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. 2018 Apr 3;18:1172-1184.
doi: 10.1016/j.dib.2018.03.132. eCollection 2018 Jun.

Supplementary Data for the Biological Age Linked to Oxidative Stress Modifies Breast Cancer Aggressiveness

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Free PMC article

Supplementary Data for the Biological Age Linked to Oxidative Stress Modifies Breast Cancer Aggressiveness

María Del Mar Sáez-Freire et al. Data Brief. .
Free PMC article

Abstract

The data presented in this article are related to the research paper entitled "The biological age linked to oxidative stress modifies breast cancer aggressiveness" (M.M. Sáez-Freire, A. Blanco-Gómez, S. Castillo-Lluva, A. Gómez-Vecino, J.M. Galvis-Jiménez, C. Martín-Seisdedos, M. Isidoro-García, L. Hontecillas-Prieto, M.B. García-Cenador, F.J. García-Criado, M.C. Patino-Alonso, P. Galindo-Villardón, J.H. Mao, C. Prieto, A. Castellanos-Martín, L. Kaderali, J. Pérez-Losada). The data shown were obtained from a population of transgenic mice, MMTV-Erbb2/Neu, with different susceptibility to breast cancer and a mixed genetic background generated by backcrossing. It was observed that the aggressiveness of breast cancer negatively correlates with age, being lower in chronologically old mice, similar to what occurs in humans. Given that oxidative stress is associated with tumour susceptibility and the degree of aging, the association between the aggressiveness of breast cancer and multiple intermediate phenotypes directly or indirectly related to oxidative stress was studied. Using a mathematical model, we defined biological age and the degree of aging as the difference between biological and chronological ages. As a result, we observed that biologically old mice predominated among those that developed the disease early on, that is, those that were chronologically young. We then identified the specific and common genetic components of Quantitative Trait loci or QTL associated with different evolution of breast cancer, the intermediate phenotypes related to oxidative stress studied, the biological age and the degree of aging. Lastly, we showed that the expression pattern in the livers of biologically old mice were enriched in signalling pathways related to inflammation and response to infections; whereas the biologically young mice exhibited enriched pathways related to mitochondrial activity. For the explanation and discussion of these data refer to the research article cited above.

Figures

Fig. 1
Fig. 1
MMTV-ErbB2/Neu transgene insertion and genetic background of the backcross cohort of mice. A) The MMTV-ErbB2/Neu transgene is inserted on chromosome 3, as shown by the linkage analysis. On the right, the heatmap shows the estimated recombination fractions (upper left) and LOD scores (lower right) for all pairs of genotyped markers in the backcross population. Red indicates pairs of markers that appear to be linked (low recombination fraction or high LOD), and blue indicates pairs that are not linked (high recombination fraction or low LOD). Red rectangles indicate low recombination fraction (or high LOD) between markers on chromosome 3 and chromosome X. The apparent linkage between markers on these chromosomes was due to the selection of female transgenic backcross mice for this study, and it shows the transgene is located on chromosome 3. B) Genotype data of the 147 backcross mice at the 244 SNPs. Red and blue spots correspond to homozygous and heterozygous genotypes, respectively. White spots indicate missing genotype data. Black vertical lines indicate the boundaries between chromosomes.
Fig. 2
Fig. 2
ERBB2 and pathophenotypes of breast cancer variability. A) The correlation studies show that the disease was less aggressive in a continuum as age advances. Units: age at onset, lifespan and duration of the disease in weeks; tumour weight in grams; tumour volume in mm3. B) ErbB2 RNA levels did not correlate with total or phosphorylated ERBB2 protein levels. In contrast, total and phosphorylated ERBB2 protein levels correlated with high statistical significance. C) Total and phosphorylated protein levels of lllERBB2 did not correlate with the variability of any of the breast cancer pathophenotypes developed in the backcross population.
Fig. 3
Fig. 3
The heatmap shows the specific associations between breast cancer pathophenotypes and intermediate phenotypes.
Fig. 4
Fig. 4
Associations of the chronological age at the time of dead with different subphenotypes related to oxidative stress. A) Comparison of the telomere length measured from tail tissue of backcross mice at the initial stage and at the time of death. Paired t-test. B) Correlation of the chronological age at the time of death with various signalling molecules determined in the liver by ELISA. C) Association of the chronological age with metabolites determined in serum at 3 to 4 months of age in a disease-free stage. D) Correlation with weight at four months (weight 1) and weight without tumours after necropsy. E) Correlation with the levels of 4-HNE as an indicator of lipid oxidation. F) The number of young and old mice based on the biological and the chronological age. The Ns indicate the animals where all of the variables were available and included within the model.
Fig. 5
Fig. 5
Behaviour of the pathophenotypes of breast cancer in the clusters generated by principal component analysis.
Fig. 6
Fig. 6
Genes in enriched pathways identified by GSEA. The heatmaps represent the running enrichment score of the genes with absolute value of Rank Metric Score >2.0 that mainly accounted for the enrichment signal found in some of the gene sets differentially enriched in biologically younger and biologically older mice. The left heatmap (A) shows the pathways that were enriched in biologically younger mice (ES < 0) and the right heatmap (B) shows the enriched pathways in biologically older mice (ES > 0).

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