Clinical and Multiomic Features Differentiate Young Black and White Breast Cancer Cohorts Derived by Machine Learning Approaches.

Background: There are documented differences in Breast cancer (BrCA) presentations and outcomes between Black and White patients. In addition to molecular factors, socioeconomic, racial, and clinical factors result in disparities in outcomes for women in the United States. Using machine learning and unsupervised biclustering methods within a multiomics framework, here we sought to shed light on the biological and clinical underpinnings of observed differences between Black and White BrCA patients.

A Lung Cancer Mouse Model Database.

Lung cancer, the leading cause of cancer mortality, exhibits diverse histological subtypes and genetic complexities. Numerous preclinical mouse models have been developed to study lung cancer, but data from these models are disparate, siloed, and difficult to compare in a centralized fashion. Here we established the Lung Cancer Mouse Model Database (LCMMDB), an extensive repository of 1,354 samples from 77 transcriptomic datasets covering 974 samples from genetically engineered mouse models (GEMMs), 368 samples from carcinogen-induced models, and 12 samples from a spontaneous model.

Multi-omics inference of differential breast cancer-related transcriptional regulatory network gene hubs between young Black and White patients.

Objective: Breast cancers (BrCA) are a leading cause of illness and mortality worldwide. Black women have a higher incidence rate relative to white women prior to age 40 years, and a lower incidence rate after 50 years. The objective of this study is to identify -omics differences between the two breast cancer cohorts to better understand the disparities observed in patient outcomes.

A Tale of Two Diseases: Exploring Mechanisms Linking Diabetes Mellitus with Alzheimer's Disease.

Dementias, including the type associated with Alzheimer's disease (AD), are on the rise worldwide. Similarly, type 2 diabetes mellitus (T2DM) is one of the most prevalent chronic diseases globally. Although mechanisms and treatments are well-established for T2DM, there remains much to be discovered.

Enhanced immortalization, HUWE1 mutations and other biological drivers of breast invasive carcinoma in Black/African American patients.

Black/African-American (B/AA) breast cancer patients tend to have more aggressive tumor biology compared to White/Caucasians. In this study, a variety of breast tumor molecular expression profiles of patients derived from the two racial groupings were investigated. Breast invasive carcinoma sample data (RNASeq version 2, Reverse Phase Protein Array, mutation, and miRSeq data) from the Cancer Genome Atlas were examined.

MSclassifier: median-supplement model-based classification tool for automated knowledge discovery.

High-throughput technologies have resulted in an exponential growth of publicly available and accessible datasets for biomedical research. Efficient computational models, algorithms and tools are required to exploit the datasets for knowledge discovery to aid medical decisions. Here, we introduce a new tool, MSclassifier, based on median-supplement approaches to machine learning to enable an automated and effective binary classification for optimal decision making.

NRF2 Activation Promotes Aggressive Lung Cancer and Associates with Poor Clinical Outcomes.

Purpose: Stabilization of the transcription factor NRF2 through genomic alterations in and occurs in a quarter of patients with lung adenocarcinoma and a third of patients with lung squamous cell carcinoma. In lung adenocarcinoma, loss often co-occurs with loss and -activating alterations. Despite its prevalence, the impact of NRF2 activation on tumor progression and patient outcomes is not fully defined.

DOKI: Domain knowledge-driven inference method for reverse-engineering transcriptional regulatory relationships among genes in cancer.

Efficient reverse-engineering methods are important for identifying transcriptional regulatory relationships among genes in cancer. These methods are becoming increasingly useful in this era where huge volumes of data are generated through the use of high-throughput technologies such as next-generation sequencing technologies and microarrays. However, it is important to improve current methods because of complications involved in modelling complex biological systems.

High-fat diet induces fibrosis in mice lacking CYP2A5 and PPARα: a new model for steatohepatitis-associated fibrosis.

Obesity is linked to nonalcoholic steatohepatitis. Peroxisome proliferator-activated receptor-α (PPARα) regulates lipid metabolism. Cytochrome -450 2A5 (CYP2A5) is a potential antioxidant and CYP2A5 induction by ethanol is CYP2E1 dependent.

Enhanced immortalization, mutations and other biological drivers of breast invasive carcinoma in Black/African American patients.

Black/African-American (B/AA) breast cancer patients tend to have more aggressive tumor biology compared to White/Caucasians. In this study, a variety of breast tumor molecular expression profiles of patients derived from the two racial groupings were investigated. Breast invasive carcinoma sample data (RNASeq version 2, Reverse Phase Protein Array, mutation, and miRSeq data) from the Cancer Genome Atlas were examined.

Restricted-derestricted dynamic Bayesian Network inference of transcriptional regulatory relationships among genes in cancer.

Understanding transcriptional regulatory relationships among genes is important for gaining etiological insights into diseases such as cancer. To this end, high-throughput biological data have been generated through advancements in a variety of technologies. These rely on computational approaches to discover underlying structures in such data.

De novo lipogenesis represents a therapeutic target in mutant Kras non-small cell lung cancer.

Oncogenic Kras mutations are one of the most common alterations in non-small cell lung cancer and are associated with poor response to treatment and reduced survival. Driver oncogenes, such as Kras are now appreciated for their ability to promote tumor growth via up-regulation of anabolic pathways. Therefore, we wanted to identify metabolic vulnerabilities in Kras-mutant lung cancer.

Machine learning approaches to decipher hormone and HER2 receptor status phenotypes in breast cancer.

Breast cancer prognosis and administration of therapies are aided by knowledge of hormonal and HER2 receptor status. Breast cancer lacking estrogen receptors, progesterone receptors and HER2 receptors are difficult to treat. Regarding large data repositories such as The Cancer Genome Atlas, available wet-lab methods for establishing the presence of these receptors do not always conclusively cover all available samples.

Brain in situ hybridization maps as a source for reverse-engineering transcriptional regulatory networks: Alzheimer's disease insights.

Microarray data have been a valuable resource for identifying transcriptional regulatory relationships among genes. As an example, brain region-specific transcriptional regulatory events have the potential of providing etiological insights into Alzheimer Disease (AD). However, there is often a paucity of suitable brain-region specific expression data obtained via microarrays or other high throughput means.

A regulatory role for the insulin- and BDNF-linked RORA in the hippocampus: implications for Alzheimer's disease.

Alzheimer's disease (AD) is the leading cause of dementia. The etiology of AD remains, in large part, unresolved. In this study, gene expression (microarray) data from postmortem brains in normal aged as well as AD-affected brains in conjunction with transcriptional regulatory networks were explored for etiological insights.

SAGA: a hybrid search algorithm for Bayesian Network structure learning of transcriptional regulatory networks.

Bayesian Networks have been used for the inference of transcriptional regulatory relationships among genes, and are valuable for obtaining biological insights. However, finding optimal Bayesian Network (BN) is NP-hard. Thus, heuristic approaches have sought to effectively solve this problem.

Pharmacokinetics of artemisinin delivered by oral consumption of Artemisia annua dried leaves in healthy vs. Plasmodium chabaudi-infected mice.

Ethnopharmacological Relevance: The Chinese have used Artemisia annua as a tea infusion to treat fever for >2000 years. The active component is artemisinin. Previously we showed that when compared to mice fed an equal amount of pure artemisinin, a single oral dose of dried leaves of Artemisia annua (pACT) delivered to Plasmodium chabaudi-infected mice reduced parasitemia at least fivefold.

Transcription factor NRF2 regulates miR-1 and miR-206 to drive tumorigenesis.

The mechanisms by which deregulated nuclear factor erythroid-2-related factor 2 (NRF2) and kelch-like ECH-associated protein 1 (KEAP1) signaling promote cellular proliferation and tumorigenesis are poorly understood. Using an integrated genomics and ¹³C-based targeted tracer fate association (TTFA) study, we found that NRF2 regulates miR-1 and miR-206 to direct carbon flux toward the pentose phosphate pathway (PPP) and the tricarboxylic acid (TCA) cycle, reprogramming glucose metabolism. Sustained activation of NRF2 signaling in cancer cells attenuated miR-1 and miR-206 expression, leading to enhanced expression of PPP genes.

Suppressed expression of T-box transcription factors is involved in senescence in chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is a major global health problem. The etiology of COPD has been associated with apoptosis, oxidative stress, and inflammation. However, understanding of the molecular interactions that modulate COPD pathogenesis remains only partly resolved.

PACAP interactions in the mouse brain: implications for behavioral and other disorders.

As an activator of adenylate cyclase, the neuropeptide Pituitary Adenylate Cyclase Activating Peptide (PACAP) impacts levels of cyclic AMP, a key second messenger available in brain cells. PACAP is involved in certain adult behaviors. To elucidate PACAP interactions, a compendium of microarrays representing mRNA expression in the adult mouse whole brain was pooled from the Phenogen database for analysis.

Subcellular location and molecular mobility of human cytosolic sulfotransferase 1C1 in living human embryonic kidney 293 cells.

Cytosolic sulfotransferases were first isolated from the hepatic cytosol, and they have been localized in the cytoplasm of formaldehyde-fixed human cell samples. The current work was carried out to determine the subcellular localization and molecular mobility of cytosolic sulfotransferases in living human embryonic kidney (HEK) 293 cells. In this work, the subcellular location of human cytosolic sulfotransferase 1C1 (SULT1C1) was studied in cultured HEK293 cells using confocal laser-scanning microscopy.

Network inference algorithms elucidate Nrf2 regulation of mouse lung oxidative stress.

A variety of cardiovascular, neurological, and neoplastic conditions have been associated with oxidative stress, i.e., conditions under which levels of reactive oxygen species (ROS) are elevated over significant periods.

Manual curation is not sufficient for annotation of genomic databases.

Motivation: Knowledge base construction has been an area of intense activity and great importance in the growth of computational biology. However, there is little or no history of work on the subject of evaluation of knowledge bases, either with respect to their contents or with respect to the processes by which they are constructed. This article proposes the application of a metric from software engineering known as the found/fixed graph to the problem of evaluating the processes by which genomic knowledge bases are built, as well as the completeness of their contents.

Ethanol sensitivity: a central role for CREB transcription regulation in the cerebellum.

Background: Lowered sensitivity to the effects of ethanol increases the risk of developing alcoholism. Inbred mouse strains have been useful for the study of the genetic basis of various drug addiction-related phenotypes. Inbred Long-Sleep (ILS) and Inbred Short-Sleep (ISS) mice differentially express a number of genes thought to be implicated in sensitivity to the effects of ethanol.

Predicting the subcellular localization of human proteins using machine learning and exploratory data analysis.

Identifying the subcellular localization of proteins is particularly helpful in the functional annotation of gene products. In this study, we use Machine Learning and Exploratory Data Analysis (EDA) techniques to examine and characterize amino acid sequences of human proteins localized in nine cellular compartments. A dataset of 3,749 protein sequences representing human proteins was extracted from the SWISS-PROT database.