A Diagnostic Method for Gastric Cancer Using Two-Photon Microscopy With Enzyme-Selective Fluorescent Probes: A Pilot Study

Abstract

Background: Endoscopy is the most important tool for gastric cancer diagnosis. However, it relies on naked-eye evaluation by endoscopists, and the histopathologic confirmation is time-consuming. We aimed to visualize and measure the activity of various enzymes through two-photon microscopy (TPM) using fluorescent probes and assess its diagnostic potential in gastric cancer. Methods: β-Galactosidase (β-gal), carboxylesterase (CES), and human NAD(P)H: quinone oxidoreductase (hNQO1) enzyme activities in the normal mucosa, ulcer, adenoma, and gastric cancer biopsy samples were measured using two-photon enzyme probes. The fluorescence emission ratio at long and short wavelengths (Ch2/Ch1) for each probe was comparatively analyzed. Approximately 8,000 – 9,000 sectional images in each group were obtained by measuring the Ch2/Ch1 ratio according to the tissue depth. Each probe was cross-validated by measuring enzymatic activity from a solution containing lysed tissue. Results: Total of 76 subjects were enrolled in this pilot study (normal 21, ulcer 18, adenoma 17, and cancer 20 patients, respectively). There were significant differences in the mean ratio values of β-gal (0.656 ± 0.142 vs. 1.127 ± 0.109, P < 0.001) and CES (0.876 ± 0.049 vs. 0.579 ± 0.089, P < 0.001) between the normal and cancer, respectively. The mean ratio value of cancer tissues was different compared to ulcer and adenoma (P < 0.001). The hNQO1 activity showed no significant difference between cancer and other conditions. Normal mucosa and cancer were visually and quantitatively distinguished through β-gal and CES analyses using TPM images, and enzymatic activity according to depth, was determined using sectional TPM ratiometric images. The results obtained from lysis buffer-treated tissue were consistent with TPM results. Conclusions: TPM imaging using ratiometric fluorescent probes enabled the discrimination of gastric cancer from normal, ulcer, and adenoma. This novel method can help in a visual differentiation and provide quantitative depth profiling in gastric cancer diagnosis.