Hepatocellular carcinoma (HCC) is an essentially incurable inflammation-related cancer. We have previously shown by network analysis of proteomic data that the flavonoids epigallocatechin gallate (EGCG) and fisetin (FIS) efficiently downregulated pro-tumor cytokines released by HCC through inhibition of Akt/mTOR/RPS6 phospho-signaling. However, their mode of action at the global transcriptome level remains unclear. Herein, we endeavor to compare gene expression alterations mediated by these compounds through a comprehensive transcriptome analysis based on RNA-seq in HEP3B, a responsive HCC cell line, upon perturbation with a mixture of prototypical stimuli mimicking conditions of tumor microenvironment or under constitutive state. Analysis of RNA-seq data revealed extended changes on HEP3B transcriptome imposed by test nutraceuticals. Under stimulated conditions, EGCG and FIS significantly modified, compared to the corresponding control, the expression of 922 and 973 genes, respectively, the large majority of which (695 genes), was affected by both compounds. Hierarchical clustering based on the expression data of shared genes demonstrated an almost identical profile in nutraceutical-treated stimulated cells which was virtually opposite in cells exposed to stimuli alone. Downstream enrichment analyses of the co-modified genes uncovered significant associations with cancer-related transcription factors as well as terms of Gene Ontology/Reactome Pathways and highlighted ECM dynamics as a nodal modulation point by nutraceuticals along with angiogenesis, inflammation, cell motility and growth. RNA-seq data for selected genes were independently confirmed by RT-qPCR. Overall, the present systems approach provides novel evidence stepping up the mechanistic understanding of test nutraceuticals, thus rationalizing their clinical exploitation in new preventive/therapeutic modalities against HCC.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7113608 | PMC |
http://dx.doi.org/10.1016/j.csbj.2020.03.006 | DOI Listing |
Gigascience
January 2025
School of Life, Health & Chemical Sciences, The Open University, Milton Keynes, Buckinghamshire, MK7 6AA, UK.
Background: Bioinformatics is fundamental to biomedical sciences, but its mastery presents a steep learning curve for bench biologists and clinicians. Learning to code while analyzing data is difficult. The curve may be flattened by separating these two aspects and providing intermediate steps for budding bioinformaticians.
View Article and Find Full Text PDFDiscov Oncol
January 2025
Department of Obstetrics and Gynecology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.
Objective: Ovarian cancer significantly impacts women's reproductive health and remains challenging to diagnose and treat. Despite advancements in understanding DNA repair mechanisms and identifying novel therapeutic targets, additional strategies are still needed. Recently, a novel form of cell death called disulfidptosis, which is triggered by glucose deprivation, has been linked to treatment resistance and changes in the tumor microenvironment (TME).
View Article and Find Full Text PDFPLoS One
January 2025
Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.
A Directed Acyclic Graph (DAG) offers an easy approach to define causal structures among gathered nodes: causal linkages are represented by arrows between the variables, leading from cause to effect. Recently, industry and academics have paid close attention to DAG structure learning from observable data, and many techniques have been put out to address the problem. We provide a two-step approach, named SEMdag(), that can be used to quickly learn high-dimensional linear SEMs.
View Article and Find Full Text PDFJ Exp Bot
January 2025
Centre for Research in Agricultural Genomics (CRAG) CSIC-IRTA-UAB-UB, Campus UAB, 08193 Cerdanyola del Vallès, Barcelona, Spain.
The complex gene regulatory landscape underlying early flower development in Arabidopsis has been extensively studied through transcriptome profiling, and gene networks controlling floral organ development have been derived from the analyses of genome wide binding of key transcription factors. In contrast, the dynamic nature of the proteome during the flower development process is much less understood. In this study, we characterized the floral proteome at different stages during early flower development and correlated it with unbiased transcript expression data.
View Article and Find Full Text PDFPLoS One
January 2025
Bioinformatics Laboratory, Institute of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan.
RNA tomography computationally reconstructs 3D spatial gene expression patterns genome-widely from 1D tomo-seq data, generated by RNA sequencing of cryosection samples along three orthogonal axes. We developed tomoseqr, an R package designed for RNA tomography analysis of tomo-seq data, to reconstruct and visualize 3D gene expression patterns through user-friendly graphical interfaces. We show the effectiveness of tomoseqr using simulated and real tomo-seq data, validating its utility for researchers.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!