Curated compendium of human transcriptional biomarker data

TidyGEO: preparing analysis-ready datasets from Gene Expression Omnibus

TidyGEO is a Web-based tool for downloading, tidying, and reformatting data series from Gene Expression Omnibus (GEO). As a freely accessible repository with data from over 6 million biological samples across more than 4000 organisms, GEO provides diverse opportunities for secondary research. Although scientists may find assay data relevant to a given research question, most analyses require sample-level annotations. In GEO, such annotations are stored alongside assay data in delimited, text-based files. However, the structure and semantics of the annotations vary widely from one series to another, and many annotations are not useful for analysis purposes. Thus, every GEO series must be tidied before it is analyzed. Manual approaches may be used, but these are error prone and take time away from other research tasks. Custom computer scripts can be written, but many scientists lack the computational expertise to create such scripts. To address these challenges, we created TidyGEO, which supports essential data-cleaning tasks for sample-level annotations, such as selecting informative columns, renaming columns, splitting or merging columns, standardizing data values, and filtering samples. Additionally, users can integrate annotations with assay data, restructure assay data, and generate code that enables others to reproduce these steps.

The ability to classify patients based on gene-expression data varies by algorithm and performance metric

By classifying patients into subgroups, clinicians can provide more effective care than using a uniform approach for all patients. Such subgroups might include patients with a particular disease subtype, patients with a good (or poor) prognosis, or patients most (or least) likely to respond to a particular therapy. Transcriptomic measurements reflect the downstream effects of genomic and epigenomic variations. However, high-throughput technologies generate thousands of measurements per patient, and complex dependencies exist among genes, so it may be infeasible to classify patients using traditional statistical models. Machine-learning classification algorithms can help with this problem. However, hundreds of classification algorithms exist-and most support diverse hyperparameters-so it is difficult for researchers to know which are optimal for gene-expression biomarkers. We performed a benchmark comparison, applying 52 classification algorithms to 50 gene-expression datasets (143 class variables). We evaluated algorithms that represent diverse machine-learning methodologies and have been implemented in general-purpose, open-source, machine-learning libraries. When available, we combined clinical predictors with gene-expression data. Additionally, we evaluated the effects of performing hyperparameter optimization and feature selection using nested cross validation. Kernel- and ensemble-based algorithms consistently outperformed other types of classification algorithms; however, even the top-performing algorithms performed poorly in some cases. Hyperparameter optimization and feature selection typically improved predictive performance, and univariate feature-selection algorithms typically outperformed more sophisticated methods. Together, our findings illustrate that algorithm performance varies considerably when other factors are held constant and thus that algorithm selection is a critical step in biomarker studies.

A broad analysis in clinical and in vitro models on regulator of G-protein signalling 10 regulation that is associated with ovarian cancer progression and chemoresistance

Ovarian cancer is one of the deadliest types of gynaecological cancers and more than half of the patients die within 5 years after diagnosis. Recurrence in advanced staged patients after chemotherapy is associated with increased chemoresistance, which results in poor prognosis. Regulator of G-protein signalling 10 (RGS10) negatively regulates cell proliferation, migration and survival by attenuating G-protein coupled-receptors mediated signalling pathways. Recent studies have shown that loss of RGS10 expression is significantly associated with proliferation and cisplatin resistance in ovarian cancer cells. SIGNIFICANCE OF THE STUDY: In this study, we analysed differential RGS10 expression levels using public microarray datasets from clinical and in vitro ovarian cancer samples. We validated that cancer progression and chemotherapy exposure change RGS10 expression. We enriched our study to evaluate the relationship between chemoresistance and differential RGS10 expression against ovarian cancer potential chemotherapeutic agent, palbociclib. Results showed that palbociclib treatment reduced cell viability, despite significantly decreased RGS10 expression in chemoresistant cells. Overall, the results confirmed that cancer progression and chemoresistance are significantly associated with the down-regulation of RGS10 while some chemotherapeutics seem to be beneficial in decreasing chemoresistance in ovarian cancer.

Host-defense peptides and their potential use as biomarkers in human diseases

Since the early 19th century, host-defense peptides (HDPs) have been known to play a crucial role in innate host defense. Subsequent work has demonstrated their role in adaptive immunity as well as their involvement in cancer and also a number of inflammatory and/or autoimmune diseases. In addition to these multiple functional activities, several studies have shown that HDP accumulation might be correlated with various human diseases and, therefore, could be used as a biomarkers for such. Thus, research has aimed to validate the clinical use of HDPs for diagnosis, prognosis, and further treatment. In this review, we outline the most recent findings related to the use of HDPs as biomarkers, their clinical and epidemiological value, and the techniques used to determine the levels of HDPs.