Biomarkers in medicine · 2025
Novel miRNA set for screening gastric cancer with high sensitivity: an ROC analysis of 6,997 samples.
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Wang Jun, Le Shilian, Wu Sien, Yang Qi, Wen Senglin, Huang Gen, Deng Peng
Аннотация
AIMS: Cancer screening is essential for reducing GC-associated deaths. Nonetheless, implementing population-based screening strategies poses challenges. In this study, a comprehensive receiver operating characteristic (ROC) analysis was conducted on 2565 microRNAs (miRNAs) from 6997 samples to identify a classifier for GC screening.
METHOD: First, batch effects were corrected across the five Gene Expression Omnibus Series (GSE) datasets. Second, a predictive model was established using the incidence method. Finally, this model was validated using randomly selected datasets, all datasets after batch effect removal, and each of the datasets separately.
RESULTS: Six miRNAs, namely miR-1290, miR-5100, miR-1343-3p, miR-8073, miR-4706, and miR-4787-3p were identified, with an area under the ROC curve (AUC) of 0.990 ± 0.002, 0.993 ± 0.002, 0.996 ± 0.004, 0.978 ± 0.010, 0.957 ± 0.015, and 0.969 ± 0.015, respectively. The miRNA set obtained by combining the six miRNAs yielded an AUC of 0.997 ± 0.001, which was higher than that of the six individual miRNAs (p < 0.001). Validation across the total dataset and five GSE datasets (GSE106817, GSE112264, GSE113486, GSE113740, and GSE164174) yielded AUCs of 0.997, 0.999, 1.000, 1.000, 0.996, and 0.994, respectively.
CONCLUSION: The novel miRNA set comprising miR-1290, miR-5100, miR-1343-3p, miR-8073, miR-4706, and miR-4787-3p holds promise as a diagnostic classifier for GC screening.
METHOD: First, batch effects were corrected across the five Gene Expression Omnibus Series (GSE) datasets. Second, a predictive model was established using the incidence method. Finally, this model was validated using randomly selected datasets, all datasets after batch effect removal, and each of the datasets separately.
RESULTS: Six miRNAs, namely miR-1290, miR-5100, miR-1343-3p, miR-8073, miR-4706, and miR-4787-3p were identified, with an area under the ROC curve (AUC) of 0.990 ± 0.002, 0.993 ± 0.002, 0.996 ± 0.004, 0.978 ± 0.010, 0.957 ± 0.015, and 0.969 ± 0.015, respectively. The miRNA set obtained by combining the six miRNAs yielded an AUC of 0.997 ± 0.001, which was higher than that of the six individual miRNAs (p < 0.001). Validation across the total dataset and five GSE datasets (GSE106817, GSE112264, GSE113486, GSE113740, and GSE164174) yielded AUCs of 0.997, 0.999, 1.000, 1.000, 0.996, and 0.994, respectively.
CONCLUSION: The novel miRNA set comprising miR-1290, miR-5100, miR-1343-3p, miR-8073, miR-4706, and miR-4787-3p holds promise as a diagnostic classifier for GC screening.