Sea-Urchin-Like Au Nanocluster with Surface-Enhanced Raman Scattering in Detecting Epidermal Growth Factor Receptor (EGFR) Mutation Status of Malignant Pleural Effusion

Somatic mutations in the epidermal growth factor receptor (EGFR) gene are common in patients with lung adenocarcinomas and are associated with sensitivity to the small-molecule tyrosine kinase inhibitors (TKIs). For 10%-50% of the patients who experienced malignant pleural effusion (MPE), pathological diagnosis might rely exclusively on finding lung cancer cells in the MPE. Current methods based on polymerase chain reaction were utilized to test EGFR mutation status of MPE samples, but the accuracy of the test data was very low, resulting in many patients losing the chance of TKIs treatment. Herein, we synthesized the sea-urchin-like Au nanocluster (AuNC) with an average diameter of 92.4 nm, composed of 15-nm nanopricks. By introducing abundant sharp nanopricks, the enhancement factor of AuNC reached at 1.97 × 10(7). After capped with crystal violet (CV), polyethylene glycol, and EGFR mutation specific antibody, the AuNC-EGFR had excellent surface-enhanced Raman scattering (SERS) activity and EGFR mutation targeted recognition capability in lung cancer cells. Characteristic SERS signal at 1617 cm(-1) of CV was linear correlation with the number of H1650 cells, demonstrating the minimum detection limit as 25 cells in a 1-mL suspension. The gold mass in single H1650 cells exposed to AuNC-E746_750 for 2 h ranged from 208.6 pg to 231.4 pg, which approximately corresponded to 56-62 AuNCs per cell. Furthermore, SERS was preclinically utilized to test EGFR mutation status in MPE samples from 35 patients with lung adenocarcinoma. Principal component analysis (PCA) and the support vector machine (SVM) algorithm were constructed for EGFR mutation diagnostic analysis, yielding an overall accuracy of 90.7%. SERS measurement based on sea-urchin-like AuNC was an efficient method for EGFR mutation detection in MPE, and it might show great potential in applications such as predicting gene typing of clinical lung cancer in the near future.