Transcriptional Signatures Connecting Inherent Aerobic Capacity to the Breast Tumor Microenvironment in Rats: Implications for Precision Oncology
Main Article Content
Abstract
Aerobic capacity, which is synonymous with the term cardiorespiratory fitness, is a strong inverse predictor of breast cancer mortality, yet the underlying mechanisms are poorly understood. In this study, RNA sequence data were used to interrogate the tumor microenvironment for potential etiological clues in a rodent model for breast cancer, widely regarded to have similar histogenesis and pathogenesis as the human disease, including about 70 percent of the tumors being ovarian hormone-responsive mammary carcinomas. What was novel about our approach is that tumor microenvironment gene expression profiles were contrasted between genetically distinct sedentary rats selectively bred to have low vs. high inherent aerobic capacity. Inherent aerobic capacity is generally overlooked as a variable in human populations, yet the observed range exceeds three-fold. RNA sequence analysis was performed on mammary carcinomas and adjacent uninvolved mammary glands, i.e., the tumor microenvironment. Investigation of effects on canonical signaling pathways, upstream regulators, and downstream effectors identified differentially expressed transcriptional signatures within the renin-angiotensin system, interferon gamma, and nitric oxide signaling pathways, distinguishing between low or high inherent aerobic capacity in the tumor microenvironment. Within these pathways, master upstream regulators: interferon gamma, interleukin-1 beta, tumor necrosis factor, and angiotensinogen emerged as part of a complex network reflecting inherent aerobic capacity-related differences, which were accompanied by differences in immune cell populations present within the tumor microenvironment. The data support the potential value of inherent aerobic capacity phenotyping in the development of precision approaches to breast cancer treatment.
Keywords:
inherent aerobic capacity, breast cancer, transcriptional signatures, intracellular signaling, biomarkers, renin-angiotensin system, interferon gamma, mammary gland, tumor microenvironment, nitric oxide signaling
Article Details
How to Cite
FITZGERALD, Vanessa K.; LUTSIV, Tymofiy; THOMPSON, Henry J..
Transcriptional Signatures Connecting Inherent Aerobic Capacity to the Breast Tumor Microenvironment in Rats: Implications for Precision Oncology.
Medical Research Archives, [S.l.], v. 13, n. 10, oct. 2025.
ISSN 2375-1924.
Available at: <https://esmed.org/MRA/mra/article/view/7043>. Date accessed: 15 nov. 2025.
doi: https://doi.org/10.18103/mra.v13i10.7043.
Section
Research Articles
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
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15. Akyol M, Tuğral A, Arıbaş Z, Bakar Y. Assessment of the cardiorespiratory fitness and the quality of life of patients with breast cancer undergoing chemotherapy: a prospective study. Breast Cancer. 2023/07/01 2023;30(4):617–626. doi:10.1007/s12282-023-01453-6
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18. Thompson HJ, McGinley JN, Rothhammer K, Singh M. Rapid induction of mammary intraductal proliferations, ductal carcinoma in situ and carcinomas by the injection of sexually immature female rats with 1-methyl-1-nitrosourea. Carcinogenesis. Oct 1995;16(10):2407–11. doi:10.1093/carcin/16.10.2407
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20. Kelahmetoglu Y, Jannig PR, Cervenka I, et al. Comparative Analysis of Skeletal Muscle Transcriptional Signatures Associated With Aerobic Exercise Capacity or Response to Training in Humans and Rats. Front Endocrinol (Lausanne). 2020;11:591476. doi:10.3389/fendo.2020.591476
21. Robinson MM, Dasari S, Konopka AR, et al. Enhanced Protein Translation Underlies Improved Metabolic and Physical Adaptations to Different Exercise Training Modes in Young and Old Humans. Cell Metab. Mar 7 2017;25(3):581–592. doi:10.1016/j.cmet.2017.02.009
22. Vellers HL, Kleeberger SR, Lightfoot JT. Inter-individual variation in adaptations to endurance and resistance exercise training: genetic approaches towards understanding a complex phenotype. Mammalian genome : official journal of the International Mammalian Genome Society. Feb 2018;29(1-2):48–62. doi:10.1007/s00335-017-9732-5
23. Thompson HJ, Singh M, McGinley J. Classification of premalignant and malignant lesions developing in the rat mammary gland after injection of sexually immature rats with 1-methyl-1-nitrosourea. Journal of mammary gland biology and neoplasia. 2000 2000;5(2):201–210. Not in File.
24. Anderson J, Burns PJ, Milroy D, Ruprecht P, Hauser T, Siegel HJ. Deploying RMACC Summit: An HPC Resource for the Rocky Mountain Region. presented at: Proceedings of the Practice and Experience in Advanced Research Computing 2017 on Sustainability, Success and Impact; 2017; New Orleans, LA, USA. https://doi.org/10.1145/3093338.3093379
25. Kim D, Paggi JM, Park C, Bennett C, Salzberg SL. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol. Aug 2019;37(8):907–915. doi:10.1038/ s41587-019-0201-4
26. Danecek P, Bonfield JK, Liddle J, et al. Twelve years of SAMtools and BCFtools. Gigascience. Feb 16 2021;10(2)doi:10.1093/gigascience/giab008
27. Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. Sep 1 2018;34(17):i884–i890. doi:10.1093/bioinfor matics/bty560
28. Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. Apr 1 2014;30(7):923–30. doi:10.1093/bioinformatics/btt656
29. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15 (12):550. doi:10.1186/s13059-014-0550-8
30. Robinson MD, Smyth GK. Small-sample estimation of negative binomial dispersion, with applications to SAGE data. Biostatistics. Apr 2008; 9(2):321–32. doi:10.1093/biostatistics/kxm030
31. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. Jan 1 2010;26(1):139–40. doi:10.10 93/bioinformatics/btp616
32. Krämer A, Green J, Pollard J, Jr, Tugendreich S. Causal analysis approaches in Ingenuity Pathway Analysis. Bioinformatics. 2013;30(4):523–530. doi:1 0.1093/bioinformatics/btt703
33. Lutsiv T, McGinley JN, Neil ES, Thompson HJ. Cell signaling pathways in mammary carcinoma induced in rats with low versus high inherent aerobic capacity. International journal of molecular sciences. 2019;20(6):1506.
34. Matthews SB, Santra M, Mensack MM, Wolfe P, Byrne PF, Thompson HJ. Metabolite profiling of a diverse collection of wheat lines using ultraperformance liquid chromatography coupled with time-of-flight mass spectrometry. PLoS One. 2012;7(8):e44179. doi:10.1371/journal.pone.0044179
35. Paz Ocaranza M, Riquelme JA, Garcia L, et al. Counter-regulatory renin-angiotensin system in cardiovascular disease. Nat Rev Cardiol. Feb 202 0;17(2):116–129. doi:10.1038/s41569-019-0244-8
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