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.

HCCR-1 is a Novel Prognostic Indicator for Gastric Cancer and Promotes Cell Proliferation.

The role of the human cervical cancer oncogene () in the development of various tumors has been elucidated; however, its expression and function in gastric cancer remains largely unknown. Accordingly, the expression of HCCR-1 and epidermal growth factor (EGF) were detected in paired gastric cancer tissues and cell lines by western blotting (WB) and immunohistochemistry (IHC). Furthermore, the correlations between HCCR-1 expression in 209 gastric cancer tissues and the clinicopathological features and disease prognosis were analyzed.