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Detecting microRNA (miRNA) biomarkers expression is of great significance for the diagnosis and treatment of lung cancer. Surface-enhanced Raman scattering (SERS) has achieved microRNA sensing for the diagnosis of primary liver cancers. In this work, we developed a SERS technology for the rapid detection of lung cancers-related miRNA (miR-196a) using bimetallic Au-Ag nanowire (AgNW@AuNPs) substrates coupled with the target hairpin DNA. The finite-difference time-domain simulation proved that a large number of "hot spots" were generated between the AgNW and AuNPs, which resulted in a huge enhancement of the signal of Raman reporters. Filter paper treated by hexadecenyl succinic anhydride hydrophobic and modified with AgNWs@AuNPs was used as capturing substrate. The detection limits of miR-196a in PBS and serum were as low as 96.58 aM and 130 aM, respectively. Studies on nonspecific sequence and single-base mismatch of miRNA demonstrated that SERS-based platform was highly selective, excellent uniform, and reproducible. Finally, the platform was used to show that the miR-196a expression in the serum of lung cancer patients was much higher than that in healthy people. The detection results indicated that the SERS platform had potential applications in cancer diagnosis and might be a viable alternative to the conventional miRNA detection method, the real-time polymerase chain reaction (RT-PCR) technology. [ABSTRACT FROM AUTHOR]

Surface-enhanced Raman spectroscopy (SERS) is a good candidate for the development of fast and easy-to-use diagnostic tools, possibly used on serum in screening tests. In this study, a potential label-free serum test based on SERS spectroscopy was developed to analyze human serum for the diagnosis of the non-small cell lung cancer (NSCLC). We firstly synthesized novel highly branched gold nanoparticles (HGNPs) at high yield through a one-step reduction of HAuCl4 with dopamine hydrochloride at 60°C. Then, HGNP substrates with good reproducibility, uniformity, and high SERS effect were fabricated by the electrostatically assisted (3-aminopropyl) triethoxysilane-(APTES-) functionalized silicon wafer surface-sedimentary self-assembly method. Using as-prepared HGNP substrates as a high-performance sensing platform, SERS spectral data of serum obtained from healthy subjects, lung adenocarcinoma patients, lung squamous carcinoma patients, and large cell lung cancer patients were collected. The difference spectra among different types of NSCLC were compared, and analysis result revealed their intrinsic difference in types and contents of nucleic acids, proteins, carbohydrates, amino acids, and lipids. SERS spectra were analyzed by principal component analysis (PCA), which was able to distinguish different types of NSCLC. Considering its time efficiency, being label-free, and sensitivity, SERS based on HGNP substrates is very promising for mass screening NSCLC and plays an important role in the detection and prevention of other diseases. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) are recognized as regulators of gene expression during the biological processes of cells as well as biomarkers of many diseases. Development of rapid and sensitive miRNA profiling methods is crucial for evaluating the pattern of miRNA expression related to normal and diseased states. This work presents a novel hollow Au nanoflowers (HAuNFs) substrate for rapid detection and identification of miRNAs by surface-enhanced Raman scattering (SERS) spectroscopy. We synthesized the HAuNFs by a seed-mediated growth approach. Then, HAuNFs substrates were fabricated by depositing HAuNFs onto the surfaces of (3-aminopropyl)triethoxysilane- (APTES-) functionalized ITO glass. The result demonstrated that HAuNFs substrates had very good reproducibility, homogeneous SERS activity, and high SERS effect. The substrates enabled us to successfully obtain the SERS spectra of miR-10a-5p, miR-125a-5p, and miR-196a-5p. The difference spectra among the three kinds of miRNAs were studied to better interpret the spectral differences and identify miRNA expression patterns with high accuracy. The principal component analysis (PCA) of the SERS spectra was used to distinguish among the three kinds of miRNAs. Considering its time efficiency, being label-free, and its sensitivity, the SERS based on HAuNFs substrates is very promising for miRNA research and plays an important role in early disease detection and prevention. [ABSTRACT FROM AUTHOR]