lncRNA NEAT1 affects granulosa cell apoptosis and steroid biosynthesis via the miR-204-5p/ESR1 axis in ovarian aging

lncRNA NEAT1 affects granulosa cell apoptosis and steroid biosynthesis via the miR-204-5p/ESR1 axis in ovarian aging | 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article lncRNA NEAT1 affects granulosa cell apoptosis and steroid biosynthesis via the miR-204-5p/ESR1 axis in ovarian aging Li Dong, Haicui Wu, Fanghua Qi, Wen Chen, Yuan Xu, Min Li, Yuqi Wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5325793/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Long non-coding RNAs (lncRNAs) play an important role in ovarian aging by affecting the biological functions of granulosa cells (GCs) through multiple mechanisms. The lncRNA NEAT1 is significantly downregulated in aging ovaries; however, the exact regulatory mechanism has not been elucidated. In the current study, we aimed to investigate the effects of the lncRNA NEAT1 in GC functions during ovarian aging and explore its therapeutic potential. We observed that NEAT1 expression is downregulated in GCs of older patients with diminished ovarian reserve (DOR), which is closely associated with ovarian reserve function and assisted reproductive cycle outcomes. Functional assays revealed that NEAT1 promotes KGN cell proliferation by increasing the proportion of S-phase cells and inhibiting apoptosis. Bioinformatics analysis and a dual-luciferase reporter assay confirmed that NEAT1 acts as a miR-204-5p sponge and identified ESR1 as a miR-204-5p target gene, both of which were significantly differentially expressed in the GCs of older patients with DOR. Mechanistic experiments demonstrated that NEAT1 acts as a competitive endogenous RNA and adsorbs miR-204-5p through molecular sponging, which in turn promotes the expression of ESR1 and upregulates the expression of key enzymes (steroidogenic acute regulatory protein and cytochrome P450 family 19 subfamily A member 1) involved in steroid hormone synthesis. This induces estradiol biosynthesis and activates the downstream mitogen-activated protein kinase (MAPK) signaling pathway to promote the phosphorylation of extracellular signaling-related kinase and cyclic adenosine monophosphate response element binding protein, which affects the cell cycle and results in the promotion of proliferation and inhibition of apoptosis of KGN cells. Our results suggest that NEAT1 activates the downstream MAPK pathway through the miR-204-5p/ESR1 axis; regulates GC proliferation, apoptosis, and the cell cycle; and affects steroid biosynthesis. Therefore, NEAT1 represents a potential therapeutic target to delay ovarian aging. lncRNA NEAT1 miR-204-5p ESR1 Ovarian aging Apoptosis Proliferation Steroid synthesis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction The ovary, a key organ in the female reproductive system that performs both reproductive and endocrine functions, is the first to age. Ovarian aging is a natural physiological process, and a decline in ovarian function occurs at an accelerated rate after age 35 [ 1 ]. Poor lifestyle habits and environmental pollution accelerate ovarian aging, and together with the global delay in the age of female fertility [ 2 ], lead to reduced fertility, manifesting as an increased risk of infertility and adverse pregnancy outcomes [ 3 ]. Although hormone replacement therapy can alleviate the symptoms associated with estrogen deficiency, it fails to restore ovarian function and has little effect on fertility. Therefore, an in-depth study of the molecular mechanisms underlying ovarian aging and the exploration of effective therapeutic strategies are important for the protection of the reproductive health of women. Granulosa cells (GCs), the key components of follicles, are the primary sites of estrogen synthesis. Communication and interaction between GCs and oocytes play a key role in oocyte development, maturation, and fertilization and are bidirectional [ 4 ]. During ovarian aging, GC dysfunction induces follicular atresia, which ultimately leads to a decrease in the number and quality of oocytes as well as in the level of related steroid hormones [ 5 , 6 ]. Evidence suggests that the apoptotic, proliferative, and steroid hormone-synthesizing capacity of GCs play a central role in ovarian aging [ 7 , 8 ]. Investigating the mechanisms that regulate the biological functions of GCs is crucial for a deeper understanding of the molecular basis of ovarian aging, and for identifying potential targets for the development of targeted therapeutic approaches. Competing endogenous RNAs (ceRNAs) constitute a complex regulatory network in which non-coding RNAs such as long non-coding RNAs (lncRNAs) or circular RNAs and mRNAs share the same miRNA response elements, thus forming a competitive relationship to regulate each other's expression levels [ 9 , 10 ]. Thus, ceRNAs play an important role in the regulation of gene expression. CeRNA-mediated regulation is involved in cell proliferation, differentiation, apoptosis, and other biological processes [ 11 ] and plays an important role in developing tumors and female reproductive endocrine diseases [ 12 , 13 ]. As important transcriptional regulatory molecules in the ceRNA network, lncRNAs such as H19, HOTAIR, and MALAT1 affect biological functions, hormone synthesis, and secretion in GCs by regulating key signaling pathways, such as the PI3K/Akt and TGFβ/SMAD [ 14 , 15 ] pathways. A previous study [ 16 ] showed that lncRNA NEAT1 expression is significantly reduced in GCs of women with diminished ovarian reserve (DOR) and can be used as a biomarker of ovarian aging; however, the specific molecular mechanism has not been fully elucidated. In the current study, we collected clinical GC samples from older patients with DOR and constructed a ceRNA regulatory axis using bioinformatics to investigate the function and potential molecular mechanism of NEAT1 in ovarian aging. The results showed that NEAT1 acts as a molecular sponge to regulate the mitogen-activated protein kinase (MAPK) signaling pathway by adsorbing miR-204-5p and upregulating estrogen receptor 1 (ESR1), thereby inhibiting apoptosis, promoting cell proliferation, and affecting cell cycle and steroid biosynthetic functions in GCs. This study explored the potential role of ceRNAs in the development of ovarian aging from a novel perspective. The discovery of a therapeutic target may provide a new scientific basis for protecting women's reproductive health, and allow for future clinical applications. Materials and methods Clinical sample collection Twelve patients who underwent assisted reproductive technology (ART)-based assisted conception using an antagonist regimen at the Department of Reproduction and Genetics (Affiliated Hospital of Shandong University of Traditional Chinese Medicine) were included in the study. These included six patients with advanced DOR (age ≥ 35 years) and six young women with normal ovarian reserve (NOR). The diagnosis of DOR was based on the fulfillment of at least two of the following criteria [ 17 ]: (i) bilateral anterior follicle count (AFC) < 6, (ii) anti- Müllerian hormone (AMH) < 1.10 ng/mL; and (iii) basal follicle-stimulating hormone (FSH) of 10–40 mIU/mL. The NOR group included women with normal ovarian reserve but infertility due to male or tubal factors. Patients with structural malformations of the reproductive organs, endometriosis, other endocrine disorders (such as thyroid dysfunction and hyperprolactinemia), or chromosomal abnormalities were excluded from the study. Follicular fluid discarded after oocyte retrieval was collected, and GCs were extracted, purified via density gradient centrifugation [ 18 ], and immediately stored in a -80°C refrigerator. This study was approved by the ethics committee of the Department of Reproductive Medicine at the hospital and was registered in the China Clinical Trial Registry on October 30, 2021 (registration number: ChiCTR2100052522). Informed consent was obtained from all the patients. Cell culture The human ovarian GC tumor cell line KGN was purchased from Procell Life Science and Technology Co. (Wuhan, China). The cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM)/F-12 medium (BasalMedia, Shanghai, China) supplemented with 10% fetal bovine serum (FBS; BasalMedia) and 1% penicillin-streptomycin solution (Biosharp, Hefei, China) in an incubator at 37°C with 5% CO 2 . Cell transfection NEAT1 overexpression plasmid (OE-NEAT1), NEAT1 small interfering RNA (siRNA; si-NEAT1), ESR1 siRNA (si-ESR1), miR-204-5p mimics (mimics-miR-204-5p), miR-204-5p inhibitor (inhibitor-miR-204-5p), and the corresponding negative controls were obtained from Tsingke Biotech Co. Ltd. (Beijing, China). The plasmids, siRNA, and miR-204-5p mimics/inhibitors were transfected into KGN cells using Lipofectamine® 3000 (Invitrogen, Carlsbad, CA, USA) according to the manufacturer's instructions. The transfection efficiency was determined using quantitative reverse transcription PCR (RT-qPCR). Fluorescence in situ hybridization (FISH) A FISH Kit (RiboBio, Guangzhou, China) was used to analyze the subcellular localization of NEAT1. KGN cells were fixed with 4% paraformaldehyde and permeabilized with 0.5% Triton X-100. Then, pre-hybridization buffer was added to the cells and incubated for 30 min, followed by overnight incubation in a hybridization buffer containing a fluorescein isothiocyanate (FITC)-labeled NEAT1 probe (Sangon Biotech, Shanghai, China). The cells were counterstained with DAPI (incubation for 8 min protected from light) and imaged under a laser confocal microscope (Leica, Germany). Dual-luciferase reporter assay Bioinformatic analysis predicted the downstream targets and complementary pairing sites of NEAT1 and miR-204-5p. Wild-type (NEAT1-WT or ESR1-WT) and mutant plasmids (NEAT1-MUT or ESR1-MUT) containing miR-204-5p binding sites were constructed and cloned into a pGL4 vector (Promega, Madison, WI, USA). KGN cells were co-transfected with the luciferase reporter plasmids, mimics-miR-204-5p, inhibitor-miR-204-5p, and the corresponding negative controls using Lipofectamine® 3000. After 48 h, the luciferase activity in the cells was measured using a Dual-Luciferase Reporter Gene Assay Kit (Promega, USA). Cell counting kit-8 (CCK-8) assay A CCK-8 Kit (Vazyme Biotech Co. Ltd., Nanjing, China) was used to analyze cellular activity. KGN cells were plated in 96-well plates at 4 × 10 3 cells/well (100 µL/well). After cell adherence, 10 µL CCK-8 solution was added to each well and incubated at 37°C for 2 h in the incubator. Absorbance was measured at 450 nm using a multifunctional enzyme marker (Bio-Rad, Hercules, CA, USA). EdU proliferation assay Cell proliferation was assessed by measuring DNA synthesis using the EdU Cell Proliferation Kit (Beyotime, Shanghai, China). KGN cells were incubated with 500 µL EdU working solution for 2 h. The cells were then fixed with 4% paraformaldehyde for 15 min, permeabilized with 0.5% Triton-100 for 10 min, and the nuclei were stained with Hoechst 33342 for 10 min in the dark. Images were captured using a fluorescence microscope (Nikon, Tokyo, Japan). Flow cytometric assay Apoptosis was detected using the Annexin V-FITC/propidium iodide (PI) Apoptosis Detection Kit (Vazyme). KGN cells were collected, washed with phosphate-buffered saline (PBS), and resuspended in a binding buffer. Subsequently, the cells were stained with Annexin V-FITC and PI by incubating for 15 min in the dark. The cells were analyzed using a flow cytometer (BD, Franklin Lakes, NJ, USA). The cell cycle was analyzed using a Cell Cycle and Apoptosis Detection Kit (Beyotime). KGN cells were fixed with pre-cooled PBS and 70% ethanol at 4 ºC overnight. The cells were then resuspended in PI staining solution for 30 min at 37 ºC protected from light and detected using a flow cytometer. RT-qPCR Total RNA was extracted from clinical GC samples and KGN cells using the Total RNA Extraction Reagent RNA Isolator (Vazyme). The concentration and purity of the extracted RNA were assessed using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). Total RNA was reverse transcribed to cDNA using HiScript II Q RT SuperMix for qPCR (Vazyme). RT-qPCR was performed using a fluorescent quantitative PCR instrument (Bio-Rad) and the ChamQ SYBR® qPCR Master Mix (Vazyme). GAPDH or U6 were used as the reference genes, and the 2−ΔΔ Ct method was used to quantify the expression level of RNAs. The specific primers used in this study were obtained from Sangon Biotech, and their sequences are listed in Table 1 . Table 1 Primer sequences for gene expression Gene Primer sequence (5'-3') NEAT1 Forward: AGTGTGAGTCCTAGCATTG Reverse: GAACTTCCTCCTCCTAAGC hsa-miR-204-5p Forward: CGCGTTCCCTTTGTCATCCT Reverse: AGTGCAGGGTCCGAGGTATT hsa-miR-125a-5p Forward: TCGGCAGGTCCCTGAGACCCTT Reverse: CTCAACTGGTGTCGTGGA StAR Forward: GCCACATTTGCCAGGAAACAATG Reverse: CTCCTGGTCACTGTAGAGAGTCT CYP19A1 Forward: GCAAAGCACCCTAATGTTGAAGA Reverse: CGAGTCTGTGCATCCTTCCAATA ESR1 Forward: CTCTAACCTCGGGCTGTGC Reverse: AGATGCTTTGGTGTGGAGGG NOTCH2 Forward: ATCCCACAAAGCCTAGCACC Reverse: CCTTGTCCCTGAGCAACCAT IGFBP5 Forward: GAAGCAGATGTGTCTCTGCCC Reverse: TTCCCTCTGCTTCCTGGTTC GAPDH Forward: TGCACCACCAACTGCTTAGC Reverse: GGCATGGACTGTGGTCATGAG U6 Forward: CAGCACATATACTAAAATTGGAACG Reverse: ACGAATTTGCGTGTCATCC CYP19A1, cytochrome P450 family 19 subfamily A member 1; ESR1, estrogen receptor 1; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; IGFBP5, insulin-like growth factor binding protein 5; NEAT1, nucleo-enriched abundant transcript 1; NOTCH2, recombinant notch homolog 2; StAR, steroidogenic acute regulatory protein; U6, RNU6. Western blotting Proteins were extracted from cells using radioimmunoprecipitation assay (RIPA) lysis buffer (Servicebio, Wuhan, China), and the protein concentration was determined using the Bicinconinic acid (BCA) Protein Assay Kit (CWBIO, Taizhou, China). Equal amounts of proteins were separated by electrophoresis on 12% sodium dodecyl sulfate-polyacrylamide gels and subsequently transferred to polyvinylidene difluoride membranes (Bio-Rad). The non-specific sites on the membranes were blocked by incubating with 5% skimmed milk for 2 h at 22°C. The membranes were then incubated with the primary antibody overnight at 4°C. The membranes were washed with TBST buffer and incubated with horseradish peroxidase (HRP)-conjugated secondary antibody for 2 h at room temperature. After washing with TBST buffer, the protein bands on the membrane were visualized using an enhanced chemiluminescence Detection Kit (Vazyme) and scanned using a gel imaging system. GAPDH was used as an internal reference. All the antibodies used in the western blotting, including ESR1 (20698-1-AP), phospho-ERK1/2 (80031-1-RR), ERK1/2 (11257-1-AP), phospho-CREB1 (28792-1-AP), CREB1 (12208-1-AP), StAR (12225-1-AP), CYP19A1 (16554-1-AP), and GAPDH (10494-1-AP) were purchased from Proteintech Group Inc. (Wuhan, China). Enzyme-linked immunosorbent assay (ELISA) KGN cells were collected, and androstenedione (10 µg/mL) was added to the culture medium. The cells were then cultured for 24 h. The estradiol level in the supernatant was measured using the Human Estrogen, E ELISA Kit (Cusabio, Wuhan, China) according to the manufacturer’s instructions. Statistical analysis The experimental data were analyzed using SPSS software (version 23.0; IBM, Armonk, NY, USA). All results are expressed as mean ± standard deviation (SD). The normality of data distribution was assessed using the Shapiro–Wilk test. Student's t -test or Wilcoxon test was used to assess the significance of the difference between the two groups of data. Pearson's correlation test was used for the correlation analysis between variables. The graphs were generated using GraphPad Prism (version 8.0; San Diego, CA, USA) software. Statistical significance was set at p < 0.05. Results Baseline characteristics The baseline characteristics of the patients in the two groups are presented in Table 2 . Serum AMH and AFC levels were significantly lower ( p < 0.001), and basal FSH levels were significantly higher ( p < 0.05) in older patients with DOR compared with that in the NOR group. The baseline profiles, such as infertility duration, body mass index (BMI), basal luteinizing hormone (LH), basal estradiol (E 2 ), and basal progesterone (P) levels, were not significantly different between the two groups of patients ( p > 0.05). Table 2 Comparison of baseline characteristics between the NOR and DOR groups. NOR (n = 6) DOR (n = 6) p -value Patient age (years) 31.33 ± 2.42 38.33 ± 1.37 < 0.001 Infertility duration (years) 2.83 ± 1.83 4.50 ± 2.26 0.191 BMI (kg/m 2 ) 20.73 ± 1.56 21.75 ± 2.05 0.356 AMH (ng/mL) 4.50 ± 1.27 0.58 ± 0.27 < 0.001 Basal FSH (mIU/mL) 7.12 ± 0.62 12.58 ± 4.50 0.031 Basal LH (mIU/mL) 7.16 ± 3.74 3.60 ± 1.66 0.059 Basal E 2 (pg/mL) 32.33 ± 12.55 44.73 ± 13.27 0.127 Basal P (ng/mL) 0.44 ± 0.28 0.39 ± 0.17 0.744 AFC (n) 19.50 ± 2.88 5.33 ± 2.42 < 0.001 Data are shown as mean ± standard deviation. AMH, anti-Müllerian hormone; AFC, antral follicle count; BMI, body mass index; E 2 , estrogen; FSH, follicle-stimulating hormone; LH, luteinizing hormone; P, progesterone. Downregulation of NEAT1 is associated with DOR occurrence and ART outcomes in older women First, the expression levels of NEAT1 in GC samples were analyzed using RT-qPCR. The results showed that NEAT1 expression was significantly downregulated in the GCs of older patients with DOR compared to those in the NOR group (*** p < 0.001; Fig. 1 a). Pearson correlation analysis showed that NEAT1 expression levels were significantly negatively correlated with patient age, basal FSH, and gonadotropin dosage (Fig. 1 b-d); and significantly positively correlated with AMH, AFC, number of oocytes retrieved, number of two-pronuclear zygotes, and the number of embryos available (Fig. 1 e-i). In addition, there was no significant correlation between NEAT1 expression level and gonadotropin duration and the number of superior embryos (Fig. 1 j, k). These results suggest that the downregulation of NEAT1 in the GCs of older patients with DOR may be associated with ovarian reserve function and ART outcomes. NEAT1 inhibits apoptosis and promotes proliferation of KGN cells To confirm the subcellular localization of NEAT1 in KGN cells, a FISH assay was performed. The assay showed that NEAT1 is mainly localized in the cytoplasm (Fig. 2 a), which suggests that NEAT1 acts as a molecular sponge for miRNAs to exert post-transcriptional regulation. To further investigate the biological functions of NEAT1, KGN cells were transfected with plasmids si-NEAT1 or OE-NEAT1. RT-qPCR results revealed significant changes in NEAT1 expression in the cells following transfection (Fig. 2 b). The CCK-8 and EdU assays revealed that NEAT1 overexpression significantly increased cell viability and proliferation, whereas NEAT1 knockdown had the opposite effect (Fig. 2 c, d). Flow cytometry results showed a significant decrease in apoptosis in the OE-NEAT1 group, whereas a significant increase in apoptosis was observed in the si-NEAT1 group (Fig. 2 e). Additionally, cell cycle analysis showed that NEAT1 overexpression led to a significant decrease in the proportion of cells in the G0/G1 phase and a significant increase in the proportion of cells in the S phase, indicative of an increase in cell proliferation. In contrast, NEAT1 knockdown significantly increased the proportion of cells in the G0/G1 phase and decreased the proportion of cells in the S phase, indicating that si-NEAT1 blocked cells in the G1/S phase, thereby inhibiting cell proliferation (Fig. 2 f). miR-204-5p is a target of NEAT1 To predict miRNAs that potentially interact with NEAT1, we used the starBase, miRcode, and LncBase databases (Fig. 3 a). We screened miR-125a-5p and miR-204-5p as miRNAs common to the three databases, verified their expression levels in GCs using RT-qPCR, and determined that miR-204-5p was significantly upregulated in the GCs of patients with advanced DOR (Fig. 3 b). Figure 3 c shows the complementary base sequences of NEAT1 and miR-204-5p. Based on the predicted binding site, dual-luciferase reporter assays were performed, which confirmed that mimics-miR-204-5p significantly reduced the luciferase activity of the NEAT1-WT vector, whereas it did not affect the luciferase activity of the NEAT1-MUT vector. In contrast, the inhibitor-miR-204-5p significantly increased the luciferase activity of the NEAT1-WT vector but not that of NEAT1-MUT (Fig. 3 d). In addition, transfection with OE-NEAT1 significantly decreased miR-204-5p expression in KGN cells, whereas transfection with si-NEAT1 significantly increased miR-204-5p expression (Fig. 3 e). These data suggest that NEAT1 directly binds to miR-204-5p and inhibits its expression. NEAT1 overexpression negatively regulates miR-204-5p to inhibit apoptosis and promote proliferation of KGN cells We transfected mimics-miR-204-5p, inhibitor-miR-204-5p, and the corresponding negative controls into KGN cells to investigate the effects of miR-204-5p on cell proliferation and apoptosis. RT-qPCR was used to confirm the transfection efficiency (Fig. 4 a). The CCK-8 and EdU assays revealed that miR-204-5p overexpression significantly inhibited cell viability and proliferation, whereas miR-204-5p inhibition had the opposite effect (Fig. 4 b, c). Flow cytometry showed that transfection with mimics-miR-204-5p induced apoptosis in KGN cells, whereas transfection with inhibitor-miR-204-5p significantly inhibited apoptosis (Fig. 4 d). In addition, cell cycle analysis revealed that miR-204-5p overexpression inhibited cell proliferation by decreasing the proportion of KGN cells in S phase. In contrast, the downregulation of miR-204-5p significantly increased the proportion of S-phase cells and decreased the proportion of G0/G1-phase cells. Thus, the low expression of miR-204-5p promoted KGN cell proliferation by promoting the G1/S transition of the cells (Fig. 4 e). To further examine the correlation between NEAT1 and miR-204-5p in the regulation of KGN cell functions, we co-transfected KGN cells with OE-NEAT1 and mimics-miR-204-5p. As shown in Figs. 5 a and b , NEAT1 overexpression increased KGN cell viability and proliferation, and this effect was reversed by miR-204-5p mimics. In addition, overexpression of miR-204-5p reversed the NEAT1-mediated effects on the apoptotic rate and G1/S transition of KGN cells (Fig. 5 c, d). These results suggest that NEAT1 affects the proliferation and apoptosis of KGN cells by targeting miR-204-5p. ESR1 is a downstream target of miR-204-5p We used multiple bioinformatics databases (starBase, miRcode, TargetScan, miRDB, and miRWalk) to predict the potential target genes of miR-204-5p (Fig. 6 a). Three potential targets were identified at the intersection of the five databases. The expression levels of these potential targets were evaluated in 12 clinical GC samples using RT-qPCR. ESR1 showed the most significant difference in expression between the two patient groups (Fig. 6 b). We predicted the potential binding site of miR-204-5p on ESR1 (Fig. 6 c). Dual-luciferase reporter assays were performed to verify the targeted regulatory relationship between the two molecules (Fig. 6 d). Transfection of KGN cells with miR-204-5p mimics decreased ESR1 mRNA and protein expression levels; conversely, transfection with miR-204-5p inhibitors increased ESR1 mRNA and protein expression (Fig. 6 e, f ) . These data suggest that miR-204-5p negatively regulates ESR1 expression. Silencing of ESR1 reverses the effects of inhibitor-miR-204-5p on proliferation, apoptosis, and steroid biosynthesis in KGN cells After transfecting KGN cells with inhibitor-miR-204-5p and si-ESR1, we performed a series of experiments to clarify the effect of ESR1 on miR-204-5p-mediated biological functions. CCK-8 and EdU assays showed that the inhibition of miR-204-5p significantly promoted the viability and proliferative capacity of KGN cells, whereas this effect was effectively reversed by silencing ESR1 (Fig. 7 a, b). In addition, flow cytometric analysis showed that the miR-204-5p inhibitor significantly reduced the apoptotic rate, increased the proportion of S-phase cells, and reduced the proportion of G2/M-phase cells. However, si-ESR1 transfection partially restored the apoptotic rate and cell cycle transition (Fig. 7 c, d). These results indicate that miR-204-5p targets the negative regulation of ESR1 to regulate proliferation, cell cycle, and apoptosis of KGN cells. ESR1 encodes estrogen receptor-α, which participates in estrogen synthesis by mediating the estrogen signaling pathway. Moreover, it regulates the MAPK signaling pathway [ 19 ] to affect cell proliferation, differentiation, and apoptosis. We further examined the expression levels of key steroidogenic genes, including steroidogenic acute regulatory protein (StAR) and cytochrome P450 family 19 subfamily A member 1 (CYP19A1), and the important proteins in the MAPK signaling pathway. The results showed that transfection of KGN cells with inhibitor-miR-204-5p significantly up-regulated the mRNA and protein expression of ESR1, StAR, and CYP19A1 and promoted E 2 biosynthesis, whereas ESR1 silencing reversed these changes (Fig. 7 e-g). In addition, the miR-204-5p inhibitor increased the levels of phosphorylated extracellular signaling-related kinase (ERK) and cyclic adenosine monophosphate response element binding protein (CREB), which were reversed by si-ESR1 (Fig. 7 f). NEAT1 mediates the MAPK signaling pathway through the miR-204-5p/ESR1 axis to affect the biological functions in KGN cells Next, we sought to investigate whether NEAT1 affects the phenotype of KGN cells by regulating ESR1. KGN cells were transfected with si-NEAT1 or OE-NEAT1, and the mRNA and protein expression levels of ESR1 were analyzed using RT-PCR and western blotting, respectively. The results showed that ESR1 levels decreased significantly following si-NEAT1 transfection, whereas they markedly increased following OE-NEAT1 transfection (Fig. 8 a, b). To investigate whether NEAT1 is involved in functional alterations in GCs during ovarian aging by regulating ESR1 expression and downstream signaling pathways through sponging of miR-204-5p, the levels of ESR1, StAR, CYP19A1, and E 2 were analyzed in KGN cells overexpressing NEAT1 in the absence of the presence of mimics-miR-204-5p. The results showed that overexpression of NEAT1 in KGN cells resulted in a significant increase in the mRNA and protein expression of ESR1, StAR, and CYP19A1 and augmentation of E 2 biosynthesis, which was reversed by co-transfection with mimics-miR-204-5p (Fig. 8 c-e). In addition, NEAT1 overexpression increased the phosphorylation of ERK and CREB, which was reversed by mimics-miR-204-5p (Fig. 8 e). These results suggest that NEAT1 acts as a miR-204-5p sponge to regulate ESR1 expression, which in turn regulates biological functions such as proliferation, apoptosis, and steroid biosynthesis in KGN cells by activating the MAPK signaling pathway. Discussion In women, ovarian aging results in decreased secretion of estrogen and P by the ovaries and a decline in the number and quality of follicles, which affects fertility and quality of life in the long term. Ovarian aging is accompanied by mitochondrial dysfunction in GCs. Oxidative stress and inflammatory responses cause further damage to the GCs. Functional abnormalities of GCs are closely related to the pathogenesis of ovarian aging. Therefore, elucidation of the underlying molecular mechanisms is of great significance for maintaining ovarian function and promoting women's health. LncRNAs are involved in ovarian aging by regulating several biological processes, including oocyte development, maturation, and luteinization [ 20 – 22 ]. In the current study, we observed that the lncRNA NEAT1 upregulates ESR1 through the adsorption of miR-204-5p, which in turn mediates the MAPK signaling pathway, promotes proliferation, inhibits apoptosis, and affects the cell cycle and steroid biosynthesis in KGN cells. Thus, these results revealed a novel regulatory mechanism of GC dysfunction, which can be exploited to improve diagnostic and therapeutic strategies for ovarian senescence. NEAT1 constitutes the skeletal structure of the subnucleosomal parafollicular patch and regulates gene expression by maintaining mRNA stability in the nucleus [ 23 ]. NEAT1 is involved in the cyclic recruitment and differentiation of follicles [ 24 ], and is closely associated with corpus luteum formation, pregnancy establishment, and maintenance [ 25 ]. NEAT1 is significantly downregulated in the ovarian tissues of patients with premature ovarian failure (POF), and overexpression of NEAT1 inhibits p53 expression by affecting its transcription and stability, which in turn ameliorates POF by inhibiting apoptosis [ 26 ]. Several recent studies have demonstrated that NEAT1 acts as a ceRNA to regulate the expression of downstream target genes, which are involved in the development of reproductive endocrine diseases at the post-transcriptional level through the competitive adsorption of miRNAs. For example, NEAT1 overexpression increases the expression of androgen receptors, follistatin, and insulin receptor substrate 2 by sponging miR-30d-5p, which is involved in developing polycystic ovarian syndrome [ 27 ]. Additionally, NEAT1 competitively binds to miR-874-3p, thereby regulating the proliferation of mouse GCs and producing E 2 and progesterone [ 28 ]. Furthermore, NEAT1 reduces miR-654 expression and regulates the STC2/MAPK pathway to reduce apoptosis and autophagy, rendering it a potential therapeutic target for POF [ 29 ]. Similarly, the current study demonstrates that NEAT1 expression levels are downregulated in the GCs of older patients with DOR and correlate with ovarian reserve function and ART outcomes. Using a series of in vitro functional assays, we demonstrated that NEAT1 promotes KGN cell proliferation by regulating the cell cycle and inhibiting apoptosis. This suggests that NEAT1 plays an important role in ovarian aging by affecting multiple biological functions of GCs and may thus serve as a potential target for the diagnosis and treatment of ovarian hypoplasia. We predicted potential targets of NEAT1 based on data from multiple bioinformatics databases and observed that miR-204-5p was upregulated in GC samples from patients with clinical DOR. In addition, using dual-luciferase reporter assays, we confirmed that miR-204-5p binds to NEAT1. Subsequent functional and salvage assays showed that miR-204-5p affects KGN cell viability, proliferation, apoptosis, and cell cycle and induces DOR, which is contrary to the biological functions of NEAT1. A recent study showing that miR-204-5p functions as an oncogene and plays an important role in various cancers by regulating cell proliferation, apoptosis, autophagy, and the immune microenvironment has received widespread attention [ 30 ]. Downregulation of miR-204-5p expression in ovarian cancer inhibits tumor cell proliferation, migration, and invasion by targeting specific genes such as ubiquitin-specific peptidase 47 and thrombospondin-1 [ 31 , 32 ]. Upregulation of NEAT1 in synovial tissues of patients with rheumatoid arthritis was observed to promote the proliferation and production of inflammatory cytokines in rheumatoid arthritis fibroblast-like synoviocytes and inhibit apoptosis by targeting miR-204-5p to mediate the nuclear factor kappa-B pathway [ 33 ]. Chronic exposure to estradiol and leptin alters endometrial homeostasis and promotes endometrial cancer via the NEAT1-miR-204-5p-Igf1 axis in women with obesity [ 34 ]. Another study demonstrated that cadmium exerts cytotoxic effects and impairs ovarian function by affecting the miR-204-5p/BCL2 axis and promoting apoptosis in GCs [ 35 ]. Similarly, our data demonstrate that low NEAT1 expression in the GCs of patients with DOR attenuates miR-204-5p adsorption, thereby affecting cell proliferation, apoptosis, and viability, accelerating ovarian senescence. miRNAs negatively regulate the expression of target genes by binding to the 3'-UTR of the target gene mRNA, promoting mRNA degradation or inhibiting protein translation [ 36 ]. We used bioinformatics to predict the potential targets of miR-204-5p and reported that it negatively regulates ESR1. ESR1 is an estrogen receptor α, which mediates estrogen signaling, affecting follicle development, maturation, and steroid hormone synthesis, and plays a crucial role in maintaining ovarian function [ 37 , 38 ]. StAR and CYP19A1, key enzymes involved in estrogen biosynthesis [ 39 ], regulate steroid biosynthesis by transporting free cholesterol from the outer mitochondrial membrane to the inner mitochondrial membrane. CYP19A1 encodes cytochrome P450 aromatase, which facilitates the catalytic conversion of androgens to estrogens by GCs and is the rate-limiting enzyme for the final step of estrogen synthesis. In addition, ESR1 has been implicated in per/polyfluoroalkyl substances-induced breast cancer development and progression through activation of the MAPK/Erk signaling pathway [ 40 ]. The MAPK signaling pathway regulates the proliferation, apoptosis, cell cycle, and steroidogenesis of GCs. Moreover, it plays an important role in the regulation of female reproduction and can therefore be used as a pharmacological target for ovarian hypoplasia [ 41 , 42 ]. The most representative member of the ERK pathway in MAPK signaling, ERK, becomes phosphorylated and activated and then translocates from the cytoplasm to the nucleus, and it directly or indirectly regulates the expression of a variety of target genes and affects a variety of cellular processes, such as proliferation, differentiation, and cell death [ 43 ]. CREB is a key downstream signaling molecule of ERK. Once activated, ERK phosphorylates the downstream CREB and affects the proliferation, apoptosis, and steroidogenesis of GCs [ 44 , 45 ]. Our findings showed that ESR1 expression was significantly reduced in GCs of older patients with DOR. Further mechanistic studies demonstrated that silencing ESR1 inhibited the MAPK signaling pathway and effectively counteracted the effects of transfection with the inhibitor-miR-204-5p on proliferation, apoptosis, and steroid biosynthesis in KGN cells. Finally, we examined the relationship between NEAT1, miR-204-5p, and ESR1. We observed that NEAT1 overexpression promoted ESR1 expression, which in turn activated the ERK and CREB phosphorylation in the MAPK signaling pathway and upregulated the expression of StAR and CYP19A1 to increase estradiol production. However, these effects were eliminated by miR-204-5p. These data suggest that NEAT1 regulates ESR1 expression by sponging miR-204-5p and mediates the MAPK signaling pathway involved in DOR. In future studies, we intend to use a larger clinical sample size and an animal model of ovarian senescence to study the molecular mechanisms that regulate ovarian pathogenesis in greater depth. Conclusion Our study revealed that NEAT1 is downregulated in the GCs of older patients with DOR. NEAT1 regulates apoptosis, proliferation, and steroid biosynthesis through the miR-204-5p/ESR1 axis-mediated MAPK signaling pathway and participates in the ovarian aging process. This novel axis of ceRNA regulation is associated with DOR development and ART cycle outcomes and provides a promising therapeutic strategy to delay ovarian aging. Abbreviations AFC anterior follicle counts AMH anti-Müllerian hormone ART Assisted Reproductive Technology BMI body mass index ceRNA Competing endogenous RNA CREB Cyclic adenosine monophosphate response element binding protein CYP19A1 cytochrome P450 family 19 subfamily A member 1 DOR diminished ovarian reserve E 2 estradiol ERK extracellular signaling-related kinase ESR1 estrogen receptor 1 FSH follicle-stimulating hormone GCs Granulosa cells LH luteinizing hormone lncRNA long non-coding RNA MAPK mitogen-activated protein kinase NOR normal ovarian reserve P progesterone POF premature ovarian failure StAR Steroidogenic acute regulatory protein siRNA small interfering RNA Declarations Ethics approval and consent to participate The study was approved by the Ethics Committee of Reproductive Medicine of the Affiliated Hospital of Shandong University of Traditional Chinese Medicine and registered with the China Clinical Trial Registry on October 30, 2021 (registration number: ChiCTR2100052522). All patients provided written signed informed consent. All methods were performed in accordance with relevant guidelines and regulations. Consent for publication Not applicable. Availability of data and materials The data supporting the findings of this study are available from the corresponding authors upon a reasonable request. Competing interests The authors declare that they have no commercial or financial relationships that could be construed as a potential conflict of interest. Funding This study was supported by the National Research and Training Program for Outstanding Clinical Talents of Traditional Chinese Medicine (grant number: National Letter of TCM Practitioners No. 1 [2022]), the Co-construction of Science and Technology Programs by the Science and Technology Department of the State Administration of Traditional Chinese Medicine (SATCM) (grant number: GZY-KJS-SD-2023-070), the Shandong Traditional Chinese Medicine Science and Technology Program (grant number: Z-2023094), and the Jinan Science and Technology Program (grant number: 202328044). Authors’ contributions LD was a major contributor in writing the manuscript. HW and DL performed the experiments and analyzed the data. FQ and WC did bioinformatics analysis. YX and ML contributed to manuscript revision. YW and RY was responsible for sample collection. PC conceived and designed this study. All authors read and approved the final submitted version of this manuscript. 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Also discoverable on Platform About In Review Editorial Policies 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-5325793","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":371292447,"identity":"88e2401a-b762-45fe-849b-5394262535f8","order_by":0,"name":"Li Dong","email":"","orcid":"","institution":"Shandong University of Traditional Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Dong","suffix":""},{"id":371292448,"identity":"d0960d3c-da31-47b8-a305-22d5bdf584b7","order_by":1,"name":"Haicui Wu","email":"","orcid":"","institution":"Affiliated Hospital of Shandong University of 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12:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5325793/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5325793/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68204187,"identity":"e1146893-a177-494f-86ee-517ab3d813bb","added_by":"auto","created_at":"2024-11-04 16:04:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":109037,"visible":true,"origin":"","legend":"\u003cp\u003eNEAT1 expression levels in human GCs correlate with ovarian reserve and ART outcome. \u003cstrong\u003e(a)\u003c/strong\u003e NEAT1 expression levels in GCs of NOR and DOR groups. \u003cstrong\u003e(b–d)\u003c/strong\u003e NEAT1 expression levels negatively correlated with age, basal FSH, and gonadotropin dosage. \u003cstrong\u003e(e–i)\u003c/strong\u003eNEAT1 expression levels positively correlated with AMH, AFC, number of oocytes retrieved, number of two-pronuclear zygotes, and number of embryos available. \u003cstrong\u003e(j–k)\u003c/strong\u003eNEAT1 expression levels did not significantly correlate with gonadotropin duration and number of superior embryos. *** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5325793/v1/1f434c48e6c627aa1012f373.png"},{"id":68204189,"identity":"3e2de424-2a54-4528-a334-46f030d5631d","added_by":"auto","created_at":"2024-11-04 16:04:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":214411,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of NEAT1 on proliferation, apoptosis, and cell cycle of KGN cells. \u003cstrong\u003e(a)\u003c/strong\u003e FISH assay was used to determine the subcellular localization of NEAT1 in KGN cells. \u003cstrong\u003e(b)\u003c/strong\u003e RT-qPCR was performed to detect the transfection efficiency of OE-NEAT1 and si-NEAT1 plasmids. \u003cstrong\u003e(c–d) \u003c/strong\u003eCCK-8 and EdU assays were used to detect the effect of NEAT1 on the viability and proliferative capacity of KGN cells. \u003cstrong\u003e(e–f)\u003c/strong\u003eFlow cytometry was used to assess the effect of NEAT1 on apoptosis and the cell cycle of KGN cells. * \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, *** \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5325793/v1/f225a54deaa45e6cc8f9c1b4.png"},{"id":68204188,"identity":"414cabbd-16d7-4588-9414-5ffe6dd89128","added_by":"auto","created_at":"2024-11-04 16:04:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":73551,"visible":true,"origin":"","legend":"\u003cp\u003emiR-204-5p is a target of lncRNA NEAT1. \u003cstrong\u003e(a)\u003c/strong\u003e Downstream targets of NEAT1 were predicted using starBase, miRcode, and LncBase databases. \u003cstrong\u003e(b)\u003c/strong\u003e RT-qPCR was used to detect the expression levels of miR-125a-5p and miR-204-5p in human GCs. \u003cstrong\u003e(c)\u003c/strong\u003e Bioinformatics software was used to predict the binding sites of NEAT1 and miR-204-5p. \u003cstrong\u003e(d)\u003c/strong\u003eDual-luciferase reporter assay was used to verify the target-binding relationship between NEAT1 and miR-204-5p. \u003cstrong\u003e(e)\u003c/strong\u003e RT-qPCR was used to detect miR-204-5p expression levels following NEAT1 overexpression or silencing. ns \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05, ** \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.01, *** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5325793/v1/be1da9ef949d36e5cbf359d6.png"},{"id":68206147,"identity":"80c22ddb-4e19-4edd-a6a6-b3cf778e1df2","added_by":"auto","created_at":"2024-11-04 16:20:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":224640,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of miR-204-5p on proliferation, apoptosis, and cell cycle of KGN cells. \u003cstrong\u003e(a)\u003c/strong\u003eRT-qPCR was used to analyze miR-204-5p expression levels following transfection with mimics-miR-204-5p or inhibitor-miR-204-5p. \u003cstrong\u003e(b–c)\u003c/strong\u003eCCK-8 and EdU assays were performed to detect the effects of miR-204-5p on KGN cell viability and proliferation. \u003cstrong\u003e(d–e)\u003c/strong\u003e Flow cytometry was performed to assess the effects of miR-204-5p on KGN cell apoptosis and cell cycle. * \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05, ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, *** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5325793/v1/b403f6fe1df9fe20a7300449.png"},{"id":68204191,"identity":"73579636-a185-465a-ab35-9d859a506e0a","added_by":"auto","created_at":"2024-11-04 16:04:27","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":197396,"visible":true,"origin":"","legend":"\u003cp\u003emiR-204-5p reverses the NEAT1-mediated biological effects in KGN cells. \u003cstrong\u003e(a–b) \u003c/strong\u003eKGN cell viability and proliferation were assessed using CCK-8 and EdU. \u003cstrong\u003e(c–d)\u003c/strong\u003eFlow cytometric analysis of KGN cell apoptosis and cell cycle. * \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, *** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5325793/v1/c3e1d55e1acc3503830ed89f.png"},{"id":68205414,"identity":"4310dbf9-bf46-446a-a568-c7e6a55602c6","added_by":"auto","created_at":"2024-11-04 16:12:27","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":93250,"visible":true,"origin":"","legend":"\u003cp\u003eESR1 is a downstream target gene of miR-204-5p.\u003cstrong\u003e (a)\u003c/strong\u003e Downstream target genes of miR-204-5p were predicted using starBase, miRcode, TargetScan, miRDB, and miRWalk.\u003cstrong\u003e (b)\u003c/strong\u003e RT-qPCR was used to analyze the mRNA expression levels of IGFBP5, NOTCH2, and ESR1 in human GCs. \u003cstrong\u003e(c)\u003c/strong\u003e Bioinformatics software was used to predict the binding sites of miR-204-5p and ESR1. \u003cstrong\u003e(d)\u003c/strong\u003eDual-luciferase reporter assay was performed to verify the target-binding relationship between miR-204-5p and ESR1. \u003cstrong\u003e(e–f)\u003c/strong\u003e Expression levels of ESR1 mRNA and protein following transfection with miR-204-5p mimics or inhibitorswere detected using RT-qPCR and western blotting, respectively. * \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, *** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, **** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5325793/v1/b26e1ac0ed1c2e3f9c2c2d33.png"},{"id":68204194,"identity":"72225b1c-8dc0-411a-b7aa-5da8cf183afa","added_by":"auto","created_at":"2024-11-04 16:04:27","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":254576,"visible":true,"origin":"","legend":"\u003cp\u003eSilencing of ESR1 reverses the inhibitor-miR-204-5p-mediated biological effects on KGN cells. \u003cstrong\u003e(a–b)\u003c/strong\u003e KGN cell viability and proliferation were assessed using CCK-8 and EdU assays. \u003cstrong\u003e(c–d)\u003c/strong\u003e Flow cytometric analysis of KGN cell apoptosis and cell cycle.\u003cstrong\u003e (e)\u003c/strong\u003e RT-qPCR was performed to detect the expression level of key genes of steroid hormone synthesis. \u003cstrong\u003e(f)\u003c/strong\u003e Western blotting was performed to detect the expression levels of proteins related to steroid hormone synthesis and MAPK signaling pathway. \u003cstrong\u003e(g)\u003c/strong\u003e ELISA was used to detect E\u003csub\u003e2\u003c/sub\u003e levels. * \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, *** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5325793/v1/67323a38474f1d90c29fd87d.png"},{"id":68205415,"identity":"b2dd0749-923d-4371-add3-3d6156538869","added_by":"auto","created_at":"2024-11-04 16:12:27","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":129506,"visible":true,"origin":"","legend":"\u003cp\u003eNEAT1 regulates steroid hormone biosynthesis and the MAPK signaling pathway in KGN cells through the miR-204-5p/ESR1 axis. \u003cstrong\u003e(a-b)\u003c/strong\u003e RT-qPCR and western blotting were performed to evaluate the effect of NEAT1 on ESR1 mRNA and protein expression levels, respectively. \u003cstrong\u003e(c)\u003c/strong\u003e RT-qPCR was performed to analyze the expression of key genes involved in steroid hormone synthesis. \u003cstrong\u003e(d) \u003c/strong\u003eELISA was performed to determine E\u003csub\u003e2 \u003c/sub\u003elevels. \u003cstrong\u003e(e)\u003c/strong\u003e Western blotting was performed to determine the levels of proteins related to steroid hormone synthesis and the MAPK signaling pathway. * \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, *** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-5325793/v1/f701d94e4dbdf1695851f9cd.png"},{"id":69647940,"identity":"6076d116-14e7-4e30-bd78-b039cbebee61","added_by":"auto","created_at":"2024-11-22 15:31:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3255656,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5325793/v1/e488c4dd-64e8-4c87-b403-6e6cb324a115.pdf"},{"id":68204195,"identity":"da8470f5-f7f8-4807-b428-d142162d4065","added_by":"auto","created_at":"2024-11-04 16:04:27","extension":"pdf","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":747593,"visible":true,"origin":"","legend":"","description":"","filename":"Originalimagesforgelsandblots.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5325793/v1/9958b26d35b30eb7be257fdd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"lncRNA NEAT1 affects granulosa cell apoptosis and steroid biosynthesis via the miR-204-5p/ESR1 axis in ovarian aging","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe ovary, a key organ in the female reproductive system that performs both reproductive and endocrine functions, is the first to age. Ovarian aging is a natural physiological process, and a decline in ovarian function occurs at an accelerated rate after age 35 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Poor lifestyle habits and environmental pollution accelerate ovarian aging, and together with the global delay in the age of female fertility [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], lead to reduced fertility, manifesting as an increased risk of infertility and adverse pregnancy outcomes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Although hormone replacement therapy can alleviate the symptoms associated with estrogen deficiency, it fails to restore ovarian function and has little effect on fertility. Therefore, an in-depth study of the molecular mechanisms underlying ovarian aging and the exploration of effective therapeutic strategies are important for the protection of the reproductive health of women.\u003c/p\u003e \u003cp\u003eGranulosa cells (GCs), the key components of follicles, are the primary sites of estrogen synthesis. Communication and interaction between GCs and oocytes play a key role in oocyte development, maturation, and fertilization and are bidirectional [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. During ovarian aging, GC dysfunction induces follicular atresia, which ultimately leads to a decrease in the number and quality of oocytes as well as in the level of related steroid hormones [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Evidence suggests that the apoptotic, proliferative, and steroid hormone-synthesizing capacity of GCs play a central role in ovarian aging [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Investigating the mechanisms that regulate the biological functions of GCs is crucial for a deeper understanding of the molecular basis of ovarian aging, and for identifying potential targets for the development of targeted therapeutic approaches.\u003c/p\u003e \u003cp\u003eCompeting endogenous RNAs (ceRNAs) constitute a complex regulatory network in which non-coding RNAs such as long non-coding RNAs (lncRNAs) or circular RNAs and mRNAs share the same miRNA response elements, thus forming a competitive relationship to regulate each other's expression levels [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Thus, ceRNAs play an important role in the regulation of gene expression. CeRNA-mediated regulation is involved in cell proliferation, differentiation, apoptosis, and other biological processes [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and plays an important role in developing tumors and female reproductive endocrine diseases [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. As important transcriptional regulatory molecules in the ceRNA network, lncRNAs such as H19, HOTAIR, and MALAT1 affect biological functions, hormone synthesis, and secretion in GCs by regulating key signaling pathways, such as the PI3K/Akt and TGFβ/SMAD [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] pathways. A previous study [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] showed that lncRNA NEAT1 expression is significantly reduced in GCs of women with diminished ovarian reserve (DOR) and can be used as a biomarker of ovarian aging; however, the specific molecular mechanism has not been fully elucidated.\u003c/p\u003e \u003cp\u003eIn the current study, we collected clinical GC samples from older patients with DOR and constructed a ceRNA regulatory axis using bioinformatics to investigate the function and potential molecular mechanism of NEAT1 in ovarian aging. The results showed that NEAT1 acts as a molecular sponge to regulate the mitogen-activated protein kinase (MAPK) signaling pathway by adsorbing miR-204-5p and upregulating estrogen receptor 1 (ESR1), thereby inhibiting apoptosis, promoting cell proliferation, and affecting cell cycle and steroid biosynthetic functions in GCs. This study explored the potential role of ceRNAs in the development of ovarian aging from a novel perspective. The discovery of a therapeutic target may provide a new scientific basis for protecting women's reproductive health, and allow for future clinical applications.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eClinical sample collection\u003c/h2\u003e \u003cp\u003eTwelve patients who underwent assisted reproductive technology (ART)-based assisted conception using an antagonist regimen at the Department of Reproduction and Genetics (Affiliated Hospital of Shandong University of Traditional Chinese Medicine) were included in the study. These included six patients with advanced DOR (age\u0026thinsp;\u0026ge;\u0026thinsp;35 years) and six young women with normal ovarian reserve (NOR). The diagnosis of DOR was based on the fulfillment of at least two of the following criteria [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]: (i) bilateral anterior follicle count (AFC)\u0026thinsp;\u0026lt;\u0026thinsp;6, (ii) anti-\u003cem\u003eM\u0026uuml;llerian\u003c/em\u003e hormone (AMH)\u0026thinsp;\u0026lt;\u0026thinsp;1.10 ng/mL; and (iii) basal follicle-stimulating hormone (FSH) of 10\u0026ndash;40 mIU/mL. The NOR group included women with normal ovarian reserve but infertility due to male or tubal factors. Patients with structural malformations of the reproductive organs, endometriosis, other endocrine disorders (such as thyroid dysfunction and hyperprolactinemia), or chromosomal abnormalities were excluded from the study. Follicular fluid discarded after oocyte retrieval was collected, and GCs were extracted, purified via density gradient centrifugation [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and immediately stored in a -80\u0026deg;C refrigerator. This study was approved by the ethics committee of the Department of Reproductive Medicine at the hospital and was registered in the China Clinical Trial Registry on October 30, 2021 (registration number: ChiCTR2100052522). Informed consent was obtained from all the patients.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCell culture\u003c/h3\u003e\n\u003cp\u003eThe human ovarian GC tumor cell line KGN was purchased from Procell Life Science and Technology Co. (Wuhan, China). The cells were cultured in Dulbecco\u0026rsquo;s modified Eagle\u0026rsquo;s medium (DMEM)/F-12 medium (BasalMedia, Shanghai, China) supplemented with 10% fetal bovine serum (FBS; BasalMedia) and 1% penicillin-streptomycin solution (Biosharp, Hefei, China) in an incubator at 37\u0026deg;C with 5% CO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e\n\u003ch3\u003eCell transfection\u003c/h3\u003e\n\u003cp\u003eNEAT1 overexpression plasmid (OE-NEAT1), NEAT1 small interfering RNA (siRNA; si-NEAT1), ESR1 siRNA (si-ESR1), miR-204-5p mimics (mimics-miR-204-5p), miR-204-5p inhibitor (inhibitor-miR-204-5p), and the corresponding negative controls were obtained from Tsingke Biotech Co. Ltd. (Beijing, China). The plasmids, siRNA, and miR-204-5p mimics/inhibitors were transfected into KGN cells using Lipofectamine\u0026reg; 3000 (Invitrogen, Carlsbad, CA, USA) according to the manufacturer's instructions. The transfection efficiency was determined using quantitative reverse transcription PCR (RT-qPCR).\u003c/p\u003e\n\u003ch3\u003eFluorescence in situ hybridization (FISH)\u003c/h3\u003e\n\u003cp\u003eA FISH Kit (RiboBio, Guangzhou, China) was used to analyze the subcellular localization of NEAT1. KGN cells were fixed with 4% paraformaldehyde and permeabilized with 0.5% Triton X-100. Then, pre-hybridization buffer was added to the cells and incubated for 30 min, followed by overnight incubation in a hybridization buffer containing a fluorescein isothiocyanate (FITC)-labeled NEAT1 probe (Sangon Biotech, Shanghai, China). The cells were counterstained with DAPI (incubation for 8 min protected from light) and imaged under a laser confocal microscope (Leica, Germany).\u003c/p\u003e\n\u003ch3\u003eDual-luciferase reporter assay\u003c/h3\u003e\n\u003cp\u003eBioinformatic analysis predicted the downstream targets and complementary pairing sites of NEAT1 and miR-204-5p. Wild-type (NEAT1-WT or ESR1-WT) and mutant plasmids (NEAT1-MUT or ESR1-MUT) containing miR-204-5p binding sites were constructed and cloned into a pGL4 vector (Promega, Madison, WI, USA). KGN cells were co-transfected with the luciferase reporter plasmids, mimics-miR-204-5p, inhibitor-miR-204-5p, and the corresponding negative controls using Lipofectamine\u0026reg; 3000. After 48 h, the luciferase activity in the cells was measured using a Dual-Luciferase Reporter Gene Assay Kit (Promega, USA).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCell counting kit-8 (CCK-8) assay\u003c/h2\u003e \u003cp\u003eA CCK-8 Kit (Vazyme Biotech Co. Ltd., Nanjing, China) was used to analyze cellular activity. KGN cells were plated in 96-well plates at 4 \u0026times; 10\u003csup\u003e3\u003c/sup\u003e cells/well (100 \u0026micro;L/well). After cell adherence, 10 \u0026micro;L CCK-8 solution was added to each well and incubated at 37\u0026deg;C for 2 h in the incubator. Absorbance was measured at 450 nm using a multifunctional enzyme marker (Bio-Rad, Hercules, CA, USA).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEdU proliferation assay\u003c/h3\u003e\n\u003cp\u003eCell proliferation was assessed by measuring DNA synthesis using the EdU Cell Proliferation Kit (Beyotime, Shanghai, China). KGN cells were incubated with 500 \u0026micro;L EdU working solution for 2 h. The cells were then fixed with 4% paraformaldehyde for 15 min, permeabilized with 0.5% Triton-100 for 10 min, and the nuclei were stained with Hoechst 33342 for 10 min in the dark. Images were captured using a fluorescence microscope (Nikon, Tokyo, Japan).\u003c/p\u003e\n\u003ch3\u003eFlow cytometric assay\u003c/h3\u003e\n\u003cp\u003eApoptosis was detected using the Annexin V-FITC/propidium iodide (PI) Apoptosis Detection Kit (Vazyme). KGN cells were collected, washed with phosphate-buffered saline (PBS), and resuspended in a binding buffer. Subsequently, the cells were stained with Annexin V-FITC and PI by incubating for 15 min in the dark. The cells were analyzed using a flow cytometer (BD, Franklin Lakes, NJ, USA).\u003c/p\u003e \u003cp\u003eThe cell cycle was analyzed using a Cell Cycle and Apoptosis Detection Kit (Beyotime). KGN cells were fixed with pre-cooled PBS and 70% ethanol at 4 \u0026ordm;C overnight. The cells were then resuspended in PI staining solution for 30 min at 37 \u0026ordm;C protected from light and detected using a flow cytometer.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRT-qPCR\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted from clinical GC samples and KGN cells using the Total RNA Extraction Reagent RNA Isolator (Vazyme). The concentration and purity of the extracted RNA were assessed using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). Total RNA was reverse transcribed to cDNA using HiScript II Q RT SuperMix for qPCR (Vazyme). RT-qPCR was performed using a fluorescent quantitative PCR instrument (Bio-Rad) and the ChamQ SYBR\u0026reg; qPCR Master Mix (Vazyme). GAPDH or U6 were used as the reference genes, and the \u003csup\u003e2\u0026minus;ΔΔ\u003c/sup\u003eCt method was used to quantify the expression level of RNAs. The specific primers used in this study were obtained from Sangon Biotech, and their sequences are listed 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\u003ePrimer sequences for gene expression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimer sequence (5'-3')\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNEAT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: AGTGTGAGTCCTAGCATTG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse: GAACTTCCTCCTCCTAAGC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ehsa-miR-204-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: CGCGTTCCCTTTGTCATCCT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse: AGTGCAGGGTCCGAGGTATT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ehsa-miR-125a-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: TCGGCAGGTCCCTGAGACCCTT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse: CTCAACTGGTGTCGTGGA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: GCCACATTTGCCAGGAAACAATG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse: CTCCTGGTCACTGTAGAGAGTCT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCYP19A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: GCAAAGCACCCTAATGTTGAAGA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse: CGAGTCTGTGCATCCTTCCAATA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eESR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: CTCTAACCTCGGGCTGTGC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse: AGATGCTTTGGTGTGGAGGG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNOTCH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: ATCCCACAAAGCCTAGCACC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse: CCTTGTCCCTGAGCAACCAT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIGFBP5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: GAAGCAGATGTGTCTCTGCCC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse: TTCCCTCTGCTTCCTGGTTC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGAPDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: TGCACCACCAACTGCTTAGC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse: GGCATGGACTGTGGTCATGAG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eU6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: CAGCACATATACTAAAATTGGAACG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse: ACGAATTTGCGTGTCATCC\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\u003eCYP19A1, cytochrome P450 family 19 subfamily A member 1; ESR1, estrogen receptor 1; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; IGFBP5, insulin-like growth factor binding protein 5; NEAT1, nucleo-enriched abundant transcript 1; NOTCH2, recombinant notch homolog 2; StAR, steroidogenic acute regulatory protein; U6, RNU6.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eWestern blotting\u003c/h2\u003e \u003cp\u003eProteins were extracted from cells using radioimmunoprecipitation assay (RIPA) lysis buffer (Servicebio, Wuhan, China), and the protein concentration was determined using the Bicinconinic acid (BCA) Protein Assay Kit (CWBIO, Taizhou, China). Equal amounts of proteins were separated by electrophoresis on 12% sodium dodecyl sulfate-polyacrylamide gels and subsequently transferred to polyvinylidene difluoride membranes (Bio-Rad). The non-specific sites on the membranes were blocked by incubating with 5% skimmed milk for 2 h at 22\u0026deg;C. The membranes were then incubated with the primary antibody overnight at 4\u0026deg;C. The membranes were washed with TBST buffer and incubated with horseradish peroxidase (HRP)-conjugated secondary antibody for 2 h at room temperature. After washing with TBST buffer, the protein bands on the membrane were visualized using an enhanced chemiluminescence Detection Kit (Vazyme) and scanned using a gel imaging system. GAPDH was used as an internal reference. All the antibodies used in the western blotting, including ESR1 (20698-1-AP), phospho-ERK1/2 (80031-1-RR), ERK1/2 (11257-1-AP), phospho-CREB1 (28792-1-AP), CREB1 (12208-1-AP), StAR (12225-1-AP), CYP19A1 (16554-1-AP), and GAPDH (10494-1-AP) were purchased from Proteintech Group Inc. (Wuhan, China).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEnzyme-linked immunosorbent assay (ELISA)\u003c/h2\u003e \u003cp\u003eKGN cells were collected, and androstenedione (10 \u0026micro;g/mL) was added to the culture medium. The cells were then cultured for 24 h. The estradiol level in the supernatant was measured using the Human Estrogen, E ELISA Kit (Cusabio, Wuhan, China) according to the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe experimental data were analyzed using SPSS software (version 23.0; IBM, Armonk, NY, USA). All results are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). The normality of data distribution was assessed using the Shapiro\u0026ndash;Wilk test. Student's \u003cem\u003et\u003c/em\u003e-test or Wilcoxon test was used to assess the significance of the difference between the two groups of data. Pearson's correlation test was used for the correlation analysis between variables. The graphs were generated using GraphPad Prism (version 8.0; San Diego, CA, USA) software. Statistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eThe baseline characteristics of the patients in the two groups are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Serum AMH and AFC levels were significantly lower (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and basal FSH levels were significantly higher (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in older patients with DOR compared with that in the NOR group. The baseline profiles, such as infertility duration, body mass index (BMI), basal luteinizing hormone (LH), basal estradiol (E\u003csub\u003e2\u003c/sub\u003e), and basal progesterone (P) levels, were not significantly different between the two groups of patients (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\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\u003eComparison of baseline characteristics between the NOR and DOR groups.\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=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNOR (n\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDOR (n\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient age (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e31.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e38.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfertility duration (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.50\u0026thinsp;\u0026plusmn;\u0026thinsp;2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e20.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e21.75\u0026thinsp;\u0026plusmn;\u0026thinsp;2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAMH (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.50\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasal FSH (mIU/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e7.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e12.58\u0026thinsp;\u0026plusmn;\u0026thinsp;4.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasal LH (mIU/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e7.16\u0026thinsp;\u0026plusmn;\u0026thinsp;3.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasal E\u003csub\u003e2\u003c/sub\u003e (pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e32.33\u0026thinsp;\u0026plusmn;\u0026thinsp;12.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e44.73\u0026thinsp;\u0026plusmn;\u0026thinsp;13.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasal P (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFC (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e19.50\u0026thinsp;\u0026plusmn;\u0026thinsp;2.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003eData are shown as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation.\u003c/p\u003e \u003cp\u003eAMH, anti-M\u0026uuml;llerian hormone; AFC, antral follicle count; BMI, body mass index; E\u003csub\u003e2\u003c/sub\u003e, estrogen; FSH, follicle-stimulating hormone; LH, luteinizing hormone; P, progesterone.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eDownregulation of NEAT1 is associated with DOR occurrence and ART outcomes in older women\u003c/h2\u003e \u003cp\u003eFirst, the expression levels of NEAT1 in GC samples were analyzed using RT-qPCR. The results showed that NEAT1 expression was significantly downregulated in the GCs of older patients with DOR compared to those in the NOR group (*** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Pearson correlation analysis showed that NEAT1 expression levels were significantly negatively correlated with patient age, basal FSH, and gonadotropin dosage (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb-d); and significantly positively correlated with AMH, AFC, number of oocytes retrieved, number of two-pronuclear zygotes, and the number of embryos available (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee-i). In addition, there was no significant correlation between NEAT1 expression level and gonadotropin duration and the number of superior embryos (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ej, k). These results suggest that the downregulation of NEAT1 in the GCs of older patients with DOR may be associated with ovarian reserve function and ART outcomes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eNEAT1 inhibits apoptosis and promotes proliferation of KGN cells\u003c/h2\u003e \u003cp\u003eTo confirm the subcellular localization of NEAT1 in KGN cells, a FISH assay was performed. The assay showed that NEAT1 is mainly localized in the cytoplasm (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), which suggests that NEAT1 acts as a molecular sponge for miRNAs to exert post-transcriptional regulation. To further investigate the biological functions of NEAT1, KGN cells were transfected with plasmids si-NEAT1 or OE-NEAT1. RT-qPCR results revealed significant changes in NEAT1 expression in the cells following transfection (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The CCK-8 and EdU assays revealed that NEAT1 overexpression significantly increased cell viability and proliferation, whereas NEAT1 knockdown had the opposite effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec, d). Flow cytometry results showed a significant decrease in apoptosis in the OE-NEAT1 group, whereas a significant increase in apoptosis was observed in the si-NEAT1 group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). Additionally, cell cycle analysis showed that NEAT1 overexpression led to a significant decrease in the proportion of cells in the G0/G1 phase and a significant increase in the proportion of cells in the S phase, indicative of an increase in cell proliferation. In contrast, NEAT1 knockdown significantly increased the proportion of cells in the G0/G1 phase and decreased the proportion of cells in the S phase, indicating that si-NEAT1 blocked cells in the G1/S phase, thereby inhibiting cell proliferation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003emiR-204-5p is a target of NEAT1\u003c/h2\u003e \u003cp\u003eTo predict miRNAs that potentially interact with NEAT1, we used the starBase, miRcode, and LncBase databases (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). We screened miR-125a-5p and miR-204-5p as miRNAs common to the three databases, verified their expression levels in GCs using RT-qPCR, and determined that miR-204-5p was significantly upregulated in the GCs of patients with advanced DOR (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec shows the complementary base sequences of NEAT1 and miR-204-5p. Based on the predicted binding site, dual-luciferase reporter assays were performed, which confirmed that mimics-miR-204-5p significantly reduced the luciferase activity of the NEAT1-WT vector, whereas it did not affect the luciferase activity of the NEAT1-MUT vector. In contrast, the inhibitor-miR-204-5p significantly increased the luciferase activity of the NEAT1-WT vector but not that of NEAT1-MUT (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). In addition, transfection with OE-NEAT1 significantly decreased miR-204-5p expression in KGN cells, whereas transfection with si-NEAT1 significantly increased miR-204-5p expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee). These data suggest that NEAT1 directly binds to miR-204-5p and inhibits its expression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eNEAT1 overexpression negatively regulates miR-204-5p to inhibit apoptosis and promote proliferation of KGN cells\u003c/h2\u003e \u003cp\u003eWe transfected mimics-miR-204-5p, inhibitor-miR-204-5p, and the corresponding negative controls into KGN cells to investigate the effects of miR-204-5p on cell proliferation and apoptosis. RT-qPCR was used to confirm the transfection efficiency (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The CCK-8 and EdU assays revealed that miR-204-5p overexpression significantly inhibited cell viability and proliferation, whereas miR-204-5p inhibition had the opposite effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, c). Flow cytometry showed that transfection with mimics-miR-204-5p induced apoptosis in KGN cells, whereas transfection with inhibitor-miR-204-5p significantly inhibited apoptosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). In addition, cell cycle analysis revealed that miR-204-5p overexpression inhibited cell proliferation by decreasing the proportion of KGN cells in S phase. In contrast, the downregulation of miR-204-5p significantly increased the proportion of S-phase cells and decreased the proportion of G0/G1-phase cells. Thus, the low expression of miR-204-5p promoted KGN cell proliferation by promoting the G1/S transition of the cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further examine the correlation between NEAT1 and miR-204-5p in the regulation of KGN cell functions, we co-transfected KGN cells with OE-NEAT1 and mimics-miR-204-5p. As shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea \u003cb\u003eand b\u003c/b\u003e, NEAT1 overexpression increased KGN cell viability and proliferation, and this effect was reversed by miR-204-5p mimics. In addition, overexpression of miR-204-5p reversed the NEAT1-mediated effects on the apoptotic rate and G1/S transition of KGN cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec, d). These results suggest that NEAT1 affects the proliferation and apoptosis of KGN cells by targeting miR-204-5p.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eESR1 is a downstream target of miR-204-5p\u003c/h2\u003e \u003cp\u003eWe used multiple bioinformatics databases (starBase, miRcode, TargetScan, miRDB, and miRWalk) to predict the potential target genes of miR-204-5p (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). Three potential targets were identified at the intersection of the five databases. The expression levels of these potential targets were evaluated in 12 clinical GC samples using RT-qPCR. ESR1 showed the most significant difference in expression between the two patient groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). We predicted the potential binding site of miR-204-5p on ESR1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). Dual-luciferase reporter assays were performed to verify the targeted regulatory relationship between the two molecules (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed). Transfection of KGN cells with miR-204-5p mimics decreased ESR1 mRNA and protein expression levels; conversely, transfection with miR-204-5p inhibitors increased ESR1 mRNA and protein expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee, f\u003cb\u003e)\u003c/b\u003e. These data suggest that miR-204-5p negatively regulates ESR1 expression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSilencing of ESR1 reverses the effects of inhibitor-miR-204-5p on proliferation, apoptosis, and steroid biosynthesis in KGN cells\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAfter transfecting KGN cells with inhibitor-miR-204-5p and si-ESR1, we performed a series of experiments to clarify the effect of ESR1 on miR-204-5p-mediated biological functions. CCK-8 and EdU assays showed that the inhibition of miR-204-5p significantly promoted the viability and proliferative capacity of KGN cells, whereas this effect was effectively reversed by silencing ESR1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea, b). In addition, flow cytometric analysis showed that the miR-204-5p inhibitor significantly reduced the apoptotic rate, increased the proportion of S-phase cells, and reduced the proportion of G2/M-phase cells. However, si-ESR1 transfection partially restored the apoptotic rate and cell cycle transition (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec, d). These results indicate that miR-204-5p targets the negative regulation of ESR1 to regulate proliferation, cell cycle, and apoptosis of KGN cells.\u003c/p\u003e \u003cp\u003eESR1 encodes estrogen receptor-α, which participates in estrogen synthesis by mediating the estrogen signaling pathway. Moreover, it regulates the MAPK signaling pathway [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] to affect cell proliferation, differentiation, and apoptosis. We further examined the expression levels of key steroidogenic genes, including steroidogenic acute regulatory protein (StAR) and cytochrome P450 family 19 subfamily A member 1 (CYP19A1), and the important proteins in the MAPK signaling pathway. The results showed that transfection of KGN cells with inhibitor-miR-204-5p significantly up-regulated the mRNA and protein expression of ESR1, StAR, and CYP19A1 and promoted E\u003csub\u003e2\u003c/sub\u003e biosynthesis, whereas ESR1 silencing reversed these changes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ee-g). In addition, the miR-204-5p inhibitor increased the levels of phosphorylated extracellular signaling-related kinase (ERK) and cyclic adenosine monophosphate response element binding protein (CREB), which were reversed by si-ESR1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ef).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eNEAT1 mediates the MAPK signaling pathway through the miR-204-5p/ESR1 axis to affect the biological functions in KGN cells\u003c/b\u003e \u003c/p\u003e \u003cp\u003eNext, we sought to investigate whether NEAT1 affects the phenotype of KGN cells by regulating ESR1. KGN cells were transfected with si-NEAT1 or OE-NEAT1, and the mRNA and protein expression levels of ESR1 were analyzed using RT-PCR and western blotting, respectively. The results showed that ESR1 levels decreased significantly following si-NEAT1 transfection, whereas they markedly increased following OE-NEAT1 transfection (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea, b). To investigate whether NEAT1 is involved in functional alterations in GCs during ovarian aging by regulating ESR1 expression and downstream signaling pathways through sponging of miR-204-5p, the levels of ESR1, StAR, CYP19A1, and E\u003csub\u003e2\u003c/sub\u003e were analyzed in KGN cells overexpressing NEAT1 in the absence of the presence of mimics-miR-204-5p. The results showed that overexpression of NEAT1 in KGN cells resulted in a significant increase in the mRNA and protein expression of ESR1, StAR, and CYP19A1 and augmentation of E\u003csub\u003e2\u003c/sub\u003e biosynthesis, which was reversed by co-transfection with mimics-miR-204-5p (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec-e). In addition, NEAT1 overexpression increased the phosphorylation of ERK and CREB, which was reversed by mimics-miR-204-5p (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ee). These results suggest that NEAT1 acts as a miR-204-5p sponge to regulate ESR1 expression, which in turn regulates biological functions such as proliferation, apoptosis, and steroid biosynthesis in KGN cells by activating the MAPK signaling pathway.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn women, ovarian aging results in decreased secretion of estrogen and P by the ovaries and a decline in the number and quality of follicles, which affects fertility and quality of life in the long term. Ovarian aging is accompanied by mitochondrial dysfunction in GCs. Oxidative stress and inflammatory responses cause further damage to the GCs. Functional abnormalities of GCs are closely related to the pathogenesis of ovarian aging. Therefore, elucidation of the underlying molecular mechanisms is of great significance for maintaining ovarian function and promoting women's health. LncRNAs are involved in ovarian aging by regulating several biological processes, including oocyte development, maturation, and luteinization [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In the current study, we observed that the lncRNA NEAT1 upregulates ESR1 through the adsorption of miR-204-5p, which in turn mediates the MAPK signaling pathway, promotes proliferation, inhibits apoptosis, and affects the cell cycle and steroid biosynthesis in KGN cells. Thus, these results revealed a novel regulatory mechanism of GC dysfunction, which can be exploited to improve diagnostic and therapeutic strategies for ovarian senescence.\u003c/p\u003e \u003cp\u003eNEAT1 constitutes the skeletal structure of the subnucleosomal parafollicular patch and regulates gene expression by maintaining mRNA stability in the nucleus [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. NEAT1 is involved in the cyclic recruitment and differentiation of follicles [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], and is closely associated with corpus luteum formation, pregnancy establishment, and maintenance [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. NEAT1 is significantly downregulated in the ovarian tissues of patients with premature ovarian failure (POF), and overexpression of NEAT1 inhibits p53 expression by affecting its transcription and stability, which in turn ameliorates POF by inhibiting apoptosis [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Several recent studies have demonstrated that NEAT1 acts as a ceRNA to regulate the expression of downstream target genes, which are involved in the development of reproductive endocrine diseases at the post-transcriptional level through the competitive adsorption of miRNAs. For example, NEAT1 overexpression increases the expression of androgen receptors, follistatin, and insulin receptor substrate 2 by sponging miR-30d-5p, which is involved in developing polycystic ovarian syndrome [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Additionally, NEAT1 competitively binds to miR-874-3p, thereby regulating the proliferation of mouse GCs and producing E\u003csub\u003e2\u003c/sub\u003e and progesterone [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Furthermore, NEAT1 reduces miR-654 expression and regulates the STC2/MAPK pathway to reduce apoptosis and autophagy, rendering it a potential therapeutic target for POF [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Similarly, the current study demonstrates that NEAT1 expression levels are downregulated in the GCs of older patients with DOR and correlate with ovarian reserve function and ART outcomes. Using a series of \u003cem\u003ein vitro\u003c/em\u003e functional assays, we demonstrated that NEAT1 promotes KGN cell proliferation by regulating the cell cycle and inhibiting apoptosis. This suggests that NEAT1 plays an important role in ovarian aging by affecting multiple biological functions of GCs and may thus serve as a potential target for the diagnosis and treatment of ovarian hypoplasia.\u003c/p\u003e \u003cp\u003eWe predicted potential targets of NEAT1 based on data from multiple bioinformatics databases and observed that miR-204-5p was upregulated in GC samples from patients with clinical DOR. In addition, using dual-luciferase reporter assays, we confirmed that miR-204-5p binds to NEAT1. Subsequent functional and salvage assays showed that miR-204-5p affects KGN cell viability, proliferation, apoptosis, and cell cycle and induces DOR, which is contrary to the biological functions of NEAT1. A recent study showing that miR-204-5p functions as an oncogene and plays an important role in various cancers by regulating cell proliferation, apoptosis, autophagy, and the immune microenvironment has received widespread attention [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Downregulation of miR-204-5p expression in ovarian cancer inhibits tumor cell proliferation, migration, and invasion by targeting specific genes such as ubiquitin-specific peptidase 47 and thrombospondin-1 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Upregulation of NEAT1 in synovial tissues of patients with rheumatoid arthritis was observed to promote the proliferation and production of inflammatory cytokines in rheumatoid arthritis fibroblast-like synoviocytes and inhibit apoptosis by targeting miR-204-5p to mediate the nuclear factor kappa-B pathway [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Chronic exposure to estradiol and leptin alters endometrial homeostasis and promotes endometrial cancer via the NEAT1-miR-204-5p-Igf1 axis in women with obesity [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Another study demonstrated that cadmium exerts cytotoxic effects and impairs ovarian function by affecting the miR-204-5p/BCL2 axis and promoting apoptosis in GCs [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Similarly, our data demonstrate that low NEAT1 expression in the GCs of patients with DOR attenuates miR-204-5p adsorption, thereby affecting cell proliferation, apoptosis, and viability, accelerating ovarian senescence.\u003c/p\u003e \u003cp\u003emiRNAs negatively regulate the expression of target genes by binding to the 3'-UTR of the target gene mRNA, promoting mRNA degradation or inhibiting protein translation [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. We used bioinformatics to predict the potential targets of miR-204-5p and reported that it negatively regulates ESR1. ESR1 is an estrogen receptor α, which mediates estrogen signaling, affecting follicle development, maturation, and steroid hormone synthesis, and plays a crucial role in maintaining ovarian function [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. StAR and CYP19A1, key enzymes involved in estrogen biosynthesis [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], regulate steroid biosynthesis by transporting free cholesterol from the outer mitochondrial membrane to the inner mitochondrial membrane. CYP19A1 encodes cytochrome P450 aromatase, which facilitates the catalytic conversion of androgens to estrogens by GCs and is the rate-limiting enzyme for the final step of estrogen synthesis. In addition, ESR1 has been implicated in per/polyfluoroalkyl substances-induced breast cancer development and progression through activation of the MAPK/Erk signaling pathway [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The MAPK signaling pathway regulates the proliferation, apoptosis, cell cycle, and steroidogenesis of GCs. Moreover, it plays an important role in the regulation of female reproduction and can therefore be used as a pharmacological target for ovarian hypoplasia [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The most representative member of the ERK pathway in MAPK signaling, ERK, becomes phosphorylated and activated and then translocates from the cytoplasm to the nucleus, and it directly or indirectly regulates the expression of a variety of target genes and affects a variety of cellular processes, such as proliferation, differentiation, and cell death [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. CREB is a key downstream signaling molecule of ERK. Once activated, ERK phosphorylates the downstream CREB and affects the proliferation, apoptosis, and steroidogenesis of GCs [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Our findings showed that ESR1 expression was significantly reduced in GCs of older patients with DOR. Further mechanistic studies demonstrated that silencing ESR1 inhibited the MAPK signaling pathway and effectively counteracted the effects of transfection with the inhibitor-miR-204-5p on proliferation, apoptosis, and steroid biosynthesis in KGN cells.\u003c/p\u003e \u003cp\u003eFinally, we examined the relationship between NEAT1, miR-204-5p, and ESR1. We observed that NEAT1 overexpression promoted ESR1 expression, which in turn activated the ERK and CREB phosphorylation in the MAPK signaling pathway and upregulated the expression of StAR and CYP19A1 to increase estradiol production. However, these effects were eliminated by miR-204-5p. These data suggest that NEAT1 regulates ESR1 expression by sponging miR-204-5p and mediates the MAPK signaling pathway involved in DOR. In future studies, we intend to use a larger clinical sample size and an animal model of ovarian senescence to study the molecular mechanisms that regulate ovarian pathogenesis in greater depth.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study revealed that NEAT1 is downregulated in the GCs of older patients with DOR. NEAT1 regulates apoptosis, proliferation, and steroid biosynthesis through the miR-204-5p/ESR1 axis-mediated MAPK signaling pathway and participates in the ovarian aging process. This novel axis of ceRNA regulation is associated with DOR development and ART cycle outcomes and provides a promising therapeutic strategy to delay ovarian aging.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.4973%;\"\u003e\n \u003cp\u003eAFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.5027%;\"\u003e\n \u003cp\u003eanterior follicle counts\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.4973%;\"\u003e\n \u003cp\u003eAMH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.5027%;\"\u003e\n \u003cp\u003eanti-M\u0026uuml;llerian hormone\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.4973%;\"\u003e\n \u003cp\u003eART\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.5027%;\"\u003e\n \u003cp\u003eAssisted Reproductive Technology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.4973%;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.5027%;\"\u003e\n \u003cp\u003ebody mass index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.4973%;\"\u003e\n \u003cp\u003eceRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.5027%;\"\u003e\n \u003cp\u003eCompeting endogenous RNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.4973%;\"\u003e\n \u003cp\u003eCREB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.5027%;\"\u003e\n \u003cp\u003eCyclic adenosine monophosphate response element binding protein\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.4973%;\"\u003e\n \u003cp\u003eCYP19A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.5027%;\"\u003e\n \u003cp\u003ecytochrome P450 family 19 subfamily A member 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.4973%;\"\u003e\n \u003cp\u003eDOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.5027%;\"\u003e\n \u003cp\u003ediminished ovarian reserve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.4973%;\"\u003e\n \u003cp\u003eE\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.5027%;\"\u003e\n \u003cp\u003eestradiol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.4973%;\"\u003e\n \u003cp\u003eERK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.5027%;\"\u003e\n \u003cp\u003eextracellular signaling-related kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.4973%;\"\u003e\n \u003cp\u003eESR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.5027%;\"\u003e\n \u003cp\u003eestrogen receptor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.4973%;\"\u003e\n \u003cp\u003eFSH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.5027%;\"\u003e\n \u003cp\u003efollicle-stimulating hormone\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.4973%;\"\u003e\n \u003cp\u003eGCs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.5027%;\"\u003e\n \u003cp\u003eGranulosa cells\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.4973%;\"\u003e\n \u003cp\u003eLH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.5027%;\"\u003e\n \u003cp\u003eluteinizing hormone\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.4973%;\"\u003e\n \u003cp\u003elncRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.5027%;\"\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\" style=\"width: 25.4973%;\"\u003e\n \u003cp\u003eMAPK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.5027%;\"\u003e\n \u003cp\u003emitogen-activated protein kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.4973%;\"\u003e\n \u003cp\u003eNOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.5027%;\"\u003e\n \u003cp\u003enormal ovarian reserve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.4973%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.5027%;\"\u003e\n \u003cp\u003eprogesterone\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.4973%;\"\u003e\n \u003cp\u003ePOF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.5027%;\"\u003e\n \u003cp\u003epremature ovarian failure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.4973%;\"\u003e\n \u003cp\u003eStAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.5027%;\"\u003e\n \u003cp\u003eSteroidogenic acute regulatory protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.4973%;\"\u003e\n \u003cp\u003esiRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.5027%;\"\u003e\n \u003cp\u003esmall interfering RNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003ch3\u003eEthics approval and consent to participate\u003c/h3\u003e\n\u003cp\u003eThe study was approved by the Ethics Committee of Reproductive Medicine of the Affiliated Hospital of Shandong University of Traditional Chinese Medicine and registered with the China Clinical Trial Registry on October 30, 2021 (registration number: ChiCTR2100052522). All patients provided written signed informed consent. All methods were performed in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003ch3\u003eConsent for publication\u003c/h3\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch3\u003eAvailability of data and materials\u003c/h3\u003e\n\u003cp\u003eThe data supporting the findings of this study are available from the corresponding authors upon a reasonable request.\u003c/p\u003e\n\u003ch3\u003eCompeting interests\u003c/h3\u003e\n\u003cp\u003eThe authors declare that they have no commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003ch3\u003eFunding\u003c/h3\u003e\n\u003cp\u003eThis study was supported by the National Research and Training Program for Outstanding Clinical Talents of Traditional Chinese Medicine (grant number: National Letter of TCM Practitioners No. 1 [2022]), the Co-construction of Science and Technology Programs by the Science and Technology Department of the State Administration of Traditional Chinese Medicine (SATCM) (grant number: GZY-KJS-SD-2023-070), the Shandong Traditional Chinese Medicine Science and Technology Program (grant number: Z-2023094), and the Jinan Science and Technology Program (grant number: 202328044).\u003c/p\u003e\n\u003ch3\u003eAuthors\u0026rsquo; contributions\u003c/h3\u003e\n\u003cp\u003eLD was a major contributor in writing the manuscript. HW and DL performed the experiments and analyzed the data. FQ and WC did bioinformatics analysis. YX and ML contributed to manuscript revision. YW and RY was responsible for sample collection. PC conceived and designed this study. All authors read and approved the final submitted version of this manuscript.\u003c/p\u003e\n\u003ch3\u003eAcknowledgments\u003c/h3\u003e\n\u003cp\u003eThe authors thank all the collaborators and participants of the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eIsola JVV, Oca\u0026ntilde;as SR, Hubbart CR, Ko S, Mondal SA, Hense JD, Carter HNC, Schneider A, Kovats S, Alberola-Ila J, et al: \u003cstrong\u003eA single-cell atlas of the aging mouse ovary.\u003c/strong\u003e \u003cem\u003eNat Aging \u003c/em\u003e2024, \u003cstrong\u003e4:\u003c/strong\u003e145-162.\u003c/li\u003e\n\u003cli\u003eBabayev E, Duncan FE: \u003cstrong\u003eAge-associated changes in cumulus cells and follicular fluid: the local oocyte microenvironment as a determinant of gamete quality.\u003c/strong\u003e \u003cem\u003eBiol Reprod \u003c/em\u003e2022, \u003cstrong\u003e106:\u003c/strong\u003e351-365.\u003c/li\u003e\n\u003cli\u003eJohnson JA, Tough S: 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\u003cstrong\u003e12\u003c/strong\u003e.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"lncRNA NEAT1, miR-204-5p, ESR1, Ovarian aging, Apoptosis, Proliferation, Steroid synthesis","lastPublishedDoi":"10.21203/rs.3.rs-5325793/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5325793/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLong non-coding RNAs (lncRNAs) play an important role in ovarian aging by affecting the biological functions of granulosa cells (GCs) through multiple mechanisms. The lncRNA NEAT1 is significantly downregulated in aging ovaries; however, the exact regulatory mechanism has not been elucidated. In the current study, we aimed to investigate the effects of the lncRNA NEAT1 in GC functions during ovarian aging and explore its therapeutic potential. We observed that NEAT1 expression is downregulated in GCs of older patients with diminished ovarian reserve (DOR), which is closely associated with ovarian reserve function and assisted reproductive cycle outcomes. Functional assays revealed that NEAT1 promotes KGN cell proliferation by increasing the proportion of S-phase cells and inhibiting apoptosis. Bioinformatics analysis and a dual-luciferase reporter assay confirmed that NEAT1 acts as a miR-204-5p sponge and identified ESR1 as a miR-204-5p target gene, both of which were significantly differentially expressed in the GCs of older patients with DOR. Mechanistic experiments demonstrated that NEAT1 acts as a competitive endogenous RNA and adsorbs miR-204-5p through molecular sponging, which in turn promotes the expression of ESR1 and upregulates the expression of key enzymes (steroidogenic acute regulatory protein and cytochrome P450 family 19 subfamily A member 1) involved in steroid hormone synthesis. This induces estradiol biosynthesis and activates the downstream mitogen-activated protein kinase (MAPK) signaling pathway to promote the phosphorylation of extracellular signaling-related kinase and cyclic adenosine monophosphate response element binding protein, which affects the cell cycle and results in the promotion of proliferation and inhibition of apoptosis of KGN cells. Our results suggest that NEAT1 activates the downstream MAPK pathway through the miR-204-5p/ESR1 axis; regulates GC proliferation, apoptosis, and the cell cycle; and affects steroid biosynthesis. Therefore, NEAT1 represents a potential therapeutic target to delay ovarian aging.\u003c/p\u003e","manuscriptTitle":"lncRNA NEAT1 affects granulosa cell apoptosis and steroid biosynthesis via the miR-204-5p/ESR1 axis in ovarian aging","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-04 16:04:22","doi":"10.21203/rs.3.rs-5325793/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c83795f3-485c-47e6-a69b-f21b358ebc76","owner":[],"postedDate":"November 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-22T15:23:42+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-04 16:04:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5325793","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5325793","identity":"rs-5325793","version":["v1"]},"buildId":"pf3fE39SIOqb-0xH_OWvX","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}