Summary: The Gene Expression Omnibus (GEO) is a public repository of gene expression data. Although GEO has its own tool, GEO2R, for data analysis, evaluation of single genes is not straightforward and survival analysis in specific GEO datasets is not possible without bioinformatics expertise. We describe a web application, shinyGEO, that allows a user to download gene expression data sets directly from GEO in order to perform differential expression and survival analysis for a gene of interest. In addition, shinyGEO supports customized graphics, sample selection, data export, and R code generation so that all analyses are reproducible. The availability of shinyGEO makes GEO datasets more accessible to non-bioinformaticians, promising to lead to better understanding of biological processes and genetic diseases such as cancer.

The protein family of aldehyde dehydrogenases (ALDH) encompasses nineteen members. The ALDH1 subfamily consists of enzymes with similar activity, having the capacity to neutralize lipid peroxidation products and to generate retinoic acid; however, only ALDH1A1 emerges as a significant risk factor in acute myeloid leukemia. Not only is the gene ALDH1A1 on average significantly overexpressed in the poor prognosis group at the RNA level, but its protein product, ALDH1A1 protects acute myeloid leukemia cells from lipid peroxidation byproducts. This capacity to protect cells can be ascribed to the stability of the enzyme under conditions of oxidant stress. The capacity to protect cells is evident both in vitro, as well as in mouse xenografts of those cells, shielding cells effectively from a number of potent antineoplastic agents. However, the role of ALDH1A1 in acute myeloid leukemia has been unclear in the past due to evidence that normal cells often have higher aldehyde dehydrogenase activity than leukemic cells. This being true, ALDH1A1 RNA expression is significantly associated with poor prognosis. It is hence imperative that ALDH1A1 is methodically targeted, particularly for the acute myeloid leukemia patients of the poor prognosis risk group that overexpress ALDH1A1 RNA.

Breast cancer (BCa) is the most frequently diagnosed malignant tumor in women and is also one of the leading causes of cancer-related death. Most breast tumors are hormone-dependent and estrogen signaling plays a critical role in promoting the survival and malignant behaviors of these cells. Estrogen signaling involves ligand-activated cytoplasmic estrogen receptors that translocate to the nucleus with various co-regulators, such as steroid receptor co-activator (SRC) family members, and bind to the promoters of target genes and regulate their expression. SRC-3 is a member of this family that interacts with, and enhances, the transcriptional activity of the ligand activated estrogen receptor. Although SRC-3 has important roles in normal homeostasis and developmental processes, it has been shown to be amplified and overexpressed in breast cancer and to promote malignancy. The malignancy-promoting potential of SRC-3 is diverse and involves both promoting malignant behavior of tumor cells and creating a tumor microenvironment that has an immunosuppressive phenotype. SRC-3 also inhibits the recruitment of tumor-infiltrating lymphocytes with effector function and promotes stemness. Furthermore, SRC-3 is also involved in the development of resistance to hormone therapy and immunotherapy during breast cancer treatment. The versatility of SRC-3 in promoting breast cancer malignancy in this way makes it a good target, and methodical targeting of SRC-3 probably will be important for the success of breast cancer treatment.

// <![CDATA[ $('.header-date').hide();$('#titleAuthors').hide();$('#abstractHeader').hide(); // ]]> Craig S. Richmond1, Darby Oldenburg1, Garrett Dancik2, David R. Meier1, Benjamin Weinhaus1, Dan Theodorescu3 and Sunny Guin1 1Medical Research, Gundersen Medical Foundation, La Crosse, WI, USA 2Department of Mathematics and Computer Science, Eastern Connecticut State University, Willimantic, CT, USA 3Department of Surgery (Urology), University of Colorado, Aurora, CO, USA Correspondence to: Sunny Guin, email: sguin@gundersenhealth.org Keywords: non-small cell lung cancer; hyaluronic acid; AGL; HAS2; RHAMM Received: September 06, 2017 Accepted: February 28, 2018 Published: March 30, 2018 ABSTRACT Glycogen debranching enzyme (AGL) and Glycogen phosphorylase (PYG) are responsible for glycogen breakdown. We have earlier shown that AGL is a regulator of bladder tumor growth. Here we investigate the role of AGL in non-small cell lung cancers (NSCLC). Short hairpin RNA (shRNA) driven knockdown of AGL resulted in increased anchorage independent and xenograft growth of NSCLC cells. We further establish that an increase in hyaluronic acid (HA) synthesis driven by Hyaluronic Acid Synthase 2 (HAS2) is critical for anchorage independent growth of NSCLC cells with AGL loss. Using gene knockdown approach against HAS2 and by using 4-methylumbelliferone (4MU), an inhibitor of HA synthesis, we show that HA synthesis is critical for growth of NSCLC cells that have lost AGL. We further show NSCLC cells without AGL expression are dependent on RHAMM for HA signaling and growth. Analysis of NSCLC patient datasets established that patients with low AGL/high HAS2 or low AGL/high RHAMM mRNA expression have poor overall survival compared to patients with high AGL/low HAS2 or high AGL/low RHAMM expression. We show for the first time that loss of AGL promotes anchorage independent growth of NSCLC cells. We further show that HAS2 driven HA synthesis and signaling via RHAMM is critical in regulating growth of these cancer cells with AGL loss. Further patients presenting with low AGL and HAS2 or RHAMM over expressing tumors might present the ideal cohort who would respond to inhibitors of HA synthesis and signaling.

In the past ~15 years, the identification of diagnostic and prognostic biomarkers from gene expression data has increased our understanding of cancer biology and has led to advances in the personalized treatment of many cancers. A diagnostic biomarker is indicative of tumor status such as tumor stage, while a prognostic biomarker is indicative of disease outcome. Despite these advances, however, there are no clinically approved biomarkers for the treatment of bladder cancer, which is the fourth most common cancer in males in the United States and one of the most expensive cancers to treat. Although gene expression profiles of bladder cancer patients are publicly available, biomarker identification requires bioinformatics expertise that is not available to many research laboratories. We collected gene expression data from 13 publicly available patient cohorts (N = 1454) and developed BC-BET, an online Bladder Cancer Biomarker Evaluation Tool for evaluating candidate diagnostic and prognostic gene expression biomarkers in bladder cancer. A user simply selects a gene, and BC-BET evaluates the utility of that gene’s expression as a diagnostic and prognostic biomarker. Specifically, BC-BET calculates how strongly a gene’s expression is associated with tumor presence (distinguishing tumor from normal samples), tumor grade (distinguishing low- from high-grade tumors), tumor stage (distinguishing non-muscle invasive from muscle invasive samples), and patient outcome (e.g., disease-specific survival) across all patients in each cohort. Patients with low-grade, non-muscle invasive tumors and patients with high-grade, muscle invasive tumors are also analyzed separately in order to evaluate whether the biomarker of interest has prognostic value independent of grade and stage. Although bladder cancer gene expression datasets are publicly available, their analysis is computationally intensive and requires bioinformatics expertise. BC-BET is an easy-to-use tool for rapidly evaluating bladder cancer gene expression biomarkers across multiple patient cohorts.