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. The results showed that the agaric-shaped nanoarray substrate has good uniformity, stability, cleanliness, and SERS enhancement effect. The trained PCA-CDNN model not only found the most important features of PLGC, but also achieved satisfactory classification results with accuracy, area under curve (AUC), sensitivity, and specificity up to 100%. This demonstrated the enormous potential of this analysis platform in the diagnosis of PLGC.

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

The role of the human cervical cancer oncogene (HCCR-1) 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. A stable HCCR-1 overexpression cell line was established, and the influence of increased HCCR-1 expression on the growth of gastric cancer cells was observed in vivo and in vitro. The expression of HCCR-1 generally increased in gastric cancer tissues. Further, increased HCCR-1 expression in gastric cancer tissues was associated with tumor T stage and was an independent factor that influenced poor postoperative prognosis in gastric cancer patients. A positive correlation was also detected between the expression of EGF and HCCR-1 in a time- and dose-dependent manner. The overexpression of HCCR-1 might enhance the growth rate of gastric cancer cells in vitro, increase the number of colony forming units, and promote the growth, volume, and weight of subcutaneous tumors in nude mice. In conclusion, HCCR-1 is a gastric cancer oncogene, and its increased expression plays a critical role in the occurrence and development of gastric cancer. Hence, HCCR-1 could serve as a valuable marker for the postoperative prognostic assessment of gastric cancer patients.