Characterization of cell cycle, inflammation, and oxidative stress signaling role in non-communicable diseases: Insights into genetic variants, microRNAs and pathways

D’Antona, Salvatore; Porro, Danilo; Gallivanone, Francesca; Bertoli, Gloria

doi:10.1016/j.compbiomed.2024.108346

Cited by 2 publications

(1 citation statement)

References 142 publications

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“…CCND2 [814], VCAM1 [615], PDGFB (platelet derived growth factor subunit B) [795], PRKCB (protein kinase C beta) [780], ACTC1 [924], DAPK1 [947], hsa-mir-17-5p [1672], hsa-mir-2110 [1673], TCF3 [1674] and SMARCA4 [1675] are associated with developing cardiovascular diseases. CCND2 [1083], VCAM1 [761], PDGFB (platelet derived growth factor subunit B) [1059], PTCH1 [1098], FOXQ1 [1044], IGF2BP1 [1162], ACTC1 [1192], DAPK1 [947], hsa-mir-2110 [1676], hsa-mir-10b-5p [1677], TCF3 [1678], NR1I2 [1679] and TRIM28 [1680] have been shown to be associated with inflammation. Studies have shown that CCND2 [1495], VCAM1 [1462], PTPRD (protein tyrosine phosphatase receptor type D) [1496], PRKCB (protein kinase C beta) [1476], IGF2BP1 [1545], hsa-mir-200a-3p [1681] and hsa-mir-10b-5p [1682] are an important biomarkers of diabetes mellitus.…”

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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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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Characterization of cell cycle, inflammation, and oxidative stress signaling role in non-communicable diseases: Insights into genetic variants, microRNAs and pathways

Cited by 2 publications

References 142 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

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

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