Supporting information

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Supporting information

Label-Free SERS Analysis of Urine Using 3D Stacked AgNW-Glass Fiber Filter Sensor for the Diagnosis of Pancreatic Cancer and Prostate Cancer

Jung Bin Phyo,1,2$ Ayoung Woo,1$ Ho Jae Yu,1 Kyongmook Lim,2 Baek Hwan Cho,1,2 Ho Sang Jung,3_{* Min-Young Lee}1,2_*

1_{Department of Medical Device Management and Research, Samsung Advanced Institute for}

Health Sciences & Technology, Sungkyunkwan University, 81, Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea

2_{Smart Healthcare Research Institute, Samsung Medical Center, 81, Irwon-ro, Gangnam-gu,}

Seoul, 06351, Republic of Korea

3_{Department of Nano-Bio Convergence, Korea Institute of Materials Science (KIMS),}

Changwon, Gyeongnam, 51508, Republic of Korea $_{These authors contributed equally.}

Contents

1. Additional materials and methods

2. Figure S1. SEM images of AgNW-GFF SERS sensor

3. Figure S2. SERS characteristics using Rhodamine b 4. Table 1. Clinical characteristics of collected urines

5. Figure S3. Raw SERS spectra of urine

6. Figure S4. Averaged raw SERS spectra of urine and raw SERS intensities at 502 cm-1 7. Figure S5. Total protein concentration

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9. Figure S7. SERS spectra and standard curves according to concentration of xanthopterin, inosine, hypoxanthine, xanthine, uric acid and urea

10. Figure S8. Difference spectra

11. Figure S9. PCA for normal vs pancreatic cancer and normal vs prostate cancer 12. Figure S10. Plots of the cumulative variance

13. Figure S11. PCA for male vs female

14. Figure S12. PCA using raw SERS spectra of urine

15. Figure S13. OPLS-DA for normal vs pancreatic cancer and normal vs prostate cancer 16. Table 2. Performance (R2Y, Q2Y and RMSEE) of OPLS-DA models

17. Figure S14. Permutation tests of OPLS-DA models 18. Figure S15. ROC curves of OPLS-DA models

Materials

AgNWs (average diameter of ca. 40 nm and an average length of ca. 8 μm, 0.3 wt%) coated with polyvinylpyrrolidone (PVP) was purchased from Nanopyxis Co., Ltd. (Seoul, Republic of Korea). Glass fiber filter with pore size of ca 0.7µm (GFF, model no. 1825‐047) was purchased from Whatman (Maidstone, UK). Xanthopterin, inosine, hypoxanthine, xanthine and uric acid were purchased from Tokyo Chemical Industry Co., Ltd (TCI, Chuo-ku, Tokyo).

Collection of urine samples

This study was approved by the Institutional Review Board of Samsung Medical Center. The prostate or pancreatic cancer patients were hospitalized and fasted after dinner, and their urines were collected the next morning before surgery. Normal people fasted after midnight, and their urines were collected on the morning of the health screenings. A 120 mL specimen cup (SPL Life Sciences, Gyeonggi-do, Korea) was used for the collection of urine samples. A total of 74 volunteers participated, including three groups: pancreatic cancer patients (n=22), prostate cancer patients (n=22), and normal controls (n=30). The cancer patient participants were

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confirmed by biopsy. Patients receiving chemotherapy, radiation therapy and surgery rejection were excluded. The normal controls were selected from people who did not have any abnormalities related to cancer in biomarkers and imaging diagnostics by health screenings. Urine samples were collected in the morning after fasting for at least 8 hours, divided into 10 mL aliquots in a 15 mL tube, and stored at -80 °C until use.

SERS characterizations

The limit of detection (LOD) was calculated according to a formula of LOD=3σ/S. σ is the standard deviation of the blank Raman intensities and S is the slope of the calibration curve. Raman mapping image of Rhodamine b on AgNW-GFF substrate was obtained by a portable Raman system (NS220, Nanoscope Systems, Inc., Daejeon, South Korea) with a laser wavelength of 633 nm, a power of 7 mW and an exposure time of 2 s, respectively. The Raman mapping was performed within a measurement area of 225 μm × 225 μm, with an interval step of 25 μm. CV value was obtained from 100 points of the Raman intensity in the mapping image, and the spectral uniformity graph was plotted from 15 random positions of the substrate.

Multivariate analysis

For multivariate analysis, unsupervised PCA and supervised OPLS-DA were performed using the built-in function of Program R. A total of 428 variables within the 502-2000 cm−1 _band were used for the multivariate analyses. For classification using PCA, the first and second principal components were taken. In the OPLS-DA classification results, sensitivity, specificity, and accuracy were calculated as follows: sensitivity = TP/(TP+FN) × 100, specificity = TN/(FP+TN) × 100, and accuracy = (TP+TN)/(TP+FP+FN+TN) × 100. (TP: true positive, FN: false negative, TN: true negative, FP: false positive).

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Figure S1. SEM images of AgNWs stacked on GFF of the AgNW-GFF SERS sensor. AgNWs

Glass fiber

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Figure S2. (a) SERS spectra of Rhodamine b on the AgNW-GFF SERS sensor at various concentrations and their standard curve at 1647 cm-1_{. (b) Raman mapping image of Rhodamine} b at a wavelength of 1647 cm-1 _{(1 µM) on AgNW-GFF SERS sensor and (c) spectral uniformity} plot.

(a)

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Table S1. Clinical characteristics of healthy subjects (normal control), pancreatic cancer patients and prostate cancer patients.

Group Number Age Gender

(M:F) CA 19-9 (U/mL) CEA (ng/mL) PSA (ng/mL) > 37 ≤ 37 > 5 ≤ 5 > 2.5 ≤ 2.5 Normal Control 30 59.5±5.5 9 : 21 0 30 0 30 0 30 Pancreatic Cancer 22 69.0±5.3 11 : 11 9 13 3 19 _ Prostate Cancer 22 65.5±6.9 22 : 0 _ _ 22 0

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Figure S4. (a) Averaged raw SERS spectra of urine on the AgNW-GFF sensor and (b) box plots of the raw SERS intensities at 502 cm-1 (***P <0.005).

(b) (a)

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Figure S5. Box plot of total protein concentration of urine after centrifugation by Bradford assay using Bovine Serum Albumin as a standard. (****P <0.0005).

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Figure S6. Baseline corrected SERS spectra of (a) xanthopterin, (b) inosine, (c) hypoxanthine, (d) xanthine, (e) uric acid and (f) urea on the AgNW-GFF SERS sensor.

(a) (b) (e) (f) (c) (d)

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Figure S7. SERS spectra and their standard curves of (a) xanthopterin, (b) inosine, (c) hypoxanthine, (d) xanthine, (e) uric acid and (f) urea according to the concentration on the AgNW-GFF SERS sensor.

(a) (b) (e) (f) (c) (d)

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Figure S8. Difference spectra of baseline corrected and averaged SERS spectra of (a) normal control – pancreatic cancer, (b) normal control – prostate cancer, and (c) pancreatic cancer – prostate cancer.

(a)

(b)

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Figure S9. Principal component analysis (PCA) of baseline corrected SERS spectra of urine on the AgNW-GFF sensor for (a) normal control and pancreatic cancer, and for (b) normal control and prostate cancer.

(a)

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Figure S10. Plots of the cumulative variance explained as a function of principal components (PCs) for the classification of (a) the normal control and cancer groups (pancreatic cancer and prostate cancer), (b) the normal control and pancreatic cancer groups, (c) the normal control and prostate cancer groups, and (d) the pancreatic cancer and prostate cancer groups.

(a) (b)

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Figure S11. PCA score plots of baseline corrected SERS spectra of urine on the AgNW-GFF sensor for males and females in (a) normal controls, (b) pancreatic cancer, and (c) combined normal control and pancreatic cancer samples.

(a)

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Figure S12. PCA score plots of raw SERS spectra of urine on the AgNW-GFF sensor. (a) PCA score plot for three groups: normal control, pancreatic cancer and prostate cancer. PCA score plots for each pair of groups: (b) normal control and pancreatic cancer, (c) normal control and prostate cancer, and (d) pancreatic cancer and prostate cancer.

(a) (b)

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Figure S13. Orthogonal partial least square discrimination analysis (OPLS-DA) baseline corrected SERS spectra of urine on the AgNW-GFF sensor for (a) classification of normal control and pancreatic cancer, and for (b) classification of normal control and prostate cancer.

(a)

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Table S2. Overview of performance (R2_{Y, Q}2_{Y and RMSEE) of the OPLS-DA models.}

Model R2_{Y Q}2_{Y RMSEE}

Normal Control vs Cancers (Pancreatic Cancer + Prostate Cancer)

0.916 0.817 0.147

Normal Control vs Pancreatic Cancer 0.911 0.873 0.152

Normal Control vs Prostate Cancer 0.957 0.856 0.108

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Figure S14. Plots for permutation tests in the OPLS-DA models for (a) normal control vs combined cancer, (b) normal control vs pancreatic cancer, (c) normal control vs prostate cancer, and (d) pancreatic cancer vs prostate cancer.

(a)

(c)

(b)

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Figure S15. Receiver operating characteristic (ROC) curves of the OPLS-DA models for (a) normal control vs combined cancer, (b) normal control vs pancreatic cancer, (c) normal control vs prostate cancer, and (d) pancreatic cancer vs prostate cancer.

(b) (a)