Discovery of immune-related diagnostic biomarkers and construction of diagnostic model in varies polycystic ovary syndrome

Qu, Junjie; Li, Bilan; Qiu, Meiting; Wang, Jingyun; Feng, Ying; Teng, Xiaoming

doi:10.1007/s00404-022-06686-y

Cited by 9 publications

(6 citation statements)

References 40 publications

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“…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 in ammation 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.…”

Section: Further Validation For New Na Pcos and Ha Pcos Markersmentioning

confidence: 86%

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

Liu¹,

Tang

Feng

et al. 2023

Preprint

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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.

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Section: Further Validation For New Na Pcos and Ha Pcos Markersmentioning

confidence: 86%

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

Liu¹,

Tang

Feng

et al. 2023

Preprint

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“…Studies have stated that hormone abnormalities in PCOS can overstimulate the immune system, and concurrently, inflammatory cell infiltration aggravates ovarian dysfunction and metabolic disorders [ 35 , 36 ]. High infiltrating abundances of macrophages [ 37 ], neutrophils, activated dendritic cells [ 38 ], and eosinophils [ 39 , 40 ] were observed in PCOS. Since chronic low-level inflammation plays a critical role in PCOS [ 41 ], we investigated and compared the immune infiltration and cytokine levels in different groups.…”

Section: Discussionmentioning

confidence: 99%

N6-methyladenosine regulator-mediated methylation modification patterns and immune infiltration characterization in Polycystic Ovary Syndrome (PCOS)

2023

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Background Polycystic ovary syndrome (PCOS) is a multisystem-related disease whose pathophysiology is still unclear. Several regulators of N6-methyladenosine (m6A) modification were confirmed to play a regulatory role in PCOS. Nonetheless, the roles of m6A regulators in PCOS are not fully demonstrated. Materials and methods Four mRNA expression profiling microarrays were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed m6A regulators between PCOS and normal patients were identified by R software. A random forest modal and nomogram were developed to assess the relationship between m6A regulators and the occurrence risk of PCOS. A consensus clustering method was utilized to distinctly divide PCOS patients into two m6A subtypes (m6A cluster A/B). The patterns of differential expression and immune infiltration were explored between the two m6A clusters. Results In this study, 22 significant m6A regulators were identified between healthy controls and PCOS patients. The random forest model determined three optimal m6A regulators which are related to the occurrence risk of PCOS, including YTHDF1, RBM15 and METTL14. A nomogram was established based on these genes, and its predictive reliability was validated by decision curve analysis. The consensus clustering algorithm distinctly divided PCOS cases into two m6A subtypes. The ssGSEA algorithm found that the immune infiltration was markedly enriched in m6A cluster B than in cluster A. The m6A-pattern related differentially expressed genes (DEGs) of the two m6A subtypes were demonstrated by differential expression analysis. We found that they were enriched in immune-related genes and various infection pathways. Based on the m6A-pattern related DEGs, the PCOS patients were classified into two m6A-pattern related genomic subtypes (gene clusters A and B). Conclusions The present study provided evidence concerning the different modification patterns of m6A regulators in PCOS compared with normal patients. This study will help clarify the overall impact of m6A modification patterns and related immune infiltration on PCOS.

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“…GO term and pathway enrichment analysis showed that DEGs were highly enriched in the present investigation, which undoubtedly verifies the reliability of our results. Signaling pathways include cytokine signaling in immune system [48], extracellular matrix organization [49], diseases of metabolism [50], hemostasis [51], innate immune system [52], metabolism of lipids [53] and metabolism [54] were linked with progression of PCOS. Altered expression of PLA2G5 [55], CASP1 [56], EDNRA (endothelin receptor type A) [57], F2RL1 [58], FOXP3 [59], DRD4 [60], COL6A3 [61], TIMP4 [62], SOCS1 [63], CD74 [64], TGFB1 [65], ATF5 [66], IRF7 [67], IRX3 [68], FOXC2 [69], STX1A [70], IL1RL1 [71], HHIP (hedgehog interacting protein) [72], ELOVL2 [73], BGN (biglycan) [74], POMC (proopiomelanocortin) [75], DOK5 [76], COL1A1 [77], POSTN (periostin) [78], SOD3 [79], ZNF423 [80], FABP5 [81], DDIT4 [82], KCTD15 [83], COL1A2 [84], MGAT2 [85], ENDOG (endonuclease G) [86], HSPA5 [87], CES1 [88], CYP19A1 [89], PLAC8 [90], CD36 [91], GIPR (gastric inhibitory polypeptide receptor) [92], ARG1 [93], MSR1 [94], DOCK2 [95], S1PR1 [96], OXTR (oxytocin receptor) [97], F11R [98], LEPR (leptin receptor) [99], AR (androgen receptor) [100], DKK3 [101], FOXO1 [102], PIK3CB [103], WWP1 [104], PHIP (pleckstrin homology domain interacting protein) [105], MPZL3 [106], FBXO2 [107], GUCY2C [108], ADRA2A [109], CHRNA5 [110], GLP2R [111], SDC3 [112], NFAT5 [113], PON2 [114], PRNP (prion protein) [115], DGKE (diacylglycerol kinase epsilon) [116], AR...…”

Section: Discussionmentioning

confidence: 99%

The Identification of Key Genes and Biological Pathways in Polycystic Ovary Syndrome and its Complications by Next Generation Squancing (NGS) and Bioinformatics Analysis

Vastrad¹,

Vastrad²

2023

Preprint

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Polycystic ovary syndrome (PCOS) is is associated with infertility, obesity, insulin resistance, hyperinsulinemia, type 2 diabetes mellitus, hypertension, cardiovascular problems, neurological and psychological problems and cancer. The specific mechanism of PCOS and its complications remains unclear. The aim of this study was to apply a bioinformatics approach to reveal related pathways or genes involved in the development of PCOS and its complications. The next generation squancing (NGS) datset GSE199225 was downloaded from the gene expression omnibus (GEO) database. Differentially expressed gene (DEG) analysis was performed using DESeq2. The g:Profiler was utilized to analyze the functional enrichment, gene ontology (GO) and REACTOME pathway of the differentially expressed genes. A protein-protein interaction (PPI) network was constructed and module analysis was performed using HiPPIE and cytoscape. The miRNA-hub gene regulatory network and TF-hub gene regulatory network were also constructed for research. The expression of the hub genes was validated using receiver operating characteristic (ROC) curve analysis. We have identified 957 DEGs in total, including 478 up regulated genes and 479 down regulated gene. GO and REACTOME illustrated that DEGs in PCOS were significantly enriched in regulation of molecular function, developmental process, interferon signaling and platelet activation, signaling and aggregation. Finally, through analyzing the PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network, we screened hub genes HSPA5, PLK1, RIN3, DBN1, CCDC85B, DISC1, AR, MTUS2, LYN and TCF4 by the Cytoscape software. This study uses a series of bioinformatics technologies to obtain hug genes and key pathways related to PCOS and its complications. These analysis results provide us with novel ideas for finding biomarkers and treatment methods for PCOS and its complications.

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Discovery of immune-related diagnostic biomarkers and construction of diagnostic model in varies polycystic ovary syndrome

Cited by 9 publications

References 40 publications

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

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

N6-methyladenosine regulator-mediated methylation modification patterns and immune infiltration characterization in Polycystic Ovary Syndrome (PCOS)

The Identification of Key Genes and Biological Pathways in Polycystic Ovary Syndrome and its Complications by Next Generation Squancing (NGS) and Bioinformatics Analysis

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