Cytoplasmic Expression of the EGFL6 Protein Is an Independent Prognostic Factor for Shortened Patient Survival in Human Hepatocellular Carcinoma.

Background/aim: Hepatocellular carcinoma (HCC) is the most common primary liver tumor and the second leading cause of cancer-related deaths worldwide. The current study aimed to investigate the clinical relevance of the epidermal growth factor-like domain multiple 6 (EGFL6) expression in HCC and to evaluate whether the expression of EGFL6 in HCC has diagnostic and prognostic significance.

Patients And Methods: This study aimed to investigate EGFL6 protein expression levels in 260 HCC tissue specimens using immunohistochemical analyses.

Correlation Between Low Cytoplasmic Expression of XBP1 and the Likelihood of Surviving Hepatocellular Carcinoma.

Background/aim: Our objectives in this study were to (i) evaluate the clinical significance of X-box-binding protein 1 (XBP1) expression in cases of hepatocellular carcinoma (HCC) and (ii) assess the potential of XBP1 to be used as a prognostic biomarker.

Patients And Methods: The expression of XBP1 protein in 267 HCC tissue specimens was measured using immunohistochemistry in order to characterize the associations among XBP1 expression, clinicopathological factors and survival outcomes. Survival analysis using follow-up data was used to assess the prognostic value of XBP1 in cases of HCC.

Clinically-observed FOXA1 mutations upregulate SEMA3C through transcriptional derepression in prostate cancer.

FOXA1 is a pioneer transcription factor that is frequently mutated in prostate, breast, bladder, and salivary gland malignancies. Indeed, metastatic castration-resistant prostate cancer (mCRPC) commonly harbour FOXA1 mutations with a prevalence of 35%. However, despite the frequent recurrence of FOXA1 mutations in prostate cancer, the mechanisms by which FOXA1 variants drive its oncogenic effects are still unclear.

Impact of gene polymorphism coupled with habitual risks on buccal mucosa cancer.

Oral squamous cell carcinoma (OSCC) is a prevalent and lethal malignancy with a diverse etiology. LINC00312 is a long intergenic non-coding RNA that functions as a signal hub to regulate the progression and treatment of head and neck cancer. The aim of this study was to evaluate the effect of single nucleotide polymorphisms (SNPs) on the development of oral cancer.

Investigation of the impact of Globo-H expression on the progression of gastric cancer.

Globo-H (GH), a globo-series glycosphingolipid antigen that is synthesized by key enzymes β1,3-galactosyltransferase V (β3GalT5), fucosyltransferase (FUT) 1 and 2, is highly expressed on a variety of epithelial cancers rendering it a promising target for cancer immunotherapy. GH-targeting antibody-drug conjugate has been demonstrated an excellent tumor growth inhibition potency in animal models across multiple cancer types including Gastric cancer (GC). This study aims to further investigate the GH roles in GC.

Improved Delivery Performance of n-Butylidenephthalide-Polyethylene Glycol-Gold Nanoparticles Efficient for Enhanced Anti-Cancer Activity in Brain Tumor.

n-butylidenephthalide (BP) has been verified as having the superior characteristic of cancer cell toxicity. Furthermore, gold (Au) nanoparticles are biocompatible materials, as well as effective carriers for delivering bio-active molecules for cancer therapeutics. In the present research, Au nanoparticles were first conjugated with polyethylene glycol (PEG), and then cross-linked with BP to obtain PEG-Au-BP nanodrugs.

Deep Modeling of Regulating Effects of Small Molecules on Longevity-Associated Genes.

Aging is considered an inevitable process that causes deleterious effects in the functioning and appearance of cells, tissues, and organs. Recent emergence of large-scale gene expression datasets and significant advances in machine learning techniques have enabled drug repurposing efforts in promoting longevity. In this work, we further developed our previous approach-DeepCOP, a quantitative chemogenomic model that predicts gene regulating effects, and extended its application across multiple cell lines presented in LINCS to predict aging gene regulating effects induced by small molecules.

Drugging the 'undruggable'. Therapeutic targeting of protein-DNA interactions with the use of computer-aided drug discovery methods.

Transcription factors (TFs) act as major oncodrivers in many cancers and are frequently regarded as high-value therapeutic targets. The functionality of TFs relies on direct protein-DNA interactions, which are notoriously difficult to target with small molecules. However, this prior view of the 'undruggability' of protein-DNA interfaces has shifted substantially in recent years, in part because of significant advances in computer-aided drug discovery (CADD).

Deep Docking: A Deep Learning Platform for Augmentation of Structure Based Drug Discovery.

Drug discovery is a rigorous process that requires billion dollars of investments and decades of research to bring a molecule "from bench to a bedside". While virtual docking can significantly accelerate the process of drug discovery, it ultimately lags the current rate of expansion of chemical databases that already exceed billions of molecular records. This recent surge of small molecules availability presents great drug discovery opportunities, but also demands much faster screening protocols.

Rapid Identification of Potential Inhibitors of SARS-CoV-2 Main Protease by Deep Docking of 1.3 Billion Compounds.

The recently emerged 2019 Novel Coronavirus (SARS-CoV-2) and associated COVID-19 disease cause serious or even fatal respiratory tract infection and yet no approved therapeutics or effective treatment is currently available to effectively combat the outbreak. This urgent situation is pressing the world to respond with the development of novel vaccine or a small molecule therapeutics for SARS-CoV-2. Along these efforts, the structure of SARS-CoV-2 main protease (Mpro) has been rapidly resolved and made publicly available to facilitate global efforts to develop novel drug candidates.

Computer-Aided Discovery of Small Molecule Inhibitors of Thymocyte Selection-Associated High Mobility Group Box Protein (TOX) as Potential Therapeutics for Cutaneous T-Cell Lymphomas.

Cutaneous T-cell lymphomas (CTCL) are the most common primary lymphomas of the skin. We have previously identified thymocyte selection-associated high mobility group (HMG) box protein (TOX) as a promising drug target in CTCL; however, there are currently no small molecules able to directly inhibit TOX. We aimed to address this unmet opportunity by developing anti-TOX therapeutics with the use of computer-aided drug discovery methods.

DeepCOP: deep learning-based approach to predict gene regulating effects of small molecules.

Motivation: Recent advances in the areas of bioinformatics and chemogenomics are poised to accelerate the discovery of small molecule regulators of cell development. Combining large genomics and molecular data sources with powerful deep learning techniques has the potential to revolutionize predictive biology. In this study, we present Deep gene COmpound Profiler (DeepCOP), a deep learning based model that can predict gene regulating effects of low-molecular weight compounds.

ETS transcription factors as emerging drug targets in cancer.

The ETS family of proteins consists of 28 transcription factors, many of which have been implicated in development and progression of a variety of cancers. While one family member, ERG, has been rigorously studied in the context of prostate cancer where it plays a critical role, other ETS factors keep emerging as potential hallmark oncodrivers. In recent years, numerous studies have reported initial discoveries of small molecule inhibitors of ETS proteins and opened novel avenues for ETS-directed cancer therapies.

Toxic Colors: The Use of Deep Learning for Predicting Toxicity of Compounds Merely from Their Graphic Images.

The majority of computational methods for predicting toxicity of chemicals are typically based on "nonmechanistic" cheminformatics solutions, relying on an arsenal of QSAR descriptors, often vaguely associated with chemical structures, and typically employing "black-box" mathematical algorithms. Nonetheless, such machine learning models, while having lower generalization capacity and interpretability, typically achieve a very high accuracy in predicting various toxicity endpoints, as unambiguously reflected by the results of the recent Tox21 competition. In the current study, we capitalize on the power of modern AI to predict Tox21 benchmark data using merely simple 2D drawings of chemicals, without employing any chemical descriptors.

Decreased cytoplasmic X-box binding protein-1 expression is associated with poor prognosis and overall survival in patients with oral squamous cell carcinoma.

Introduction: Squamous cell carcinoma is the most common cancer of the oral cavity. In spite of advancements in surgical, chemoradiological and targeted therapies, these therapeutic strategies still have had little impact on survival rates. X-box binding protein-1 (XBP-1) is a potent transcription factor that is involved in the unfolded protein response (UPR) pathway, which itself is activated in response to endoplasmic reticulum stress as a method to restore cellular homeostasis.

Discovery and characterization of small molecules targeting the DNA-binding ETS domain of ERG in prostate cancer.

Genomic alterations involving translocations of the ETS-related gene ERG occur in approximately half of prostate cancer cases. These alterations result in aberrant, androgen-regulated production of ERG protein variants that directly contribute to disease development and progression. This study describes the discovery and characterization of a new class of small molecule ERG antagonists identified through rational in silico methods.

Local hemostatic matrix for endoscope-assisted removal of intracerebral hemorrhage is safe and effective.

Background/purpose: Minimally invasive endoscope-assisted (MIE) evacuation of spontaneous intracerebral hemorrhage (ICH) is simple and effective, but the limited working space may hinder meticulous hemostasis and might lead to rebleeding. Management of intraoperative hemorrhage is therefore a critical issue of this study. This study presents experience in the treatment of patients with various types of ICH by MIE evacuation followed by direct local injection of FloSeal Hemostatic Matrix (Baxter Healthcare Corp, Fremont, CA, USA) for hemostasis.

Androgen receptor transcriptionally regulates semaphorin 3C in a GATA2-dependent manner.

The androgen receptor (AR) is a member of the nuclear receptor superfamily of transcription factors and is central to prostate cancer (PCa) progression. Ligand-activated AR engages androgen response elements (AREs) at androgen-responsive genes to drive the expression of gene batteries involved in cell proliferation and cell fate. Understanding the transcriptional targets of the AR has become critical in apprehending the mechanisms driving treatment-resistant stages of PCa.

Differentiation of tumor vasculature heterogeneity levels in small animals based on total hemoglobin concentration using magnetic resonance-guided diffuse optical tomography in vivo.

Insight into the vasculature of the tumor in small animals has the potential to impact many areas of cancer research. The heterogeneity of the vasculature of a tumor is directly related to tumor stage and disease progression. In this small scale animal study, we investigated the feasibility of differentiating tumors with different levels of vasculature heterogeneity in vivo using a previously developed hybrid magnetic resonance imaging (MRI) and diffuse optical tomography (DOT) system for small animal imaging.

Mitf is a master regulator of the v-ATPase, forming a control module for cellular homeostasis with v-ATPase and TORC1.

The v-ATPase is a fundamental eukaryotic enzyme that is central to cellular homeostasis. Although its impact on key metabolic regulators such as TORC1 is well documented, our knowledge of mechanisms that regulate v-ATPase activity is limited. Here, we report that the Drosophila transcription factor Mitf is a master regulator of this holoenzyme.

Identification of a potent antiandrogen that targets the BF3 site of the androgen receptor and inhibits enzalutamide-resistant prostate cancer.

There has been a resurgence of interest in the development of androgen receptor (AR) inhibitors with alternative modes of action to overcome the development of resistance to current therapies. We demonstrated previously that one promising strategy for combatting mutation-driven drug resistance is to target the Binding Function 3 (BF3) pocket of the receptor. Here we report the development of a potent BF3 inhibitor, 3-(2,3-dihydro-1H-indol-2-yl)-1H-indole, which demonstrates excellent antiandrogen potency and anti-PSA activity and abrogates the androgen-induced proliferation of androgen-sensitive (LNCaP) and enzalutamide-resistant (MR49F) PCa cell lines.

Orphan nuclear receptors as drug targets for the treatment of prostate and breast cancers.

Nuclear receptors (NRs), a family of 48 transcriptional factors, have been studied intensively for their roles in cancer development and progression. The presence of distinctive ligand binding sites capable of interacting with small molecules has made NRs attractive targets for developing cancer therapeutics. In particular, a number of drugs have been developed over the years to target human androgen- and estrogen receptors for the treatment of prostate cancer and breast cancer.