Increased ApoE Expression in Follicular Fluid and the ApoE Genotype Are Associated With Endometriosis in Chinese Women

Liu, Ya‐Jing; Xing, Fen; Zong, Kai; Wang, Mengyao; Ji, Dongmei; Zhao, Yuhang; Xia, Yun-He; Wang, An; Shi, Ling-Ge; Ding, Si-Min; Wei, Zhaolian; Qiao, Jin-Ping; Du, Xin; Cao, Yunxia

doi:10.3389/fendo.2021.779183

Cited by 4 publications

(1 citation statement)

References 31 publications

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“…Signaling pathways include extracellular matrix organization [101], nervous system development [102], signal transduction [103], hemostasis [104], muscle contraction [105], signaling by retinoic acid [106] and diseases of glycosylation [107] were responsible for advancement of endometriosis. Altered expression of L1CAM [108], HSD17B2 [109], VCAM1 [110], SOX6 [111], FGF10 [112], MMP12 [113], CCR1 [114], PROK1 [115], PRL (prolactin) [116], TIMP3 [117], ADAMTS9 [118], NDNF (neuron derived neurotrophic factor) [119], LHCGR (luteinizing hormone/choriogonadotropin receptor) [120], PDGFB (platelet derived growth factor subunit B) [121], LDLR (low density lipoprotein receptor) [122], CD4 [123], FOXL2 [124], TRPA1 [125], ADRB2 [126], PLAU (plasminogen activator, urokinase) [127], EPCAM (epithelial cell adhesion molecule) [128], UCN2 [129], CYP1A1 [130], NTN1 [131], IL15 [132], BMP2 [133], APOE (apolipoprotein E) [134], CASP1 [135], ABCG2 [136], ACE (angiotensin I converting enzyme) [137], PGR (progesterone receptor) [138], ALPP (alkaline phosphatase, placental) [139], LPAR4 [140], ATRNL1 [141], HLA-C [142], MMP3 [143], PDLIM3 [144], NFASC (neurofascin) [145], IL33 [146], NGF (nerve growth factor) [147], COMP (cartilage oligomeric matrix protein) [148], FST (follistatin) [149], EFEMP1 [150], GATA6 [151], TCF21 [152], PTGS2 [153], HOXC8 [154], AKR1C3 [155], BDNF (brain derived neurotrophic factor) [119], EPHA3 [156], INHBA (inhibin subunit beta A) [157], RAP1GAP [158], TLR3 [159], NOX4 [160], TGFBI (transforming growth factor beta induced) [161], IGF2BP1 [162], DLX5 [163], VDR (vitamin D receptor) [164], F...…”

Section: Discussionmentioning

confidence: 99%

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2024

Preprint

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Endometriosis is a common cause of endometrial-type mucosa outside the uterine cavity with symptoms such as painful periods, chronic pelvic pain, pain with intercourse and infertility. However, the early diagnosis of endometriosis is still restricted. The purpose of this investigation is to identify and validate the key biomarkers of endometriosis. Next generation sequencing (NGS) dataset GSE243039 was obtained from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) between endometriosis and normal control samples were identified. After screening of DEGs, gene ontology (GO) and REACTOME pathway enrichment analyses were performed. Furthermore, a protein-protein interaction (PPI) network was constructed and modules were analysed using the Human Integrated Protein-Protein Interaction rEference (HIPIE) database and Cytoscape software, and hub genes were identified. Subsequantely, a network between miRNAs and hub genes, and network between TFss and hub genes were constructed using the miRNet and NetworkAnalyst tool, and possible key miRNAs and TFs were predicted. Finally, receiver operating characteristic curve (ROC) analysis was used to validate the hub genes. A total of 958 DEGs, including 479 up regulated genes and 479 down regulated genes, were screened between endometriosis and normal control samples. GO and REACTOME pathway enrichment analyses of the 958 DEGs showed that they were mainly involved in multicellular organismal process, developmental process, signaling by GPCR and muscle contraction. Further analysis of the PPI network and modules identified 10 hub genes, including VCAM1, SNCA, PRKCB, ADRB2, FOXQ1, MDFI, ACTBL2, PRKD1, DAPK1 and ACTC1. Possible target miRNAs, including hsa-mir-3143 and hsa-mir-2110, and target TFs, including TCF3 and CLOCK, were predicted by constructing a miRNA-hub gene regulatory network and TF-hub gene regulatory network. This investigation used bioinformatics techniques to explore the potential and novel biomarkers. These biomarkers might provide new ideas and methods for the early diagnosis, treatment, and monitoring of endometriosis.

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Section: Discussionmentioning

confidence: 99%

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2024

Preprint

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Cell cycle, apoptosis, cell differentiation, and lipid metabolism gene expression in endometriotic tissue and exposure to parabens and benzophenones

Peinado

Olivas-Martínez

Iribarne-Durán

et al. 2023

Science of The Total Environment

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Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next-generation sequencing data analysis

Vastrad,

Vastrad

2024

Egypt J Med Hum Genet

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Background Endometriosis is a common cause of endometrial-type mucosa outside the uterine cavity with symptoms such as painful periods, chronic pelvic pain, pain with intercourse and infertility. However, the early diagnosis of endometriosis is still restricted. The purpose of this investigation is to identify and validate the key biomarkers of endometriosis. Methods Next-generation sequencing dataset GSE243039 was obtained from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) between endometriosis and normal control samples were identified. After screening of DEGs, gene ontology (GO) and REACTOME pathway enrichment analyses were performed. Furthermore, a protein–protein interaction (PPI) network was constructed and modules were analyzed using the Human Integrated Protein–Protein Interaction rEference database and Cytoscape software, and hub genes were identified. Subsequently, a network between miRNAs and hub genes, and network between TFs and hub genes were constructed using the miRNet and NetworkAnalyst tool, and possible key miRNAs and TFs were predicted. Finally, receiver operating characteristic curve analysis was used to validate the hub genes. Results A total of 958 DEGs, including 479 upregulated genes and 479 downregulated genes, were screened between endometriosis and normal control samples. GO and REACTOME pathway enrichment analyses of the 958 DEGs showed that they were mainly involved in multicellular organismal process, developmental process, signaling by GPCR and muscle contraction. Further analysis of the PPI network and modules identified 10 hub genes, including vcam1, snca, prkcb, adrb2, foxq1, mdfi, actbl2, prkd1, dapk1 and actc1. Possible target miRNAs, including hsa-mir-3143 and hsa-mir-2110, and target TFs, including tcf3 (transcription factor 3) and clock (clock circadian regulator), were predicted by constructing a miRNA-hub gene regulatory network and TF-hub gene regulatory network. Conclusions This investigation used bioinformatics techniques to explore the potential and novel biomarkers. These biomarkers might provide new ideas and methods for the early diagnosis, treatment and monitoring of endometriosis.

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Increased ApoE Expression in Follicular Fluid and the ApoE Genotype Are Associated With Endometriosis in Chinese Women

Cited by 4 publications

References 31 publications

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next generation sequencing data analysis

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next generation sequencing data analysis

Cell cycle, apoptosis, cell differentiation, and lipid metabolism gene expression in endometriotic tissue and exposure to parabens and benzophenones

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next-generation sequencing data analysis

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