Establishment of a novel lysosomal signature for the diagnosis of gastric cancer with in-vitro and in-situ validation
BackgroundGastric cancer (GC) represents a malignancy with a multi-factorial combination of genetic, environmental, and microbial factors. Targeting lysosomes presents significant potential in the treatment of numerous diseases, while lysosome-related genetic markers for early GC detection have not...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-05-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1182277/full |
| _version_ | 1797833065208217600 |
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| author | Qi Wang Ying Liu Zhangzuo Li Yidan Tang Weiguo Long Huaiyu Xin Xufeng Huang Shujing Zhou Longbin Wang Bochuan Liang Zhengrui Li Zhengrui Li Zhengrui Li Min Xu |
| author_facet | Qi Wang Ying Liu Zhangzuo Li Yidan Tang Weiguo Long Huaiyu Xin Xufeng Huang Shujing Zhou Longbin Wang Bochuan Liang Zhengrui Li Zhengrui Li Zhengrui Li Min Xu |
| author_sort | Qi Wang |
| collection | DOAJ |
| description | BackgroundGastric cancer (GC) represents a malignancy with a multi-factorial combination of genetic, environmental, and microbial factors. Targeting lysosomes presents significant potential in the treatment of numerous diseases, while lysosome-related genetic markers for early GC detection have not yet been established, despite implementing this process by assembling artificial intelligence algorithms would greatly break through its value in translational medicine, particularly for immunotherapy.MethodsTo this end, this study, by utilizing the transcriptomic as well as single cell data and integrating 20 mainstream machine-learning (ML) algorithms. We optimized an AI-based predictor for GC diagnosis. Then, the reliability of the model was initially confirmed by the results of enrichment analyses currently in use. And the immunological implications of the genes comprising the predictor was explored and response of GC patients were evaluated to immunotherapy and chemotherapy. Further, we performed systematic laboratory work to evaluate the build-up of the central genes, both at the expression stage and at the functional aspect, by which we could also demonstrate the reliability of the model to guide cancer immunotherapy.ResultsEight lysosomal-related genes were selected for predictive model construction based on the inclusion of RMSE as a reference standard and RF algorithm for ranking, namely ADRB2, KCNE2, MYO7A, IFI30, LAMP3, TPP1, HPS4, and NEU4. Taking into account accuracy, precision, recall, and F1 measurements, a preliminary determination of our study was carried out by means of applying the extra tree and random forest algorithms, incorporating the ROC-AUC value as a consideration, the Extra Tree model seems to be the optimal option with the AUC value of 0.92. The superiority of diagnostic signature is also reflected in the analysis of immune features.ConclusionIn summary, this study is the first to integrate around 20 mainstream ML algorithms to construct an AI-based diagnostic predictor for gastric cancer based on lysosomal-related genes. This model will facilitate the accurate prediction of early gastric cancer incidence and the subsequent risk assessment or precise individualized immunotherapy, thus improving the survival prognosis of GC patients. |
| first_indexed | 2024-04-09T14:17:45Z |
| format | Article |
| id | doaj.art-b9b036cd8bd34b55ba3bd1667ca2f0e1 |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-04-09T14:17:45Z |
| publishDate | 2023-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj.art-b9b036cd8bd34b55ba3bd1667ca2f0e12023-05-05T05:57:07ZengFrontiers Media S.A.Frontiers in Immunology1664-32242023-05-011410.3389/fimmu.2023.11822771182277Establishment of a novel lysosomal signature for the diagnosis of gastric cancer with in-vitro and in-situ validationQi Wang0Ying Liu1Zhangzuo Li2Yidan Tang3Weiguo Long4Huaiyu Xin5Xufeng Huang6Shujing Zhou7Longbin Wang8Bochuan Liang9Zhengrui Li10Zhengrui Li11Zhengrui Li12Min Xu13Department of Gastroenterology, Affiliated Hospital of Jiangsu University, Jiangsu University, Zhenjiang, ChinaDepartment of Cardiology, Sixth Medical Center, PLA General Hospital, Beijing, ChinaDepartment of Cell Biology, School of Medicine, Jiangsu University, Zhenjiang, ChinaFaculty of Medicine, University of Debrecen, Debrecen, HungaryDepartment of Pathology, Affiliated Hospital of Jiangsu University, Jiangsu University, Zhenjiang, ChinaDepartment of Gastroenterology, Affiliated Hospital of Jiangsu University, Jiangsu University, Zhenjiang, ChinaFaculty of Dentistry, University of Debrecen, Debrecen, HungaryFaculty of Medicine, University of Debrecen, Debrecen, HungaryDepartment of Clinical Veterinary Medicine, Huazhong Agricultural University, Wuhan, ChinaFaculty of Chinese Medicine, Nanchang Medical College, Nanchang, ChinaDepartment of Oral and Maxillofacial-Head and Neck Oncology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, Shanghai, China0National Center for Stomatology and National Clinical Research Center for Oral Diseases, Shanghai JiaoTong University, Shanghai, China1Shanghai Key Laboratory of Stomatology, Shanghai JiaoTong University, Shanghai, ChinaDepartment of Gastroenterology, Affiliated Hospital of Jiangsu University, Jiangsu University, Zhenjiang, ChinaBackgroundGastric cancer (GC) represents a malignancy with a multi-factorial combination of genetic, environmental, and microbial factors. Targeting lysosomes presents significant potential in the treatment of numerous diseases, while lysosome-related genetic markers for early GC detection have not yet been established, despite implementing this process by assembling artificial intelligence algorithms would greatly break through its value in translational medicine, particularly for immunotherapy.MethodsTo this end, this study, by utilizing the transcriptomic as well as single cell data and integrating 20 mainstream machine-learning (ML) algorithms. We optimized an AI-based predictor for GC diagnosis. Then, the reliability of the model was initially confirmed by the results of enrichment analyses currently in use. And the immunological implications of the genes comprising the predictor was explored and response of GC patients were evaluated to immunotherapy and chemotherapy. Further, we performed systematic laboratory work to evaluate the build-up of the central genes, both at the expression stage and at the functional aspect, by which we could also demonstrate the reliability of the model to guide cancer immunotherapy.ResultsEight lysosomal-related genes were selected for predictive model construction based on the inclusion of RMSE as a reference standard and RF algorithm for ranking, namely ADRB2, KCNE2, MYO7A, IFI30, LAMP3, TPP1, HPS4, and NEU4. Taking into account accuracy, precision, recall, and F1 measurements, a preliminary determination of our study was carried out by means of applying the extra tree and random forest algorithms, incorporating the ROC-AUC value as a consideration, the Extra Tree model seems to be the optimal option with the AUC value of 0.92. The superiority of diagnostic signature is also reflected in the analysis of immune features.ConclusionIn summary, this study is the first to integrate around 20 mainstream ML algorithms to construct an AI-based diagnostic predictor for gastric cancer based on lysosomal-related genes. This model will facilitate the accurate prediction of early gastric cancer incidence and the subsequent risk assessment or precise individualized immunotherapy, thus improving the survival prognosis of GC patients.https://www.frontiersin.org/articles/10.3389/fimmu.2023.1182277/fulllysosomegastric cancerdiagnosismachine learningimmunotherapychemotherapy |
| spellingShingle | Qi Wang Ying Liu Zhangzuo Li Yidan Tang Weiguo Long Huaiyu Xin Xufeng Huang Shujing Zhou Longbin Wang Bochuan Liang Zhengrui Li Zhengrui Li Zhengrui Li Min Xu Establishment of a novel lysosomal signature for the diagnosis of gastric cancer with in-vitro and in-situ validation Frontiers in Immunology lysosome gastric cancer diagnosis machine learning immunotherapy chemotherapy |
| title | Establishment of a novel lysosomal signature for the diagnosis of gastric cancer with in-vitro and in-situ validation |
| title_full | Establishment of a novel lysosomal signature for the diagnosis of gastric cancer with in-vitro and in-situ validation |
| title_fullStr | Establishment of a novel lysosomal signature for the diagnosis of gastric cancer with in-vitro and in-situ validation |
| title_full_unstemmed | Establishment of a novel lysosomal signature for the diagnosis of gastric cancer with in-vitro and in-situ validation |
| title_short | Establishment of a novel lysosomal signature for the diagnosis of gastric cancer with in-vitro and in-situ validation |
| title_sort | establishment of a novel lysosomal signature for the diagnosis of gastric cancer with in vitro and in situ validation |
| topic | lysosome gastric cancer diagnosis machine learning immunotherapy chemotherapy |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1182277/full |
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