Blood-based machine learning classifiers for early diagnosis of gastric cancer multiple miRNAs.

[BACKGROUND] Early screening methods for gastric cancer (GC) are lacking; therefore, the disease often progresses to an advanced stage when patients first start to exhibit typical symptoms. Endoscopy and pathological biopsy remain the primary diagnostic approaches, but they are invasive and not yet widely applicable for early population screening. miRNA is a highly conserved type of RNA that exists stably in plasma. Dysfunction of miRNA is linked to tumorigenesis and progression, indicating that individual miRNAs or combinations of multiple miRNAs may serve as potential biomarkers.

[AIM] To identify effective plasma miRNA biomarkers and investigate the clinical value of combining multiple miRNAs for early detection of GC.

[METHODS] Plasma samples from multiple centres were collected. Differentially expressed genes among healthy controls, early-stage GC patients, and advanced-stage GC patients were identified through small RNA sequencing (sRNA-seq) and validated real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR). A Wilcoxon signed-rank test was used to investigate the differences in miRNAs. Sequencing datasets of GC serum samples were retrieved from the Gene Expression Omnibus (GEO), ArrayExpress, and The Cancer Genome Atlas databases, and a multilayer perceptron-artificial neural network (MLP-ANN) model was constructed for the key risk miRNAs. The pROC package was used to assess the discriminatory efficacy of the model.

[RESULTS] Plasma samples of 107 normal, 71 early GC and 97 advanced GC patients were obtained from three centres, and serum samples of 8443 normal and 1583 GC patients were obtained from the GEO database. The sRNA-seq and RT-qPCR experiments revealed that miR-452-5p, miR-5010-5p, miR-27b-5p, miR-5189-5p, miR-552-5p and miR-199b-5p were significantly increased in early GC patients compared with healthy controls and in advanced GC patients compared with early GC patients ( < 0.05). An MLP-ANN model was constructed for the six key miRNAs. The area under the curve (AUC) within the training cohort was 0.983 [95% confidence interval (CI): 0.980-0.986]. In the two validation cohorts, the AUCs were 0.995 (95%CI: 0.987 to nearly 1.000) and 0.979 (95%CI: 0.972-0.986), respectively.

[CONCLUSION] Potential miRNA biomarkers, including miR-452-5p, miR-5010-5p, miR-27b-5p, miR-5189-5p, miR-552-5p and miR-199b-5p, were identified. A GC classifier based on these miRNAs was developed, benefiting early detection and population screening.