Search

Motivation: Cancer classification based on gene expression profiles has provided insight on the causes of cancer and cancer treatment. Recently, machine learning-based approaches have been attempted in downstream cancer analysis to address the large differences in gene expression values, as determined by single-cell RNA sequencing (scRNA-seq)., Results: We designed cancer classifiers that can identify 21 types of cancers and normal tissues based on bulk RNA-seq as well as scRNA-seq data. Training was performed with 7398 cancer samples and 640 normal samples from 21 tumors and normal tissues in TCGA based on the 300 most significant genes expressed in each cancer. Then, we compared neural network (NN), support vector machine (SVM), k-nearest neighbors (kNN) and random forest (RF) methods. The NN performed consistently better than other methods. We further applied our approach to scRNA-seq transformed by kNN smoothing and found that our model successfully classified cancer types and normal samples., Availability and Implementation: Cancer classification by neural network., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Regenerative therapeutic approaches for myocardial diseases often involve delivery of stem cells expanded ex vivo. Prior studies indicate that cell culture conditions affect functional and phenotypic characteristics, but relationship(s) of cultured cells derived from freshly isolated populations and the heterogeneity of the cultured population remain poorly defined. Functional and phenotypic characteristics of ex vivo expanded cells will determine outcomes of interventional treatment for disease, necessitating characterization of the impact that ex vivo expansion has upon isolated stem cell populations. Single-cell RNA-Seq profiling (scRNA-Seq) was performed to determine consequences of culture expansion upon adult cardiac progenitor cells (CPCs) as well as relationships with other cell populations. Bioinformatic analyses demonstrate that identity marker genes expressed in freshly isolated cells become undetectable in cultured CPCs while low level expression emerges for thousands of other genes. Transcriptional profile of CPCs exhibited greater degree of similarity throughout the cultured population relative to freshly isolated cells. Findings were validated by comparative analyses using scRNA-Seq datasets of various cell types generated by multiple scRNA-Seq technology. Increased transcriptome diversity and decreased population heterogeneity in the cultured cell population may help account for reported outcomes associated with experimental and clinical use of CPCs for treatment of myocardial injury.

Iron supplementation is a common method for alleviating symptoms of iron deficiency, but excessive iron intake may lead to systemic copper deficiencies and hypercholesterolemia. In our study, we explored the intricate relationship between dietary iron and copper levels and their impact on cholesterol metabolism. Using a rat model, we conducted dietary interventions with varying iron and copper concentrations and analyzed hepatic transcriptomes. High iron intake coupled with low copper intake induced hypercholesterolemia and altered the expression of genes associated with cholesterol and lipid metabolism, thereby, exacerbating cardiovascular disease risks. Conversely, copper supplementation mitigated these hepatic gene expression alterations, suggesting that dietary copper plays a role in cholesterol regulation. Transcriptomic analysis revealed significant upregulation of genes involved in cholesterol synthesis and antioxidative pathways in response to high iron intake, while genes involved in cholesterol elimination were downregulated. Furthermore, high iron consumption was associated with cellular apoptosis and the activation of cholesterol synthesis. Our findings underscore the importance of balanced iron and copper intake in cholesterol homeostasis and highlight the potential of copper supplementation for mitigating iron-induced hypercholesterolemia. [ABSTRACT FROM AUTHOR]