Transcriptomic and bioinformatic analyses in the sacral ligament tissue of postmenopausal women have revealed the pathogenesis of pelvic organ prolapse disease

Transcriptomic and bioinformatic analyses in the sacral ligament tissue of postmenopausal women have revealed the pathogenesis of pelvic organ prolapse disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Transcriptomic and bioinformatic analyses in the sacral ligament tissue of postmenopausal women have revealed the pathogenesis of pelvic organ prolapse disease yanfeng yang, bingjie rui, ZhiJun xia, jing zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4575197/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Background Pelvic organ prolapse (POP) is a prevalent gynecological disorder, characterized by anomalies in the function or position of the pelvic organs, frequently manifesting as prolapse of the uterus and both the anterior and posterior vaginal walls. POP is primarily linked to damage to both the pelvic floor muscles and connective tissue, with the majority of molecules and genetic mutations associated with POP pertaining to the synthesis and degradation of pelvic support tissues. Recently, the significant role of non-coding RNA (ncRNA) in epigenetic regulation has garnered extensive attention. However, the functions of various RNAs including microRNA (miRNA), long non-coding RNA (lncRNA), circular RNA (circRNA), and messenger RNA (mRNA) in the pathogenesis of POP remain elusive. Results This study collected uterosacral ligament tissue from five POP patients and three age-matched controls for Illumina PE150 sequencing, identifying 60 mRNAs, 176 miRNAs, 29 lncRNAs, and 176 circRNAs with statistically significant differences in abundance between the POP and control groups. The accuracy of the high-throughput next-generation sequencing results was further validated through qRT-PCR analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses demonstrated that differentially expressed genes were predominantly involved in the MAPK, Erk1/2, rap1, FOXO, and ErbB signaling pathways. KEGG disease analysis indicated that these genes are closely associated with inflammation, adhesion plaques, cellular aging, apoptosis, and the cytoskeleton. Utilizing the competitive endogenous RNA (ceRNA) regulatory mechanism, we constructed a lncRNA/circRNA-miRNA-mRNA network. Finally, from the RNAs identified in the high-throughput whole transcriptome sequencing, we randomly selected ENSG00000254531 and confirmed that this molecule influences fibroblast proliferative capacity. Conclusion Our comprehensive transcriptome study reveals the gene expression characteristics in the uterosacral ligament tissues of postmenopausal women with POP. This study provides essential data support for identifying key mRNAs and non-coding RNAs associated with the potential molecular mechanisms of POP. We screened differentially expressed miRNAs, lncRNAs, circRNAs, and mRNAs, evaluated their functional enrichments, and constructed ceRNA network to elucidate potential regulatory mechanisms and their corresponding functions. Finally, we validated the differential expression of a critical lncRNA in tissues and cells through in vitro experiments. Our findings demonstrate that the dysregulated lncRNA significantly impacts fibroblast proliferation. The identification of key lncRNAs in our study provides valuable insights into POP-related lncRNAs and may serve as important factors in the diagnosis and treatment of pop. This research introduces new candidate markers for exploring the pathogenesis of pelvic organ prolapse. Pelvic Organ Prolapse Uterosacral Ligament Non-coding RNA High-throughput Sequencing Fibroblasts Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Background Pelvic Organ Prolapse (POP) is principally characterized by the descent of pelvic organs—including the bladder, intestines, and uterus—through the vaginal wall and pelvic floor, constituting the predominant form of pelvic floor organ dysfunction[ 1 ]. According to a report grounded in clinical consensus, approximately 30–76% of women exhibit signs of POP during routine gynecological examinations[ 2 ]. Luber et al., in a comprehensive demographic study, demonstrated that the incidence of prolapse symptoms peaks at approximately 61.5 years, although POP symptoms remain prevalent among younger women[ 3 ]. It is widely acknowledged that 50% of women may experience prolapse[ 4 ]; however, prevalence rates for POP vary from 3–6% based on symptom definition, to as high as 50% according to vaginal examination findings[ 5 ]. Presently, reports on the overall prevalence of POP exhibit a broad variance, spanning from 3–50%, with surgical intervention rates approximately at 1.5 to 1.8 per 1,000 women[ 5 ]. Post-operative observations reveal persistent bladder and intestinal symptoms, as well as sexual dysfunction; however, complications such as vault prolapse, urinary incontinence, and challenges in sexual intercourse continue to manifest[ 6 ]. POP is characterized by a lack of noticeable symptoms in its early stages[ 7 ], delayed treatment, and recurrence after surgery[ 8 ].Given the aging demographic, POP significantly undermines women’s social, physical, and mental well-being, leading to diminished quality of life, labor productivity, and escalated costs for both individuals and the healthcare system at large[ 9 ]. Consequently, elucidating the molecular mechanisms underlying the onset and progression of POP and identifying effective diagnostic targets are paramount for enhancing the prognosis and quality of life of patients with POP. High-throughput sequencing technologies facilitate not only large-scale genome sequencing but also the analysis of gene expression, identification of non-coding RNAs, and selection of transcription factor target genes, thereby elucidating processes such as growth, differentiation, proliferation, apoptosis, and gene reprogramming, which are intimately associated with human diseases[ 10 , 11 ]. Recent findings indicate that approximately 3% of the human genome is transcribed into mRNA encoding proteins, while about 75% is transcribed into non-coding RNA (ncRNA), encompassing miRNA, lncRNA, and circRNA[ 12 ]. The differentially expressed ncRNAs are posited to play a pivotal role in diverse biological processes pertinent to the onset and progression of POP[ 13 , 14 ], and can be identified through Next-Generation Sequencing (NGS). This study employs NGS technology for whole-transcriptome sequencing of tissues implicated in the emergence of POP, with the aim of quantifying the expression levels of circRNA, lncRNA, miRNA, and mRNA. It seeks to identify targets among differentially expressed ncRNAs and their associated molecular signaling pathways, elucidate the regulatory roles of differentially expressed mRNA and ncRNA, analyze their interactions and co-expression networks, and integrate circRNA/lncRNA-miRNA-mRNA competitive regulatory networks. Real-time fluorescence quantitative PCR is employed to validate the differential expression of ncRNAs. Ultimately, differentially expressed ncRNAs were validated at the organizational and cellular levels, and their effects on the proliferative capacity of POP-related fibroblasts were assessed. This investigation offers novel insights into the molecular mechanisms underlying POP. Methods Patients and Clinical Samples The clinical samples used in this study were obtained from Shengjing Hospital affiliated with China Medical University. Sample collection was approved by the Ethics Committee of Shengjing Hospital affiliated with China Medical University (2024PS020K), and patient consent was obtained. The studies were conducted in accordance with Declaration of Helsinki ethical guidelines.We used standard methods to obtain the uterosacral ligaments of 5 women with uterine prolapse and the uterosacral ligaments of 3 control individuals undergoing total hysterectomy, and cultured sacrouterine ligament fibroblasts. Patient information is shown in Table 1 . Table 1 Statistical analysis of clinical characteristics of participants. Variable POP group (n = 5) Control group(n = 3) p-value Birth frequency, median 1 1 0.285 Age, mean ± SD, years 60.00 ± 4.796 57.67 ± 3.215 0.392857 Body mass index, mean ± SD, kg/m 2 26.35 ± 3.403 27.50 ± 4.574 1.000000 Postmenopausal,mean ± SD, years 9.6000 ± 3.91152 7.3333 ± 1.15470 0.250000 Hormone replacement therapy, n (%) 0 0 1.000000 Chronic cervicitis or vaginitis history, n (%) 0 0 1.000000 History of malignancy, n (%) 0 0 1.000000 Smoking habit, n (%) 0 0 1.000000 Endocrine diseases historye, n (%) 0 0 1.000000 Immune disorders historye, n (%) 0 0 1.000000 Note: Descriptive data are given as numbers (%), mean ± standard deviation. The Mann-Whitney U test was used to compare differences between groups for age and body mass index. All others were analyzed using the rank-sum test. p ≤ 0.05 (statistically significant) was analyzed using SPSS software package (version 27.0, SPSS Inc., Chicago, Illinois, USA). Postmenopause is defined as at least 1 year after menstruation stops. The history of endocrine diseases includes hypertension, thyroid disease, and diabetes. The history of immune diseases includes asthma, systemic lupus erythematosus, rheumatism, or osteoarthritis. Library Construction A strand-specific library was constructed utilizing a method dedicated to the removal of ribosomal RNA (rRNA)[ 15 ]. Following the experimental guidelines, the purified total RNA was subjected to several procedures including rRNA removal, fragmentation, first- and second-strand cDNA synthesis, end repair, A-tailing and adapter ligation, fragment selection, second cDNA strand degradation, and enrichment to finalize the construction of the sequencing sample library. Subsequent to library construction, initial quantification was conducted with Qubit 2.0, while the library's efficacy was assessed using Agilent 2100, ensuring an effective library concentration exceeding 3 nM. The Illumina NovaSeq6000 sequencing instrument (Illumina, San Diego, CA, USA) facilitated sequencing in PE150 mode. The Hisat2 software facilitated alignment analysis of RNA-Seq sequencing data. For gene-level quantification, a straightforward reads count model was employed, where read counts were determined using the featureCount function within the Subread software package, succeeded by differential analysis via DESeq2. Differential expression analysis was performed using the DESeq2 package in R, applying padj 1 as thresholds for significant differences. Volcano plots and clustering heatmaps were generated to illustrate the differential expression of ncRNA and mRNA. Functional Enrichment Analysis The clusterProfiler software was employed to select widely utilized gene annotation databases for the pathway enrichment analysis of differentially expressed genes (DEGs). Pathway enrichment analyses of DEGs were conducted using the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome databases. The GO analysis elucidates molecular functions (MF), biological processes (BP), and cellular components (CC), whereas KEGG and Reactome analyses facilitate the understanding of signaling pathways associated with DEGs. Construction of the lncRNA/circRNA-miRNA-mRNA ceRNA Network A regulatory network focused on miRNA interactions within lncRNA-miRNA-mRNA and circRNA-miRNA-mRNA configurations was constructed. Consequently, at the whole-transcriptome level, the ceRNA regulatory network reveals the mechanisms of ncRNA regulation of gene expression. The lncRNA/circRNA-miRNA-mRNA network was visualized using Cytoscape software. RNA Extraction and Real-Time Fluorescent Quantitative PCR (RT-qPCR) Total RNA was extracted from tissues and cells using the TRIzol reagent kit (Thermo Fisher Scientific), and subsequently reverse transcribed into cDNA following the protocol provided by the PrimeScript RT reagent kit. RT-qPCR analysis was conducted using the Applied Biosystems instrument with TBGreen Premix Ex Taq II (Tli RNase H Plus). The reaction protocol comprised an initial incubation at 95℃ for 3 minutes, followed by denaturation at 95℃for 10 seconds, and annealing and extension at 60℃ for 10 seconds over a total of 39 cycles. Quantitative analysis of gene expression was performed utilizing the 2 −ΔΔCt method. Primer sequences are detailed in Table 2 . Table 2 Primers used in the PCR identification. Primers Sequence (5′−3′) Exon location ENSG00000254531-fp AAAAGCGGGTCTCCGTCTAC 4q24 ENSG00000254531-rp AACCACGTTGCCAGTCCTTG 4q24 β-Actin-fp GGGAAATCGTGCGTGACATTAAG 7p22.1 β-Actin-rp TGTGTTGGCGTACAGGTCTTTG 7p22.1 Cell Culture In this study, fresh Uterosacral ligament tissues from the clinical samples with uterine prolapse were dissected into 1×1×1 mm pieces, distributed, and subsequently cultured in DMEM high glucose medium supplemented with 10% fetal bovine serum, 0.5 mg/ml streptomycin, 0.5 kU/ml penicillin, and 1.25 µg/ml amphotericin B, under a humidified atmosphere containing 5% CO 2 at 37℃. Cells from the fourth to sixth generations were selected for experimentation. Human uterosacral ligament fibroblasts were characterized through immunostaining using an anti-vimentin antibody. siRNA and plasmid transfection methods lncRNA-ENSG00000254531 siRNA, commercial negative control, lncRNA-ENSG00000254531 overexpression plasmid, and lncRNA-ENSG00000254531 empty plasmid used in this study were purchased from HanBio (Shanghai, China). Transfection of siRNA and overexpression plasmids was performed according to the manufacturer's instructions using Lipofectamine 3000 Transfection Reagent (Thermo Fisher Scientific). Cell Proliferation Detection CCK8 Detection Cells were seeded in triplicate in a 96-well plate at a density of 2,000 cells per well. Following a 24-hour incubation at 37℃ in a 5% CO 2 atmosphere, 10 µl of CCK-8 solution (Beyotime Biotechnology, Nantong, China) was introduced to the cell culture medium. Incubation proceeded for an additional hour, after which absorbance at 450 nm was measured using a Spectra Max 250 spectrophotometer (Molecular Devices, Sunnyvale, CA, USA). Continuous monitoring was conducted over a period of five days following cell seeding. Blank wells, containing solely the culture medium, were utilized to adjust for the absorbance attributed to phenol red. EDU Experiment Cells were seeded in a 6-well plate at a density of 6×10⁵ cells per well and assessed upon reaching approximately 50% confluence. The 2× EdU working solution was prepared as per the provided instructions and preheated to 37℃. For the assay, the culture medium in each well of the 6-well plate was adjusted to 1 ml, followed by the addition of an equal volume of 2× EdU working solution. Incubation then continued at 37℃ in a 5% CO 2 atmosphere for 2 hours to facilitate EdU labeling. Following labeling, the culture media were discarded, and cells were fixed with 4% polyformaldehyde. Subsequently, the fixative was removed, and cells were washed thrice with PBS, for 5 minutes each. Subsequently, permeabilization solution was added for immunostaining, and the cells were permeabilized at room temperature for 15 minutes. Cells were then washed twice with PBS, for 5 minutes each. The Click solution was prepared following the instructions, with 0.5 ml of the working solution added to each well, and incubated in the dark at room temperature for 30 minutes. Subsequently, the Click working solution was removed, and the cells were washed three times with PBS, for 5 minutes each, completing the EdU staining process. DAPI staining solution, at a concentration of 5 µg/ml, was then added and the cells were stained in the dark at room temperature for 5 minutes, followed by three washes with PBS, for 5 minutes each. EdU-labeled specimens were observed and photographed with a laser confocal microscope (Nikon), and the proportion of positive cells was quantified using ImageJ software. Statistical Analysis Statistical analysis was performed using SPSS version 27.0 (IBM SPSS Inc., Chicago, IL, U.S.A.). Continuous variables are expressed as mean ± standard deviation (SD). Rank tests were used to analyze differences between groups as appropriate. Results Summary of Differentially Expressed lncRNAs, mRNAs, circRNAs, and miRNAs Based on Gene Sequencing RNA sequencing was conducted to compare control samples with POP samples. Genes with P 1 were considered significantly expressed. Clustering heatmap analyses were utilized to assess significant differences in expression profiles, displaying lncRNAs, circRNAs, miRNAs, and mRNAs in non-POP and POP samples (Fig. 1 A-D). Volcano plots revealed 8 significantly upregulated and 21 significantly downregulated lncRNAs between non-POP and POP tissues (Fig. 1 A). Additionally, 105 circRNAs were significantly upregulated, whereas 71 were significantly downregulated (Fig. 1 B). For miRNAs, 72 were significantly upregulated and 74 significantly downregulated (Fig. 1 C). Finally, analysis identified 25 significantly upregulated and 35 significantly downregulated mRNAs (Fig. 1 D). Functional Enrichment of dif-miRNA, dif-lncRNA, dif-circRNA, and dif-mRNA Related Genes Functional enrichment analyses were independently conducted on the sets of target genes associated with differentially expressed miRNAs, lncRNAs, and circRNAs utilizing both GO and KEGG analyses. The target genes of differentially expressed miRNAs were predicted utilizing the miRDB database. In the GO Molecular Function (GO-MF) category, miRNA-related genes were predominantly enriched in DNA binding and GTPase activity (Fig. 2 A). Furthermore, GO analysis of biological processes indicated enrichment in DEGs related to Ras protein signaling, axon and synapse functions, and regulation of neurons and the urogenital system (Fig. 2 B). Cellular components of DEGs were primarily identified in the neuronal cell body, neuron-to-neuron synapses, glutamatergic synapses, postsynaptic density, synaptic membrane, and postsynaptic membrane (Fig. 2 C). KEGG pathway analysis showed enrichment in axon guidance, adhesion plaques, cellular aging, autophagy, proteoglycan-related cancer pathways, FoxO signaling pathway, MAPK signaling pathway, among others (Fig. 2 D). To identify the co-location regulatory mechanism, a threshold of 100kb upstream and downstream of lncRNAs was established. The Pearson correlation coefficient between lncRNA and mRNA was calculated, with an absolute value exceeding 0.98 serving as the co-expression screening criterion. The target genes of lncRNAs were identified at the intersection of co-location and co-expression criteria. LncRNA-related genes exhibited significant enrichment in molecular functions such as receptor ligand activity, RNA polymerase II specificity, DNA binding, and MAP kinase phosphatase activity (Fig. 3 A). In terms of GO-BP, enrichment was primarily observed in leukocyte migration, ERK1 and ERK2 cascades, and MAPK cascade regulation (Fig. 3 B). For cellular components, the main enrichment was found in the extracellular matrix structural components, specifically collagen, and the endoplasmic reticulum lumen. Enrichment was also identified in pathways related to immune defense, such as the complement and coagulation cascades, and the TNF signaling pathway (Fig. 3 C). Additionally, aging and inflammation-related pathways, including the AGE-RAGE signaling pathway and ECM receptor interactions, were identified (Fig. 3 D). GO analysis of circRNA source genes in the MF category revealed significant enrichment in ATP hydrolysis activity, protein serine/threonine kinase activity, and GTPase binding (Fig. 4 A). In the BP category, significant enrichment was observed in cellular catabolic metabolism processes, protein phosphorylation, and nucleocytoplasmic transport, among others (Fig. 4 B). For CC, enrichment was predominantly found in the nuclear membrane, early endosome, and cytoplasm (Fig. 4 C). KEGG analysis indicated that differentially expressed circRNAs play significant roles in pathways such as ABC transporters, lysine degradation, actin cytoskeleton regulation, autophagy, adhesion plaques, the Rap1 signaling pathway, and various cancer pathways (Fig. 4 E). Insights into differentially expressed mRNAs were further obtained through GO analysis in Biological Processes, revealing that DEGs were enriched in processes such as keratinization, morphogenesis of the prostate and prostatic acini, and reproductive organ development (Fig. 4 D). Interaction Network Constructed by lncRNAs/circRNAs, miRNAs, and mRNAs ncRNAs exhibit a broad spectrum of regulatory functions; they not only directly regulate DNA structure, RNA transcription, and translation but also act as miRNA "sponges" by possessing miRNA binding sites, competitively binding to miRNAs, and thus inhibiting the regulatory effects of miRNAs on target genes, thereby indirectly regulating gene expression. Leveraging the ceRNA theory, we constructed a miRNA-centric regulatory co-expression network of lncRNA-miRNA-mRNA and circRNA-miRNA-mRNA interactions, systematically investigating the impact of this network on the expression of genes associated with POP. The MiRanda and RNAhybrid software tools were employed to predict differentially expressed (DE) circRNA-miRNA and DE lncRNA-miRNA target pairs, respectively. DE miRNAs, mRNAs, lncRNAs, and circRNAs, exhibiting targeted and negatively correlated relationships with differentially expressed miRNAs, were identified through screening. The regulatory co-expression network was constructed using obtained interaction information, including 11 differentially expressed miRNAs, 11 lncRNAs, and 40 mRNAs (Fig. 5 A), as well as 5 differentially expressed miRNAs, 4 circRNAs, and 16 mRNAs (Fig. 5 B). Furthermore, GO enrichment analysis, conducted on the genes within the ceRNA network, revealed significant enrichment in molecular functions such as interleukin-6 receptor binding and low-density lipoprotein particle receptor activity for lncRNA-ceRNA interactions (Fig. 5 C). For circRNA-ceRNA interactions, significant enrichment was observed in calcium-dependent phospholipid binding and synaptosomal fusion protein binding, among others (Fig. 5 D). Morphological Identification of Uterosacral Ligament Fibroblasts Immunofluorescence staining of human uterosacral ligament fibroblasts (HULFs) from the second to third generations demonstrated high expression of vimentin—a fibroblast marker—and the absence of expression for desmin and pan-cytokeratin. This confirms the identification of the primary cells isolated from uterosacral ligament tissue as HULFs (Fig. 6 ). Validation of Candidate lncRNA To validate the reliability of the differentially expressed RNA data obtained via Next-Generation Sequencing (NGS), real-time quantitative PCR (qPCR) was employed to measure RNA expression levels. The candidate gene ENSG00000254531 was randomly selected for validation in uterosacral ligament samples and fibroblasts from both POP and non-POP patients. qPCR results indicated that ENSG00000254531 expression was lower in POP tissues compared to control tissues (Fig. 7 A). Furthermore, ENSG00000254531 expression in POP fibroblasts was significantly reduced compared to control fibroblasts (Fig. 7 B). The Biological Function of Candidate lncRNA is Confirmed In Vitro To investigate the potential function of the candidate lncRNA ENSG00000254531, specific siRNA, negative control, overexpression plasmid, and empty plasmid constructs were transfected into POP fibroblasts, respectively. The CCK-8 assay revealed that knockdown of ENSG00000254531 significantly inhibited fibroblast proliferation (Fig. 8 A). Conversely, the EDU assay demonstrated that overexpression of ENSG00000254531 significantly promoted fibroblast growth (Fig. 8 B). In summary, these findings suggest that RNAs identified through high-throughput whole-transcriptome sequencing influence fibroblast proliferation and play a crucial role in the progression of POP. Discussion POP is a prevalent condition affecting nearly 50% of women worldwide over the age of 50[ 16 ]. Although rarely fatal, POP significantly impacts the quality of life of affected individuals[ 17 ]. For instance, targeted studies have suggested that prolapse detrimentally affects women's personal and professional lives, leading to a loss of interest in activities and reduced capability in performing daily and work-related tasks[ 18 ]. Additionally, women with prolapse are at risk of experiencing poorer genital body image and diminished sexual health[ 19 ]. Consequently, an in-depth understanding of these understudied POP-related molecules could lead to enhanced diagnostic and prognostic capabilities for this risk group, potentially unveiling new prevention strategies and treatment options, thereby ultimately improving clinical outcomes. The recent advancements in technologies like NGS have significantly facilitated the study of ncRNA. In the past decade, the roles and molecular mechanisms of genes associated with POP have been extensively explored. This study employed whole-transcriptome sequencing to analyze the expression patterns of miRNA, lncRNA, circRNA, and mRNA in human uterosacral ligament tissues associated with uterine prolapse. Our data yielded numerous functional annotations,and insights into non-coding RNAs and ceRNA network of POP-related genes, providing valuable information and contributing to a more comprehensive understanding of pathogenesis. Initially, we identified differentially expressed miRNAs, lncRNAs, circRNAs, and mRNAs and analyzed their functional enrichment. Subsequently, we constructed a competing ceRNA network and delineated potential regulatory mechanisms along with their functions. Finally, through in vitro experiments, we confirmed the differential expression of candidate lncRNA in tissues and cells and demonstrated that dysregulated lncRNA significantly affects fibroblast proliferation. Consequently, the data derived from NGS technology emerge as a promising biomarker for the diagnosis, monitoring, and surveillance of POP. Among various studies on ncRNA, research on miRNA is the most comprehensive. Several miRNAs we identified have been implicated in the onset and progression of POP. For instance, inhibition of miR-138 expression results in enhanced vitality of bone marrow mesenchymal stem cells (BMSCs), increased secretion of elastin, and reduced expression of interleukin-1β (IL-1β), positioning miR-138 as a potential therapeutic target for integrating tissue engineering in POP treatment[ 20 ]. Additionally, we observed that miR-92, ranging from 19 to 25 nucleotides in length, regulates gene expression by binding to the 3' untranslated region (3'UTR) of its target mRNA, either inhibiting translation or facilitating mRNA degradation[ 21 ]. The expression level of miR-92 in POP patients' uterosacral ligaments was significantly higher compared to those of non-POP patients, aligning with the findings of our current study. Furthermore, immunohistochemistry (IHC) analysis revealed that miR-92 expression negatively correlates with estrogen receptor β1 (ERβ1) expression levels, potentially facilitating the onset and progression of POP[ 22 ]. Current evidence indicates that the expression of estrogen receptors influences the risk of POP progression, with ERβ involved in regulating estrogen activity in fibroblasts[ 23 ] and enhancing extracellular matrix synthesis[ 24 ]. The suppression of elastin expression by the miR-29 family[ 25 ][ 26 ], and the negative regulation of the Akt/mTOR/p70S6K pathway and inhibition of collagen 1 (COL-1) secretion by miR-19-3p[ 27 ], further elucidate the molecular mechanisms contributing to POP development. Recent studies have indicated that whole-transcriptome RNA sequencing of anterior vaginal wall tissue revealed 71 significantly differentially expressed circRNAs, among which hsa_circ_0002190, hsa_circ_0046843, hsa_circ_0001326, and hsa_circ_0007733 were selected to construct ceRNA networks related to the ROCK2, PPP1R12B, and VEGFD target genes, respectively[ 28 ]. hsa_circ_0002190, which is highly expressed in POP tissues, regulates the expression of ROCK2 by sponging miR-23a-3p. ROCK2 can regulate actin-mediated cellular cytoskeletal contractility[ 29 ]. Furthermore, hsa_circ_0001326 is posited to inhibit fibroblast proliferation via the miR-205-5p/VEGFD axis, potentially influencing the onset and progression of POP[ 28 ]. Zhao et al. reported that RNA sequencing identified 21 upregulated and 20 downregulated differentially expressed lncRNAs in POP patients, with significant enrichment observed in the extracellular matrix (ECM), cell receptor interactions, the Wnt signaling pathway, the p53 signaling pathway, among others[ 14 ]. Connell et al. discovered that upregulated HOXA11 expression might enhance the proliferation of uterosacral ligament cells by inhibiting the p53 pathway[ 30 ]. At present, knowledge regarding the regulation of lncRNAs in the onset and progression of POP remains limited. Our in vitro experiments confirmed significant differential expression of ENSG00000254531 in POP versus non-POP tissues and cells. Furthermore, knocking down and overexpressing ENSG00000254531 significantly affected fibroblast proliferation. LncRNAs may significantly influence POP pathology, necessitating further exploration and research into many yet-uncovered aspects. This study's enrichment analysis of ncRNAs reveals that differentially expressed genes predominantly participate in the MAPK, Erk1/2, rap1, FOXO, and ErbB signaling pathways. KEGG disease analysis demonstrates their close association with inflammation, adhesions, cellular aging, apoptosis, and the cytoskeleton. Furthermore, we constructed lncRNA/circRNA-ceRNA regulatory network centered on miRNA, which is regulated by miRNA and shares the same miRNA binding sites[ 31 ]. Functional analysis indicates that the lncRNA-ceRNA network is involved in regulating inflammation[ 32 ] and metabolic processes[ 33 , 34 ], while the circRNA-ceRNA network contributes to cytoskeletal stability[ 35 ]. Dysregulation of cholesterol intake and synthesis, along with increased infusion of lipoproteins, may result in a chronic inflammatory state[ 36 ]. Concurrently, the inhibition of mitochondrial respiration and glycolysis, resulting in reduced intracellular ATP levels, induces apoptosis[ 37 ]. Consequently, we hypothesize that in POP patients, opportunistic modes of inflammatory response and basic metabolic processes are utilized through the ceRNA mechanism, culminating in the destruction of cell membranes and cytoskeleton, thus facilitating POP's onset and progression. Presently, validation experiments for ncRNA and ceRNA predominantly involve overexpression and knockout studies in cellular and animal models. However, whether these findings accurately represent the underlying mechanisms of POP progression has yet to be confirmed at the physiological level. Thus, further animal studies and clinical trials are imperative for analysis, aiming to translate basic research findings into clinical applications. In summary, the ncRNAs identified in this study may significantly contribute to the pathogenesis and progression of pelvic organ prolapse . Abbreviations Abbreviation Meaning POP Pelvic organ prolapse ncRNA non-coding RNA GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes ceRNA competitive endogenous RNA NGS Next-Generation Sequencing rRNA ribosomal RNA DEGs differentially expressed genes MF molecular functions BP biological processes CC cellular components RT-qPCR Real-Time Fluorescent Quantitative PCR DE differentially expressed HULFs human uterosacral ligament fibroblasts miRNA microRNA lncRNA long non-coding RNA circRNA circular RNA mRNA messenger RNA BMSCs bone marrow mesenchymal stem cells IL-1β interleukin-1β 3'UTR 3' untranslated region IHC immunohistochemistry ERβ1 estrogen receptor β1 COL-1 collagen 1 ECM extracellular matrix Declarations Ethics approval and consent to participate The sample collection was approved by the Ethics Committee of Shengjing Hospital affiliated with China Medical University (Ethical Approval Number: 2024PS020K). All clinical specimens were collected in accordance with surgical standards. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding This work was supported by grants from National Natural Science Foundation of China(82271613) to ZhiJun Xia. Author Contribution Y.F .Y and Z.J. X conceived and designed the study. Y.F .Y and B.J. R performed the experiments and analyzed the data. B.J. R and J. Z prepared the figures. Y.F .Y drafted the manuscript. Z.J. X approved the final version of the manuscript.All authors edited and contributed to the manuscript. Availability of data and materials The data and materials used in this study are available upon reasonable requests to the corresponding author. References Smith TA, Poteat TA, Shobeiri SA. Pelvic organ prolapse. J Am Acad Physician Assistants. 2014;27(3):20–4. Barber MD. Pelvic organ prolapse. BMJ, 2016: p. i3853. Luber KM, Boero S, Choe JY. 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Yin Y, et al. miR-19-3p Promotes Autophagy and Apoptosis in Pelvic Organ Prolapse Through the AKT/mTOR/p70S6K Pathway: Function of miR-19-3p on Vaginal Fibroblasts by Targeting IGF-1. Female Pelvic Med Reconstr Surg. 2021;27(9):e630–8. Yu X et al. Construction of a focal adhesion signaling pathway-related ceRNA network in pelvic organ prolapse by transcriptome analysis. Front Genet, 2022. 13. Weber AJ, Herskowitz JH. Perspectives on ROCK2 as a Therapeutic Target for Alzheimer’s Disease. Front Cell Neurosci, 2021. 15. Connell KA, et al. HOXA11 promotes fibroblast proliferation and regulates p53 in uterosacral ligaments. Reprod Sci. 2009;16(7):694–700. Karreth FA, Pandolfi PP. ceRNA cross-talk in cancer: when ce-bling rivalries go awry. Cancer Discov. 2013;3(10):1113–21. Liu G, Jin S, Jiang Q. Interleukin-6 Receptor and Inflammatory Bowel Disease: A Mendelian Randomization Study. Gastroenterology. 2019;156(3):823–4. Keshavarz R, et al. MicroRNA-mediated Regulation of LDL Receptor: Biological and Pharmacological Implications. Curr Med Chem. 2024;31(14):1830–8. Zhou Y-X, et al. Delivery of low-density lipoprotein from endocytic carriers to mitochondria supports steroidogenesis. Nat Cell Biol. 2023;25(7):937–49. Xi Y, Ju R, Wang Y. Roles of Annexin A protein family in autophagy regulation and therapy. Volume 130. Biomedicine & Pharmacotherapy; 2020. p. 110591. O'Hagan R, et al. Systemic consequences of abnormal cholesterol handling: Interdependent pathways of inflammation and dyslipidemia. Front Immunol. 2022;13:972140. Woldetsadik AD, et al. Hexokinase II–derived cell-penetrating peptide targets mitochondria and triggers apoptosis in cancer cells. FASEB J. 2017;31(5):2168–84. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 13 Jun, 2024 Submission checks completed at journal 13 Jun, 2024 First submitted to journal 13 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4575197","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":314272250,"identity":"eb792ab5-8f63-4d7e-b8c8-78213d4bef37","order_by":0,"name":"yanfeng yang","email":"","orcid":"","institution":"China Medical University","correspondingAuthor":false,"prefix":"","firstName":"yanfeng","middleName":"","lastName":"yang","suffix":""},{"id":314272251,"identity":"31d8a0e9-de96-4628-8260-09238f7f5527","order_by":1,"name":"bingjie rui","email":"","orcid":"","institution":"fushun central hospital","correspondingAuthor":false,"prefix":"","firstName":"bingjie","middleName":"","lastName":"rui","suffix":""},{"id":314272252,"identity":"67a0b492-5a2d-4e00-ad91-45085528a5cc","order_by":2,"name":"ZhiJun xia","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCklEQVRIiWNgGAWjYJACCQYDCTl+hoONDz42gPiMjQcIaymwMJZsPNxsOLMByGFgbCBCy4eKxA2Hj7cJ84K1MDDg1WJwvPfgbR4DCcaGYwfbmG132NTpth8G2lJjE41Ty5lzydZALcyMPQfbHueeSZMwO5MI1HIsLbcBhxazGzlm0kAtbMwSB9uNc9sOS5gdAGphbDiMW8v9N2AtPGzyD9ukLUFazj8koOUGD1iLBA/DwTZpRpCWGwRssT+TY2w5x0DCQILhYLNhb1ua5LYbQFsS8PhFsv2M4Y03f+rq9x84/vDBzzYbfrPz6Q8ffKixwakFBJh4MIQS8CgHAcYfBBSMglEwCkbBCAcAL01mDVOM9JIAAAAASUVORK5CYII=","orcid":"","institution":"Obstetrics and Gynecology Hospital of Fudan University","correspondingAuthor":true,"prefix":"","firstName":"ZhiJun","middleName":"","lastName":"xia","suffix":""},{"id":314272253,"identity":"c800ca4f-2a0d-4a6f-9792-a6e6d080024f","order_by":3,"name":"jing zhu","email":"","orcid":"","institution":"Qinhuangdao Maternal and Child Health Hospital","correspondingAuthor":false,"prefix":"","firstName":"jing","middleName":"","lastName":"zhu","suffix":""}],"badges":[],"createdAt":"2024-06-13 09:42:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4575197/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4575197/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60201625,"identity":"aee10202-fbb0-418e-8276-83020a609b2d","added_by":"auto","created_at":"2024-07-13 02:47:19","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":287281,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Clustering heatmap and volcano plot of differentially expressed lncRNA molecules. (B) Clustering heatmap and volcano plot of differentially expressed circRNAs. (C) Clustering heatmap and volcano plot of differentially expressed miRNAs. (D) Clustering heatmap and volcano plot of differentially expressed mRNAs. Red indicates upregulation, blue indicates downregulation.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4575197/v1/16b69615d560694ece7e9ae5.jpg"},{"id":60200100,"identity":"40d78b62-fe39-4704-b794-283f4e444e01","added_by":"auto","created_at":"2024-07-13 02:31:19","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":104904,"visible":true,"origin":"","legend":"\u003cp\u003eGene Ontology enrichment and signaling pathway analysis of miRNAs. (A) Molecular function. (B) Biological process. (C) Cellular component. (D) KEGG pathway\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4575197/v1/9aa44621d0306ac28de18149.jpg"},{"id":60200094,"identity":"cdcd34ae-861b-4f58-accd-c7557a09b781","added_by":"auto","created_at":"2024-07-13 02:31:19","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":105788,"visible":true,"origin":"","legend":"\u003cp\u003eGene Ontology enrichment and signaling pathway analysis of lncRNAs. (A) Molecular function. (B) Biological process. (C) Cellular component. (D) KEGG pathway\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4575197/v1/09036702ed922b12ecaf0f30.jpg"},{"id":60201626,"identity":"28f94896-d970-4281-982d-91d4766ebc54","added_by":"auto","created_at":"2024-07-13 02:47:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":182319,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Molecular function of circRNA-regulated genes. (B) Biological process of circRNA-regulated genes. (C) Cellular component of circRNA-regulated genes. (D) mRNA genes GO-BP. (E) KEGG pathway.\u003c/p\u003e","description":"","filename":"Picture4.png","url":"https://assets-eu.researchsquare.com/files/rs-4575197/v1/fac720e72443e7c2ec803b81.png"},{"id":60200893,"identity":"679a197b-4526-4abf-a5f8-77a9e740db7c","added_by":"auto","created_at":"2024-07-13 02:39:19","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":49105,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of the construction and GO enrichment of the ceRNA network. (A) Extracting LncRNA-miRNA-mRNA and (B) circRNA-miRNA-mRNA networks. Squares represent lncRNA or circRNA, circles represent miRNA, diamonds represent mRNA. Green indicates upregulation, blue or red indicates downregulation. Black lines indicate the correlation between lncRNA/circRNA-miRNA-mRNA. (C) Gene enrichment in the lncRNA-miRNA-mRNA network. (D) Gene enrichment in the circRNA-miRNA-mRNA network.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4575197/v1/10f9c62c34cf2b2207282539.jpg"},{"id":60200891,"identity":"97562bee-276e-4c07-a44a-358bead55ea1","added_by":"auto","created_at":"2024-07-13 02:39:19","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":75585,"visible":true,"origin":"","legend":"\u003cp\u003eImmunofluorescence of fibroblasts shows positive for Vimentin, negative for desmin, and Pancytokeratin negative.\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4575197/v1/889d1d94ad3a533d90dde655.jpg"},{"id":60200095,"identity":"2080c9b2-0590-4a94-a32d-31b15f88f14b","added_by":"auto","created_at":"2024-07-13 02:31:19","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":60432,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression difference of ENSG00000254531 in sacrouterine ligament tissues and cells of POP and non-POP groups is significant, and it is expressed lower in the POP group. (3 independent experiments, * indicates P\u0026lt;0.05, ** indicates P\u0026lt;0.01, *** indicates P\u0026lt;0.001)\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4575197/v1/30cbd5bb93a63e86f3dbf74b.jpg"},{"id":60200098,"identity":"d85e1461-d3ad-48f1-b6ce-b38fe31fd638","added_by":"auto","created_at":"2024-07-13 02:31:19","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":99800,"visible":true,"origin":"","legend":"\u003cp\u003eThe effect of knocking down and overexpressing ENSG00000254531 on fibroblast proliferation ability. (A) CCK8 experiment, (B) EDU experiment.\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4575197/v1/833bb01076470feea0ffd5cd.jpg"},{"id":60201627,"identity":"c6f5bdee-28ee-4483-99f7-019e03eb8330","added_by":"auto","created_at":"2024-07-13 02:47:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1396724,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4575197/v1/d2d21549-8aa5-4ea2-abed-50214e0e99f6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transcriptomic and bioinformatic analyses in the sacral ligament tissue of postmenopausal women have revealed the pathogenesis of pelvic organ prolapse disease","fulltext":[{"header":"Background","content":"\u003cp\u003ePelvic Organ Prolapse (POP) is principally characterized by the descent of pelvic organs\u0026mdash;including the bladder, intestines, and uterus\u0026mdash;through the vaginal wall and pelvic floor, constituting the predominant form of pelvic floor organ dysfunction[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. According to a report grounded in clinical consensus, approximately 30\u0026ndash;76% of women exhibit signs of POP during routine gynecological examinations[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Luber et al., in a comprehensive demographic study, demonstrated that the incidence of prolapse symptoms peaks at approximately 61.5 years, although POP symptoms remain prevalent among younger women[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. It is widely acknowledged that 50% of women may experience prolapse[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]; however, prevalence rates for POP vary from 3\u0026ndash;6% based on symptom definition, to as high as 50% according to vaginal examination findings[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Presently, reports on the overall prevalence of POP exhibit a broad variance, spanning from 3\u0026ndash;50%, with surgical intervention rates approximately at 1.5 to 1.8 per 1,000 women[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Post-operative observations reveal persistent bladder and intestinal symptoms, as well as sexual dysfunction; however, complications such as vault prolapse, urinary incontinence, and challenges in sexual intercourse continue to manifest[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. POP is characterized by a lack of noticeable symptoms in its early stages[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], delayed treatment, and recurrence after surgery[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].Given the aging demographic, POP significantly undermines women\u0026rsquo;s social, physical, and mental well-being, leading to diminished quality of life, labor productivity, and escalated costs for both individuals and the healthcare system at large[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Consequently, elucidating the molecular mechanisms underlying the onset and progression of POP and identifying effective diagnostic targets are paramount for enhancing the prognosis and quality of life of patients with POP.\u003c/p\u003e \u003cp\u003eHigh-throughput sequencing technologies facilitate not only large-scale genome sequencing but also the analysis of gene expression, identification of non-coding RNAs, and selection of transcription factor target genes, thereby elucidating processes such as growth, differentiation, proliferation, apoptosis, and gene reprogramming, which are intimately associated with human diseases[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Recent findings indicate that approximately 3% of the human genome is transcribed into mRNA encoding proteins, while about 75% is transcribed into non-coding RNA (ncRNA), encompassing miRNA, lncRNA, and circRNA[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The differentially expressed ncRNAs are posited to play a pivotal role in diverse biological processes pertinent to the onset and progression of POP[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and can be identified through Next-Generation Sequencing (NGS). This study employs NGS technology for whole-transcriptome sequencing of tissues implicated in the emergence of POP, with the aim of quantifying the expression levels of circRNA, lncRNA, miRNA, and mRNA. It seeks to identify targets among differentially expressed ncRNAs and their associated molecular signaling pathways, elucidate the regulatory roles of differentially expressed mRNA and ncRNA, analyze their interactions and co-expression networks, and integrate circRNA/lncRNA-miRNA-mRNA competitive regulatory networks. Real-time fluorescence quantitative PCR is employed to validate the differential expression of ncRNAs. Ultimately, differentially expressed ncRNAs were validated at the organizational and cellular levels, and their effects on the proliferative capacity of POP-related fibroblasts were assessed. This investigation offers novel insights into the molecular mechanisms underlying POP.\u003c/p\u003e "},{"header":"Methods","content":" \u003cp\u003ePatients and Clinical Samples\u003c/p\u003e \u003cp\u003e The clinical samples used in this study were obtained from Shengjing Hospital affiliated with China Medical University. Sample collection was approved by the Ethics Committee of Shengjing Hospital affiliated with China Medical University (2024PS020K), and patient consent was obtained. The studies were conducted in accordance with Declaration of Helsinki ethical guidelines.We used standard methods to obtain the uterosacral ligaments of 5 women with uterine prolapse and the uterosacral ligaments of 3 control individuals undergoing total hysterectomy, and cultured sacrouterine ligament fibroblasts. Patient information is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e Statistical analysis of clinical characteristics of participants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePOP group (n\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl group(n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirth frequency, median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.00\u0026thinsp;\u0026plusmn;\u0026thinsp;4.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.67\u0026thinsp;\u0026plusmn;\u0026thinsp;3.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.392857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.35\u0026thinsp;\u0026plusmn;\u0026thinsp;3.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.50\u0026thinsp;\u0026plusmn;\u0026thinsp;4.574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostmenopausal,mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.6000\u0026thinsp;\u0026plusmn;\u0026thinsp;3.91152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.3333\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.250000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHormone replacement therapy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic cervicitis or vaginitis history, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of malignancy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking habit, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndocrine diseases historye, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmune disorders historye, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNote: Descriptive data are given as numbers (%), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation.\u003c/p\u003e \u003cp\u003eThe Mann-Whitney U test was used to compare differences between groups for age and body mass index. All others were analyzed using the rank-sum test.\u003c/p\u003e \u003cp\u003ep\u0026thinsp;\u0026le;\u0026thinsp;0.05 (statistically significant) was analyzed using SPSS software package (version 27.0, SPSS Inc., Chicago, Illinois, USA).\u003c/p\u003e \u003cp\u003ePostmenopause is defined as at least 1 year after menstruation stops.\u003c/p\u003e \u003cp\u003eThe history of endocrine diseases includes hypertension, thyroid disease, and diabetes.\u003c/p\u003e \u003cp\u003eThe history of immune diseases includes asthma, systemic lupus erythematosus, rheumatism, or osteoarthritis.\u003c/p\u003e \u003cp\u003eLibrary Construction\u003c/p\u003e \u003cp\u003eA strand-specific library was constructed utilizing a method dedicated to the removal of ribosomal RNA (rRNA)[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Following the experimental guidelines, the purified total RNA was subjected to several procedures including rRNA removal, fragmentation, first- and second-strand cDNA synthesis, end repair, A-tailing and adapter ligation, fragment selection, second cDNA strand degradation, and enrichment to finalize the construction of the sequencing sample library. Subsequent to library construction, initial quantification was conducted with Qubit 2.0, while the library's efficacy was assessed using Agilent 2100, ensuring an effective library concentration exceeding 3 nM. The Illumina NovaSeq6000 sequencing instrument (Illumina, San Diego, CA, USA) facilitated sequencing in PE150 mode. The Hisat2 software facilitated alignment analysis of RNA-Seq sequencing data. For gene-level quantification, a straightforward reads count model was employed, where read counts were determined using the featureCount function within the Subread software package, succeeded by differential analysis via DESeq2. Differential expression analysis was performed using the DESeq2 package in R, applying padj\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2FoldChange| \u0026gt; 1 as thresholds for significant differences. Volcano plots and clustering heatmaps were generated to illustrate the differential expression of ncRNA and mRNA.\u003c/p\u003e \u003cp\u003eFunctional Enrichment Analysis\u003c/p\u003e \u003cp\u003eThe clusterProfiler software was employed to select widely utilized gene annotation databases for the pathway enrichment analysis of differentially expressed genes (DEGs). Pathway enrichment analyses of DEGs were conducted using the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome databases. The GO analysis elucidates molecular functions (MF), biological processes (BP), and cellular components (CC), whereas KEGG and Reactome analyses facilitate the understanding of signaling pathways associated with DEGs.\u003c/p\u003e \u003cp\u003eConstruction of the lncRNA/circRNA-miRNA-mRNA ceRNA Network\u003c/p\u003e \u003cp\u003eA regulatory network focused on miRNA interactions within lncRNA-miRNA-mRNA and circRNA-miRNA-mRNA configurations was constructed. Consequently, at the whole-transcriptome level, the ceRNA regulatory network reveals the mechanisms of ncRNA regulation of gene expression. The lncRNA/circRNA-miRNA-mRNA network was visualized using Cytoscape software.\u003c/p\u003e \u003cp\u003eRNA Extraction and Real-Time Fluorescent Quantitative PCR (RT-qPCR)\u003c/p\u003e \u003cp\u003eTotal RNA was extracted from tissues and cells using the TRIzol reagent kit (Thermo Fisher Scientific), and subsequently reverse transcribed into cDNA following the protocol provided by the PrimeScript RT reagent kit. RT-qPCR analysis was conducted using the Applied Biosystems instrument with TBGreen Premix Ex Taq II (Tli RNase H Plus). The reaction protocol comprised an initial incubation at 95℃ for 3 minutes, followed by denaturation at 95℃for 10 seconds, and annealing and extension at 60℃ for 10 seconds over a total of 39 cycles. Quantitative analysis of gene expression was performed utilizing the 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method. Primer sequences are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrimers used in the PCR identification.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSequence (5\u0026prime;\u0026minus;3\u0026prime;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExon location\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSG00000254531-fp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAAAAGCGGGTCTCCGTCTAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4q24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSG00000254531-rp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAACCACGTTGCCAGTCCTTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4q24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-Actin-fp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGGGAAATCGTGCGTGACATTAAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7p22.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-Actin-rp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGTGTTGGCGTACAGGTCTTTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7p22.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCell Culture\u003c/p\u003e \u003cp\u003eIn this study, fresh Uterosacral ligament tissues from the clinical samples with uterine prolapse were dissected into 1\u0026times;1\u0026times;1 mm pieces, distributed, and subsequently cultured in DMEM high glucose medium supplemented with 10% fetal bovine serum, 0.5 mg/ml streptomycin, 0.5 kU/ml penicillin, and 1.25 \u0026micro;g/ml amphotericin B, under a humidified atmosphere containing 5% CO\u003csub\u003e2\u003c/sub\u003e at 37℃. Cells from the fourth to sixth generations were selected for experimentation. Human uterosacral ligament fibroblasts were characterized through immunostaining using an anti-vimentin antibody.\u003c/p\u003e \u003cp\u003esiRNA and plasmid transfection methods\u003c/p\u003e \u003cp\u003elncRNA-ENSG00000254531 siRNA, commercial negative control, lncRNA-ENSG00000254531 overexpression plasmid, and lncRNA-ENSG00000254531 empty plasmid used in this study were purchased from HanBio (Shanghai, China). Transfection of siRNA and overexpression plasmids was performed according to the manufacturer's instructions using Lipofectamine 3000 Transfection Reagent (Thermo Fisher Scientific).\u003c/p\u003e \u003cp\u003eCell Proliferation Detection\u003c/p\u003e \u003cp\u003eCCK8 Detection\u003c/p\u003e \u003cp\u003eCells were seeded in triplicate in a 96-well plate at a density of 2,000 cells per well. Following a 24-hour incubation at 37℃ in a 5% CO\u003csub\u003e2\u003c/sub\u003e atmosphere, 10 \u0026micro;l of CCK-8 solution (Beyotime Biotechnology, Nantong, China) was introduced to the cell culture medium. Incubation proceeded for an additional hour, after which absorbance at 450 nm was measured using a Spectra Max 250 spectrophotometer (Molecular Devices, Sunnyvale, CA, USA). Continuous monitoring was conducted over a period of five days following cell seeding. Blank wells, containing solely the culture medium, were utilized to adjust for the absorbance attributed to phenol red.\u003c/p\u003e \u003cp\u003eEDU Experiment\u003c/p\u003e \u003cp\u003eCells were seeded in a 6-well plate at a density of 6\u0026times;10⁵ cells per well and assessed upon reaching approximately 50% confluence. The 2\u0026times; EdU working solution was prepared as per the provided instructions and preheated to 37℃. For the assay, the culture medium in each well of the 6-well plate was adjusted to 1 ml, followed by the addition of an equal volume of 2\u0026times; EdU working solution. Incubation then continued at 37℃ in a 5% CO\u003csub\u003e2\u003c/sub\u003e atmosphere for 2 hours to facilitate EdU labeling. Following labeling, the culture media were discarded, and cells were fixed with 4% polyformaldehyde. Subsequently, the fixative was removed, and cells were washed thrice with PBS, for 5 minutes each. Subsequently, permeabilization solution was added for immunostaining, and the cells were permeabilized at room temperature for 15 minutes. Cells were then washed twice with PBS, for 5 minutes each. The Click solution was prepared following the instructions, with 0.5 ml of the working solution added to each well, and incubated in the dark at room temperature for 30 minutes. Subsequently, the Click working solution was removed, and the cells were washed three times with PBS, for 5 minutes each, completing the EdU staining process. DAPI staining solution, at a concentration of 5 \u0026micro;g/ml, was then added and the cells were stained in the dark at room temperature for 5 minutes, followed by three washes with PBS, for 5 minutes each. EdU-labeled specimens were observed and photographed with a laser confocal microscope (Nikon), and the proportion of positive cells was quantified using ImageJ software.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed using SPSS version 27.0 (IBM SPSS Inc., Chicago, IL, U.S.A.). Continuous variables are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). Rank tests were used to analyze differences between groups as appropriate.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eSummary of Differentially Expressed lncRNAs, mRNAs, circRNAs, and miRNAs Based on Gene Sequencing\u003c/p\u003e \u003cp\u003eRNA sequencing was conducted to compare control samples with POP samples. Genes with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2(fold change)| \u0026gt; 1 were considered significantly expressed. Clustering heatmap analyses were utilized to assess significant differences in expression profiles, displaying lncRNAs, circRNAs, miRNAs, and mRNAs in non-POP and POP samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-D). Volcano plots revealed 8 significantly upregulated and 21 significantly downregulated lncRNAs between non-POP and POP tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Additionally, 105 circRNAs were significantly upregulated, whereas 71 were significantly downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). For miRNAs, 72 were significantly upregulated and 74 significantly downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Finally, analysis identified 25 significantly upregulated and 35 significantly downregulated mRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFunctional Enrichment of dif-miRNA, dif-lncRNA, dif-circRNA, and dif-mRNA Related Genes\u003c/p\u003e \u003cp\u003eFunctional enrichment analyses were independently conducted on the sets of target genes associated with differentially expressed miRNAs, lncRNAs, and circRNAs utilizing both GO and KEGG analyses. The target genes of differentially expressed miRNAs were predicted utilizing the miRDB database. In the GO Molecular Function (GO-MF) category, miRNA-related genes were predominantly enriched in DNA binding and GTPase activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Furthermore, GO analysis of biological processes indicated enrichment in DEGs related to Ras protein signaling, axon and synapse functions, and regulation of neurons and the urogenital system (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Cellular components of DEGs were primarily identified in the neuronal cell body, neuron-to-neuron synapses, glutamatergic synapses, postsynaptic density, synaptic membrane, and postsynaptic membrane (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). KEGG pathway analysis showed enrichment in axon guidance, adhesion plaques, cellular aging, autophagy, proteoglycan-related cancer pathways, FoxO signaling pathway, MAPK signaling pathway, among others (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo identify the co-location regulatory mechanism, a threshold of 100kb upstream and downstream of lncRNAs was established. The Pearson correlation coefficient between lncRNA and mRNA was calculated, with an absolute value exceeding 0.98 serving as the co-expression screening criterion. The target genes of lncRNAs were identified at the intersection of co-location and co-expression criteria. LncRNA-related genes exhibited significant enrichment in molecular functions such as receptor ligand activity, RNA polymerase II specificity, DNA binding, and MAP kinase phosphatase activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). In terms of GO-BP, enrichment was primarily observed in leukocyte migration, ERK1 and ERK2 cascades, and MAPK cascade regulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). For cellular components, the main enrichment was found in the extracellular matrix structural components, specifically collagen, and the endoplasmic reticulum lumen. Enrichment was also identified in pathways related to immune defense, such as the complement and coagulation cascades, and the TNF signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Additionally, aging and inflammation-related pathways, including the AGE-RAGE signaling pathway and ECM receptor interactions, were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGO analysis of circRNA source genes in the MF category revealed significant enrichment in ATP hydrolysis activity, protein serine/threonine kinase activity, and GTPase binding (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). In the BP category, significant enrichment was observed in cellular catabolic metabolism processes, protein phosphorylation, and nucleocytoplasmic transport, among others (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). For CC, enrichment was predominantly found in the nuclear membrane, early endosome, and cytoplasm (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). KEGG analysis indicated that differentially expressed circRNAs play significant roles in pathways such as ABC transporters, lysine degradation, actin cytoskeleton regulation, autophagy, adhesion plaques, the Rap1 signaling pathway, and various cancer pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Insights into differentially expressed mRNAs were further obtained through GO analysis in Biological Processes, revealing that DEGs were enriched in processes such as keratinization, morphogenesis of the prostate and prostatic acini, and reproductive organ development (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInteraction Network Constructed by lncRNAs/circRNAs, miRNAs, and mRNAs\u003c/p\u003e \u003cp\u003encRNAs exhibit a broad spectrum of regulatory functions; they not only directly regulate DNA structure, RNA transcription, and translation but also act as miRNA \"sponges\" by possessing miRNA binding sites, competitively binding to miRNAs, and thus inhibiting the regulatory effects of miRNAs on target genes, thereby indirectly regulating gene expression. Leveraging the ceRNA theory, we constructed a miRNA-centric regulatory co-expression network of lncRNA-miRNA-mRNA and circRNA-miRNA-mRNA interactions, systematically investigating the impact of this network on the expression of genes associated with POP. The MiRanda and RNAhybrid software tools were employed to predict differentially expressed (DE) circRNA-miRNA and DE lncRNA-miRNA target pairs, respectively. DE miRNAs, mRNAs, lncRNAs, and circRNAs, exhibiting targeted and negatively correlated relationships with differentially expressed miRNAs, were identified through screening. The regulatory co-expression network was constructed using obtained interaction information, including 11 differentially expressed miRNAs, 11 lncRNAs, and 40 mRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), as well as 5 differentially expressed miRNAs, 4 circRNAs, and 16 mRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Furthermore, GO enrichment analysis, conducted on the genes within the ceRNA network, revealed significant enrichment in molecular functions such as interleukin-6 receptor binding and low-density lipoprotein particle receptor activity for lncRNA-ceRNA interactions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). For circRNA-ceRNA interactions, significant enrichment was observed in calcium-dependent phospholipid binding and synaptosomal fusion protein binding, among others (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMorphological Identification of Uterosacral Ligament Fibroblasts\u003c/p\u003e \u003cp\u003eImmunofluorescence staining of human uterosacral ligament fibroblasts (HULFs) from the second to third generations demonstrated high expression of vimentin\u0026mdash;a fibroblast marker\u0026mdash;and the absence of expression for desmin and pan-cytokeratin. This confirms the identification of the primary cells isolated from uterosacral ligament tissue as HULFs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eValidation of Candidate lncRNA\u003c/p\u003e \u003cp\u003eTo validate the reliability of the differentially expressed RNA data obtained via Next-Generation Sequencing (NGS), real-time quantitative PCR (qPCR) was employed to measure RNA expression levels. The candidate gene ENSG00000254531 was randomly selected for validation in uterosacral ligament samples and fibroblasts from both POP and non-POP patients. qPCR results indicated that ENSG00000254531 expression was lower in POP tissues compared to control tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Furthermore, ENSG00000254531 expression in POP fibroblasts was significantly reduced compared to control fibroblasts (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Biological Function of Candidate lncRNA is Confirmed In Vitro\u003c/p\u003e \u003cp\u003eTo investigate the potential function of the candidate lncRNA ENSG00000254531, specific siRNA, negative control, overexpression plasmid, and empty plasmid constructs were transfected into POP fibroblasts, respectively. The CCK-8 assay revealed that knockdown of ENSG00000254531 significantly inhibited fibroblast proliferation (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Conversely, the EDU assay demonstrated that overexpression of ENSG00000254531 significantly promoted fibroblast growth (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). In summary, these findings suggest that RNAs identified through high-throughput whole-transcriptome sequencing influence fibroblast proliferation and play a crucial role in the progression of POP.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePOP is a prevalent condition affecting nearly 50% of women worldwide over the age of 50[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Although rarely fatal, POP significantly impacts the quality of life of affected individuals[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. For instance, targeted studies have suggested that prolapse detrimentally affects women's personal and professional lives, leading to a loss of interest in activities and reduced capability in performing daily and work-related tasks[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Additionally, women with prolapse are at risk of experiencing poorer genital body image and diminished sexual health[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Consequently, an in-depth understanding of these understudied POP-related molecules could lead to enhanced diagnostic and prognostic capabilities for this risk group, potentially unveiling new prevention strategies and treatment options, thereby ultimately improving clinical outcomes.\u003c/p\u003e \u003cp\u003eThe recent advancements in technologies like NGS have significantly facilitated the study of ncRNA. In the past decade, the roles and molecular mechanisms of genes associated with POP have been extensively explored. This study employed whole-transcriptome sequencing to analyze the expression patterns of miRNA, lncRNA, circRNA, and mRNA in human uterosacral ligament tissues associated with uterine prolapse. Our data yielded numerous functional annotations,and insights into non-coding RNAs and ceRNA network of POP-related genes, providing valuable information and contributing to a more comprehensive understanding of pathogenesis. Initially, we identified differentially expressed miRNAs, lncRNAs, circRNAs, and mRNAs and analyzed their functional enrichment. Subsequently, we constructed a competing ceRNA network and delineated potential regulatory mechanisms along with their functions. Finally, through in vitro experiments, we confirmed the differential expression of candidate lncRNA in tissues and cells and demonstrated that dysregulated lncRNA significantly affects fibroblast proliferation. Consequently, the data derived from NGS technology emerge as a promising biomarker for the diagnosis, monitoring, and surveillance of POP.\u003c/p\u003e \u003cp\u003eAmong various studies on ncRNA, research on miRNA is the most comprehensive. Several miRNAs we identified have been implicated in the onset and progression of POP. For instance, inhibition of miR-138 expression results in enhanced vitality of bone marrow mesenchymal stem cells (BMSCs), increased secretion of elastin, and reduced expression of interleukin-1β (IL-1β), positioning miR-138 as a potential therapeutic target for integrating tissue engineering in POP treatment[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Additionally, we observed that miR-92, ranging from 19 to 25 nucleotides in length, regulates gene expression by binding to the 3' untranslated region (3'UTR) of its target mRNA, either inhibiting translation or facilitating mRNA degradation[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The expression level of miR-92 in POP patients' uterosacral ligaments was significantly higher compared to those of non-POP patients, aligning with the findings of our current study. Furthermore, immunohistochemistry (IHC) analysis revealed that miR-92 expression negatively correlates with estrogen receptor β1 (ERβ1) expression levels, potentially facilitating the onset and progression of POP[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Current evidence indicates that the expression of estrogen receptors influences the risk of POP progression, with ERβ involved in regulating estrogen activity in fibroblasts[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and enhancing extracellular matrix synthesis[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The suppression of elastin expression by the miR-29 family[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e][\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and the negative regulation of the Akt/mTOR/p70S6K pathway and inhibition of collagen 1 (COL-1) secretion by miR-19-3p[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], further elucidate the molecular mechanisms contributing to POP development.\u003c/p\u003e \u003cp\u003eRecent studies have indicated that whole-transcriptome RNA sequencing of anterior vaginal wall tissue revealed 71 significantly differentially expressed circRNAs, among which hsa_circ_0002190, hsa_circ_0046843, hsa_circ_0001326, and hsa_circ_0007733 were selected to construct ceRNA networks related to the ROCK2, PPP1R12B, and VEGFD target genes, respectively[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. hsa_circ_0002190, which is highly expressed in POP tissues, regulates the expression of ROCK2 by sponging miR-23a-3p. ROCK2 can regulate actin-mediated cellular cytoskeletal contractility[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Furthermore, hsa_circ_0001326 is posited to inhibit fibroblast proliferation via the miR-205-5p/VEGFD axis, potentially influencing the onset and progression of POP[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Zhao et al. reported that RNA sequencing identified 21 upregulated and 20 downregulated differentially expressed lncRNAs in POP patients, with significant enrichment observed in the extracellular matrix (ECM), cell receptor interactions, the Wnt signaling pathway, the p53 signaling pathway, among others[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Connell et al. discovered that upregulated HOXA11 expression might enhance the proliferation of uterosacral ligament cells by inhibiting the p53 pathway[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. At present, knowledge regarding the regulation of lncRNAs in the onset and progression of POP remains limited. Our in vitro experiments confirmed significant differential expression of ENSG00000254531 in POP versus non-POP tissues and cells. Furthermore, knocking down and overexpressing ENSG00000254531 significantly affected fibroblast proliferation. LncRNAs may significantly influence POP pathology, necessitating further exploration and research into many yet-uncovered aspects.\u003c/p\u003e \u003cp\u003eThis study's enrichment analysis of ncRNAs reveals that differentially expressed genes predominantly participate in the MAPK, Erk1/2, rap1, FOXO, and ErbB signaling pathways. KEGG disease analysis demonstrates their close association with inflammation, adhesions, cellular aging, apoptosis, and the cytoskeleton. Furthermore, we constructed lncRNA/circRNA-ceRNA regulatory network centered on miRNA, which is regulated by miRNA and shares the same miRNA binding sites[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Functional analysis indicates that the lncRNA-ceRNA network is involved in regulating inflammation[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and metabolic processes[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], while the circRNA-ceRNA network contributes to cytoskeletal stability[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Dysregulation of cholesterol intake and synthesis, along with increased infusion of lipoproteins, may result in a chronic inflammatory state[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Concurrently, the inhibition of mitochondrial respiration and glycolysis, resulting in reduced intracellular ATP levels, induces apoptosis[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Consequently, we hypothesize that in POP patients, opportunistic modes of inflammatory response and basic metabolic processes are utilized through the ceRNA mechanism, culminating in the destruction of cell membranes and cytoskeleton, thus facilitating POP's onset and progression. Presently, validation experiments for ncRNA and ceRNA predominantly involve overexpression and knockout studies in cellular and animal models. However, whether these findings accurately represent the underlying mechanisms of POP progression has yet to be confirmed at the physiological level. Thus, further animal studies and clinical trials are imperative for analysis, aiming to translate basic research findings into clinical applications. In summary, the ncRNAs identified in this study may significantly contribute to the pathogenesis and progression of pelvic organ prolapse .\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAbbreviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMeaning\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePelvic organ prolapse\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003encRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003enon-coding RNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGene Ontology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eceRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ecompetitive endogenous RNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNext-Generation Sequencing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003erRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eribosomal RNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDEGs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edifferentially expressed genes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003emolecular functions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ebiological processes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ecellular components\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRT-qPCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReal-Time Fluorescent Quantitative PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edifferentially expressed\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHULFs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ehuman uterosacral ligament fibroblasts\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003emiRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003emicroRNA\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003elncRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003elong non-coding RNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ecircRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ecircular RNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003emRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003emessenger RNA\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBMSCs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ebone marrow mesenchymal stem cells\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIL-1\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003einterleukin-1\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u0026apos;UTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u0026apos; untranslated region\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eimmunohistochemistry\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eER\u0026beta;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eestrogen receptor\u0026nbsp;\u0026beta;1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCOL-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ecollagen 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eECM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eextracellular matrix\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e The sample collection was approved by the Ethics Committee of Shengjing Hospital affiliated with China Medical University (Ethical Approval Number: 2024PS020K). All clinical specimens were collected in accordance with surgical standards.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by grants from National Natural Science Foundation of China(82271613) to ZhiJun Xia.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eY.F .Y and Z.J. X conceived and designed the study. Y.F .Y and B.J. R performed the experiments and analyzed the data. B.J. R and J. Z prepared the figures. Y.F .Y drafted the manuscript. Z.J. X approved the final version of the manuscript.All authors edited and contributed to the manuscript.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eThe data and materials used in this study are available upon reasonable requests to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSmith TA, Poteat TA, Shobeiri SA. Pelvic organ prolapse. J Am Acad Physician Assistants. 2014;27(3):20\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarber MD. Pelvic organ prolapse. BMJ, 2016: p. i3853.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuber KM, Boero S, Choe JY. The demographics of pelvic floor disorders: Current observations and future projections. Am J Obstet Gynecol. 2001;184(7):1496\u0026ndash;503.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMattsson NK et al. \u003cem\u003ePelvic organ prolapse surgery and quality of life\u0026mdash;a nationwide cohort study.\u003c/em\u003e American Journal of Obstetrics and Gynecology, 2020. 222(6): p. 588.e1-588.e10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarber MD, Maher C. Epidemiology and outcome assessment of pelvic organ prolapse. Int Urogynecol J. 2013;24(11):1783\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaher C et al. Surgical management of pelvic organ prolapse in women. Cochrane Database Syst Rev, 2013(4): p. Cd004014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSlieker-ten Hove MCP, et al. Prediction model and prognostic index to estimate clinically relevant pelvic organ prolapse in a general female population. Int Urogynecol J. 2009;20(9):1013\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVergeldt TFM, et al. Risk factors for pelvic organ prolapse and its recurrence: a systematic review. Int Urogynecol J. 2015;26(11):1559\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu JM, et al. Forecasting the Prevalence of Pelvic Floor Disorders in U.S. Women. Obstet Gynecol. 2009;114(6):1278\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDillies MA, et al. A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief Bioinform. 2013;14(6):671\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSoon WW, Hariharan M, Snyder MP. High-throughput sequencing for biology and medicine. Mol Syst Biol. 2013;9:640.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKimura T. 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FASEB J. 2017;31(5):2168\u0026ndash;84.\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":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Pelvic Organ Prolapse, Uterosacral Ligament, Non-coding RNA, High-throughput Sequencing, Fibroblasts","lastPublishedDoi":"10.21203/rs.3.rs-4575197/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4575197/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePelvic organ prolapse (POP) is a prevalent gynecological disorder, characterized by anomalies in the function or position of the pelvic organs, frequently manifesting as prolapse of the uterus and both the anterior and posterior vaginal walls. POP is primarily linked to damage to both the pelvic floor muscles and connective tissue, with the majority of molecules and genetic mutations associated with POP pertaining to the synthesis and degradation of pelvic support tissues. Recently, the significant role of non-coding RNA (ncRNA) in epigenetic regulation has garnered extensive attention. However, the functions of various RNAs including microRNA (miRNA), long non-coding RNA (lncRNA), circular RNA (circRNA), and messenger RNA (mRNA) in the pathogenesis of POP remain elusive.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThis study collected uterosacral ligament tissue from five POP patients and three age-matched controls for Illumina PE150 sequencing, identifying 60 mRNAs, 176 miRNAs, 29 lncRNAs, and 176 circRNAs with statistically significant differences in abundance between the POP and control groups. The accuracy of the high-throughput next-generation sequencing results was further validated through qRT-PCR analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses demonstrated that differentially expressed genes were predominantly involved in the MAPK, Erk1/2, rap1, FOXO, and ErbB signaling pathways. KEGG disease analysis indicated that these genes are closely associated with inflammation, adhesion plaques, cellular aging, apoptosis, and the cytoskeleton. Utilizing the competitive endogenous RNA (ceRNA) regulatory mechanism, we constructed a lncRNA/circRNA-miRNA-mRNA network. Finally, from the RNAs identified in the high-throughput whole transcriptome sequencing, we randomly selected ENSG00000254531 and confirmed that this molecule influences fibroblast proliferative capacity.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur comprehensive transcriptome study reveals the gene expression characteristics in the uterosacral ligament tissues of postmenopausal women with POP. This study provides essential data support for identifying key mRNAs and non-coding RNAs associated with the potential molecular mechanisms of POP. We screened differentially expressed miRNAs, lncRNAs, circRNAs, and mRNAs, evaluated their functional enrichments, and constructed ceRNA network to elucidate potential regulatory mechanisms and their corresponding functions. Finally, we validated the differential expression of a critical lncRNA in tissues and cells through in vitro experiments. Our findings demonstrate that the dysregulated lncRNA significantly impacts fibroblast proliferation. The identification of key lncRNAs in our study provides valuable insights into POP-related lncRNAs and may serve as important factors in the diagnosis and treatment of pop. This research introduces new candidate markers for exploring the pathogenesis of pelvic organ prolapse.\u003c/p\u003e","manuscriptTitle":"Transcriptomic and bioinformatic analyses in the sacral ligament tissue of postmenopausal women have revealed the pathogenesis of pelvic organ prolapse disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-13 02:31:14","doi":"10.21203/rs.3.rs-4575197/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2024-06-14T03:47:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-14T02:33:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomics","date":"2024-06-13T09:41:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1679c8d3-4f19-4acb-9ed3-ddce6bb0a415","owner":[],"postedDate":"July 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-07-13T02:31:14+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-13 02:31:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4575197","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4575197","identity":"rs-4575197","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}