Evaluation of DNA Versus Collagen Perception in Scientific Articles Examining Cancer Chemotherapy: Implication for Collagen-Based Approaches

Year 2022, Volume: 4 Issue: 1, 27 - 31, 01.03.2022

Şule Karaman Özge Karaçay Yavuz Dizdar

https://doi.org/10.38175/phnx.1053721

Abstract

Objective: Although cancer chemotherapy has been used for more than seventy years, its definitive mechanism of action is not known. Many studies indicate that beyond DNA the collagen connective tissue matrix is also affected. This database analysis aims to determine the extent of DNA versus collagen perception in scientific papers indexed under PubMed.

Materials and methods: The PubMed database scanned on September, 15, 2021 using following keywords and combinations; "cancer", "cancer chemotherapy", "cancer chemotherapy AND damage”, “chemotherapy AND mechanism AND damage”, "chemotherapy AND clinical" as nominator. The number of items found for each search was proportioned in terms “DNA versus collagen” and the ratio was accepted as the perception shift coefficient.

Results: Tested with the p1-p2 analysis to calculate the difference between the two proportions in both search items. Based on the main rule under the assumption that “all cells have DNA and all cells live in the collagen matrix”. In the p1-p2 analysis of the data, a significant (p <0.001) difference was obtained for all dichotomy scans.

Conclusion: This data analysis supports the argument that both cancer and chemotherapy perception is DNA-based rather than collagen, since the synthesis and degradation process of very slow; it is not possible to observe it in short term studies. Chemotherapy should be further analyzed by this manner in purpose of collagen matrix.

Keywords

DNA, collagen, dichotomy, chemotherapy

Kanser Kemoterapisini İnceleyen Bilimsel Makalelerde DNA'ya Karşı Kolajen Algısının Değerlendirilmesi: Kollajen Temelli Yaklaşımlar için Çıkarım

Abstract

Amaç: Kemoterapi yetmiş yıldan fazla bir süredir kullanılmasına rağmen, kesin etki mekanizması bilinmemektedir. Birçok çalışma, DNA'nın ötesinde kollajen bağ dokusu matrisinin de etkilendiğini göstermektedir. Bu veri tabanı analizi, PubMed altında indekslenen bilimsel makalelerde DNA'ya karşı kolajen algısının kapsamını belirlemeyi amaçlar.

Gereçler ve yöntemler: Aşağıdaki anahtar kelimeler ve kombinasyonlar kullanılarak 15 Eylül 2021'de taranan PubMed veri tabanı; Aday olarak "kanser", "kanser kemoterapisi", "kanser kemoterapisi VE hasarı", "kemoterapi VE mekanizması VE hasarı", "kemoterapi VE klinik". Her arama için bulunan öğe sayısı "DNA'ya karşı kollajen" ve oran algı kayması katsayısı olarak kabul edildi.

Sonuçlar: Her iki arama öğesindeki iki oran arasındaki farkı hesaplamak için p1-p2 analiziyle test edildi. "Bütün hücrelerin DNA'sı vardır ve tüm hücreler kolajen matriks içinde yaşar" varsayımı altındaki ana kurala dayanmaktadır. Verilerin p1-p2 analizinde tüm dikotomi taramaları için anlamlı (p <0,001) fark elde edildi.

Sonuç: Bu veri analizi, hem kanser hem de kemoterapi algısının, sentez ve degradasyon süreci çok yavaş olduğu için kollajenden ziyade DNA bazlı olduğu argümanını desteklemektedir; kısa süreli çalışmalarda bunu gözlemlemek mümkün değildir. Kemoterapi, kollajen matriks amacıyla bu şekilde daha fazla analiz edilmelidir.

Keywords

kemoterapi, dna, kolajen, dikotomi

References

• Maddams J, Utley M, Møller H. Projections of cancer prevalence in the United Kingdom, 2010-2040. Br J Cancer 2012;107(7):1195-1202.
• Smith BD, Smith GL, Hurria A, Hortobagyi GN, Buchholz TA. Future of cancer incidence in the United States: burdens upon an aging, changing nation. J Clin Oncol 2009;27(17):2758-2765.
• D’Souza ND, Murthy NS, Aras RY. Projection of cancer incident cases for India -till 2026. Asian Pac J Cancer Prev 2013;14(7):4379-4386.
• Rosalie David A, Zimmerman MR. Cancer: An old disease, a new disease or something in between? Nat Rev Cancer 2010;10(10):728-733.
• Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394-424.
• Plaut A. Virchow’s ‘Cellular Pathology’ in the framework of biology and medicine. J Wash Acad Sci 1960;50 (1):2–18.
• Jeggo P, Pearl L, Carr A. DNA repair, genome stability and cancer: a historical perspective. Nat Rev Cancer 2016;16: 35–42.
• Basu AK. DNA damage, mutagenesis and cancer. Int J Mol Sc. 2018;19: 970.
• Palmer VJ. The participatory zeitgeist in health care: It is time for a science of participation. J Participat Med 2020;12(1):e15101.
• Vincent T, DeVita Jr, Edward Chu. A History of Cancer Chemotherapy. Cancer Res 2008;68(21):8643-8653
• Galmarini D, Galmarini CM, Galmarini FC. Cancer chemotherapy: A critical analysis of its 60 years of history. Crit Rev Oncol Hemat 2012;84(2):181-199.
• Vermeulen K, Van Bockstaele DR, Berneman ZN. The cell cycle: a review of regulation, deregulation and therapeutic targets in cancer. Cell Prolif 2003;36(3):131-149.
• Ricard-Blum S. The collagen family. Cold Spring Harb Perspect Biol 2011;3(1):a004978.
• Gordon MK, Hahn RA. Collagens. Cell Tissue Res. 2010;339(1):247-257.
• Gelse K, Pöschl E, Aigner T. Collagens—structure, function, and biosynthesis. Adv Drug Deliv Rev 2003;55:1531–1546.
• Stratton MR, Campbell PJ, Futreal PA. The cancer genome. Nature. 2009;458(7239):719-724.
• Garber JE, Offit K. Hereditary cancer predisposition syndromes. J Clin Oncol 2005;23:276-292.
• Brooks B, Kilgour FG. A comparison of Library of Congress subject headings and medical subject headings. Bull Med Libr Assoc 1964;52(2):414-419.
• Man Sze Lau A. ‘Formative good, summative bad?’ – A review of the dichotomy in assessment literature, J Furth High Edu 2016;40(4):509-525.
• Asuero AG, Sayago A, Gonzalez AG The correlation coefficient: an overview. Crit Rev Anal Chem 2006;36:41–59
• Jean D. Gibbons & John W. Pratt P-values: Interpretation and Methodology, Am Stat 1975; 29(1):20-25.
• Garg S, Sharma AK. Comparative analysis of data mining techniques on educational dataset. Int J Comput App 2013;74(5):1-5.
• Batterham M, Neale E, Martin A, Tapsell L. Data mining: Potential applications in research on nutrition and health. Nutr Diet 2017;74(1):3-10.
• Yang H, Lee HJ. Research trend visualization by MeSH terms from PubMed. Int J Environ Res Public Health. 2018;15(6):1113.
• Baumann N. How to use the medical subject headings (MeSH). Int J Clin Pract 2016;70(2):171-174.
• Loeb LA, Harris CC. Advances in chemical carcinogenesis: A historical review and prospective. Cancer Res 2008;68(17):6863-6872.
• Bhattacharya B and Mukherjee S. Cancer therapy using antibiotics. J Cancer Ther 2015;6:849-858.
• Russell RG, Sparano JA, Schwartz EL. Inhibition of endothelial cell function in vitro and angiogenesis in vivo by docetaxel (Taxotere): association with impaired repositioning of the microtubule organizing center. Mol Cancer Ther. 2002;1(13):1191-1200.
• Pérez-Tamayo R. Pathology of collagen degradation. A review. Am J Pathol. 1978;92(2):508-566
• Risteli L, Risteli J. Biochemical markers of bone metabolism, Ann Med 1993;25(4):385-393.
• Hendricks T, Martens MF, Huyben CM, Wobbes T. Inhibition of basal and TGF beta-induced fibroblast collagen synthesis by antineoplastic agents. Implications for wound healing. Br J Cancer 1993;67(3):545-550.
• Zenda M, Yasui H, Oishi S, Masuda R, Fujii N, Koide T. A cisplatin derivative that inhibits collagen fibril-formation in vitro. Chem Biol Drug Des 2015;85(5):519-526.
• Muszyńska A, Wolczyński S, Pałka J. The mechanism for anthracycline-induced inhibition of collagen biosynthesis. Eur J Pharmacol 2001;411(1-2):17-25.
• McGuigan LJ, Quigley HA, Lutty G, Enger C, Young E. The effects of D-penicillamine and daunorubicin on conjunctival fibroblast proliferation and collagen synthesis. Invest Ophthalmol Vis Sci 1988;29(1):112-118.
• Sterling KM Jr, DiPetrillo TA, Kotch JP, Cutroneo KR. Bleomycin-induced increase of collagen turnover in IMR-90 fibroblasts: an in vitro model of connective tissue restructuring during lung fibrosis. Cancer Res 1982;42(9):3502-3506.
• Adamcová M, Pelouch V, Gersl V, Kaplanová J, Mazurová Y, Simůnek T, Klimtová I, Hrdina R. Protein profiling in daunorubicin-induced cardiomyopathy. Gen Physiol Biophys 2003;22(3):411-419.