VCAM-1 complements CA-125 in detecting recurrent ovarian cancer

Song, Jin; Sokoll, Lori J.; Zhang, Zhen; Chan, Daniel W.

doi:10.1186/s12014-023-09414-z

Cited by 3 publications

(3 citation statements)

References 48 publications

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“…VCAM1 [110], ADRB2 [126], and DAPK1 [183] might serve as genetic markers of endometriosis. The expression of VCAM1 [190], ADRB2 [237], FOXQ1 [210], ACTBL2 [357], DAPK1 [379], ACTC1 [359], CST4 [229], NFIB (nuclear factor I B) [363], NFIX (nuclear factor I X) [1657] and ERG (ETS transcription factor ERG) [1658] were altered in the ovarian cancer. VCAM1 [478], FOXQ1 [492], DAPK1 [609] and ERG (ETS transcription factor ERG) [1659] promotes the development of cervical cancer.…”

Section: Discussionmentioning

confidence: 99%

“…These analyses could help to find some miRNAs, TFs and hub genes involved in the regulation of endometriosis. CCND2 [246], VCAM1 [190], PDGFB (platelet derived growth factor subunit B) [221], PTCH1 [263], FOXQ1 [210], IGF2BP1 [324], ACTC1 [359], EPB41L3 [315], DAPK1 [379], hsa-mir-17-5p [1663], TCF3 [1664], RNF2 [1665], CLOCK (clock circadian regulator) [1666], SMARCA4 [1667] and TRIM28 [1668] provided a clear picture of the prognosis of patients with ovarian cancer. CCND2 [511], VCAM1 [478], PTCH1 [523], FOXQ1 [492], IGF2BP1 [568], EPB41L3 [560], DAPK1 [609], hsa-mir-17-5p [1669], TCF3 [1670] and TRIM28 [1671] as a biomarkers for cervical cancer.…”

Section: Discussionmentioning

confidence: 99%

“…In this investigation, we identified enriched genes in GO terms and signaling pathways that might be utilized as diagnostic and/or therapeutic targets in endometriosis. Signaling pathways include extracellular matrix organization [101], nervous system development [102], signal transduction [103], hemostasis [104], muscle contraction [105], signaling by retinoic acid [106] [189], VCAM1 [190], GRP (gastrin releasing peptide) [191], AQP8 [192], WNT6 [193], FABP4 [194], SOX6 [195], NTRK1 [196], CNTN1 [197], MMP12 [198], LAG3 [199], SOX18 [200], CCR1 [201], FLT1 [202], PRDM1 [203], TRPC3 [204], DKK2 [205], RNF157 [206], DHCR24 [207], BMP4 [208], PRL (prolactin) [209],…”

Section: Go and Pathway Enrichment Analyses Of Degsmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Go and Pathway Enrichment Analyses Of Degsmentioning

confidence: 99%

See 1 more Smart Citation

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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MUC16/CA125 in cancer: new advances

Zhang,

Hong,

Ling

2025

Clinica Chimica Acta

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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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VCAM-1 complements CA-125 in detecting recurrent ovarian cancer

Cited by 3 publications

References 48 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

MUC16/CA125 in cancer: new advances

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

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