Multiomics Analysis–Based Biomarkers in Diagnosis of Polycystic Ovary Syndrome

Rani, Shikha; Chandna, Piyush

doi:10.1007/s43032-022-00863-9

Cited by 9 publications

(3 citation statements)

References 285 publications

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“…Another study explored the classification and etiology of PCOS from a genomic perspective (5). Moreover, some groups attempted to investigate the PCOS bio-markers and pathogenic mechanisms using multi-omics and bioinformatics (6)(7)(8). Despite the efforts made in recent decades, the etiology of PCOS is not yet fully understood due to its complex pathogenesis and variability.…”

Section: Introductionmentioning

confidence: 99%

A novel model based on necroptosis to assess progression for polycystic ovary syndrome and identification of potential therapeutic drugs

Wang,

An,

Huang

et al. 2023

Front. Endocrinol.

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BackgroundPolycystic ovary syndrome (PCOS), a common endocrine and reproductive disorder, lacks precise diagnostic strategies. Necroptosis was found to be crucial in reproductive and endocrine disorders, but its function in PCOS remains unclear. We aimed to identify differentially diagnostic genes for necroptosis (NDDGs), construct a diagnostic model to assess the progression of PCOS and explore the potential therapeutic drugs.MethodsGene expression datasets were combined with weighted gene co-expression network analysis (WGCNA) and necroptosis gene sets to screen the differentially expressed genes for PCOS. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a necroptosis-related gene signatures. Independent risk analyses were performed using nomograms. Pathway enrichment of NDDGs was conducted with the GeneMANIA database and gene set enrichment analysis (GSEA). Immune microenvironment analysis was estimated based on ssGSEA algorithm analysis. The Comparative Toxicogenomics Database (CTD) was used to explore potential therapeutic drugs for NDDGs. The expression of NDDGs was validated in GSE84958, mouse model and clinical samples.ResultsFour necroptosis-related signature genes, IL33, TNFSF10, BCL2 and PYGM, were identified to define necroptosis for PCOS. The areas under curve (AUC) of receiver operating characteristic curve (ROC) for training set and validation in diagnostic risk model were 0.940 and 0.788, respectively. Enrichment analysis showed that NDDGs were enriched in immune-related signaling pathways such as B cells, T cells, and natural killer cells. Immune microenvironment analysis revealed that NDDGs were significantly correlated with 13 markedly different immune cells. A nomogram was constructed based on features that would benefit patients clinically. Several compounds, such as resveratrol, tretinoin, quercetin, curcumin, etc., were mined as therapeutic drugs for PCOS. The expression of the NDDGs in the validated set, animal model and clinical samples was consistent with the results of the training sets.ConclusionIn this study, 4 NDDGs were identified to be highly effective in assessing the progression and prognosis of PCOS and exploring potential targets for PCOS treatment.

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Section: Introductionmentioning

confidence: 99%

A novel model based on necroptosis to assess progression for polycystic ovary syndrome and identification of potential therapeutic drugs

Wang,

An,

Huang

et al. 2023

Front. Endocrinol.

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“…A study found that the interaction between LncRNA ZFHX4-AS1 and MRPS11 may be related to the immune microenvironment in ovarian cancer and promote ovarian cancer progression [ 29 ]. Another study found that the expression of MRPS12 is positively correlated with the infiltration of macrophages and neutrophils in ovarian cancer, and MRPS12 is a potential oncogene and a promising prognostic candidate in ovarian cancer [ 30 ].…”

Section: Discussionmentioning

confidence: 99%

Identification of key biomarkers in ischemic stroke: single-cell sequencing and weighted co-expression network analysis

Tao

Xie

Luo

et al. 2023

Aging

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Purpose: At present, there is a lack of accurate early diagnostic markers for ischemic stroke. Methods: By using dimensionality reduction cluster analysis, differential expression analysis, weighted coexpression network analysis, protein-protein interaction network analysis, cell heterogeneity and key pathogenic genes were identified in ischemic stroke. Immunomicroenvironment analysis was used to explore the immune landscape and immune associations of key genes in ischemic stroke. The analysis platform we use is R software (version 4.0.5). PCR experiments were used to verify the expression of key genes. Results: Single cell sequencing data in ischemic stroke can be annotated as fibroblast cells, pre-B cell CD34, neutrophils cells, bone marrow (BM), keratinocytes, macrophage, neurons and mesenchymal stem cells (MSC). By the intersection of differential expression analysis and WGCNA analysis, 385 genes were obtained. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that these genes were highly correlated with multiple functions and pathways. Protein-protein interaction network analysis revealed that MRPS11 and MRPS12 were key genes, both of which were down-regulated in ischemic stroke. The Pseudo-time series analysis found that the expression of MRPS12 decreased gradually with the differentiation of pre-B cell CD34 cells in ischemic stroke, suggesting that the downregulation of MRPS12 expression may play an important role in ischemic stroke. At last, PCR showed that MRPS11 and MRPS12 were significantly down-regulated in peripheral blood of patients with ischemic stroke. Conclusions: Our study provides a reference for the study of pathogenesis and key targets of ischemic stroke.

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“…Omics and multi-omics studies have been performed on PCOS transcriptome data to investigate the pathogenesis for PCOS and search PCOS biomarkers [13][14][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.…”

Section: Introductionmentioning

confidence: 99%

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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Multiomics Analysis–Based Biomarkers in Diagnosis of Polycystic Ovary Syndrome

Cited by 9 publications

References 285 publications

A novel model based on necroptosis to assess progression for polycystic ovary syndrome and identification of potential therapeutic drugs

A novel model based on necroptosis to assess progression for polycystic ovary syndrome and identification of potential therapeutic drugs

Identification of key biomarkers in ischemic stroke: single-cell sequencing and weighted co-expression network analysis

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

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