Integrated multi-omics analysis to investigate the pathogenesis of intrauterine adhesion

Integrated multi-omics analysis to investigate the pathogenesis of intrauterine adhesion | 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 Article Integrated multi-omics analysis to investigate the pathogenesis of intrauterine adhesion Bao Liu, Mingqian Chen, Yugang Chi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7079007/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Background Intrauterine adhesion (IUA) represents a prevalent cause of infertility and reproductive dysfunction; however, the underlying molecular mechanisms contributing to the development of IUA remain inadequately characterized. Consequently, this study aimed to elucidate key genes implicated in IUA through comprehensive multi-omics analyses. Methods Transcriptome data from 6 IUA and 6 control endometrial tissue samples, microbiome (16S rRNA gene sequencing) data from 6 IUA and 6 control uterine lavage samples, and metabolome data from 21 IUA and 21 control uterine lavage samples were utilized. Initially, differential analyses were performed separately on transcriptome, microbiome, and metabolome data to identify genes, microbes, and metabolites of interest, respectively. Subsequently, multi-omics integration through Spearman correlation analysis identified key genes, microbes, and metabolites. Additionally, functional annotation, regulatory network construction, and drug prediction analyses were performed to further clarify the molecular mechanisms associated with the identified key genes. Results In this study, 46 genes, 4 microbes, and 11 metabolites of interest were identified. Through comprehensive multi-omics analyses, 7 key genes (DUSP2, IL1A, POF1B, ICAM4, CX3CL1, HTR2C, and SIX1), 2 key microbes (g__Clostridium_sensu_stricto_1 and g__Acidisoma), and 2 key metabolites (Pe(18:3(6Z,9Z,12Z)/18:1(9Z)) and 1,3,6-trihydroxy-2-(3-methylbut-2-enyl)xanthen-9-one) were pinpointed, all showing strong intercorrelations. Moreover, functional pathways were involved in various biological processes, including ribosome function, fatty acid metabolism, cell cycle regulation, DNA replication, and cytokine signaling. The regulatory networks revealed complex interactions, such as NEAT1-hsa-miR-185-5p-SIX1 and hsa_circ_0013870-hsa-miR-4498-CX3CL1. Additionally, olanzapine was predicted as a potential therapeutic drug based on its predicted targeting of two key genes (IL1A and HTR2C) through drug-gene interaction analysis. Conclusion This research identified seven key genes, two key microbes, and two key metabolites associated with IUA, offering novel insights into its molecular mechanisms and underscoring potential therapeutic targets for subsequent investigation. Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Biological sciences/Microbiology Biological sciences/Molecular biology Intrauterine adhesion Transcriptome Microbiome Metabolome Key genes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. Introduction Intrauterine adhesions (IUA), commonly referred to as Asherman syndrome, represent a pathological condition resulting from damage to the basal layer of the endometrium, often culminating in partial or complete obliteration of the uterine cavity. Common causes include dilation and curettage (D&C) following miscarriage, endometrial injury, cesarean section, and other intrauterine procedures. The incidence of IUA ranges from 1.5–40%, with a prevalence as high as 21.5% in women with a history of postpartum curettage and over 40% in those undergoing secondary removal of retained placental tissue or repeat curettage for incomplete abortion [ 1 ] . IUA significantly impacts women's reproductive health, manifesting as menstrual abnormalities, infertility, and recurrent miscarriages. The burden of IUA on both patients and healthcare systems underscores the urgency for better understanding its pathophysiology and improving treatment options. Current therapeutic strategies, such as hysteroscopic adhesiolysis and hormonal therapy, are often limited by high recurrence rates and inconsistent efficacy, necessitating a more profound exploration of the underlying biological mechanisms that contribute to IUA. Despite the well-defined clinical manifestations and diagnostic methods for IUA, its pathogenesis remains incompletely understood. Current research primarily focuses on inflammatory responses, abnormal deposition of extracellular matrix (ECM), and dysfunction of endometrial stem cells [ 2 ] . In recent times, the swift advancement of multi-omics technologies—encompassing genomics, transcriptomics, microbiome analysis, proteomics, and metabolomics—has introduced novel methodologies for investigating complex diseases. Multi-omics analysis integrates multiple levels of biological information, such as the transcriptome, microbiome, and metabolome [ 3 ] . This integration enables researchers to observe gene function and regulatory networks from different perspectives and reveals complex molecular interactions and signal transduction pathways. Prior studies have indicated that the interplay of gene expression changes, metabolic disruptions, and microbial community dynamics may significantly influence the uterine environment's stability and its ability to heal following injury [ 4 ] . Despite these promising leads, the multifaceted nature of IUA remains poorly understood, and integrating these diverse omics approaches could offer new perspectives on its pathogenesis. Compared to single-omics approaches, multi-omics analysis offers a more holistic view of the pathophysiological processes of IUA; it provides specific insights into molecular mechanisms, thereby aiding in the identification of new therapeutic targets and biomarkers. This research utilizes transcriptomic profiling, metabolomic analysis, and microbiome assessment to attain a thorough understanding of the factors associated with IUA. Endometrial tissue and lavage samples were obtained from individuals diagnosed with IUA as well as healthy controls for transcriptomic, microbiome, and metabolomic sequencing. And then, candidate genes, microbes, and metabolites were identified through differential analysis. Ultimately, the biological functions of the identified key genes were explored through functional analysis, regulatory network examination, and drug prediction. By leveraging the power of integrative omics, we hope to advance the current understanding of IUA and contribute to improved clinical outcomes for patients affected by this challenging condition. 2. Materials and methods 2.1 Sample collection The study was conducted in compliance with the Declaration of Helsinki, and the protocol received approval from the Ethics Committee of Chongqing Health Center for Women and Children (Approval NO. 2021044).This study collected endometrial tissue samples from 6 IUA patients and 6 controls for transcriptome sequencing, uterine lavage samples from 6 IUA patients and 6 controls for microbiome sequencing (16S rRNA gene sequencing), and uterine lavage samples from 21 IUA patients and 21 controls for metabolome sequencing. 2.2 Transcriptome sequencing and data pre-processing Total RNA was extracted from the tissue samples utilizing TRIzol® Reagent in accordance with the manufacturer's instructions. The quality of the RNA was assessed with the 5300 Bioanalyser (Agilent) and quantified using the ND-2000 (NanoDrop Technologies). The parameters for acceptable DNA quality included a concentration ranging from 1.8 to 2.2 ng/µL, an OD260/230 ratio of at least 2.0, a RNA Quality Number (RQN) of 6.5 or greater, and a 28S:18S ratio of 1.0 or higher, with a minimum yield of 1g.Following sequencing, the detection accuracy and error rate of each base were assessed using a specified formula: \(\:Q=-10{log}_{10}\left(e\right)\) ,where Q represented the quality score of each base, and e denoted the probability of that base being incorrectly identified. Low-quality reads were subsequently removed using the fastpQC (v 0.12.0) package [ 5 ] , and clean data were obtained using the cutadapt function. The sequencing data were then aligned to the genome ( Homo sapiens , GRCh38) using Hierarchical Indexing for Spliced Transcript Alignment version 2 (HISAT2) (v 2.2.1) software [ 6 ] . Following alignment, the data were processed with StringTie (v 3.0.0) software [ 7 ] to generate gene count data, which were subsequently converted to transcripts per million (TPM) based on gene length. 2.3 Principal component analysis (PCA) and gene set variation analysis (GSVA) Based on the pre-processed transcriptome sequencing data, PCA was performed using the PCA function from the FactoMineR (v 2.9) package [ 8 ] to assess the differences between IUA and control samples. To further examine the divergent biological functions between these groups, the curated gene set "c2.all.kegg.symbols.gmt" was referenced from the Molecular Signatures Database (MSigDB) ( https://www.gsea-msigdb.org/gsea/msigdb ). Employing single-sample GSEA (ssGSEA) function from GSVA (v 1.50.0) package [ 9 ] , the GSVA scores of all samples were quantified, thereby allowing the biological functions distinctive to IUA and control samples to be compared by the limma (v 3.58.1) package [ 10 ] . These differentially enriched gene sets were displayed, with |t| >2 and p < 0.05 demarcating statistical significance. 2.4 Differential expression analysis and functional analysis Following pre-processing of the transcriptome sequencing data, differential expression analysis was conducted using the DESeq2 (v 3.54.0) package [ 11 ] [|log 2 Fold Change (FC)| >2.0, adj.p < 0.05] to identify differentially expressed genes (DEGs) referred to as candidate genes. The volcano plot was generated to visualize candidate genes using the ggplot (v 3.5.1) package [ 12 ] , with the top ten up-regulated and down-regulated genes highlighted according to their |log2FC| values (ranked from high to low).Meantime, the candidate genes were visualized utilizing a heatmap employing the ComplexHeatmap (v 2.21.1) package [ 13 ] . To delve into the cellular functions associated with these candidate genes and their relevant pathways, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted utilizing the clusterProfiler (v 4.10.0) package, with statistical significance set at p < 0.05 [ 14 ] [ 15 ] . The GO analysis encompassed three primary categories: biological process (BP), cellular component (CC), and molecular function (MF). Subsequently, these candidate genes were uploaded to the Search Tool for the Retrieval of Interacting Genes (STRING) database ( http://string-db.org ) to elucidate their protein-level interactions (confidence level > 0.15). These interactions were then visualized utilizing Cytoscape (v 3.10.2) software [ 16 ] to construct a protein-protein interaction (PPI) network. In addition, to investigate the chromosomal localization of these candidate genes, the Circos (v 1.38.0) package [ 17 ] was employed to visualize their positions on chromosomes. 2.5 Microbiome sequencing and data pre-processing Total microbial genomic DNA was extracted from uterine lavage samples utilizing the FastPure Stool DNA Isolation Kit (MJYH, Shanghai, China) in accordance with the manufacturer's guidelines. The quality and concentration of the extracted DNA were assessed through 1.0% agarose gel electrophoresis and a NanoDrop® ND-2000 spectrophotometer (Thermo Scientific Inc., USA), with samples stored at -80 ℃ prior to further analysis. The present investigation, the hypervariable regions V3-V4 of the bacterial 16S rRNA gene were amplified employing primer pairs 338F (5'-ACTCCTACGGGAGGCAGCAG-3') and 806R (5'-GGACTACHVGGGTWTCTAAT-3') [1], utilizing a T100 Thermal Cycler (BIO-RAD, USA). The PCR reaction mixture including 4 µL 5 × Fast Pfu buffer, 2 µL 2.5 mM dNTPs, 0.8 µL each primer (5 µM), 0.4 µL Fast Pfu polymerase, 10 ng of template DNA, and ddH2O to a final volume of 20 µL. PCR amplification cycling conditions were as follows: an initial denaturation step at 95°C for 3 minutes, followed by 27 cycles of denaturation at 95°C for 30 seconds, annealing at 55°C for 30 seconds, and extension at 72°C for 30 seconds. The final extension was conducted at 72°C for 5 minutes. The amplified products were quantified using a Synergy HTX (Biotek, USA). The purified amplicons were subsequently combined in equimolar concentrations and subjected to paired-end sequencing on an Illumina NextSeq 2000 PE300 platform (Illumina, San Diego, USA), following the standard protocols established by Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China). After sequencing, quality control (QC) was performed using DADA2 and Vsearch, resulting in the identification of amplicon sequence variants (ASVs). For species annotation of each ASV, the classify-sklearn algorithm from the QIIME2 (v 2023.3) software [ 18 ] was employed. To evaluate the adequacy of the sequencing data, rarefaction curves were generated for each group. Rank abundance curves were also plotted to visualize the species richness and evenness within each group. The relationship between species diversity and sample size was analyzed using the vegan (v 2.6.4) package [ 19 ] . 2.6 Alpha and beta diversity analyses and species composition profiling Alpha diversity refers to the richness, diversity, and evenness of species within a locally homogeneous habitat, also known as within-habitat diversity [ 20 ] . In this study, the Chao1, Observed species, Shannon, and Simpson indices were calculated to assess the richness and diversity of microbiome sequencing data using the cal alphadiv function from the microeco (v 1.8.0) package [ 21 ] . The Wilcoxon test was then applied to compare the differences in these 4 indices between IUA and control samples (p < 0.05), and the results were visualized using box plots generated by the ggpubr (v 0.6.0) package [ 22 ] . Beta diversity, on the other hand, reflects the species composition differences or species turnover between communities along an environmental gradient, and is also referred to as between-habitat diversity [ 20 ] . In this study, the Bray-Curtis and Jaccard indices were computed using the cal_betadiv function from the microeco (v 1.8.0) pcakage, followed by Principal Coordinates Analysis (PCoA) conducted using the cal_ordination function (method = "PCoA"). Next, non-metric multidimensional scaling (NMDS) analysis was conducted to further verify the differences between IUA and control samples (Stress < 0.1), using the cal_ordination function from the microeco (v 1.8.0) package (method = "NMDS"). Besides, to explore the microbial community composition between IUA and control samples, the relative abundance of each sample in each group was determined. Bar plots were then generated using the ggplot2 (v 3.5.1) package [ 23 ] to visualize the top 10 most abundant microbes at the 2 taxonomic levels (phylum and genus). 2.7 Functional and linear discriminant analysis effect size (LEfSe) analyses Subsequently, based on microbiome sequencing data, functional analysis was performed using the trans_func function to investigate the biological functions in IUA and control samples. The Linear discriminant analysis effect size (LEfSe) is a statistical method that combines non-parametric testing with linear discriminant analysis to assess the effect size of features, enabling the identification of differentially abundant taxa across groups [ 24 ] . In this study, LEfSe was performed using the trans diff function from the microeco (v 1.8.0) package (method = "lefse") to identify microbes with significant differences in abundance between IUA and control samples. Taxa with the adj.p 2 were considered significant. and microbes exhibiting significant differences in abundance at the genus levels between IUA and control samples were defined as candidate microbes. The distribution of LDA scores and the differences in abundance of candidate microbes (sorted in descending order based on LDA values) were visualized using bar plots generated with the ggplot2 (v3.5.1) package. 2.8 Metabolome sequencing and data pre-processing A total of 100 µL of liquid sample was introduced into a 1.5 mL centrifuge tube, accompanied by 400 µL of a solvent mixture composed of acetonitrile and methanol in a 1:1 volume ratio, which included an internal standard, L-2-chlorophenylalanine, at a concentration of 0.02 mg/mL for the purpose of metabolite extraction. The combination underwent vortex mixing for 30 seconds, followed by low-temperature sonication for 30 minutes at 5°C and 40 kHz. Subsequently, the samples were stored at -20°C for 30 minutes to allow for protein precipitation. Following this, centrifugation was performed for 15 minutes at 4°C and 13,000 g, after which the supernatant was carefully removed and evaporated under a nitrogen stream. The residues were then re-dissolved in 100 µL of a solution (acetonitrile: water = 1:1) and extracted using low-temperature ultrasonication for 5 minutes at 5°C and 40 KHz, followed by a further centrifugation at 13,000 g and 4°C for 10 minutes. The resulting supernatant was transferred into sample vials for subsequent LC-MS/MS analysis. After sequencing, the raw data from mass spectrometry were converted into mzXML format using Proteowizard (v 3.0) tool [ 25 ] . Peak detection, extraction, alignment, and integration were carried out with XCMS [ 26 ] . For initial compound identification, the primary m/z values of the ions detected by XCMS were matched against databases such as Human Metabolome Database (HMDB) ( https://hmdb.ca/ ) and KEGG database ( https://www.genome.jp/kegg/ ) using the MetaX (v 2.0.0) software [ 27 ] , providing the primary metabolites identification results. Additionally, fragmentation of each ion within the mass spectrometer generated a secondary spectrum, which was then matched to a curated database for secondary metabolites identification. 2.9 PCA and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) Following this, PCA was employed to assess the quality of metabolome data among the IUA, control, and QC samples. SIMCA software (v 16.0.2) was used to process the data, including normalization and log transformation, followed by automated modeling analysis. Additionally, Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) was performed to further investigate the differences among the three groups in liver, fecal, and bile acid metabolomics. For OPLS-DA, unit variance scaling (UV) normalization and log transformation were applied to the ion data, and the model was constructed accordingly. To validate the quality of the models, 7-fold cross-validation was conducted. 2.10 Differential expression analysis and metabolic pathway enrichment analysis Univariate statistical analysis methods, such as the t-test and analysis of variance (ANOVA), are primarily focused on identifying independent changes in metabolite levels. In this study, the Wilcoxon-Mann-Whitney test was utilized to identify differentially expressed metabolites (DEMs) between IUA and control groups, with DEMs being defined as candidate metabolites for further analyses. The threshold for selection was set at p 0.5, and Variable Importance in the Projection (VIP) > 1, where VIP reflected the contribution of each variable to the projection of the first principal component in the OPLS-DA model. To investigate the signaling pathways associated with candidate metabolites, the metabolic pathway analysis was conducted by inputting these candidate metabolites into the MetaboAnalyst (v 6.0) platform ( https://www.metaboanalyst.ca/ ). 2.11 Multi-omics integration analyses To investigate the relationships between candidate genes and candidate microbes, Spearman correlation analysis was performed on transcriptome (IUA: control = 6: 6) and microbiome (IUA: control = 6: 6) sequencing data using the psych (v 2.4.3) package [ 28 ] . Significant associations were identified (|correlation coefficient (cor)| >0.7, p < 0.05), resulting in the identification of key genes and key microbes. The correlation relationships were visualized with a heatmap generated using the ggplot2 (v 3.5.1) package [ 22 ] , and an interaction network between key genes and key microbes was also constructed and visualized using Cytoscape (v 3.7.1) software. In the same way, candidate genes gained from transcriptome (IUA: control = 6: 6) sequencing were analyzed in conjunction with candidate metabolites derived from metabolome (IUA: control = 6: 6, using samples matched to the transcriptome) sequencing. Spearman correlation analysis was used to explore the relationships between candidate genes and candidate metabolites (|cor| >0.7, p < 0.05), leading to the identification of key genes 2 and key metabolites 1. In addition, a heatmap generated with the ggplot2 (v 3.5.1) package was employed to visualize the correlations, while a regulatory interaction network between key genes 2 and key metabolites 1 was constructed using Cytoscape (v 3.7.1) software. Likewise, candidate microbes obtained from microbiome (IUA: control = 6: 6) sequencing were subjected to combined analysis with candidate metabolites identified through metabolome (IUA: control = 6: 6, using samples matched to the transcriptome) sequencing. Spearman correlation analysis was employed to uncover the relationships between candidate microbes and candidate metabolites (|cor| >0.7, p < 0.05), yielding the identification of key microbes 2 and key metabolites 2. To visualize these correlations, a heatmap was generated with the ggplot2 (v 3.5.1) package, and a network depicting the interactions between key microbes 2 and key metabolites 2 was built using Cytoscape (v 3.7.1) software. Further analysis was conducted to identify key genes, key microbes, and key metabolites. To be specific, the key genes 1 and the key genes 2 were intersected to identify the key genes for this study. In an analogous manner, the intersection of key microbes 1 and key microbes 2 identified the key microbes in this study, while the overlap between key metabolites 1 and key metabolites 2 highlighted the key metabolites. These intersections were all visualized using ggvenn (v 0.1.10) package [ 29 ] . Leveraging the key genes, key microbes, and key metabolites, the construction of a regulatory network was facilitated through Cytoscape (v 3.9.1) software. 2.12 Comprehensive functional characterization analysis of key genes After identifying the key genes, a series of analyses were conducted to explore their characteristics. Originally, to explore the functional similarity among key genes, the functional similarity of key genes was scored using the GOSemSim (v 3.19) package [ 30 ] . Subsequently, genes related to the functions of these key genes and their shared activities were predicted using the GeneMANIA database ( http://www.genemania.org/ ), leading to the construction of a gene-gene interaction (GGI) network for visualization. Further analysis included obtaining the FASTA files of key genes from the national center for biotechnology information (NCBI) database ( https://www.ncbi.nlm.nih.gov/gene/ ) and using the mRNALocater ( http://bio-bigdata.cn/mRNALocater/ ) to predict the subcellular localization of key genes. Moreover, to clarify the potential biological pathways of the key genes, the Spearman analyses between each key gene and other genes were performed through psych (v 2.1.6) package in the transcriptome sequencing data. The correlation coefficients were then ranked in descending order to obtain a gene ranking list for each key gene. Subsequently, the "c2.cp.kegg.v2023.1.Hs.symbols.gmt" gene set from the MSigDB served as the reference for gene set enrichment analysis (GSEA), conducted for each key gene using the clusterProfiler (v 4.11.0) package with |normalized enrichment score (NES)| >1 and p < 0.05. Finally, the top 5 pathways of the enrichment results were visualized using the enrichplot (v 1.18.3) package [ 31 ] , ordered by p values from low to high. 2.13 Construction of regulatory network and drug prediction In order to delve into the molecular regulatory mechanisms governing key genes, the miRDB and DIANA-microT databases from the multiMiR (v 3.19) package (PMID: 25063298) were employed to predict miRNAs potentially targeting these key genes, and the common miRNAs that appeared across both databases were pinpointed. Subsequently, the starbase database ( http://starbase.sysu.edu.cn/index.php ) was used to predict the lncRNAs corresponding to these common miRNAs, facilitating the construction of an lncRNA-miRNA-mRNA regulatory network. Furthermore, the circbank database ( https://rnasysu.com/encori/ ) was leveraged to predict the interactions between cirRNAs and common miRNAs, leading to the creation of a cirRNA-miRNA-mRNA regulatory network. The results from these networks were graphically represented through Cytoscape (v 3.10.2) software. Additionally, the potential drugs (approved) were predicted based on key genes from the drug gene interaction database (DGIdb) ( https://dgidb.org/ ), and the interaction relationships between potential drugs and key genes were visualized by Cytoscape (v 3.8.2) software. 2.14 Statistical Analysis All analyses were conducted in R (v 4.2.2) software. To determine whether there were statistical differences between the 2 groups, the Wilcoxon test and Wilcoxon-Mann-Whitney test was employed. The value of p < 0.05 was considered statistically significant. 3. Results 3.1 Acquisition of robustness transcriptome sequencing data In this study, the detection accuracy and error rate of each measured base were evaluated ( Supplementary Table 1 ), and the quality of the transcriptome sequencing raw data was assessed ( Supplementary Table 2 ). After QC and comparison, the alignment rate of all 12 samples was found to be higher than 85%, indicating high-quality sequencing data that met the requirements for subsequent analysis ( Supplementary Table 3 ). Overall, these results confirmed the robustness of the transcriptome sequencing data for further analysis. 3.2 Exploration of biological pathway differences between IUA and control samples The PCA provided insights into the mingling distribution of samples between IUA and control samples, underscoring its capability to discriminate between them effectively (Fig. 1 A). Following the GSVA conducted between IUA and control samples, a total of 10 significant differential pathways were identified, with 9 up-regulated and 1 down-regulated in IUA samples. The up-regulated pathways included "fatty acid metabolism", "propanoate metabolism", "glyoxylate and dicarboxylate metabolism", "butanoate metabolism", "valine leucine and isoleucine degradation", "beta alanine metabolism", "amino sugar and nucleotide sugar metabolism", "SNARE interactions in vesicular transport", and "citrate cycle (TCA cycle)". The only down-regulated pathway was the "circadian rhythm mammal" (Fig. 1 B). These insights significantly contributed to elucidating the complex biological mechanisms underlying the IUA and could inform future clinical interventions. 3.3 Identification and enrichment analysis of 46 candidate genes Following the differential expression analysis conducted in the transcriptome sequencing data, a sum of 46 DEGs were identified between IUA and control samples, comprising 29 up-regulated and 17 down-regulated genes for subsequent analyses (Fig. 2 A, 2 B), and these genes were selected as candidate genes. Further analysis of these 46 candidate genes utilizing GO and KEGG pathway analysis led to the identification of 82 BP entries, such as "steroid metabolic process", "hormone metabolic process", and "positive regulation of T cell activation" (p < 0.05) (Fig. 2 C, Supplementary Table 4 ). No CC entries were found, While 2 MF entries, encompassed "heme binding" and "tetrapyrrole binding", were identified (p < 0.05) (Fig. 2 C, Supplementary Table 4 ). Moreover, there were 17 KEGG pathways enriched and the top 10 results of KEGG enrichment were visualized, such as "ovarian steroidogenesis", "efferocytosis", and "steroid hormone biosynthesis" (p < 0.05) (Fig. 2 D, Supplementary Table 4 ). These analyses provided a significant foundation for the functional significance of candidate genes in the progression of IUA. Additionally, after removing 1 discrete protein, the remaining 45 candidate genes were interacted in the PPI network, with IL1A and CD36 exhibiting strong interactions with other genes (Fig. 2 E). Chromosomal localization results determined that the presence of multiple genes on chromosomes 7, 11, and 20. Specifically, there were 5 candidate genes located on chromosome 7, 5 candidate genes on chromosome 11, and 4 candidate genes on chromosome 20 (Fig. 2 F). These findings suggested that the candidate genes might be involved in region-specific genetic regulation, potentially contributing to distinct biological processes. 3.4 Microbiome sequencing depth assessment To evaluate the adequacy of sequencing depth in microbiome data, rarefaction curves were employed. The stabilization of the curve suggested that any further increase in sequencing depth would not significantly uncover new amplicon sequence variants (ASVs), indicating that the existing sequencing depth was sufficient to capture the diversity present within the samples (Fig. 3 A). Rank abundance curves provided insights into the richness and evenness of each sample (Fig. 3 B). The species accumulation boxplot illustrated an increase in observed species count correlating with the number of samples analyzed (Fig. 3 C). Overall, these analyses indicated that the sequencing depth was adequate, revealing distinct differences in microbial diversity between IUA and control samples. 3.5 Differences in Alpha diversity indices and Beta diversity The Alpha diversity indices (Chao1, Observed species, Shannon, and Simpson) were assessed ( Supplementary Table 5 ). Differential analysis between IUA and control samples showed significant differences in Chao1 and Observed species indices between IUA and control samples (p 0.05) (Fig. 4 A). Furthermore, the Beta diversity analysis, through PCoA, showed clear differences in microbial community structure between IUA and control samples (Fig. 4 B). NMDS analysis confirmed the reliability of the results with a stress value of 0.08 (Fig. 4 C). Subsequently, the composition of species between IUA and control samples was analyzed at 2 taxonomic levels (phylum and genus), and the Proteobacteria phylum, Firmicutes phylum, Lactobacillus genus, and Acinetobacter genus exhibited the higher relative abundance, highlighting their dominance in the microbiome composition (Fig. 4 D, 4 E). These results offered valuable insights into the microbial diversity between different samples, which might help in understanding the relationship between microbes and individual conditions. 3.6 Distinct microbial signatures in IUA and control samples Further analysis of the microbes from the IUA and control samples led to the identification of functional characteristics. The IUA samples showed an enhancement in anaerobic chemoheterotrophy (energy source) and fermentation (carbon cycling). In contrast, the control samples exhibited a high abundance in aerobic chemohetertrophy function (Fig. 5 A). These findings suggested that specific functions might play a crucial role in distinguishing the microbes profiles between IUA and control samples. Moreover, LEfSe analysis revealed 14 differential microbes between IUA and control samples, with 4 of them showing significant differences at the genus level (Fig. 5 B, 5 C). These 4 differential microbes were identified as candidate microbes, all of which exhibited higher abundance in the IUA samples (p < 0.05) (Fig. 5 D). These results underscored the potential functional and taxonomic differences that might contribute to the microbial signature associated with IUA, providing insights into the underlying mechanisms that distinguished it from the control group. 3.7 Generation of stable and consistent metabolome sequencing data The PCA provided insights into the mingling distribution of samples from IUA and control (Fig. 6 A), while OPLS-DA showcased a dispersed distribution between IUA and control samples, underscoring its capability to discriminate between them effectively (Fig. 6 B). To validate the robustness of the OPLS-DA model, permutation testing, with 200 iterations, was carried out. For the comparison between IUA and control samples, the model demonstrated commendable performance (Fig. 6 C). These consistent data generations underscored the reliability of the metabolome sequencing data, ensuring that the results could be confidently applied for further biological insights. 3.8 Identification and function exploration of candidate metabolites Subsequent analysis, employing p 0.5, and VIP > 1 as threshold, led to the identification of 11 DEMs which were selected as candidate metabolites, comprising 4 up-regulated and 7 down-regulated metabolites (Fig. 7 A, 7 B). Enrichment analysis identified 11 shared pathways among these candidate metabolites, including "felbamate metabolism pathway", "GPCR downstream signaling", and "signal transduction" (Fig. 7 C). These findings suggested that the candidate metabolites and their associated pathways may play critical roles in regulating metabolic and signaling processes, potentially influencing the underlying mechanisms of the studied condition. 3.9 Strong correlations among candidate genes, candidate metabolites, and candidate microbes Employing the previously discerned 46 candidate genes and 4 candidate microbes for an investigation into the symbiotic interplay between genes and microbes revealed a distinct pattern: 18 candidate genes manifested significantly correlations with 4 candidate microbes (|cor| >0.7, p < 0.05) (Fig. 8 A; Supplementary Table 6 ). Thus, within this framework, 18 key genes 1 along with 4 key microorganisms 1 were obtained. Concurrently, a regulatory network was constructed, comprising 22 nodes and 35 edges, to elucidate the correlations between key genes 1 and key microbes 1 (Fig. 8 B). In a parallel probing using the ascertained 46 candidate genes and 11 candidate metabolites to delve into the gene-metabolite nexus, findings revealed that 17 candidate genes were interlinked with 7 candidate metabolites (|cor| >0.7, p < 0.05) (Fig. 8 C; Supplementary Table 7 ). Based on this information, 17 key genes 2 and 7 key metabolites 1 were derived. A sophisticated regulatory network comprising 12 nodes and 20 edges materialized from this analysis, mapping out the intricate gene-metabolite associations (Fig. 8 D). Harnessing the insights from the designated 4 candidate microbes and 11 candidate metabolites to trace the connective threads between candidate microbes and candidate metabolites, the examination culminated in 2 candidate microbes exhibiting correlations with 2 candidate metabolites (|cor| >0.7, p < 0.05) (Fig. 8 E; Supplementary Table 8 ). Employing this results, 2 candidate microbes 2 and 2 candidate metabolites 2 were extracted. This culminated in the crafting of a comprehensive correlation regulatory network with 4 nodes and 3 edges (Fig. 8 F). 3.10 Identification of 7 key genes, 2 key microbes, and 2 key metabolites The intersection of key genes 1 and key genes 2 revealed 7 key genes within the scope of this study: DUSP2, IL1A, POF1B, ICAM4, CX3CL1, HTR2C, and SIX1 (Fig. 9 A). Simultaneously, an intersection between key microbes 1 and key microbes 2 brought 2 key microbes ( g__Clostridium_sensu_stricto_1 and g__Acidisoma )(Fig. 9 B). Besides, the culmination of this meticulous research revealed 2 key metabolites (Pe(18:3(6Z,9Z,12Z)/18:1(9Z)) and 1,3,6-Trihydroxy-2-(3-Methylbut-2-Enyl)Xanthen-9-One) by intersected key metabolites 1 and key metabolites 2 (Fig. 9 C). Under these circumstances, by retaining these interactions that simultaneously involved key genes, key microbes, and key metabolites, a comprehensive regulatory network was carefully constructed. This network comprised 11 nodes and 15 edges, forming an intricate web of biological relationships pivotal to the themes explored in this research (Fig. 9 D). 3.11 Functional analysis of key genes The results of the functional similarity analysis indicated that CX3CL1 had the highest similarity score (Fig. 10 A). Based on the key genes, a GGI network was constructed by GeneMANIA, yielding 20 genes related to the key genes, such as CXCL2, IL6, and CCL3, which were collectively involved in functions like the "cellular response to molecule of bacterial origin", "response to lipopolysaccharide", and "cellular response to biotic stimulus" (Fig. 10 B). Subcellular localization results indicated that DUSP2, IL1A, POF1B, ICAM4, CX3CL1, and SIX1 primarily presented within the cytoplasm, whlie HTR2C was located in the cell nucleus (Fig. 10 C). These findings implied that key genes functioned through diverse molecular interactions and cellular localization, highlighting their complex roles. Subsequently, the pathway enrichment analysis revealed significant enrichment of key genes in multiple pathways: DUSP2 was associated with 24 pathways (such as "fatty acid metabolism"), IL1A with 7 pathways (such as "cell cycle"), POF1B with 18 pathways (such as "DNA replication"), ICAM4 with 18 pathways (such as "adipocytokine signaling pathway"), CX3CL1 with 18 pathways (such as "cell cycle"), HTR2C with 30 pathways (such as "fatty acid metabolism"), and SIX1 with 5 pathways (such as "inositol phosphate metabolism") ( Supplementary Table 9 ). 3.12 Decipherment of regulatory networks and key gene-targeted drug discovery Employing the miRDB and DIANA-microT databases within the multiMiR (v 3.19) package, a prediction analysis was performed to identify associations between key genes and miRNAs. By intersecting the results from both databases, 52 common miRNAs were identified (Fig. 11 A, Supplementary Table 10 ). Additionally, 29 lncRNAs were retrieved from the starbase database, enabling the construction of a lncRNA-miRNA-mRNA regulatory network (Fig. 11 B, Supplementary Table 11 ). Furthermore, the circbank database was utilized to predict circRNAs associated with the common miRNAs, yielding 18 circRNAs, which were used to construct a circRNA-miRNA-mRNA network (Fig. 11 C, Supplementary Table 12 ). Within these networks, complex interaction relationships were identified, such as NEAT1-hsa-miR-185-5p-SIX1, XIST-hsa-miR-106a-5p-DUSP2, hsa_circ_0013871-hsa-miR-4533-HTR2C, and hsa_circ_0013870-hsa-miR-4498-CX3CL1. These findings provide a foundation for further investigation into the roles of these interactions in the mechanisms underlying IUA. Using the DGIdb database, potential drugs targeting these key genes were predicted to identify therapeutic candidates. A total of 61 drugs were identified, including 1 targeting CX3CL1, 3 targeting DUSP2, 53 targeting HTR2C, and 4 targeting IL1A, while no drugs were predicted for the remaining key genes ( Supplementary Table 13 ). Based on these results, a drug-biomarker interaction network was constructed (Fig. 11 D), and olanzapine was predicted as a potential therapeutic drug due to its association with 2 key genes (IL1A and HTR2C). These findings highlight potential therapeutic strategies for targeting these key genes, offering valuable insights for further experimental validation and clinical translation. 4. Discussion IUA can lead to severe complications such as infertility, amenorrhea (absence of menstruation), reduced menstrual flow (hypomenorrhea), and recurrent abortions. Current treatments include TCRA, hormonal therapy, and stem cell-based therapies; however, these approaches often have limitations, including high recurrence rates and incomplete restoration of fertility. Given these limitations, multi-omics integration is a powerful tool for unraveling the complexity of IUA, offering new avenues for diagnosis, treatment, and management of this challenging condition. In this article, we obtained candidate genes, candidate microorganisms, and candidate metabolites from sequencing data of the transcriptome, microbiome, and metabolome, respectively. Through multi-omics analysis, we identified seven key genes, two key microorganisms, and two key metabolites. Finally, we explored the functional characteristics, regulatory networks, and drugs targeting. After differential gene expression analysis, a total of 46 DEGs were identified between IUA and control samples. The identification of these DEGs was crucial, as they may serve as biomarkers for diagnosis or therapeutic targets. Several of these genes have been reported to be involved in the occurrence of fibrosis, such as CD36, which has strong interactions with other genes in the PPI network. Furthermore, some studies have suggested that inhibiting CD36 or targeting CD36 can alleviate fibrotic lesions in pulmonary [ 32 ] . Thus, the DEG that we discovered may be involved in endometrial fibrosis. However, this biological process can be very complex, involving multiple pathways and biological processes. Meanwhile, microbial diversity analysis showed that the phyla with relatively high abundance of endometrial microbiota were Proteobacteria, Firmicutes, Bacteroidetes, and Actinobacteria in the present study. These results were consistent with previous studies on the uterine microbiota in patients with endometrial cancer, infertility, and endometrial polyps [ 33 , 34 ] . In terms of diversity, the IUA group and the control group showed significant differences in α-diversity and β-diversity. These differences indicated that the changes in the microbiota structure may be closely related to the occurrence of IUA. The identification of specific candidate microbes with differential abundance further supported the hypothesis that microbial dysbiosis may play a role in IUA. The functional analysis of the microbiome revealed distinct metabolic capabilities, with IUA samples exhibiting enhanced anaerobic chemoheterotrophy, which may reflect an adaptive response to the altered uterine environment. The candidate metabolite, perlolyrine, a strongly anti-inflammatory β-carboline, has been found to suppress vaginal inflammation in mice and was depleted in people with BV [ 35 ] . In our study, the level of perlolyrine, a specific metabolite, was found to be decreased in patients with intrauterine adhesions. It was speculated that the reduction of perlolyrine may weaken the inhibition of the inflammatory response, thereby accelerating the development of intrauterine adhesions. Moreover, the metabolomic profiling identified 11 differentially expressed metabolites, with pathways such as "GPCR downstream signaling" being significantly enriched. This suggested that metabolic alterations may be integral to the pathophysiology of IUA, potentially influencing cellular signaling and metabolic processes. The strong correlations observed among candidate genes, candidate metabolites, and candidate microbes underscored the interconnectedness of these biological systems, providing a comprehensive view of the molecular landscape in IUA. Collectively, these findings not only enhance our understanding of the underlying mechanisms of IUA but also pave the way for future research aimed at developing targeted therapeutic strategies. Through multi-omics analysis, we identified seven key genes, two key microorganisms, and two key metabolites with strong correlations. Among these, the key gene CX3CL1 exhibited the highest similarity score in functional similarity analysis, which assesses the functional relatedness of genes based on their biological roles. Fractalkine (CX3CL1), a chemotactic membrane-bound adhesion molecule, binds specifically to CX3C chemokine receptor 1 (CX3CR1). Notably, numerous studies have demonstrated that the CX3CL1/CX3CR1 signaling pathway is closely associated with fibrogenesis in multiple organs [ 36 ] .Jiali Wang found that the number of CX3CR1 + monocyte/macrophages were significantly elevated in the endometrial tissue of IUA patients. Furthermore, blocking IL-34 in the LPS-IUA model improved endometrial fibrosis and reduced the number of CX3CR1 + monocyte/macrophages [ 37 ] . In a mouse model of liver fibrosis, the levels of liver CX3CL1 and CX3CR1 mRNA at 8 weeks post-infection were notably upregulated, which indicated that CX3CL1 and CX3CR1 might accelerate the process of liver fibrosis after Schistosoma haematobium infection [ 38 , 39 ] . In addition, there were also studies indicating that CX3CL1 was associated with fibrosis of the kidneys, pulmonary and heart. This is the first report of an association between CX3CL1/CX3CR1 and IUA. Other key genes have also been reported to be related to the process of fibrosis .IL-1α mainly promoted lung and liver fibrosis in the early stage of organ repair, thus IL-1α produced by endothelial cells plays a unique role in promoting organ fibrosis [ 40 ] . SIX1 was the key transcription factor of EMT, and EMT of airway epithelium was the key pathological process of pulmonary fibrosis [ 41 ] . Furthermore, the key microorganism Clostridium_sensu_stricto_1 has been reported to be associated with various fibrosis and gynecological diseases. It was significantly increased in mice with nonalcoholic steatohepatitis (NASH), which may be related to inflammatory infiltration and liver fibrosis [ 42 ] . Notably, patients with polycystic ovary syndrome (PCOS) showed an increased abundance of Clostridium_sensu_stricto_1 compared to other groups [ 43 ] . The process of fibrosis was related to multiple metabolic pathways. In this study, differential metabolites were enriched in multiple pathways, among which GPCR downstream signaling was a core signaling pathway related to fibrosis. Specifically, GPCR activates phospholipase Cβ (PLCβ) via Gαq/11 to generate IP3 and DAG. This triggered Ca²⁺ release, and PKC activation, which promoted the fibroblast proliferation and collagen deposition, such as in liver fibrosis and pulmonary fibrosis [ 44 , 45 ] ‌.In summary, the interactions among genes, metabolites, and microorganisms were found to play a critical role in the local inflammatory microenvironment, fibrotic formation, and immune repair processes within the uterus, offering new insights into the pathological mechanisms of intrauterine adhesion and potential targeted interventions. The enrichment analysis of key genes revealed significant pathways among multiple pathways, such as "fatty acid metabolism," "cell cycle," and "DNA replication." Notably, the fatty acid metabolism pathways have been widely studied in fibrotic diseases. Previous research indicated that the fatty acid metabolism pathway played an important role in cardiac, liver, and renal fibrotic diseases [ 46 , 47 ] . There was a close relationship between cell cycle arrest and fibrosis, especially in pathological processes such as renal fibrosis. In several renal injury models, a causal link has been identified between the G2/M phase arrest of renal tubular epithelial cells and the subsequent development of renal fibrosis. For instance, research has demonstrated that G2/M phase-arrested renal tubular epithelial cells can stimulate the production of TGF-β1 and CTGF by activating the JNK signaling pathway [ 48 ] . This discovery underscored the pivotal role these key genes played in maintaining fundamental processes within organisms and suggested that they might occupy crucial roles in regulating complex fibrotic pathways. In recent years, some lncRNAs, miRNAs, circRNAs, and mRNAs related to IUA have been discovered through microarray analysis. Therefore, in-depth research on the lncRNA-miRNA-mRNA/circRNA-miRNA-mRNA ceRNA network is conducive to deepening the understanding of the occurrence and development of IUA. This study identified 52 miRNAs, 29 lncRNAs, and 18 circRNAs to construct a ceRNA network. Some RNAs have been reported in previous studies; moreover, we also discovered some novel non-coding RNAs (ncRNAs) potentially involved in the pathogenesis of IUA, which provided new targets for further research on the mechanism of IUA occurrence. MiR-543 negatively regulated collagen XVI, and this regulation may promote the formation of IUA by affecting the component proteins of Col XVI and ECM [ 49 ] . Furthermore, the exosome miR-543 derived from Umbilical Cord Mesenchymal Stem Cells(UCMSC) can alleviate endometrial fibrosis in IUA mice by down-regulating N-cadherin. This suggested that miR-543 played an important role in the anti-fibrotic effect of UCMSC-derived exosomes [ 50 ] .In this study, we found that miR-543, as a differentially expressed ncRNA, was predicted to interact with the IL1A and the lncRNAs NORAD, SNHG7, and NEAT1 to participate in the occurrence of intrauterine adhesions. Knockdown of lncRNA H19 could significantly reverse the up-regulation of fibronectin, COL1A1, and α-SMA in HK-2 cells induced by TGF-β1, as well as the down-regulation of E-cadherin, accompanied by an up-regulation of let-7b-5p. Therefore, lncRNA H19 functions as a ceRNA targeting the let-7b-5p–TGF-βR1–COL1A1 axis to regulate renal tubular epithelial fibrosis [ 51 ] . CircRNA played a role in fibrosis, including cardiac fibrosis and liver fibrosis. Overexpression of CircPlekha 7 can inhibit the expression levels of a-SMA, type I collagen and Smad 3 [ 52 ] . Consequently, both circRNA and lncRNA may participate in the fibrosis-related pathways of IUA by targeting miRNAs or modulating their host genes. In this study, a multi-omics joint analysis was conducted based on sequencing data from the transcriptome, microbiome, and metabolome. As a result, seven key genes, two key microorganisms, and two key metabolites were identified. Subsequently, the functional characteristics, regulatory networks, and related drugs of the key genes were explored. However, certain limitations were noted within this study, including a small sample size and the absence of experimental validation. In the future, the underlying mechanisms will be experimentally validated through cell experiments, animal experiments, and other experimental approaches. In addition, we will continue to investigate the functions of these mechanisms. Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee of Chongqing Health Center for Women and Children (Approval NO. 2021044). All patients provided written informed consent when clinical samples were collected for RT-qPCR experiments to ensure that the research process was in accordance with ethical norms and that the patients' rights and wishes were fully respected. Competing interests The authors declare no conflict of interest. Funding This research was funded by Chongqing Science and Technology Commission, grant number CSTB2022NSCQ-MSX0907. This project was supported by grants from Chongqing Municipal Education Commission Science and Technology Research Program Project (KJZD-K202300407). This work was supported by the National Key Clinical Specialty Construction Project (Obstetrics and Gynecology), 2022 Author Contribution Bao Liu and Mingqian Chen wrote the main manuscript text and Yugang Chi prepared figures 1-11. All authors reviewed the manuscript. Acknowledgment We would like to express my gratitude to all those who helped me during the writing of this manuscript. Data Availability Sequence data that support the findings of this study have been deposited in the National Genomics Data Center with the primary accession code HRA012451.Shared URL:https://ngdc.cncb.ac.cn/gsa-human/s/8K5yxV1v References Zhao, G. & Hu, Y. Mechanistic insights into intrauterine adhesions [J]. Semin. Immunopathol. 47 (1), 3 (2024). Luo, Y. et al. Effects and safety of hyaluronic acid gel on intrauterine adhesion and fertility after intrauterine surgery: a systematic review and meta-analysis with trial sequential analysis of randomized controlled trials [J]. Am. J. Obstet. Gynecol. , 231 (1). (2024). Jin, C. et al. Multi-omics reveal mechanisms of high enteral starch diet mediated colonic dysbiosis via microbiome-host interactions in young ruminant [J]. Microbiome 12 (1), 38 (2024). Wu, F. et al. 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Supplementary Files SupplementaryTable1.xlsx SupplementaryTable2.xlsx SupplementaryTable3.xlsx SupplementaryTable4.xlsx SupplementaryTable6.xlsx SupplementaryTable5.xlsx SupplementaryTable8.xlsx SupplementaryTable12.xlsx SupplementaryTable7.xlsx SupplementaryTable11.xlsx SupplementaryTable9.xlsx SupplementaryTable10.xlsx SupplementaryTable13.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 14 Apr, 2026 Reviews received at journal 09 Apr, 2026 Reviewers agreed at journal 03 Apr, 2026 Reviewers agreed at journal 03 Apr, 2026 Reviews received at journal 01 Apr, 2026 Reviewers agreed at journal 01 Apr, 2026 Reviews received at journal 09 Aug, 2025 Reviewers agreed at journal 06 Aug, 2025 Reviewers invited by journal 05 Aug, 2025 Editor assigned by journal 05 Aug, 2025 Editor invited by journal 30 Jul, 2025 Submission checks completed at journal 23 Jul, 2025 First submitted to journal 23 Jul, 2025 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. 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02:38:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7079007/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7079007/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88660845,"identity":"4fe07a59-2add-4c2c-a180-6dad5c2eb570","added_by":"auto","created_at":"2025-08-08 20:53:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":111550,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferences in biological pathways between IUA and control samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A)PCA illustrates the distribution of samples from both IUA and control groups\u003c/p\u003e\n\u003cp\u003e(B) GSVA identified ten significant differential pathways, with upregulated pathways highlighted in red and downregulated pathways in blue.\u003c/p\u003e","description":"","filename":"Slide1.png","url":"https://assets-eu.researchsquare.com/files/rs-7079007/v1/685eae60f089985dcedc3f22.png"},{"id":88659891,"identity":"6f5033be-c785-4dfd-814e-fff33cc2e248","added_by":"auto","created_at":"2025-08-08 20:37:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":244459,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification and enrichment analysis of candidate genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A)(B) Differentially expressed genes in IUA tissues VS the control tissues.\u003c/p\u003e\n\u003cp\u003e(C) Gene Ontology (GO) term enrichment analysis of differentially expressed genes (DEGs)\u003c/p\u003e\n\u003cp\u003e(D) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of differentially expressed genes (DEGs)\u003c/p\u003e\n\u003cp\u003e(E) Protein-protein interaction (PPI) network for the 45 differentially expressed genes (DEGs)\u003c/p\u003e\n\u003cp\u003e(F) Chromosomal localization results revealing multiple genes located on chromosomes 7, 11, and 20\u003c/p\u003e","description":"","filename":"Slide2.png","url":"https://assets-eu.researchsquare.com/files/rs-7079007/v1/31ff77373d90d978e6d22463.png"},{"id":88659897,"identity":"1212f92b-b998-4fdf-bdeb-7788d2aa7d37","added_by":"auto","created_at":"2025-08-08 20:37:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":118483,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDepth assessment of microbiome sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Rarefaction curves of the samples\u003c/p\u003e\n\u003cp\u003e(B) Rank abundance curves foreach group\u003c/p\u003e\n\u003cp\u003e(C) Species accumulation box plot for the samples from across groups\u003c/p\u003e","description":"","filename":"Slide3.png","url":"https://assets-eu.researchsquare.com/files/rs-7079007/v1/043b2d2fa34db2b228e9da2f.png"},{"id":88659893,"identity":"a4c6d0a6-a7fd-450e-83dc-00fde8795088","added_by":"auto","created_at":"2025-08-08 20:37:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":158262,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAlpha and Beta diversity differences between IUA and control group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Alpha diversity distances between the IUA and control groups\u003c/p\u003e\n\u003cp\u003e(B) Beta diversity distances between the IUA and control groups\u003c/p\u003e\n\u003cp\u003e(C) NMDS analysis for the two groups\u003c/p\u003e\n\u003cp\u003e(D) Comparison of microbial compositions of the top 10 enriched microorganisms among the two groups at the phylum level\u003c/p\u003e\n\u003cp\u003e(E) Comparison of microbial compositions of the top 10 enriched microorganisms among the two groups at the genus level\u003c/p\u003e","description":"","filename":"Slide4.png","url":"https://assets-eu.researchsquare.com/files/rs-7079007/v1/244ccb55f0822b4b9a501245.png"},{"id":88659924,"identity":"9259a44d-63f3-4fd9-b212-351805190494","added_by":"auto","created_at":"2025-08-08 20:37:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":171197,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional characteristics analysis of the microbes from the IUA and control samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The IUA samples showed an enhancement in anaerobic chemoheterotrophy (energy source) and fermentation (carbon cycling)\u003c/p\u003e\n\u003cp\u003e(B)(C) LEfSe analysis revealed 14 differential microbes between IUA and control samples\u003c/p\u003e\n\u003cp\u003e(D) 4 differential microbes showing significant differences at the genus level\u003c/p\u003e","description":"","filename":"Slide5.png","url":"https://assets-eu.researchsquare.com/files/rs-7079007/v1/289502a95be043b6eba9b8a5.png"},{"id":88660993,"identity":"d810ec47-bcf3-4e72-b7ee-baae2d5611cc","added_by":"auto","created_at":"2025-08-08 21:01:56","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":108494,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStability and consistency analysis of metabolomics data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) PCA analysis of IUA and control groups\u003c/p\u003e\n\u003cp\u003e(B) OPLS-DA analysis for the two groups\u003c/p\u003e\n\u003cp\u003e(C) Permutation testing to validate the robustness of the OPLS-DA model\u003c/p\u003e","description":"","filename":"Slide6.png","url":"https://assets-eu.researchsquare.com/files/rs-7079007/v1/f80a2e85423e308fb3d5fe19.png"},{"id":88659922,"identity":"b0f7284f-b5ee-43c3-ae20-7bb903396608","added_by":"auto","created_at":"2025-08-08 20:37:56","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":157565,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification and function exploration of candidate metabolite\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A)(B) Differentially expressed metabolites (DEMs) were identified by Wilcoxon-Mann-Whitney test\u003c/p\u003e\n\u003cp\u003e(C) Enrichment analysis revealed 11 common pathways associated with these candidate metabolites\u003c/p\u003e","description":"","filename":"Slide7.png","url":"https://assets-eu.researchsquare.com/files/rs-7079007/v1/1fdc9b1e9c63dcf4420789d8.png"},{"id":88660335,"identity":"506445d6-5a0f-41a9-bdc1-587553664447","added_by":"auto","created_at":"2025-08-08 20:45:56","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":231191,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMulti-omics integration analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) A heatmap of the correlation relationships between candidate genes and candidate microbes\u003c/p\u003e\n\u003cp\u003e(B)A interaction network between key genes and key microbes\u003c/p\u003e\n\u003cp\u003e(C) A heatmap of the correlation relationships between candidate genes and candidate metabolites\u003c/p\u003e\n\u003cp\u003e(D) A interaction network between key genes and key metabolites\u003c/p\u003e\n\u003cp\u003e(E) A heatmap of the correlation relationships between candidate microbes and candidate metabolites\u003c/p\u003e\n\u003cp\u003e(F) A interaction network between key microbes and key metabolites\u003c/p\u003e","description":"","filename":"Slide8.png","url":"https://assets-eu.researchsquare.com/files/rs-7079007/v1/2c47dac2d61d4f91b6085fe9.png"},{"id":88659933,"identity":"9d67991a-1781-4a8f-9103-3e0adbe559e5","added_by":"auto","created_at":"2025-08-08 20:37:57","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":137919,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of key genes, key microbes, and key metabolites\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e（A） The intersection of key genes 1 and key genes 2\u003c/p\u003e\n\u003cp\u003e（B） The intersection of key microbes 1 and key microbes 2\u003c/p\u003e\n\u003cp\u003e（C） The intersection of key metabolites 1 and key metabolites 2\u003c/p\u003e\n\u003cp\u003e（D） The interaction network of key microbes, key metabolites and key genes\u003c/p\u003e","description":"","filename":"Slide9.png","url":"https://assets-eu.researchsquare.com/files/rs-7079007/v1/f269e61dea1b5f011f21814e.png"},{"id":88659905,"identity":"de1cf6a9-a1d7-4bae-b537-aa8ab9937c20","added_by":"auto","created_at":"2025-08-08 20:37:56","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":147039,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional analysis of key genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The functional similarity analysis of key genes\u003c/p\u003e\n\u003cp\u003e(B) The GGI network constructed by yielding 20 genes related to the key genes\u003c/p\u003e\n\u003cp\u003e(C) Subcellular localization analysis of key genes\u003c/p\u003e","description":"","filename":"Slide10.png","url":"https://assets-eu.researchsquare.com/files/rs-7079007/v1/e993dcd1932bc07e9dce49a0.png"},{"id":88661412,"identity":"5320e710-c4ba-43cc-9ae3-81da57df87d9","added_by":"auto","created_at":"2025-08-08 21:09:56","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":380884,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegulatory networks and key gene-targeted drug\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The intersection of miRNAs from two databases\u003c/p\u003e\n\u003cp\u003e（B）The network of lncRNA-miRNA-mRNA\u003c/p\u003e\n\u003cp\u003e(C) The network of circRNA-miRNA-mRNA\u003c/p\u003e","description":"","filename":"Slide11.png","url":"https://assets-eu.researchsquare.com/files/rs-7079007/v1/f45b35c5995d43e3273e8aaf.png"},{"id":89063005,"identity":"c5ef533c-89c0-4138-b23b-9803873059f4","added_by":"auto","created_at":"2025-08-14 09:56:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3701908,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7079007/v1/a8f644b1-53d4-40ed-a575-5933a6c08910.pdf"},{"id":88659890,"identity":"afc8dd5e-7d7c-43cc-a766-e584f270470c","added_by":"auto","created_at":"2025-08-08 20:37:55","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":9915,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7079007/v1/2bc60c1fab8a17ee9d4503f5.xlsx"},{"id":88659903,"identity":"d8b1d8fc-6e33-4a50-8cf5-352b7cdc60ef","added_by":"auto","created_at":"2025-08-08 20:37:56","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10404,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7079007/v1/bd2460090e37111b9a76c3ad.xlsx"},{"id":88659901,"identity":"61903887-9993-48f6-9d39-bbc61c4aec53","added_by":"auto","created_at":"2025-08-08 20:37:56","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10462,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7079007/v1/5244ac0e1ad88903ca45f740.xlsx"},{"id":88659906,"identity":"cec76b1d-e923-4bba-b8ea-6878e9200a92","added_by":"auto","created_at":"2025-08-08 20:37:56","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":20354,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7079007/v1/cfac1f418c1987680199c8ab.xlsx"},{"id":88660339,"identity":"1c87b207-4459-4b9b-8923-69b4f2d6a0a3","added_by":"auto","created_at":"2025-08-08 20:45:56","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":18629,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7079007/v1/1d2f68396b65c5b49de237f8.xlsx"},{"id":88659932,"identity":"00a3e4a7-a6f2-4c52-924a-56ca4bbba716","added_by":"auto","created_at":"2025-08-08 20:37:57","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":10672,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7079007/v1/8e198693e1a594f9bd5b863b.xlsx"},{"id":88660342,"identity":"7d2a4613-e071-42ca-9585-b3fd66e361ba","added_by":"auto","created_at":"2025-08-08 20:45:56","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":11674,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable8.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7079007/v1/eaba6d5124f3ecc2934daf9c.xlsx"},{"id":88659904,"identity":"22dacea8-28a2-4c9f-b9a3-51fc3e6116ec","added_by":"auto","created_at":"2025-08-08 20:37:56","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":11508,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable12.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7079007/v1/0048d3b1f764f1f0b8585000.xlsx"},{"id":88659917,"identity":"9710fcbe-8dcd-4f6c-866d-f570b3da5782","added_by":"auto","created_at":"2025-08-08 20:37:56","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":27212,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable7.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7079007/v1/9dabf7aa1757bd310d8d65db.xlsx"},{"id":88659923,"identity":"b0628354-f8e4-496e-9452-af767dbc9ce3","added_by":"auto","created_at":"2025-08-08 20:37:57","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":13649,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable11.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7079007/v1/e97161806066b889d2c3ddaa.xlsx"},{"id":88659910,"identity":"1a0805a8-552b-4677-9935-cd586314e7c1","added_by":"auto","created_at":"2025-08-08 20:37:56","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":41221,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable9.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7079007/v1/c5638356a953e0dc18384a2e.xlsx"},{"id":88659926,"identity":"b7a0ec62-049f-465e-839d-edb11255267f","added_by":"auto","created_at":"2025-08-08 20:37:57","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":52863,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable10.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7079007/v1/ea08d39bf4b1331ebc12f3fb.xlsx"},{"id":88659921,"identity":"09722bc2-ed07-4db2-844c-71019f97f22c","added_by":"auto","created_at":"2025-08-08 20:37:56","extension":"xlsx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":12726,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable13.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7079007/v1/6d7a158de5fe7cafbd627d74.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated multi-omics analysis to investigate the pathogenesis of intrauterine adhesion","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIntrauterine adhesions (IUA), commonly referred to as Asherman syndrome, represent a pathological condition resulting from damage to the basal layer of the endometrium, often culminating in partial or complete obliteration of the uterine cavity. Common causes include dilation and curettage (D\u0026amp;C) following miscarriage, endometrial injury, cesarean section, and other intrauterine procedures. The incidence of IUA ranges from 1.5\u0026ndash;40%, with a prevalence as high as 21.5% in women with a history of postpartum curettage and over 40% in those undergoing secondary removal of retained placental tissue or repeat curettage for incomplete abortion\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. IUA significantly impacts women's reproductive health, manifesting as menstrual abnormalities, infertility, and recurrent miscarriages.\u003c/p\u003e\u003cp\u003eThe burden of IUA on both patients and healthcare systems underscores the urgency for better understanding its pathophysiology and improving treatment options. Current therapeutic strategies, such as hysteroscopic adhesiolysis and hormonal therapy, are often limited by high recurrence rates and inconsistent efficacy, necessitating a more profound exploration of the underlying biological mechanisms that contribute to IUA. Despite the well-defined clinical manifestations and diagnostic methods for IUA, its pathogenesis remains incompletely understood. Current research primarily focuses on inflammatory responses, abnormal deposition of extracellular matrix (ECM), and dysfunction of endometrial stem cells\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn recent times, the swift advancement of multi-omics technologies\u0026mdash;encompassing genomics, transcriptomics, microbiome analysis, proteomics, and metabolomics\u0026mdash;has introduced novel methodologies for investigating complex diseases. Multi-omics analysis integrates multiple levels of biological information, such as the transcriptome, microbiome, and metabolome\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. This integration enables researchers to observe gene function and regulatory networks from different perspectives and reveals complex molecular interactions and signal transduction pathways. Prior studies have indicated that the interplay of gene expression changes, metabolic disruptions, and microbial community dynamics may significantly influence the uterine environment's stability and its ability to heal following injury \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Despite these promising leads, the multifaceted nature of IUA remains poorly understood, and integrating these diverse omics approaches could offer new perspectives on its pathogenesis. Compared to single-omics approaches, multi-omics analysis offers a more holistic view of the pathophysiological processes of IUA; it provides specific insights into molecular mechanisms, thereby aiding in the identification of new therapeutic targets and biomarkers.\u003c/p\u003e\u003cp\u003eThis research utilizes transcriptomic profiling, metabolomic analysis, and microbiome assessment to attain a thorough understanding of the factors associated with IUA. Endometrial tissue and lavage samples were obtained from individuals diagnosed with IUA as well as healthy controls for transcriptomic, microbiome, and metabolomic sequencing. And then, candidate genes, microbes, and metabolites were identified through differential analysis. Ultimately, the biological functions of the identified key genes were explored through functional analysis, regulatory network examination, and drug prediction. By leveraging the power of integrative omics, we hope to advance the current understanding of IUA and contribute to improved clinical outcomes for patients affected by this challenging condition.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Sample collection\u003c/h2\u003e\u003cp\u003e The study was conducted in compliance with the Declaration of Helsinki, and the protocol received approval from the Ethics Committee of Chongqing Health Center for Women and Children (Approval NO. 2021044).This study collected endometrial tissue samples from 6 IUA patients and 6 controls for transcriptome sequencing, uterine lavage samples from 6 IUA patients and 6 controls for microbiome sequencing (16S rRNA gene sequencing), and uterine lavage samples from 21 IUA patients and 21 controls for metabolome sequencing.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Transcriptome sequencing and data pre-processing\u003c/h2\u003e\u003cp\u003eTotal RNA was extracted from the tissue samples utilizing TRIzol\u0026reg; Reagent in accordance with the manufacturer's instructions. The quality of the RNA was assessed with the 5300 Bioanalyser (Agilent) and quantified using the ND-2000 (NanoDrop Technologies). The parameters for acceptable DNA quality included a concentration ranging from 1.8 to 2.2 ng/\u0026micro;L, an OD260/230 ratio of at least 2.0, a RNA Quality Number (RQN) of 6.5 or greater, and a 28S:18S ratio of 1.0 or higher, with a minimum yield of 1g.Following sequencing, the detection accuracy and error rate of each base were assessed using a specified formula:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Q=-10{log}_{10}\\left(e\\right)\\)\u003c/span\u003e\u003c/span\u003e,where \u003cem\u003eQ\u003c/em\u003e represented the quality score of each base, and \u003cem\u003ee\u003c/em\u003e denoted the probability of that base being incorrectly identified. Low-quality reads were subsequently removed using the fastpQC (v 0.12.0) package \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e, and clean data were obtained using the cutadapt function. The sequencing data were then aligned to the genome (\u003cem\u003eHomo sapiens\u003c/em\u003e, GRCh38) using Hierarchical Indexing for Spliced Transcript Alignment version 2 (HISAT2) (v 2.2.1) software \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Following alignment, the data were processed with StringTie (v 3.0.0) software \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e to generate gene count data, which were subsequently converted to transcripts per million (TPM) based on gene length.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Principal component analysis (PCA) and gene set variation analysis (GSVA)\u003c/h2\u003e\u003cp\u003eBased on the pre-processed transcriptome sequencing data, PCA was performed using the PCA function from the FactoMineR (v 2.9) package \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003eto assess the differences between IUA and control samples. To further examine the divergent biological functions between these groups, the curated gene set \"c2.all.kegg.symbols.gmt\" was referenced from the Molecular Signatures Database (MSigDB) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/msigdb\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/msigdb\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Employing single-sample GSEA (ssGSEA) function from GSVA (v 1.50.0) package \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e, the GSVA scores of all samples were quantified, thereby allowing the biological functions distinctive to IUA and control samples to be compared by the limma (v 3.58.1) package \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. These differentially enriched gene sets were displayed, with |t| \u0026gt;2 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 demarcating statistical significance.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Differential expression analysis and functional analysis\u003c/h2\u003e\u003cp\u003eFollowing pre-processing of the transcriptome sequencing data, differential expression analysis was conducted using the DESeq2 (v 3.54.0) package \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e[|log\u003csub\u003e2\u003c/sub\u003eFold Change (FC)| \u0026gt;2.0, adj.p\u0026thinsp;\u0026lt;\u0026thinsp;0.05] to identify differentially expressed genes (DEGs) referred to as candidate genes. The volcano plot was generated to visualize candidate genes using the ggplot (v 3.5.1) package \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e, with the top ten up-regulated and down-regulated genes highlighted according to their |log2FC| values (ranked from high to low).Meantime, the candidate genes were visualized utilizing a heatmap employing the ComplexHeatmap (v 2.21.1) package \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTo delve into the cellular functions associated with these candidate genes and their relevant pathways, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted utilizing the clusterProfiler (v 4.10.0) package, with statistical significance set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. The GO analysis encompassed three primary categories: biological process (BP), cellular component (CC), and molecular function (MF). Subsequently, these candidate genes were uploaded to the Search Tool for the Retrieval of Interacting Genes (STRING) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://string-db.org\u003c/span\u003e\u003cspan address=\"http://string-db.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to elucidate their protein-level interactions (confidence level\u0026thinsp;\u0026gt;\u0026thinsp;0.15). These interactions were then visualized utilizing Cytoscape (v 3.10.2) software \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e to construct a protein-protein interaction (PPI) network. In addition, to investigate the chromosomal localization of these candidate genes, the Circos (v 1.38.0) package \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e was employed to visualize their positions on chromosomes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Microbiome sequencing and data pre-processing\u003c/h2\u003e\u003cp\u003e Total microbial genomic DNA was extracted from uterine lavage samples utilizing the FastPure Stool DNA Isolation Kit (MJYH, Shanghai, China) in accordance with the manufacturer's guidelines. The quality and concentration of the extracted DNA were assessed through 1.0% agarose gel electrophoresis and a NanoDrop\u0026reg; ND-2000 spectrophotometer (Thermo Scientific Inc., USA), with samples stored at -80 ℃ prior to further analysis. The present investigation, the hypervariable regions V3-V4 of the bacterial 16S rRNA gene were amplified employing primer pairs 338F (5'-ACTCCTACGGGAGGCAGCAG-3') and 806R (5'-GGACTACHVGGGTWTCTAAT-3') [1], utilizing a T100 Thermal Cycler (BIO-RAD, USA). The PCR reaction mixture including 4 \u0026micro;L 5 \u0026times; Fast Pfu buffer, 2 \u0026micro;L 2.5 mM dNTPs, 0.8 \u0026micro;L each primer (5 \u0026micro;M), 0.4 \u0026micro;L Fast Pfu polymerase, 10 ng of template DNA, and ddH2O to a final volume of 20 \u0026micro;L. PCR amplification cycling conditions were as follows: an initial denaturation step at 95\u0026deg;C for 3 minutes, followed by 27 cycles of denaturation at 95\u0026deg;C for 30 seconds, annealing at 55\u0026deg;C for 30 seconds, and extension at 72\u0026deg;C for 30 seconds. The final extension was conducted at 72\u0026deg;C for 5 minutes. The amplified products were quantified using a Synergy HTX (Biotek, USA). The purified amplicons were subsequently combined in equimolar concentrations and subjected to paired-end sequencing on an Illumina NextSeq 2000 PE300 platform (Illumina, San Diego, USA), following the standard protocols established by Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China).\u003c/p\u003e\u003cp\u003eAfter sequencing, quality control (QC) was performed using DADA2 and Vsearch, resulting in the identification of amplicon sequence variants (ASVs). For species annotation of each ASV, the classify-sklearn algorithm from the QIIME2 (v 2023.3) software \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e was employed. To evaluate the adequacy of the sequencing data, rarefaction curves were generated for each group. Rank abundance curves were also plotted to visualize the species richness and evenness within each group. The relationship between species diversity and sample size was analyzed using the vegan (v 2.6.4) package \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Alpha and beta diversity analyses and species composition profiling\u003c/h2\u003e\u003cp\u003eAlpha diversity refers to the richness, diversity, and evenness of species within a locally homogeneous habitat, also known as within-habitat diversity \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. In this study, the Chao1, Observed species, Shannon, and Simpson indices were calculated to assess the richness and diversity of microbiome sequencing data using the cal alphadiv function from the microeco (v 1.8.0) package \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. The Wilcoxon test was then applied to compare the differences in these 4 indices between IUA and control samples (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and the results were visualized using box plots generated by the ggpubr (v 0.6.0) package \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eBeta diversity, on the other hand, reflects the species composition differences or species turnover between communities along an environmental gradient, and is also referred to as between-habitat diversity \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. In this study, the Bray-Curtis and Jaccard indices were computed using the cal_betadiv function from the microeco (v 1.8.0) pcakage, followed by Principal Coordinates Analysis (PCoA) conducted using the cal_ordination function (method = \"PCoA\"). Next, non-metric multidimensional scaling (NMDS) analysis was conducted to further verify the differences between IUA and control samples (Stress\u0026thinsp;\u0026lt;\u0026thinsp;0.1), using the cal_ordination function from the microeco (v 1.8.0) package (method = \"NMDS\").\u003c/p\u003e\u003cp\u003eBesides, to explore the microbial community composition between IUA and control samples, the relative abundance of each sample in each group was determined. Bar plots were then generated using the ggplot2 (v 3.5.1) package \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e to visualize the top 10 most abundant microbes at the 2 taxonomic levels (phylum and genus).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Functional and linear discriminant analysis effect size (LEfSe) analyses\u003c/h2\u003e\u003cp\u003eSubsequently, based on microbiome sequencing data, functional analysis was performed using the trans_func function to investigate the biological functions in IUA and control samples. The Linear discriminant analysis effect size (LEfSe) is a statistical method that combines non-parametric testing with linear discriminant analysis to assess the effect size of features, enabling the identification of differentially abundant taxa across groups \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. In this study, LEfSe was performed using the trans diff function from the microeco (v 1.8.0) package (method = \"lefse\") to identify microbes with significant differences in abundance between IUA and control samples. Taxa with the adj.p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and the LDA score\u0026thinsp;\u0026gt;\u0026thinsp;2 were considered significant. and microbes exhibiting significant differences in abundance at the genus levels between IUA and control samples were defined as candidate microbes. The distribution of LDA scores and the differences in abundance of candidate microbes (sorted in descending order based on LDA values) were visualized using bar plots generated with the ggplot2 (v3.5.1) package.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Metabolome sequencing and data pre-processing\u003c/h2\u003e\u003cp\u003eA total of 100 \u0026micro;L of liquid sample was introduced into a 1.5 mL centrifuge tube, accompanied by 400 \u0026micro;L of a solvent mixture composed of acetonitrile and methanol in a 1:1 volume ratio, which included an internal standard, L-2-chlorophenylalanine, at a concentration of 0.02 mg/mL for the purpose of metabolite extraction. The combination underwent vortex mixing for 30 seconds, followed by low-temperature sonication for 30 minutes at 5\u0026deg;C and 40 kHz. Subsequently, the samples were stored at -20\u0026deg;C for 30 minutes to allow for protein precipitation. Following this, centrifugation was performed for 15 minutes at 4\u0026deg;C and 13,000 g, after which the supernatant was carefully removed and evaporated under a nitrogen stream. The residues were then re-dissolved in 100 \u0026micro;L of a solution (acetonitrile: water\u0026thinsp;=\u0026thinsp;1:1) and extracted using low-temperature ultrasonication for 5 minutes at 5\u0026deg;C and 40 KHz, followed by a further centrifugation at 13,000 g and 4\u0026deg;C for 10 minutes. The resulting supernatant was transferred into sample vials for subsequent LC-MS/MS analysis.\u003c/p\u003e\u003cp\u003eAfter sequencing, the raw data from mass spectrometry were converted into mzXML format using Proteowizard (v 3.0) tool \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Peak detection, extraction, alignment, and integration were carried out with XCMS \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. For initial compound identification, the primary m/z values of the ions detected by XCMS were matched against databases such as Human Metabolome Database (HMDB) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://hmdb.ca/\u003c/span\u003e\u003cspan address=\"https://hmdb.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and KEGG database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genome.jp/kegg/\u003c/span\u003e\u003cspan address=\"https://www.genome.jp/kegg/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using the MetaX (v 2.0.0) software \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e, providing the primary metabolites identification results. Additionally, fragmentation of each ion within the mass spectrometer generated a secondary spectrum, which was then matched to a curated database for secondary metabolites identification.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9 PCA and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA)\u003c/h2\u003e\u003cp\u003eFollowing this, PCA was employed to assess the quality of metabolome data among the IUA, control, and QC samples. SIMCA software (v 16.0.2) was used to process the data, including normalization and log transformation, followed by automated modeling analysis. Additionally, Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) was performed to further investigate the differences among the three groups in liver, fecal, and bile acid metabolomics. For OPLS-DA, unit variance scaling (UV) normalization and log transformation were applied to the ion data, and the model was constructed accordingly. To validate the quality of the models, 7-fold cross-validation was conducted.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.10 Differential expression analysis and metabolic pathway enrichment analysis\u003c/h2\u003e\u003cp\u003eUnivariate statistical analysis methods, such as the t-test and analysis of variance (ANOVA), are primarily focused on identifying independent changes in metabolite levels. In this study, the Wilcoxon-Mann-Whitney test was utilized to identify differentially expressed metabolites (DEMs) between IUA and control groups, with DEMs being defined as candidate metabolites for further analyses. The threshold for selection was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, |log\u003csub\u003e2\u003c/sub\u003eFC| \u0026gt;0.5, and Variable Importance in the Projection (VIP)\u0026thinsp;\u0026gt;\u0026thinsp;1, where VIP reflected the contribution of each variable to the projection of the first principal component in the OPLS-DA model. To investigate the signaling pathways associated with candidate metabolites, the metabolic pathway analysis was conducted by inputting these candidate metabolites into the MetaboAnalyst (v 6.0) platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.metaboanalyst.ca/\u003c/span\u003e\u003cspan address=\"https://www.metaboanalyst.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.11 Multi-omics integration analyses\u003c/h2\u003e\u003cp\u003eTo investigate the relationships between candidate genes and candidate microbes, Spearman correlation analysis was performed on transcriptome (IUA: control\u0026thinsp;=\u0026thinsp;6: 6) and microbiome (IUA: control\u0026thinsp;=\u0026thinsp;6: 6) sequencing data using the psych (v 2.4.3) package \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Significant associations were identified (|correlation coefficient (cor)| \u0026gt;0.7, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), resulting in the identification of key genes and key microbes. The correlation relationships were visualized with a heatmap generated using the ggplot2 (v 3.5.1) package \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e, and an interaction network between key genes and key microbes was also constructed and visualized using Cytoscape (v 3.7.1) software.\u003c/p\u003e\u003cp\u003eIn the same way, candidate genes gained from transcriptome (IUA: control\u0026thinsp;=\u0026thinsp;6: 6) sequencing were analyzed in conjunction with candidate metabolites derived from metabolome (IUA: control\u0026thinsp;=\u0026thinsp;6: 6, using samples matched to the transcriptome) sequencing. Spearman correlation analysis was used to explore the relationships between candidate genes and candidate metabolites (|cor| \u0026gt;0.7, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), leading to the identification of key genes 2 and key metabolites 1. In addition, a heatmap generated with the ggplot2 (v 3.5.1) package was employed to visualize the correlations, while a regulatory interaction network between key genes 2 and key metabolites 1 was constructed using Cytoscape (v 3.7.1) software.\u003c/p\u003e\u003cp\u003eLikewise, candidate microbes obtained from microbiome (IUA: control\u0026thinsp;=\u0026thinsp;6: 6) sequencing were subjected to combined analysis with candidate metabolites identified through metabolome (IUA: control\u0026thinsp;=\u0026thinsp;6: 6, using samples matched to the transcriptome) sequencing. Spearman correlation analysis was employed to uncover the relationships between candidate microbes and candidate metabolites (|cor| \u0026gt;0.7, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), yielding the identification of key microbes 2 and key metabolites 2. To visualize these correlations, a heatmap was generated with the ggplot2 (v 3.5.1) package, and a network depicting the interactions between key microbes 2 and key metabolites 2 was built using Cytoscape (v 3.7.1) software.\u003c/p\u003e\u003cp\u003eFurther analysis was conducted to identify key genes, key microbes, and key metabolites. To be specific, the key genes 1 and the key genes 2 were intersected to identify the key genes for this study. In an analogous manner, the intersection of key microbes 1 and key microbes 2 identified the key microbes in this study, while the overlap between key metabolites 1 and key metabolites 2 highlighted the key metabolites. These intersections were all visualized using ggvenn (v 0.1.10) package \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Leveraging the key genes, key microbes, and key metabolites, the construction of a regulatory network was facilitated through Cytoscape (v 3.9.1) software.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.12 Comprehensive functional characterization analysis of key genes\u003c/h2\u003e\u003cp\u003eAfter identifying the key genes, a series of analyses were conducted to explore their characteristics. Originally, to explore the functional similarity among key genes, the functional similarity of key genes was scored using the GOSemSim (v 3.19) package \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Subsequently, genes related to the functions of these key genes and their shared activities were predicted using the GeneMANIA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.genemania.org/\u003c/span\u003e\u003cspan address=\"http://www.genemania.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), leading to the construction of a gene-gene interaction (GGI) network for visualization. Further analysis included obtaining the FASTA files of key genes from the national center for biotechnology information (NCBI) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/gene/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/gene/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and using the mRNALocater (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bio-bigdata.cn/mRNALocater/\u003c/span\u003e\u003cspan address=\"http://bio-bigdata.cn/mRNALocater/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to predict the subcellular localization of key genes.\u003c/p\u003e\u003cp\u003eMoreover, to clarify the potential biological pathways of the key genes, the Spearman analyses between each key gene and other genes were performed through psych (v 2.1.6) package in the transcriptome sequencing data. The correlation coefficients were then ranked in descending order to obtain a gene ranking list for each key gene. Subsequently, the \"c2.cp.kegg.v2023.1.Hs.symbols.gmt\" gene set from the MSigDB served as the reference for gene set enrichment analysis (GSEA), conducted for each key gene using the clusterProfiler (v 4.11.0) package with |normalized enrichment score (NES)| \u0026gt;1 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Finally, the top 5 pathways of the enrichment results were visualized using the enrichplot (v 1.18.3) package \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e, ordered by p values from low to high.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e2.13 Construction of regulatory network and drug prediction\u003c/h2\u003e\u003cp\u003eIn order to delve into the molecular regulatory mechanisms governing key genes, the miRDB and DIANA-microT databases from the multiMiR (v 3.19) package (PMID: 25063298) were employed to predict miRNAs potentially targeting these key genes, and the common miRNAs that appeared across both databases were pinpointed. Subsequently, the starbase database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://starbase.sysu.edu.cn/index.php\u003c/span\u003e\u003cspan address=\"http://starbase.sysu.edu.cn/index.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to predict the lncRNAs corresponding to these common miRNAs, facilitating the construction of an lncRNA-miRNA-mRNA regulatory network. Furthermore, the circbank database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rnasysu.com/encori/\u003c/span\u003e\u003cspan address=\"https://rnasysu.com/encori/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was leveraged to predict the interactions between cirRNAs and common miRNAs, leading to the creation of a cirRNA-miRNA-mRNA regulatory network. The results from these networks were graphically represented through Cytoscape (v 3.10.2) software.\u003c/p\u003e\u003cp\u003eAdditionally, the potential drugs (approved) were predicted based on key genes from the drug gene interaction database (DGIdb) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dgidb.org/\u003c/span\u003e\u003cspan address=\"https://dgidb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the interaction relationships between potential drugs and key genes were visualized by Cytoscape (v 3.8.2) software.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e2.14 Statistical Analysis\u003c/h2\u003e\u003cp\u003eAll analyses were conducted in R (v 4.2.2) software. To determine whether there were statistical differences between the 2 groups, the Wilcoxon test and Wilcoxon-Mann-Whitney test was employed. The value of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Acquisition of robustness transcriptome sequencing data\u003c/h2\u003e\u003cp\u003eIn this study, the detection accuracy and error rate of each measured base were evaluated (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e), and the quality of the transcriptome sequencing raw data was assessed (\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e). After QC and comparison, the alignment rate of all 12 samples was found to be higher than 85%, indicating high-quality sequencing data that met the requirements for subsequent analysis (\u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e). Overall, these results confirmed the robustness of the transcriptome sequencing data for further analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Exploration of biological pathway differences between IUA and control samples\u003c/h2\u003e\u003cp\u003eThe PCA provided insights into the mingling distribution of samples between IUA and control samples, underscoring its capability to discriminate between them effectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Following the GSVA conducted between IUA and control samples, a total of 10 significant differential pathways were identified, with 9 up-regulated and 1 down-regulated in IUA samples. The up-regulated pathways included \"fatty acid metabolism\", \"propanoate metabolism\", \"glyoxylate and dicarboxylate metabolism\", \"butanoate metabolism\", \"valine leucine and isoleucine degradation\", \"beta alanine metabolism\", \"amino sugar and nucleotide sugar metabolism\", \"SNARE interactions in vesicular transport\", and \"citrate cycle (TCA cycle)\". The only down-regulated pathway was the \"circadian rhythm mammal\" (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). These insights significantly contributed to elucidating the complex biological mechanisms underlying the IUA and could inform future clinical interventions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Identification and enrichment analysis of 46 candidate genes\u003c/h2\u003e\u003cp\u003eFollowing the differential expression analysis conducted in the transcriptome sequencing data, a sum of 46 DEGs were identified between IUA and control samples, comprising 29 up-regulated and 17 down-regulated genes for subsequent analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), and these genes were selected as candidate genes. Further analysis of these 46 candidate genes utilizing GO and KEGG pathway analysis led to the identification of 82 BP entries, such as \"steroid metabolic process\", \"hormone metabolic process\", and \"positive regulation of T cell activation\" (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, \u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e). No CC entries were found, While 2 MF entries, encompassed \"heme binding\" and \"tetrapyrrole binding\", were identified (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, \u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e). Moreover, there were 17 KEGG pathways enriched and the top 10 results of KEGG enrichment were visualized, such as \"ovarian steroidogenesis\", \"efferocytosis\", and \"steroid hormone biosynthesis\" (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, \u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e). These analyses provided a significant foundation for the functional significance of candidate genes in the progression of IUA.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAdditionally, after removing 1 discrete protein, the remaining 45 candidate genes were interacted in the PPI network, with IL1A and CD36 exhibiting strong interactions with other genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Chromosomal localization results determined that the presence of multiple genes on chromosomes 7, 11, and 20. Specifically, there were 5 candidate genes located on chromosome 7, 5 candidate genes on chromosome 11, and 4 candidate genes on chromosome 20 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). These findings suggested that the candidate genes might be involved in region-specific genetic regulation, potentially contributing to distinct biological processes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Microbiome sequencing depth assessment\u003c/h2\u003e\u003cp\u003eTo evaluate the adequacy of sequencing depth in microbiome data, rarefaction curves were employed. The stabilization of the curve suggested that any further increase in sequencing depth would not significantly uncover new amplicon sequence variants (ASVs), indicating that the existing sequencing depth was sufficient to capture the diversity present within the samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Rank abundance curves provided insights into the richness and evenness of each sample (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The species accumulation boxplot illustrated an increase in observed species count correlating with the number of samples analyzed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Overall, these analyses indicated that the sequencing depth was adequate, revealing distinct differences in microbial diversity between IUA and control samples.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Differences in Alpha diversity indices and Beta diversity\u003c/h2\u003e\u003cp\u003eThe Alpha diversity indices (Chao1, Observed species, Shannon, and Simpson) were assessed (\u003cb\u003eSupplementary Table\u0026nbsp;5\u003c/b\u003e). Differential analysis between IUA and control samples showed significant differences in Chao1 and Observed species indices between IUA and control samples (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). No significant differences in Shannon and Simpson indices were found between IUA and control samples (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Furthermore, the Beta diversity analysis, through PCoA, showed clear differences in microbial community structure between IUA and control samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). NMDS analysis confirmed the reliability of the results with a stress value of 0.08 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSubsequently, the composition of species between IUA and control samples was analyzed at 2 taxonomic levels (phylum and genus), and the Proteobacteria phylum, Firmicutes phylum, \u003cem\u003eLactobacillus\u003c/em\u003e genus, and \u003cem\u003eAcinetobacter\u003c/em\u003e genus exhibited the higher relative abundance, highlighting their dominance in the microbiome composition (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). These results offered valuable insights into the microbial diversity between different samples, which might help in understanding the relationship between microbes and individual conditions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Distinct microbial signatures in IUA and control samples\u003c/h2\u003e\u003cp\u003eFurther analysis of the microbes from the IUA and control samples led to the identification of functional characteristics. The IUA samples showed an enhancement in anaerobic chemoheterotrophy (energy source) and fermentation (carbon cycling). In contrast, the control samples exhibited a high abundance in aerobic chemohetertrophy function (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). These findings suggested that specific functions might play a crucial role in distinguishing the microbes profiles between IUA and control samples. Moreover, LEfSe analysis revealed 14 differential microbes between IUA and control samples, with 4 of them showing significant differences at the genus level (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). These 4 differential microbes were identified as candidate microbes, all of which exhibited higher abundance in the IUA samples (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). These results underscored the potential functional and taxonomic differences that might contribute to the microbial signature associated with IUA, providing insights into the underlying mechanisms that distinguished it from the control group.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Generation of stable and consistent metabolome sequencing data\u003c/h2\u003e\u003cp\u003eThe PCA provided insights into the mingling distribution of samples from IUA and control (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), while OPLS-DA showcased a dispersed distribution between IUA and control samples, underscoring its capability to discriminate between them effectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). To validate the robustness of the OPLS-DA model, permutation testing, with 200 iterations, was carried out. For the comparison between IUA and control samples, the model demonstrated commendable performance (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). These consistent data generations underscored the reliability of the metabolome sequencing data, ensuring that the results could be confidently applied for further biological insights.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e3.8 Identification and function exploration of candidate metabolites\u003c/h2\u003e\u003cp\u003eSubsequent analysis, employing p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, |log\u003csub\u003e2\u003c/sub\u003eFC| \u0026gt;0.5, and VIP\u0026thinsp;\u0026gt;\u0026thinsp;1 as threshold, led to the identification of 11 DEMs which were selected as candidate metabolites, comprising 4 up-regulated and 7 down-regulated metabolites (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Enrichment analysis identified 11 shared pathways among these candidate metabolites, including \"felbamate metabolism pathway\", \"GPCR downstream signaling\", and \"signal transduction\" (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). These findings suggested that the candidate metabolites and their associated pathways may play critical roles in regulating metabolic and signaling processes, potentially influencing the underlying mechanisms of the studied condition.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e3.9 Strong correlations among candidate genes, candidate metabolites, and candidate microbes\u003c/h2\u003e\u003cp\u003eEmploying the previously discerned 46 candidate genes and 4 candidate microbes for an investigation into the symbiotic interplay between genes and microbes revealed a distinct pattern: 18 candidate genes manifested significantly correlations with 4 candidate microbes (|cor| \u0026gt;0.7, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA; \u003cb\u003eSupplementary Table\u0026nbsp;6\u003c/b\u003e). Thus, within this framework, 18 key genes 1 along with 4 key microorganisms 1 were obtained. Concurrently, a regulatory network was constructed, comprising 22 nodes and 35 edges, to elucidate the correlations between key genes 1 and key microbes 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn a parallel probing using the ascertained 46 candidate genes and 11 candidate metabolites to delve into the gene-metabolite nexus, findings revealed that 17 candidate genes were interlinked with 7 candidate metabolites (|cor| \u0026gt;0.7, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC; \u003cb\u003eSupplementary Table\u0026nbsp;7\u003c/b\u003e). Based on this information, 17 key genes 2 and 7 key metabolites 1 were derived. A sophisticated regulatory network comprising 12 nodes and 20 edges materialized from this analysis, mapping out the intricate gene-metabolite associations (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003eHarnessing the insights from the designated 4 candidate microbes and 11 candidate metabolites to trace the connective threads between candidate microbes and candidate metabolites, the examination culminated in 2 candidate microbes exhibiting correlations with 2 candidate metabolites (|cor| \u0026gt;0.7, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE; \u003cb\u003eSupplementary Table\u0026nbsp;8\u003c/b\u003e). Employing this results, 2 candidate microbes\u003c/p\u003e\u003cp\u003e2 and 2 candidate metabolites 2 were extracted. This culminated in the crafting of a comprehensive correlation regulatory network with 4 nodes and 3 edges (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eF).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\u003ch2\u003e3.10 Identification of 7 key genes, 2 key microbes, and 2 key metabolites\u003c/h2\u003e\u003cp\u003eThe intersection of key genes 1 and key genes 2 revealed 7 key genes within the scope of this study: DUSP2, IL1A, POF1B, ICAM4, CX3CL1, HTR2C, and SIX1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). Simultaneously, an intersection between key microbes 1 and key microbes 2 brought 2 key microbes (\u003cem\u003eg__Clostridium_sensu_stricto_1\u003c/em\u003e and \u003cem\u003eg__Acidisoma\u003c/em\u003e)(Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB). Besides, the culmination of this meticulous research revealed 2 key metabolites (Pe(18:3(6Z,9Z,12Z)/18:1(9Z)) and 1,3,6-Trihydroxy-2-(3-Methylbut-2-Enyl)Xanthen-9-One) by intersected key metabolites 1 and key metabolites 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC). Under these circumstances, by retaining these interactions that simultaneously involved key genes, key microbes, and key metabolites, a comprehensive regulatory network was carefully constructed. This network comprised 11 nodes and 15 edges, forming an intricate web of biological relationships pivotal to the themes explored in this research (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003e3.11 Functional analysis of key genes\u003c/h2\u003e\u003cp\u003eThe results of the functional similarity analysis indicated that CX3CL1 had the highest similarity score (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA). Based on the key genes, a GGI network was constructed by GeneMANIA, yielding 20 genes related to the key genes, such as CXCL2, IL6, and CCL3, which were collectively involved in functions like the \"cellular response to molecule of bacterial origin\", \"response to lipopolysaccharide\", and \"cellular response to biotic stimulus\" (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eB). Subcellular localization results indicated that DUSP2, IL1A, POF1B, ICAM4, CX3CL1, and SIX1 primarily presented within the cytoplasm, whlie HTR2C was located in the cell nucleus (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eC). These findings implied that key genes functioned through diverse molecular interactions and cellular localization, highlighting their complex roles.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSubsequently, the pathway enrichment analysis revealed significant enrichment of key genes in multiple pathways: DUSP2 was associated with 24 pathways (such as \"fatty acid metabolism\"), IL1A with 7 pathways (such as \"cell cycle\"), POF1B with 18 pathways (such as \"DNA replication\"), ICAM4 with 18 pathways (such as \"adipocytokine signaling pathway\"), CX3CL1 with 18 pathways (such as \"cell cycle\"), HTR2C with 30 pathways (such as \"fatty acid metabolism\"), and SIX1 with 5 pathways (such as \"inositol phosphate metabolism\") (\u003cb\u003eSupplementary Table\u0026nbsp;9\u003c/b\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003e3.12 Decipherment of regulatory networks and key gene-targeted drug discovery\u003c/h2\u003e\u003cp\u003eEmploying the miRDB and DIANA-microT databases within the multiMiR (v 3.19) package, a prediction analysis was performed to identify associations between key genes and miRNAs. By intersecting the results from both databases, 52 common miRNAs were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eA, \u003cb\u003eSupplementary Table\u0026nbsp;10\u003c/b\u003e). Additionally, 29 lncRNAs were retrieved from the starbase database, enabling the construction of a lncRNA-miRNA-mRNA regulatory network (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eB, \u003cb\u003eSupplementary Table\u0026nbsp;11\u003c/b\u003e). Furthermore, the circbank database was utilized to predict circRNAs associated with the common miRNAs, yielding 18 circRNAs, which were used to construct a circRNA-miRNA-mRNA network (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eC, \u003cb\u003eSupplementary Table\u0026nbsp;12\u003c/b\u003e). Within these networks, complex interaction relationships were identified, such as NEAT1-hsa-miR-185-5p-SIX1, XIST-hsa-miR-106a-5p-DUSP2, hsa_circ_0013871-hsa-miR-4533-HTR2C, and hsa_circ_0013870-hsa-miR-4498-CX3CL1. These findings provide a foundation for further investigation into the roles of these interactions in the mechanisms underlying IUA.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eUsing the DGIdb database, potential drugs targeting these key genes were predicted to identify therapeutic candidates. A total of 61 drugs were identified, including 1 targeting CX3CL1, 3 targeting DUSP2, 53 targeting HTR2C, and 4 targeting IL1A, while no drugs were predicted for the remaining key genes (\u003cb\u003eSupplementary Table\u0026nbsp;13\u003c/b\u003e). Based on these results, a drug-biomarker interaction network was constructed (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eD), and olanzapine was predicted as a potential therapeutic drug due to its association with 2 key genes (IL1A and HTR2C). These findings highlight potential therapeutic strategies for targeting these key genes, offering valuable insights for further experimental validation and clinical translation.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIUA can lead to severe complications such as infertility, amenorrhea (absence of menstruation), reduced menstrual flow (hypomenorrhea), and recurrent abortions. Current treatments include TCRA, hormonal therapy, and stem cell-based therapies; however, these approaches often have limitations, including high recurrence rates and incomplete restoration of fertility. Given these limitations, multi-omics integration is a powerful tool for unraveling the complexity of IUA, offering new avenues for diagnosis, treatment, and management of this challenging condition. In this article, we obtained candidate genes, candidate microorganisms, and candidate metabolites from sequencing data of the transcriptome, microbiome, and metabolome, respectively. Through multi-omics analysis, we identified seven key genes, two key microorganisms, and two key metabolites. Finally, we explored the functional characteristics, regulatory networks, and drugs targeting.\u003c/p\u003e\u003cp\u003eAfter differential gene expression analysis, a total of 46 DEGs were identified between IUA and control samples. The identification of these DEGs was crucial, as they may serve as biomarkers for diagnosis or therapeutic targets. Several of these genes have been reported to be involved in the occurrence of fibrosis, such as CD36, which has strong interactions with other genes in the PPI network. Furthermore, some studies have suggested that inhibiting CD36 or targeting CD36 can alleviate fibrotic lesions in pulmonary \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Thus, the DEG that we discovered may be involved in endometrial fibrosis. However, this biological process can be very complex, involving multiple pathways and biological processes. Meanwhile, microbial diversity analysis showed that the phyla with relatively high abundance of endometrial microbiota were Proteobacteria, Firmicutes, Bacteroidetes, and Actinobacteria in the present study. These results were consistent with previous studies on the uterine microbiota in patients with endometrial cancer, infertility, and endometrial polyps \u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. In terms of diversity, the IUA group and the control group showed significant differences in α-diversity and β-diversity. These differences indicated that the changes in the microbiota structure may be closely related to the occurrence of IUA. The identification of specific candidate microbes with differential abundance further supported the hypothesis that microbial dysbiosis may play a role in IUA. The functional analysis of the microbiome revealed distinct metabolic capabilities, with IUA samples exhibiting enhanced anaerobic chemoheterotrophy, which may reflect an adaptive response to the altered uterine environment. The candidate metabolite, perlolyrine, a strongly anti-inflammatory β-carboline, has been found to suppress vaginal inflammation in mice and was depleted in people with BV\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. In our study, the level of perlolyrine, a specific metabolite, was found to be decreased in patients with intrauterine adhesions. It was speculated that the reduction of perlolyrine may weaken the inhibition of the inflammatory response, thereby accelerating the development of intrauterine adhesions. Moreover, the metabolomic profiling identified 11 differentially expressed metabolites, with pathways such as \"GPCR downstream signaling\" being significantly enriched. This suggested that metabolic alterations may be integral to the pathophysiology of IUA, potentially influencing cellular signaling and metabolic processes. The strong correlations observed among candidate genes, candidate metabolites, and candidate microbes underscored the interconnectedness of these biological systems, providing a comprehensive view of the molecular landscape in IUA. Collectively, these findings not only enhance our understanding of the underlying mechanisms of IUA but also pave the way for future research aimed at developing targeted therapeutic strategies.\u003c/p\u003e\u003cp\u003eThrough multi-omics analysis, we identified seven key genes, two key microorganisms, and two key metabolites with strong correlations. Among these, the key gene CX3CL1 exhibited the highest similarity score in functional similarity analysis, which assesses the functional relatedness of genes based on their biological roles. Fractalkine (CX3CL1), a chemotactic membrane-bound adhesion molecule, binds specifically to CX3C chemokine receptor 1 (CX3CR1). Notably, numerous studies have demonstrated that the CX3CL1/CX3CR1 signaling pathway is closely associated with fibrogenesis in multiple organs\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e.Jiali Wang found that the number of CX3CR1\u0026thinsp;+\u0026thinsp;monocyte/macrophages were significantly elevated in the endometrial tissue of IUA patients. Furthermore, blocking IL-34 in the LPS-IUA model improved endometrial fibrosis and reduced the number of CX3CR1\u0026thinsp;+\u0026thinsp;monocyte/macrophages\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. In a mouse model of liver fibrosis, the levels of liver CX3CL1 and CX3CR1 mRNA at 8 weeks post-infection were notably upregulated, which indicated that CX3CL1 and CX3CR1 might accelerate the process of liver fibrosis after \u003cem\u003eSchistosoma haematobium infection\u003c/em\u003e \u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. In addition, there were also studies indicating that CX3CL1 was associated with fibrosis of the kidneys, pulmonary and heart. This is the first report of an association between CX3CL1/CX3CR1 and IUA. Other key genes have also been reported to be related to the process of fibrosis .IL-1α mainly promoted lung and liver fibrosis in the early stage of organ repair, thus IL-1α produced by endothelial cells plays a unique role in promoting organ fibrosis\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. SIX1 was the key transcription factor of EMT, and EMT of airway epithelium was the key pathological process of pulmonary fibrosis\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. Furthermore, the key microorganism \u003cem\u003eClostridium_sensu_stricto_1\u003c/em\u003e has been reported to be associated with various fibrosis and gynecological diseases. It was significantly increased in mice with nonalcoholic steatohepatitis (NASH), which may be related to inflammatory infiltration and liver fibrosis \u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. Notably, patients with polycystic ovary syndrome (PCOS) showed an increased abundance of \u003cem\u003eClostridium_sensu_stricto_1\u003c/em\u003e compared to other groups\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. The process of fibrosis was related to multiple metabolic pathways. In this study, differential metabolites were enriched in multiple pathways, among which GPCR downstream signaling was a core signaling pathway related to fibrosis. Specifically, GPCR activates phospholipase Cβ (PLCβ) via Gαq/11 to generate IP3 and DAG. This triggered Ca\u0026sup2;⁺ release, and PKC activation, which promoted the fibroblast proliferation and collagen deposition, such as in liver fibrosis and pulmonary fibrosis \u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e\u0026zwnj;.In summary, the interactions among genes, metabolites, and microorganisms were found to play a critical role in the local inflammatory microenvironment, fibrotic formation, and immune repair processes within the uterus, offering new insights into the pathological mechanisms of intrauterine adhesion and potential targeted interventions.\u003c/p\u003e\u003cp\u003eThe enrichment analysis of key genes revealed significant pathways among multiple pathways, such as \"fatty acid metabolism,\" \"cell cycle,\" and \"DNA replication.\" Notably, the fatty acid metabolism pathways have been widely studied in fibrotic diseases. Previous research indicated that the fatty acid metabolism pathway played an important role in cardiac, liver, and renal fibrotic diseases\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e. There was a close relationship between cell cycle arrest and fibrosis, especially in pathological processes such as renal fibrosis. In several renal injury models, a causal link has been identified between the G2/M phase arrest of renal tubular epithelial cells and the subsequent development of renal fibrosis. For instance, research has demonstrated that G2/M phase-arrested renal tubular epithelial cells can stimulate the production of TGF-β1 and CTGF by activating the JNK signaling pathway\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e. This discovery underscored the pivotal role these key genes played in maintaining fundamental processes within organisms and suggested that they might occupy crucial roles in regulating complex fibrotic pathways.\u003c/p\u003e\u003cp\u003eIn recent years, some lncRNAs, miRNAs, circRNAs, and mRNAs related to IUA have been discovered through microarray analysis. Therefore, in-depth research on the lncRNA-miRNA-mRNA/circRNA-miRNA-mRNA ceRNA network is conducive to deepening the understanding of the occurrence and development of IUA. This study identified 52 miRNAs, 29 lncRNAs, and 18 circRNAs to construct a ceRNA network. Some RNAs have been reported in previous studies; moreover, we also discovered some novel non-coding RNAs (ncRNAs) potentially involved in the pathogenesis of IUA, which provided new targets for further research on the mechanism of IUA occurrence. MiR-543 negatively regulated collagen XVI, and this regulation may promote the formation of IUA by affecting the component proteins of Col XVI and ECM \u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e. Furthermore, the exosome miR-543 derived from Umbilical Cord Mesenchymal Stem Cells(UCMSC) can alleviate endometrial fibrosis in IUA mice by down-regulating N-cadherin. This suggested that miR-543 played an important role in the anti-fibrotic effect of UCMSC-derived exosomes \u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e.In this study, we found that miR-543, as a differentially expressed ncRNA, was predicted to interact with the IL1A and the lncRNAs NORAD, SNHG7, and NEAT1 to participate in the occurrence of intrauterine adhesions. Knockdown of lncRNA H19 could significantly reverse the up-regulation of fibronectin, COL1A1, and α-SMA in HK-2 cells induced by TGF-β1, as well as the down-regulation of E-cadherin, accompanied by an up-regulation of let-7b-5p. Therefore, lncRNA H19 functions as a ceRNA targeting the let-7b-5p\u0026ndash;TGF-βR1\u0026ndash;COL1A1 axis to regulate renal tubular epithelial fibrosis\u003csup\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e. CircRNA played a role in fibrosis, including cardiac fibrosis and liver fibrosis. Overexpression of CircPlekha 7 can inhibit the expression levels of a-SMA, type I collagen and Smad 3 \u003csup\u003e[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e. Consequently, both circRNA and lncRNA may participate in the fibrosis-related pathways of IUA by targeting miRNAs or modulating their host genes.\u003c/p\u003e\u003cp\u003eIn this study, a multi-omics joint analysis was conducted based on sequencing data from the transcriptome, microbiome, and metabolome. As a result, seven key genes, two key microorganisms, and two key metabolites were identified. Subsequently, the functional characteristics, regulatory networks, and related drugs of the key genes were explored. However, certain limitations were noted within this study, including a small sample size and the absence of experimental validation. In the future, the underlying mechanisms will be experimentally validated through cell experiments, animal experiments, and other experimental approaches. In addition, we will continue to investigate the functions of these mechanisms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Chongqing Health Center for Women and Children (Approval NO. 2021044). All patients provided written informed consent when clinical samples were collected for RT-qPCR experiments to ensure that the research process was in accordance with ethical norms and that the patients\u0026apos; rights and wishes were fully respected.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research was funded by Chongqing Science and Technology Commission, grant number CSTB2022NSCQ-MSX0907. This project was supported by grants from Chongqing Municipal Education Commission Science and Technology Research Program Project (KJZD-K202300407). This work was supported by the National Key Clinical Specialty Construction Project (Obstetrics and Gynecology), 2022\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eBao Liu and Mingqian Chen wrote the main manuscript text and Yugang Chi prepared figures 1-11. All authors reviewed the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgment\u003c/h2\u003e\n\u003cp\u003eWe would like to express my gratitude to all those who helped me during the writing of this manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eSequence data that support the findings of this study have been deposited in the National Genomics Data Center with the primary accession code HRA012451.Shared URL:https://ngdc.cncb.ac.cn/gsa-human/s/8K5yxV1v\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZhao, G. \u0026amp; Hu, Y. Mechanistic insights into intrauterine adhesions [J]. \u003cem\u003eSemin. Immunopathol.\u003c/em\u003e \u003cb\u003e47\u003c/b\u003e (1), 3 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLuo, Y. et al. 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Signal.\u003c/em\u003e \u003cb\u003e123\u003c/b\u003e, 111373 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu, H-D. \u0026amp; Wang, S-W. Role of noncoding RNA in the pathophysiology and treatment of intrauterine adhesion [J]. \u003cem\u003eFront. Genet.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 948628 (2022).\u003c/span\u003e\u003c/li\u003e\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Intrauterine adhesion, Transcriptome, Microbiome, Metabolome, Key genes","lastPublishedDoi":"10.21203/rs.3.rs-7079007/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7079007/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eIntrauterine adhesion (IUA) represents a prevalent cause of infertility and reproductive dysfunction; however, the underlying molecular mechanisms contributing to the development of IUA remain inadequately characterized. Consequently, this study aimed to elucidate key genes implicated in IUA through comprehensive multi-omics analyses.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eTranscriptome data from 6 IUA and 6 control endometrial tissue samples, microbiome (16S rRNA gene sequencing) data from 6 IUA and 6 control uterine lavage samples, and metabolome data from 21 IUA and 21 control uterine lavage samples were utilized. Initially, differential analyses were performed separately on transcriptome, microbiome, and metabolome data to identify genes, microbes, and metabolites of interest, respectively. Subsequently, multi-omics integration through Spearman correlation analysis identified key genes, microbes, and metabolites. Additionally, functional annotation, regulatory network construction, and drug prediction analyses were performed to further clarify the molecular mechanisms associated with the identified key genes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eIn this study, 46 genes, 4 microbes, and 11 metabolites of interest were identified. Through comprehensive multi-omics analyses, 7 key genes (DUSP2, IL1A, POF1B, ICAM4, CX3CL1, HTR2C, and SIX1), 2 key microbes (g__Clostridium_sensu_stricto_1 and g__Acidisoma), and 2 key metabolites (Pe(18:3(6Z,9Z,12Z)/18:1(9Z)) and 1,3,6-trihydroxy-2-(3-methylbut-2-enyl)xanthen-9-one) were pinpointed, all showing strong intercorrelations. Moreover, functional pathways were involved in various biological processes, including ribosome function, fatty acid metabolism, cell cycle regulation, DNA replication, and cytokine signaling. The regulatory networks revealed complex interactions, such as NEAT1-hsa-miR-185-5p-SIX1 and hsa_circ_0013870-hsa-miR-4498-CX3CL1. Additionally, olanzapine was predicted as a potential therapeutic drug based on its predicted targeting of two key genes (IL1A and HTR2C) through drug-gene interaction analysis.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis research identified seven key genes, two key microbes, and two key metabolites associated with IUA, offering novel insights into its molecular mechanisms and underscoring potential therapeutic targets for subsequent investigation.\u003c/p\u003e","manuscriptTitle":"Integrated multi-omics analysis to investigate the pathogenesis of intrauterine adhesion","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-08 20:37:50","doi":"10.21203/rs.3.rs-7079007/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-14T11:41:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-09T11:20:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"315924521036444157537503156882602229369","date":"2026-04-03T22:07:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"32116543989130167202488496078667248815","date":"2026-04-03T08:11:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-01T12:09:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"189433726882840447620954172160724722415","date":"2026-04-01T11:16:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-09T07:41:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"261448340760311473284503708489651159747","date":"2025-08-06T19:31:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-05T16:20:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-05T16:16:35+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-30T15:42:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-24T02:18:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-24T02:14:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"71fc1170-14e2-42ad-a3cd-5c0d67c104a1","owner":[],"postedDate":"August 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":52770638,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":52770639,"name":"Health sciences/Diseases"},{"id":52770640,"name":"Biological sciences/Microbiology"},{"id":52770641,"name":"Biological sciences/Molecular biology"}],"tags":[],"updatedAt":"2025-08-08T20:37:50+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-08 20:37:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7079007","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7079007","identity":"rs-7079007","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}