Klotho promoter methylation status and its prognostic value in ovarian cancer

Al-Zahrani, Maryam H.; Yahya, Fatimah M.; Assidi, Mourad; Dallol, Ashraf; Buhmeida, Abdelbaset

doi:10.3892/mco.2021.2343

Cited by 4 publications

(1 citation statement)

References 63 publications

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“…α-Klotho protein has been described as an extremely sensitive and early marker in CKD, as its levels reflect the degree of renal insufficiency, and it can be used as an indicator of CKD progression [33]. α-Klotho (i) may be also a novel biomarker and potential treatment target for diabetic-related complications [34] and (ii) may act as a tumor suppressor and modulator of carcinomas (e.g., hepatocellular and ovarian carcinomas), representing a potential biomarker for their diagnosis [35,36]. Furthermore, a recently published study suggests α-Klotho protein as a useful biomarker in the diagnosis and prognosis of respiratory diseases (e.g., bronchopulmonary dysplasia and pulmonary hypertension) in young populations [5].…”

Section: Discussionmentioning

confidence: 99%

References Values of Soluble α-Klotho Serum Levels Using an Enzyme-Linked Immunosorbent Assay in Healthy Adults Aged 18–85 Years

Espuch-Oliver

Vázquez-Lorente

Jurado‐Fasoli

et al. 2022

JCM

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α-Klotho protein is a powerful predictor of the aging process and lifespan. Although lowered circulating soluble α-Klotho levels have been observed in aged non-healthy individuals, no specific reference values across a wide range of ages and sex using an enzyme-linked immunosorbent assay (ELISA) are available for larger cohorts of healthy individuals. The present analytical cross-sectional study was aimed to establish the reference values of soluble α-Klotho serum levels in healthy adults by age and sex groups. A total of 346 (59% women) healthy individuals aged from 18 to 85 years were recruited. Subjects were divided by sex and age as: (i) young (18–34.9 years), (ii) middle-aged (35–54.9 years), and (iii) senior (55–85 years) individuals. The soluble α-Klotho levels were measured in serum using ELISA. Senior adults were the age-group that presented the lowest soluble α-Klotho serum levels (p < 0.01), with age showing a negative association with soluble α-Klotho serum levels (p < 0.001). No differences between sexes were observed. Therefore, soluble α-Klotho levels were especially decreased—regardless of sex—in our cohort of healthy individuals because of the physiological decline derived from the aging process. We recommend routine assessments of soluble α-Klotho levels using ELISA as a simple and cheap detectable marker of aging that improves quality of life in the elderly.

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Section: Discussionmentioning

confidence: 99%

References Values of Soluble α-Klotho Serum Levels Using an Enzyme-Linked Immunosorbent Assay in Healthy Adults Aged 18–85 Years

Espuch-Oliver

Vázquez-Lorente

Jurado‐Fasoli

et al. 2022

JCM

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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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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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Klotho promoter methylation status and its prognostic value in ovarian cancer

Cited by 4 publications

References 63 publications

References Values of Soluble α-Klotho Serum Levels Using an Enzyme-Linked Immunosorbent Assay in Healthy Adults Aged 18–85 Years

References Values of Soluble α-Klotho Serum Levels Using an Enzyme-Linked Immunosorbent Assay in Healthy Adults Aged 18–85 Years

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

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