Identification and Validation of Key m7G-Related Genes and Cuproptosis-Related Genes Related to Immunity in Polycystic Ovary Syndrome (PCOS) by Comprehensive Bioinformatics Analysis

doi:10.21203/rs.3.rs-2222217/v1

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Identification and Validation of Key m7G-Related Genes and Cuproptosis-Related Genes Related to Immunity in Polycystic Ovary Syndrome (PCOS) by Comprehensive Bioinformatics Analysis

https://doi.org/10.21203/rs.3.rs-2222217/v1

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Background

Polycystic ovary syndrome (PCOS) is a complex endocrine disease that is one of the most common disease of reproductive age and is one of the important factors leading to infertility in young women, but the potential molecular mechanism remains unclear. In this study, we identified previously unrecognized m⁷G-related and cuproptosis-related genes that are significantly correlated with PCOS via bioinformatics.

Materials and methods

Three transcriptome profiles of granulosa cells of PCOS sample and normal samples were obtained from online databases. Comprehensive bioinformatic analyses, such as differential expression analysis, functional and pathway enrichment analysis, immune infiltration analysis, were utilized.

Results

Three PCOS datasets (GSE34526, GSE80432, and GSE137684) from the Gene Expression Omnibus (GEO) database were integrated and the background correction and normalization were performed by R language. The corrected gene cuproptosis-related genes and m⁷G-related genes respectively to obtain intersection genes. There were 12 intersection genes relate to cuproptosis-related genes and 21 intersection genes relate to m⁷G-related genes in comparison between PCOS group and normal group. After the correction gene crossed with the immune infiltrating gene, it crossed with 12 intersection genes relate to cuproptosis-related genes and 21 intersection genes relate to m⁷G-related genes respectively. Ultimately, it was identified that 8 genes related to cuproptosis are related to immune infiltration (CIGs), among which DLAT was the most significant (P = 0.049); 5 m⁷G related genes are associated with immune infiltration (MIGs), among which NUDT16 was the most significant (P = 0.0246). There were significant differences in clinicopathological characteristics between the PCOS group and the normal group by ROC curve analysis.

Conclusions

This study addresses the information gap in PCOS research about the interaction between immunology, m⁷G, and copper metabolism. According to the PCOS prediction model, the genes m⁷G and those associated to copper metabolism are crucial in the onset and progression of the condition. This study has further explored the microenvironment of ovarian granulosa cell of PCOS development and immune factor related genes. This work will support future research.

Polycystic Ovary Syndrome (PCOS)

m7G methylation

Cuproptosis

Immune Infiltration

Characteristics

Biomarkers

Bioinformatics

Among women of reproductive age, polycystic ovarian syndrome (PCOS), one of the most prevalent endocrine illnesses, affects 5–10% of them. Obesity, hirsutism, infrequent ovulation or anovulation, and polycystic ovarian changes are the typical clinical characteristics of PCOS. Insulin resistance, dyslipidemia, and obesity are frequently coexisting conditions, and there is a high risk of developing cardiovascular disease and metabolic sequelae (such as diabetes and metabolic syndrome) [1]. Although the condition is quite prevalent in clinical practice, little is known about its origin and pathophysiology processes. Consistent evidence suggests that PCOS may be a complicated polygenic illness with significant environmental and epigenetic influences [2]. Therefore, identifying new disease biomarkers has significant therapeutic ramifications for treating PCOS and enhancing patient outcomes.

Chronic low-grade inflammatory conditions are essential to the pathophysiology of PCOS, and in particular, the local ovarian tissue's inflammatory response is believed to be crucial to the onset and progression of PCOS[3]. The mechanism of PCOS immune escape can be clarified and new diagnostics and treatment approaches can be more easily developed with the help of a systematic examination of the association between hub genes and PCOS immune infiltration.

N7 methyladenosine, also known as m⁷G RNA methylation, is a modification in which a methyl group is added to the RNA guanine (Gseventh )'s position N by a methyltransferase [4]. One of the most frequent base modification types in post-transcriptional control, m⁷G modification is also the most often applied to tRNA modification, which aids in translation by supplying amino acids to ribosomes in accordance with particular mRNA codons. [5]. As a cofactor in vital enzymes, copper is crucial to human life processes. Recent research has revealed that mitochondrial respiration is necessary for cuproptosis. In this process, copper binds directly to the lipogenic part of the tricarboxylic acid cycle, which causes tagged proteins to aggregate and iron-sulfur cluster proteins to disappear, ultimately causing proteotoxic stress and cell death. Copper homeostasis is closely related to immune infiltration, cell death (apoptosis and autophagy), various mechanisms (active oxygen accumulation, proteasome inhibition and anti angiogenesis), etc. Unbalanced copper concentrations can cause irreversible damage to cells and mechanism activity[6]. As a cofactor in vital enzymes, copper is crucial to human life processes. Recent research has revealed that mitochondrial respiration is necessary for cuproptosis. This process involves the direct binding of copper to the lipogenic portion of the tricarboxylic acid cycle, which results in the accumulation of tagged proteins and the disappearance of iron-sulfur cluster proteins, leading to proteotoxic stress and cell death. Immune infiltration, cell death (apoptosis and autophagy), and numerous processes (active oxygen buildup, proteasome inhibition, anti angiogenesis, etc.) are all strongly tied to copper homeostasis. Unbalanced copper concentrations can cause irreversible damage to cells and mechanism activity[6]. According to certain studies, some differently expressed genes in PCOS granule cells are linked to oxidative stress and metabolic problems[7]. The gene CHRDL1 was found potentially responsible for the obesity status of PCOS patients[8]. It has been identified that ferroptosis and apoptosis are strongly connected in PCOS granule cells[9]. Granulosa cells in PCOS underwent luteinization due to low methylation FoxO3 mRNA[10]. In conclusion, the alteration of the granulosa cell gene, the epigenetic modification of mRNA, and the metabolism of metal ions are all strongly associated to the development of PCOS.

According to recent research, m⁷G and cuproptosis control transcription as well as cell development and apoptosis. There has not been any research on the mechanism of genes and m⁷G genes in PCOS that is bioinformatics-based. In order to evaluate the genes expressed in the granulosa cells of PCOS patients and healthy individuals, we used data mining and data analysis approaches. These genes were then intersected with the immune cell genes dataset to obtain immune DEGs, and then intersected with the curoptosis genes and the m⁷G genes respectively. We investigated at significant biomarkers and established the molecular etiology of PCOS. Our results will contribute to a better understanding of the cuproptosis and m⁷G epigenetic status in PCOS, as well as new approaches to the clinical diagnosis and management of PCOS. Our results will help to understand the cuproptosis and m⁷G epigenetic status of PCOS, and provide new ideas for the clinical diagnosis and treatment of PCOS.

2.1 Data extraction

The original datasets comparing the gene expression profiles of the granulosa cells between PCOS patients and normal controls were downloaded from NCBI GEO databases (Table 1). The accession number was GSE34526(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE34526), GSE80432 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE80432), GSE137684(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE137684) respectively. The microarray data of GSE34526 was based on GPL570 ([HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array), GSE80432 were based on GPL6244 and GSE137684 ([HuGene-1_0-st] Affymetrix Human Gene 1.0 ST Array [transcript (gene) version]) were based on GPL17077 (Agilent-039494 SurePrint G3 Human GE v2 8x60K Microarray 039381). The chip contains 23 PCOS patients and 15 healthy individuals. This study will examine the mRNA in ovarian granulosa cells from PCOS patients and healthy persons. The analytic workflow was shown in Figure 1.

Table 1: The basic backgrounds of the datasets included.

	Platforms	Country	Year	NP	NC
GSE34526	[HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array	China	2011	7	3
GSE80432	[HuGene-1_0-st] Affymetrix Human Gene 1.0 ST Array	China	2021	8	8
GSE137684	Agilent-039494 SurePrint G3 Human GE v2 8x60K Microarray 039381	China	2019	8	4

1. Number of PCOS Sample(NP);2. Number of Control Sample(NC)

2.2 Data pre-processing

The three raw datasets were pre-processed by “limma”, “sva” in R, including merge, background calibration, normalization.

2.3 Immune Infiltration Analysis

We investigated for a correlation between the hub gene expression and immune infiltration in PCOS. By ssGSEA in R, the infiltration of 29 immune cells that were obtained by GSEA download (https://www.gsea-msigdb.org/gsea/index.jsp)was analysed, with the ssGSEA score set as the standard and visualized using heat map generated by the “pheatmap” package. The “corrplot” package was employed to reveal the correlation among immune cells and immune cell functions regulators. The immune cells and immune cell functions between PCOS and normal groups were visualized by “ggpubr” package. Analyze the original data and take p<0.05 and | logFC |＞0.5 as the screening conditions

2.3 Bioinformatics Analysis of curoptosis and m7G

We found 29 m⁷G-related genes and 13 curoptosis-related genes from previous systematic reviews [6, 11, 12]. The correlation between immune related gene matrix, curoptosis genes matrix and m⁷G related gene matrix was analyzed by "ggcorrplot", "ROCR" package, and a clinical prediction model was constructed and drew a correlation heat map.

2.4 Functional Enrichment Analyses

Since the number of key genes we obtained is small, we chose to conduct online analysis of KEGG and GO Enrichr database (https://maayanlab.cloud/Enrichr/). The difference was statistically significant at p < 0.05.

2.5 Experimental Validation

The Chengdu Xinan Women's Hospital provided the follicular fluid for this validation test, and three patients with polycystic ovary syndrome and three healthy volunteers signed an informed consent form. By centrifuging granular cells from the follicular fluid, quantitative real-time PCR (qRT-PCR) was utilized to confirm the expression of two sets of important genes.

2.5.1 Inclusion Criteria

1. The PCOS group met the Rotterdam diagnostic criteria[13]: ①. Rare ovulation and / or no ovulation;②. There is clinical and / or biochemical evidence of androgen excess; ③. Ultrasound showed polycystic ovary [12 or more follicles with a diameter of 2-9mm and/or ovarian volume increased (>10mL) in one ovary; calculation formula: 0.5 × high × wide × Thick)]. (2 of the 3 standards need to be satisfied).

2. In the normal group, patients with blocked fallopian tubes and normal ovarian function were selected.

3. Patients aged 20-35 years who need artificial assisted reproductive technology

(ART).

4. Patients using an antagonist regimen during the stimulation cycle.

2.5.2 Exclusion Criteria

1. The patients at the post refused to sign consent forms;

2. Patients with cardiovascular, immune, nervous system and other diseases;

3. Patients with ovarian cancer, ovarian cysts, and other ovarian disorders.

2.5.3 Granular Cells (GCs) Extraction

At the Chengdu Southwest Gynecological Hospital, follicular fluid from patients undergoing controlled ovulation induction was collected and centrifuged at 1500 rpm for five minutes. Follicular fluid makes up the supernatant, and GCs, red blood cells, and white blood cells make up the precipitate. Generating and mixing the precipitate with a tiny amount of follicular fluid is followed by centrifuging it at 3000 rpm for 10 minutes, absorbing the upper white layer as GCS after centrifugation, and then washing it twice with PBS.

2.5.4 Quantitative Real-Time PCR

In preparation for further analysis, the GCs from PCOS and normal individuals were extracted, added to RNA protection solution, and then frozen at -80°C. The total RNA was extracted by the TRIzol method. All reagents are purchased from Beijing Baori Medical Biotechnology Co., Ltd. TRIzol reagent was used to extract the total RNA from granulosa cells. Total RNA was reverse transcribed into cDNA using a template of 1μG. 40 °C for 60 min, 25 °C for 5 min, and 75 °C for 5 min were the reaction temperatures. GAPDH was utilized as an internal control after 1 L of cDNA was setup using an RTFQ-PCR system, and the RT-PCR amplification settings were set to 95 °C for 3 minutes, 40 cycles at 95 °C for 5s, and 60 °C for 32s. The primers used are as follows: NUDT16 (forward 5′- TCCCAATTTCCTCTTCTCCCAAAGC -3′, reverse 5′- GATCACGATCCCAACTCCACCAAG -3′) and DATL (forward 5′- GGGTTATTGCACAGCGATTAAT -3′, reverse 5′- GAAGAATTTGCTTCGGGAACTT -3′). After amplification, the relative expression amount is 2- ΔΔ Ct method calculation.

2.6 Statistical analysis

All statistical analyses were performed in the R language (Version 4.1.3). All statistical tests were bilateral, and p< 0.05 was considered statistically significant.

3.1 The Corrected Genes and Immune Infiltration Analysis

With the help of the “limma”, “sva” R package, data from GSE34526, GSE80432 , GSE137684were merged to form a new sequence. Immune cells and immune cell function are associated with the corrected genes via the ssGSEA package, as shown in the heatmap. The results showed that CD⁺⁸, macrophages, neophills, T hepler cell, TIL and Treg were highly expressed in immune cells. In immune function, cyclicity activity, HLA, MHC class I, parinflumation and Type I IFN reply were more obvious (Fig 2A). The correlation analysis between immune cells and immune cells, immune function and immune function was obtained by "corrplot" in R. We found that all immune cells were positively correlated, suggesting that all immune cells had synergistic effects in the activation of immune cells, among which neurophils had the most significant correlation with Treg and TIL (cor>0.9). We found that all immune cells are positively correlated, suggesting that all immune cells have synergistic effects in the activation of immune cells, among which APC co inhibitions and CCR, check point, T cell co dependence of aggression, APC co stimulation and check point relevance, check point and CCR, inflammation promoting,T cell co inhibition，T cell co directional correlation of simulation, inflammation promoting and cytologic correlation of activities, CCR and T cell co stimulation，T cell co The correlation of stimulation is obvious (cor>0.9) (Fig 2B and Fig 2C). By comparing the expression of immune cells and immune function between the PCOS group and the normal group, it was found that the expression of macrophages in the PCOS group was significantly higher than that in the normal group, while the parianflammation in the PCOS group was significantly higher than that in the normal group, indicating that there was long-term low-grade inflammation in the PCOS group, which may be an important immunological mechanism leading to PCOS(Fig 2D and Fig 2E).

3.2 Correlation Analysis between Differential Genes and Immune Infiltration

Correlations between curoptosis-related genes and m⁷G-related genes and immune-related genes were analyzed in R using the ggcorrplot package. Finally, we found that 8 curoptosis-related genes were correlated with immune related genes(CIGs), and 5 m⁷G-related genes were correlated with immune related genes(MIGs) (p <0.05). SNUPN, NUDT16, GEMIN5 and DCPS are negatively correlated with immune cells and immune function, while EIF4E3 is positively correlated with immune cells and immune function in MIGs (Fig 3A). SLC31A1, PDHB, PDHA1, DLST, DLD, DLAT and DBT are negatively correlated with immune cells and immune function, while ATP7A is positively correlated with immune cells and immune function in CIGs (Fig 3B).

3.3 Development and Validation of the m7G-Related Genes and Curoptosis Related Genes Prognostic Signature

To further investigate the predictive value of these genes, we aimed to establish a prognostic signature for analog computation. Next, using the data from the above intersection genes, 5 MIGs (SNUPN, NUDT16, GEMIN5, DCPS and EIF4E3) and 8 CIGs (SLC31A1, PDHB, PDHA1, DLST, DLD, DLAT, DBT and ATP7A) were selected to form the signature. The median risk value of all genes was 1.5. Compared with the two groups, the genes above the median value were high expression, and the genes below the median value were low expression. PCOS group is considered as high risk, while normal group is considered as low risk. We found that in the prognostic signature constructed by MIGs, NUDT16 had significant difference between the PCOS group and the normal group (p=0.0242), and the OR of NUDT16 was 0.1538360, indicating that NUDT16 was low expression in PCOS and high expression in the normal group. In the prognostic signature constructed by CIGs, DLAT had significant difference between the PCOS group and the normal group (p=0.049), and the OR of DLAT was 0.07263146, indicating that DLAT was low expression in PCOS and high expression in the normal group (Table 2). The nomogram incorporated various prognostic genes to assess the prediction. The ROC curve demonstrated the predictive ability of MIGs and CIGs. The nomogram incorporated various prognostic genes to assess the pathological factors probability of an individual graphically. To further forecast the pathogenic factors and therapeutic target of PCOS patients, we structured a nomogram comprised of key genes. Nomography predicted the pathological factors of patients with PCOS(Fig. 4A-B). The C index of the nomograms respectively was 0.736 in prognostic signature of MIGs and 0.767 in prognostic signature of CIGs, which confirmed the favorable prediction ability of the nomograms. The calibration curve indicated that the practical pathological factors of the PCOS was in line with the predicted value(Fig. 4C-D). The area under the curve (AUC) was 0.767 in prognostic signature of CIGs and AUC was 0.736 in prognostic signature of MIGs, suggesting good prediction performances (Fig. 4E-F).

Table 2: The prognostic signature of Coefficients and OR

Gene ID	Coefficients(p)	OR
DCPSHigh	0.5327	0.545
EIF4E3High	0.6926	0.641
GEMIN5High	0.7685	1.290
NUDT16High	0.0242*	0.154
SNUPNHigh	0.8812	1.179
ATP7AHigh	0.5590	1.655
DBTHigh	0.4913	0.528
DLATHigh	0.0490*	0.073
DLDHigh	0.2987	4.266
DLSTHigh	0.3108	0.422
PDHA1High	0.2011	0.286
PDHBHigh	0.8167	0.803
SLC31A1High	0.0828	6.330

The m⁷G-immune-related genes were enriched in 46 biological processes (BP), 6 cellular components (CC), and 29 molecular functions (MF). Among GO-BP. There was significant enrichment of differential genes in the regulation of mRNA splicing, via spliceosome, spliceosomal snRNP assembly, nucleocytoplasmic transport, import into nucleus and ribonucleoprotein complex assembly. Significantly enriched GO-CC included P-body.The significantly enriched GO-MF included RNA cap binding, RNA 7-methylguanosine cap binding, m⁷G(5') pppN diphosphatase activity, pyrophosphatase activity, nucleotide phosphatase activity, acting on free nucleotides, U4 snRNA binding, mRNA binding, U1 snRNA binding, nucleoside-triphosphate diphosphatase activity,chloride ion binding and RNA binding. The cuproptosis-immune-related genes were enriched in 73 BP, 33 CC, and 15 MF. Among GO-BP, there was significant enrichment of differential genes in the regulation of ‘acetyl-CoA biosynthetic process from pyruvate’, ‘pyruvate metabolic process’, ‘acetyl-CoA biosynthetic process’, ‘monocarboxylic acid metabolic process’, ‘cellular amino acid catabolic process’, ‘copper ion transport’, ‘lysine catabolic process’, ‘lysine metabolic process’, ‘cellular copper ion homeostasis’, ‘copper ion homeostasis’, ‘aspartate family amino acid catabolic process’, ‘branched-chain amino acid catabolic process’, ‘branched-chain amino acid metabolic process’, ‘transition metal ion transport’ and ‘cellular transition metal ion homeostasis’. Significantly enriched GO-CC included ‘mitochondrial matrix’, ‘intracellular organelle lumen’, ‘oxoglutarate dehydrogenase complex’, ‘mitochondrial alpha-ketoglutarate dehydrogenase complex’, ‘melanosome membrane’, ‘pigment granule membrane’, ‘chitosome’, ‘trans-Golgi network transport vesicle’ and ‘melanosome’. The significantly enriched GO-MF included ‘oxidoreductase activity, acting on the aldehyde or oxo group of donors, disulfide as acceptor’, ‘transition metal ion transmembrane transporter activity’, ‘oxidoreductase activity, acting on a sulfur group of donors, NAD(P) as acceptor’, ‘cuprous ion binding’, ‘P-type ion transporter activity’, ‘acetyltransferase activity’, ‘copper ion binding’ and ‘flavin adenine dinucleotide binding’(Fig. 5A-B).

Enrichment analysis by KEGG showed that the MIGs were mainly enriched in the 'RNA degradation', 'RNA transport' and 'purine metabolism', The CIGs were mainly enriched in the 'Citrate cycle', 'Pyruvate metabolism', 'Glycolysis / Gluconeogenesis', 'Propanoate metabolism', 'Tryptophan metabolism', 'Valine, leucine and isoleucine degradation', 'Mineral absorption', 'Lysine degradation', 'Central carbon metabolism in cancer', 'Glucagon signaling pathway', 'HIF-1 signaling pathway', 'Diabetic cardiomyopathy', 'Glyoxylate and dicarboxylate metabolism' and 'Glycine, serine and threonine metabolism' (Fig. 5C-D). The KEGG pathway of CIGs is mostly enriched in amino acid, sugar metabolism and other pathways, indicating that these genes affect the development of PCOS by regulating nutrient metabolism. The pathway of purine metabolism is a common enrichment pathway of two cell clusters, which indicates that both MIGs and CIGs can regulate ribonucleic acid metabolic pathway and affect the process of PCOS disease.

Two genes were verified. They had high reliability and were ralated to purine metabolism, and DLAT was related to HIF-1 signaling that was essential for the body to respond to hypoxia concentration or hypoxia. Therefore, the development of PCOS disease is closely related to the metabolism of various substances, such as sugar, amino acid, ribonucleic acid, oxygen metabolism, etc. DLAT and NUDT16 were identified by ROCR as the genes with significant differences in the 13 genes. Then, the filtered biomarkers, including DLAT and NUDT16, were verified in PCOS samples using qRT-PCR.The results indicated that the expression levels of DLAT and NUDT16 were visibly lower in granule cells of PCOS patients than in the granule cells of nomral controls (Fig. 6).

The qRT-PCR results show that the expression levels of DLAT (p<0.05), NUDT16 (p<0.05) in PCOS group were significantly lower than that in control group.

The previous research further investigated the causes of PCOS and found the essential genes involved in curoptosis and m⁷G. 15 normal samples and 23 PCOS samples were included in the data we collected from the GSE34526, GSE80432, and GSE137684 studies. To acquire the distinct gene cluster with immune cells and immunological function, the obtained gene cluster was intersected with genes relevant to immune infiltration. By fusing immune-associated genes with genes related to m⁷G and cuproptosis, two gene clusters, dubbed MIGs and CIGs, were produced. Afterward, create a clinical prediction model using MIGs and CIGs. We found 8 immune invasion genes related to cuproptosis and 5 m⁷G related immune invasion genes. The two gene clusters were used to build a clinical PCOS prediction model and demonstrate the validity of the bioinformatics study. Finally, we discovered that the most significant expression difference was between the PCOS group and the normal group.

The effects of PCOS on women's health are not limited to the childbearing age, but are felt throughout their lives. PCOS's particular etiology and pathophysiology are still poorly understood. It is believed to have some genetic roots due to its familial clustering. Recent research suggests that PCOS may be a polygenic genetic illness, and that some genetic variables (gene variation, epigenetic alterations, etc.) may combine with environmental factors to cause PCOS (socio-economic, diet, lifestyle, etc.) [14, 15]. Visceral obesity, insulin resistance, and hyperandrogenism intersect in the disease, resulting in a vicious cycle that progresses and makes it challenging to determine the etiology[16]. According to certain studies, immune regulatory molecules are essential for maintaining metabolic homeostasis and controlling immunological reactions during PCOS[17]. As a result of inflammatory microenvironmental changes in the follicular fluid of PCOS patients, inflammatory pathways are activated in the GCs, and the ovaries are in a chronically inflammatory state for an extended period of time. This chronic inflammatory infiltration is a significant contributor to the development of adverse symptoms in patients[18]. Our research demonstrates that immune activity, as measured by immune cells, macrophages, and parainflammation, differs significantly between the PCOS group and the normal group. According to several research, PCOS is characterized by hyperandrogenism, obesity, hyperinsulinemia, and inflammation[19, 20]. This suggests that the differentially screened genes are indicators of the inflammatory response associated with PCOS, have a substantial impact on macrophage expression, and influence PCOS development by modifying the parainflammatory response.

The concentration of copper ions in cells causes cell death, as was recently revealed. This indicates that copper transporters as well as copper ions play a role in cell death[6].Copper ions are an indispensable element in the growth and development of the body. When copper ions exceed the threshold, toxicity is produced and cell death is induced[21–23]. Genetic variation in copper homeostasis results in life-threatening disease[24, 25]. There is still insufficient research on the effects of copper death on PCOS, so this study intersect cuproptosis -related genes with immune infiltration genes and build a predictive model of PCOS pathogenic factors to see the effects of the associated genes on PCOS. We obtained 8 target genes related to PCOS development, including SLC31A1, PDHB, PDHA1, DLST, DLD, DLAT, DBT and ATP7A. The clinical prediction model constructed by these genes is reliable. Enrichment analysis showed that these genes were significantly enriched in the HIF signaling pathway. The expression of DLAT is most closely related to the development of PCOS disease in the constructed clinical model. DLAT is a gene related to glycolysis, which can promote glycolysis[26]. It was found that there was obvious hypoxia reaction in granular cells of PCOS patients, and the HIF signal pathway was inhibited[27]. HIF signaling pathway is a pathway that hypoxia response and glucose metabolism and plays a key role in the development of mammalian ovarian follicular development and ovulation [28, 29]. The DLAT in the PCOS group was lower than in the normal group, so we hypothesize that DLAT may influence the metabolism of granulosa cells by mediating the HIF signaling pathway, thereby promoting PCOS development.

As a result of the continuous advancement of gene sequencing technology, more than 160 RNA modification mechanisms have currently been identified. N7-methylguanosine (m⁷G), 5-hydroxymethylcytosine (hm5C), 5-methylcytosine (m⁵C), N6-methyladenosine (m⁶A), and other modifications are among the most prevalent RNA modifications[30]. METTL1 exhibits oncogenic activity in hepatocellular carcinoma[31]. It was found that overexpression of METTL1 may inhibit the proliferation, invasion and migration of colon cancer cells and promote cell death. Over-expression of METTL1 inhibits the proliferation, invasion and migration of CC cells and promotes cell death. METTL1 inhibits the proliferation and differentiation of tumor cells by downstream activation of the m⁷G-regulated − 7e miRNA/HMGA2 axis[23, 32]. These results indicate that METTLI plays an important role in the regulation of cell proliferation depending on m⁷G. NUDT16 regulates the level of capped RNA that flavin adenine dinucleotide (FAD) that plays a role in hydrogen transfer in biological oxidation system[33, 34]. NUDT16 does also cause DNA strand breakage, leading to apoptosis[35, 36]. NUDT16 was found to be the most significant pathogenic factor in clinical prediction of m⁷G-related gene constructions. In comparison between the normal and PCOS groups, NUDT16 was significantly reduced. Enrichment analysis shows that all genes from which predictive models are constructed are included in the RNA and RNA modification pathways. This means that in PCOS patients, a lack of NUDT16 may lead to abnormal biological oxidation systems and DNA damage in granulosa cells. Experimentally validated, we found that NUDT16 and DLAT in GCs in PCOS patients were significantly lower than in normal patients. Taken together, these results suggest that NUDT16 and DLAT might regulate m⁷A methylation and cuproptosis to promote PCOS concurrence, development, and progression. Hormone replacement medication and lifestyle changes are the mainstays of PCOS treatment currently, however their clinical efficacy falls short of expectations. The majority of PCOS patients have fertility needs and are in desperate need of safe and efficient treatment choices. A number of clinical studies have revealed that acupuncture is quite beneficial in the treatment of PCOS and is a safe, effective, and non-toxic alternative therapy[37, 38]. Treatment with acupuncture for PCOS has been demonstrated to alter the progression of the condition by controlling adipose tissue gene expression, insulin resistance, granulocyte apoptosis, and autophagy[39–42]. The causes of PCOS are complicated and varied. Researching the major contributing variables that cause PCOS at various levels and with various aims not only advances our understanding of the condition, but also gives us fresh ideas for future studies on alternative remedies, such as acupuncture.

We examined 13 genes associated with patient prognosis and used them to create a risk score model based on the link between immune infiltration of polycystic ovary syndrome and m⁷G and goblet disease. The two that have had the most recent effects on the prediction model are DLAT and NUDT16, which offer fresh perspectives and approaches for assessing the prognosis of polycystic ovarian syndrome.

Authors’ contributions

Z.Y.M. and L.L.Y. performed the literature research, collated the data and drafted the initial manuscript. Y.H., Z.X.Y. and Z.Y.M. provided statistical analysis method. H.J.H. completed qRT-PCR validation experiment. Y.J. revised the manuscript critically for important intellectual content. J.L.X. provided professional suggestions and resolved any problems and disagreements. All authors read and approved the final manuscript.

Funding

This study were supported by grants from the National Natural Science Foundation of China (NO. 82174517 and NO.82074549).

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Li Y, Chen C, Ma Y, Xiao J, Luo G, Li Y, Wu D: Multi-system reproductive metabolic disorder: significance for the pathogenesis and therapy of polycystic ovary syndrome (PCOS). Life Sciences 2019, 228:167-175.
Ruan X, Song J, Gu M, Wang L, Wang H, Mueck AO: Effect of Diane-35, alone or in combination with orlistat or metformin in Chinese polycystic ovary syndrome patients. Arch Gynecol Obstet 2018, 297(6):1557-1563.
Amaral GA, Alves JD, Honorio-França AC, Fagundes DL, Araujo GG, Lobato NS, Lima VV, Giachini FR: Interleukin 1-beta is Linked to Chronic Low-Grade Inflammation and Cardiovascular Risk Factors in Overweight Adolescents. Endocr Metab Immune Disord Drug Targets 2020, 20(6):887-894.
Luo Y, Yao Y, Wu P, Zi X, Sun N, He J: The potential role of N(7)-methylguanosine (m7G) in cancer. J Hematol Oncol 2022, 15(1):63.
Purta E, van Vliet F, Tricot C, De Bie LG, Feder M, Skowronek K, Droogmans L, Bujnicki JM: Sequence-structure-function relationships of a tRNA (m7G46) methyltransferase studied by homology modeling and site-directed mutagenesis. Proteins 2005, 59(3):482-488.
Tsvetkov P, Coy S, Petrova B, Dreishpoon M, Verma A, Abdusamad M, Rossen J, Joesch-Cohen L, Humeidi R, Spangler RD et al: Copper induces cell death by targeting lipoylated TCA cycle proteins. Science 2022, 375(6586):1254–1261
Kaur S, Archer KJ, Devi MG, Kriplani A, Strauss JF, 3rd, Singh R: Differential gene expression in granulosa cells from polycystic ovary syndrome patients with and without insulin resistance: identification of susceptibility gene sets through network analysis. J Clin Endocrinol Metab 2012, 97(10):E2016-2021.
Zhou Y, Zhou J, Xu X, Du F, Nie M, Hu L, Ma Y, Liu M, Yu S, Zhang J et al: Matrigel/Umbilical Cord-Derived Mesenchymal Stem Cells Promote Granulosa Cell Proliferation and Ovarian Vascularization in a Mouse Model of Premature Ovarian Failure. Stem Cells Dev 2021, 30(15):782-796.
Zhang D, Yi S, Cai B, Wang Z, Chen M, Zheng Z, Zhou C: Involvement of ferroptosis in the granulosa cells proliferation of PCOS through the circRHBG/miR-515/SLC7A11 axis. Ann Transl Med 2021, 9(16):1348.
Zhang S, Deng W, Liu Q, Wang P, Yang W, Ni W: Altered m(6) A modification is involved in up-regulated expression of FOXO3 in luteinized granulosa cells of non-obese polycystic ovary syndrome patients. J Cell Mol Med 2020, 24(20):11874-11882.
Tomikawa C: 7-Methylguanosine Modifications in Transfer RNA (tRNA). Int J Mol Sci 2018, 19(12).
Zhang LS, Liu C, Ma H, Dai Q, Sun HL, Luo G, Zhang Z, Zhang L, Hu L, Dong X et al: Transcriptome-wide Mapping of Internal N(7)-Methylguanosine Methylome in Mammalian mRNA. Mol Cell 2019, 74(6):1304-1316 e1308.
Rotterdam EA-SPcwg: Revised 2003 consensus on diagnostic criteria and long-term health risks related to polycystic ovary syndrome (PCOS). Hum Reprod 2004, 19(1):41-47.
Norman RJ, Dewailly D, Legro RS, Hickey TE: Polycystic ovary syndrome. The Lancet 2007, 370(9588):685-697.
Insenser M, Montes-Nieto R, Murri M, Escobar-Morreale HF: Proteomic and metabolomic approaches to the study of polycystic ovary syndrome. Mol Cell Endocrinol 2013, 370(1-2):65-77.
Mancini A, Bruno C, Vergani E, d'Abate C, Giacchi E, Silvestrini A: Oxidative Stress and Low-Grade Inflammation in Polycystic Ovary Syndrome: Controversies and New Insights. Int J Mol Sci 2021, 22(4).
Rostamtabar M, Esmaeilzadeh S, Tourani M, Rahmani A, Baee M, Shirafkan F, Saleki K, Mirzababayi SS, Ebrahimpour S, Nouri HR: Pathophysiological roles of chronic low-grade inflammation mediators in polycystic ovary syndrome. J Cell Physiol 2021, 236(2):824-838.
Liu Y, Liu H, Li Z, Fan H, Yan X, Liu X, Xuan J, Feng D, Wei X: The Release of Peripheral Immune Inflammatory Cytokines Promote an Inflammatory Cascade in PCOS Patients via Altering the Follicular Microenvironment. Front Immunol 2021, 12:685724.
Gonzalez F, Considine RV, Abdelhadi OA, Acton AJ: Inflammation Triggered by Saturated Fat Ingestion Is Linked to Insulin Resistance and Hyperandrogenism in Polycystic Ovary Syndrome. J Clin Endocrinol Metab 2020, 105(6).
Gonzalez F: Nutrient-Induced Inflammation in Polycystic Ovary Syndrome: Role in the Development of Metabolic Aberration and Ovarian Dysfunction. Semin Reprod Med 2015, 33(4):276-286.
Tsvetkov P, Detappe A, Cai K, Keys HR, Brune Z, Ying W, Prathapan Thiru4 MR, Kugener G, Rossen J, Kocak M et al: Mitochondrial metabolism promotes adaptation to proteotoxic stress. Nat Chem Biol 2019, 15(7):681–689.
Corsello SM, Nagari RT, Spangler RD, Rossen J, Kocak M, Bryan JG, Humeidi R, Peck D, Wu X, Tang AA et al: Discovering the anti-cancer potential of non-oncology drugs by systematic viability profiling. Nat Cancer 2020, 1(2):235-248.
Xu J, Zheng T, Hong W, Ye H, Hu C, Zheng Y: Mechanism for the Decision of Ovarian Surface Epithelial Stem Cells to Undergo Neo-Oogenesis or Ovarian Tumorigenesis. Cell Physiol Biochem 2018, 50(1):214-232.
Bandmann O, Weiss KH, Kaler SG: Copper Homeostasis and Neurodegenerative Disorders (Alzheimer’s, Prion, and Parkinson’s Diseases and Amyotrophic Lateral Sclerosis). Lancet Neurol 2015, 14(1):103–113.
Bandmann O, Weiss KH, Kaler SG: Wilson's disease and other neurological copper disorders. The Lancet Neurology 2015, 14(1):103-113.
Chen Q, Wang Y, Yang L, Sun L, Wen Y, Huang Y, Gao K, Yang W, Bai F, Ling L et al: PM2.5 promotes NSCLC carcinogenesis through translationally and transcriptionally activating DLAT-mediated glycolysis reprograming. Journal of Experimental & Clinical Cancer Research 2022, 41(1).
Zou J, Li Y, Liao N, Liu J, Zhang Q, Luo M, Xiao J, Chen Y, Wang M, Chen K et al: Identification of key genes associated with polycystic ovary syndrome (PCOS) and ovarian cancer using an integrated bioinformatics analysis. J Ovarian Res 2022, 15(1):30.
Nagao A, Kobayashi M, Koyasu S, Chow CCT, Harada H: HIF-1-Dependent Reprogramming of Glucose Metabolic Pathway of Cancer Cells and Its Therapeutic Significance. Int J Mol Sci 2019, 20(2).
Balamurugan K: HIF-1 at the crossroads of hypoxia, inflammation, and cancer. Int J Cancer 2016, 138(5):1058-1066.
Rong D, Sun G, Wu F, Cheng Y, Sun G, Jiang W, Li X, Zhong Y, Wu L, Zhang C et al: Epigenetics: Roles and therapeutic implications of non-coding RNA modifications in human cancers. Mol Ther Nucleic Acids 2021, 25:67-82.
Chiao MT, Cheng WY, Yang YC, Shen CC, Ko JL: Suberoylanilide hydroxamic acid (SAHA) causes tumor growth slowdown and triggers autophagy in glioblastoma stem cells. Autophagy 2013, 9(10):1509-1526.
Liu Y, Yang C, Zhao Y, Chi Q, Wang Z, Sun B: Overexpressed methyltransferase-like 1 (METTL1) increased chemosensitivity of colon cancer cells to cisplatin by regulating miR149-3p/S100A4/p53 axis. Aging (Albany NY) 2019, 11(24):12328–12344.
Zhang F, Lou L, Peng B, Song X, Reizes O, Almasan A, Gong Z: Nudix Hydrolase NUDT16 Regulates 53BP1 Protein by Reversing 53BP1 ADP-Ribosylation. Cancer Res 2020, 80(5):999-1010.
Sharma S, Grudzien-Nogalska E, Hamilton K, Jiao X, Yang J, Tong L, Kiledjian M: Mammalian Nudix proteins cleave nucleotide metabolite caps on RNAs. Nucleic Acids Res 2020, 48(12):6788-6798.
Peng S, Guo P, Lin X, An Y, Sze KH, Lau MHY, Chen ZS, Wang Q, Li W, Sun JK et al: CAG RNAs induce DNA damage and apoptosis by silencing NUDT16 expression in polyglutamine degeneration. Proc Natl Acad Sci U S A 2021, 118(19).
Iyama T, Abolhassani N, Tsuchimoto D, Nonaka M, Nakabeppu Y: NUDT16 is a (deoxy)inosine diphosphatase, and its deficiency induces accumulation of single-strand breaks in nuclear DNA and growth arrest. Nucleic Acids Res 2010, 38(14):4834-4843.
Pundir J, Charles D, Sabatini L, Hiam D, Jitpiriyaroj S, Teede H, Coomarasamy A, Moran L, Thangaratinam S: Overview of systematic reviews of non-pharmacological interventions in women with polycystic ovary syndrome. Hum Reprod Update 2019, 25(2):243-256.
Lim CED, Ng RWC, Cheng NCL, Zhang GS, Chen H: Acupuncture for polycystic ovarian syndrome. Cochrane Database Syst Rev 2019, 7:CD007689.
Kokosar M, Benrick A, Perfilyev A, Nilsson E, Kallman T, Ohlsson C, Ling C, Stener-Victorin E: A Single Bout of Electroacupuncture Remodels Epigenetic and Transcriptional Changes in Adipose Tissue in Polycystic Ovary Syndrome. Sci Rep 2018, 8(1):1878.
Manni L, Lundeberg T, Holmang A, Aloe L, Stener-Victorin E: Effect of electro-acupuncture on ovarian expression of alpha (1)- and beta (2)-adrenoceptors, and p75 neurotrophin receptors in rats with steroid-induced polycystic ovaries. Reprod Biol Endocrinol 2005, 3:21.
J C, Y Z, X Y, Y W, H H, M W: Anti-polycystic ovary syndrome effect of electroacupuncture: IMD inhibits ER stress-mediated apoptosis and autophagy in granulosa cells. Biochem Biophys Res Commun 2022, 634:159-167.
Gonzalez Garcia B, Garcia Vicente AM, Palomar Munoz A, Poblete Garcia VM, Jimenez Londono GA, Soriano Castrejon AM: Incidental pathologic extracardiac uptake of (99m)Tc-tetrofosmin in myocardial perfusion imaging: Importance of patient background evaluation. Rev Esp Med Nucl Imagen Mol 2015, 34(6):383-386.

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Identification and Validation of Key m7G-Related Genes and Cuproptosis-Related Genes Related to Immunity in Polycystic Ovary Syndrome (PCOS) by Comprehensive Bioinformatics Analysis

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1. Introduction

2. Methods

3. Results

4. Functional Enrichment And Durg Sensitivity Of Analysis

5. Potential Biomarker Expression By Qrt-pcr

6. Disscusion

7. Conclusion

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