Knowledge Graph Construction based on Granulosa Cells Transcriptome from polycystic ovary syndrome with Normoandrogen and Hyperandrogen

doi:10.21203/rs.3.rs-2752985/v1

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Knowledge Graph Construction based on Granulosa Cells Transcriptome from polycystic ovary syndrome with Normoandrogen and Hyperandrogen

https://doi.org/10.21203/rs.3.rs-2752985/v1

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PCOS is a widespread disease that primarily caused in-pregnancy in pregnant-age women. Normoandrogen (NA) and Hyperandrogen (HA) PCOS are distinct subtypes of PCOS, while bio-markers and expression patterns for NA PCOS and HA PCOS have not been disclosed. We performed microarray analysis on granusola cells from NA PCOS, HA PCOS and normal tissue from 12 individuals. Afterwards, microarray data were processed and specific genes for NA PCOS and HA PCOS were identified. Further functional analysis selected IL6R and CD274 as new NA PCOS functional markers, and meanwhile selected CASR as new HA PCOS functional marker. IL6R, CD274 and CASR were afterwards experimentally validated on mRNA and protein level. Subsequent causal relationship analysis based on Apriori Rules Algorithm and co-occurrence methods identified classification markers for NA PCOS and HA PCOS. According to classification markers, downloaded transcriptome datasets were merged with our microarray data. Based on merged data, causal knowledge graph was constructed for NA PCOS or HA PCOS and female infertility on NA PCOS and HA PCOS. Gene-drug interaction analysis was then performed and drugs for HA PCOS and NA PCOS were predicted. Our work was among the first to indicate the NA PCOS and HA PCOS functional and classification markers and using markers to construct knowledge graphs and afterwards predict drugs for NA PCOS and HA PCOS based on transcriptome data. Thus, our study possessed biological and clinical value on further understanding the inner mechanism on the difference between NA PCOS and HA PCOS.

Polycystic ovary syndrome

Hyperandrogenic polycystic ovary syndrome

Normoandrogenic polycystic ovary syndrome

knowledge graph

drug-gene interaction

Polycystic ovary syndrome (PCOS) is a widespread heterogeneous endocrine and metabolic disease affecting 7%-12% reproductive-age women globally affected by the applied criteria^{1, 2}. As defined by the well accepted diagnostic criteria Rotterdam Standard, patients with two of the three syndrome including hyperandrogenism, ovulatory dysfunction, and polycystic ovarian morphologic features on ultrasound were considered as PCOS³. However, it was also proposed that on the condition with the presence of both oligo- or anovulation and hyperandrogenism, ultrasound is not necessity for diagnosis. For adolescents, both oligo-anovulation and hyperandrogenism are required, while ultrasound was not recommended for diagnosis⁴. PCOS patients were normally suffered from cases such as obesity, dyslipidemia, insulin resistance, metabolic disorders, high risk of female infertility, hirsutism, cardiovascular disease and endometrial cancer⁵. However, since these traits were dramatically distinct and involved in several differentiated clinical phenotype, the research on etiology of PCOS is still inadequate.

Hyperandrogenemia is the core symptom of PCOS⁶ and abundant evidences prove that excessive androgen may be the critical factor in the pathogenesis of PCOS. The animal model of PCOS also confirmed that high androgen level caused polycystic ovarian morphologic features^7,8. However, since not all PCOS patients were hyperandrogenic physiologically and biochemically manifested⁹, hyperandrogenism alone was not abundant to explain the pathogenesis of patients with normal androgen levels. To elucidate the problem, PCOS is divided into two subtypes according to the presence or absence of hyperandrogen, which are normoandrogen PCOS (NA PCOS) and hyperandrogen PCOS (HA PCOS). NA PCOS patients express syndrome of ovulatory dysfunction accompanied with polycystic ovarian morphologic features and meanwhile HA PCOS patients express syndrome of hyperandrogenism, ovulatory dysfunction and polycystic ovarian morphologic features³. As syndromes of two subtypes of PCOS were greatly differentiated, it has been proposed the two subtypes might actually be different types of diseases^3,9. However, the research on the mechanism on the difference of HA PCOS and NA PCOS was rare and unreliable.

In order to distinguish the difference between NA and HA PCOS, it was necessary to ensure the most essential cell subtype for ovary. Ovary is composed of four major cell subtypes: theca cells, granulosa cells, cumulus cells and stromal cells. Androgen acts via androgen receptor (AR) in ovary to promote follicle growth and steroidogenesis. AR is expressed in oocytes, granulosa cells, and theca cells. AR is temporally regulated during follicular development. In former studies, AR was specifically knocked out in all four major ovarian cell subtypes, and the results disclosed that androgen played distinct roles in each cell subtype. Mouse knockout studies also showed that AR was expressed most critically in granulosa cells within the four cell subtypes for normal follicular development and subsequent ovulation^10–12 and thus might be significantly correlated to PCOS. Thus study on transcriptome of granulosa cells might be essential to discover the traits on NA PCOS and HA PCOS and further understand the pathogenesis of the two subtypes of PCOS .

Omics and multi-omics studies have been performed on PCOS transcriptome data to investigate the pathogenesis for PCOS and search PCOS biomarkers^13–15. While however, the microarray or RNA-seq analysis on NA PCOS and HA PCOS were rare in NCBI Gene Expression Omnibus (GEO) datasets or published papers, and comparative transcriptome study on NA PCOS and HA PCOS was not yet published. To fill-in this vacancy, we performed microarray analysis on granulosa cells from NA and HA PCOS patients and normal individuals (4 female subjects for each group) to further identify potential marker genes for NA PCOS and HA PCOS subtypes. Sample numbers were comparably limited due to the relative difficulty on acquiring PCOS samples. The microarray data were afterwards processed to identify differentiated and afterwards specific genes for NA PCOS and HA PCOS. Further functional analysis filtered the specific genes into list of potential marker genes for NA PCOS and HA PCOS. Deeper analysis finally identified NA PCOS marker IL6R and CD274, and meanwhile HA PCOS marker CASR. Subsequent RT-qPCR and Western blotting experiments validated the differential expression of these markers on NA PCOS and HA PCOS. Afterwards, we applied combined methods of Apriori Rules Algorithm^16,17 and co-occurrence analysis on downloaded PubMed PCOS abstracts and drug-gene interaction analysis to construct causal knowledge graph for HA PCOS and NA PCOS markers and female infertility. For our last step, drug-gene interaction analysis was performed to predict drugs for HA PCOS and NA PCOS.

Patient selection and ethical statement

All study participants were women at the Center of Reproductive Medicine, Shengjing Hospital of China Medical University (Shenyang, China). A diagnosis of PCOS was based on the Rotterdam criteria (2021). We enrolled 12 women in this study (Fig. 1e), 8 women with PCOS (PCOS group) and 4 body mass index- and age-matched healthy women (control group). The subjects’ characteristics are provided in Table S1. Among the 8 women with PCOS, we separated the patients into HA PCOS group and NA PCOS group according to the different level of testosterone, androstenedione, free androgen index and clinical signs of hyperandrogenism. All patients were with age between 22–35 years since birth. The study protocol was approved by the Research Ethics Committee of China Medical University. All participants provided signed informed consent.

Ovarian Stimulation, Granulosa Cell Collection And Rna Extraction

Control and PCOS patient ovaries were stimulated with recombinant follicle-stimulating hormone (FSH) (Merck-Serono, Switzerland) after pretreatment with gonadotropin-releasing hormone agonist (Diphereline, Ipsen Pharma Biotech, France). FSH stimulation was started when down-regulation was confirmed by vaginal ultrasonography and by measurements of serum estradiol and luteinizing hormone. Ultrasonography was used to evaluate follicular development from day 5 of stimulation until the day of follicular maturation. When at least 1 follicle grew to 18 mm in diameter, 250 µg of recombinant hCG (Ovidrel, Merck-Serono, Switzerland) was administered, and oocyte retrieval was performed 34–36 hours later. Follicle size was determined based upon the average follicular diameter, and follicular fluid samples from mature follicles (17–22 mm) and matched-immature follicles (8–13 mm) were stored separately. The granulosa cell samples were centrifuged at 12,000 g for 10 minutes to remove blood, and the remaining fluid was used in subsequent studies. PCOS was diagnosed according to the 2021 Rotterdam criteria (Rotterdam, 2021). The subjects’ characteristics are provided.The quality and integrity of RNA samples were analyzed using the Agilent 2100 Bioanalyzer (Agilent Technologies, Inc., Palo Alto, CA, USA)¹⁸.

Microarray Data Analysis

Each RNA sample was generated by Agilent SurePrint G3 Human GE 8 × 60 K Microarray kit (Agilent Technologies Canada Inc., Mississauga, ON) with a standardized protocol as described¹⁹. Microarray data were possessed applying Feature Extraction software (version 10.7.1.1) and analyzed using Genespring software (version 13.1) in reference genome matching and tpm calculation.Genes with mean tpm < 0.1 were removed. Normalization and Student’s t test were performed using R limma package (version 3.34.9). Student’s t test was calculated based on the expression value of the HA PCOS group, NA PCOS group and normal tissue sample group and then applied to identify HA PCOS and NA PCOS differentiated genes.

Further Analysis For Microarray Data

Pie plot was generated using R package plotrix²⁰ (version 3.7.4). Barplots were plotted by R ggplot2 package²¹ (version 3.1.0). Venn analysis was visualized by R package VennDiagram²² (version 1.6.20). Heatmaps were generated using R gplots package²³(version 3.0.1.1). Gene ontology (GO) analysis was performed according to DAVID database and GO figure was generated using R package ggplot2 (version 3.1.0). Colors for all figures were generated by R RColorBrewer package²⁴ (version 1.1.2).

Co-occurrence Analysis Using Pubmed

Co-occurrence calculation was realized by self-written R code. Co-occurrence was calculated by R RISmed package²⁵ (version 2.1.7) and our self-written R code. The input searching word was organized by the format “Term A and Term B”. The output co-occurrence number was then reflecting the strength of relationship between term A and term B²⁶. Term A and Term B might be genes or diseases or Medical Subject Headings (MeSH) terms.

Protein-protein Interaction Network Analysis

Protein-protein interaction network was calculated by STRING online database using human version of 9606.protein.links.v11.0 and the “Multi-protein” function^27,28. “+” was used on the situation on which the inputted proteins were not linked. “Experiment” and “Databases” filtering was used on the situation on which the links between inputted proteins were substantially high.

Deeper Gene Search Based On Na Pcos Gene Ontology Analysis

The Biological Process analysis from DAVID on expanded NA PCOS specific genes were firstly applied²⁹. The immunity terms and the terms correlated with immunity were manually picked and the genes on these terms with appearing frequency > 5 were regarded as potential marker genes. This process was afterwards repeated on the top 20 GO terms, all KEGG pathway terms and top 20 KEGG pathway terms. The genes satisfying the four conditions were regarded as potential marker genes. STRING ppi analysis (using Experimental and Database filtering) was applied on these potential marker genes to remove genes with no connection with other genes. Further filtering was applied by co-occurrence text-mining with the inputting format “potential marker gene name and PCOS” and the marker gene with co-occurrence number > 0 was reserved. Finally, a co-GO analysis was applied to select the Biological Process Gene Ontology terms which included at least two potential marker genes. The selected GO terms were applied with co-occurrence analysis using the inputting term “GO term and PCOS”. The GO term with the most co-occurrence number was reserved. The genes on this GO term were regarded as NA PCOS functional marker.

Deeper Gene Search Based On Ha Pcos Gene Ontology Analysis

The Biological Process analysis from DAVID on expanded HA PCOS specific genes were firstly applied. The terms highly correlated with glucose were manually picked and the genes on these terms with appearing frequency > 1 were regarded as potential marker genes. Further filtering was applied by co-occurrence text-mining with the inputting format “potential marker gene name and PCOS”. The gene with co-occurrence number > 0 was reserved as HA PCOS functional marker.

Classification Markers Identification And Data Integration

Genes were classified into HA PCOS, NA PCOS and normal (NM) groups based on mean highest expression on each group. 10–50 genes were randomly selected for each group. Then the mean rank of expression for HA PCOS, NA PCOS and NM group was used to divide samples into groups. This process was repeated for 100 million times and the calculated sample groupings identical with the actual sample grouping were reserved. Then our microarray data was merged with downloaded gene omnibus (GEO) transciptome datasets on PCOS and then normalized. The groupings with the highest matching ratio for downloaded datasets (with PCOS/NM grouping as reference) were reserved. The HA PCOS, NA PCOS and NM genes on the reserved grouping were regarded as classification markers.

Apriori Rule Analysis On Merged Transcriptome Data

The merged transcriptome expression table were independently discretized based on the expression level rank for each gene on different samples. The samples possessing less than two genes with expression > 0 were removed. On total, the discretization was performed for a 4-element quantile discretization (separated by 75%, 50% and 25% rank) and a 2-element quantile discretization (separated by 50% rank). Minimum discretization value was defined as the value less than 25% rank for 4-element discretization and 50% rank for 2-element discretization. Association rules calculation was performed independently for each discretization method. Each gene pair (gene A and gene B) from the gene co-expression filtering results was nominated as a rule and afterwards the confidence and support for each rule were calculated using the following equations:

$$\text{c}\text{o}\text{n}\text{f}\text{i}\text{d}\text{e}\text{n}\text{c}\text{e} \text{A}\to \text{B}=\frac{\text{n}(\text{A}=\text{B} \cap \text{A}>\text{m}\text{i}\text{n}(\text{A}) \cap \text{B}>\text{m}\text{i}\text{n}(\text{B}\left)\right)}{\text{n}(\text{A}>\text{m}\text{i}\text{n}(\text{A}\left)\right)}$$

$$\text{s}\text{u}\text{p}\text{p}\text{o}\text{r}\text{t} \text{A}\to \text{B}=\text{n}(\text{A}>\text{m}\text{i}\text{n}(\text{A}\left)\right)+\text{n}(\text{B}>\text{m}\text{i}\text{n}(\text{B}\left)\right)$$

n() means the number of the subject, for instance, n(A > min(A)) means number of A whose value is above minimum value of A, and also n(A = B) means number of paired A and B which satisfies A value equaling B value.$\cap$ means intersection for the left object and the right object.

Afterwards, confidence and support for the two different discretization methods were summed. Rules with summed confidence > 0.6 and summed support > 30 were reserved. The direction (arrow) showed potential causal relationship pointing from the potential driver gene (upstream, or cause) to the potential driven gene (downstream, or result).

Pubmed Abstracts Download And Preprocessing

All available PubMed abstracts from 2010.8.1-2022.8.1 were downloaded and processed by R pubMR package³⁰(version 0.0.0.2). The abstracts containing HA PCOS markers and HA PCOS differentiated genes were deemed as HA PCOS abstracts. The abstracts containing NA PCOS markers and NA PCOS differentiated genes were deemed as NA PCOS abstracts. The MeSH words with the sub major words containing “metabolism”, “chemical”, “genetics” etc. were extracted for each abstract. All possible combination of paired pmid and MeSH term/marker genes were recorded for the next step.

Apriori Rules Algorithm Analysis And Knowledge Graph Construction

The association rules were calculated on the paired pmid and MeSH term/marker gene table from the previous step. This step was respectively performed on NA PCOS and HA PCOS. Each two MeSH term/marker gene (A and B) were forming a pair and the confidence of the rule was calculated as the division of number of intersecting pmids for A and B by number of the pmids of the A. The support of the rule was calculated by adding number of pmids of A and B.The result showed the causal relationship pointing from A to B. This part was using the same equations as the previous described association rules equations for transcriptome data. The causal relationship for B pointing to A was also calculated. The rules with confidence > 0.1 and support > 0.001 were reserved. Afterwards, the Apriori Rules Method was applied on the calculated rules to form rules with 3 elements. The confidence of rules with more elements were selected by multiplying all confidences of the corresponding 2-element rules and then the 3-element rules with confidence < 0.1 were removed. The rules with the 3rd element as “female infertility” were reserved. The reserved 3 rules were splitted into 2-element rules, and then combined with the association rules results for the existing genes. The causal knowledge graph was respectively built for NA PCOS rules and HA PCOS rules. The causal network visualization was realized by Cytoscape³¹ (Version 3.9.1) .

Kaplan-meier’s Survival Analysis

Kaplan-Meier’s Survival analysis was performed by GEPIA 2021^32,33. p-value for GENE + between GENE- for each tumor was extracted. Hazard ratio value for each gene on each tumor was extracted.

Drug-gene Interaction Analysis

Drug-Gene interaction data were downloaded from Gene Regulatory Network database (GRNdb)³⁴. Drugs interacted with HA PCOS functional/classifcation markers were regarded as drugs for HA PCOS. Drugs interacted with NA PCOS functional/classifcation markers were regarded as drugs for NA PCOS. Drugs both regarded as HA PCOS and NA PCOS drugs were removed from the 2 groups. The reserved drug-gene interaction was then visualized by Cytoscape (version 3.9.1).

Quantitative Real-time Pcr

Total RNAs were isolated from cells using the TRIzol reagent(TAKARA). RNAs were reversely transcribed into cDNA with PrimeScript RT-qPCR kit (TAKARA) in a 20 ml reaction. qPCR assays were performed with SYBR premerase Taq kit (TAKARA) on LightCycler96 (Roche) and the sequences of primer pairs used in the present study are shown as followed: CASR: 5’AACGCTTGACCTGAGTCTTG;3’: ATGGTTCTGCCGTCTCTCCA. IL-6R: 5’: TTGCCATTGTTCTGAGGTT ; 3’: GTAGTCTGTATTGCTGATGTC. VWC2: 5’: CTGTCTTACTGAACCTACTTAC;3’:GGAGCAATGAGAACCTGAT.CD274:5’: CGTGACAAGAGGAAGGAAT; 3’: AGGATGAGCAATGGATGATT.

Western Blotting And Antibodies

For potential gene (IL6R and CD274), total protein was extracted from an additional three non-PCOS granulosa cells and three PCOS granulosa cells, and 20µg of protein per lane was loaded and separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis. Proteins were then transferred to polyvinylidene fluoride (PVDF) membranes and blocked for 1h with 5% nonfat milk in Tris-buffered saline with Tween-20 (TAKARA). The membranes were incubated overnight at 4°C with one of the following primary antibodies: IL6R (p-ERK; diluted 1:500), CD274(diluted 1:1000), and glyceraldehyde 3-phosphate dehydrogenase (GAPDH; diluted 1:5000) (Santa Cruz Biotechnology, Inc., Santa Cruz, CA, USA). Next, PVDF membranes were incubated for 60min at room temperature with secondary antibodies (Santa Cruz Biotechnology, Inc.), and immune complexes were detected by chem-iluminescence using a Western blotting analysis system (TAKARA). Relative protein levels per sample were then normalized to GAPDH signals.

Statistical analysis

Clinical features of interest were selected as dependent variables. The Mann–Whitney U test, Student’s t-test and paired t-test of variance were used where appropriate for statistical analyses. Data from at least three independent measurements were presented as means ± standard deviation of the mean (SEM). The SYSTAT statistical graphics software package (SYSTAT Software, Inc., San Jose, CA, USA) was used for analysis, and a probability (P) value of < 0.05 was considered statistically significant. *p < 0.05, **p < 0.01, ***p < 0.001.

Differential Analysis on the Granulosa Cells Microarray Data in Identifying HA and NA PCOS Specific Genes

The combined flowchart for first part of our study was illustrated (Fig. 1a). Co-occurrence analysis based on the abstract of PubMed published papers was performed to stat the correlation between PCOS and four subtypes of ovarian cells including granulosa cells, cumulus cells, theca cells and stromal cells. Granulosa cells were with the most text-mining correlation within the four cell subtypes with PCOS (Fig S2a). Microarray analysis was then performed on granulosa cells from 12 individuals. Clinic information illustrated these individuals were within three groups: normal individuals, HA PCOS individuals and NA PCOS individuals (Table S1). Groups were separated based on clinical symptoms and testosterone level, and afterwards granulosa cells in all groups were collected and assayed. Pearson’s correlation analysis for all genes based on the expression value of the 12 samples showed high quality for our microarray data (Fig S2b). We further validated the precision of the microarray result using quantitative real-time PCR (RT-qPCR) method to check the 14 top genes in microarray data and found the RT-qPCR result was in concordance with microarray data and some published work (Fig. 1c). Box Whisker plot, matrix plot, principal component analysis (PCA) and sample clustering analysis further confirmed the quality of our microarray data(Fig S1a-d).

The microarray data was then preprocessed and normalized. Differential analysis was afterwards performed and showed 615 HA PCOS, NA PCOS or NM differentiated genes including a series of known PCOS and feminine tumor markers (Fig. 1b). These results confirmed the clinical research value for our microarray data. We further filtered the genes expressed highest in HA PCOS or NA PCOS, and with statistical difference with the other 2 groups, and these genes were named HA or NA PCOS specific genes. On this filtering, 130 NA PCOS specific genes and 43 HA PCOS specific genes were identified. Functional analysis was afterwards further applied on the selected specific genes to deeper filter NA PCOS and HA PCOS potential marker genes.

Functional Analysis On Ha Pcos Specific Genes

DAVID Gene Ontology (GO) analysis was performed on the 43 HA PCOS specific genes (Fig. 1d)²⁹. Our most interesting term were the terms associated with glucose metabolism according to the close interaction between PCOS and insulin. The glucose metabolism terms possessed approximately 25% of all terms (Fig S2c). The DAVID KEGG Pathway (KEGG) results for HA PCOS were not showing patterns for our interest. Thus for the next step, genes on these glucose metabolism biological process (BP) terms were extracted, and the number of terms which these genes appeared were counted (Fig. 1e). CASR and SOX4 were appearing on 2 terms and then selected. STRING ppi analysis were afterwards performed on CASR and SOX4 using multi-protein function and pressing “+” once to display all the inter-proteins between CASR and SOX4 (Fig S2d). CASR possessed more ppi interactions than SOX4. Meanwhile, co-occurrence analysis showed that CASR was co-occurred with PCOS, HA PCOS, NA PCOS and androgen receptor, while SOX4 was merely co-occurred with androgen receptor (Fig. 1f, Fig S2e). According to these results, SOX4 was consequentially removed, and CASR was selected as new HA PCOS functional marker.

Functional Analysis On Na Pcos Specific Genes

DAVID GO analysis was performed on the 130 NA PCOS specific genes²⁹. BP analysis results showed the majority terms within the top 20 terms were immunity terms or immunity related terms (Fig. 2a,b). Meanwhile, the majority terms within all BP terms were immunity or immunity related terms (Fig S3a,b). The KEGG pathway analysis results showed the majority pathways within the top 20 pathways were immunity pathways or immunity related pathways (Fig S3c,d). Meanwhile, the majority pathways within all KEGG pathways were immunity pathways or immunity related pathways (Fig S3e,f). These results indicated the close relationship between NA PCOS and immunity. For this reason, NA PCOS functional markers should also be immunity or immunity related genes. We extracted 100 genes which appeared on immunity or immunity related terms/pathways on all 4 conditions (BP top 20, BP all, KEGG top 20, KEGG all).

For further filtering, appearance frequency of the potential marker genes on all BP terms were counted (Fig S4a). The 100 genes with more than 5 frequency were reserved. Subsequent STRING protein-protein interaction (ppi) analysis on the 100 genes using experimental and database links found 80 of the 100 genes were connected together (Fig S4b). The same procedure was also performed on top 20 BP terms, and the 86 genes out of 100 genes were with frequency > 2 and connected together (Fig S4c,d). 55 genes both appeared within the all BP results and top 20 BP results (Fig. 2c). And thus the 55 genes were regarded as potential marker genes. Further co-occurrence analysis with the word “PCOS” reserved 26 genes out of the 55 genes with co-occurrence number > 0 (Fig. 2d). Afterwards, we selected the GO BP terms which at least appeared 2 times within the 26 genes and performed co-occurrence analysis with the word “PCOS” on these terms. The term “cytokine” was mostly co-occurred (Fig. 2e). The 7 genes on the term “cytokine” out of the 26 genes were selected (Fig. 2f). Within all “cytokine” terms, the “response to cytokine” term was more related to immunity than the other terms. For this reason, the genes on this term, IL6R and CD274, were selected as new NA PCOS functional markers.

Further Validation For New Na Pcos And Ha Pcos Markers

Within new NA PCOS markers, IL6R (interleukin 6 receptor) belongs to a subunit of the interleukin 6 (IL6) receptor complex. As an IL6R ligand, IL6 is a potent pleiotropic cytokine that regulates cell growth and differentiation and plays important role in immune response.IL6R has been studied to be correlated with PCOS. The research found between PCOS patients and normal individuals, the most observed inflammation status of IL6R has been resulted from relative obesity or insulin resistance, and meanwhile not independent character of PCOS^35,36. The IL6R difference in NA and HA PCOS has not been reported. CD274 (CD274 molecule), also named PDL1, encodes an immune inhibitory receptor ligand which was involved in the immune escape for tumor. The expression of CD274 in tumor cells is regarded as prognostic in many types of human malignancies such as colon cancer and ovarian cancer^37–39. However, relationship of CD274 with PCOS has not yet been reported. Meanwhile, for new HA PCOS markers, CASR (calcium sensing receptor) is a plasma membrane G protein-coupled receptor that senses small changes in circulating calcium concentration. Previous studies have suggested that the pathological mechanism of insulin resistance in PCOS is related to calcium homeostasis, and CASR as an important calcium regulator may play an important role in PCOS pathogenesis^40–42. Conclusively, these studies indicated that IL6R, CD274 and CASR might engage in important biological processes in NA PCOS or HA PCOS.

RT-qPCR was then applied to further confirm the expression of IL6R, CD274 and CASR in human granulosa cells. Groups were set as HA PCOS group and NA PCOS group. Notably, RT-qPCR showed that the expression level of IL6R and CD274 were significantly higher in NA PCOS than HA PCOS (Fig. 3a), and the result was in accordance with our former analysis that IL6R and CD274 were new NA PCOS markers. Meanwhile, RT-qPCR also illustrated that the expression level of CASR was higher in HA PCOS than NA PCOS (Fig. 3a), and it is also consistent with the result that CASR was new HA PCOS markers.

The protein level of NA PCOS specific genes IL6R and CD274 was further measured by Western blotting. Groups were set as HA PCOS group and NA PCOS group. Western blotting results further showed IL6R and CD274 were expressed on significantly higher protein level in NA PCOS than HA PCOS (Fig. 3b) and the similar results were also illustrated in level of band intensity (Fig. 3c). Combined with the experimental result of RT-qPCR and Western blotting, IL6R and CD274 were validated to be NA PCOS specific genes and new markers, and meanwhile CASR was validated to be HA PCOS specific genes and new marker.

Identifying Classification Markers For Ha Pcos And Na Pcos

Which markers were capable of classifying unassorted PCOS samples into HA and NA PCOS samples was of high clinical importance. We applied a random method on classification markers identification (refer to Methods, Fig. 4a). Firstly, genes were divided into HA PCOS, NA PCOS and NM groups according to expression value in each group. Secondly, 50 million cycles of calculations were performed on the random set of 10–50 genes within each group. The mean ranking of each group for expression was used to classify samples. If the calculated sample groups were identical with original groups, the random set of genes were reserved. For instance, 30 NA PCOS genes were randomly picked, and the mean ranking of the 30 genes on one sample was 17,000 of the 20,000 genes, and was higher than HA PCOS (11,000) and NM (9,000) and thus this sample was classified as NA PCOS. If this sample was also NA PCOS from clinical information, and if the same was true for all samples, this random set of genes might be classification markers.

Subsequently, the 578 reserved sets of genes were further filtered using most highly confident downloaded transciptome data from GEO datasets GSE34526, GSE102293 and GSE98595. Though the 3 datasets were unclassified, we could still apply the known PCOS and NM groups for these datasets, with NA PCOS or HA PCOS comparing PCOS, and NM comparing NM. The set of genes with highest matching rate were selected and regarded as classification markers (Fig. 4b). The matching rate for the classification markers on our microarray data and downloaded datasets was also illustrated (Fig. 4c). Afterwards, the classification markers were applied on the downloaded datasets and classified the PCOS samples into HA and NA PCOS samples. These HA and NA PCOS samples were merged into our microarray data and the heatmap of combined datasets were visualized (Fig S5a). HA or NA PCOS functional and classification markers were merged and regarded as HA PCOS or NA PCOS markers.

Discovering The Characteristic Difference Between Ha And Na Pcos

Co-occurrence analysis was applied on HA PCOS and NA PCOS up-regulated with common metabolism terms appeared on PCOS patients (Fig. 4d). HA PCOS was more correlated with obesity, vitamin D deficiency, hyperandrogenism, hirsutism, depression, insomnia and cardiac diseases. However, NA PCOS was more correlated with inflammation, immunity and insulin resistance. Meanwhile, we were interested on the relationship with feminine cancer for HA and NA PCOS. Survival p value (Fig. 4e) and hazard ratio (Fig. 4f) were calculated for HA and NA PCOS on 4 types of feminine cancers using GEPIA database³³. No substantial difference was found on HA and NA PCOS on feminine cancer risks.

Causal Network Construction For Ha/na Pcos Markers And Female Infertility

The subsequent question to be solved was how HA or NA PCOS markers resulted in female infertility. An effective tool to solve the problem was the Apriori Rules Algorithm^16,17 which was specifically designed to calculate causal relationship between terms (refer to Methods). The causal relationship might be promoting or inhibiting relationship. The algorithm was applied on two different form of data: merged transcriptome data (Fig. 5a) and PubMed paper abstracts (Fig. 5b). Transcriptome data were using the merged transcriptome data from the final step of classification markers identification, and the causal relationship between each pair of genes were calculated. For abstract data, 9832 PubMed paper abstracts on PCOS from the last 12 years were downloaded. NA PCOS and HA PCOS up-regulated genes were used to classify the downloaded abstracts as NA PCOS papers and HA PCOS papers. NCBI MeSH words⁴³ with specific subheadings were applied as the measurement for the following Apriori Rules calculation. The number of MeSH terms co-occurred on the same abstract with each specific gene or MeSH term were recorded. Causal relationship was afterwards calculated based on the co-occurred number of MeSH terms. Apriori Rules method was then applied on calculated causal relationship to construct higher order rules with 3 elements for NA PCOS and HA PCOS. The term “Female infertility” was designed as the terminal for the causal cascade.

The 3 element rules ending as “Female infertility” were combined and merged with the rules from merged transcriptome data. Genes or terms with less connections were removed, and afterwards the the causal knowledge graph for HA PCOS (Fig. 5c) or NA PCOS (Fig. 5d) with female infertility was constructed. Mutual terms of HA and NA PCOS were removed from the knowledge graph (Fig S5b). From the knowledge graph, we were capable of deriving the causal route for HA or NA PCOS markers and important MeSH terms which finally resulted in female infertility. The arrows on the network pointed from the cause to the effect (promote or suppress), or the sub-term to the term. Most of the causal relationships on the network were accordant with public knowledge and published papers. Unreported causal relationship might be prediction of the hidden mechanism for HA and NA PCOS. From the knowledge graph, we also noticed the important role for our newly discovered HA and NA PCOS functional markers IL6R, CD274 and CASR on female infertility.

Drug Interaction Analysis Predict Specific Drugs For Ha And Na Pcos

The next clinical question to answer was the difference in drugs applied in HA and NA PCOS. We downloaded and applied the reported drug-gene interaction relationship from GRNdb³⁴ database on identifying the drugs specifically interacted with HA markers and NA markers. The flowchart of the analysis was shown (Fig. 6a). 23 HA PCOS specific drugs and 6 NA PCOS specific drugs were discovered (Fig. 6b). The co-occurrence counts with PCOS were illustrated (Fig S5c,d). Further filtering removed drugs with less connections with marker genes. Then we constructed network based on the drug-gene interaction between selected drugs and HA/NA PCOS markers for HA and NA PCOS (Fig. 6c,d). The selected drugs were promoting or restraining the corresponding interacted markers. From the interaction network, we noticed androgen composed of most part, which was consistent with HA PCOS characteristics and probably promoting HA PCOS, while we also discovered flutamide and tamoxifen as specific drugs promoting or inhibiting HA PCOS. Within these drugs, tamoxifen had the potential to inhibit HA PCOS and promoted ovulation and pregnancy⁴⁴. For NA PCOS, we found human albumin, herapin, insulin, adenosine, liothyronine sodium and antibiotic might promote or inhibit NA PCOS. According to reported drug function, liothyronine sodium might promote NA PCOS⁴⁵, while antibiotic might potentially inhibit NA PCOS⁴⁶. Human albumin might also inhibit NA PCOS by regulating immunity. Further drug experiments were required for effects of other drugs and to disclose the suitable drugs for HA and NA PCOS.

Considering the vacancy on the studies on HA PCOS and NA PCOS, we applied microarray on granulosa cell samples for HA PCOS, NA PCOS and normal patients. Differential analysis identified specific genes for HA PCOS and NA PCOS. Further deeper functional analysis selected IL6R and CD274 as new NA PCOS functional markers, and meanwhile selected CASR as new HA PCOS functional marker. RT-qPCR and Western blotting validated that the expression pattern of IL6R, CD274 and CASR on mRNA and protein level was accordant with microarray data. Classification markers for HA PCOS, NA PCOS and normal groups were identified. Afterwards, combined methods of Apriori Rules and co-occurrence analysis were applied on downloaded PubMed PCOS abstracts. The causal network disclosed how HA PCOS or NA PCOS markers resulted in female infertility. Further drug-gene interaction analysis selected specific drugs promoting or inhibiting HA and NA PCOS. Our work was to our knowledge the first to apply microarray or sequencing technology on NA PCOS and HA PCOS mRNA samples, and among the first on applying transcriptome data on screening NA PCOS and HA PCOS markers and drugs, and thus our work was with considerable clinical significance. The methods applied in our studies were original methods and capable of detecting causal or interactive relationship between different types of datasets including transcriptome data, drug data and text mining data.

Meanwhile, HA PCOS and NA PCOS markers have not been reported. Our work successfully identified NA PCOS markers IL6R and CD274, as well as HA PCOS markers CASR. Works from other researchers discussed the relationship of some of these markers including IL6R and CASR in PCOS^47,48 ,but these work failed to describe the mechanism for the relation and also barely explain why they were expressed differently in NA and HA PCOS. Within these potential markers, CD274 (also named PDL1) is a special gene both identified by our method as NA PCOS specific gene and reported to be related to immunity and inflammation^37–39, and might indicate important implications between NA PCOS and tumor. Similarly, CASR was reported to be highly related to PCOS^40–42 and also correlated with adipose⁴⁹. Our study might further implied internal relationship among HA PCOS, adipose and insulin. Thus finding and experimentally validating potential markers for NA and HA PCOS might dramatically promote the understanding of PCOS and assist disclosing the mechanism that resulted in distinct syndrome of NA PCOS and HA PCOS.

Previous studies showed the similarity in syndrome for HA and NA PCOS patients including high levels of body mass index, insulin, total cholesterol and etc..⁵⁰ Other works were concentrated on the difference for HA and NA PCOS and stated that HA PCOS patients possessed higher irisin level, adipocytokines and maternal complications^51–53. Increasing number of evidence showed the high correlation between PCOS and inflammation^54–57, and also the correlation between PCOS and vitamin D has been reported^58,59,while however these studies did not distinguish PCOS between subtypes of NA PCOS and HA PCOS. The main difference between NA PCOS and HA PCOS was still dilemma. Our causal knowledge graph showed that the causing pathogenesis for female infertility for HA PCOS and NA PCOS was mostly similar. Insulin / insulin resistance, aromatase / aromatase inhibitors, gonadotropin, inositol and even vitamin D were appearing on both HA PCOS and NA PCOS causes (Fig. 5c,d). This implied that the mechanism on how HA PCOS and NA PCOS caused female infertility might be similar. The specific cause for female infertility on HA PCOS included hyperandrogenism, cholesterol and adiponectin, while specific cause for NA PCOS included immunity terms and hyperinsulinism, indicating that HA PCOS was more correlated to metabolism disorders while however NA PCOS was more correlated to immunity disorders.

Our work is primarily based on bioinformatical and data mining technology, and thus our work is a prediction of the pattern, potential markers and specific drugs for NA PCOS and HA PCOS. The reliability of our conclusion is based on the fact that high percentage of our prediction was accordant with public or reported knowledge. It would be interesting for us to further illuminate the deeper mechanism underlying the function of IL6R, CD274 and CASR, and also HA/NA PCOS classification markers on the formation of PCOS and female infertility. Meanwhile, the specific HA and NA PCOS drugs on HA and NA PCOS were requiring further drug testing and might be of substantial clinical value.

Ethics approval and Consent to Participate

The study protocol was approved by the Research Ethics Committee of China Medical University. All participants provided signed informed consent.

Consent for Publication

Not applicable

Data Availability

All data generated or analyzed during this study are included in this published article or in the data repositories listed in References.

Microarray data were uploaded on NCBI Gene Expression Omnibus with Accession Number GSE137684.

Conflict of interest

No competing interests declared.

Funding

National Natural Science Foundation of China [32170603, 31871286 for Yue Zhao, 81902889 for Wensu Liu, 82273123 for Chunyu Wang], Foundation of Liaoning Province of China (LJKZ0756 for Shengli Wang), Local projects supported by the central government (2022JH6/100100035 for Yue Zhao), Foreign expert project of Ministry of Science and Technology (G2022006007L for Yue Zhao).

Authors’ Contributions

Yue Zhao, Zeyu Yang and Chunyu Wang designed the project. Tianyu Tang and Kai Zeng assisted collecting samples and experiments. Wensu Liu was participated in bioinformatics analysis and paper writing. Tianyu Tang, Jianwei Feng, Renlong Zou, Lin Lin and Shengli Wang provided technical support. All authors read and approved the final manuscript.

Acknowledgement

We appreciate Miss. Linlin and Mr. Tao Wen for helpful technique support. We thank Dr. Jiao Jiao and Dr. Da Li for collecting the granulosa cells from polycystic ovary syndrome patients in Shengjing Hospital. We also appreciate Compass Biotechnology Co., Ltd Company, Beijing, China for RNA microarray analysis.

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No competing interests reported.

TableS1.docx
FigureS1.tif
Figure S1: Quality assessment of our microarray analysis results (A) Box Whisker Plot showing the average expression value of all samples. (B) Matrix plot illustrating the correlation of either of the two samples for all samples. (C) 2D PCA figure in showing the distribution of all samples. NA samples were labeled in color green, HA samples were labeled in color blue, and normal tissue were labeled in red. (D) Sample cluster analysis results for all samples. Distance between two samples showed the level of similarity within them. Samples with label ended with letter ‘d’ were NA samples, and also letter ‘h’ for HA samples, and letter ‘c’ for normal tissues.
FigureS2.tif
Figure S2: Supplemental Figures for PCOS Data Acquisition and Gene Ontology analysis results for HA PCOS specific genes (A) Boxblot showing the PubMed co-occurrence analysis with the four subtypes of ovarian cells as keyword and the keyword “PCOS”, “HA PCOS” and “NA PCOS”. (B) Pearson’s correlation map showing correlation of all samples for each gene. (C) Pie plot illustrating the percentage of glucose metabolism terms within HA PCOS specific genes gene ontology terms. (D) STRING multi-protein network for the four candidate marker genes. The intermediate proteins were generated using the “more” function on STRING once. (E) PubMed co-occurrence results for SOX4 with PCOS, NA PCOS, HA PCOS and androgen receptor respectfully.
FigureS3.tif
Figure S3: Supplemental Figures for Gene ontology analysis results for NA PCOS specific genes (A) Barplot showing the all DAVID biological process analysis terms within the NA PCOS specific genes. Immunity terms and immunity related terms were labeled red and green, and other terms were labeled as blue (Figure S2b-2f were using the identical color labeling as Figure S2a). (B) Pie plot illustrating the percentage of immunity terms, immunity-related terms and other terms within all DAVID biological process analysis terms. (C) Barplot showing the top 20 DAVID KEGG pathways within the NA PCOS specific genes. (D) Pie plot illustrating the percentage of immunity pathways, immunity-related pathways and other pathways within the top 20 DAVID KEGG pathway analysis results. (E) Barplot showing all DAVID KEGG pathways within the NA PCOS specific genes. (F) Pie plot illustrating the percentage of immunity pathways, immunity-related pathways and other pathways within all DAVID KEGG pathway analysis results.
FigureS4.tif
Figure S4: Deep Analysis on NA PCOS GO terms to further identify potential NA PCOS markers (A) Barplot illustrating the NA PCOS specific genes (x-axis) and the corresponding number of immunity GO terms which each gene belonged (y-axis). The genes existed on at least 5 immunity GO terms were plotted. (B) STRING network for the NA PCOS specific genes on at least 5 immunity GO terms. Connection with the type experimental validation and database were reserved. The genes with no connection with other genes were removed. (C) Barplot illustrating the NA PCOS specific genes (x-axis) and the corresponding number of top 20 immunity GO terms which each gene belonged (y-axis). The genes existed on at least 2 immunity GO terms were plotted. (D) STRING network for the NA PCOS specific genes on at least 2 of top 20 immunity GO terms. Connection with the type experimental validation and database were reserved. The genes with no connection with other genes were removed.
FigureS5.tif
Figure S5: Further analysis on merged PCOS data to extract deeper information (A) Heatmap showing the expression pattern of HA PCOS, NA PCOS and NM groups after merging with downloaded PCOS datasets for each sample on x axis and each gene on y-axis. (B) Knowledge graph plotted by Cytoscape using causal relationship predictions by Apriori Rules Algorithm for HA PCOS and NA PCOS mutual terms. The arrow implied causal relationship pointing from the cause side (upstream side) to the result side (downstream side). The HA and NA PCOS potential marker was labeled green. Female Infertility was labeled red. Width of lines implied confidence of the causal relationship. (C) Co-occurrence counts (log) on PCOS for specific drugs on HA PCOS. (D) Co-occurrence counts (log) from co-occurrence on PCOS for specific drugs on NA PCOS.

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Knowledge Graph Construction based on Granulosa Cells Transcriptome from polycystic ovary syndrome with Normoandrogen and Hyperandrogen

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Abstract

Figures

Introduction

Material And Method

Patient selection and ethical statement

Ovarian Stimulation, Granulosa Cell Collection And Rna Extraction

Microarray Data Analysis

Further Analysis For Microarray Data

Co-occurrence Analysis Using Pubmed

Protein-protein Interaction Network Analysis

Deeper Gene Search Based On Na Pcos Gene Ontology Analysis

Deeper Gene Search Based On Ha Pcos Gene Ontology Analysis

Classification Markers Identification And Data Integration

Apriori Rule Analysis On Merged Transcriptome Data

Pubmed Abstracts Download And Preprocessing

Apriori Rules Algorithm Analysis And Knowledge Graph Construction

Kaplan-meier’s Survival Analysis

Drug-gene Interaction Analysis

Quantitative Real-time Pcr

Western Blotting And Antibodies

Statistical analysis

Results

Functional Analysis On Ha Pcos Specific Genes

Functional Analysis On Na Pcos Specific Genes

Further Validation For New Na Pcos And Ha Pcos Markers

Identifying Classification Markers For Ha Pcos And Na Pcos

Discovering The Characteristic Difference Between Ha And Na Pcos

Causal Network Construction For Ha/na Pcos Markers And Female Infertility

Drug Interaction Analysis Predict Specific Drugs For Ha And Na Pcos

Discussion

Declarations

References

Additional Declarations

Supplementary Files

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