Gao Xue-Feng, Zhang Can-Gui, Huang Kun, Zhao Xiao-Lin, Liu Ying-Qiao, Wang Zi-Kai, Ren Rong-Rong, Mai Geng-Hui, Yang Ke-Ren, Chen Ye
Integrative Microecology Clinical Center, Shenzhen Clinical Research Center for Digestive Disease, Shenzhen Technology Research Center of Gut Microbiota Transplantation, The Clinical Innovation & Research Center, Shenzhen Key Laboratory of Viral Oncology, Department of Clinical Nutrition, Shenzhen Hospital, Southern Medical University, Shenzhen, China.
Department of Gastroenterology, State Key Laboratory of Organ Failure Research, Guangdong Provincial Key Laboratory of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
J Oral Microbiol. 2025 Jan 17;17(1):2451921. doi: 10.1080/20002297.2025.2451921. eCollection 2025.
BACKGROUND: This study aims to develop an oral microbiota-based model for gastric cancer (GC) risk stratification and prognosis prediction. METHODS: Oral microbial markers for GC prognosis and risk stratification were identified from 99 GC patients, and their predictive potential was validated on an external dataset of 111 GC patients. The identified bacterial markers were used to construct a Deep Neural Network (DNN) model, a Random Forest (RF) model, and a Support Vector Machine (SVM) model for predicting GC prognosis. RESULTS: GC patients with <3 years of survival showed a higher abundance of and diminished abundances of and Moryella than those who survived ≥3 years. The Boruta algorithm unearthed Leptotrichia as another significant marker for GC prognosis. Consequently, a DNN model was constructed based on the relative abundances of these bacteria, predicting 3-year and 5-year survival in GC patients with Area Under Curve of 0.814 and 0.912, respectively. Notably, the DNN model outperformed the TNM staging system, SVM and RF models. The prognostic value of these bacterial markers was further reinforced by external validation. CONCLUSION: The oral microbiota-based DNN model may advance GC prognosis. The biological functions of these oral bacterial markers warrant further investigation from the perspective of GC progression.
背景：本研究旨在开发一种基于口腔微生物群的模型，用于胃癌（GC）风险分层和预后预测。 方法：从99例GC患者中鉴定出用于GC预后和风险分层的口腔微生物标志物，并在111例GC患者的外部数据集中验证其预测潜力。所鉴定的细菌标志物用于构建预测GC预后的深度神经网络（DNN）模型、随机森林（RF）模型和支持向量机（SVM）模型。 结果：生存时间<3年的GC患者中，[未提及具体细菌名称]的丰度较高，而[未提及具体细菌名称]和莫雷拉菌的丰度较低，比生存时间≥3年的患者低。博鲁塔算法挖掘出纤毛菌属作为GC预后的另一个重要标志物。因此，基于这些细菌的相对丰度构建了一个DNN模型，预测GC患者3年和5年生存率的曲线下面积分别为0.814和0.912。值得注意的是，DNN模型优于TNM分期系统、SVM模型和RF模型。外部验证进一步强化了这些细菌标志物的预后价值。 结论：基于口腔微生物群的DNN模型可能改善GC的预后。这些口腔细菌标志物的生物学功能值得从GC进展的角度进一步研究。
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