Ma Fu-Chao, Zhang Guan-Lan, Chi Bang-Teng, Tang Yu-Lu, Peng Wei, Liu Ai-Qun, Chen Gang, Gao Jin-Biao, Wei Dan-Ming, Ge Lian-Ying
Department of Medical Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi Zhuang Autonomous Region, China.
Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi Zhuang Autonomous Region, China.
World J Gastrointest Oncol. 2025 Apr 15;17(4):103679. doi: 10.4251/wjgo.v17.i4.103679.
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.
To identify effective plasma miRNA biomarkers and investigate the clinical value of combining multiple miRNAs for early detection of GC.
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.
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.
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.
胃癌（GC）缺乏早期筛查方法；因此，当患者首次开始出现典型症状时，该病往往已进展到晚期。内镜检查和病理活检仍是主要的诊断方法，但它们具有侵入性，尚未广泛应用于早期人群筛查。微小RNA（miRNA）是一种在血浆中稳定存在的高度保守的RNA类型。miRNA功能异常与肿瘤发生和进展有关，这表明单个miRNA或多个miRNA的组合可能作为潜在的生物标志物。
鉴定有效的血浆miRNA生物标志物，并研究联合多个miRNA用于GC早期检测的临床价值。
收集来自多个中心的血浆样本。通过小RNA测序（sRNA-seq）鉴定健康对照、早期GC患者和晚期GC患者之间差异表达的基因，并通过实时定量逆转录聚合酶链反应（RT-qPCR）进行验证。采用Wilcoxon符号秩检验研究miRNA的差异。从基因表达综合数据库（GEO）、ArrayExpress和癌症基因组图谱数据库中检索GC血清样本的测序数据集，并为关键风险miRNA构建多层感知器-人工神经网络（MLP-ANN）模型。使用pROC软件包评估模型的判别效能。
从三个中心获得了107例正常、71例早期GC和97例晚期GC患者的血浆样本，并从GEO数据库中获得了8443例正常和1583例GC患者的血清样本。sRNA-seq和RT-qPCR实验显示，与健康对照相比，早期GC患者中miR-452-5p、miR-5010-5p、miR-27b-5p、miR-5189-5p、miR-552-5p和miR-199b-5p显著升高，与早期GC患者相比，晚期GC患者中这些miRNA也显著升高（<0.05）。为这六个关键miRNA构建了MLP-ANN模型。训练队列中的曲线下面积（AUC）为0.983 [95%置信区间（CI）：0.980-0.986]。在两个验证队列中，AUC分别为0.995（95%CI：0.987至接近1.000）和0.979（95%CI：0.972-0.986）。
鉴定出了潜在的miRNA生物标志物，包括miR-452-5p、miR-5010-5p、miR-27b-5p、miR-5189-5p、miR-552-5p和miR-199b-5p。基于这些miRNA开发了一种GC分类器，有助于早期检测和人群筛查。
