Identification of transcriptome characteristics of granulosa cells and the possible role of UBE2C in the pathogenesis of premature ovarian insufficiency

Liu, Dan; Guan, Xiaohong; Liu, Wenqiang; Jia, Yanping; Zhou, Hong; Xi, Chenxiang; Zhao, Mei; Fang, Yuan; Wu, Li; Li, Kunming

doi:10.1186/s13048-023-01266-3

Cited by 4 publications

(2 citation statements)

References 85 publications

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“…For example, granulosa cells (GCs) from ovary tissue were significantly associated with premature ovarian insufficiency (POI) (Figure 3a). This result is relevant as granulosa cells surrounding oocytes play a pivotal role in folliculogenesis, emerging as an important etiological factor in POI (Liu et al, 2023).…”

Section: Resultsmentioning

confidence: 99%

Differential expression and co-expression reveal cell types relevant to rare disease phenotypes

Alías-Segura,

Pazos,

Chagoyen

2024

Preprint

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Disease phenotypes, serving as valuable descriptors for delineating the spectrum of human pathologies, play a critical role in understanding disease mechanisms. Integration of these phenotypes with single-cell RNA sequencing (scRNA-seq) data facilitates the elucidation of potential associations between phenotypes and specific cell types underlying them, which sheds light on the underlying physiological processes related to these phenotypes. In this study, we utilized scRNA-seq data to infer potential associations between rare disease phenotypes and cell types. Differential expression and co-expression analyses of genes linked to abnormal phenotypes were employed as metrics to identify the involved cell types. Comparative assessments were made against existing phenotype-cell type associations documented in the literature. Our findings underscore the utility of differential expression and co-expression analyses in identifying significant relationships. Moreover, co-expression analysis unveils cell types potentially linked to abnormal phenotypes not extensively characterized in prior studies.Key points-Cell types underling rare disease phenotypes remain largely unknown-Single-cell RNA-seq data from healthy tissues can be analyzed to reveal these cell types-We employed differential expression and co-expression analysis to identify cell types associated with rare disease phenotypes-We validated our results with known relations described in the literature

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Section: Resultsmentioning

confidence: 99%

Differential expression and co-expression reveal cell types relevant to rare disease phenotypes

Alías-Segura,

Pazos,

Chagoyen

2024

Preprint

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“…Moreover, CDC20 can be regulated by estrogen and progesterone, which could offer a new strategy to improve the success rate of assisted reproduction 28 . Using bioinformatics analysis, CDC20 and TOP2A were found to be differentially expressed in POF, revealing low expression 29 . CDCA8 encodes Borealin/Desra B protein, which is an important component of the chromosome passenger complex 30 .…”

Section: Discussionmentioning

confidence: 99%

The shared mechanism and potential diagnostic markers for premature ovarian failure and dry eye disease

Long,

Wu,

Jiang

et al. 2024

Sci Rep

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Premature ovarian failure (POF), which is often comorbid with dry eye disease (DED) is a key issue affecting female health. Here, we explored the mechanism underlying comorbid POF and DED to further elucidate disease mechanisms and improve treatment. Datasets related to POF (GSE39501) and DED (GSE44101) were identified from the Gene Expression Omnibus (GEO) database and subjected to weighted gene coexpression network (WGCNA) and differentially expressed genes (DEGs) analyses, respectively, with the intersection used to obtain 158 genes comorbid in POF and DED. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses of comorbid genes revealed that identified genes were primarily related to DNA replication and Cell cycle, respectively. Protein–Protein interaction (PPI) network analysis of comorbid genes obtained the 15 hub genes: CDC20, BIRC5, PLK1, TOP2A, MCM5, MCM6, MCM7, MCM2, CENPA, FOXM1, GINS1, TIPIN, MAD2L1, and CDCA3. To validate the analysis results, additional POF- and DED-related datasets (GSE48873 and GSE171043, respectively) were selected. miRNAs-lncRNAs-genes network and machine learning methods were used to further analysis comorbid genes. The DGIdb database identified valdecoxib, amorfrutin A, and kaempferitrin as potential drugs. Herein, the comorbid genes of POF and DED were identified from a bioinformatics perspective, providing a new strategy to explore the comorbidity mechanism, opening up a new direction for the diagnosis and treatment of comorbid POF and DED.

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Exploring the molecular mechanisms by which per- and polyfluoroalkyl substances induce polycystic ovary syndrome through in silico toxicogenomic data mining

Xu,

Zhang,

Chen

et al. 2024

Ecotoxicology and Environmental Safety

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Identification of transcriptome characteristics of granulosa cells and the possible role of UBE2C in the pathogenesis of premature ovarian insufficiency

Cited by 4 publications

References 85 publications

Differential expression and co-expression reveal cell types relevant to rare disease phenotypes

Differential expression and co-expression reveal cell types relevant to rare disease phenotypes

The shared mechanism and potential diagnostic markers for premature ovarian failure and dry eye disease

Exploring the molecular mechanisms by which per- and polyfluoroalkyl substances induce polycystic ovary syndrome through in silico toxicogenomic data mining

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