Complementary role of p57kip2 immunostaining in diagnosing hydatidiform mole subtypes.

OBJECTIVES: Gestational trophoblastic disease comprises of a spectrum of pregnancy-related tumours which includes complete (CHM) and partial hydatidiform moles (PHM). Accurate diagnosis and subclassification of HM subtypes are crucial as prognosis differs. Histopathological examination using haemotoxylin and eosin (H&E) staining remains the basis for diagnosing HM, with only 80% accuracy. p57kip2 is a cyclin-dependent kinase inhibitor (CDKI) protein and is strongly paternally imprinted, being expressed from maternal allele. Therefore, complete mole (CHM) with only paternal genome has nearly absent expression of p57kip2 compared to partial mole (PHM) having both paternal and maternal genomes. This study is aimed to determine usefulness of p57kip2 immunohistochemistry (IHC) analysis in the diagnosis of HM subtypes. METHODS: A total of 82 archived paraffin embedded HM tissues with subtypes classified based on H&E staining - 39 (47.5%) CHM, 41 (50.0%) PHM and two (2.43%) unclassified molar pregnancy were retrieved. All tissue samples were subjected for p57kip2 IHC analysis and HM subtypes were then reclassified. RESULTS: A total of 66 cases (80.5%) were re-classified as CHM, 14 cases (17.1%) as PHM and two cases (2.4%) were decidual and cystic tissues. Analysis using p57kip2 immunostaining showed a diagnostic discrepancy of 33.0% from routine H&E staining and helps to improve the characterisation of the HM subtypes specifically at early gestations which have less distinctive morphologies. CONCLUSIONS: IHC using p57kip2 monoclonal antibody should be considered as a routine ancillary test to H&E in improving the diagnosis of HM subtypes particularly in developing countries with limited resources.

A Review of Candidate Genes and Pathways in Preeclampsia-An Integrated Bioinformatical Analysis.

: Preeclampsia is a pregnancy-specific disorder characterized by the presence of hypertension with the onset of either proteinuria, maternal organ or uteroplacental dysfunction. Preeclampsia is one of the leading causes of maternal and fetal mortality and morbidity worldwide. However, the etiopathologies of preeclampsia are not fully understood. Many studies have indicated that genes are differentially expressed between normal and in the disease state. Hence, this study systematically searched the literature on human gene expression that was differentially expressed in preeclampsia. An electronic search was performed through 2019 through PubMed, Scopus, Ovid-Medline, and Gene Expression Omnibus where the following MeSH (Medical Subject Heading) terms were used and they had been specified as the primary focus of the articles: Gene, placenta, preeclampsia, and pregnancy in the title or abstract. We also found additional MeSH terms through Cochrane Library: Transcript, sequencing, and profiling. From 687 studies retrieved from the search, only original publications that had performed high throughput sequencing of human placental tissues that reported on differentially expressed genes in pregnancies with preeclampsia were included. Two reviewers independently scrutinized the titles and abstracts before examining the eligibility of studies that met the inclusion criteria. For each study, study design, sample size, sampling type, and method for gene analysis and gene were identified. The genes listed were further analyzed with the DAVID, STRING and Cytoscape MCODE. Three original research articles involving preeclampsia comprising the datasets in gene expression were included. By combining three studies together, 250 differentially expressed genes were produced at a significance setting of p < 0.05. We identified candidate genes: LEP, NRIP1, SASH1, and ZADHHC8P1. Through GO analysis, we found extracellular matrix organization as the highly significant enriched ontology in a group of upregulated genes and immune process in downregulated genes. Studies on a genetic level have the potential to provide new insights into the regulation and to widen the basis for identification of changes in the mechanism of preeclampsia. Integrated bioinformatics could identify differentially expressed genes which could be candidate genes and potential pathways in preeclampsia that may improve our understanding of the cause and underlying molecular mechanisms that could be used as potential biomarkers for risk stratification and treatment.