Prior knowledge transfer across transcriptional data sets and technologies using compositional statistics yields new mislabelled ovarian cell line

Here, we describe gene expression compositional assignment (GECA), a powerful, yet simple method based on compositional statistics that can validate the transfer of prior knowledge, such as gene lists, into independent data sets, platforms and technologies. Transcriptional profiling has been used to derive gene lists that stratify patients into prognostic molecular subgroups and assess biomarker performance in the pre-clinical setting. Archived public data sets are an invaluable resource for subsequent in silico validation, though their use can lead to data integration issues. We show that GECA can be used without the need for normalising expression levels between data sets and can outperform rank-based correlation methods. To validate GECA, we demonstrate its success in the cross-platform transfer of gene lists in different domains including: bladder cancer staging, tumour site of origin and mislabelled cell lines. We also show its effectiveness in transferring an epithelial ovarian cancer prognostic gene signature across technologies, from a microarray to a next-generation sequencing setting. In a final case study, we predict the tumour site of origin and histopathology of epithelial ovarian cancer cell lines. In particular, we identify and validate the commonly-used cell line OVCAR-5 as non-ovarian, being gastrointestinal in origin. GECA is available as an open-source R package.

Exploring the needs, concerns and knowledge of women diagnosed with gestational diabetes: A qualitative study

Objective
To explore the concerns, needs and knowledge of women diagnosed with Gestational Diabetes Mellitus (GDM).
Design
A qualitative study of women with GDM or a history of GDM.
Methods
Nineteen women who were both pregnant and recently diagnosed with GDM or post- natal with a recent history of GDM were recruited from outpatient diabetes care clinics. This qualitative study utilised focus groups. Participants were asked a series of open-ended questions to explore 1) current knowledge of GDM; 2) anxiety when diagnosed with GDM, and whether this changed overtime; 3) understanding and managing GDM and 4) the future impact of GDM. The data were analysed using a conventional content analysis approach.
Findings
Women experience a steep learning curve when initially diagnosed and eventually become skilled at managing their disease effectively. The use of insulin is associated with fear and guilt. Diet advice was sometimes complex and not culturally appropriate. Women appear not to be fully aware of the short or long-term consequences of a diagnosis of GDM.
Conclusions
Midwives and other Health Care Professionals need to be cognisant of the impact of a diagnosis of GDM and give individual and culturally appropriate advice (especially with regards to diet). High quality, evidence based information resources need to be made available to this group of women. Future health risks and lifestyle changes need to be discussed at diagnosis to ensure women have the opportunity to improve their health.