Investigation of Fourier Transform Infrared (FT-IR) Spectroscopy and Chemometric Analysis Method as an Alternative Method in the Diagnosis of Prostate Cancer

Öz

Prostate cancer is one of the most common types of cancer in men. It usually grows slowly and may not show obvious symptoms at first. Prostate cancer can be diagnosed by symptoms or by a doctor performing certain tests during routine health checkups. These tests include physical examination, PSA (Prostate Specific Antigen) Test, biopsy, imaging techniques, and Gleason score. In addition, Fourier transform infrared spectroscopy (FT-IR) is an analysis method used for prostate cancer diagnosis. This study aims to demonstrate FT-IR spectroscopy as an alternative method to other diagnostic methods in the diagnosis of prostate cancer. The FT-IR spectroscopy method is used to examine the molecular structure of samples. For prostate cancer diagnosis, FT-IR spectroscopy can be used to identify molecular changes in prostate tissue and identify characteristics by which cancerous cells differ from healthy cells. FT-IR spectroscopy is based on spectral data obtained by exposing samples to infrared radiation. These spectral data are based on properties associated with the movements of the molecules contained in the samples, such as vibration, rotation and bending. Molecular changes caused by diseases such as prostate cancer may be evident in these spectral data. These changes can provide information about the presence or stage of cancerous cells. Data obtained using FT-IR spectroscopy is processed with statistical analysis methods. These analyses are used to identify molecular differences between cancerous and healthy prostate tissues. In this way, FTIR spectroscopy can help obtain sensitive and accurate results in the diagnosis of prostate cancer.

Anahtar Kelimeler

• FT-IR Spectroscopy
• Prostate cancer
• Diagnostic methods
• Chemometric Analysis

Kaynakça

1. M. Bilal, A. Javaid, F. Amjad, T. A. Youssif, and S. Afzal, “An overview of prostate cancer (PCa) diagnosis: Potential role of miRNAs,” Transl. Oncol., vol. 26, p. 101542, Dec. 2022, doi: 10.1016/j.tranon.2022.101542.
2. T. T. Wheeler, P. Cao, M. D. Ghouri, T. Ji, G. Nie, and Y. Zhao, “Nanotechnological strategies for prostate cancer imaging and diagnosis,” Sci. China Chem., vol. 65, no. 8, pp. 1498–1514, Aug. 2022, doi: 10.1007/s11426-022-1271-0.
3. W. Hou et al., “Nanoparticles for multi-modality cancer diagnosis: Simple protocol for self-assembly of gold nanoclusters mediated by gadolinium ions,” Biomaterials, vol. 120, pp. 103–114, Mar. 2017, doi: 10.1016/j.biomaterials.2016.12.027.
4. B. Shi et al., “Multifunctional gap-enhanced Raman tags for preoperative and intraoperative cancer imaging,” Acta Biomater., vol. 104, pp. 210–220, Mar. 2020, doi: 10.1016/j.actbio.2020.01.006.
5. K.-Y. Su and W.-L. Lee, “Fourier Transform Infrared Spectroscopy as a Cancer Screening and Diagnostic Tool: A Review and Prospects,” Cancers (Basel)., vol. 12, no. 1, p. 115, Jan. 2020, doi: 10.3390/cancers12010115.
6. K. M. G. Lima, K. B. Gajjar, P. L. Martin-Hirsch, and F. L. Martin, “Segregation of ovarian cancer stage exploiting spectral biomarkers derived from blood plasma or serum analysis: ATR-FTIR spectroscopy coupled with variable selection methods,” Biotechnol. Prog., vol. 31, no. 3, pp. 832–839, May 2015, doi: 10.1002/btpr.2084.
7. M. J. Walsh et al., “FTIR Microspectroscopy Coupled with Two-Class Discrimination Segregates Markers Responsible for Inter- and Intra-Category Variance in Exfoliative Cervical Cytology,” Biomark. Insights, vol. 3, p. BMI.S592, Jan. 2008, doi: 10.4137/BMI.S592.
8. Çırak Olgun, “Yıldız Teknik Üniversitesi Fen Bilimleri Enstitüsü Fourier Transform İnfrared Spektroskopisi (FT-IR) İle Sütte Tür Tayini Yapılması,” 2017. Accessed: Dec. 26, 2023. [Online]. Available: chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://food.yildiz.edu.tr/media/files/25(4).pdf

9. M. J. Baker et al., “Using Fourier transform IR spectroscopy to analyze biological materials,” Nat. Protoc., vol. 9, no. 8, pp. 1771–1791, Aug. 2014, doi: 10.1038/nprot.2014.110.
10. W. J. Zheng Y, Xiao M, Jiang S, Ding F, “Coating fabrics with gold nanorods for colouring, UV-protection, and antibacterial functions,” Nanoscale, vol. 5:788, p. 95, 1951.
11. L. Wang and B. Mizaikoff, “Application of multivariate data-analysis techniques to biomedical diagnostics based on mid-infrared spectroscopy,” Anal. Bioanal. Chem., vol. 391, no. 5, pp. 1641–1654, Jul. 2008, doi: 10.1007/s00216-008-1989-9.
12. M. ALBAYRAK, “Fourier Dönüşümlü Kızılötesi Spektroskopisinin Prostat Kanseri Teşhisinde Kullanılabilirliğinin Araştırılması,” Iğdır Üniversitesi Fen Bilim. Enstitüsü Derg. , vol. 8, no. 4, pp. 223–227, Jun. 2018.
13. M. Çınar, M. Engin, E. Z. Engin, and Y. Ziya Ateşçi, “Early prostate cancer diagnosis by using artificial neural networks and support vector machines,” Expert Syst. Appl., vol. 36, no. 3, pp. 6357–6361, Apr. 2009, doi: 10.1016/j.eswa.2008.08.010.
14. B. A. Akinnuwesi et al., “Application of support vector machine algorithm for early differential diagnosis of prostate cancer,” Data Sci. Manag., vol. 6, no. 1, pp. 1–12, Mar. 2023, doi: 10.1016/j.dsm.2022.10.001.
15. Z. Rustam and N. Angie, “Prostate Cancer Classification Using Random Forest and Support Vector Machines,” J. Phys. Conf. Ser., vol. 1752, no. 1, p. 012043, Feb. 2021, doi: 10.1088/1742-6596/1752/1/012043.
16. A. W. Simonetti, W. J. Melssen, M. van der Graaf, G. J. Postma, A. Heerschap, and L. M. C. Buydens, “A Chemometric Approach for Brain Tumor Classification Using Magnetic Resonance Imaging and Spectroscopy,” Anal. Chem., vol. 75, no. 20, pp. 5352–5361, Oct. 2003, doi: 10.1021/ac034541t.
17. “Prostate Cancer Serum Spectra as Indicators of Early Diagnosis Using FTIR-ATR Fourier Transform Infrared Spectroscopy,” J. Pharm. Negat. Results, vol. 13, no. SO3, Jan. 2022, doi: 10.47750/pnr.2022.13.S03.126.
18. B. Holmstrom, M. Johansson, A. Bergh, U.-H. Stenman, G. Hallmans, and P. Stattin, “Prostate specific antigen for early detection of prostate cancer: longitudinal study,” BMJ, vol. 339, no. sep24 1, pp. b3537–b3537, Sep. 2009, doi: 10.1136/bmj.b3537.
19. M. J. Baker, E. Gazi, M. D. Brown, J. H. Shanks, P. Gardner, and N. W. Clarke, “FTIR-based spectroscopic analysis in the identification of clinically aggressive prostate cancer,” Br. J. Cancer, vol. 99, no. 11, pp. 1859–1866, Dec. 2008, doi: 10.1038/sj.bjc.6604753.
20. O. V. Gul and M. Duzova, “Effect of different CTV shrinkage and skin flash margins on skin dose for left chest wall IMRT: A dosimetric study,” Radiat. Phys. Chem., vol. 216, p. 111445, Mar. 2024, doi: 10.1016/j.radphyschem.2023.111445.
21. X.-L. Yap, B. Wood, T.-A. Ong, J. Lim, B.-H. Goh, and W.-L. Lee, “Detection of Prostate Cancer via IR Spectroscopic Analysis of Urinary Extracellular Vesicles: A Pilot Study,” Membranes (Basel)., vol. 11, no. 8, p. 591, Jul. 2021, doi: 10.3390/membranes11080591.