biostudies-arrayexpressCELINE CIVATIMus musculushttps://www.ebi.ac.uk/biostudies/studies/E-MTAB-15506We hypothesized that treating heart failure mitigates its impact on cancer progression and that different treatments (carvedilol, enalapril, empagliflozin) may have distinct effects. For this reason we have created an mouse model combining both heart failure (LAD ligation) and 4T1 metastatic breast cancer to study the effects of these treatments on breast cancer development at different levels: primary tumor growth in vivo, metastasis development and RNA-sequencing of the primary tumor. Twenty samples divided in five different groups were analyzed with RNA sequencing: group 1= Ctrl; group 2= HF/veh; group 3= HF/car; group 4= HF/emp group 5 HF/ena. HF medications with different mechanisms of action had distinct effects on 4T1 progression when the two diseases coexisted. These effects varied from isolated effects on the tumor transcriptome, to effects on the expansion of the primary tumor and on metastatic spread in the lungs.biostudies-arrayexpressNucleic Acid Extraction - Total RNA was extracted from the tissue samples using the RNA XS kit (Machery-Nagel, 740902.250) following the manufacturer’s protocol.Library Construction - Enrichment, cDNA synthesis, and adapter ligation. PolyA+ RNA Selection was used.Sequencing - RNA quality control and sequencing libraries were prepared, and paired-end sequencing was performed by Azenta Life Sciences, using an Illumina NovaSeq 6000 platform and a configuration of 2x150 bp, generating an average of 10 million paired-end reads per sample. Raw sequencing data were returned in FASTQ format.Sample Collection - Tumors were removed from the 4th mammary fat path of BALB/c female mice at day 22 after injection, weighted and snap frozen and preserved at -80°C.OrganizationMINSEQE ScoreAssays and DataProcessed DataMAGE-TAB FilesData Transformation - The reads were processed by trimming low-quality bases and adaptor sequences using Trimmomatic (v0.39) and aligned to the human and mouse reference genome (GRCh38) using HISAT2 (v2.1.0). Raw count data were filtered by retaining only genes with at least 10 counts in at least 20% of the samples, which identified 23,960 informative genes. Next, raw count data were normalized using negative binomial models using the BioC-package DESeq2. The design matrix was set up to only consider the treatment conditions (i.e. Ctrl vs. HF/veh vs. HF/car vs. HF/emp vs. HF/ena). Finally, data were subjected to variance stabilizing transformation prior to further analysis (i.e. vst-function in DESeq2). Global expression themes were analyzed using unsupervised hierarchical clustering (UHC; R-package pheatmap) and principal component analysis (PCA). For UHC analysis, the Manhattan distance was used to calculate the dissimilarity matrix, and clustering was performed using Ward linkage. For PCA, data were median centered and scaled to unit variance. Additional removal of technical noise, identified following the first round of unsupervised analysis, was performed using the BioC-package sva. Data were corrected using the fsva-function with the number of surrogate variables set to 6. Differential expression analysis was performed using generalized linear models (BioC-package limma), and results were visualized using volcano plots and upset plots (R-package ComplexUpset). Genes with false discovery rate adjusted p-values inferior to 10% were considered significant. Finally, vectors of log2 fold changes were inspected for enrichment of the hallmark gene sets (Molecular Signatures Database; Broad Institute) using the BioC-package fgsea and results were visualized using dotplots. Hallmarks with a false discovery rate adjusted p-values inferior to 10% were considered significant.UnknownTranscriptomicsGenomicsProteomicsIllumina NovaSeq 6000RNA-seq of coding RNAMus musculusCELINE CIVATIfalseBreast Cancer Progression in the Presence of Treated and Untreated Left Ventricular DysfunctionWe hypothesized that treating heart failure mitigates its impact on cancer progression and that different treatments (carvedilol, enalapril, empagliflozin) may have distinct effects. For this reason we have created an mouse model combining both heart failure (LAD ligation) and 4T1 metastatic breast cancer to study the effects of these treatments on breast cancer development at different levels: primary tumor growth in vivo, metastasis development and RNA-sequencing of the primary tumor. Twenty samples divided in five different groups were analyzed with RNA sequencing: group 1= Ctrl; group 2= HF/veh; group 3= HF/car; group 4= HF/emp group 5 HF/ena. HF medications with different mechanisms of action had distinct effects on 4T1 progression when the two diseases coexisted. These effects varied from isolated effects on the tumor transcriptome, to effects on the expansion of the primary tumor and on metastatic spread in the lungs.2025-09-01T00:00:00Z2025-09-01T01:02:32.495Z2025-08-14T11:35:07.545ZE-MTAB-15506ERP178779EFO_0002944EFO_0004170EFO_0005518EFO_0003816EFO_0003738EFO_0004184