Analysis of differentially expressed genes in placental tissues of preeclampsia patients using microarray combined with the Connectivity Map database

Song, Yeguang; Liu, J.; Huang, S.; Zhang, L.

doi:10.1016/j.placenta.2013.09.013

Cited by 18 publications

(11 citation statements)

References 43 publications

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“…When the subtypes were merged, a prediction value similar to past publications was observed (w70%), 88 thus confirming that previous biomarker assessment has been confounded by disease heterogeneity.…”

Section: Mirnasupporting

confidence: 82%

“…77,78 Other genes detected in PE microarray studies, such as LEP, HTRA1, INHA, INHBA, PAPPA2, and FSTL3, have all been investigated as potential biomarkers of this pathology, 9,79-81 and have been found to be enriched in cell signaling, lipid response, apoptosis, hypoxia, immune, inflammation, and oxidative stress pathways. [82][83][84][85][86][87][88][89] Additionally, very similar changes in placental gene expression have been noted to occur in HELLP (hemolysis, elevated liver enzymes, and low platelets) syndrome by microarray analysis. 90 Meta and integrative analysis of mRNA microarray data in PE While each published data set of PE placentas is relatively small (1-23 PE patients), when combined they represent nearly 250 PE samples.…”

Section: Mirnamentioning

confidence: 77%

See 1 more Smart Citation

Placental transcriptome in development and pathology: expression, function, and methods of analysis

Cox¹,

Leavey²,

Nosi³

et al. 2015

American Journal of Obstetrics and Gynecology

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Section: Mirnasupporting

confidence: 82%

Section: Mirnamentioning

confidence: 77%

Placental transcriptome in development and pathology: expression, function, and methods of analysis

Cox¹,

Leavey²,

Nosi³

et al. 2015

American Journal of Obstetrics and Gynecology

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“…Moreover we can’t confirm that these up or down-regulated genes can be used as a risk evaluator or predictive measure without further experimental analysis in a longitudinal design. Even with all previous considerations, microarrays information is used for bioinformatics analysis and gene prioritization suggesting that here are genes that can be probably related with pathogenesis [6–12]. …”

Section: Introductionmentioning

confidence: 99%

Consensus strategy in genes prioritization and combined bioinformatics analysis for preeclampsia pathogenesis

et al. 2017

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BackgroundPreeclampsia is a multifactorial disease with unknown pathogenesis. Even when recent studies explored this disease using several bioinformatics tools, the main objective was not directed to pathogenesis. Additionally, consensus prioritization was proved to be highly efficient in the recognition of genes-disease association. However, not information is available about the consensus ability to early recognize genes directly involved in pathogenesis. Therefore our aim in this study is to apply several theoretical approaches to explore preeclampsia; specifically those genes directly involved in the pathogenesis.MethodsWe firstly evaluated the consensus between 12 prioritization strategies to early recognize pathogenic genes related to preeclampsia. A communality analysis in the protein-protein interaction network of previously selected genes was done including further enrichment analysis. The enrichment analysis includes metabolic pathways as well as gene ontology. Microarray data was also collected and used in order to confirm our results or as a strategy to weight the previously enriched pathways.ResultsThe consensus prioritized gene list was rationally filtered to 476 genes using several criteria. The communality analysis showed an enrichment of communities connected with VEGF-signaling pathway. This pathway is also enriched considering the microarray data. Our result point to VEGF, FLT1 and KDR as relevant pathogenic genes, as well as those connected with NO metabolism.ConclusionOur results revealed that consensus strategy improve the detection and initial enrichment of pathogenic genes, at least in preeclampsia condition. Moreover the combination of the first percent of the prioritized genes with protein-protein interaction network followed by communality analysis reduces the gene space. This approach actually identifies well known genes related with pathogenesis. However, genes like HSP90, PAK2, CD247 and others included in the first 1% of the prioritized list need to be further explored in preeclampsia pathogenesis through experimental approaches.Electronic supplementary materialThe online version of this article (doi:10.1186/s12920-017-0286-x) contains supplementary material, which is available to authorized users.

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“…Expression profiling studies at the pre-eclamptic maternal–foetal interface frequently and consistently reveal dysregulated expression of genes involved in the oxidative stress and inflammatory pathways ( Eide et al , 2008 ; Loset et al , 2011 ; Tsai et al , 2011 ; Song et al , 2013 ; Yong et al , 2015 ; Tong et al , 2018 ). Moreover, abnormal gene expression signatures of extravillous trophoblast cells, which was identified by the first single-cell RNA sequencing study of pre-eclamptic placenta, were also detectable in the maternal circulation in pre-eclampsia and may potentially serve as a non-invasive marker or ‘liquid biopsy’ of anomalous placental function in the future ( Tsang et al , 2017 ).…”

Section: Placental Transcriptome Studies Of Pregnancy Complicationsmentioning

confidence: 99%

Current approaches and developments in transcript profiling of the human placenta

Yong

Chan

2020

Human Reproduction Update

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BACKGROUND The placenta is the active interface between mother and foetus, bearing the molecular marks of rapid development and exposures in utero. The placenta is routinely discarded at delivery, providing a valuable resource to explore maternal-offspring health and disease in pregnancy. Genome-wide profiling of the human placental transcriptome provides an unbiased approach to study normal maternal–placental–foetal physiology and pathologies. OBJECTIVE AND RATIONALE To date, many studies have examined the human placental transcriptome, but often within a narrow focus. This review aims to provide a comprehensive overview of human placental transcriptome studies, encompassing those from the cellular to tissue levels and contextualize current findings from a broader perspective. We have consolidated studies into overarching themes, summarized key research findings and addressed important considerations in study design, as a means to promote wider data sharing and support larger meta-analysis of already available data and greater collaboration between researchers in order to fully capitalize on the potential of transcript profiling in future studies. SEARCH METHODS The PubMed database, National Center for Biotechnology Information and European Bioinformatics Institute dataset repositories were searched, to identify all relevant human studies using ‘placenta’, ‘decidua’, ‘trophoblast’, ‘transcriptome’, ‘microarray’ and ‘RNA sequencing’ as search terms until May 2019. Additional studies were found from bibliographies of identified studies. OUTCOMES The 179 identified studies were classifiable into four broad themes: healthy placental development, pregnancy complications, exposures during pregnancy and in vitro placental cultures. The median sample size was 13 (interquartile range 8–29). Transcriptome studies prior to 2015 were predominantly performed using microarrays, while RNA sequencing became the preferred choice in more recent studies. Development of fluidics technology, combined with RNA sequencing, has enabled transcript profiles to be generated of single cells throughout pregnancy, in contrast to previous studies relying on isolated cells. There are several key study aspects, such as sample selection criteria, sample processing and data analysis methods that may represent pitfalls and limitations, which need to be carefully considered as they influence interpretation of findings and conclusions. Furthermore, several areas of growing importance, such as maternal mental health and maternal obesity are understudied and the profiling of placentas from these conditions should be prioritized. WIDER IMPLICATIONS Integrative analysis of placental transcriptomics with other ‘omics’ (methylome, proteome and metabolome) and linkage with future outcomes from longitudinal studies is crucial in enhancing knowledge of healthy placental development and function, and in enabling the underlying causal mechanisms of pregnancy complications to be identified. Such understanding could help in predicting risk of future adversity and in designing interventions that can improve the health outcomes of both mothers and their offspring. Wider collaboration and sharing of placental transcriptome data, overcoming the challenges in obtaining sufficient numbers of quality samples with well-defined clinical characteristics, and dedication of resources to understudied areas of pregnancy will undoubtedly help drive the field forward.

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Analysis of differentially expressed genes in placental tissues of preeclampsia patients using microarray combined with the Connectivity Map database

Cited by 18 publications

References 43 publications

Placental transcriptome in development and pathology: expression, function, and methods of analysis

Placental transcriptome in development and pathology: expression, function, and methods of analysis

Consensus strategy in genes prioritization and combined bioinformatics analysis for preeclampsia pathogenesis

Current approaches and developments in transcript profiling of the human placenta

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