Khosroshahi Mohammad E, Gaoiran Christine, Umashanker Vithurshan, Veeru Hayagreev, Panday Pranav
Nanobiophotonics & Biomedical Research Laboratory, M.I.S. Electronics Inc., Richmond Hill, ON L4B 1B4, Canada.
Institute for Advanced Non-Destructive and Non-Invasive Diagnostic Technologies (IANDIT), University of Toronto, Toronto, ON M5S 3G8, Canada.
Biosensors (Basel). 2025 Jul 11;15(7):447. doi: 10.3390/bios15070447.
In clinical applications of surface-enhanced Raman spectroscopy (SERS) immunosensors, accurately determining analyte biomarker concentrations is essential. This study presents a non-invasive approach for quantifying various breast cancer biomarkers-including human epidermal growth factor receptor II (HER-II) (2+, 3+ (I), 3+ (II), 3+ (III), and positive IV) and CA 15-3-using a directional, plasmonically active, label-free SERS sensor. Each stage of sensor functionalization, conjugation, and biomarker interaction was verified by UV-Vis spectroscopy. Atomic force microscopy (AFM) characterized the morphology of gold nanourchin (GNU)-immobilized printed circuit board (PCB) substrates. An enhancement factor of ≈ 0.5 × 10 was achieved using Rhodamine 6G as the probe molecule. Calibration curves were initially established using standard HER-II solutions at concentrations ranging from 1 to 100 ng/mL and CA 15-3 at concentrations from 10 to 100 U/mL. The SERS signal intensities in the 620-720 nm region were plotted against concentration, yielding linear sensitivity with R values of 0.942 and 0.800 for HER-II and CA15-3, respectively. The same procedure was applied to breast cancer serum (BCS) samples, allowing unknown biomarker concentrations to be determined based on the corresponding calibration curves. SERS data were processed using the filter from for smoothing and then baseline-corrected with the Improved Asymmetric Least Squares (IASLS) algorithm from the library. Principal Component Analysis (PCA) effectively distinguished the sample groups and revealed spectral differences before and after biomarker interactions. Key Raman peaks were attributed to functional groups including N-H (primary and secondary amines), C-H antisymmetric stretching, C-N (amines), C=O antisymmetric stretching, NH (amines), carbohydrates, glycine, alanine, amides III, C=N stretches, and NH in primary amides.
在表面增强拉曼光谱（SERS）免疫传感器的临床应用中，准确测定分析物生物标志物浓度至关重要。本研究提出了一种非侵入性方法，用于使用定向、具有等离子体活性、无标记的SERS传感器定量各种乳腺癌生物标志物，包括人表皮生长因子受体II（HER-II）（2 +、3 +（I）、3 +（II）、3 +（III）和阳性IV）以及CA 15-3。通过紫外可见光谱对传感器功能化、共轭和生物标志物相互作用的每个阶段进行了验证。原子力显微镜（AFM）表征了固定有金纳米海胆（GNU）的印刷电路板（PCB）基板的形态。使用罗丹明6G作为探针分子实现了约0.5×10的增强因子。最初使用浓度范围为1至100 ng/mL的标准HER-II溶液和浓度为10至100 U/mL的CA 15-3建立校准曲线。将620 - 720 nm区域的SERS信号强度与浓度作图，HER-II和CA15-3的线性灵敏度R值分别为0.942和0.800。将相同程序应用于乳腺癌血清（BCS）样本，从而能够根据相应校准曲线确定未知生物标志物浓度。使用来自 的 滤波器对SERS数据进行处理以进行平滑，然后使用来自 库的改进非对称最小二乘法（IASLS）算法进行基线校正。主成分分析（PCA）有效地区分了样本组，并揭示生物标志物相互作用前后的光谱差异。关键拉曼峰归因于包括N - H（伯胺和仲胺）、C - H反对称拉伸、C - N（胺）、C = O反对称拉伸、NH（胺）、碳水化合物、甘氨酸、丙氨酸、酰胺III、C = N拉伸和伯酰胺中的NH在内的官能团。
