Machine learning-driven SERS analysis platform for rapid and accurate detection of precancerous lesions of gastric cancer.

A novel approach is proposed leveraging surface-enhanced Raman spectroscopy (SERS) combined with machine learning (ML) techniques, principal component analysis (PCA)-centroid displacement-based nearest neighbor (CDNN). This label-free approach can identify slight abnormalities between SERS spectra of gastric lesions at different stages, offering a promising avenue for detection and prevention of precancerous lesion of gastric cancer (PLGC). The agaric-shaped nanoarray substrate was prepared using gas-liquid interface self-assembly and reactive ion etching (RIE) technology to measure SERS spectra of serum from mice model with gastric lesions at different stages, and then a SERS spectral recognition model was trained and constructed using the PCA-CDNN algorithm.

Dowregulation of OTX1 attenuates gastric cancer cell proliferation, migration and invasion.

Orthodenticle homolog 1 (OTX1) has previously been revealed to be tightly associated with the development and progression of several human tumors. However, the functional roles and underlying molecular mechanisms of OTX1 in gastric cancer (GC) remain poorly understood. In the present study, we observed that OTX1 was highly expressed in GC tissues compared with adjacent non‑tumor tissues based on a large cohort of samples from The Cancer Genome Atlas (TCGA) database.