The expression of Platelet-derived Growth factor receptors (PDGFRs) and their correlation with overall survival of patients with ovarian cancer

Szubert, Sebastian; Moszyński, Rafał; Szpurek, Dariusz; Romaniuk, Bartlomiej; Sajdak, Stefan; Nowicki, Michał; Michalak, Sławomir

doi:10.5603/gp.a2019.0045

Cited by 9 publications

(8 citation statements)

References 32 publications

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“…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%

“…FOXQ1 [210], WNT5A [211], MEOX1 [212], DOCK4 [213], TIMP3 [214], ADAMTS9 [215], NDNF (neuron derived neurotrophic factor) [216], NETO1 [217], CD24 [218], LHCGR (luteinizing hormone/choriogonadotropin receptor) [219], SCD (stearoyl-CoA desaturase) [220], PDGFB (platelet derived growth factor subunit B) [221], MMRN1 [222], LDLR (low density lipoprotein receptor) [223], CD4 [224], FOXL2 [225], TRPA1 [226], EPHA5 [227], TOX (thymocyte selection associated high mobility group box) [228] [253], CLDN3 [254], LEPR (leptin receptor) [255], IL15 [256], BMP2 [257], LAMA5 [258], NTNG1 [259], KRT19 [260], ROS1 [261], APOE (apolipoprotein E) [262], PTCH1 [263], ITPKA (inositol-trisphosphate 3-kinase A) [264], CASP1 [265], NID1 [266], ABCG2 [267], ACE (angiotensin I converting enzyme) [268], PGR (progesterone receptor) [269], WLS (Wnt ligand secretion mediator) [270], KLK3 [271], LRP1B [272], LY6K [273], ALPP (alkaline phosphatase, placental) [274], PRAME (PRAME nuclear receptor transcriptional regulator) [275], SLCO4A1 [276], EGFL6…”

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: Go and Pathway Enrichment Analyses Of Degsmentioning

confidence: 99%

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

Vastrad,

Vastrad

2024

Preprint

View full text Add to dashboard Cite

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“…Tumor angiogenesis is closely related to platelets. 38 Platelets come into contact with prethrombus structures such as collagen in blood vessels, causing changes in blood flow, and endothelial cells release von Willebrand factor to activate platelets. Activated platelets mainly release regulatory factors that promote angiogenesis, such as VEGF and PDGF.…”

Section: Discussionmentioning

confidence: 99%

Prognostic Significance of Platelet (PLT) and Platelet to Mean Platelet Volume (PLT/MPV) Ratio During Apatinib Second-Line or Late-Line Treatment in Advanced Esophageal Squamous Cell Carcinoma Patients

Jian

et al. 2022

Technol Cancer Res Treat

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Background: Apatinib has a certain efficacy for advanced esophageal squamous cell carcinoma (ESCC). This study aimed to investigate the prognostic significance of platelet (PLT) and platelet to mean platelet volume (PLT/MPV) ratio for advanced ESCC patients with apatinib second-line or late-line treatment. Methods: A retrospective study included 80 patients with advanced ESCC who received Apatinib ≥ 2 lines targeted therapy. We collected baseline clinical characteristics and blood parameters from the patients. Kaplan–Meier plots and univariate and multivariate analysis were used to find the factors related to progression-free survival (PFS). Results: The optimal cut-off values of PLT and PLT/MPV ratio were determined by X-tile software. Kaplan–Meier analysis demonstrated that patients in the high PLT group had better PFS than those in the low PLT group (156 d vs 80 d, P <.001), and patients in the high PLT/MPV ratio group had better PFS than those in low PLT/MPV ratio group (157 d vs 85 d, P <.001). Univariate analysis revealed pretreatment PLT and PLT/MPV ratio were significantly correlated with PFS. Multivariate analysis revealed high levels of pretreatment PLT/MPV ratio was an independent predictor of longer PFS (HR: 0.257, 95% CI: 0.089-0.743, P = .012). Conclusion: High levels of baseline PLT and PLT/MPV may indicate a better prognosis in apatinib ≥ 2 lines treatment for advanced ESCC patients.

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“…Embryonic development, cell proliferation, survival, differentiation, chemotaxis, and migration are all regulated by the protein that the PDGFRB gene produces [ 35 , 36 ]. Previous studies have established the link between PDGFRB overexpression and a poor prognosis in patients with renal cell carcinoma [ 37 ], oral squamous cell carcinoma [ 38 ], ovarian cancer [ 39 ], and colorectal cancer [ 40 ]. Involved in cell proliferation, the transcriptional activator FOXM1 may have an impact on the expression of several cell cycle genes, including cyclin B1 and cyclin D. Numerous studies have revealed the association of elevated FOXM1 expression levels with a poor prognosis in patients with ovarian cancer [ 41 ], pancreatic and esophageal cancers [ 42 ], malignant rhabdoid tumors [ 41 ], and small-cell lung cancer [ 43 ].…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Combined Ferroptosis and Immune Prognostic Model for Melanoma

Tang

Wang

et al. 2022

Journal of Oncology

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Background. Melanoma development and progression are significantly influenced by ferroptosis and the immune microenvironment. However, there are no reliable biomarkers for melanoma prognosis prediction based on ferroptosis and immunological response. Methods. Ferroptosis-related genes (FRGs) were retrieved from the FerrDb website. Immune-related genes (IRGs) were collected in the ImmPort dataset. The TCGA (The Cancer Genome Atlas) and GSE65904 datasets both contained prognostic FRGs and IRGs. The model was created using multivariate Cox regression, the least absolute shrinkage and selection operator (LASSO) Cox regression analysis, and the analysis and comparison between the expression patterns of ferroptosis and immune cell infiltration were done. Last but not least, research was conducted to assess the expression and involvement of the genes in the comprehensive index of ferroptosis and immune (CIFI). Results. Two prognostic ferroptosis- and immune-related markers (PDGFRB and FOXM1) were utilized to develop a CIFI. In various datasets and patient subgroups, CIFI exhibits consistent predictive performance. The fact that CIFI is an independent prognostic factor for melanoma patients was revealed. Patients in the CIFI-high group further exhibited immune-suppressive characteristics and had elevated ferroptosis gene expression levels. The results of in vitro research point to the possibility that the PDGFRB and FOXM1 genes function as oncogenes in melanoma. Conclusion. In this study, a novel prognostic classifier for melanoma patients was developed and validated using ferroptosis and immune expression profiles.

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The expression of Platelet-derived Growth factor receptors (PDGFRs) and their correlation with overall survival of patients with ovarian cancer

Cited by 9 publications

References 32 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

Prognostic Significance of Platelet (PLT) and Platelet to Mean Platelet Volume (PLT/MPV) Ratio During Apatinib Second-Line or Late-Line Treatment in Advanced Esophageal Squamous Cell Carcinoma Patients

Development and Validation of a Combined Ferroptosis and Immune Prognostic Model for Melanoma

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