Immune dysfunction mediated by the competitive endogenous RNA network in fetal side placental tissue of polycystic ovary syndrome.

Polycystic ovary syndrome (PCOS) is a prevalent illness that impacts women of reproductive age. It is distinguished by the existence of hyperandrogenism, menstrual irregularities, and the formation of polycystic ovaries [ 1 ]. Based on a recent meta-analysis involving 69 studies, approximately 10% of Chinese women of reproductive age are suffering from PCOS [ 2 ]. Moreover, another meta-analysis showed that PCOS is linked to a significantly elevated prevalence of obstetric and neonatal complications when contrasted with non-PCOS controls [ 3 ]. It is essential to highlight that PCOS women may exhibit a heightened susceptibility, ranging from two to four times to experiencing preeclampsia, pregnancy-induced hypertension, spontaneous preterm labor, gestational diabetes, and an elevated likelihood of requiring cesarean delivery [ 4 – 6 ]. Besides being the primary factor contributing to the occurrence of absence or irregularity in menstrual cycles and infertility due to lack of ovulation, PCOS is correlated with an elevated likelihood of cardiovascular disease, type 2 diabetes mellitus, obesity, and specific types of cancer [ 7 , 8 ]. The diagnostic criteria for PCOS primarily consist of the guidelines put forth by the National Institutes of Health (NIH) [ 9 ], the 2003 Rotterdam Consensus raised by the European Society of Human Reproduction and Embryology (ESHRE), and the American Society for Reproductive Medicine (ASRM) [ 7 ] and the criteria proposed by Androgen Excess Society (AES) [ 10 ]. Among them, the Rotterdam definition represents the prevailing and extensively adopted classification system for PCOS [ 11 ]. The maternal immune system function is essential in facilitating the progression of a viable pregnancy. Various immune cells and molecules are critical in placental and fetal maintenance and function [ 12 ]. A good balance between anti-inflammatory and pro-inflammatory effects is needed. The placenta, an essential component for fetal development, is the initial organ to develop in mammals. The maternal-placental-fetal units can adequately meet the requirements of the developing conceptus while providing simultaneous support to the mother [ 13 , 14 ]. Numerous studies have shown that abnormal function of the placenta is linked to a variety of major obstetric syndromes, including, but not limited to, miscarriage, preeclampsia, FGR, and preterm labor [ 15 – 18 ]. At present, our understanding of the pathologic state of the placenta in PCOS is limited. This is attributed to the heterogeneity of the PCOS phenotype, as well as other comorbidities, including obesity, diabetes, and hypertension. A limited number of studies demonstrated that The PCOS placenta exhibited a notable decrease in placental weight, density, thickness, and volume [ 19 ]. Additionally, it was noted that women diagnosed with PCOS exhibited elevated occurrences of funisitis, chorioamnionitis, vascular thrombosis, villitis, infarction, and villous immaturity when compared to individuals without PCOS [ 20 ]. An increasing body of scholarly literature has indicated that alterations in the placenta-associated gene expression may result in abnormalities in their function and pregnancy complications [ 21 – 24 ]. However, the immune characteristics of peripheral blood and placental immune function throughout pregnancy in PCOS women have not been investigated in depth yet. Over the last decade, significant advancements in high-throughput sequencing technology and various RNAs that play crucial roles in disease have been revealed [ 25 ]. In the mammalian genome, merely a minute proportion is transcribed into mRNAs that encode proteins, and the great majority produces non-coding RNAs (lncRNAs), including miRNA, lncRNA, and circular RNA (circRNA) [ 26 ]. An increasing amount of scholarly research emerged that non-coding RNAs (ncRNAs) were the key regulator of many female diseases, such as endometrial cancer, ovarian cancer, endometriosis, preeclampsia, and also, without a doubt, PCOS [ 27 – 30 ]. A growing amount of research suggests that ncRNAs result in the pathogenesis and progression of PCOS. Numerous markedly distinct ncRNA expressions were detected in granulosa cells (GCs), serum, follicular fluid (FF), and FF exosomes when contrasting PCOS women to those without PCOS [ 31 – 33 ]. A paper was conducted to identify the lncRNAs’ and mRNAs’ profiles present in exosomes. The study also aimed to uncover the pathways linked to PCOS, encompassing the MAPK and inflammation-related pathways [ 34 ]. Thus, the differential expression of ncRNAs in PCOS women may be an effective marker for PCOS diagnosis [ 35 ]. The academic community has recently shown considerable interest in the phenomenon of competitive endogenous RNA (ceRNA). It is hypothesized that mRNAs and ncRNAs cross-talk with and regulate each other through the bridge miRNA based on microRNA response elements (MREs) [ 36 , 37 ]. Many researchers have noticed that ceRNA interactions play important roles in several complicated diseases, encompassing immune-related diseases [ 38 ] and cardiovascular diseases [ 39 ]. Significantly, ceRNA also assumes a pivotal role in the domain of women’s reproductive health [ 40 ], including endometriosis [ 41 ], fetal and organ development [ 42 ], spontaneous abortion [ 43 ], intrahepatic cholestasis and eclampsia in pregnancy [ 44 , 45 ]. Previous studies have been conducted to construct competing ceRNA networks in PCOS. A lncRNA-miRNA-mRNA network was developed utilizing the granulosa cell profile in PCOS, which unveiled significant pathways associated with oxidative stress, inflammation, and apoptosis [ 46 ]. A separate investigation was conducted with the objective of establishing ceRNA networks through the utilization of exosomal lnRNA and circRNA. This study also elucidated the potential functions of exosomes of follicular fluid (FF) in relation to PCOS [ 47 ]. Although there were some ceRNA network studies in some tissues or cells of PCOS, unfortunately, up to now, to our knowledge, the ceRNA networks in placenta tissue of PCOS have not been explored. Therefore, a comprehensive, systematic, and in-depth exploration of the changes that occur in the placental tissue in PCOS can help to reveal the molecular mechanisms underlying pregnancy and adverse outcomes in PCOS. In the present investigation, the alteration of the immune system of PCOS women at the end of pregnancy was investigated, and the differentially expressed lncRNAs (DElncRNAs), DEmiRNAs, and DEmRNAs were found deploying whole transcriptome sequencing of fetal side placental tissues of PCOS. The DElncRNA-DEmiRNA-DEmRNA ceRNA networks were created depending on the principles of ceRNA. Some DERNAs involved in the ceRNA network were chosen to verify by quantitative reverse transcription polymerase chain reaction (qRT-PCR). These findings helped unravel the underlying mechanisms of the ceRNA network in the physiological and pathological processes in PCOS and in finding novel targets for the development of treatments.

Table 1 lists the anthropometric and laboratory profiles of both control and PCOS groups. The age and BMI were similar between the two groups. Regarding the hematological parameters, red blood cell count, neutrophil count, and lymphocyte count were significantly elevated in PCOS women compared to the controls. Moreover, PCOS women exhibited markedly heightened hs-CRP and procalcitonin concentrations but lower levels of PLGF. There was an increased likelihood of neonatology transfer among the progeny of women with PCOS (46.60% vs. 24.26%). BMI: body mass index; WBC: white blood cell; RBC: red blood cell; hs-CRP: hypersensitive C-reactive protein; PLGF: placental growth factor. Data are presented as mean ± standard deviation (SD). A p-value of < 0.05 was considered statistically significant. Ten tissues (including five normal and five PCOS placentas) were deployed for the whole transcriptome analysis. Based on the established screening criteria, a comprehensive set of 308 DEmRNAs (138 up-regulated and 170 hindered), 332 significant DElncRNAs (149 elevated and 183 hindered), and 77 significant DEmiRNAs (39 elevated and 38 hindered) were acquired and displayed by volcano plot ( Fig 1A ) and heatmaps ( Fig 1B ), respectively. (A) Volcano plots of DERNAs with log 2 (foldchange) as the abscissa and -log10 (P value) as the ordinate. The RNAs that exhibited significant up- or down-regulation in PCOS are symbolically represented by red and blue splashes, respectively (|log 2 FC| ≥ 1, P < 0.05). Gray splashes represent RNAs whose expression is not significantly altered. (B) Heatmaps for DEmRNAs, DEmiRNAs, and DElncRNAs in PCOS and control group (n = 5 in each group). Each row in the dataset corresponds to a specific RNA molecule, while each column represents a unique sample. The color red is indicative of a heightened level of expression, while the color blue signifies a diminished level of expression. Moreover, to examine the possible functional functions of these DEmRNAs in PCOS, GO, KEGG, and GSEA analysis was conducted. Fig 2 displays the top 20 BP, CC, and MF-GO terms. Specifically, for BP terms, DEmRNAs exhibited substantial improvements primarily in terms related to the immune system, encompassing inflammatory response, chemotaxis, leukocyte chemotaxis, cytokine-mediated pathway, chemokine-mediated pathway, positive regulation of positive chemotaxis, neutrophil chemotaxis, and regulation of immune response. For CC terms, the terms of extracellular region and extracellular space were most significantly changed. Moreover, the transcription factor AP-1 complex term was most enriched ( S1A Fig ). Notably, immune-related terms were also involved in MF terms, like CXCR chemokine receptor binding, CXCR3 chemokine receptor binding, chemokine activity, interleukin-8 receptor activity, and interleukin-8 binding. The scatter plot of enriched top 20 Gene Ontology (GO) terms in (A) biological process (BP), (B) cell components (CC), and (C) molecular function (MF). (D) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment scatter plot of DEmRNAs in PCOS and control group. The x-axis denotes the rich factor, while the y-axis corresponds to the pathway nomenclature. The dot size corresponds to the gene number, and dot color indicates the P-value. All the significantly enriched KEGG pathways are shown. KEGG pathway analysis emerged that these DEmRNAs to be improved in immune system (IL-17 signaling pathway, chemokine pathway), endocrine system (aldosterone synthesis and secretion, regulation of lipolysis in adipocytes, relaxin pathway); molecules and interaction (cytokine-cytokine receptor interaction, neuroactive ligand-receptor interaction, cell adhesion molecules (CAMs), ECM-receptor interaction); signal transduction (cAMP pathway, PI3K-Akt pathway, phospholipase D pathway, calcium pathway); and infectious disease (pertussis, leishmaniasis, epithelial cell signaling in Helicobacter pylori infection). Fig 2D shows all significantly enriched pathways. In addition, GSEA was performed in the DEmRNAs between control and PCOS groups. Interestingly, immune-related pathways showed remarkable enrichment, including natural killer cell-mediated cytotoxicity, autoimmune thyroid disease, systemic lupus erythematosus, B cell receptor pathway, Fc gamma R-mediated phagocytosis, complement and coagulation cascades, and intestinal immune network for IgA generation ( S1B and S1C Fig ) ( S1 Table ). To fully understand the key genes in PCOS pathogenesis, the PPI network of DEmRNAs was created by the STRING website with a confidence score of 0.70. Three hundred and eight DEmRNAs were submitted to the STRING database, and the network was composed of 119 nodes and 162 edges. CytoHubba, a plug-in that uses multiple topological algorithms in Cytoscape, was employed to screen out hub genes. The intersection of the top 10 genes derived from three distinct algorithms, namely MNC, EPC, and degree, is computed. Additionally, those genes that were shared by at least two algorithms were considered hub genes. Fig 3A displays the top 10 genes found by three algorithms. The shared genes were shown in a Venn diagram ( Fig 3B ). Finally, eight genes, including FOS , JUN , EGR1 , CXCL10 , CXCR1 , CXCR2 , CXCL11 , and CXCL8 , were considered as hub genes. (A) Top 10 hub genes obtained by three different algorithms (Degree, MNC, and EPC) using Cytohubba plug-in app. MNC: Maximum Neighborhood Component; EPC: Edge Percolated Component. Darker colors indicate higher rankings. (B) Venn diagram of the hub genes from Degree, MNC, and EPC algorithms. (C) PPI network constructed by Metascape analysis of DEmRNAs. (D) Seven sub-network modules were aggregated and extracted from PPI network. A clustering analysis was utilized to reveal the core functions in DEmRNAs. In the results of Metascape analysis, seven sub-network modules were aggregated and extracted ( Fig 3C and 3D ). 3Among these modules, the top three show a complicated network. MCODE1 contained 15 nodes and 104 edges, in which the immune-related mRNAs were most included, such as CXCL10 , CXCR1 , CXCR2 , CXCL11 and CXCL8 . MCODE2 contained 14 nodes and 41 edges, in which some transcription factors were involved, including JUN , FOS , FOSB , EGR1 , EGR2 , and EGR3 . MCODE3 contained 9 nodes and 16 edges, enriched by hormone and lipid metabolism-related genes. The above results demonstrated that the immune-related pathways and hub genes exhibited significant dissimilarities between the PCOS and control groups. To further explain the difference in immune systems between PCOS women and healthy controls, immune infiltration analysis was conducted using the CIBERSORT algorithm. First, the number of 22 types of infiltrating immune cells in placental tissues was calculated and shown in the bar plot ( Fig 4A ). The boxplot indicated the difference between each immune cell in control and PCOS group ( Fig 4B ). The outcomes emerged that the degree of Neutrophils infiltration (P = 0.021) was dramatically decreased in PCOS tissues compared with controls. Subsequently, the Pearson correlation coefficient between 22 infiltrating immune cells was calculated and illustrated in correlation heatmaps ( Fig 4C ). Neutrophils correlated positively with resting dendritic cells and negatively with resting memory CD4 T cells. An interesting conclusion can be drawn that the infiltrating immune cells in the fetal side placenta tissue of PCOS were remarkably changed. (A) Bar plot of the proportion of 22 infiltrated immune cells between PCOS and control group. Each color represents a type of immune cell. (B) The violin plot depicted the differences in immune infiltrating between PCOS and normal samples (n = 5 in each group). (C) Heatmap of correlations among 22 types of immune cells. (D) Correlation analysis between eight hub genes and 22 infiltrated immune cells. The colors blue and red are employed to symbolize positive and negative correlation, respectively. The darker the color, the greater the connection. *P < 0.05, **P < 0.01, ***P < 0.001. Next, we analyzed the connection between hub genes and immune cells based on expression level. Fig 4D presents that neutrophils were significantly positively connected to CXCL8 (rho = 0.828, P = 0.003), CXCR2 (rho = 0.835, P = 0.003); CXCL10 and CXCL11 were significantly positively linked to 6/22 infiltrating immune cells. Moreover, to address the DElncRNAs functions, the potential cis target transcripts were identified. First, the Pearson correlation test was conducted to determine the connection of expression levels between lncRNA and mRNA. Furthermore, the FEELnc software was employed to detect the differentially expressed transcripts within a genomic region spanning 100 kb upstream and downstream of the DElncRNAs. Transcripts that meet both of these conditions were regarded as target transcripts. Ultimately, 35 lncRNA-mRNA pairs were screened. A total of 27 cis-transcripts were regulated by 30 regulatory DELncRNAs ( S2 Table ). Moreover, KEGG enrichment analysis was performed for these target transcripts. It is surprising that the most significantly changed pathways were also immune-related pathways, encompassing the chemokine signaling pathway and leukocyte transendothelial migration ( Fig 5 ). Top 20 significantly enriched KEGG pathways of DEmRNAs in PCOS and control group. The x-axis denotes the rich factor, while the y-axis corresponds to the pathway nomenclature. The dot size corresponds to the gene number, and dot color indicates the P-value. After identifying the regulation relationships of DEmiRNAs-DElncRNAs and DEmiRNAs-DEmRNAs with two algorithms (TargetScan5.0 and miRanda3.3a), the DElncRNA (known lncRNA) and DEmRNA that were regulated by the same DEmiRNA were screened. As a result, 29 DElncRNAs, 13 DEmiRNAs, and 18 DEmRNAs were utilized in order to construct ceRNA networks, with the aim of investigating their respective roles in PCOS placental tissues. In total, 57 ceRNA events were finally obtained ( Fig 6A ). Moreover, the ceRNA networks of hub or immune mRNAs ( CXCL11 , EGR1 , EGLN3 , and PRG2 ) were visualized in the Sankey plot ( Fig 6B ). These potential ceRNA networks might provide an illuminating insight into the pathogenesis of PCOS. (A) The ceRNA regulatory network included 18 DEmRNAs, 13 DEmiRNAs, and 29 DElncRNAs in PCOS and control group. DEmRNAs, DEmiRNAs, and DElncRNAs are denoted as green triangles, pink circles, and blue diamonds, respectively. (B) A Sankey diagram of the ceRNA network associated with the immune system. The miRNAs interacting with them are in the middle. Each ceRNA network is represented by the same color. To confirm the hub mRNAs’ and DERNAs’ expression included in the ceRNA network, we measured the levels of eight mRNAs, ten lncRNAs, and five miRNAs using qRT-PCR methods. All primers are listed in S3 Table . The expression variations of the vast majority of chosen DERNAs were in accordance with the whole transcriptome sequencing data. All the immune-related mRNAs ( EGR1 , CXCL8 , CXCL10 , CXCL11 , CXCR1 , CXCR2 , EGLN3 and PRG2 ) were significantly decreased in PCOS group. lncRNA NOP14-AS1 , ERICH6-AS1 , LINC00963 , LINC02128 and STAG3L5P-PVPIG2P-PILRB were dramatically upregulated, while GARS1-DT , GMDS-DT , MIR205HG , MIR210HG , and STX18-AS1 were down-regulated in PCOS group. Of the five selected DEmiRNAs, hsa-miR-133b_R-1 was up-regulated, and other four DEmiRNAs ( hsa-miR-423-5p , hsa-miR-4685-3p_R-2 , PC-5p-51254_51 , and hsa-miR-760_R+2 ) were downregulated in PCOS group ( Fig 7 and S2 Fig ). The quantification of mRNA, lncRNA, and miRNA expression levels was conducted utilizing the 2- ΔΔCT approach. ACTB was used as reference genes for mRNA and lncRNA, and U6 was used as reference genes for miRNA. Data were reported as means ± SEM; n = 30 in each group. Two-tailed student’s t-tests were used, and significant differences were considered when the P-value < 0.05. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. CMap, a computational screening approach, was used to predict prospective small-molecule PCOS therapeutic drugs. A total of 138 up-regulated DEmRNAs were imported into the CMap web tool. After conducting the signature query, we identified the top 15 drugs with the greatest enrichment score. These drugs have been identified as potential therapeutic options for PCOS ( S4 Table ). These drugs consisted of receptor antagonists (clobetasol, aminopentamide, phentolamine, hydrocortisone-phosphate, RS-100329, brompheniramine), enzyme inhibitors (tubacin, enalapril, cilostamide, tipifarnib) and others.

Taken together, we assessed the immune status of women with PCOS at the end of pregnancy and explored possible molecular mechanisms. Herein, we comprehensively analyzed of the expression profiles of mRNAs, miRNAs, and lncRNAs derived from fetal side placental tissues. Our investigation focused on PCOS women as well as a control group. Additionally, we present the constructed ceRNA networks based on our findings. Furthermore, hub-DEmRNAs were retrieved, and KEGG, GO, and GSEA analysis revealed that immune systems, especially chemokines, were highly linked to PCOS pathogenesis. These outcomes would offer novel insight into the molecular mechanisms in the pathophysiological development of PCOS. Moreover, several small-molecule drugs that could be potential therapies for PCOS were screened.

Between June 1st, 2019 and June 1st, 2020, typically, 103 PCOS women and 136 healthy control women who had only one female fetus at the time of cesarean delivery were recruited at the Women’s Hospital, School of Medicine, Zhejiang University. The individuals included in this study were identified as having PCOS according to the diagnostic criteria outlined in the Rotterdam Consensus, a set of guidelines jointly established by the European Society of Human Reproduction and Embryology and the American Society for Reproductive Medicine [ 7 ]. The female participants within the control group demonstrated consistent menstrual cycles while retaining typical levels of sex hormones prior to conception. All women had normal uterine and ovarian structures. The exclusion criteria encompassed various diseases that exhibit comparable clinical manifestations, encompassing androgen-secreting tumors originating from the ovary or adrenal gland, congenital adrenal hyperplasia, thyroid dysfunction, Cushing’s syndrome, primary ovarian insufficiency, hyperprolactinemia, functional hypothalamic amenorrhea, and premature ovarian failure. Peripheral blood, cord blood, and placenta samples were obtained throughout the cesarean delivery procedure. The current investigation received approval from the Ethics Committee of the Women’s Hospital, School of Medicine, Zhejiang University. The consent was informed, and the individuals in this study have given written informed consent to use the samples and data details. Following obtaining informed consent from the patients, we collected placental tissue from the term fetal side of those women who underwent cesarean section deliveries, as described in a previous study. Immediately after the placental tissue was stripped, it was snap-frozen in liquid nitrogen for 30 min, after which it was stored at –80°C. Complete blood counts encompassing red blood cells, white blood cells, lymphocytes, neutrophils, eosinophils, monocytes, and basophils were performed using whole blood. The biochemical tests were performed using the serum samples, including procalcitonin, hypersensitive C-reactive protein (hs-CRP), and placental growth factor (PLGF). The test results were obtained from hospital records. Total RNA extraction was carried out by deploying TRIzol reagent (Invitrogen) from fetal side placental tissue. Typically, five specimens were encompassed in each group, and the extraction protocol provided by the manufacturer was followed. A Bioanalyzer 2100 (RRID: SCR_019715) and RNA 6000 Nano LabChip Kit (Agilent, Santa Clara, CA, USA) were employed to assess the amount and purity of total RNA. RNA with RNA integrity number (RIN) ≥ 7 and 28S/18S ≥ 0.7 was considered to be high-quality RNA. Two libraries were constructed for the purpose of whole transcriptome sequencing. The TruSeq Small RNA Sample Prep Kits (Illumina, San Diego, USA) were employed to create the small RNA library. A 1 μg of total RNA was utilized in accordance with the provided protocol. Following the completion of library preparation, the created libraries underwent sequencing by employing the Illumina Hiseq2000 platform, employing a single-end configuration with a read length of 1×50 bp. For the mRNA and circRNA libraries, first, ribosomal RNA was depleted, and the remaining RNA was fragmented. Following the purification process, the poly(A)–or poly(A)+ RNA fractions were subjected to fragmentation by means of divalent cations at an increased temperature. Additionally, the cDNA library was constructed utilizing the mRNA-Seq sample preparation kit (Illumina, San Diego, CA, USA). Finally, the paired-end sequencing was conducted on the Illumina NovaSeq 6000 system with a 2 × 150 bp read length (LC Bio, China). To perform analysis on mRNAs, the initial step involved utilizing Cutadapt (RRID: SCR_011841) and Perl scripts to eliminate reads containing adaptor contamination, low-quality bases, and unclear bases [ 48 ]. Then, FastQC ( http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ , RRID: SCR_014583) was employed for the purpose of confirming the sequence quality. The standard-compliant reads were mapped to the human genome by Bowtie2 (RRID: SCR_016368) and Tophat2 [ 49 , 50 ]. The assembled mapped reads of each specimen were processed by deploying StringTie (RRID: SCR_016323) [ 51 ]. The determination of transcript expression values was achieved through the computation of fragments per kilobase of exon per million reads mapped (FPKM) by deploying the software tools StringTie and Ballgown [ 52 ]. Finally, the DEmRNAs were screened out with |log2 (fold-change)| ≥ 1 and P-value < 0.05 by edgeR [ 53 ]. For lncRNAs analysis, following the reads assembly using StringTie, transcripts smaller than 200 bp, as well as known mRNAs, were subsequently excluded from subsequent analysis. Subsequently, the remaining transcripts were subjected to lncRNA prediction. The utilized prediction software consisted of the Coding Potential Calculator (CPC), the Coding-Non-Coding Index (CNCI), and the Coding Potential Assessment Tool (CPAT) [ 54 – 56 ]. Transcripts that demonstrate the ability to encode protein were categorized as newly discovered mRNA and subsequently designated as such through the implementation of filtration methodologies. The FRKM method was employed to determine the lncRNA expression level. Subsequently, DElncRNAs were identified using a set of criteria that included a two-fold change and a P-value below 0.05. For miRNA analysis, the ACGT101-miR software, which was self-developed by LC Sciences, was used. The analytical procedure of this software can be outlined in the following manner: (1) The clean data were acquired through the elimination of 3’ junction and extraneous sequences; (2) Sequences ranging from 18–26 nucleotides in length were preserved; (3) Non-miRNA sequences, such as rRNA, tRNA, snRNA, and snoRNA, were eliminated by cross-referencing with mRNA, RFam, and Repbase databases; (4) Valid data was retrieved and subsequently contrasted to precursors and genomes to identify miRNAs; (5) If |log2FC| ≥1 and P-value < 0.05, the miRNAs were deemed as differently expressed miRNAs (DEmiRNAs). The DEmRNAs, DElncRNAs, and DEmiRNAs were visualized by volcano plots and heatmaps [ 57 ]. To further ascertain the DEmRNAs bio-function, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and GO enrichment analysis, encompassing biological process (BP), cellular component (CC), and molecular function (MF), were executed using R package clusterProfiler v3.10.1 [ 58 , 59 ]. Gene Set Enrichment Analysis (GSEA) analysis was conducted on the normalized gene expression dataset with the KEGG dataset from c2.cp.kegg.v7.4 using clusterProfiler V3.10.1. A P-value < 0.05 was deemed to be significant [ 60 ]. The Protein-protein interaction (PPI) network was created with a confidence score ≥ 0.7 by the STRING online database ( https://cn.string-db.org/ ; version 11.5) to determine the hub genes in these DEmRNAs. The Cytoscape plug-in app Cytohubba was used to find important nodes in biological networks [ 61 ]. The outcomes visualization used Cytoscape software (version 3.9.1) [ 62 ]. In order to examine the clustering modules of greatest importance, we employed the Metascape platform ( http://metascape.org ; V3.5.20230101), an online tool designed for comprehensive analyses [ 63 ]. CIBERSORT is a computational technique utilized for the assessment of immune cell presence and abundance within intricate biological tissues depending on the analysis of gene expression patterns [ 64 ]. The abundance of immune infiltrating cells was quantified depending on the mRNA profile in the control and PCOS group. P value was determined employing the Mann-Whitney U test. A percentage bar graph was deployed for the purpose of showing the immune cell composition in each group. Boxplots were utilized to show the disparities in these cells between the control and PCOS specimens. Pearson correlation analysis was conducted by deploying R package stats V4.0.3 to evaluate the interactive connection between 22 immune cells and the association between hub genes and the content of immune cells. The Sankey plot was used to depict the selected ceRNA network (lncRNA-miRNA-mRNA). The R package ggalluvial V3.6.1 was utilized to generate the Sankey plot [ 65 ]. LncRNAs often participate in the regulation of nearby mRNAs through cis-regulation. The cis-regulation of lncRNA was forecasted through the examination of the co-expression network between lncRNA and mRNA. Hence, the Pearson correlation coefficient was deployed for the purpose of ascertaining the association between the lncRNA and mRNA expression levels. P-value < 0.05 was considered significant. LncRNA cis-regulatory target genes are predicted mainly based on positional relationships, defining the presence of DElncRNAs with DEmRNAs within 100 kb upstream and downstream in the chromosome. First, two individual pairs, lncRNA–miRNA and miRNA–mRNA pairs, were constructed using two algorithms (TargetScan5.0 and miRanda3.3a) [ 66 , 67 ]. The target pairs were selected according to the criteria that the TargetScan score is ≥ 90 and the miRanda Energy is < -20. Subsequently, to create a ceRNA network, we selected DElncRNAs and DEmRNAs that exhibit binding affinity towards the same DEmiRNA. This ceRNA network was constructed employing Cytoscape 3.9.1. For the purpose of finding the possible drugs for the PCOS treatment, we used a web tool called Clue (the updated Connectivity Map; https://clue.io .) [ 68 ]. The web tool was employed to upload the list of up-regulated DEmRNAs. L1000, a highly reproducible method, was selected to determine the connections between drugs and genes. Ultimately, the top 15 drugs were considered as candidate drugs. The RNA expressions involved in the ceRNA network in each group were verified by qRT-PCR. For mRNA and lncRNA verifications, PrimeScript™ RT reagent Kit with gDNA Eraser (Perfect Real Time) kit (TaKaRa, RR047A, Japan) was deployed in reverse transcription procedure. Subsequently, qPCR was conducted, and TB Green® Premix Ex Taq ™ (Tli RNaseH Plus) (TaKaRa, RR420A, Japan) was deployed. The miRNA verification process involved the utilization of the miDETECT A TrackTM miRNA qRT-PCR Starter Kit ( C10712 , RIBOBIO, China) and the miDETECT A TrackTM miRNA qPCR Kit ( C10711 , RIBOBIO, China). The quantification of mRNA, lncRNA, and miRNA expression levels was conducted utilizing the 2- ΔΔCT approach. ACTB and GAPDH were used as reference genes for mRNA and lncRNA, U6 and 5S were used as reference genes for miRNA. The bioinformatic analysis was conducted utilizing the OmicStudio tools available at https://www.omicstudio.cn/tool [ 69 ]. The qRT-PCR data underwent statistical analysis using GraphPad Prism software (version 9.2.0). A significance level of P < 0.05 was deemed to denote significance.

(A) Bubble plot of enriched top 20 cell components (CC) terms ranking by rich factor. Enriched terms of (B) B cell receptor signaling pathway and (C) natural killer cell mediated cytotoxicity in Gene Set Enrichment Analysis (GSEA) analysis. (TIF) The quantification of mRNA, lncRNA, and miRNA expression levels was conducted utilizing the 2- ΔΔCT approach. GAPDH was used as reference gene for mRNA and lncRNA, and 5S was used as reference gene for miRNA. Data were reported as means ± SEM; n = 30 in each group. Two-tailed student’s t-tests were used, and significant differences were considered when the P-value < 0.05. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. (TIF) (XLSX) (XLSX) (DOCX) (XLSX)