Abstract

   Polycystic ovary syndrome (PCOS) is a known risk factor for uterine
   endometrial cancer (UCEC), but its underlying mechanisms remain
   unclear. MicroRNAs (miRNAs) could provide insights into these
   mechanisms and reveal potential therapeutic targets. Differential miRNA
   expression was analyzed in plasma exosomes from 15 PCOS and 15 control
   samples. Survival analysis assessed the prognostic value of these
   miRNAs in UCEC. MiRNA-target gene interaction networks and gene
   co-expression analyses were used to explore molecular mechanisms.
   Validation was performed using experimental data from Ishikawa cells
   treated with six candidate drugs. Among the 15 differentially expressed
   miRNAs, 12 were up-regulated and 3 were down-regulated in PCOS. Twelve
   of these miRNAs were associated with UCEC overall survival, with
   miR-142, miR-424, and miR-331 acting as protective factors, while the
   remaining 9 miRNAs were identified as risk factors. MiRNA-target
   network highlighted key genes such as PHF8, LCOR, SFT2D3, E2F1, and
   ESR1, which were found to be prognostic for patient survival. Further
   gene expression and co-expression analyses based on miR-424 and miR-330
   expression revealed significant alterations in gene expression and
   cellular processes related to UCEC. Two-sample Mendelian randomization
   analysis identified potential causal relationships between AURKA gene
   expression and PCOS or UCEC. Testosterone and estradiol might have
   adverse roles in UCEC, while drugs like troglitazone, valproic acid,
   retinoic acid, and progesterone demonstrated various effects on gene
   expression and cellular processes. Our findings suggest that aberrant
   miRNA expression, particularly miR-330 and miR-424, may play crucial
   roles in UCEC progression. The identified miRNAs and candidate drugs
   may serve as potential therapeutic targets for UCEC, but further
   research is required to validate and explore their clinical
   applications.

Supplementary Information

   The online version contains supplementary material available at
   10.1007/s12672-025-01861-4.

   Keywords: miRNAs, Endometrial cancer, Polycystic ovary syndrome (PCOS),
   Integrative analysis, Gene expression

Introduction

   Polycystic ovary syndrome (PCOS) is a complex endocrine disorder marked
   by hyperandrogenism, ovulatory dysfunction, polycystic ovarian
   morphology, and insulin resistance, with both genetic and environmental
   factors playing a role [[32]1]. PCOS is associated with various
   metabolic and reproductive abnormalities, and growing evidence
   indicates it may also elevate the risk of endometrial cancer (UCEC),
   one of the most common gynecological malignancies [[33]2]. UCEC is
   characterized by a wide range of molecular and genetic alterations
   [[34]3]. Understanding the link between PCOS and UCEC is particularly
   valuable, as it may shed light on the molecular mechanisms connecting
   endocrine disorders with cancer development.

   Recent research has increasingly highlighted the role of microRNAs
   (miRNAs) as key regulators in cancer biology, influencing various
   aspects of tumor progression, including growth, metastasis, and
   resistance to therapy [[35]4]. MiRNAs, small non-coding RNAs that
   modulate gene expression post-transcriptionally, have emerged as
   significant biomarkers and potential therapeutic targets in numerous
   cancers. Plasma exosomal miRNAs are small non-coding RNAs encapsulated
   within exosomes, nano-vesicles secreted by various cell types [[36]5].
   These miRNAs play a pivotal role in intercellular communication,
   influencing gene expression and cellular functions in distant tissues.
   They can promote cancer cell invasion, migration, and
   epithelial-to-mesenchymal transition (EMT) by modulating pathways such
   as Wnt/β-catenin and STAT3 [[37]6]. Exosomal miRNAs are also important
   as biomarkers for cancer progression and immune evasion, offering
   potential targets for therapeutic intervention [[38]7, [39]8]. While
   some exosomal miRNAs can drive tumor growth, others may suppress it by
   targeting tumor suppressor genes [[40]9]. Due to their stability in
   circulation, exosomal miRNAs from various sources, including adipose
   tissue macrophages, can influence systemic processes such as insulin
   sensitivity and glucose homeostasis [[41]10].

   This study conducted an integrative analysis to identify miRNAs that
   may link PCOS with UCEC. We found distinct miRNA expression patterns in
   both PCOS patients and UCEC tissues, with several miRNAs significantly
   associated with both conditions. Functional validation using cell line
   experiments explored the effects of candidate drugs targeting
   miRNA-related genes on gene expression and cellular processes in UCEC.
   Our findings suggest these miRNAs could serve as potential biomarkers
   and therapeutic targets, offering insights into the molecular
   connection between PCOS and UCEC and aiding in personalized diagnosis
   and treatment strategies.

Materials and methods

Data acquisition

   Plasma exosomal miRNA data were obtained from the Gene Expression
   Omnibus (GEO) database ([42]GSE142819) which included 15 PCOS samples
   and 15 control samples [[43]11]. 571 UCEC patients from TCGA (The
   Cancer Genome Atlas) with both gene expression data and miRNA profiles
   were used for comprehensive analysis [[44]12]. Expression data for
   Ishikawa cells treated with 6 different chemicals were also downloaded
   from the NCBI GEO database ([45]GSE69849). For each chemical, three
   replicates were performed in control and treatment groups [[46]13]. The
   dataset [47]GSE158317 comprises RNA-Seq data from six ovarian cancer
   cell line samples (COV318), including three transfected with the
   miR-330-3p mimic and three with the control [[48]14].

Differential miRNA expression analysis

   Differential miRNA expression was analyzed using the R software to
   preprocess and normalize the data. We applied the limma package to
   identify differentially expressed miRNAs between PCOS and control
   samples. A volcano plot was generated to visualize significant miRNAs
   with adjusted P-values < 0.01 and log2 fold change > 1.

Survival analysis

   Survival analysis was conducted using the KM plotter web tool [[49]15].
   Kaplan–Meier survival curves were plotted for UCEC patients stratified
   by high and low expression levels of the identified miRNAs. Statistical
   significance was determined using the log-rank test [[50]16].

miRNA-target gene interaction network analysis

   To identify key genes associated with miRNAs, we constructed miRNA-gene
   interaction networks using the MIENTURNET web tool [[51]17].
   Up-regulated and down-regulated miRNAs from PCOS were analyzed
   separately. Target gene predictions were obtained from databases such
   as TargetScan and miRDB. The networks were visualized to determine key
   gene interactions. Survival analysis was conducted on the genes with
   high connectivity to assess their prognostic value by KM plotter web
   tool.

Differential gene expression and co-expression analysis

   For UCEC tissues, differential gene expression was assessed based on
   miR-424 and miR-330 expression levels. The limma package in R was used
   for differential analysis with adjusted P value < 0.05 and log2 fold
   change > 0.3. Gene Ontology (GO) and pathway enrichment analyses were
   performed using the clusterProfiler package [[52]18].

   Gene co-expression analysis was conducted using weighted gene
   co-expression network analysis (WGCNA) to identify gene modules
   associated with miR-424 and miR-330 [[53]19]. Correlation between
   module eigengenes and clinical traits was analyzed to identify
   significant modules [[54]20].

Mendelian randomization analysis

   To explore potential causal relationships between AURKA gene expression
   and POCS, UCEC, we performed a two-sample Mendelian randomization (MR)
   analysis. Summary statistics for AURKA were obtained from
   eqtl-a-ENSG00000087586, while data for PCOS and UCEC were retrieved
   from finn-b-E4_POCS and ebi-a-GCST006466 in the MRC IEU OpenGWAS
   database [[55]21]. Single nucleotide polymorphisms (SNPs) significantly
   associated with eqtl-a-ENSG00000087586 (p < 5 × 10⁻⁸) were selected as
   instrumental variables (IVs), ensuring independence (r^2 < 0.001) and
   absence of linkage disequilibrium. The inverse-variance weighted (IVW)
   method was applied as the primary MR approach to estimate causal
   effects, with complementary sensitivity analyses conducted using
   MR-Egger regression and the weighted median method to account for
   potential pleiotropy and heterogeneity. The F-statistic was calculated
   to assess the strength of the IVs. Additionally, heterogeneity was
   evaluated using Cochran's Q test, and potential horizontal pleiotropy
   was tested using the intercept from MR-Egger regression. All analyses
   were performed using the R package TwoSampleMR [[56]22]. Results were
   reported as odds ratios (OR) or beta coefficients with 95% confidence
   intervals (CI), representing the effect of eqtl-a-ENSG00000087586 on
   PCOS and UCEC.

Cellular composition analysis

   Cellular composition profiles of UCEC samples were retrieved using the
   Ecotyper deconvolution algorithm [[57]23, [58]24]. Heat maps were
   generated to visualize cellular subtype abundance differences between
   high and low miR-330 expression samples. The top differentially
   expressed cell subtypes were identified by t test and their association
   with patient survival was analyzed using the survminer and survival
   packages in R. Kaplan–Meier survival curves were plotted for UCEC
   patients stratified by high and low expression levels of cell subtype
   abundance.

Drug candidate identification

   Up-regulated and down-regulated genes from miR-330 high samples were
   used to identify candidate drugs through the enrichr web tool, DSigDB,
   and literature searches [[59]25, [60]26]. We focused on drugs with
   known interactions related to hormone signaling and cancer treatment.
   The Ishikawa cell line experimental data was used for the validation of
   candidate drugs. Cells were treated with six selected drugs for 6 h.
   Gene expression changes were identified by limma.

Statistical analysis

   All statistical analyses were performed using R software.
   P-values < 0.05 were considered statistically significant. Multiple
   testing corrections were applied where appropriate, using methods such
   as the Benjamini–Hochberg false discovery rate.

Results

Differential expression of plasma exosomal miRNAs in polycystic ovary
syndrome patients

   Deep sequencing analysis of the miRNA transcriptome (dataset
   [61]GSE142819) was performed on plasma samples from 15 control subjects
   and 15 patients with polycystic ovary syndrome (PCOS). This
   differential expression analysis identified 15 miRNAs with significant
   expression changes (Fig. [62]1A). Among them, 12 miRNAs were
   up-regulated in PCOS patients, including miR-483, miR-206, miR-142,
   miR-330, miR-3120, miR-203a, miR-1228, miR-1246, miR-3656-p3, miR-4792,
   miR-193b, and miR-1 (Fig. [63]1B). In contrast, three miRNAs—miR-331,
   let-7f, and miR-424—were found to be down-regulated in PCOS samples.

Fig. 1.

   [64]Fig. 1
   [65]Open in a new tab

   Differential analysis identified 15 differentially expressed plasma
   exosomal miRNAs in PCOS patients. A Volcano plot showing differential
   expression of plasma exosomal miRNAs. B Top ten miRNAs with the most
   significant fold changes

miRNAs as prognostic markers in UCEC tissue

   PCOS has been associated with an elevated risk of developing UCEC. To
   explore the prognostic significance of the 15 differentially expressed
   miRNAs identified in PCOS, we analyzed their association with overall
   survival in UCEC patients. Notably, 12 of the 15 miRNAs were
   significantly linked to overall survival, except for miR-1, let-7f, and
   miR-483. Among these, miR-142 (up-regulated in PCOS), miR-424, and
   miR-331 (both down-regulated in PCOS) were identified as protective
   factors. In contrast, the remaining nine miRNAs, all up-regulated in
   PCOS, were associated with increased risk (Fig. [66]2). These findings
   suggest that these miRNAs may play a critical role in the heightened
   risk of developing UCEC in PCOS patients.

Fig. 2.

   [67]Fig. 2
   [68]Open in a new tab

   Survival analysis of tissue miRNAs in UCEC patients grouped by miR
   expression. Survival curves for UCEC patients stratified by low and
   high expression levels of miR-142, miR-3120, miR-330, miR-206,
   miR-3656, miR-4792, miR-1246, miR-1228, miR-193b, miR-203a, miR-424,
   and miR-331

miRNA and target gene network analysis identifies key genes

   To elucidate the relationships between miRNAs and their target genes,
   two miRNA-gene interaction networks were constructed—one for
   up-regulated miRNAs and the other for down-regulated miRNAs. Through
   these networks, several key genes were identified. In Network A, genes
   such as PHF8, LCOR, and SFT2D3 were strongly associated with three
   specific miRNAs (Fig. [69]3A). In Network B, genes like E2F1, ESR1,
   C5orf51, VEGFA, and others showed significant connections with various
   miRNAs (Fig. [70]3B). To validate the relevance of these genes,
   survival analysis was conducted, revealing that most of the key genes
   in both networks were prognostic for overall survival in UCEC patients
   (Fig. [71]3C). These findings suggest that these genes may play
   critical roles in the progression and prognosis of UCEC.

Fig. 3.

   [72]Fig. 3
   [73]Open in a new tab

   miRNA-gene interaction network reveals key genes. A miRNA-gene
   interaction network for down-regulated miRNAs. B miRNA-gene interaction
   network for up-regulated miRNAs. C Survival curves for key genes
   SFT2D3, E2F1, ESR1, C5orf51, and VEGFA

Differential gene expression analysis and co-expression analysis of UCEC
stratified by tissue miR-424 expression

   Given that miR-424 is a protective factor but is down-regulated in
   PCOS, we explored the differences between UCEC patients with high
   versus low miR-424 expression. Differential gene expression analysis
   identified 550 genes (250 up-regulated and 300 down-regulated) in
   patients with high miR-424 expression compared to those with low
   expression (Fig. [74]4A). Notably, the most significantly up-regulated
   genes included SFRP4, ALDH1A2, APOD, CCL21, TWIST2, PAMR1, ECM1, CD248,
   WNT2, and LTBP4 (Fig. [75]4B). These up-regulated genes were mainly
   involved in extracellular matrix organization and response to TGF-β
   signaling (Fig. [76]4C), while the down-regulated genes were associated
   with ribosome function and mitochondrial gene expression (Fig. [77]4D).
   Additionally, pathways related to the up-regulated genes were enriched
   in focal adhesion processes (Fig. [78]4E).

Fig. 4.

   [79]Fig. 4
   [80]Open in a new tab

   Differential gene expression and co-expression analysis based on tissue
   miR-424 expression in UCEC tissues. A Differential gene expression
   analysis identified 550 genes in samples with high miR-424 compared to
   low miR-424. B Boxplots illustrating the top ten significantly changed
   genes. C–E Gene Ontology (GO) biological processes and pathway
   enrichment analyses for up-regulated and down-regulated genes. F
   Heatmap depicting the relationship between four co-expression modules
   and miR-424. Each cell represents the correlation between the
   expression of specific modules and distinct clinical parameters, with
   numerical values in brackets indicating the statistical significance of
   the correlation

   Gene co-expression analysis revealed four distinct gene co-expression
   modules (Fig. [81]4F). Functional enrichment analysis showed that
   up-regulated module ME1 was related to focal adhesion, while
   down-regulated module ME2 was linked to the cell cycle. Modules ME3 and
   ME5 were both associated with ncRNA metabolism. Intriguingly, eight of
   the top ten significantly up-regulated genes—SFRP4, APOD, CCL21,
   TWIST2, PAMR1, ECM1, CD248, and WNT2—were all correlated with longer
   overall survival (Figure S1) and were enriched in the regulation of T
   cell migration.

   Further, correlation analysis identified ten genes most significantly
   associated with miR-424 expression, several of which, including SFRP4,
   ALDH1A2, and CD248, were linked to the positive regulation of
   apoptosis. This may help explain why UCEC patients with high miR-424
   expression had improved survival rates.

Differential gene expression analysis and co-expression analysis of UCEC
stratified by tissue miR-330 expression

   As miR-330 is a risk factor and is up-regulated in PCOS, we explored
   the differences between UCEC patients with high and low miR-330
   expression. Differential gene expression analysis identified 1289 genes
   (715 up-regulated and 574 down-regulated) in patients with high miR-330
   expression compared to those with low expression (Fig. [82]5A). The
   most significantly altered genes were predominantly up-regulated,
   including AURKA, ABHD3, NCAPG, YARS, RRM2, AUNIP, and MTFR2
   (Fig. [83]5B). These up-regulated genes were primarily involved in
   nuclear division processes (Fig. [84]5C), while down-regulated genes
   were associated with the cellular response to TGF-β signaling
   (Fig. [85]5D). In an independent ovarian cancer cell line dataset
   [[86]14], we observed that all the most significantly altered genes,
   except AUNIP, displayed consistent directional changes following
   treatment with the miR-330 mimic.

Fig. 5.

   [87]Fig. 5
   [88]Open in a new tab

   Differential gene expression and co-expression analysis based on tissue
   miR-330 expression in UCEC tissues. A Differential gene expression
   analysis identified 1289 genes in high miR-330 samples compared to low
   miR-330 samples. B Boxplots showing the top ten significantly changed
   genes. C–F Gene Ontology (GO) biological processes and pathway
   enrichment analyses for up-regulated and down-regulated genes

   Notably, seven of the top ten significantly altered genes were
   up-regulated, and six of them (AURKA, NCAPG, YARS, RRM2, AUNIP, and
   MTFR2) were linked to shorter overall survival (Figure S2). These genes
   were enriched in pathways related to the positive regulation of the
   cell cycle (Fig. [89]5E). In contrast, the three down-regulated
   genes—WBP1L, WFS1, and AXIN2—were associated with longer overall
   survival and were involved in the positive regulation of protein
   ubiquitination. Pathway enrichment analysis revealed that up-regulated
   genes were primarily associated with the cell cycle, while
   down-regulated genes were linked to focal adhesion (Fig. [90]5F).

   Gene co-expression analysis identified five distinct co-expression
   modules (Fig. [91]5F). Functional enrichment analysis revealed that the
   up-regulated modules ME1 and ME3 were involved in cell cycle regulation
   and immune system processes, while the down-regulated modules ME2, ME4,
   and ME5 were associated with focal adhesion, TGF-β signaling, and
   cilium assembly. Interestingly, the top ten genes most strongly
   correlated with miR-330 expression, identified through correlation
   analysis, were predominantly involved in the positive regulation of the
   cell cycle, including AURKA. These findings provide a potential
   explanation for the poorer survival outcomes in UCEC patients with high
   miR-330 expression.

Validation of the prognostic gene AURKA in UCEC by Mendelian randomization

   The Mendelian randomization (MR) analysis revealed a significant
   positive association between AURKA eQTL SNPs and PCOS (Fig. [92]6A). A
   funnel plot showed a generally symmetric distribution of the genetic
   variants' estimates, suggesting minimal evidence of directional
   pleiotropy (Fig. [93]6B). The robustness of these results was further
   confirmed through sensitivity analyses (Fig. [94]6C). Leave-one-out
   analysis was performed to assess whether any single SNP
   disproportionately influenced the overall causal estimate. After
   sequentially excluding each SNP, the effect estimates remained
   consistent, demonstrating the stability of the results (Fig. [95]6C).
   This suggests that no single genetic variant was driving the
   association between AURKA and PCOS. The similar analysis was also
   performed for casual relationship between AURKA and UCEC (Fig. [96]6D,
   [97]F). Taken together, these analyses support robust causal and
   positive relationships between AURKA and PCOS and UCEC, with minimal
   evidence of bias from pleiotropy or outlier SNPs.

Fig. 6.

   [98]Fig. 6
   [99]Open in a new tab

   Two-sample Mendelian randomization analysis illustrating the causal
   effects of AURKA gene expression on PCOS and UCEC. A Scatter plot of
   effects of cis-eQTL SNPs on AURKA expression and PCOS occurence. B
   Funnel plot for PCOS. C Leave-one-out sensitivity analysis for PCOS. D
   Scatter plot of effects of cis-eQTL SNPs on AURKA expression and UCEC
   occurence. E Funnel plot for UCEC. F Leave-one-out sensitivity analysis
   for UCEC

Comparative analysis reveals differential cell subtypes in UCEC patients
stratified by tissue miR-330 expression

   To investigate the cellular composition differences between UCEC groups
   stratified by tissue miR-330 expression, we applied the
   state-of-the-art deconvolution algorithm Ecotyper to the TCGA UCEC
   samples. The analysis revealed significant heterogeneity in the
   cellular composition across UCEC samples, as illustrated by the heatmap
   (Fig. [100]7A). Notably, we identified distinct differences in cellular
   composition between groups with high and low miR-330 expression.

Fig. 7.

   [101]Fig. 7
   [102]Open in a new tab

   Differences in tissue cellular composition in UCEC patients stratified
   by miR-330 expression. A Heatmap showing the abundance of cell subtypes
   in UCEC patients. B Identification of the top 12 differential cell
   subtypes in high miR-330 samples compared to low miR-330 samples. C
   Survival curve for samples stratified by abundance of fibroblasts.8. D
   Survival curve for samples stratified by the abundance of
   CD4.T.cells.1. In both survival curves, the green line represents high
   expression and the red line represents low expression

   The top five significantly altered cell subtypes were
   epithelial.cells.4, epithelial.cells.2, fibroblasts.8, CD8.T.cells.3,
   and dendritic.cells.5 (Fig. [103]7B). According to cell state
   annotation, epithelial.cells.4 is a pro-inflammatory subset,
   fibroblasts.8 is a pro-migratory-like subset, and CD8.T.cells.3
   represents an exhausted T cell state. All three of these subtypes were
   up-regulated in miR-330 high samples. In contrast, epithelial.cells.2
   and dendritic.cells.5, which are normal-enriched subtypes, were
   down-regulated in miR-330 high samples. Additionally, CD4.T.cells.1,
   annotated as another exhausted T cell subtype, was up-regulated.

   Moreover, survival analysis revealed that fibroblasts.8 and
   CD4.T.cells.1 were both significantly linked to poorer overall survival
   in UCEC patients (Fig. [104]7C, [105]D). These findings suggest that
   the aberrant immune cell composition in miR-330 high patients,
   characterized by increased pro-inflammatory and exhausted cell types,
   may contribute to the poorer clinical outcomes observed in this group.

Identification and validation of UCEC-related drugs based on DEGs from
miR-330 high samples

   We utilized the differentially expressed genes (DEGs) from miR-330 high
   samples to screen for candidate drugs associated with UCEC. A total of
   twenty candidate drugs were identified, several of which were
   hormone-related (Table [106]1). Notably, testosterone was correlated
   with the up-regulated genes, while two phytoestrogens, enterolactone
   and coumestrol, were also associated with the up-regulated gene set.
   Additionally, hormone-related drugs such as calcitriol, retinoic acid,
   progesterone, and estradiol were identified. These findings highlight
   the potential involvement of aberrant hormone signaling pathways in the
   progression of UCEC.

Table 1.

   Identification of candidate drugs based on differential gene expression
   in high miR-330 samples
   Name Adjusted P Odds ratio Combined score
   Up-regulated genes Testosterone 4.1e-104 8.7 2143.7
   Enterolactone 5.01e-91 8.9 1925.7
   Coumestrol 3.0e-89 6.4 1357.9
   Lucanthone 9.3e-89 29.3 6143.0
   Dasatinib 1.1e-84 12.8 2572.7
   Calcitriol Dihydroxyvitamin D3 1.5e-76 5.5 1007.2
   Resveratrol 3.0e-74 5.9 1040.6
   Troglitazone 3.3e-59 8.0 1138.5
   Etoposide 3.6e-57 316.0 42936.6
   Phytoestrogens 5.2e-40 85.1 8194.2
   Down-regulated genes Valproic acid 1.6e-24 2.5 160.8
   Trichostatin A 2.1e-14 2.2 87.7
   Retinoic acid 1.2e-13 2.1 78.4
   Progesterone 1.9e-11 2.4 75.9
   Vitamin E 1.6e-7 2.2 49.7
   Tert-Butyl hydroperoxide 1.6e-7 2.3 50.9
   Estradiol 0.0000023 1.7 32.6
   Methaneseleninic acid 0.0000023 3.3 63.7
   Decitabine 0.0000053 2.0 36.0
   Cytarabine 0.0000088 2.7 48.1
   [107]Open in a new tab

   To validate the associations of the candidate drugs, experimental data
   from the Ishikawa cell line treated with six selected drugs was
   analyzed. Our findings revealed that testosterone down-regulated
   estrogen 16-alpha-hydroxylase activity while up-regulating cholesterol
   metabolism. Estradiol was observed to down-regulate extracellular
   exosome production while up-regulating developmental processes. These
   effects suggest potential adverse roles of testosterone and estradiol
   in UCEC progression. In addition, troglitazone was found to
   down-regulate artery development while up-regulating cholesterol
   metabolism. Valproic acid down-regulated nucleoplasm activity and
   up-regulated cell junction formation. Retinoic acid down-regulated cell
   migration while up-regulating programmed cell death, indicating its
   potential as an anti-tumor agent. Lastly, progesterone down-regulated
   the cell cycle while up-regulating primary metabolism (Fig. [108]8).
   These experimental data suggest that these drugs could modulate
   critical biological pathways in UCEC, potentially impacting disease
   progression and providing insights into therapeutic strategies.

Fig. 8.

   [109]Fig. 8
   [110]Open in a new tab

   Impact of six candidate drugs on gene expression in the Ishikawa cell
   line of endometrial carcinoma. The bars represent the number of
   down-regulated and up-regulated genes following 6-h drug treatment.
   Functional enrichment analysis terms are annotated around the bars,
   with numbers in parentheses indicating the statistical significance of
   the enrichment

Discussion

   In this study, we performed an integrative analysis to investigate the
   role of miRNAs in linking PCOS with UCEC, identifying several miRNAs as
   potential biomarkers and therapeutic targets. Our analysis revealed 15
   differentially expressed miRNAs in PCOS, many of which were also
   associated with UCEC prognosis. Notably, up-regulated miRNAs were
   generally linked to poorer survival outcomes, while down-regulated
   miRNAs were protective. For instance, miR-331 and miR-424 are involved
   in processes like metabolic reprogramming and hormonal regulation,
   which may drive cancer progression by influencing pathways such as
   estrogen signaling and cellular proliferation [[111]27, [112]28].
   MiR-1246 was identified as a signature that could discriminate
   high-grade serous ovarian cancer patients with high accuracy [[113]29].
   The ceRNA networks MALAT1/miR-203a/TGFβR2 and
   [114]AK128202/miR-483-5p/GOT2 are implicated in regulating
   proliferation, angiogenesis, and insulin resistance in PCOS. As such,
   they have been proposed as potential biomarkers and therapeutic targets
   for PCOS [[115]30]. MiR-330, known for promoting tumorigenesis in
   various cancers, was highly expressed in PCOS and linked to adverse
   outcomes in UCEC, suggesting its role in connecting the two conditions
   [[116]31]. MiR-330 is highly expressed in both cancer tissues and serum
   of breast cancer patients, and it can enhance axillary lymph node
   metastasis, a significant factor influencing breast cancer prognosis
   [[117]32]. MiR-330 is significantly up-regulated in gestational
   diabetes mellitus (GDM) patients and is a potential biomarker for
   glucose homeostasis and pregnancy outcomes in GDM [[118]33]. These
   results underscore the potential of these miRNAs as prognostic markers
   for UCEC.

   Gene co-expression analysis highlighted key interactions between
   miR-330 and genes like SFT2D3 and E2F1, while miR-424 was associated
   with PHF8 and LCOR. Interestingly, we used miWalk to confirm the
   associations of these miR-target gene pairs (data not shown) [[119]34].
   Functional enrichment indicated that up-regulated genes in miR-330-high
   samples were involved in nuclear division and cell cycle regulation,
   while TGF-β signaling was more active in miR-424-high samples. AURKA is
   a vital gene in the cell cycle and TGF-β signaling, which may induce an
   inflammatory response during PCOS progression [[120]35]. These results
   underscore the importance of miRNA regulation in processes like cell
   division, extracellular matrix organization, and TGF-β signaling, all
   critical in cancer progression [[121]36, [122]37].

   We also found significant immune cell composition changes linked to
   miR-330 expression, with pro-inflammatory and exhausted immune subsets
   being up-regulated in high miR-330 samples, indicating an unfavorable
   immune microenvironment in UCEC. These findings suggest that miR-330
   may shape the immune landscape in a way that worsens prognosis.

   Our drug screening identified hormone-related agents such as
   testosterone, estradiol, and progesterone as influential in
   miR-330-related pathways. Interestingly, estradiol and progesterone
   have been identified as potential therapeutic drugs for PCOS [[123]38].
   Testosterone was found to up-regulate cholesterol metabolism while
   down-regulating estrogen 16-alpha-hydroxylase activity, reflecting its
   complex role in UCEC. The up-regulation of cholesterol metabolism may
   indicate a metabolic reprogramming in UCEC cells that could be
   detrimental to disease progression. Progesterone, a hormone frequently
   used in UCEC treatment, was observed to down-regulate the cell cycle
   while up-regulating primary metabolism. These effects are consistent
   with progesterone’s known ability to inhibit proliferation and promote
   metabolic stability in cancer cells [[124]39]. These findings align
   with previous research indicating that hormonal imbalances play a
   crucial role in UCEC development and progression [[125]40–[126]43].

   In addition to hormone-related agents, other drugs identified in our
   screening also showed significant potential in modulating key pathways
   in UCEC. Troglitazone, known for its impact on metabolic pathways
   [[127]13], was found to down-regulate artery development while
   up-regulating cholesterol metabolism. This dual action suggests that
   troglitazone may influence both tumor vascularization and lipid
   management, processes critical to tumor growth and survival. Valproic
   acid, an HDAC inhibitor [[128]43], demonstrated the ability to
   down-regulate nucleoplasm components, potentially suppressing gene
   expression or cell division, while simultaneously up-regulating cell
   junction integrity, which may enhance cellular adhesion and slow cancer
   cell dissemination. Retinoic acid, a modulator of cellular
   differentiation [[129]42], down-regulated cell migration while
   promoting programmed cell death, positioning it as a promising
   candidate for UCEC therapy by inhibiting tumor spread and encouraging
   apoptosis. Together, these findings highlight the diverse mechanisms by
   which non-hormonal drugs can influence UCEC progression and offer
   potential therapeutic avenues beyond hormonal regulation.

   In conclusion, this study, for the first time, provides valuable
   insights into the molecular link between PCOS and UCEC by identifying
   key miRNAs and their associated pathways. The role of miRNAs in immune
   modulation, hormonal signaling, and metabolic reprogramming offers new
   opportunities for therapeutic intervention. Future research should
   focus on validating these findings and developing targeted therapies to
   improve outcomes for patients with both PCOS and UCEC.

Limitations

   This study has several limitations. First, although the sample size in
   both PCOS and UCEC datasets was sufficient for exploratory analysis,
   larger cohorts may enhance the robustness of the findings. Second, the
   identified miRNA-gene interactions and potential therapeutic targets
   are based on computational predictions; therefore, further experimental
   validation, including qRT-PCR, Western blotting, miRNA knockdown
   models, and immune cell models in both clinical and in vitro settings,
   will be important to confirm their biological significance and
   elucidate the underlying mechanisms. Finally, our drug screening
   results are promising, but preclinical studies will be needed to
   substantiate their potential therapeutic applications.

Supplementary Information

   [130]12672_2025_1861_MOESM1_ESM.docx^ (320.6KB, docx)

   Additional file 1: Figure S1 Up-regulated genesSFRP4, APOD, CCL21,
   TWIST2, PAMR1, ECM1, CD248, and WNT2were all correlated with longer
   overall survival in UCEC samples with high miR-424 expression. Figure
   S2 Up-regulated genes AURKA, NCAPG, YARS, RRM2, AUNIP, and MTFR2 were
   linked to shorter overall survival. Down-regulated genesWBP1L, WFS1,
   and AXIN2were associated with longer overall survival in UCEC samples
   with high miR-330 expression.

Author contributions

   J.Y. Conceived and designed the study. X.L. and L.W. performed the
   experiments. P.L. performed the data statistical analysis. L.Y.
   prepared the final figures. X.L. wrote the paper. All authors read and
   approved the final manuscript.

Funding

   The authors received no specific funding for this work.

Data availability

   The datasets used in this study are available from the GEO and TCGA
   databases.

Declarations

Ethics approval and consent to participate

   Not applicable.

Competing interests

   The authors declare no competing interests.

Footnotes

   Publisher's Note

   Springer Nature remains neutral with regard to jurisdictional claims in
   published maps and institutional affiliations.

   Xuedan Lai and Ling Wu contributed equally to this work.

References