Raw FTIR Spectral Data of Urinary Extracellular Vesicles from Prostate Cancer Patients and Healthy Controls

This dataset comprises raw Fourier-transform infrared (FTIR) spectral data obtained from urinary extracellular vesicles (EVs) isolated from a well-characterized cohort of 53 individuals. The cohort consists of 22 patients clinically diagnosed with prostate cancer and 31 healthy control participants. The FTIR spectra were acquired in the mid-infrared region, spanning wavenumbers from 4000 cm⁻¹ to 400 cm⁻¹, capturing the molecular fingerprint region relevant for biomolecular analysis. The urinary EVs were isolated following standardized protocols to ensure purity and reproducibility, and the resulting spectra represent the biochemical composition of these vesicles, reflecting underlying physiological and pathological states. The dataset includes only de-identified spectral intensity values at each wavenumber, with no accompanying direct patient identifiers to maintain participant confidentiality and compliance with ethical standards. The dataset supports the findings presented in the research article: Wong, LW., Bu, ZH., Supramaniam, J. et al. Revolutionizing prostate cancer screening: Machine learning-driven FTIR analysis of biofluidic EVs integrated with clinical profiles. Health Technol. 15, 563–576 (2025). https://doi.org/10.1007/s12553-025-00967-7 The study highlights the potential of combining spectroscopic data with machine learning techniques to enhance early detection of prostate cancer, reducing reliance on invasive procedures and improving patient outcomes. This dataset enables further exploration and validation of spectral biomarkers and analytical methods in the context of non-invasive cancer diagnostics.