Introduction
Polycystic ovary syndrome (PCOS) is one of the most common endocrine diseases in women of reproductive age, with multiple aetiologies and highly heterogeneous clinical manifestations [1]. It usually presents with ovulation disorders, hyperandrogenaemia, and polycystic ovarian changes. PCOS also shows abnormal lipid metabolism, obesity, and insulin resistance (IR) [2]. Abnormal lipid metabolism is an important biochemical feature of PCOS patients, which is manifested as increased body fat content and obesity, and these factors are also high-risk factors for cardiovascular diseases [3]. IR has an inhibitory effect on the proliferation of ovarian granulosa cells, causing granulosa cell dysfunction, affecting normal follicular growth and development, and ultimately causing ovarian dysfunction [4]. While studying the pathogenesis of PCOS, more and more studies have found that alterations in cytokines or genetic regulatory molecules can exacerbate alterations in insulin receptor sensitivity by affecting the IR, which ultimately affects the course of disease progression in PCOS [5, 6]. There are many studies on the aetiological aspects of PCOS-IR, but there is no exact aetiological and pathophysiological mechanism. It is promising to explore the molecular mechanism of PCOS-IR from the aetiological level and to improve the endocrine level and clinical manifestations of patients through molecularly targeted therapy to improve the pregnancy rate.
microRNA (miRNA) is a small non-coding single-stranded RNA produced endogenously, which can regulate gene expression at the post-transcriptional level [7]. miRNAs are widely involved in female follicular maturation, fertilisation, implantation, and early embryonic development. miRNAs are involved in the regulation of insulin synthesis, secretion, and glucose-lipid metabolism [8, 9]. Numerous studies have shown that miRNAs are abnormally expressed in PCOS and are involved in follicular growth and development by regulating follicular granulosa cell proliferation and apoptosis. For example, miR-223 can affect lipid peroxidation damage and exacerbate changes in insulin receptor sensitivity on the surface of adipocyte membranes, ultimately affecting the development of IR and leading to the disturbance of sex hormone levels in PCOS patients [10, 11]. miR-16 expression was down-regulated in ovarian cortical tissues and serum of PCOS patients. miR-16 overexpression could promote granulosa cell proliferation and inhibit apoptosis by targeting programmed cell death 4 (PDCD4) [12]. miRNAs play a crucial role in the abnormal proliferation and apoptosis of granulosa cells in PCOS patients. Exploring the differentially expressed miRNAs in PCOS and further clarifying their roles and mechanisms of action may provide new molecular targets for improving PCOS. Several studies have pointed out that miR-181d-5p expression is closely related to metabolism-related diseases. For example, miR-181d-5p has been discovered to be involved in the regulation of glycogen synthesis [14]. It regulates pancreatic b-cell dysfunction and contributes to the development of diabetes mellitus by mediating insulin receptor substrate 2 (IRS2) [15]. The study of Wang et al. also pointed out that miR-181d-5p is involved in the regulation of hypercholesterolaemia [16]. In obese patients, miR-181d-5p was abnormally expressed and could regulate lipid metabolism by targeting angiopoietin-like 3 (ANGPTL3) [17]. In addition, miR-181 plays a role in cell proliferation, apoptosis, and differentiation. It was reported that miR-181 can inhibit the proliferation of granular cells and mesenchymal stem cells (MSC) [18, 19]. Mulato et al. found that miR-181d-5p response sensitively showed abnormal expression during ovarian stimulation [20]. It is speculated that miR-181d-5p may participate in PCOS by regulating factors related to glucose and lipid metabolism and the development of ovarian granulosa cells.
This study aims to explore the relationship between serum miR-181d-5p level and insulin resistance and clinical endocrine metabolism in PCOS patients, and clarify its role and mechanism in PCOS, to provide a reference for improving PCOS.
Material and methods
Study subjects and grouping
A total of 101 PCOS patients admitted to Huanggang Central Hospital of Yangtze University from 2017 to 2019 were selected, and the inclusion criteria were as follows: (1) PCOS subjects met the diagnostic criteria for PCOS [21]; (2) All patients were primarily diagnosed; (3) Informed consent was obtained from patients and their families. Exclusion criteria were as follows: (1) other reproductive diseases (such as endometriosis, uterine malformation, endometrial abnormalities, ovarian surgery history); (2) Endocrine diseases (such as congenital adrenal hyperplasia, thyroid disease, hyperprolactinaemia); (3) Family genetic history (such as diabetes, hypertension); (4) Systemic diseases (such as abnormal liver function, abnormal kidney function); (5) Medication < 6 months before study (oral contraceptives, oestrogen or antiandrogen, metformin, beta-blockers, antihypertensive, etc.); (6) History of recurrent abortion; and (7) Chromosomal abnormalities. At the same time, 95 healthy women were selected as the control group (HC). This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Huanggang Central Hospital of Yangtze University.
Referring to the article by Feng et al., based on the guidance of the Insulin Resistance Research Group of the Chinese Diabetes Association, the insulin resistance index suitable for Chinese people was defined as Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) ≥ 2.69 [22]. The PCOS patients were divided into an IR group (HOMA-IR ≥ 2.69, n = 60) and a non-IR group (HOMA-IR < 2.69, n = 41). HOMA-IR was calculated according to the following equation:
IR index (HOMA-IR) = fasting plasma glucose (FPG) (mmol/L) × fasting insulin (FINS) (mIU/L) /22.5.
According to the WHO and International Obesity Task Force in 2000 [23], patients with a body mass index (BMI) ≥ 25 were classified into the overweight group (ow), and patients with a BMI < 25 were classified into the non-overweight group (non-ow).
Specimen collection
Venous blood samples were collected early in the morning on days 2–4 of the natural menstrual cycle or at amenorrhoea when the mean maximal follicular diameter was less than 10 mm on transvaginal ultrasound, with at least 10 hours of fasting. Blood samples were centrifuged for 5 min at 4000 rpm at 4°C to separate the serum, and all the specimens were preserved at 2–8°C, and the test was completed within 48 h after collection.
Laboratory index detection
Serum collected from volunteers was used to detect the following indicators. Fasting plasma glucose (FPG) and fasting insulin (FINS) were measured using UniCel DxI 800 automatic chemiluminescence immunoassay analyser (Beckman Coulter, USA) and kits from Bayer. Triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density protein cholesterol (LDL-C) were measured using a 7600 automatic biochemistry analyser (Hitachi, Japan) and kits from Roche, USA. A fully automated chemiluminescent immunoassay system (Siemens, Shanghai, China) was used to measure endocrine-related markers, including luteinising hormone (LH), follicle stimulating hormone (FSH), testosterone (T), and oestradiol (E2) levels.
RT-qPCR
Total RNA was extracted from both patient serum and experimental group cells using the Trizol method (UNISOL Bio, Shanghai, China). RNA concentration was detected and quantified by NanoDropND-1000 spectrophotometer (Agilent Technologies, CA, USA). RNA was reverse transcribed to cDNA using a reverse transcription kit (Thermo Fisher, USA). Amplification was carried out by configuring the reaction system and setting the reaction conditions according to the instructions of the PCR amplification kit (Thermo Fisher, USA), and real-time reverse transcription polymerase chain reaction (RT-PCR) was performed with the assistance of a quantitative 7500 PCR detector (ABI, USA). The reaction system consisted of 10 μL, including 1.0 μL complementary DNA (cDNA) (50 ng/μL), 0.5μL upstream and downstream primers of 10 μM, 5.0 μL mix, and 3.0 μL double-distilled water (ddH2O). The reaction conditions were: predenaturation at 95°C of 2 min, denaturation at 95°C of 10 s, annealing at 60°C of 30 s, cycling 35 times, and extension at 72°C of 15 s. At least 3 replicates were ensured for each group. Relative expression was calculated by the 2-DDct method using U6 and glyceraldehyde 3-phosphate dehydrogenase (GAPDH) as internal controls for miRNA and mRNA, respectively. The primer sequence is shown in Supplementary File — Table S1.
Cell culture and IR cell modelling
Human ovarian granule cell line KGN (from the American Type Culture Collection) was used for the experiments and was cultured in DMEM medium (Hyclone, China) containing foetal bovine serum and dual antibodies (Invitrogen, USA). Incubation was carried out in an incubator with constant parameters of 37°C and 5% carbon dioxide (CO2).
The KGN cell suspension was inoculated into a 12-well plate, incubated for 24 hours, and starved in a blank Dulbecco’s modified eagle medium (DMEM) substrate for 12 hours to obtain digested single-cell suspension, which was placed in 90% Dulbecco’s Modified Eagle Medium (DMEM) + 10% foetal bovine serum (FBS) + D-glucose (an aqueous, Sigma-Aldrich, USA) + insulin (Sigma-Aldrich, USA) in the culture medium, and subsequent functional experiments were performed after treatment for 48 h. Cells with HOMA-IR < 2.57 were selected, which was successfully established for the IR cell model, to carry out subsequent transfection experiments. The cells of the constructed model were assayed by glucose kit, the glucose content of the cells was measured by glucose oxidase assay, and the glucose consumption and insulin sensitivity index were calculated by the formula.
Cell transfection
The control, IR model, IR + miR-181d-5p inhibitor negative control (miR inhibitor-NC), and IR + miR-inhibitor group KGN cells were grown into 12-well plates in serum-free blank medium and incubated at a constant temperature until the cell density reached 60–80%, which was replaced with full DMEM medium containing serum before transfection. The transfection process was performed according to Lipofectamine 2000 (Invitrogen, USA), and the reagents were mixed with miR-inhibitor and miR inhibitor-NC sequences, respectively (the transfection sequence is shown in Supplementary File — Table S2). The mixed complexes were transfected into each group of KGN cells, which were co-located in a suitable environment in the incubator for culture, and subsequent experiments could be carried out after 40 h.
CCK-8 and cell apoptosis assay
Counting cell kit 8 (CCK-8) assay was performed according to the kit instructions (WST-8, Dojindi Labs, Kumamoto, Japan). The control, IR model, IR + miR inhibitor-NC, and IR + miR-inhibitor group cells required for the experiment were inoculated in 96-well plates at a density of 3000 cells/well and treated with ethanol. At 0, 24, 48, and 72 h after inoculation, 10 μL of CCK8 solution was added to each well of the plate, and the wells were incubated for 2 h. Absorbance values at 450 nm (OD450) were evaluated using a Multi-scan (MS) spectrophotometer (Stockholm Laboratory Systems, Sweden).
In the apoptosis experiment, the control, IR model, IR + miR inhibitor-NC, and IR + miR-inhibitor group KGN cells were first subjected to trypsin treatment and centrifuged. Afterward, the cell suspension was then thoroughly mixed with Annexin V binding buffer, as per the Annexin V-FITC apoptosis detection kit instructions (Beyotime, China), and transferred to a fresh Eppendorf tube. Then the propidium iodide (PI) solution and Annexin V-FITC were added to the mix. They underwent a 15-minute light-free incubation period at room temperature before being subjected to fascia flow cytometry analysis (BD Biosciences).
Dual-luciferase reporter assay
The binding sites of miR-181d-5p in the 3′-UTR (untranslated region) of the human SIRT1 gene transcript were predicted using Target Scan (http://targetscan.org/), which was used to construct wild-type (WT) or mutant type (MT) luciferase plasmids. The miR-181d-5p mimic or inhibitor was co-transfected into KGN cells with the above 2 plasmids, respectively, with the assistance of transfection reagents. Relative luciferase activity was measured with a dual luciferase reporter system (Promega) according to the manufacturer’s instructions.
Statistical analysis
Statistical processing: All the collected data were statistically processed by GraphPad Prism 8.0 and SPSS19.0 software. Cell experiments related to this study were repeated 3 times in each group. The measurement data were expressed as mean ± standard, and an independent sample t-test was used to compare the mean of the 2 samples. One-way analysis of variance was used to compare multiple sample means. The clinical diagnostic value of miR-221-3p was calculated by the receiver operating characteristic (ROC) curve. The linear relationship between variables was performed using Pearson correlation. P < 0.05 indicates that the difference is statistically significant.
Results
Comparison of baseline data
The specific flow chart of this study is shown in Figure S1. Compared with the HC non-ow group, the PCOS non-ow group exhibited considerably higher FINS, TG, HOMA-IR, LH, T, and E2 levels, and lower levels of HDL-C and FSH (p < 0.05), whereas there were no significant differences in BMI, FPG, TC, and LDL-C levels between the 2 groups (p > 0.05). Comparison between the HC ow group and PCOS ow was consistent with the above (Tab. 1). In addition, serum miR-181d-5p was significantly up-regulated in PCOS non-ow patients compared to HC non-ow (Fig. 1A). The ROC curve for predicting the occurrence of PCOS-non-ow based on miR-181d-5p expression level was plotted with an area under the curve (AUC) of 0.881 and 95% confidence interval (CI) of 0.823–0.939, and the optimal threshold corresponded to a sensitivity and specificity of 84.21% and 81.01%, respectively (Fig. 1B). Similarly, serum miR-181d-5p expression was significantly higher in patients with PCOS ow than in those in the HC ow group (Fig. 1C), and the ROC curve also showed that miR-181d-5p could better assist in the diagnosis of patients with PCOS ow: AUC was 0.881, with 95% CI: 0.792–0.970, and the optimal threshold corresponded to the sensitivity and specificity of 84.09% and 81.25%, respectively (Fig. 1D).
|
|
PCOS |
HC |
||
|
Non-ow (n = 57) |
Ow (n = 44) |
Non-ow (n = 79) |
Ow (n = 16) |
|
|
Age |
27.28 ± 3.66 |
25.58 ± 4.82 |
28.27 ± 4.21 |
28.88 ± 4.33 |
|
BMI |
22.17 ± 1.52 |
25.62 ± 3.27 |
22.57 ± 1.73 |
26.56 ± 1.09 |
|
FPG [mmol/L] |
5.18 ± 0.73 |
5.53 ± 0.61 |
5.04 ± 0.62 |
4.89 ± 0.65 |
|
FINS [mIU/L] |
11.48 ± 3.42a |
15.16 ± 6.28b |
9.05 ± 1.7 |
8.74 ± 1.59 |
|
HOMA-IR |
2.66 ± 0.91a |
4.68 ± 2.56b |
2.03 ± 0.46 |
1.89 ± 0.37 |
|
TG [mmol/L] |
2.6 ± 0.64a |
1.52 ± 0.30b |
1.04 ± 0.35 |
1.01 ± 0.4 |
|
TC [mmol/L] |
4.02 ± 0.72 |
4.53 ± 0.88 |
4.07 ± 0.72 |
3.92 ± 0.59 |
|
LDL-C [mmol/L] |
3.03 ± 0.42 |
3.14 ± 0.82 |
2.89 ± 0.44 |
3.12 ± 0.47 |
|
HDL-C [mmol/L] |
0.98 ± 0.2a |
0.84 ± 0.26b |
1.42 ± 0.08 |
1.4 ± 0.08 |
|
LH [IU/L] |
11.44 ± 1.97a |
8.82 ± 1.40b |
7.07 ± 2.67 |
6.48 ± 2.66 |
|
FSH [U/L] |
7.29 ± 2.02a |
5.80 ± 1.23b |
9.36 ± 3.43 |
8.77 ± 3.78 |
|
T [ng/dL] |
57.65 ± 9.61a |
43.30 ± 9.10b |
25.27 ± 11.08 |
42.47 ± 16.63 |
|
E2 [pg/mL] |
46.11 ± 12.18a |
57.88 ± 6.02b |
36.15 ± 6.82 |
35.49 ± 6.35 |
Correlation between miR-181d-5p expression and indicators
To further explore the relationship between miR-181d-5p expression and PCOS, Pearson correlation analysis was performed. It was found that indicators in both PCOS ow and PCOS non-ow groups showed the following correlations with miR-181d-5p (Tab. 2): miR-181d-5p was positively correlated with patients’ FINS, HOMA-IR, and T (r > 0.5, p < 0.05), and negatively correlated with HDL-C and E2 (r < –0.5, p < 0.05). In addition, logistic regression analysis showed that miR-181d-5p (p = 0.014), BMI (p = 0.042), FINS (p = 0.032), TG (p = 0.032), LH (p = 0.04), and T (p = 0.03) can be used as an independent risk factor indicator for patients with PCOS-IR (Tab. 3).
|
|
PCOS non-ow |
PCOS ow |
||
|
Pearson r |
p-value |
Pearson r |
p-value |
|
|
Age |
–0.006 |
0.964 |
–0.037 |
0.811 |
|
FPG [mmol/L] |
0.385 |
0.003 |
0.357 |
0.018 |
|
FINS [mIU/L] |
0.684 |
< 0.001 |
0.641 |
< 0.0001 |
|
HOMA-IR |
0.760 |
< 0.001 |
0.717 |
< 0.0001 |
|
TG [mmol/L] |
0.196 |
0.144 |
0.280 |
0.066 |
|
TC [mmol/L] |
0.158 |
0.241 |
0.185 |
0.230 |
|
LDL-C [mmol/L] |
0.252 |
0.058 |
0.179 |
0.245 |
|
HDL-C [mmol/L] |
–0.522 |
< 0.001 |
–0.658 |
< 0.0001 |
|
LH [IU/L] |
0.190 |
0.157 |
0.274 |
0.072 |
|
FSH [U/L] |
–0.162 |
0.229 |
–0.156 |
0.313 |
|
T [ng/dL] |
0.630 |
< 0.001 |
0.588 |
< 0.0001 |
|
E2 [pg/mL] |
–0.559 |
< 0.001 |
–0.698 |
< 0.0001 |
|
|
p-value |
OR |
95% CI |
|
miR-20a-5p |
0.014 |
3.729 |
1.303-10.667 |
|
Age |
0.381 |
1.561 |
0.576-4.233 |
|
BMI |
0.042 |
0.312 |
0.102-0.96 |
|
FPG [mmol/L] |
0.235 |
1.87 |
0.666-5.25 |
|
FINS [mIU/L] |
0.032 |
3.1 |
1.102-8.727 |
|
TG [mmol/L] |
0.032 |
3.443 |
1.109-10.688 |
|
TC [mmol/L] |
0.345 |
1.71 |
0.562-5.2 |
|
LDL-C [mmol/L] |
0.826 |
1.114 |
0.425-2.918 |
|
HDL-C [mmol/L] |
0.05 |
2.874 |
1.002-8.244 |
|
LH [IU/L] |
0.04 |
2.893 |
1.05-7.967 |
|
FSH [U/L] |
0.486 |
1.435 |
0.519-3.965 |
|
T [ng/dL] |
0.03 |
3.37 |
1.124-10.109 |
|
E2 [pg/mL] |
0.778 |
0.867 |
0.321-2.339 |
Correlation between miR-181d-5p and PCOS-IR
As shown in Figure 2A, PCOS patients were divided into 2 groups according to IR values, and the expression of miR-181d-5p in the IR group was significantly higher than that in the non-IR group. ROC curve showed that miR-181d-5p could assist in the diagnosis of PCOS-IR patients: AUC was 0.898 (95% CI: 0.838 ~ 0.958). The specificity and sensitivity of the optimal cut-off point were 83.33% and 82.93, respectively (Fig. 2B). To study the effect of miR-181d-5p on IR-induced cells, we constructed an IR model of KGN cells. miR-181d-5p expression in the IR model was higher than that of control KGN cells (Fig. 2C), and it was found that IR decreased the proliferation (Fig. 2D) and prompted apoptosis (Fig. 2E) of KGN cells. Figure 3A demonstrates how well the miR-181d-5p inhibitor was able to suppress miR-181d-5p expression. Interfering with miR-181d-5p expression could significantly improve IR-inhibited KGN cell proliferation (Fig. 3B) and inhibit IR-induced KGN cell apoptosis (Fig. 3C). According to the findings, miR-181d-5p may be involved in the occurrence of PCOS by regulating the proliferation and apoptosis of KGN induced by IR.
Interaction between miR-181d-5p and SIRT1
Through the Target Scan the Human (https://www.targetscan.org/vert_72/) found that miR-181d-5p and SIRT1 possessed multiple binding sites (Fig. 4A). SIRT1 was downregulated in PCOS patients (Fig. 4B) and lower in patients with PCOS combined with IR (Fig. 4C). SIRT1 showed a negative correlation with miR-181d-5p expression in PCOS patients (r = –0.717, p < 0.0001, Fig. 4D). To verify the targeting relationship, a dual luciferase reporter gene assay was conducted, which indicated that SIRT1 luciferase activity was reduced with increasing miR-181d-5p (Fig. 4E). At the same time, down-regulation of miR-181d-5p inhibited the expression of SIRT1 in the IR model (Fig. 4F).
Discussion
As patients with PCOS are prone to concomitant occurrence of obesity, patients were initially divided into non-ow and ow groups to avoid the interference of BMI in this study. Comprehensive analysis of patients’ baseline data revealed significant differences in FINS, HOMA-IR, TG, HDL-C, LH, FSH, T, and E2 between the PCOS and HC groups in both the non-ow and ow patients. In this study, the expression of miR-181d-5p in PCOS patients was significantly higher than that in the control group in both non-ow and ow patients, and it could be a good aid in the diagnosis of PCOS. Wang et al. also discovered that up-regulated miR-222-3p was present in PCOS ow and non-ow patients, suggesting its potential as a diagnostic marker for PCOS [24]. Fan et al. demonstrated that miR-141-3p has the potential to be a diagnostic marker for PCOS and plays an important role in regulating glycolipid metabolism in PCOS [25]. Hou et al. concluded that miR-26b expression was noticeably higher in the PCOS group and was associated with the disorders of glucose-lipid metabolism and IR in PCOS patients [26]. Therefore, it is initially hypothesised that the up-regulation of miR-181d-5p may assist in the early diagnosis of PCOS patients.
Further analysis revealed that miR-181d-5p was closely related to the occurrence of IR and lipid metabolism in patients with PCOS. Guo et al. found that miR-181d-5p could be correlated with insulin sensitivity while exploring islet function in miRNA diabetic rats [27]. So, the present study speculated that there was a correlation between miR-181d-5p and the occurrence of IR in patients with PCOS. The study initially found that miR-181d-5p was elevated in patients with PCOS combined with IR, and it could be used as a good marker and independent risk predictor in patients with PCOS-IR. This tentatively confirms our speculation that miR-181d-5p is highly associated with PCOS-IR, but the specific role of miR-181d-5p in PCOS-IR is unclear. For this reason, we constructed an IR cell model in this study and found that, compared with normal granulosa cells, the viability of the IR cell model was significantly reduced and the apoptosis rate was significantly increased. In addition, we found that miR-181d-5p expression was significantly up-regulated in the IR cell model in this study. miR-483-3p has been shown to inhibit the proliferation and cell cycle of KGN cells by inhibiting insulin growth factor [28]. Therefore, it is hypothesised that miR-181d-5p is involved in PCOS progression through the regulation of IR. It was shown that the knockdown of miR-181d-5p could improve the IR-inhibited granulosa cells and reverse the IR-induced apoptosis of granulosa cells. Yang et al. found that up-regulation of miR-133a-3p can promote IR in granular cells of PCOS patients [29]. Jiang et al. found that miR-204 could prevent insulin release and promote granulosa cell proliferation by regulating the inactivation of the TLR4/NF-kB pathway [30]. It indicates that miRNA is crucial for the development of PCOS-IR patients. Therefore, we suggested that miR-181d-5p could affect the development of oocyte granules by participating in IR.
In-depth investigation revealed that SIRTI is a target gene of miR-181d-5p, and its expression was considerably lower in PCOS patients with IR and was inversely associated with that of miR-181d-5p. Recent studies have shown that SIRT1 expression in polycystic ovary syndrome patients is regulated by miRNAs. For example, miR-34a can regulate SIRT1 expression [31]. Similarly, overexpression of miR-23a inhibits SIRT1 expression in PCOS [32]. More and more evidence shows that SIRTI is an important regulator involved in IR, and activation of SIRTI can improve the sensitivity of adipose tissue to insulin and protect the function of pancreatic B cells [33]. Zhang et al. found that metformin treatment upregulated the expression of SIRTI in PCOS rats, which improved hyperandrogenaemia and IR [34]. Tao et al. found that the PCOS mouse model was in an obvious IR status, and overexpression of AMPKa-SIRT1 could significantly improve IR [35]. Therefore, it is speculated that miR-181d-5p downregulation could activate SIRT1 to promote insulin sensitivity in ovarian cells, which improved PCOS-IR patients.
There are certain limitations to this study. 1. This study is a retrospective study, based on the analysis of existing data or records, which may not have been collected with scientific research in mind, and thus there may be selection bias and confounding factors (like unpredictable eating habits), which may affect the internal validity of the study. 2. This study included a small sample size, which may also have contributed to the lack of statistical power. 3. Due to time constraints, further plasmid transfection validation and proteomics studies could not be conducted for the functional mechanism study. 4. The present study only preliminarily elucidated the role of miR-181d-5p in the development of PCOS-IR, and PCOS pathogenesis is also affected by many other mechanisms and uncontrollable factors, for example, the correlation with lipid metabolism. In future experiments, we will need to dig deeper into the mechanistic study of miR-181d-5p on PCOS and conduct more detailed investigations or clinical studies to provide new theoretical support for the prevention and treatment of clinical PCOS.
Conclusions
In conclusion, this study revealed that there is a high expression of serum miR-181d-5p in both PCOS ow and non-ow patients, which is a phenomenon that could potentially be beneficial for the diagnosis and assessment of the severity of PCOS. Moreover, miR-181d-5p high expression indicates that PCOS patients may be associated with IR, implying an increased risk of diabetic complications. In further cellular experiments, it was found that knockdown of miR-181d-5p reversed the effects of IR on proliferation and apoptosis of KGN cells, which may be achieved by targeting SIRT1. This study provides a new reference for the diagnosis and treatment of patients with PCOS and combined IR. The limitation of this study is the relatively small number of cases and events included and analysed. Future studies should include patients with PCOS involving a larger number, wider range, and more phenotypes, and expand the study of miR-181d-5p target genes to clarify the role of miR-181d-5p in PCOS and increase the confidence of the results.
Data availability statement
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
Ethics statement
This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Huanggang Central Hospital of Yangtze University.
Author contributions
M.S. and H.P.C. contributed to the study conception and design. Y.Y. and Y.Q.Z. contributed sample collection and statistical analysis. All authors contributed to analysis and interpretation of data. MS drafted the manuscript, and HPC primarily revised and finalised the manuscript. All authors reviewed and edited the manuscript and approved the final version of the manuscript.
Funding
This work was supported by 2024 Huanggang Municipal Science and Technology Innovation General Project (No. YBXM20240039).
Conflict of interest
The authors declare that they have no conflict of interest.
