SLCO4A1 expression is associated with activated inflammatory pathways in high-grade serous ovarian cancer

Koller, Stephanie; Kendler, Jonatan; Karacs, Jasmine; Wolf, Andrea; Kreuzinger, Caroline; Decken, Isabel von der; Mungenast, Felicitas; Mechtcheriakova, Diana; Schreiner, Wolfgang; Gleiß, Andreas; Jäger, Walter; Castillo‐Tong, Dan Cacsire; Thalhammer, Theresia

doi:10.3389/fphar.2022.946348

Cited by 4 publications

(1 citation statement)

References 48 publications

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“…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%

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: 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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Identification of novel key genes and signaling pathways in hypertrophic cardiomyopathy: evidence from bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2024

Preprint

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Hypertrophic cardiomyopathy (HCM) is a global health problem characterized by left ventricle become thick and stiff with effect of indication including chest pain, fluttering, fainting and shortness of breath. In this investigation, we aimed to identify diagnostic markers and analyzed the therapeutic potential of essential genes. Next generation sequencing (NGS) dataset GSE180313 was obtained from the Gene Expression Omnibus (GEO) database and used to identify differentially expressed genes (DEGs) in HCM. DEGs were screened using the DESeq2 Rbioconductor tool. Then, Gene Ontology (GO) and REACTOME pathway enrichment analyses were performed. Moreover, a protein-protein interaction (PPI) network of the DEGs was constructed, and module analysis was performed. Next, miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed and analyzed. Finally, diagnostic values of hub genes were assessed by by receiver operating characteristic (ROC) curve analysis. By performing DEGs analysis, total 958 DEGs ( 479 up regulated genes and 479 down regulated genes) were successfully identified from GSE180313, respectively. GO and REACTOME pathway enrichment analyses revealed that GO functions and signaling pathways were significantly enriched including response to stimulus, multicellular organismal process, metabolism and extracellular matrix organization. The hub genes of FN1, SOX2, TUBA4A, RPS2, TUBA1C, ESR1, SNCA, LCK, PAK1 and APLNR might be associated with HCM. The hub gens of FN1 and TPM3, together with corresponding predicted miRNAs (e.g., hsa-mir-374a-5p and hsa-miR-8052), and SH3KBP1 and ESR1 together with corresponding predicted TFs (e.g PRRX2 and STAT3) were found to be significantly correlated with HCM. This investigation could serve as a basis for further understanding the molecular pathogenesis and potential therapeutic targets of HCM.

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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.

show abstract

SLCO4A1 expression is associated with activated inflammatory pathways in high-grade serous ovarian cancer

Cited by 4 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

Identification of novel key genes and signaling pathways in hypertrophic cardiomyopathy: evidence from 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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