Gene Signatures Induced by Ionizing Radiation as Prognostic Tools in an In Vitro Experimental Breast Cancer Model
| Autor: | Francisco Aguayo, Tammy C. Bleak, Gloria M. Calaf, Juan P. Muñoz, Leodan A Crispin, Debasish Roy |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Cancer Research
medicine.medical_treatment breast cancer model Cancer Neoplasms. Tumors. Oncology. Including cancer and carcinogens Review Gene signature Biology medicine.disease Metastasis Radiation therapy radiation Oncology Cell culture Gene expression medicine Cancer research gene expression Interleukin-7 receptor Gene RC254-282 estrogens |
| Zdroj: | Cancers, Vol 13, Iss 4571, p 4571 (2021) Cancers |
| ISSN: | 2072-6694 |
| Popis: | Simple Summary The present work analyzed the expression of genes involved in radiation, using an in vitro experimental breast cancer model developed by the combined treatment of low doses of high linear energy transfer (LET) radiation α particle radiation and estrogen yielding different stages in a malignantly transformed breast cancer cell model called Alpha model. Results showed important findings of genes involved in cancers of the breast, lung, and nervous system, and others. Most of those genes analyzed in these studies such as ATM, selenoproteins, GABA receptor, interleukins, epsin, and cathepsin inhibitors like stefins, and metallothioneins can be used for new prognostic tools and future therapies since they affect cancer progression and metastasis. In conclusion, gene signature demonstrated to be specific to cell line types, hence cell-dependency must be considered in future radiotherapy treatment planning since molecular and clinical features affect such results. Thus, using gene technology and molecular information is possible to improve therapies and reduction of side effects. Abstract This study aimed to analyze the expression of genes involved in radiation, using an Affymetrix system with an in vitro experimental breast cancer model developed by the combined treatment of low doses of high linear energy transfer (LET) radiation α particle radiation and estrogen yielding different stages in a malignantly transformed breast cancer cell model called Alpha model. Altered expression of different molecules was detected in the non-tumorigenic Alpha3, a malignant cell line transformed only by radiation and originally derived from the parental MCF-10F human cell line; that was compared with the Alpha 5 cell line, another cell line exposed to radiation and subsequently grown in the presence 17β-estradiol. This Alpha5, a tumorigenic cell line, originated the Tumor2 cell line. It can be summarized that the Alpha 3 cell line was characterized by greater gene expression of ATM and IL7R than control, Alpha5, and Tumor2 cell lines, it presented higher selenoprotein gene expression than control and Tumor2; epsin 3 gene expression was higher than control; stefin A gene expression was higher than Alpha5; and metallothionein was higher than control and Tumor2 cell line. Therefore, radiation, independently of estrogen, induced increased ATM, IL7R, selenoprotein, GABA receptor, epsin, stefin, and metallothioneins gene expression in comparison with the control. Results showed important findings of genes involved in cancers of the breast, lung, nervous system, and others. Most genes analyzed in these studies can be used for new prognostic tools and future therapies since they affect cancer progression and metastasis. Most of all, it was revealed that in the Alpha model, a breast cancer model developed by the authors, the cell line transformed only by radiation, independently of estrogen, was characterized by greater gene expression than other cell lines. Understanding the effect of radiotherapy in different cells will help us improve the clinical outcome of radiotherapies. Thus, gene signature has been demonstrated to be specific to tumor types, hence cell-dependency must be considered in future treatment planning. Molecular and clinical features affect the results of radiotherapy. Thus, using gene technology and molecular information is possible to improve therapies and reduction of side effects while providing new insights into breast cancer-related fields. |
| Databáze: | OpenAIRE |
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