factor 6 Declarations Ethics approval and consent to participate All experimental procedures were approved by the ethics committee of the First Hospital of Lanzhou University and obtained written informed consent from all the patients (Approval no. LDYYLL2025-799). All methods were performed in accordance with relevant guidelines and regulations. Consent for publication Not applicable. Availability of data and materials The datasets supporting the conclusions of this article are included within the article and its additional files. Competing Interests The authors declare no competing interests. Funding This work was supported by National Natural Science Foundation of China(82460298) & Science and Technology Planning Project of Gansu Province (24JRRA319) & Science and Technology Project of Gansu Province (Key Research and Development Program) (24YFFA039) & The Youth Fund of the First Affiliated Hospital of Lanzhou University(ldyyyn2020-67) & College Teacher Innovation Fund project of Gansu Provincial Department of Education(2023B-008） Authors’ contribution Xuehong Zhang, Wei Wang and Lirong Wang discussed and established the plan of the manuscript. Xiaorong Luo, Lili Zhang, Jiajing He conducted the literature search and analyzed the literature, Luni Tan, Yuzi Li, Ning Zhang and Rui Zhang prepared the figures and tables. Xinyue Zhou, Haofei Shen, Feng Yue and Lirong Wang wrote the draft with input of all authors. Acknowledgements The authors thank the technical support provided by the reproductive medicine center of the First Hospital of Lanzhou University and Key Laboratory for Reproductive Medicine and Embryo References Sharara FI, Scott RT Jr, Seifer DB. The detection of diminished ovarian reserve in infertile women. Am J Obstet Gynecol . 1998;179(3 Pt 1):804-812. doi:10.1016/s0002-9378(98)70087-0 Pastore LM, Christianson MS, Stelling J, Kearns WG, Segars JH. Reproductive ovarian testing and the alphabet soup of diagnoses: DOR, POI, POF, POR, and FOR. J Assist Reprod Genet . 2018;35(1):17-23. doi:10.1007/s10815-017-1058-4 Jiao Z, Bukulmez O. Potential roles of experimental reproductive technologies in infertile women with diminished ovarian reserve. J Assist Reprod Genet . 2021;38(10):2507-2517. doi:10.1007/s10815-021-02246-6 Bukulmez O. The International Society for Mild Approaches in Assisted Reproduction (ISMAAR) Definitions for Mild Stimulation and Their Rationale for Assisted Reproductive Technologies [J]. Diminished Ovarian Reserve and Assisted Reproductive Technologies, 2019.DOI:10.1007/978-3-030-23235-1_10. Oakes SA, Papa FR. The role of endoplasmic reticulum stress in human pathology. Annu Rev Pathol .2015;10:173-194. doi:10.1146/annurev-pathol-012513-104649 Guzel E, Arlier S, Guzeloglu-Kayisli O, et al. Endoplasmic Reticulum Stress and Homeostasis in Reproductive Physiology and Pathology. Int J Mol Sci . 2017;18(4):792. Published 2017 Apr 8. doi:10.3390/ijms18040792 Harada M, Nose E, Takahashi N, et al. Evidence of the activation of unfolded protein response in granulosa and cumulus cells during follicular growth and maturation. Gynecol Endocrinol . 2015;31(10):783-787. doi:10.3109/09513590.2015.1062862 Lin P, Yang Y, Li X, et al. Endoplasmic reticulum stress is involved in granulosa cell apoptosis during follicular atresia in goat ovaries. Mol Reprod Dev . 2012;79(6):423-432. doi:10.1002/mrd.22045 Galgani M, Insabato L, Calì G, et al. Regulatory T cells, inflammation, and endoplasmic reticulum stress in women with defective endometrial receptivity. Fertil Steril . 2015;103(6):1579-1586.e1. doi:10.1016/j. fertnstert.2015.03.014 Mathieu M, Martin-Jaular L, Lavieu G, Théry C. Specificities of secretion and uptake of exosomes and other extracellular vesicles for cell-to-cell communication. Nat Cell Biol . 2019;21(1):9-17. doi:10.1038/s41556-018-0250-9 Zhang X, Zhang R, Hao J, et al. miRNA-122-5p in POI ovarian-derived exosomes promotes granulosa cell apoptosis by regulating BCL9. Cancer Med . 2022;11(12):2414-2426. doi:10.1002/cam4.4615 Sun B, Ma Y, Wang F, Hu L, Sun Y. miR-644-5p carried by bone mesenchymal stem cell-derived exosomes targets regulation of p53 to inhibit ovarian granulosa cell apoptosis. Stem Cell Res Ther . 2019;10(1):360. Published 2019 Nov 29. doi:10.1186/s13287-019-1442-3 Yang M, Lin L, Sha C, et al. Bone marrow mesenchymal stem cell-derived exosomal miR-144-5p improves rat ovarian function after chemotherapy-induced ovarian failure by targeting PTEN. Lab Invest . 2020;100(3):342-352. doi:10.1038/s41374-019-0321-y Nejabati HR, Roshangar L, Nouri M. Follicular fluid extracellular vesicle miRNAs and ovarian aging. Clin Chim Acta . 2023; 538:29-35. doi:10.1016/j. cca.2022.11.003 Davis S, Meltzer PS. GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics . 2007;23(14):1846-1847. doi:10.1093/bioinformatics/btm254 Barrett T, Wilhite SE, Ledoux P, et al. NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res . 2013;41(Database issue): D991-D995. doi:10.1093/nar/gks1193 Kao KJ, Chang KM, Hsu HC, Huang AT. Correlation of microarray-based breast cancer molecular subtypes and clinical outcomes: implications for treatment optimization. BMC Cancer . 2011;11:143. Published 2011 Apr 18. doi:10.1186/1471-2407-11-143 Stelzer G, Rosen N, Plaschkes I, et al. The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses. Curr Protoc Bioinformatics . 2016; 54:1.30.1-1.30.33. Published 2016 Jun 20. doi:10.1002/cpbi.5 Lai Y, Lin X, Lin C, Lin X, Chen Z, Zhang L. Identification of endoplasmic reticulum stress-associated genes and subtypes for prediction of Alzheimer's disease based on interpretable machine learning. Front Pharmacol . 2022;13: 975774. Published 2022 Aug 19. doi:10.3389/fphar.2022.975774 Shen Y, Cao Y, Zhou L, Wu J, Mao M. Construction of an endoplasmic reticulum stress-related gene model for predicting prognosis and immune features in kidney renal clear cell carcinoma. Front Mol Biosci . 2022; 9:928006. Published 2022 Sep 2. doi:10.3389/fmolb.2022.928006 Xu X, Tang Y, Zhu J, Luo J. Endoplasmic Reticulum Stress-Related Four-Biomarker Risk Classifier for Survival Evaluation in Esophageal Cancer. J Oncol . 2022; 2022:5860671. Published 2022 Mar 18. doi:10.1155/2022/5860671 Ritchie ME, Phipson B, Wu D, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res . 2015;43(7):e47. doi:10.1093/nar/gkv007 Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol . 2014;15(12):550. doi:10.1186/s13059-014-0550-8 Robin X, Turck N, Hainard A, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics . 2011;12:77. Published 2011 Mar 17. doi:10.1186/1471-2105-12-77 Mi H, Muruganujan A, Ebert D, Huang X, Thomas PD. PANTHER version 14: more genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools. Nucleic Acids Res . 2019;47(D1):D419-D426. doi:10.1093/nar/gky1038 Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res . 2000;28(1):27-30. doi:10.1093/nar/28.1.27 Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS . 2012;16(5):284-287. doi:10.1089/omi.2011.0118 Subramanian A, Tamayo P, Mootha VK, 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):15545-15550. doi:10.1073/pnas.0506580102 Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics . 2013;14:7. Published 2013 Jan 16. doi:10.1186/1471-2105-14-7 Liberzon A, Subramanian A, Pinchback R, Thorvaldsdottir H, Tamayo P, Molecular signatures database (MSigDB) 3.0. Bioinformatics 2011; 27 (12): 1739-1740. doi:10.1093/bioinformatics/btr260 Sanz H, Valim C, Vegas E, Oller JM, Reverter F. SVM-RFE: selection and visualization of the most relevant features through non-linear kernels. BMC Bioinformatics . 2018;19(1):432. Published 2018 Nov 19. doi:10.1186/s12859-018-2451-4 Engebretsen S, Bohlin J. Statistical predictions with glmnet. Clin Epigenetics . 2019;11(1):123. Published 2019 Aug 23. doi:10.1186/s13148-019-0730-1 Wu J, Zhang H, Li L, et al. A nomogram for predicting overall survival in patients with low-grade endometrial stromal sarcoma: A population-based analysis. Cancer Commun (Lond) . 2020;40(7):301-312. doi:10.1002/cac2.12067 Van Calster B, Wynants L, Verbeek JFM, et al. Reporting and Interpreting Decision Curve Analysis: A Guide for Investigators. Eur Urol . 2018;74(6):796-804. doi:10.1016/j.eururo.2018.08.038 Yu G, Li F, Qin Y, Bo X, Wu Y, Wang S. GOSemSim: an R package for measuring semantic similarity among GO terms and gene products. Bioinformatics . 2010;26(7):976-978. doi:10.1093/bioinformatics/btq064 Li JH, Liu S, Zhou H, Qu LH, Yang JH. starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res . 2014;42(Database issue):D92-D97. doi:10.1093/nar/gkt1248 Chen Y, Wang X. miRDB: an online database for prediction of functional microRNA targets. Nucleic Acids Res . 2020;48(D1):D127-D131. doi:10.1093/nar/gkz757 Zhou KR, Liu S, Sun WJ, et al. ChIPBase v2.0: decoding transcriptional regulatory networks of non-coding RNAs and protein-coding genes from ChIP-seq data. Nucleic Acids Res . 2017;45(D1):D43-D50. doi:10.1093/nar/gkw965 Shannon P, Markiel A, Ozier O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res . 2003;13(11):2498-2504. doi:10.1101/gr.1239303 Singh A. RNA-binding protein kinetics. Nat Methods . 2021;18(4):335. doi:10.1038/s41592-021-01122-6 Grondin CJ, Davis AP, Wiegers JA, et al. Predicting molecular mechanisms, pathways, and health outcomes induced by Juul e-cigarette aerosol chemicals using the Comparative Toxicogenomics Database. Curr Res Toxicol . 2021;2:272-281. Published 2021 Aug 5. doi:10.1016/j.crtox.2021.08.001 Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res . 2019;47(D1):D607-D613. doi:10.1093/nar/gky1131 Franz M, Rodriguez H, Lopes C, et al. GeneMANIA update 2018. Nucleic Acids Res. 2018;46(W1):W60-W64. doi:10.1093/nar/gky311 Newman AM, Liu CL, Green MR, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods . 2015;12(5):453-457. doi:10.1038/nmeth.3337 LeBleu VS, Kalluri R. Exosomes as a Multicomponent Biomarker Platform in Cancer. Trends Cancer . 2020;6(9):767-774. doi:10.1016/j. trecan.2020.03.007 Kucharzewska P, Christianson HC, Welch JE, et al. Exosomes reflect the hypoxic status of glioma cells and mediate hypoxia-dependent activation of vascular cells during tumor development. Proc Natl Acad Sci U S A . 2013;110(18):7312-7317. doi:10.1073/pnas.1220998110 Barile L, Vassalli G. Exosomes: Therapy delivery tools and biomarkers of diseases. Pharmacol Ther. 2017; 174:63-78. doi:10.1016/j. pharmthera.2017.02.020 Voros C, Athanasiou D, Mavrogianni D, et al. Exosomal Communication Between Cumulus-Oocyte Complexes and Granulosa Cells: A New Molecular Axis for Oocyte Competence in Human-Assisted Reproduction. Int J Mol Sci . 2025;26(11):5363. Published 2025 Jun 3. doi:10.3390/ijms26115363 Oakes SA, Papa FR. The role of endoplasmic reticulum stress in human pathology. Annu Rev Pathol . 2015; 10:173-194. doi:10.1146/annurev-pathol-012513-104649 Yang D, Jiang T, Lin P, et al. Knock-down of apoptosis inducing factor gene protects endoplasmic reticulum stress-mediated goat granulosa cell apoptosis. Theriogenology . 2017; 88:89-97. doi:10.1016/j. theriogenology.2016.10.001 Martínez-Laorden E, Navarro-Zaragoza J, Milanés MV, Laorden ML, Almela P. Cardiac Protective Role of Heat Shock Protein 27 in the Stress Induced by Drugs of Abuse. Int J Mol Sci . 2020;21(10):3623. Published 2020 May 21. doi:10.3390/ijms21103623 Couly S, Yasui Y, Su TP. SIGMAR1 Confers Innate Resilience against Neurodegeneration. Int J Mol Sci . 2023;24(9):7767. Published 2023 Apr 24. doi:10.3390/ijms24097767 Jiang L, Cui J, Zhang C, et al. Sigma-1 receptor is involved in diminished ovarian reserve possibly by influencing endoplasmic reticulum stress-mediated granulosa cells apoptosis. Aging (Albany NY) . 2020;12(10):9041-9065. doi:10.18632/aging.103166 Lamothe B, Campos AD, Webster WK, Gopinathan A, Hur L, Darnay BG. The RING domain and first zinc finger of TRAF6 coordinate signaling by interleukin-1, lipopolysaccharide, and RANKL. J Biol Chem . 2008;283(36):24871-24880. doi:10.1074/jbc.M802749200 Wang YT, Liu TY, Shen CH, et al. K48/K63-linked polyubiquitination of ATG9A by TRAF6 E3 ligase regulates oxidative stress-induced autophagy. Cell Rep . 2022;38(8):110354. doi:10.1016/j. celrep.2022.110354 Meng Z, Zhao T, Zhou K, et al. A20 Ameliorates Intracerebral Hemorrhage-Induced Inflammatory Injury by Regulating TRAF6 Polyubiquitination. J Immunol . 2017;198(2):820-831. doi:10.4049/jimmunol.1600334 Zeng Q, Ye L, Ling M, et al. TLR4/TRAF6/NOX2 signaling pathway is involved in ventilation-induced lung injury via endoplasmic reticulum stress in murine model. Int Immunopharmacol . 2021; 96:107774. doi:10.1016/j. intimp.2021.107774 Yang J, Zhang M, Luo Y, et al. Protopine ameliorates OVA-induced asthma through modulatingTLR4/MyD88/NF-κB pathway and NLRP3 inflammasome-mediated pyroptosis. Phytomedicine . 2024; 126:155410. doi:10.1016/j. phymed.2024.155410 Additional Declarations No competing interests reported. Supplementary Files Supplementaryfile.docx TableS1ERStressRGs.csv TableS2ERStressRDEGs.csv TableS3Cogenes.docx TableS4GSVADiffAnalysisSig.txt TableS5mRNAmiRNAnodes.csv TableS6ceRNAlncRNAnodes.csv TableS7DrugmRNAmiRNAnodesnodes.csv TableS8RBPmRNAmiRNAnodesnodes.csv TableS9TFmRNAmiRNAnodesnodes.csv Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 07 May, 2026 Reviewers agreed at journal 28 Apr, 2026 Reviewers invited by journal 24 Apr, 2026 Editor assigned by journal 07 Apr, 2026 Submission checks completed at journal 04 Apr, 2026 First submitted to journal 04 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9228384","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":634205660,"identity":"f2db17f7-751f-4d7c-ac4c-d0cb42afa837","order_by":0,"name":"Lirong Wang","email":"","orcid":"","institution":"The First Clinical Medical College of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Lirong","middleName":"","lastName":"Wang","suffix":""},{"id":634205663,"identity":"787adb8e-e02b-4999-8c56-040e10c8c3b4","order_by":1,"name":"Jiajing He","email":"","orcid":"","institution":"The First Clinical Medical College of 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02:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9228384/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9228384/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108978679,"identity":"b1eeef3a-ebeb-4017-be86-c9e933ce433d","added_by":"auto","created_at":"2026-05-11 11:47:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":378170,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of the technical workflow\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDEGs, differentially expressed genes; ER-StressRGs, endoplasmic reticulum stress-related genes; ER-StressRDEGs, endoplasmic reticulum stress-related differentially expressed genes; GSEA, Gene Set Enrichment Analysis; GSVA, Gene Set Variation Analysis; PPI, protein–protein interaction; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; SVM, Support Vector Machine; LASSO, Least Absolute Shrinkage and Selection Operator; ExpDiff\u0026amp;ROC, expression difference and receiver operating characteristic; TF, transcription factors; ceRNAs, competing endogenous RNAs; RBP, RNA-binding proteins.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9228384/v1/ef15a7c6f8f57b6b3b3cd2f5.png"},{"id":108978636,"identity":"057b702d-2a75-4347-83ae-6cfe60467a22","added_by":"auto","created_at":"2026-05-11 11:47:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":257717,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eData cleaning\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Boxplot showing the distribution of the GSE87201 dataset before standardization. (B) Boxplot showing the distribution of the GSE87201 dataset after standardization. Abbreviations: PCA, principal component analysis; DOR, diminished ovarian reserve. Yellow represents NOR samples, and brown represents DOR samples.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9228384/v1/ca92d6fd33d6919da6bd054d.png"},{"id":108978624,"identity":"ced2dcc1-edab-4b7e-b337-3a683f2e03e8","added_by":"auto","created_at":"2026-05-11 11:46:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":446994,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential gene expression analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Volcano plot showing differential miRNA expression analysis between DOR and NOR samples in the miRNA dataset. (B) Volcano plot showing differentially expressed miRNAs between DOR and NOR samples in the GSE87201 dataset. (C) Venn diagram illustrating the intersection between DEGs and ER-StressRGs in the GSE87201 dataset. (D) Heat-map showing the expression patterns of ER-StressRDEGs in the GSE87201 dataset. (E–H) Venn diagrams showing the intersections between predicted target genes of differentially expressed miRNAs and DEGs: has-miR-10a-5p (E), has-miR-128-3p (F), has-miR-195-5p (G), and has-miR-1299 (H). Abbreviations: DOR, diminished ovarian reserve; DEGs, differentially expressed genes; ER-StressRGs, endoplasmic reticulum stress-related genes; ER-StressRDEGs, endoplasmic reticulum stress-Related differentially expressed genes. In the plots, DOR samples are shown in brown and NOR samples are shown in yellow. In the heat-map, red represents high expression, and purple represents low expression.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9228384/v1/2af4cf3e1a0ea71811004c3e.png"},{"id":108978615,"identity":"c5ac3103-70ba-4c1c-9439-0ded5d3cfc4a","added_by":"auto","created_at":"2026-05-11 11:46:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":463228,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential expression validation and ROC curve analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Group comparison plots showing the expression levels of co-genes in DOR and NOR samples from dataset GSE87201. (B–E) ROC curves of the diagnostic performance of co-genes \u003cem\u003eEIF3H\u003c/em\u003e, \u003cem\u003eCD109\u003c/em\u003e, and \u003cem\u003eSAMD8\u003c/em\u003e (B) \u003cem\u003eSNRPA1\u003c/em\u003e, \u003cem\u003eMAP1B\u003c/em\u003e, and \u003cem\u003eUVRAG\u003c/em\u003e (C), \u003cem\u003eUBE4B\u003c/em\u003e, \u003cem\u003eYAP1\u003c/em\u003e, and \u003cem\u003eTRAF6\u003c/em\u003e (D), and \u003cem\u003eBAG2\u003c/em\u003e, \u003cem\u003eUSP25\u003c/em\u003e, and \u003cem\u003ePPIF\u003c/em\u003e(E) in dataset GSE87201. Statistical significance is indicated as follows: ns, \u003cem\u003ep\u003c/em\u003e≥ 0.05 (not significant); *, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 (significant); **, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.01 (highly significant). AUC \u0026gt; 0.5 indicates that higher gene expression is associated with the event of interest, with values approaching 1 reflecting stronger diagnostic performance. In this study, the AUC ranged from 0.7 to 0.9, indicating moderate to good accuracy.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9228384/v1/e76c421919b2ab07a6b0658b.png"},{"id":108978616,"identity":"a4298489-da0e-4fb8-8b6c-3c313bb8c461","added_by":"auto","created_at":"2026-05-11 11:46:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":563875,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGO and KEGG enrichment analysis for co-genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Bubble diagram showing the GO and KEGG enrichment analysis results for co-genes across the BP, CC, MF, and KEGG categories. GO and KEGG terms are shown on the abscissa. The bubble size represents the number of genes associated with each term, and the color gradient indicates significance: red corresponds to smaller \u003cem\u003ep\u003c/em\u003e-values, while blue indicates larger \u003cem\u003ep\u003c/em\u003e-values. (B–E) Network diagrams illustrating the relationships between co-genes and enriched GO and KEGG terms: BP (B), CC (C), MF (D), and KEGG (E). In the networks, purple nodes represent GO and KEGG entries, yellow nodes represent molecules, and lines represent the relationship between entries and molecules.\u003c/p\u003e\n\u003cp\u003eAbbreviations: GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; BP, biological process; CC, cellular component; MF, molecular function. The screening criteria for GO and KEGG enrichment analysis were \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 and FDR (\u003cem\u003eq\u003c/em\u003e-value) \u0026lt; 0.25.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9228384/v1/73caa09608bd1266ce1596c4.png"},{"id":108979482,"identity":"61e17ea1-0d71-443e-b7fa-32bead613f56","added_by":"auto","created_at":"2026-05-11 11:59:19","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":391341,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential gene expression analysis and GSEA for GSE87201\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Mountain plot illustrating GSEA results for GSE87201 across four categories of biological functions. (B–E) GSEA revealed significant enrichment in the following pathways: Reactome Metallothioneins Bind Metals (B), Zheng Response to Arsenite Up (C), Hebert Matrisome Tnbc Bone Metastasis (D), and Reactome Response to Metal Ions (E). Abbreviations: DOR, diminished ovarian reserve; GSEA, gene set enrichment analysis.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-9228384/v1/d5efdc972622b7bb1e802322.png"},{"id":108978687,"identity":"135d3d73-faf3-4414-82d4-abd8a69fdccc","added_by":"auto","created_at":"2026-05-11 11:47:25","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":892231,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGSVA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A, B) Heatmap (A) and group comparison plot (B) showing the results of GSVA for the DOR and NOR groups in GSE87201. Abbreviations: DOR, diminished ovarian reserve; GSVA, gene set variation analysis. Statistical significance is indicated as follows: ns, not significant; *, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01. Brown indicates the DOR group, and yellow indicates the NOR group. The screening criterion for GSVA was\u003cem\u003e p\u003c/em\u003e \u0026lt; 0.05. In the heatmap, blue indicates low pathway enrichment, and red indicates high enrichment.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-9228384/v1/4de47da1028790cb4c1b467f.png"},{"id":108978638,"identity":"2b13f2ec-e423-4bdc-9b43-45ec2c16c953","added_by":"auto","created_at":"2026-05-11 11:47:12","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":230012,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiagnostic model of DOR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A, B) Visualization of the SVM model results: (A) the number of genes with the lowest error rate, and (B) the number of genes with the highest prediction accuracy. (C, D) Visualization of the LASSO regression model: (C) diagnostic model plot and (D) variable trajectory plot. Abbreviations: DOR, diminished ovarian reserve; SVM, support vector machine; LASSO, least absolute shrinkage and selection operator.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-9228384/v1/eb5bc96c5e0c34bea8fa55df.png"},{"id":109203743,"identity":"75eeb709-3c17-4530-bc68-1192bf8bd129","added_by":"auto","created_at":"2026-05-13 14:45:14","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":383575,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiagnostic and validation analysis of the DOR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Nomogram of key genes included in the diagnostic model for DOR based on dataset GSE87201. (B) DCA based on the key genes in dataset GSE87201 for the diagnostic model of DOR. (C) ROC curve of the risk score derived from dataset GSE87201. (D) Bar chart showing the results of the functional similarity (Friends) analysis for the key genes. In the DCA plot, the ordinate represents the net benefit, and the abscissa represents the probability threshold or threshold probability. Abbreviations: DOR, diminished ovarian reserve; DCA, decision curve analysis; ROC, receiver operating characteristic; AUC, area under the curve; TPR, true positive rate; FPR, false positive rate. AUC \u0026gt; 0.5 indicates that the expression of the molecule is a trend to promote the occurrence of the event, with values closer to 1 indicating better diagnostic performance. AUC values between 0.5 and 0.7 indicate low accuracy, AUCs between 0.7 and 0.9 indicate moderate accuracy, and AUCs above 0.9 indicate high accuracy.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-9228384/v1/79ca26fa1028cfefd772e6bd.png"},{"id":109081433,"identity":"1744e1c1-ba6a-4d17-86cc-0247b092c3f2","added_by":"auto","created_at":"2026-05-12 12:18:09","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":371708,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegulatory networks\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) mRNA–miRNA regulatory network of the key genes. (B) mRNA–ceRNA regulatory network of the key genes. In the network diagram, mRNAs are shown in orange, miRNAs in green, and lncRNAs in light blue.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-9228384/v1/fc727403f8b2961bb7dd328b.png"},{"id":108978623,"identity":"cd2824b9-f1a8-4dcd-a3c7-4e16bd5a06fa","added_by":"auto","created_at":"2026-05-11 11:46:57","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":543151,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegulatory Network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) miRNA–mRNA–drug regulatory network of key genes. (B) miRNA–mRNA–RBP regulatory network of the key genes. (C) miRNA–mRNA–TF regulatory network of key genes. In these networks, orange represents mRNA, green represents miRNA, yellow represents a drug or molecular compound, purple represents an RBP, and blue represents a TF.\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-9228384/v1/ca72b341b68ac35a88a22f1c.png"},{"id":108978614,"identity":"e993b392-4777-40ab-bbd4-862ad7aa6764","added_by":"auto","created_at":"2026-05-11 11:46:42","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":606999,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePPI network analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) PPI network of key genes generated using the STRING database. (B) Interaction network of functionally similar genes predicted using GeneMANIA. In the figure, circles represent the key genes and their functionally similar genes. The colored lines represent the interrelated functions. Abbreviation: PPI, protein–protein interaction network.\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-9228384/v1/688730f8aa49e306b1dda381.png"},{"id":108978641,"identity":"aa9163be-123b-400a-8383-b4aa1f770d3b","added_by":"auto","created_at":"2026-05-11 11:47:12","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":516449,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune infiltration analysis using the CIBERSORT algorithm\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Bar chart showing the proportion of immune cells in GSE87201. (B) Correlation heatmap of immune cell populations in GSE87201. (C) Bubble plot illustrating the correlation between immune cell infiltration abundance and key genes in GSE87201. Abbreviations: DOR, diminished ovarian reserve; NOR, normal ovarian reserve. The absolute value of the correlation coefficient (r value) below 0.3 indicated weak or no correlation, between 0.3 and 0.5 indicated weak correlation, between 0.5 and 0.8 indicated moderate correlation, and over 0.8 indicated strong correlation. In all plots, yellow represents the NOR group and brown represent the DOR group. Red shows a positive correlation, and blue shows a negative correlation. The depth of the color reflects the strength of the correlation.\u003c/p\u003e","description":"","filename":"floatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-9228384/v1/bea2a5092a9d92009f0526de.png"},{"id":108978681,"identity":"a362f8ba-8ecc-4b50-aaff-8dd1575133a3","added_by":"auto","created_at":"2026-05-11 11:47:15","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":422103,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTRAF6 expression is upregulated in human follicular fluid and granulosa cells in clinical samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A–E) mRNA expression levels of five key co-genes (\u003cem\u003eTRAF6\u003c/em\u003e, \u003cem\u003eCD109\u003c/em\u003e, \u003cem\u003eBAG2\u003c/em\u003e, \u003cem\u003eUSP25\u003c/em\u003e, and \u003cem\u003eEIF3H\u003c/em\u003e) in GCs from the DOR and NOR samples (n = 15 per group). (F) TRAF6 concentration (pg/mL) in human FF from the DOR and NOR samples (n = 42 per group). (G–I) Correlation analyses between TRAF6 and AMH, AFC, and FSH. *** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-9228384/v1/d98e9361b95a486846c653e3.png"},{"id":108978648,"identity":"eae60404-d470-4aea-87fb-77c4aa97b1bf","added_by":"auto","created_at":"2026-05-11 11:47:12","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":642545,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEstablishment of the CTX-induced DOR mouse\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Animal experimental protocol. (B) Giemsa-stained photomicrographs of vaginal smears from mice (scale = 100 μm). (C) Line chart depicting estrus cycle progression. (D) Body weight (g). (E) Measurement of the hormone levels of AMH, FSH, and E2. (F) H\u0026amp;E staining of ovarian tissue (scale = 200 μm). (G) Quantitative analysis of follicle counts by type: primordial, primary, secondary, antral, atretic, and total follicles. Data are presented as mean ± SD (N = 5 mice per group). Statistical significance is indicated as follows: *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, ****\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001; ns, not significant, compared with the control group.\u003c/p\u003e","description":"","filename":"floatimage15.png","url":"https://assets-eu.researchsquare.com/files/rs-9228384/v1/b48d282b3c568e312eb56c94.png"},{"id":108979737,"identity":"9248b6a1-03c5-4e03-b3f3-61ccea04752b","added_by":"auto","created_at":"2026-05-11 12:00:57","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":514082,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression of TRAF6 and apoptosis-related proteins in mouseovarian tissue\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Western blot analysis of target protein TRAF6 in ovarian tissues, showing significantly higher expression levels in the CTX 100 mg/kg group. (B) Western blot analysis of apoptosis-related proteins (Bax, Bcl-2, and Bax/Bcl-2 ratio) in ovarian tissues. Data are presented as mean ± SD from three independent experiments. (C) Representative immunofluorescence images of TRAF6 expression in mouse ovarian tissue (scale = 100 μm). (D) Positive cell ratio analysis of TRAF6 expression using immunofluorescence. (E) Representative TUNEL assay images of ovarian sections from each group (scale = 200 μm). Data are presented as mean ± SD (N = 5 mice per group). Statistical significance is indicated as follows: *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, ****\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001; ns, not significant, compared with the Control group.\u003c/p\u003e","description":"","filename":"floatimage16.png","url":"https://assets-eu.researchsquare.com/files/rs-9228384/v1/2e97ac152c0d7e8edf572664.png"},{"id":109205924,"identity":"15171df4-2cb5-4435-9345-4817363f863d","added_by":"auto","created_at":"2026-05-13 15:09:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8141850,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9228384/v1/43228e8f-2a0a-436f-88db-8c96f661a4c3.pdf"},{"id":108978696,"identity":"0ee3801d-4180-49c5-9938-f483627e911c","added_by":"auto","created_at":"2026-05-11 11:47:32","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1554360,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-9228384/v1/86bee2d2366d7438f69cdbe2.docx"},{"id":108978680,"identity":"1dc167f1-6197-4da5-a000-609886619639","added_by":"auto","created_at":"2026-05-11 11:47:15","extension":"csv","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17044,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1ERStressRGs.csv","url":"https://assets-eu.researchsquare.com/files/rs-9228384/v1/af1a34975d84d506f1474001.csv"},{"id":108978690,"identity":"20ad6964-b675-4eb9-95b2-d52673a934c6","added_by":"auto","created_at":"2026-05-11 11:47:26","extension":"csv","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1043,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2ERStressRDEGs.csv","url":"https://assets-eu.researchsquare.com/files/rs-9228384/v1/24825d4ce35d27321ba33f9d.csv"},{"id":108979588,"identity":"df9e0f8e-b4c1-4e08-8e9e-a2729120a592","added_by":"auto","created_at":"2026-05-11 11:59:58","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":15478,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3Cogenes.docx","url":"https://assets-eu.researchsquare.com/files/rs-9228384/v1/730413a0ec731c564237e2cc.docx"},{"id":108978637,"identity":"0ff221a9-c92d-41da-9630-9d4abdcba7c0","added_by":"auto","created_at":"2026-05-11 11:47:12","extension":"txt","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":3335,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4GSVADiffAnalysisSig.txt","url":"https://assets-eu.researchsquare.com/files/rs-9228384/v1/5ad8a1dffccccb026fe43cfa.txt"},{"id":108978689,"identity":"06a7df2f-cc82-46b1-8434-021b58644c45","added_by":"auto","created_at":"2026-05-11 11:47:26","extension":"csv","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":632,"visible":true,"origin":"","legend":"","description":"","filename":"TableS5mRNAmiRNAnodes.csv","url":"https://assets-eu.researchsquare.com/files/rs-9228384/v1/bd572f21d891fe84c29d5ad7.csv"},{"id":108979590,"identity":"174f0527-8411-45ef-9944-d52cb82a6726","added_by":"auto","created_at":"2026-05-11 12:00:04","extension":"csv","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":1347,"visible":true,"origin":"","legend":"","description":"","filename":"TableS6ceRNAlncRNAnodes.csv","url":"https://assets-eu.researchsquare.com/files/rs-9228384/v1/bcff740a91e96d7df9ee25a4.csv"},{"id":108979602,"identity":"a7de4025-2784-4970-9d7a-8465d9574087","added_by":"auto","created_at":"2026-05-11 12:00:13","extension":"csv","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":695,"visible":true,"origin":"","legend":"","description":"","filename":"TableS7DrugmRNAmiRNAnodesnodes.csv","url":"https://assets-eu.researchsquare.com/files/rs-9228384/v1/315948ea0ab84575e5689ad5.csv"},{"id":108978640,"identity":"70d17662-6762-4bbc-96ba-daa274299a58","added_by":"auto","created_at":"2026-05-11 11:47:12","extension":"csv","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":858,"visible":true,"origin":"","legend":"","description":"","filename":"TableS8RBPmRNAmiRNAnodesnodes.csv","url":"https://assets-eu.researchsquare.com/files/rs-9228384/v1/4d77ee8bb47593ae2c89b073.csv"},{"id":108979631,"identity":"740b2358-e2fc-41a8-bd22-d272d0ebfc0e","added_by":"auto","created_at":"2026-05-11 12:00:23","extension":"csv","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":873,"visible":true,"origin":"","legend":"","description":"","filename":"TableS9TFmRNAmiRNAnodesnodes.csv","url":"https://assets-eu.researchsquare.com/files/rs-9228384/v1/530a48f492155426519f65e3.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Endoplasmic reticulum stress-related gene TRAF6 as a potential biomarker for diminished ovarian reserve","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eA decline in oocyte quantity and quality with advancing age is a normal physiologic process known as diminished ovarian reserve (DOR) \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. However, some women experience this decline much earlier than expected, leading to premature infertility; this condition is often referred to as pathologic DOR \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. In recent years, the incidence of DOR has gradually increased, with a noticeable trend toward earlier onset in younger populations. The reported prevalence of DOR ranges from 10% to 35% \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e, making it a significant contributor to female infertility. Currently, there is no internationally unified consensus regarding the diagnosis or management of DOR \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Existing treatment strategies\u0026mdash;such as hormonal therapies and assisted reproductive technologies \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e\u0026mdash;often yield limited effectiveness and may involve substantial financial costs. Furthermore, most current treatment modalities focus primarily on symptomatic management rather than addressing the underlying pathophysiological mechanisms of the condition. This limitation highlights a critical gap in our understanding of DOR. Thus, identifying novel diagnostic biomarkers and potential therapeutic targets is imperative.\u003c/p\u003e \u003cp\u003eEndoplasmic reticulum stress (ERS)-induced apoptosis is involved in the pathogenesis of various diseases, including neurodegenerative disorders, stroke, breast cancer, and diabetes \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Despite their apparent diversity, these conditions share a common feature: intracellular and/or extracellular factors that disrupt protein folding and lead to the accumulation of misfolded proteins in the endoplasmic reticulum (ER). Under normal physiological conditions, ERS plays a crucial role in the growth and maturation of ovarian follicles, follicular closure, corpus luteum formation, and implantation. However, disruption of ER homeostasis by ERS may negatively impact female fertility \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Harada M reported that oocyte growth and granulosa cell proliferation in ovarian tissue cause hypoxia, leading to ER dysfunction resulting in ERS \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Additionally, apoptosis of ovarian granulosa cells during early follicular atresia has been associated with ERS \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Previous studies have indicated a potential link between ERS and various reproductive disorders \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Nevertheless, the specific role of ER stress-related differentially expressed genes (ER-StressRDEGs) in DOR remains poorly elucidated. Therefore, this study seeks to explore the relationship between ER-StressRDEGs and DOR to uncover novel molecular mechanisms that may inform the development of more effective diagnostic and therapeutic strategies.\u003c/p\u003e \u003cp\u003eResearch has shown that exosomes can transport proteins and RNA from donor cells to recipient cells, playing a crucial role in cell communication, signal transduction, and the regulation of various physiological and pathological processes \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Among these, exosomal microRNAs (ex-miRNAs), encapsulated within extracellular vesicles, play a vital role in facilitating paracrine communication among granulosa cells (GCs), cumulus cells, and the oocyte inside follicular fluid (FF). Previous studies have primarily focused on the molecular functions and regulatory mechanisms of miRNAs carried by these exosomes. For example, exosomal miR-122-5p has been shown to promote apoptosis of ovarian GCs by targeting BCL9 \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Conversely, exosomal miR-644-5p derived from bone marrow mesenchymal stem cells \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e and miR-144-5p \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e have been reported to inhibit the apoptosis of damaged GCs and prevent follicular atresia after chemotherapy. Additional studies suggest that changes in ex-miRNA expression in FF may contribute to declines in ovarian reserve and oocyte quality in patients with DOR \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. In this study, a comprehensive suite of bioinformatics tools was applied to identify the key genes and signaling pathways associated with ovarian reserve decline. Using these results, an excellent predictive diagnostic model was constructed. These findings not only deepen our understanding of the molecular mechanisms underlying this pathological process but also lay the foundation for the future development of novel diagnostic and therapeutic strategies.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Sample collection\u003c/h2\u003e \u003cp\u003eFor the self-test ex-miRNA analysis, FF samples were collected from six patients with DOR and six cases of normal ovarian reserve (NOR). The raw count data were used for subsequent differential analyses.\u003c/p\u003e \u003cp\u003ePatients undergoing in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI) were recruited from the Reproductive Center of the First Hospital of Lanzhou University between March 2024 and June 2024.\u003c/p\u003e \u003cp\u003eThe diagnostic criteria for patients with DOR were as follows: age\u0026thinsp;\u0026le;\u0026thinsp;35 years; antral follicular count (AFC)\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026ndash;7 follicles; anti-M\u0026uuml;llerian hormone (AMH)\u0026thinsp;\u0026lt;\u0026thinsp;1.1 ng/ml; and basic follicle-stimulating hormone (bFSH) at 10\u0026ndash;25 mIU/mL. Patients with NOR included individuals whose infertility was mainly attributable to tubal or male factors. Their diagnostic criteria were as follows: age\u0026thinsp;\u0026le;\u0026thinsp;35 years; unilateral AFC between 5 and 12; AMH\u0026thinsp;\u0026gt;\u0026thinsp;2.0 ng/mL; bFSH\u0026thinsp;\u0026lt;\u0026thinsp;10 mIU/mL; and basic luteinizing hormone (bLH)\u0026thinsp;\u0026lt;\u0026thinsp;10 mIU/mL.\u003c/p\u003e \u003cp\u003eExclusion criteria for all participants included the following: age\u0026thinsp;\u0026gt;\u0026thinsp;35 years, the presence of female chromosomal abnormalities, previous ovarian or fallopian tube surgery, endometriosis, polycystic ovary syndrome, gonadal dysplasia, related endocrine disorders (e.g., thyroid dysfunction, hyperprolactinemia, diabetes), and autoimmune diseases (e.g., systemic lupus erythematosus).\u003c/p\u003e \u003cp\u003eMedical histories and clinical characteristics were obtained from electronic medical records of patients with DOR (n\u0026thinsp;=\u0026thinsp;42) and NOR (n\u0026thinsp;=\u0026thinsp;42). All patients underwent controlled ovarian stimulation using either the antagonist protocol or the progesterone-primed ovarian stimulation protocol. FF and GCs were collected on the day of oocyte retrieval and stored separately at \u0026minus;\u0026thinsp;80\u0026deg;C for subsequent use in reverse transcription quantitative polymerase chain reaction (RT-qPCR) and enzyme-linked immunosorbent assay (ELISA) validation experiments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data download\u003c/h2\u003e \u003cp\u003eThe DOR dataset GSE87201\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e was downloaded from the Gene Expression Omnibus (GEO) database \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using the R package GEOquery \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e (version 2.70.0). Raw data were processed and standardized using the R package limma \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e (version 3.58.1), including normalization of probe annotations. All samples in GSE87201 were from \u003cem\u003eHomo sapiens\u003c/em\u003e, and the tissue source was mature MII oocytes. The chip platform for GSE87201 was GPL17586; detailed information is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The dataset included 18 DOR samples and 17 NOR samples, all of which were included in this study.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGEO Microarray Chip Information\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePlatform\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSE87201\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGPL17586\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperiment type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExpression profiling by array\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHomo sapiens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMature MII oocytes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSamples in DOR group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSamples in Control group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28586423\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eAbbreviations: GEO, Gene Expression Omnibus; DOR, diminished ovarian reserve.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of GO and KEGG Enrichment Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eONTOLOGY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGeneRatio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBgRatio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003epvalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep.adjust\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eqvalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0010639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enegative regulation of organelle organization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5/19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e365/18888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.43904E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01746493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.009634467\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0035666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTRIF-dependent toll-like receptor signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2/19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11/18888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.24438E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01746493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.009634467\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:1903050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eregulation of proteolysis involved in protein catabolic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4/19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e228/18888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.9514E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01746493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.009634467\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0007250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eactivation of NF-kappaB-inducing kinase activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2/19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14/18888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.66145E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01746493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.009634467\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0002756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMyD88-independent toll-like receptor signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2/19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16/18888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00011408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01746493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.009634467\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0140672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eATAC complex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2/19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14/19894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.81044E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.009294425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.006083923\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0070461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSAGA-type complex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2/19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39/19894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000627003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.037306688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.024420123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0000123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ehistone acetyltransferase complex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2/19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94/19894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00358467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.092276356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.060402037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0031248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eprotein acetyltransferase complex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2/19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e104/19894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004367593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.092276356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.060402037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:1902493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eacetyltransferase complex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2/19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e105/19894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004449873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.092276356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.060402037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0034450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eubiquitin-ubiquitin ligase activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2/19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14/18522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.00587E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.009906456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.005687917\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0031625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eubiquitin protein ligase binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3/19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e305/18522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003523281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.084733626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.048650886\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0044389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eubiquitin-like protein ligase binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3/19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e324/18522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004174353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.084733626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.048650886\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0043130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eubiquitin binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2/19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99/18522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004557884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.084733626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.048650886\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0101005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003edeubiquitinase activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2/19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e115/18522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006099126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.084733626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.048650886\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa03250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eViral life cycle - HIV-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3/17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64/8538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000253445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.012250649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.009861203\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa04140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAutophagy - animal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4/17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e169/8538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000288251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.012250649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.009861203\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa04657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIL-17 signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3/17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95/8538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000810439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.022962436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.018483695\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa05145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eToxoplasmosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3/17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e112/8538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001306841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.027770364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.022353853\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa05162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMeasles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3/17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e139/8538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002429144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.041295442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.033240913\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eAbbreviations: GO, Gene Ontology; BP, biological process; CC, cellular component; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genomes.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGSEA enrichment analysis of the Disease versus Control Groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003esetSize\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eenrichmentScore\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003epvalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep.adjust\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eqvalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREACTOME_METALLOTHIONEINS_BIND_METALS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.887774845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.96187129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.25E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.016673964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.016106786\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZHENG_RESPONSE_TO_ARSENITE_UP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.864426208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.033759036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.22E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.010295164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.009944966\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHEBERT_MATRISOME_TNBC_BONE_METASTASIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.854905334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.889233784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000219442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.046176892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.044606149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREACTOME_RESPONSE_TO_METAL_IONS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.854093035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.009447899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.43E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.014324357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.013837103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of clinical baseline data between DOR group and NOR group.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge(year)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNOR(n\u0026thinsp;=\u0026thinsp;42)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDOR(n\u0026thinsp;=\u0026thinsp;42)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.74\u0026thinsp;\u0026plusmn;\u0026thinsp;3.04\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.00\u0026thinsp;\u0026plusmn;\u0026thinsp;2.49\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.73\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.47 -1.05\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of infertility (y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.62\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.26\u0026thinsp;\u0026plusmn;\u0026thinsp;1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.44 0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e21.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e21.81\u0026thinsp;\u0026plusmn;\u0026thinsp;2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.26 0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAMH (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.24\u0026thinsp;\u0026plusmn;\u0026thinsp;1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.33 4.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e15.92\u0026thinsp;\u0026plusmn;\u0026thinsp;3.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.07 13.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFSH (mIU/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e6.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e13.34\u0026thinsp;\u0026plusmn;\u0026thinsp;3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-10.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-7.75 -5.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLH (mIU/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e5.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5.22\u0026thinsp;\u0026plusmn;\u0026thinsp;2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.34 1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE2 (pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e40.33\u0026thinsp;\u0026plusmn;\u0026thinsp;13.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e41.50\u0026thinsp;\u0026plusmn;\u0026thinsp;19.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-8.59 6.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: AMH, anti-M\u0026uuml;llerian hormone; BMI, Body Mass Index; AFC, antral follicular count; FSH, follicle-stimulating hormone; LH, luteinizing hormone; E2, estradiol\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eEndoplasmic reticulum stress-related genes (ER-StressRGs) were obtained from the GeneCards database \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which provides comprehensive information on human genes. Using\u0026ldquo;Endoplasmic Reticulum Stress\u0026rdquo;as the search keyword and restricting the results to \u0026ldquo;protein-coding,\u0026rdquo; 2,414 ER-StressRGs were identified. Additional ER-StressRGs were collected from published literature indexed in PubMed (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using \u0026ldquo;Endoplasmic Reticulum Stress\u0026rdquo; as the keyword \u003csup\u003e[\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. After removing duplicates and irrelevant entries, 37 ER-StressRGs were identified. Merging genes from the two sources and deduplicating yielded a final set of 2,425 ER-StressRGs. Detailed information on these genes is provided in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Differentially expressed genes related to endoplasmic reticulum stress in diminished ovarian reserve\u003c/h2\u003e \u003cp\u003eBased on the miRNA dataset samples, the samples were grouped into DOR and NOR groups. Differential gene expression analysis between DOR and NOR was performed using the R package DESeq2 \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e (version 1.42.0). Differentially expressed genes (DEGs) were defined using a threshold of |logFC| \u0026gt; 0 and adj.\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, with Benjamini\u0026ndash;Hochberg (BH) correction applied for multiple testing. Genes with logFC\u0026thinsp;\u0026gt;\u0026thinsp;0 and adj.\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered upregulated, while those with logFC\u0026thinsp;\u0026lt;\u0026thinsp;0 and adj.\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered downregulated. Volcano plots illustrating the differential expression result were generated using the R package ggplot2 (version 3.4.4). Similarly, for the GSE87201 dataset, samples were divided into DOR and NOR groups. Differential expression analysis was conducted using the R package limma \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e (version 3.58.1). DEGs were identified using the threshold of |logFC| \u0026gt; 0 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Genes with logFC\u0026thinsp;\u0026gt;\u0026thinsp;0 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were classified as upregulated DEGs, whereas genes with logFC\u0026thinsp;\u0026lt;\u0026thinsp;0 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were classified as downregulated DEGs. Volcano plots illustrating the differential expression results were generated using the R package ggplot2 (version 3.4.4). To identify ER-StressRDEGs associated with DOR, all DEGs from GSE87201 were intercrossed with the previously obtained ER-StressRGs. A Venn diagram was used to illustrate the overlap. The resulting ER-StressRDEGs were further visualized using a heatmap of the top 20 genes, generated with the R package pheatmap (version 1.0.12). Finally, the corresponding target genes of the differentially expressed miRNAs were predicted using the MicroRNA Target Prediction Database (miRDB; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mirdb.org/\u003c/span\u003e\u003cspan address=\"https://mirdb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). These predicted target genes were then intersected with the ER-StressRDEGs from GSE87201 to identify overlapping genes, referred to as co-genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Differential expression validation and receiver operating characteristic curve analysis of co-genes\u003c/h2\u003e \u003cp\u003eTo further examine the expression levels of co-genes between the DOR and NOR groups in the GSE87201 dataset, a group comparison plot was generated using the expression profiles of the identified co-genes. Subsequently, receiver operating characteristic (ROC) curve analysis was performed to evaluate the potential diagnostic performance of the co-genes. The ROC curves were generated using the R package pROC \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e (version 1.18.5), and the corresponding area under the curve (AUC) values were calculated. AUC values were used to evaluate the diagnostic value of co-gene expression in the occurrence of DOR. Generally, AUC values range from 0.5 to 1. Values closer to 1 indicate stronger diagnostic performance, whereas values closer to 0.5 indicate limited diagnostic ability. AUC values between 0.5 and 0.7 were considered to indicate low accuracy, values between 0.7 and 0.9 were considered to indicate moderate accuracy, and values greater than 0.9 were considered to indicate high accuracy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Gene Ontology and pathway enrichment analysis\u003c/h2\u003e \u003cp\u003eGene Ontology (GO) analysis \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e is widely used for large-scale functional enrichment studies. It classifies gene functions into three independent domains: biological process (BP), cellular component (CC), and molecular function (MF). The Kyoto Encyclopedia of Genes and Genomes (KEGG) \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e is a widely used resource that integrates information on genomes, biological pathways, diseases, and drugs. The R package cluster-Profiler \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e (version 4.10.0) was used to perform GO functional enrichment analysis and KEGG pathway enrichment analysis for the co-genes. Enrichment results were considered statistically significant when the \u003cem\u003ep\u003c/em\u003e-value was \u0026lt;\u0026thinsp;0.05 and the false discovery rate (FDR; \u003cem\u003eq\u003c/em\u003e-value) was \u0026lt;\u0026thinsp;0.25.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Gene set enrichment analysis\u003c/h2\u003e \u003cp\u003eGene set enrichment analysis (GSEA) \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e is used to evaluate the distribution of genes within a predefined gene set in a gene table ranked by correlation with the phenotype, thereby determining their contribution to the phenotype. In this study, genes from GSE87201 were first ranked according to their logFC values between the DOR and NOR groups. GSEA was then performed on the ranked gene list using the R package cluster-Profiler \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e (version 4.10.0). The analysis was conducted using the c2 gene set obtained from the Molecular Signatures Database (MSigDB), version v2023.2.Hs. The following parameters were applied: a random seed of 2020, a minimum gene set size of 10, and a maximum gene set size of 500. Enrichment significance was evaluated using adj.\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and an FDR value (\u003cem\u003eq\u003c/em\u003e-value)\u0026thinsp;\u0026lt;\u0026thinsp;0.25. \u003cem\u003eP\u003c/em\u003e-value adjustment was performed using the BH method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Gene set variation analysis\u003c/h2\u003e \u003cp\u003eGene set variation analysis (GSVA) \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e is a nonparametric, unsupervised analytical method used to estimate variations in gene set enrichment across samples in transcriptomic datasets. Instead of evaluating differential expression at the level of individual genes, GSVA converts the gene expression matrix into pathway-level enrichment scores for each sample. This approach allows the identification of biological pathways that may be differentially enriched across samples. In this study, GSVA was performed using all genes from the GSE87201 dataset to evaluate functional enrichment differences between the DOR and NOR groups. The gene set used for the analysis was obtained from the MSigDB \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e, specifically the c2.cp.v2023.2.Hs. symbols gene set in GMT format. The analysis was conducted using the R package GSVA (version 1.50.0). Pathways with a \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Establishment of a diagnostic model for diminished ovarian reserve\u003c/h2\u003e \u003cp\u003eTo construct a diagnostic model for DOR using GSE87201, logistic regression analysis was first performed on the co-genes. Logistic regression is used to analyze the association between an independent variable and a binary dependent variable. In this analysis, the dependent variable represents the sample group classification (DOR or NOR). Co-genes with \u003cem\u003ep\u003c/em\u003e-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were selected as the criterion for screening co-genes and constructing a logistic regression model. A forest plot was generated to visualize the group-wise expression of the co-genes included in the model. Subsequently, a model based on the support vector machine (SVM) \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e algorithm was constructed using the co-genes retained from the logistic regression analysis. The SVM model was used to further screen genes by evaluating model performance, with gene selection based on achieving the highest accuracy and the lowest error rate. Next, least absolute shrinkage and selection operator (LASSO) regression analysis was performed using the R package glmnet \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e (version 4.1-8). The analysis was conducted with the parameters set.seed (500) and family = \"binomial,\" using the co-genes retained from the SVM model. LASSO regression analysis introduces a penalty term to the regression model to reduce overfitting and improve model generalizability by shrinking regression coefficients toward zero. The strength of this penalty is controlled by the regularization parameter (lambda \u0026times; absolute value of slope). The diagnostic model plot and variable trajectory plot were used to visualize the results of the LASSO regression analysis. Genes retained in the final LASSO model were defined as key genes, and the resulting model was considered the diagnostic model for DOR. Finally, a LASSO-based risk score was computed using the risk coefficients obtained from the LASSO model. The risk score was calculated using the following formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{r}\\text{i}\\text{s}\\text{k}\\:score\\:=\\:\\sum\\:_{i}Coefficient\\:\\left({gene}_{i}\\right)\\text{*}mRNA\\:Expression\\:\\left({gene}_{i}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Validation of the diagnostic model for diminished ovarian reserve\u003c/h2\u003e \u003cp\u003eA nomogram \u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e is a graphical tool that uses a cluster of disjoint line segments to represent the functional relationship between multiple independent variables in a rectangular coordinate system. The R package rms (version 6.7-1) was used to construct a nomogram based on the logistic regression results to visualize the relationship among key genes. A calibration curve was generated from the LASSO regression results to evaluate the accuracy and discrimination of the diagnostic model for DOR. The R package ggDCA (version 1.1) was used to generate the decision curve analysis (DCA) plot based on the key genes identified in the GSE87201 dataset \u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. DCA is a method used for evaluating clinical prediction models, diagnostic tests, or molecular markers. Next, the R package pROC \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e (version 1.18.5) was used to plot the ROC curve for the GSE87201 dataset and to calculate the corresponding AUC. This analysis evaluates the diagnostic performance of the LASSO-derived risk score in distinguishing samples with DOR from those with NOR.\u003c/p\u003e \u003cp\u003eThe semantic comparison of GO \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e annotation provides a quantitative method for calculating the similarity between genes and genomes and has become an important basis for many bioinformatics analysis methods. The R package GOSemSim \u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e (version 2.28.0) was used to calculate the functional correlation of key genes, and the functional correlation between key genes was analyzed using functional similarity (Friends).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Construction of the regulatory network\u003c/h2\u003e \u003cp\u003eThe ENCORI database (starBase v3.0) \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://starbase.sysu.edu.cn/\u003c/span\u003e\u003cspan address=\"https://starbase.sysu.edu.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) provides various visual tools for examining miRNA\u0026ndash;target interactions. Furthermore, the miRDB database \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e is used for miRNA target gene prediction and functional annotation. In this study, the ENCORI and miRDB databases were used to predict miRNAs that interact with the identified key genes. The predicted interactions were intersected with the mRNA\u0026ndash;miRNA interaction data in the ENCORI database to obtain the intersected mRNA\u0026ndash;miRNA interaction network. Subsequently, the ENCORI database was used to predict long noncoding RNAs (lncRNAs) that interact with the identified miRNAs. Interactions with clipExpNum\u0026thinsp;\u0026ge;\u0026thinsp;30 were retained as the screening criterion to construct the miRNA\u0026ndash;lncRNA interaction network. The mRNA\u0026ndash;miRNA and miRNA\u0026ndash;lncRNA interactions were then integrated to establish an mRNA\u0026ndash;miRNA\u0026ndash;lncRNA network. Additionally, transcription factors (TFs) regulate gene expression by interacting with key genes at the post-transcriptional stage. TF\u0026ndash;gene regulatory relationships were retrieved from the ChIPBase database \u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://rna.sysu.edu.cn/chipbase/\u003c/span\u003e\u003cspan address=\"http://rna.sysu.edu.cn/chipbase/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). These data were used to analyze TF regulation of key genes, and the resulting miRNA\u0026ndash;mRNA\u0026ndash;TF regulatory network was visualized using Cytoscape \u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e software. RNA-binding proteins (RBPs) \u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e play a key role in the process of gene regulation, including RNA synthesis, alternative splicing, modification, transport, and translation. Based on interaction information from the ENCORI (starBase v3.0) database \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://starbase.sysu.edu.cn/\u003c/span\u003e\u003cspan address=\"https://starbase.sysu.edu.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), key genes of the target RBP were identified. The resulting miRNA\u0026ndash;mRNA\u0026ndash;RBP regulatory network was also visualized in Cytoscape. Finally, the Comparative Toxicogenomics Database \u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ctdbase.org/\u003c/span\u003e\u003cspan address=\"https://ctdbase.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to identify potential drugs that directly or indirectly target the key genes. These interactions were used to explore gene\u0026ndash;drug associations. The resulting miRNA\u0026ndash;mRNA\u0026ndash;drug regulatory network was visualized in Cytoscape, thereby completing its construction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11 Protein\u0026ndash;protein interaction network\u003c/h2\u003e \u003cp\u003eThe protein\u0026ndash;protein interaction (PPI) network is composed of proteins that interact with one another and participate in a wide range of cellular processes, including biological signaling, regulation of gene expression, energy and substance metabolism, and cell cycle regulation. Systematic analysis of protein interactions in biological systems is important for understanding how proteins function within cellular networks, how biological signals and energy are transmitted, and how metabolic processes operate under specific physiological and pathological conditions. Such analyses may also help clarify the functional relationships among proteins. The STRING database \u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.string-db.org/\u003c/span\u003e\u003cspan address=\"https://cn.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a platform for identifying interactions among known and predicted proteins. In this study, a PPI network was constructed using the STRING database based on the identified key genes with a minimum required interaction score set to \u0026gt;\u0026thinsp;0.400, corresponding to medium confidence (0.400), which served as the threshold for network construction. Tightly connected local regions within the resulting PPI network may represent molecular complexes with specific biological functions. The GeneMANIA database \u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://genemania.org/\u003c/span\u003e\u003cspan address=\"https://genemania.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is used to generate hypotheses about gene function, analyze gene lists, and prioritize genes for functional analysis. Given a list of query genes, GeneMANIA identifies genes with similar functional characteristics using a large set of genomics and proteomics data. In this framework, each functional genomic dataset is weighted according to its predicted relevance to the query. GeneMANIA can also be used to predict gene function by identifying genes that are likely to share functional properties with a query gene based on interaction patterns. The PPI network was constructed by predicting functionally similar genes to key genes using the GeneMANIA online website.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.12 Immune infiltration analysis of diminished ovarian reserve\u003c/h2\u003e \u003cp\u003eCIBERSORT \u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e applies linear support vector regression to deconvolute bulk transcriptome expression matrices and estimate the composition and abundance of immune cell types within mixed cell populations. Using the LM22 signature matrix as a reference, the CIBERSORT algorithm was applied to retain only samples with immune cell enrichment scores greater than zero. The immune cell infiltration matrix for the GSE87201 dataset was generated, and the relative proportions of immune cells were visualized using a bar chart. Pairwise correlations among immune cells were calculated using Spearman\u0026rsquo;s correlation, and the R package heatmap (version 1.0.12) was used to generate a correlation heatmap. Correlations between key genes and immune cell proportions were calculated using Spearman\u0026rsquo;s correlation, and the results showing statistically significant associations (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were retained. The R package ggplot2 (version 3.4.4) was used to generate correlation bubble plots illustrating the correlations between the key genes and immune cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.13 Statistical analysis\u003c/h2\u003e \u003cp\u003eAll data processing and analyses were performed using R software (version 4.2.2). For comparisons of continuous variables between two groups, the statistical significance of normally distributed variables was estimated using independent Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-tests, unless otherwise specified. The Mann\u0026ndash;Whitney \u003cem\u003eU\u003c/em\u003e Test (Wilcoxon rank-sum test) was used to analyze the differences between variables that did not follow a normal distribution. The Kruskal\u0026ndash;Wallis test was used to compare three or more groups. Spearman correlation analysis was used to calculate correlation coefficients between different molecules. All \u003cem\u003ep\u003c/em\u003e-values were two-sided unless otherwise specified, and a \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.14 Real‑time quantitative PCR\u003c/h2\u003e \u003cp\u003eThe mRNA expression levels of five key co-genes (\u003cem\u003eTRAF6\u003c/em\u003e, \u003cem\u003eCD109\u003c/em\u003e, \u003cem\u003eBAG2\u003c/em\u003e, \u003cem\u003eUSP25\u003c/em\u003e, and \u003cem\u003eEIF3H\u003c/em\u003e) were measured using RT-qPCR. Total cellular RNA was extracted using Trizol reagent (Coolaber, RE600), and RNA concentration was measured using a Nanodrop One spectrophotometer (Thermo Fisher Scientific). The RNA specimen was subjected to reverse transcription to generate a single-stranded cDNA in a 20 \u0026micro;L reaction volume using FastKing gDNA Dispelling RT SuperMix (TIANGEN, KR118). Quantitative real-time PCR was performed using SuperReal PreMix Plus (SYBR Green) (TIANGEN, FP205). \u003cem\u003eGAPDH\u003c/em\u003e primers were used to normalize the relative expression of target genes, which was calculated using the 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method. All primer sequences were synthesized by Sangong Biotech Ltd. (Shanghai, China) and are listed in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe primer sequences used for qRT-PCR.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward 5\u0026prime;\u0026thinsp;\u0026rarr;\u0026thinsp;3\u0026prime; primer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReverse 5\u0026prime;\u0026thinsp;\u0026rarr;\u0026thinsp;3\u0026prime; primer\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTRAF6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTTTGCTCTTATGGATTGTCCCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCATTGATGCAGCACAGTTGTC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCD109\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGAAGCCATCTCTCAACTTCACA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTTCCACTGTTAGATCCGCTCC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEIF3H\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAGATGGAAATGATGCGGAGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAGTATGTGGACTGATACCAGCC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBAG2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGGGAAGAACTCTCACCGTTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCCTCATCAATAATCCTTGTGGCA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eUSP25\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCACCAGCAGACGTTTTTGAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAGCATTCTTCGCAGTAAGGAAA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGAPDH\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGCGAGATCCCTCCAAAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAAATGAGCCCCAGCCTT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2.15 ELISA\u003c/h2\u003e \u003cp\u003eThe concentration of TRAF6 in the FF was measured using an ELISA kit (JINGMEI, JM-5953H1). The levels of the reproductive hormones AMH (Solarbio, SEKM-0310), follicle-stimulating hormone (FSH; Elabscience, E-EL-M0511), and estradiol (E2; Elabscience, E-OSEL-M0008) were also determined using ELISA kits according to the manufacturer\u0026rsquo;s instructions. The concentrations of these proteins were determined by measuring absorbance at 450 nm using a spectrophotometer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e2.16 Animals and DOR model establishment\u003c/h2\u003e \u003cp\u003eFemale wild-type C57BL/6J mice (aged 6\u0026ndash;8 weeks) were obtained from the Animal Experimental Center of Lanzhou University and housed in the Specific Pathogen-Free facility at the Medical Experimental Center of Lanzhou University. Mice were maintained in a temperature-controlled environment (20\u0026ndash;25\u0026deg;C) with 45%\u0026ndash;55% humidity and a 12-h light/dark cycle. All experimental procedures were approved by the ethics committee of the First Hospital of Lanzhou University (Approval no. LDYYLL2025-799), and all methods were performed in accordance with relevant guidelines and regulations. After 1 week of adaptive feeding, mice were randomly assigned to four groups (n\u0026thinsp;=\u0026thinsp;5 per group) and administered intraperitoneal injections of Cyclophosphamide (CTX, MedChem Express, HY-17420) at varying doses:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eControl group: 200 \u0026micro;L normal saline\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCTX 50 mg/kg group: 200 \u0026micro;L of CTX (50 mg/kg, single dose)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCTX 75 mg/kg group: 200 \u0026micro;L of CTX (75 mg/kg, single dose)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCTX 100 mg/kg group: 200 \u0026micro;L of CTX (100 mg/kg, single dose)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eTwo weeks post-injection, mice were weighed and anesthetized through an intraperitoneal injection of 3% sodium pentobarbital. Blood samples were collected for hormone analysis. Ovarian tissues were either fixed in 4% paraformaldehyde (PFA) or snap-frozen and stored at \u0026minus;\u0026thinsp;80\u0026deg;C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e2.17 The estrous cycle\u003c/h2\u003e \u003cp\u003eVaginal smears were collected daily between 9:00 and 10:00 AM for 14 days. After air-drying, the smears were stained using Giemsa Stain Solution (Solarbio, G1010) and examined under a light microscope. The murine estrus cycle lasts approximately 4\u0026ndash;5 days and comprises four stages: Proestrus (P), Estrus (E), Metestrus (M), and Diestrus (D).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e2.18 Histopathological analysis and follicle counting\u003c/h2\u003e \u003cp\u003eOvarian tissues were fixed in 4% PFA, dehydrated in an ascending ethanol series, and embedded in paraffin. Serial sections of 5 \u0026micro;m thickness were prepared using a microtome, and every fifth section was stained with hematoxylin and eosin. Follicles were categorized into primordial, primary, secondary, antral, or atretic based on established criteria.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e2.19 Immunofluorescence\u003c/h2\u003e \u003cp\u003eFollowing deparaffinization and rehydration, antigen retrieval was performed in a pressure cooker for 10 min. Sections were then blocked with 10% donkey serum to prevent nonspecific binding and incubated overnight at 4\u0026deg;C with the primary antibody anti-TRAF6 (Abclonal, A23385). Subsequently, the sections were washed with PBS and incubated with the appropriate secondary antibody at room temperature (RT) for 50 min. Finally, the sections were counterstained with DAPI and mounted for imaging.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e2.20 Western blotting\u003c/h2\u003e \u003cp\u003eThe expression levels of target proteins in mouse ovarian tissue were quantitatively analyzed by Western blotting. Ovarian tissues were lysed with RIPA lysis buffer (Coolaber, SL1020) and homogenized using a tissue crusher. Total protein was extracted using low-temperature ultracentrifugation, and protein concentration was determined using the BCA Protein Assay Kit (Coolabe, SK1070). Equal amounts of protein were separated in sodium dodecyl sulfate\u0026ndash;polyacrylamide gel electrophoresis (Coolaber, SK6010) and transferred onto polyvinylidene fluoride (Millipore) membranes. Membranes were blocked with 5% skim milk for 2 h and incubated overnight at 4\u0026deg;C using the following primary antibodies: anti-TRAF6 (1:1000), GAPDH (1:5000), Bax (1:1000), Bcl-2 (1:500), and β-actin (1:3000). After washing with PBS, the membranes were incubated with the corresponding secondary antibodies for 1 h at RT. Protein bands were visualized using enhanced chemiluminescence, and the grayscale values of the stripes were analyzed using Image-J software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e2.21 TUNEL\u003c/h2\u003e \u003cp\u003eTUNEL staining was performed to assess the extent of apoptosis in ovarian tissues. Ovarian sections were first deparaffinized, rehydrated, and permeabilized using proteinase K for 30 min. Sections were then incubated with the TUNEL cell apoptosis detection kit (Servicebio, G1504) for 1 h in the dark. After washing, nuclei were then counterstained with DAPI for 10 min, and the sections were mounted using an antifade medium (Servicebio, G1401). Fluorescence microscopy was used to capture the images, and TUNEL-positive apoptotic cells were labeled green.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eAbbreviations: DOR, diminished ovarian reserve; ER-StressRDEGs, endoplasmic reticulum stress-related differentially expressed genes; ROC, receiver operating characteristic; AUC, area under the curve; TPR, true positive rate; FPR, false positive rate. In the plots, yellow represents NOR samples and brown represent DOR samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e.5 Gene Ontology and pathway enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGO and KEGG enrichment analyses were performed to further explore the biological significance of the 19 co-genes in the context of DOR, focusing on the BP, CC, MF, and KEGG pathways. The detailed results are presented in Table 2. The enrichment analysis revealed that the 19 co-genes were mainly enriched in specific BPs related to DOR, including the negative regulation of organelle organization, TRIF-dependent toll-like receptor signaling pathway, regulation of proteolysis involved in protein catabolic process, activation of NF-\u0026kappa;B\u0026ndash;inducing kinase activity, and the MyD88-independent toll-like receptor signaling pathway. For CC, the co-genes were enriched in the ATAC complex, SAGA-type complex, histone acetyltransferase complex, protein acetyltransferase complex, and other acetyltransferase-related complexes. Regarding MF, the co-genes were enriched ubiquitin\u0026ndash;ubiquitin ligase activity, ubiquitin protein ligase binding, ubiquitin-like protein ligase binding, ubiquitin binding, and deubiquitinase activity. KEGG pathway analysis indicated enrichment in Viral life cycle\u0026ndash;HIV-1, Autophagy\u0026ndash;animal, IL-17 signaling pathway, toxoplasmosis, measles, and additional relevant pathways. The overall GO and KEGG enrichment results were visualized using a bubble diagram (Fig. 5A).\u003c/p\u003e\n\u003cp\u003eFurthermore, network maps were generated for BP, CC, MF, and KEGG based on the enrichment analyses (Fig. 5B\u0026ndash;E). In these network diagrams, the lines represent the corresponding molecules and annotations of the corresponding entries, while node size corresponds to the number of molecules associated with each entry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e.6 Gene set enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the impact of gene expression on the onset of DOR, GSEA was performed using logFC values for all genes in the GSE87201 dataset between the DOR and NOR groups. GSEA was used to evaluate the association of gene expression with BP, CC, and MF, and the results were visualized using a mountain plot (Fig. 6A). Detailed results are provided in Table 3. The analysis revealed that all genes in GSE87201 were significantly enriched in several biologically relevant pathways and functions, including Reactome Metallothioneins Bind Metals (Fig. 6B), Zheng Response to Arsenite Up (Fig. 6C), Zheng Response to Arsenite Up (Fig. 6C), Hebert Matrisome Tnbc Bone Metastasis (Fig. 6D), and Reactome Response to Metal Ions (Fig. 6E).\u003c/p\u003e\n\u003cp\u003eIn the mountain plot, color indicates the adj.\u003cem\u003ep\u003c/em\u003e: red represents smaller adj.\u003cem\u003ep\u003c/em\u003e values, and blue represents larger adj.\u003cem\u003ep\u003c/em\u003e values. The screening criteria for GSEA were adj.\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 and FDR (\u003cem\u003eq\u003c/em\u003e-value) \u0026lt; 0.25, with \u003cem\u003ep\u003c/em\u003e-value correction performed using the BH method.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e.7 Gene set variation analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore pathway-level differences between the DOR and NOR groups in GSE87201, the c2.Cp.v2023.2.Hs.symbols.gmt gene set was used for GSVA. Detailed results are provided in Table S4. From the results, the top 20 pathways were selected based on \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 and descending absolute logFC values. The differential expression of these 20 pathways between the DOR and NOR groups was analyzed and visualized using a heat\u0026ndash;map (Fig. 7A). Differences were further evaluated using the Mann\u0026ndash;Whitney \u003cem\u003eU\u003c/em\u003e test, and a group comparison plot was generated (Fig. 7B). The GSVA revealed that the following pathways showed statistically significant differences between the DOR and NOR groups (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05): Signaling by PDGFR in Disease, LPA GNA12/13 RhoA signaling pathway, receptor-type tyrosine protein phosphatases, lectin pathway of the coagulation cascade (fibrinogen to fibrin), nitric oxide stimulates guanylate cyclase, depolymerization of the nuclear lamina, repression of WNT target genes, recycling of bile acids and salts, regulation of gene expression in late-stage branching morphogenesis, pancreatic bud precursor cells, glucocorticoid biosynthesis, elevation of cytosolic Ca\u003csup\u003e2+\u003c/sup\u003e levels, Biocarta HSP27 signaling pathway, sumoylation of immune response proteins, activated TAK1 mediates p38 MAPK activation, and Variant Mutation Inactivated Sigmar1 to Ca\u003csup\u003e2+\u003c/sup\u003e apoptosis pathway.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e.8 Construction of a diagnostic model for diminished ovarian reserve\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo determine the diagnostic value of the 19 co-genes in DOR, a logistic regression analysis was first performed, and a logistic regression model was constructed using all 19 co-genes. The analysis identified 10 co-genes with statistically significant contributions (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05): \u003cem\u003eUBE4B\u003c/em\u003e, \u003cem\u003eBAG2\u003c/em\u003e, \u003cem\u003eCD109\u003c/em\u003e, \u003cem\u003eEIF3H\u003c/em\u003e, \u003cem\u003eSAMD8\u003c/em\u003e, \u003cem\u003ePPIF\u003c/em\u003e, \u003cem\u003eUVRAG\u003c/em\u003e, \u003cem\u003eYAP1\u003c/em\u003e, \u003cem\u003eTRAF6\u003c/em\u003e, and \u003cem\u003eUSP25\u003c/em\u003e. Subsequently, an SVM model was constructed using these 10 co-genes. The SVM algorithm was applied to identify the number of genes with the lowest error rate (Fig. 8A) and the highest accuracy rate (Fig. 8B). The results showed that the SVM model achieved the highest accuracy with five co-genes: \u003cem\u003eTRAF6\u003c/em\u003e, \u003cem\u003eCD109\u003c/em\u003e, \u003cem\u003eBAG2\u003c/em\u003e, \u003cem\u003eUSP25\u003c/em\u003e, and \u003cem\u003eEIF3H\u003c/em\u003e. Finally, a LASSO regression analysis was performed using these five co-genes to construct a diagnostic model for DOR. The resulting LASSO regression model and variable trajectory are visualized in Fig. 8C and Fig. 8D, respectively. The results showed that the five co-genes (key genes) included in the LASSO regression model were \u003cem\u003eTRAF6\u003c/em\u003e, \u003cem\u003eCD109\u003c/em\u003e, \u003cem\u003eBAG2\u003c/em\u003e, \u003cem\u003eUSP25\u003c/em\u003e, and \u003cem\u003eEIF3H\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e.9 Validation of the diagnostic model for diminished ovarian reserve\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further verify the diagnostic performance of the model for DOR, a nomogram was constructed using key genes to illustrate their contributions in the GSE87201 dataset (Fig. 9A). The results showed that \u003cem\u003eTRAF6\u003c/em\u003e contributed the most to the predictive power of the model, whereas \u003cem\u003eEIF3H\u003c/em\u003e showed comparatively lower diagnostic utility.\u003c/p\u003e\n\u003cp\u003eDCA was performed to evaluate the clinical usefulness of the model based on the key genes in GSE87201 (Fig. 9B). The results showed that the model consistently provided a higher net benefit than the \u0026ldquo;All positive\u0026rdquo; and \u0026ldquo;All negative\u0026rdquo; strategies across a certain range, indicating strong clinical applicability. Additionally, the R package pROC was used to generate the ROC curve based on the risk score in the GSE87201 dataset. The ROC curve (Fig. 9C) indicated high predictive accuracy with an AUC \u0026gt; 0.9. The risk score was calculated using the following formula:\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"624\" height=\"33\" src=\"data:image/png;base64,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\" v:shapes=\"_x0000_i1025\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003eFunctional similarity (Friends) analysis was conducted to identify genes that play critical roles in the biological processes underlying DOR (Fig. 9D). The results suggest that \u003cem\u003eUSP25\u003c/em\u003e plays an important role in DOR pathophysiology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e.10 Construction of regulatory networks\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst, miRNAs related to the key genes were obtained from the StarBase database, and the mRNA\u0026ndash;miRNA regulatory network was constructed and visualized using Cytoscape software (Fig. 10A). The resulting network consisted of four mRNAs and 26 miRNAs. Detailed information on these interactions is provided in Table S5.\u003c/p\u003e\n\u003cp\u003eNext, the ENCORI database was used to predict the lncRNAs associated with the identified miRNAs. Finally, Cytoscape was used to map the mRNA\u0026ndash;miRNA\u0026ndash;lncRNA interaction network for visualization (Fig. 10B). This network included four mRNAs (\u003cem\u003eBAG2\u003c/em\u003e, \u003cem\u003eCD109\u003c/em\u003e, \u003cem\u003eTRAF6\u003c/em\u003e, and \u003cem\u003eUSP25\u003c/em\u003e), 26 miRNAs, and 10 lncRNAs. Detailed information on the mRNA\u0026ndash;miRNA\u0026ndash;lncRNA interaction relationships is provided in Table S6.\u003c/p\u003e\n\u003cp\u003ePotential drugs or molecular compounds associated with the key genes were identified using the CTD database. Based on these associations, an miRNA\u0026ndash;mRNA\u0026ndash;drug regulatory network was constructed and visualized using Cytoscape (Fig. 11A). Only miRNAs, mRNAs, and drugs or molecular compounds with documented interactions in the miRNA\u0026ndash;mRNA\u0026ndash;drug network are shown. This network included four mRNAs, 26 miRNAs, and two drugs or molecular compounds. Detailed information on these interactions is provided in Table S7.\u003c/p\u003e\n\u003cp\u003eNext, RBPs associated with the key genes were predicted using the StarBase database. A miRNA\u0026ndash;mRNA\u0026ndash;RBP regulatory network was constructed and visualized using Cytoscape (Fig. 11B). Only miRNAs, mRNAs, and RBPs with documented interactions within the miRNA\u0026ndash;mRNA\u0026ndash;RBP network are shown. This network included four mRNAs, 26 miRNAs, and 17 RBPs. Detailed information on these interactions is provided in Table S8.\u003c/p\u003e\n\u003cp\u003eFinally, TFs that bind to the key genes were obtained through the ChIPBase database. A miRNA\u0026ndash;mRNA\u0026ndash;TF regulatory network was constructed and visualized using Cytoscape (Fig. 11C). Only miRNAs, mRNAs, and TFs documented within the miRNA\u0026ndash;mRNA\u0026ndash;TF network are shown. This network included four mRNAs, 26 miRNAs, and 22 TFs. Detailed information on these interactions is provided in Table S9.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e.11 Protein\u0026ndash;protein interaction network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst, a PPI analysis was performed. A PPI network for five key genes\u0026mdash;\u003cem\u003eEIF3H\u003c/em\u003e, \u003cem\u003eUSP25\u003c/em\u003e, \u003cem\u003eTRAF6\u003c/em\u003e, \u003cem\u003eIFIH1\u003c/em\u003e, and \u003cem\u003eUBE2I\u003c/em\u003e\u0026mdash;was constructed using the STRING database (Fig. 12A). The results showed that three key genes (\u003cem\u003eEIF3H\u003c/em\u003e, \u003cem\u003eUSP25\u003c/em\u003e, and \u003cem\u003eTRAF6\u003c/em\u003e) exhibited interactions within the network. Second, an interaction network comprising the five key genes and their functionally similar genes was predicted and constructed using GeneMANIA (Fig. 12B). In this network, lines of different colors represent the coexpression between them and share information, such as protein domains. The resulting network contained five hub genes and 20 functionally similar proteins.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e.12 Immune infiltration analysis of diminished ovarian reserve (CIBERSORT)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGSE87201 was used to estimate the relative abundance of 22 immune cells using the CIBERSORT algorithm. First, based on the results of the immune infiltration analysis, a bar chart illustrating the proportions of immune cells in the dataset was generated (Fig. 13A). The results showed that 20 immune cells were enriched in the DOR samples, including the following: na\u0026iuml;ve B cells, plasma cells, CD\u003csup\u003e8+\u003c/sup\u003e T cells, na\u0026iuml;ve CD\u003csup\u003e4+\u003c/sup\u003e T cells, resting memory CD\u003csup\u003e4+\u003c/sup\u003e T cells, activated memory CD\u003csup\u003e4+\u003c/sup\u003e T cells, follicular helper T cells, regulatory T cells (Tregs), gamma\u0026ndash;delta T cells, resting natural killer (NK) cells, activated NK cells, monocytes, M0 macrophages, M1 macrophages, M2 macrophages, resting dendritic cells, resting mast cells, activated mast cells, eosinophils, and neutrophils. Next, the correlations among the abundances of the 20 infiltrating immune cell types in the DOR samples were visualized using a correlation heatmap (Fig. 13B). The heatmap showed that many immune cell types showed strong correlations with one another. Among these, resting memory CD\u003csup\u003e4+\u003c/sup\u003e T cells and Tregs showed the strongest negative correlation (\u003cem\u003er\u003c/em\u003e = \u0026minus;0.783, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). Finally, the correlations between key genes and the abundance of immune cell infiltration were visualized using a correlation bubble plot (Fig. 13C). The results showed that most immune cells showed strong correlations. Among these, \u003cem\u003eCD109\u003c/em\u003e and resting NK cells showed the strongest negative correlation (\u003cem\u003er\u003c/em\u003e = \u0026minus;0.452, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.13\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eClinical sample validation and correlation study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 84 individuals who met the inclusion criteria were included in this study, with 42 participants in the DOR group and 42 in the NOR group. The clinical baseline data of the patients are summarized in Table 4. No statistically significant differences were observed between the DOR and NOR groups in terms of duration of infertility, body mass index (BMI), basal luteinizing hormone (LH), or E2 levels (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05). However, age and basal FSH were higher in the DOR group than in the NOR group (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). Conversely, AMH levels and AFC were significantly lower in the DOR group than those in the NOR group (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). The mRNA expression of \u003cem\u003eFRAF6\u003c/em\u003e in the GCs of patients with DOR was significantly higher than that in the NOR group (\u003cem\u003ep\u003c/em\u003e＜0.001), whereas the mRNA expression levels of \u003cem\u003eCD109\u003c/em\u003e, \u003cem\u003eBAG2\u003c/em\u003e, \u003cem\u003eUSP25\u003c/em\u003e, and \u003cem\u003eEIF3H\u003c/em\u003e did not differ significantly between the two groups (Fig.14A\u0026ndash;E).\u003c/p\u003e\n\u003cp\u003eFurthermore, the concentration of TRAF6 in FF was measured using ELISA. The FRAF6 protein level was significantly higher in the FF of patients with DOR (2591.88 \u0026plusmn; 237.83 pg/mL vs. 2800.68 \u0026plusmn; 307.77 pg/mL, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001; mean \u0026plusmn; SD) (Fig. 14F). Correlation analysis further showed that TRAF6 levels were negatively correlated with AMH (Spearman \u003cem\u003er\u003c/em\u003e = \u0026minus;0.3205, \u003cem\u003ep\u003c/em\u003e = 0.003) and AFC (Spearman \u003cem\u003er\u003c/em\u003e = \u0026minus;0.30, \u003cem\u003ep\u003c/em\u003e = 0.0062), and positively correlated with FSH (Spearman \u003cem\u003er\u003c/em\u003e = 0.2239, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.004) (Fig. 14 G\u0026ndash;I).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e.14\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003cstrong\u003enimals\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwenty healthy female rats (6\u0026ndash;8 weeks) were randomly divided into four groups and treated as described in Fig. 15A. The DOR model was established by a single administration of CTX at doses of 50, 75, and 100 mg/kg on the first day, whereas the Control group received saline. Vaginal smears were collected daily between 9:00 and 10:00 AM for 14 consecutive days. Giemsa staining revealed characteristic changes at each stage (Fig. 15B). Compared with the Control group, the CTX-treated groups exhibited irregular estrus cycles (Fig. 15C). During the treatment period, the mice\u0026rsquo;s body weight was recorded every other day. As shown in Fig. 15D, two weeks after CTX injection (day 15), rats in the CTX 75 mg/kg and CTX 100 mg/kg groups exhibited significantly higher body weights than the control groups. Serum AMH levels decreased significantly in the CTX-treated groups (50, 75, and 100 mg/kg) compared to the Control groups. FSH levels were significantly elevated in the CTX 100 mg/kg group, and serum E2 levels were significantly altered in the CTX 75 mg/kg and CTX 100 mg/kg groups compared with the controls (Fig. 15E). Histological results of the mice\u0026rsquo;s ovaries showed that the total number of follicles in the CTX-treated groups was significantly lower than in the control group. Primordial and primary follicles in the CTX 75 mg/kg and CTX 100 mg/kg groups were significantly reduced compared to the controls. Secondary and antral follicles in the four groups showed no significant differences. The number of atretic follicles was higher in the CTX 100 mg/kg group compared with the controls (Fig. 15F, G).\u003c/p\u003e\n\u003cp\u003eAs shown in Fig. 16A, Western blot analysis revealed that compared with the control group, TRAF6 expression in the CTX 100 mg/kg group was significantly increased. Immunofluorescence analysis also showed significantly increased TRAF6 expression in the CTX 75 mg/kg and CTX 100 mg/kg groups compared to the control group (Fig. 16C, D). Furthermore, Western blot analysis of apoptosis-related proteins (Bax/Bcl-2 ratio) and the TUNEL assay indicated that in the CTX 75 mg/kg and CTX 100 mg/kg groups, granule cell apoptosis significantly increased compared with the control group (Fig. 16B\u0026ndash;E). Statistical significance is indicated as follows: *\u003cem\u003ep\u003c/em\u003e \u0026lt;\u0026thinsp;0.05\u003cem\u003e, ** p\u0026thinsp;\u003c/em\u003e\u0026lt;\u0026thinsp;0.01\u003cem\u003e, *** p\u0026thinsp;\u003c/em\u003e\u0026lt;\u003cem\u003e\u0026thinsp;\u003c/em\u003e0.001\u003cem\u003e, **** p\u0026thinsp;\u003c/em\u003e\u0026lt;\u003cem\u003e\u0026thinsp;\u003c/em\u003e0.0001.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eDOR contributes significantly to female infertility, and its management remains one of the most challenging areas in reproductive medicine. The bioactive substances carried by exosomes reflect changes occurring within the parent cells and can indicate their physiological and pathological states \u003csup\u003e[45]\u003c/sup\u003e. The close relationship between the contents of exosomes and those of the parent cells has been confirmed in several studies\u003csup\u003e\u0026nbsp;[46]\u003c/sup\u003e, and exosomes are suitable biomarkers \u003csup\u003e[47]\u003c/sup\u003e. Recent evidence also indicates that exosomes serve as crucial regulators of intercellular signaling in the ovary \u003csup\u003e[48]\u003c/sup\u003e. Therefore, we isolated exosomes from FF and performed miRNA sequencing to explore their regulatory roles.\u003c/p\u003e\n\u003cp\u003eApoptosis induced by ERS is involved in the occurrence of various diseases. Beyond tumors, these include neurodegenerative diseases, liver diseases, and chronic metabolic diseases \u003csup\u003e[49]\u003c/sup\u003e. Some studies have also found that under normal physiological conditions, ERS plays a crucial role in the growth and maturation of ovarian follicles, the closure of follicles, corpus luteum formation, and implantation. However, the disruption of ER homeostasis can lead to negative pathological conditions that affect female fertility\u003csup\u003e\u0026nbsp;[6\u0026minus;8]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe core design of this study is to identify key genes through cross-integration of multi-omics data. By cross-referencing miRNA target genes with ER-StressRDEGs, we identified 19 co-genes. The innovation of this approach lies in linking intercellular communication mediated by FF exosomes to the ERS status of oocytes, thereby identifying a set of candidate genes that are both subject to epigenetic regulation, have core functions, and are highly related to the pathology of DOR. Subsequent GO and KEGG enrichment analyses suggest that the GCs of patients with DOR may experience disrupted protein homeostasis and heightened ERS. This may eventually lead to the activation of apoptotic signaling pathways, resulting in a large number of GC deaths, accelerating follicular closure, and a more rapid decline in ovarian reserve function. These findings align with previous reports indicating that ERS is involved in GC apoptosis and early follicular closure\u003csup\u003e\u0026nbsp;[50]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn our GSVA analysis, two pathways highly correlated with ERS\u0026mdash;Biocarta HSP27 Signaling Pathway \u003csup\u003e[51]\u0026nbsp;\u003c/sup\u003eand Variant Mutation Inactivated Sigmar1 to Ca\u003csup\u003e2+\u003c/sup\u003e Apoptosis Pathway \u003csup\u003e[52]\u003c/sup\u003e\u0026mdash;were found to be abnormally activated in DOR. Consistently, Lile Jiang\u0026rsquo;s study also revealed that this pathway is involved in DOR pathogenesis\u003csup\u003e\u0026nbsp;[53]\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eValidation of our diagnostic model, which incorporates five key co-genes (\u003cem\u003eTRAF6\u003c/em\u003e, \u003cem\u003eCD109\u003c/em\u003e, \u003cem\u003eBAG2\u003c/em\u003e, \u003cem\u003eUSP25\u003c/em\u003e, and \u003cem\u003eEIF3H\u003c/em\u003e), yielded an AUC exceeding 0.9, indicating excellent accuracy in distinguishing patients with DOR from healthy controls. Correlation analysis also indicated that \u003cem\u003eTRAF6\u003c/em\u003e was negatively correlated with ovarian reserve indicators, including AMH and AFC, and positively correlated with FSH. To validate these findings, RT‑qPCR and ELISA were performed on clinical samples, which confirmed that \u003cem\u003eTRAF6\u003c/em\u003e was highly expressed in the DOR group. Our \u003cem\u003ein vivo\u003c/em\u003e experimental results indicate that increasing doses of CTX led to elevated expression of both the target protein TRAF6 and apoptosis-related proteins in mouse ovarian tissues, consistent with observations in human ovarian GCs. Additionally, H\u0026amp;E staining, serum hormone analysis, and follicle counting revealed that CTX 100 mg/kg may be the appropriate dose for establishing the DOR model.\u003c/p\u003e\n\u003cp\u003eTumor necrosis factor receptor-associated factor 6 (TRAF6), a member of the TRAF protein family, is broadly expressed in mammalian tissues and is conserved among species. Structurally, TRAF6 comprises an N-terminal RING finger domain, a series of four zinc finger motifs, a coiled-coil structure domain, and a C-terminal TRAF-C domain \u003csup\u003e[54]\u003c/sup\u003e. TRAF6 is a central hub for multiple intracellular signaling pathways, playing significant roles in regulating cell apoptosis, proliferation, and autophagy \u003csup\u003e[55]\u003c/sup\u003e. As a key hub of the signaling pathways of pattern recognition receptors, such as TLRs and IL-1R, TRAF6 undergoes K63-linked ubiquitination through its E3 ubiquitin ligase activity, which amplifies downstream signals such as NF-\u0026kappa;B, MAPK, and ROS production, thereby driving inflammatory responses, oxidative stress, and cell death \u003csup\u003e[55\u003c/sup\u003e\u003csup\u003e\u0026ndash;\u003c/sup\u003e\u003csup\u003e58]\u003c/sup\u003e. Therefore, activated TRAF6 may promote the production of substantial proinflammatory cytokines by GCs and ovarian macrophages through the NF-\u0026kappa;B pathway, creating a chronic inflammatory ovarian microenvironment. Simultaneously, TRAF6 activation induced NOX2-mediated ROS bursts, triggering ERS and mitochondrial dysfunction, which directly impair GC function. Together, these processes accelerate follicular depletion. Despite its well-established roles in other systems, studies on TRAF6 in obstetrics and gynecology remain scarce. Therefore, we plan to examine TRAF6 function through both \u003cem\u003ein vivo\u003c/em\u003e and \u003cem\u003ein vitro\u003c/em\u003e experiments and explore its interacting proteins to elucidate the underlying mechanisms in DOR. These findings may provide potential therapeutic targets for preserving ovarian function and offer new insights to improve assisted reproductive technologies.\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. First, although our core findings have been preliminarily verified using internal clinical samples, independent validation in larger prospective external cohorts is still required. Furthermore, this study primarily focuses on transcriptomic data and does not integrate proteomic or metabolomic analyses; thus, it is unable to comprehensively capture the dynamic changes in protein modifications and metabolic networks that may occur during the progression of DOR.\u003c/p\u003e\n\u003cp\u003eThrough bioinformatics analyses and clinical validation, we identified the E3 ubiquitin ligase \u003cem\u003eTRAF6\u003c/em\u003e as a key driver molecule for DOR. \u003cem\u003eTRAF6\u003c/em\u003e may participate in processes involving ERS, ubiquitination, and inflammatory signaling, suggesting a central role in the decline of ovarian reserve. These findings not only contribute to a deeper understanding of the molecular mechanisms underlying DOR but also provide new tools for early diagnosis and risk stratification of the disease. Furthermore, they may support the future exploration of therapeutic strategies targeting \u003cem\u003eTRAF6\u003c/em\u003e or related pathways to delay the decline in ovarian function and preserve female fertility.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThrough bioinformatics analysis, five co‑genes (TRAF6, CD109, BAG2, USP25, and EIF3H) were identified. Further validation in CTX‑induced DOR mouse models and clinical samples confirmed that TRAF6 expression was significantly upregulated in the DOR group compared with the control group. These results suggest that TRAF6 may serve as a novel biomarker for diagnosing DOR.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eAbbreviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 393px;\"\u003e\n \u003cp\u003eFull form\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eER-StressRDEGs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 393px;\"\u003e\n \u003cp\u003eendoplasmic reticulum stress-related differentially expressed genes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eDOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 393px;\"\u003e\n \u003cp\u003ediminished ovarian reserve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eNOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 393px;\"\u003e\n \u003cp\u003enormal ovarian reserve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eGEO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 393px;\"\u003e\n \u003cp\u003egene expression omnibus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eER-StressRGs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 393px;\"\u003e\n \u003cp\u003eendoplasmic reticulum stress-related genes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eCTX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 393px;\"\u003e\n \u003cp\u003ecyclophosphamide\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eERS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 393px;\"\u003e\n \u003cp\u003eendoplasmic reticulum stress\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 393px;\"\u003e\n \u003cp\u003eendoplasmic reticulum\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eGCs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 393px;\"\u003e\n \u003cp\u003egranulosa cells\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eFF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 393px;\"\u003e\n \u003cp\u003efollicular fluid\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eIVF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 393px;\"\u003e\n \u003cp\u003ein vitro fertilization \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eICSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 393px;\"\u003e\n \u003cp\u003eintracytoplasmic sperm injection\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eAMH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 393px;\"\u003e\n \u003cp\u003eanti-M\u0026uuml;llerian hormone\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eAFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 393px;\"\u003e\n \u003cp\u003eantral follicular count\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003ebFSH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 393px;\"\u003e\n \u003cp\u003ebasic follicle-stimulating hormone\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 393px;\"\u003e\n \u003cp\u003ereceiver operating characteristic\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 393px;\"\u003e\n \u003cp\u003earea under the curve\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eGO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 393px;\"\u003e\n \u003cp\u003egene ontology\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 393px;\"\u003e\n \u003cp\u003ebiological process\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 393px;\"\u003e\n \u003cp\u003ecellular component\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 393px;\"\u003e\n \u003cp\u003emolecular function\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 393px;\"\u003e\n \u003cp\u003ekyoto encyclopedia of genes and genomes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eGSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 393px;\"\u003e\n \u003cp\u003egene set enrichment analysis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eGSVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 393px;\"\u003e\n \u003cp\u003egene set variation analysis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eSVM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 393px;\"\u003e\n \u003cp\u003esupport vector machine\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eLASSO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 393px;\"\u003e\n \u003cp\u003eleast absolute shrinkage and selection operator\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003ePPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 393px;\"\u003e\n \u003cp\u003eprotein\u0026ndash;protein interaction\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eTRAF6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 393px;\"\u003e\n \u003cp\u003etumor necrosis factor receptor-associated factor 6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll experimental procedures were approved by the ethics committee of the First Hospital of Lanzhou University and obtained written informed consent from all the patients (Approval no. LDYYLL2025-799). All methods were performed in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the conclusions of this article are included within the article and its additional files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by National Natural Science Foundation of China(82460298) \u0026amp; Science and Technology Planning Project of Gansu Province (24JRRA319) \u0026amp;\u0026nbsp;Science and Technology Project of Gansu Province (Key Research and Development Program)\u0026nbsp;(24YFFA039) \u0026amp; The Youth Fund of the First Affiliated Hospital of Lanzhou University(ldyyyn2020-67) \u0026amp; College Teacher Innovation Fund project of Gansu Provincial Department of Education(2023B-008）\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contribution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXuehong Zhang, Wei Wang and Lirong Wang discussed and established the plan of the manuscript. Xiaorong Luo, Lili Zhang, Jiajing He conducted the literature search and analyzed the literature, Luni Tan, Yuzi Li, Ning Zhang and Rui Zhang prepared the figures and tables. Xinyue Zhou, Haofei Shen, Feng Yue and Lirong Wang wrote the draft with input of all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the technical support provided by the reproductive medicine center of the First Hospital of Lanzhou University and Key Laboratory for Reproductive Medicine and Embryo\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSharara FI, Scott RT Jr, Seifer DB. The detection of diminished ovarian reserve in infertile women. \u003cem\u003eAm J Obstet Gynecol\u003c/em\u003e. 1998;179(3 Pt 1):804-812. doi:10.1016/s0002-9378(98)70087-0 \u003c/li\u003e\n\u003cli\u003ePastore LM, Christianson MS, Stelling J, Kearns WG, Segars JH. Reproductive ovarian testing and the alphabet soup of diagnoses: DOR, POI, POF, POR, and FOR. \u003cem\u003eJ Assist Reprod Genet\u003c/em\u003e. 2018;35(1):17-23. doi:10.1007/s10815-017-1058-4\u003c/li\u003e\n\u003cli\u003eJiao Z, Bukulmez O. Potential roles of experimental reproductive technologies in infertile women with diminished ovarian reserve. \u003cem\u003eJ Assist Reprod Genet\u003c/em\u003e. 2021;38(10):2507-2517. doi:10.1007/s10815-021-02246-6 \u003c/li\u003e\n\u003cli\u003eBukulmez O. The International Society for Mild Approaches in Assisted Reproduction (ISMAAR) Definitions for Mild Stimulation and Their Rationale for Assisted Reproductive Technologies [J]. Diminished Ovarian Reserve and Assisted Reproductive Technologies, 2019.DOI:10.1007/978-3-030-23235-1_10.\u003c/li\u003e\n\u003cli\u003eOakes SA, Papa FR. The role of endoplasmic reticulum stress in human pathology. \u003cem\u003eAnnu Rev Pathol\u003c/em\u003e.2015;10:173-194. doi:10.1146/annurev-pathol-012513-104649 \u003c/li\u003e\n\u003cli\u003eGuzel E, Arlier S, Guzeloglu-Kayisli O, et al. Endoplasmic Reticulum Stress and Homeostasis in Reproductive Physiology and Pathology. \u003cem\u003eInt J Mol Sci\u003c/em\u003e. 2017;18(4):792. Published 2017 Apr 8. doi:10.3390/ijms18040792 \u003c/li\u003e\n\u003cli\u003eHarada M, Nose E, Takahashi N, et al. Evidence of the activation of unfolded protein response in granulosa and cumulus cells during follicular growth and maturation. \u003cem\u003eGynecol Endocrinol\u003c/em\u003e. 2015;31(10):783-787. doi:10.3109/09513590.2015.1062862 \u003c/li\u003e\n\u003cli\u003eLin P, Yang Y, Li X, et al. Endoplasmic reticulum stress is involved in granulosa cell apoptosis during follicular atresia in goat ovaries. \u003cem\u003eMol Reprod Dev\u003c/em\u003e. 2012;79(6):423-432. doi:10.1002/mrd.22045 \u003c/li\u003e\n\u003cli\u003eGalgani M, Insabato L, Cal\u0026igrave; G, et al. Regulatory T cells, inflammation, and endoplasmic reticulum stress in women with defective endometrial receptivity. \u003cem\u003eFertil Steril\u003c/em\u003e. 2015;103(6):1579-1586.e1. doi:10.1016/j. fertnstert.2015.03.014 \u003c/li\u003e\n\u003cli\u003eMathieu M, Martin-Jaular L, Lavieu G, Th\u0026eacute;ry C. Specificities of secretion and uptake of exosomes and other extracellular vesicles for cell-to-cell communication. \u003cem\u003eNat Cell Biol\u003c/em\u003e. 2019;21(1):9-17. doi:10.1038/s41556-018-0250-9 \u003c/li\u003e\n\u003cli\u003eZhang X, Zhang R, Hao J, et al. miRNA-122-5p in POI ovarian-derived exosomes promotes granulosa cell apoptosis by regulating BCL9. \u003cem\u003eCancer Med\u003c/em\u003e. 2022;11(12):2414-2426. doi:10.1002/cam4.4615\u003c/li\u003e\n\u003cli\u003eSun B, Ma Y, Wang F, Hu L, Sun Y. miR-644-5p carried by bone mesenchymal stem cell-derived exosomes targets regulation of p53 to inhibit ovarian granulosa cell apoptosis. \u003cem\u003eStem Cell Res Ther\u003c/em\u003e. 2019;10(1):360. Published 2019 Nov 29. doi:10.1186/s13287-019-1442-3\u003c/li\u003e\n\u003cli\u003eYang M, Lin L, Sha C, et al. Bone marrow mesenchymal stem cell-derived exosomal miR-144-5p improves rat ovarian function after chemotherapy-induced ovarian failure by targeting PTEN.\u003cem\u003e Lab Invest\u003c/em\u003e. 2020;100(3):342-352. doi:10.1038/s41374-019-0321-y\u003c/li\u003e\n\u003cli\u003eNejabati HR, Roshangar L, Nouri M. Follicular fluid extracellular vesicle miRNAs and ovarian aging. \u003cem\u003eClin Chim Acta\u003c/em\u003e. 2023; 538:29-35. doi:10.1016/j. cca.2022.11.003 \u003c/li\u003e\n\u003cli\u003eDavis S, Meltzer PS. GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. \u003cem\u003eBioinformatics\u003c/em\u003e. 2007;23(14):1846-1847. doi:10.1093/bioinformatics/btm254 \u003c/li\u003e\n\u003cli\u003eBarrett T, Wilhite SE, Ledoux P, et al. NCBI GEO: archive for functional genomics data sets--update. \u003cem\u003eNucleic Acids Res\u003c/em\u003e. 2013;41(Database issue): D991-D995. doi:10.1093/nar/gks1193 \u003c/li\u003e\n\u003cli\u003eKao KJ, Chang KM, Hsu HC, Huang AT. Correlation of microarray-based breast cancer molecular subtypes and clinical outcomes: implications for treatment optimization. \u003cem\u003eBMC Cancer\u003c/em\u003e. 2011;11:143. Published 2011 Apr 18. doi:10.1186/1471-2407-11-143 \u003c/li\u003e\n\u003cli\u003eStelzer G, Rosen N, Plaschkes I, et al. The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses. \u003cem\u003eCurr Protoc Bioinformatics\u003c/em\u003e. 2016; 54:1.30.1-1.30.33. Published 2016 Jun 20. doi:10.1002/cpbi.5 \u003c/li\u003e\n\u003cli\u003eLai Y, Lin X, Lin C, Lin X, Chen Z, Zhang L. Identification of endoplasmic reticulum stress-associated genes and subtypes for prediction of Alzheimer\u0026apos;s disease based on interpretable machine learning. \u003cem\u003eFront Pharmacol\u003c/em\u003e. 2022;13: 975774. Published 2022 Aug 19. doi:10.3389/fphar.2022.975774 \u003c/li\u003e\n\u003cli\u003eShen Y, Cao Y, Zhou L, Wu J, Mao M. Construction of an endoplasmic reticulum stress-related gene model for predicting prognosis and immune features in kidney renal clear cell carcinoma. \u003cem\u003eFront Mol Biosci\u003c/em\u003e. 2022; 9:928006. Published 2022 Sep 2. doi:10.3389/fmolb.2022.928006\u003c/li\u003e\n\u003cli\u003eXu X, Tang Y, Zhu J, Luo J. Endoplasmic Reticulum Stress-Related Four-Biomarker Risk Classifier for Survival Evaluation in Esophageal Cancer. \u003cem\u003eJ Oncol\u003c/em\u003e. 2022; 2022:5860671. Published 2022 Mar 18. doi:10.1155/2022/5860671\u003c/li\u003e\n\u003cli\u003eRitchie ME, Phipson B, Wu D, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. \u003cem\u003eNucleic Acids Res\u003c/em\u003e. 2015;43(7):e47. doi:10.1093/nar/gkv007 \u003c/li\u003e\n\u003cli\u003eLove MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. \u003cem\u003eGenome Biol\u003c/em\u003e. 2014;15(12):550. doi:10.1186/s13059-014-0550-8\u003c/li\u003e\n\u003cli\u003eRobin X, Turck N, Hainard A, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. \u003cem\u003eBMC Bioinformatics\u003c/em\u003e. 2011;12:77. Published 2011 Mar 17. doi:10.1186/1471-2105-12-77 \u003c/li\u003e\n\u003cli\u003eMi H, Muruganujan A, Ebert D, Huang X, Thomas PD. PANTHER version 14: more genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools. \u003cem\u003eNucleic Acids Res\u003c/em\u003e. 2019;47(D1):D419-D426. doi:10.1093/nar/gky1038 \u003c/li\u003e\n\u003cli\u003eKanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. \u003cem\u003eNucleic Acids Res\u003c/em\u003e. 2000;28(1):27-30. doi:10.1093/nar/28.1.27 \u003c/li\u003e\n\u003cli\u003eYu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. \u003cem\u003eOMICS\u003c/em\u003e. 2012;16(5):284-287. doi:10.1089/omi.2011.0118 \u003c/li\u003e\n\u003cli\u003eSubramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. \u003cem\u003eProc Natl Acad Sci U S A\u003c/em\u003e. 2005;102(43):15545-15550. doi:10.1073/pnas.0506580102 \u003c/li\u003e\n\u003cli\u003eH\u0026auml;nzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. \u003cem\u003eBMC Bioinformatics\u003c/em\u003e. 2013;14:7. Published 2013 Jan 16. doi:10.1186/1471-2105-14-7 \u003c/li\u003e\n\u003cli\u003eLiberzon A, Subramanian A, Pinchback R, Thorvaldsdottir H, Tamayo P, Molecular signatures database (MSigDB) 3.0. \u003cem\u003eBioinformatics\u003c/em\u003e 2011; 27 (12): 1739-1740. doi:10.1093/bioinformatics/btr260\u003c/li\u003e\n\u003cli\u003eSanz H, Valim C, Vegas E, Oller JM, Reverter F. SVM-RFE: selection and visualization of the most relevant features through non-linear kernels. \u003cem\u003eBMC Bioinformatics\u003c/em\u003e. 2018;19(1):432. Published 2018 Nov 19. doi:10.1186/s12859-018-2451-4 \u003c/li\u003e\n\u003cli\u003eEngebretsen S, Bohlin J. Statistical predictions with glmnet. \u003cem\u003eClin Epigenetics\u003c/em\u003e. 2019;11(1):123. Published 2019 Aug 23. doi:10.1186/s13148-019-0730-1\u003c/li\u003e\n\u003cli\u003eWu J, Zhang H, Li L, et al. A nomogram for predicting overall survival in patients with low-grade endometrial stromal sarcoma: A population-based analysis. \u003cem\u003eCancer Commun (Lond)\u003c/em\u003e. 2020;40(7):301-312. doi:10.1002/cac2.12067 \u003c/li\u003e\n\u003cli\u003eVan Calster B, Wynants L, Verbeek JFM, et al. Reporting and Interpreting Decision Curve Analysis: A Guide for Investigators. \u003cem\u003eEur Urol\u003c/em\u003e. 2018;74(6):796-804. doi:10.1016/j.eururo.2018.08.038 \u003c/li\u003e\n\u003cli\u003eYu G, Li F, Qin Y, Bo X, Wu Y, Wang S. GOSemSim: an R package for measuring semantic similarity among GO terms and gene products. \u003cem\u003eBioinformatics\u003c/em\u003e. 2010;26(7):976-978. doi:10.1093/bioinformatics/btq064 \u003c/li\u003e\n\u003cli\u003eLi JH, Liu S, Zhou H, Qu LH, Yang JH. starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. \u003cem\u003eNucleic Acids Res\u003c/em\u003e. 2014;42(Database issue):D92-D97. doi:10.1093/nar/gkt1248 \u003c/li\u003e\n\u003cli\u003eChen Y, Wang X. miRDB: an online database for prediction of functional microRNA targets. \u003cem\u003eNucleic Acids Res\u003c/em\u003e. 2020;48(D1):D127-D131. doi:10.1093/nar/gkz757 \u003c/li\u003e\n\u003cli\u003eZhou KR, Liu S, Sun WJ, et al. ChIPBase v2.0: decoding transcriptional regulatory networks of non-coding RNAs and protein-coding genes from ChIP-seq data. \u003cem\u003eNucleic Acids Res\u003c/em\u003e. 2017;45(D1):D43-D50. doi:10.1093/nar/gkw965\u003c/li\u003e\n\u003cli\u003eShannon P, Markiel A, Ozier O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. \u003cem\u003eGenome Res\u003c/em\u003e. 2003;13(11):2498-2504. doi:10.1101/gr.1239303 \u003c/li\u003e\n\u003cli\u003eSingh A. RNA-binding protein kinetics. \u003cem\u003eNat Methods\u003c/em\u003e. 2021;18(4):335. doi:10.1038/s41592-021-01122-6 \u003c/li\u003e\n\u003cli\u003eGrondin CJ, Davis AP, Wiegers JA, et al. Predicting molecular mechanisms, pathways, and health outcomes induced by Juul e-cigarette aerosol chemicals using the Comparative Toxicogenomics Database. \u003cem\u003eCurr Res Toxicol\u003c/em\u003e. 2021;2:272-281. Published 2021 Aug 5. doi:10.1016/j.crtox.2021.08.001 \u003c/li\u003e\n\u003cli\u003eSzklarczyk D, Gable AL, Lyon D, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. \u003cem\u003eNucleic Acids Res\u003c/em\u003e. 2019;47(D1):D607-D613. doi:10.1093/nar/gky1131 \u003c/li\u003e\n\u003cli\u003eFranz M, Rodriguez H, Lopes C, et al. GeneMANIA update 2018. Nucleic Acids Res. 2018;46(W1):W60-W64. doi:10.1093/nar/gky311\u003c/li\u003e\n\u003cli\u003eNewman AM, Liu CL, Green MR, et al. Robust enumeration of cell subsets from tissue expression profiles. \u003cem\u003eNat Methods\u003c/em\u003e. 2015;12(5):453-457. doi:10.1038/nmeth.3337\u003c/li\u003e\n\u003cli\u003eLeBleu VS, Kalluri R. Exosomes as a Multicomponent Biomarker Platform in Cancer. \u003cem\u003eTrends Cancer\u003c/em\u003e. 2020;6(9):767-774. doi:10.1016/j. trecan.2020.03.007 \u003c/li\u003e\n\u003cli\u003eKucharzewska P, Christianson HC, Welch JE, et al. Exosomes reflect the hypoxic status of glioma cells and mediate hypoxia-dependent activation of vascular cells during tumor development. \u003cem\u003eProc Natl Acad Sci U S A\u003c/em\u003e. 2013;110(18):7312-7317. doi:10.1073/pnas.1220998110 \u003c/li\u003e\n\u003cli\u003eBarile L, Vassalli G. Exosomes: Therapy delivery tools and biomarkers of diseases. \u003cem\u003ePharmacol Ther.\u003c/em\u003e 2017; 174:63-78. doi:10.1016/j. pharmthera.2017.02.020 \u003c/li\u003e\n\u003cli\u003eVoros C, Athanasiou D, Mavrogianni D, et al. Exosomal Communication Between Cumulus-Oocyte Complexes and Granulosa Cells: A New Molecular Axis for Oocyte Competence in Human-Assisted Reproduction. \u003cem\u003eInt J Mol Sci\u003c/em\u003e. 2025;26(11):5363. Published 2025 Jun 3. doi:10.3390/ijms26115363\u003c/li\u003e\n\u003cli\u003eOakes SA, Papa FR. The role of endoplasmic reticulum stress in human pathology. \u003cem\u003eAnnu Rev Pathol\u003c/em\u003e. 2015; 10:173-194. doi:10.1146/annurev-pathol-012513-104649\u003c/li\u003e\n\u003cli\u003eYang D, Jiang T, Lin P, et al. Knock-down of apoptosis inducing factor gene protects endoplasmic reticulum stress-mediated goat granulosa cell apoptosis. \u003cem\u003eTheriogenology\u003c/em\u003e. 2017; 88:89-97. doi:10.1016/j. theriogenology.2016.10.001\u003c/li\u003e\n\u003cli\u003eMart\u0026iacute;nez-Laorden E, Navarro-Zaragoza J, Milan\u0026eacute;s MV, Laorden ML, Almela P. Cardiac Protective Role of Heat Shock Protein 27 in the Stress Induced by Drugs of Abuse. \u003cem\u003eInt J Mol Sci\u003c/em\u003e. 2020;21(10):3623. Published 2020 May 21. doi:10.3390/ijms21103623\u003c/li\u003e\n\u003cli\u003eCouly S, Yasui Y, Su TP. SIGMAR1 Confers Innate Resilience against Neurodegeneration. \u003cem\u003eInt J Mol Sci\u003c/em\u003e. 2023;24(9):7767. Published 2023 Apr 24. doi:10.3390/ijms24097767\u003c/li\u003e\n\u003cli\u003eJiang L, Cui J, Zhang C, et al. Sigma-1 receptor is involved in diminished ovarian reserve possibly by influencing endoplasmic reticulum stress-mediated granulosa cells apoptosis. \u003cem\u003eAging (Albany NY)\u003c/em\u003e. 2020;12(10):9041-9065. doi:10.18632/aging.103166\u003c/li\u003e\n\u003cli\u003eLamothe B, Campos AD, Webster WK, Gopinathan A, Hur L, Darnay BG. The RING domain and first zinc finger of TRAF6 coordinate signaling by interleukin-1, lipopolysaccharide, and RANKL. \u003cem\u003eJ Biol Chem\u003c/em\u003e. 2008;283(36):24871-24880. doi:10.1074/jbc.M802749200\u003c/li\u003e\n\u003cli\u003eWang YT, Liu TY, Shen CH, et al. K48/K63-linked polyubiquitination of ATG9A by TRAF6 E3 ligase regulates oxidative stress-induced autophagy. \u003cem\u003eCell Rep\u003c/em\u003e. 2022;38(8):110354. doi:10.1016/j. celrep.2022.110354\u003c/li\u003e\n\u003cli\u003eMeng Z, Zhao T, Zhou K, et al. A20 Ameliorates Intracerebral Hemorrhage-Induced Inflammatory Injury by Regulating TRAF6 Polyubiquitination. \u003cem\u003eJ Immunol\u003c/em\u003e. 2017;198(2):820-831. doi:10.4049/jimmunol.1600334\u003c/li\u003e\n\u003cli\u003eZeng Q, Ye L, Ling M, et al. TLR4/TRAF6/NOX2 signaling pathway is involved in ventilation-induced lung injury via endoplasmic reticulum stress in murine model. \u003cem\u003eInt Immunopharmacol\u003c/em\u003e. 2021; 96:107774. doi:10.1016/j. intimp.2021.107774 \u003c/li\u003e\n\u003cli\u003eYang J, Zhang M, Luo Y, et al. Protopine ameliorates OVA-induced asthma through modulatingTLR4/MyD88/NF-\u0026kappa;B pathway and NLRP3 inflammasome-mediated pyroptosis. \u003cem\u003ePhytomedicine\u003c/em\u003e. 2024; 126:155410. doi:10.1016/j. phymed.2024.155410 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Exosomal microRNAs, endoplasmic reticulum stress, diminished ovarian reserve, bioinformatics, biomarker","lastPublishedDoi":"10.21203/rs.3.rs-9228384/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9228384/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eIn this study, the role of endoplasmic reticulum stress-related differentially expressed genes (ER-StressRDEGs) in diminished ovarian reserve (DOR) is examined. Comprehensive bioinformatics analyses were performed to identify key genes and pathways associated with DOR, providing a foundation for future clinical applications.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eFollicular fluid samples from six patients with DOR and six patients with normal ovarian reserve (NOR) were used for exosomal miRNA analysis. Gene expression data were retrieved from the Gene Expression Omnibus (GEO) database using the GEOquery package, and endoplasmic reticulum stress-related genes (ER-StressRGs) were retrieved from the GeneCards database. miRNA target genes were predicted using the MicroRNA Target Prediction Database and overlapped with ER-StressRDEGs to identify co-genes. Clinical data and biological samples (follicular fluid and granulosa cells) were collected from 42 patients with DOR and 42 patients with NOR for validation using reverse transcription quantitative polymerase chain reaction (RT-qPCR) and enzyme-linked immunosorbent assay (ELISA). After identifying the target gene, additional validation was conducted in vivo using experimental models. Female C57BL/6 J mice received single intraperitoneal injections of cyclophosphamide (CTX) at three dosage regimens. Ovarian tissue was then evaluated using histopathological analysis (hematoxylin and eosin staining), hormone level assessment, and protein expression analysis, including TUNEL assays, immunofluorescence staining, and Western blotting.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAnalysis of the GSE87201 dataset identified 1,389 differentially expressed genes, including 606 upregulated genes and 783 downregulated genes, with 115 classified as ER-StressRDEGs. A support vector machine model was constructed incorporating five key co-genes (\u003cem\u003eTRAF6, CD109, BAG2, USP25, EIF3H\u003c/em\u003e). Validation experiments using RT-qPCR and ELISA showed that \u003cem\u003eTRAF6\u003c/em\u003e was highly expressed in the DOR group and was statistically significant compared to the control group. Furthermore, female mice induced by CTX 100 mg/kg exhibited characteristic features of DOR. Consistently, the expression level of the target protein TRAF6 was increased in ovarian tissues.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThese findings indicate that \u003cem\u003eTRAF6\u003c/em\u003e is highly expressed in DOR and is associated with clinical indicators of ovarian reserve. These results suggest that \u003cem\u003eTRAF6\u003c/em\u003e may serve as a novel biomarker for diagnosing DOR.\u003c/p\u003e","manuscriptTitle":"Endoplasmic reticulum stress-related gene TRAF6 as a potential biomarker for diminished ovarian reserve","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-09 00:12:19","doi":"10.21203/rs.3.rs-9228384/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-08T03:29:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"246252824486337389256215435289450207246","date":"2026-04-28T07:36:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-24T13:53:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-07T14:05:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-04T10:01:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Women's Health","date":"2026-04-04T09:54:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2e349f9f-dbb2-4155-8138-df3630266a5d","owner":[],"postedDate":"May 9th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-08T03:29:17+00:00","index":75,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-09T00:12:20+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-09 00:12:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9228384","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9228384","identity":"rs-9228384","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}