Breast cancer is a complex heterogeneous disease caused by the interactions of both intrinsic (genetic and non-genetic factors) and extrinsic (Age, gender, and family history) as the major factors (Cappetta et al, 2024). It is the main cause of cancer death and the most diagnosed cancer case in women in 140 of the world’s 184 countries (Tzenois et al 2024). #2
Description
Breast cancer is a complex heterogeneous disease caused by the interactions of both intrinsic (genetic and non-genetic factors) and extrinsic (Age, gender, and family history) as the major factors (Cappetta et al, 2020). It is the main cause of cancer death and the most diagnosed cancer case in women in 140 of the world’s 184 countries (Tzenois et al 2024).
In a report in 2012 by the International Agency for Research on Cancer, it showed 1.7 million breast cancer new cases, and with 6.3 million new patients already diagnosed and reported in the previous 5 years (Tzenois et al 2024). Every year, an estimated 1.5 million women globally are diagnosed and confirmed with breast cancer, (Zahmatkesh et al 2017).
DNA methylation is a key process involved in the regulation of gene expression. DNA methylation is a molecular modification of DNA that is tightly associated with gene function and cell- type-specific gene function, it therefore provides an exquisite identity descriptor of a cell (Szyf, 2012). Interestingly, DNA methylation is potentially modifiable and is related to age and the strongest breast cancer risk predictor as a molecular biomarker for cancer (Horvath et al. 2012 & Capetta et al. 2020).
Several studies have investigated peripheral blood DNA methylation biomarkers in different cancer types including head and neck, breast, lung, bladder, gastric cancer, prostate, colorectal, and ovarian cancers (Langevin et al. 2012 & Sun et al. 2018).
Few studies have attempted to investigate the role of loci-specific DNA methylation in leukocytes as a marker of breast cancer, most of them by candidate gene approaches, and did not use a validation set to confirm their results (Guan et al. 2020; Tang et al. 2016; 2019 & Capetta et al. 2020). However, there is no research reporting the uncovering DNA Methylation signatures in breast cancer using an in-silico approach. Hence, the need to delve into the study.
Hypothesis and critical questions.
DNA methylation states could serve as an important diagnostic tool in breast cancer care.
Therefore the pertinent questions to look into are;
How can DNA Methylation signature In breast cancer be uncovered using in-silico approach?
What are the changes in the breast cancer gene expressions that is impacted by DNA Methylation?
How can DNA Methylation be use as a molecular tool for diagnosing and early breast cancer detection?
It is also important to elucidate whether a set of specific gene methylation events would be sufficient in breast cancer diagnostics, or whether this would require more complex ‘signatures’ that involve coordinated changes in groups of genes. Hence, the need to uncover the DNA Methylation signatures in breast cancer using different datasets.
DNA methylation in humans.
DNA methylation, is a biological process that involves the addition of a methyl (CH3) group to a cytosine, most commonly at cytosine-guanine (CpG) sites, is a stable and ubiquitous epigenetic modification in humans (Peters et al., 2024).
DNA methylation pathways. A family of DNA methyltransferases (Dnmts) catalyzes the transfer of a methyl group from S-adenyl methionine (SAM) to the fifth carbon of cytosine residue to form 5-methylcytosine (5mC). (a) Dnmt3a and Dnmt3b are the de novo Dnmts and transfer methyl groups (red) onto naked DNA. (b) Dnmt1 is the maintenance Dnmt and maintains DNA methylation pattern during replication. When DNA undergoes semiconservative replication, the parental DNA stand retains the original DNA methylation pattern (gray). Dnmt1 associates at the replication foci and precisely replicates the original DNA methylation pattern by adding methyl groups (red) onto the newly formed daughter strand (blue), (Moore, 2012).
Originally posted by @Benthai05 in https://github.com/omicscodeathon/brcamethyl/issues/1
Reference
Ballan, F., Battistini, B., Xavier-Santos, D. and Saad, M.S. (2020). Interactions of probiotics and prebiotics with the gut microbiota. Progress in Molecular Biology and Translational Science, 171:265-300.
Szyf, M. (2012). DNA methylation signatures for breast cancer classification and prognosis. Genomic Medicine, 4:26.
Moore, D.L., Le, T. and Fan, G. (2013). DNA Methylation and Its Basic Function. Neuropsychopharmacology Reviews, 38: 23-38.
Zahmatkesh BH, Alavi N, Keramat A, Khosravi A, and Chaman, R. (2017). Body mass index and risk of breast cancer: a systematic review and meta-analysis in Iran. International Journal of Cancer Management, 10(14):e5921.
Tzenios, N.,Tazanios, M.E., Chahine, M. (2024). The impact of BMI on breast cancer – an updated systematic review and meta-analysis. Medicine, 103(5):p e36831.
International Agency for Research on Cancer,(IARC). GLOBOCAN (2012): Estimated age-standardised incidence and mortality rates. Fact Sheets. Population Fact Sheets: women 2012.
Horvath S, Zhang Y, Langfelder P, Kahn RS, Boks MPM, van Eijk K, and Ophoff RA (2012) Aging effects on DNA methylation modules in human brain and blood tissue. Genome Biol 13, R97.
Langevin SM, Koestler DC, Christensen BC, Butler RA, Wiencke JK, Nelson HH, Houseman EA, Marsit CJ & Kelsey KT (2012) Peripheral blood DNA Methylation profiles are indicative of head and neck squamous cell carcinoma: an epigenome-wide association study. Epigenetics 7, 291–299.
Sun H, Zhou H, Zhang Y, Chen J, Han X, Huang D, Ren X, Jia Y, Fan Q, Tian W et al. (2018) Aberrant methylation of FAT4 and SOX11 in peripheral blood leukocytes and their association with gastric cancer risk. J Cancer 9, 2275–2283.
Guan Z, Yu H, Cuk K, Zhang Y & Brenner H (2019) Whole-blood DNA methylation markers in early detection of breast cancer: a systematic literature review. Cancer Epidemiol Biomarkers Prev 28, 496–505.
Tang Q, Cheng J, Cao X, Surowy H & Burwinkel B (2016) Blood-based DNA methylation as biomarker for breast cancer: a systematic review. Clin Epigenetics 8, 115.
Cappetta, M., Fernandez, L., Brignoni, L., Artagaveytia, N., Bonilla, C., López, M., Esteller, M., Bertoni, B., and Berdasco, M. (2020).Discovery of novel DNA methylation biomarkers for non-invasive sporadic breast cancer detection in the Latino population. Molecular Oncology, 15, (2):473-486.
Timothy J. Peters, Braydon Meyer, Lauren Ryan, Joanna Achinger-Kawecka, Jenny Song, Elyssa M. Campbell, Wenjia Qu, Shalima Nair, Phuc Loi-Luu, Phillip Stricker, Elgene Lim, Clare Stirzaker, Susan J. Clark & Ruth Pidsley (2024). Characterisation and reproducibility of the HumanMethylationEPIC v2.0 BeadChip for DNA methylation profiling. BMC Genomics 25, 251.
Buist DSM, Abraham L, Lee CI, et al. Breast biopsy intensity and findings following breast cancer screening in women with and without a personal history of breast cancer. JAMA Intern Med. 2018;178:458–68.
Obeagu,EI. and Obeagu, GU. (2024). Breast cancer: A review of risk factors and diagnosi. Medicine, 103:3(e36905).
Maio F, Tari DU, Granata V, et al. Breast cancer screening during COVID-19 emergency: patients and department management in a local experience. J Pers Med. 2021;11:380.
Nelson H. D., Zakher B., Cantor A., Fu R., Griffin J., O′Meara E. S., Buist D. S., Kerlikowske K., van Ravesteyn N. T., Trentham-Dietz A., Mandelblatt J. S., and Miglioretti D. L., Risk factors for breast cancer for women aged 40 to 49 years: a systematic review and meta-analysis, Annals of Internal Medicine. (2012) 156, no. 9, 635–648.
Winters S., Martin C., Murphy D., and Shokar N. K., Breast cancer epidemiology, prevention, and screening, Prog Mol Biol Transl Sci. (2017) 151, 1–32.
Sarah Wolf, Ingrid Zechmeister-Koss, Irmgard Fruehwirth. (2024). The Prognostic Quality of Risk Prediction Models to Assess the Individual Breast Cancer Risk in Women: An Overview of Reviews. The Breast Journal, (1): 1711696.
Thigpen D, Kappler A, Brem R. The role of ultrasound in screening dense breasts—a review of the literature and practical solutions for implementation. Diagnostics. 2018;8:20.
Gutierrez, C., Owens, A., Medeiros, L. et al. Breast cancer detection using enhanced IRI-numerical engine and inverse heat transfer modeling: model description and clinical validation. Sci Rep 14, 3316 (2024).
Kyuwan Lee, Laura Kruper, Christina M Dieli-Conwright, Joanne E Mortimer. (2019). The impact of obesity on breast cancer diagnosis and treatment. Current oncology reports, 21, 1-6.
Daniel F Argolo, Clifford A Hudis, Neil M Iyengar
(2018). The impact of obesity on breast cancer. Current oncology reports 20, 1-8.
Petrelli F, Cortellini A, Indini A, et al. Association of obesity with survival outcomes in patients with cancer. JAMA Netw Open. 2021;4:e213520.
WHO. Obesity and overweight. Available at: https://www.who.int/ news-room/fact-sheets/detail/obesity-and-overweight. Published 2021.
Mills ZB, Faull RLM, Kwakowsky A. Is hormone replacement therapy a risk factor or a therapeutic option for Alzheimer’s disease? Int J Mol Sci. 2023;24:3205.