An oral microbiota-based deep neural network model for risk stratification and prognosis prediction in gastric cancer
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 ex...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
|
| Series: | Journal of Oral Microbiology |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/20002297.2025.2451921 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841524916025819136 |
|---|---|
| author | Xue-Feng Gao Can-Gui Zhang Kun Huang Xiao-Lin Zhao Ying-Qiao Liu Zi-Kai Wang Rong-Rong Ren Geng-Hui Mai Ke-Ren Yang Ye Chen |
| author_facet | Xue-Feng Gao Can-Gui Zhang Kun Huang Xiao-Lin Zhao Ying-Qiao Liu Zi-Kai Wang Rong-Rong Ren Geng-Hui Mai Ke-Ren Yang Ye Chen |
| author_sort | Xue-Feng Gao |
| collection | DOAJ |
| description | 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 Aggregatibacter and diminished abundances of Filifactor 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. |
| format | Article |
| id | doaj-art-a9103893fac948238f7a231ba9bf5c6b |
| institution | Kabale University |
| issn | 2000-2297 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Oral Microbiology |
| spelling | doaj-art-a9103893fac948238f7a231ba9bf5c6b2025-01-18T04:46:54ZengTaylor & Francis GroupJournal of Oral Microbiology2000-22972025-12-0117110.1080/20002297.2025.2451921An oral microbiota-based deep neural network model for risk stratification and prognosis prediction in gastric cancerXue-Feng Gao0Can-Gui Zhang1Kun Huang2Xiao-Lin Zhao3Ying-Qiao Liu4Zi-Kai Wang5Rong-Rong Ren6Geng-Hui Mai7Ke-Ren Yang8Ye Chen9Integrative 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, ChinaIntegrative 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, ChinaDepartment of Gastroenterology, Civil Aviation General Hospital, Beijing, ChinaDepartment of Gastroenterology, Civil Aviation General Hospital, Beijing, ChinaDepartment of Oral and Maxillofacial Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, ChinaDepartment of Gastroenterology and Hepatology, The First Medical Center, Chinese PLA General Hospital, Beijing, ChinaDepartment of Gastroenterology and Hepatology, The First Medical Center, Chinese PLA General Hospital, Beijing, ChinaDepartment of Gastroenterology, State Key Laboratory of Organ Failure Research, Guangdong Provincial Key Laboratory of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, ChinaIntegrative 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, ChinaIntegrative 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, ChinaBackground 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 Aggregatibacter and diminished abundances of Filifactor 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.https://www.tandfonline.com/doi/10.1080/20002297.2025.2451921Gastric cancerprognosisrisk stratificationoral microbiotadeep neural network |
| spellingShingle | Xue-Feng Gao Can-Gui Zhang Kun Huang Xiao-Lin Zhao Ying-Qiao Liu Zi-Kai Wang Rong-Rong Ren Geng-Hui Mai Ke-Ren Yang Ye Chen An oral microbiota-based deep neural network model for risk stratification and prognosis prediction in gastric cancer Journal of Oral Microbiology Gastric cancer prognosis risk stratification oral microbiota deep neural network |
| title | An oral microbiota-based deep neural network model for risk stratification and prognosis prediction in gastric cancer |
| title_full | An oral microbiota-based deep neural network model for risk stratification and prognosis prediction in gastric cancer |
| title_fullStr | An oral microbiota-based deep neural network model for risk stratification and prognosis prediction in gastric cancer |
| title_full_unstemmed | An oral microbiota-based deep neural network model for risk stratification and prognosis prediction in gastric cancer |
| title_short | An oral microbiota-based deep neural network model for risk stratification and prognosis prediction in gastric cancer |
| title_sort | oral microbiota based deep neural network model for risk stratification and prognosis prediction in gastric cancer |
| topic | Gastric cancer prognosis risk stratification oral microbiota deep neural network |
| url | https://www.tandfonline.com/doi/10.1080/20002297.2025.2451921 |
| work_keys_str_mv | AT xuefenggao anoralmicrobiotabaseddeepneuralnetworkmodelforriskstratificationandprognosispredictioningastriccancer AT canguizhang anoralmicrobiotabaseddeepneuralnetworkmodelforriskstratificationandprognosispredictioningastriccancer AT kunhuang anoralmicrobiotabaseddeepneuralnetworkmodelforriskstratificationandprognosispredictioningastriccancer AT xiaolinzhao anoralmicrobiotabaseddeepneuralnetworkmodelforriskstratificationandprognosispredictioningastriccancer AT yingqiaoliu anoralmicrobiotabaseddeepneuralnetworkmodelforriskstratificationandprognosispredictioningastriccancer AT zikaiwang anoralmicrobiotabaseddeepneuralnetworkmodelforriskstratificationandprognosispredictioningastriccancer AT rongrongren anoralmicrobiotabaseddeepneuralnetworkmodelforriskstratificationandprognosispredictioningastriccancer AT genghuimai anoralmicrobiotabaseddeepneuralnetworkmodelforriskstratificationandprognosispredictioningastriccancer AT kerenyang anoralmicrobiotabaseddeepneuralnetworkmodelforriskstratificationandprognosispredictioningastriccancer AT yechen anoralmicrobiotabaseddeepneuralnetworkmodelforriskstratificationandprognosispredictioningastriccancer AT xuefenggao oralmicrobiotabaseddeepneuralnetworkmodelforriskstratificationandprognosispredictioningastriccancer AT canguizhang oralmicrobiotabaseddeepneuralnetworkmodelforriskstratificationandprognosispredictioningastriccancer AT kunhuang oralmicrobiotabaseddeepneuralnetworkmodelforriskstratificationandprognosispredictioningastriccancer AT xiaolinzhao oralmicrobiotabaseddeepneuralnetworkmodelforriskstratificationandprognosispredictioningastriccancer AT yingqiaoliu oralmicrobiotabaseddeepneuralnetworkmodelforriskstratificationandprognosispredictioningastriccancer AT zikaiwang oralmicrobiotabaseddeepneuralnetworkmodelforriskstratificationandprognosispredictioningastriccancer AT rongrongren oralmicrobiotabaseddeepneuralnetworkmodelforriskstratificationandprognosispredictioningastriccancer AT genghuimai oralmicrobiotabaseddeepneuralnetworkmodelforriskstratificationandprognosispredictioningastriccancer AT kerenyang oralmicrobiotabaseddeepneuralnetworkmodelforriskstratificationandprognosispredictioningastriccancer AT yechen oralmicrobiotabaseddeepneuralnetworkmodelforriskstratificationandprognosispredictioningastriccancer |