Saliva-derived transcriptomic signature for gastric cancer detection using machine learning and leveraging publicly available datasets
Abstract Saliva, a non-invasive, self-collected liquid biopsy, holds promise for early gastric cancer (GC) screening. This study aims to assess the potential of saliva as a proxy for malignant gastric transformation and its diagnostic value through transcriptomic profiling. Leveraging transcriptomic...
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Nature Portfolio
2025-05-01
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| Online Access: | https://doi.org/10.1038/s41598-025-96864-0 |
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| author | Catarina Lopes Andreia Brandão Manuel R. Teixeira Mário Dinis-Ribeiro Carina Pereira |
| author_facet | Catarina Lopes Andreia Brandão Manuel R. Teixeira Mário Dinis-Ribeiro Carina Pereira |
| author_sort | Catarina Lopes |
| collection | DOAJ |
| description | Abstract Saliva, a non-invasive, self-collected liquid biopsy, holds promise for early gastric cancer (GC) screening. This study aims to assess the potential of saliva as a proxy for malignant gastric transformation and its diagnostic value through transcriptomic profiling. Leveraging transcriptomic data from the Gene Expression Omnibus (GEO), we constructed and validated predictive models through machine learning algorithms within the tidymodels framework. Tissue-based models were validated on independent tissue datasets, and subsequently applied to saliva. Additionally, an independent saliva-derived model was created and evaluated using sensitivity, specificity, accuracy, area under the curve (AUC), and likelihood ratio (LR) metrics. Tissue-derived models demonstrated excellent performance, with AUC values exceeding 0.9, but did not translate effectively to saliva, suggesting distinct molecular landscapes between tissue and saliva in GC. The saliva-specific model using support vector machine (SVM) achieved the highest performance, with an AUC of 0.87 (95% CI 0.72–0.97), a sensitivity of 0.79 (95% CI 0.58–0.95) and a specificity of 0.70 (95% CI 0.40–0.90). While saliva may not mirror tissue gene expression profile, it represents a promising non-invasive predictive tool for the early detection of GC. Further research is warranted to optimize saliva-derived molecular signatures, increasing their sensitivity and specificity for early cancer detection and advance the use of liquid biopsies in personalized medicine for improved screening, diagnostic and prognostic capabilities. |
| format | Article |
| id | doaj-art-247a0de6048449df895a22d5e88eb75a |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
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| spelling | doaj-art-247a0de6048449df895a22d5e88eb75a2025-08-20T03:22:07ZengNature PortfolioScientific Reports2045-23222025-05-0115111210.1038/s41598-025-96864-0Saliva-derived transcriptomic signature for gastric cancer detection using machine learning and leveraging publicly available datasetsCatarina Lopes0Andreia Brandão1Manuel R. Teixeira2Mário Dinis-Ribeiro3Carina Pereira4Precancerous Lesions and Early Cancer Management Group, Research Center of IPO Porto (CI-IPOP)/CI-IPOP@RISE (Health Research Group), Portuguese Institute of Oncology of Porto (IPO Porto)/Porto Comprehensive Cancer Center Raquel Seruca (Porto.CCC)Cancer Genetics Group, Research Center of IPO Porto (CI-IPOP)/CI-IPOP@RISE (Health Research Group), Portuguese Institute of Oncology of Porto (IPO Porto)/Porto Comprehensive Cancer Center Raquel Seruca (Porto.CCC)ICBAS – School of Medicine and Biomedical Sciences, University of PortoPrecancerous Lesions and Early Cancer Management Group, Research Center of IPO Porto (CI-IPOP)/CI-IPOP@RISE (Health Research Group), Portuguese Institute of Oncology of Porto (IPO Porto)/Porto Comprehensive Cancer Center Raquel Seruca (Porto.CCC)Precancerous Lesions and Early Cancer Management Group, Research Center of IPO Porto (CI-IPOP)/CI-IPOP@RISE (Health Research Group), Portuguese Institute of Oncology of Porto (IPO Porto)/Porto Comprehensive Cancer Center Raquel Seruca (Porto.CCC)Abstract Saliva, a non-invasive, self-collected liquid biopsy, holds promise for early gastric cancer (GC) screening. This study aims to assess the potential of saliva as a proxy for malignant gastric transformation and its diagnostic value through transcriptomic profiling. Leveraging transcriptomic data from the Gene Expression Omnibus (GEO), we constructed and validated predictive models through machine learning algorithms within the tidymodels framework. Tissue-based models were validated on independent tissue datasets, and subsequently applied to saliva. Additionally, an independent saliva-derived model was created and evaluated using sensitivity, specificity, accuracy, area under the curve (AUC), and likelihood ratio (LR) metrics. Tissue-derived models demonstrated excellent performance, with AUC values exceeding 0.9, but did not translate effectively to saliva, suggesting distinct molecular landscapes between tissue and saliva in GC. The saliva-specific model using support vector machine (SVM) achieved the highest performance, with an AUC of 0.87 (95% CI 0.72–0.97), a sensitivity of 0.79 (95% CI 0.58–0.95) and a specificity of 0.70 (95% CI 0.40–0.90). While saliva may not mirror tissue gene expression profile, it represents a promising non-invasive predictive tool for the early detection of GC. Further research is warranted to optimize saliva-derived molecular signatures, increasing their sensitivity and specificity for early cancer detection and advance the use of liquid biopsies in personalized medicine for improved screening, diagnostic and prognostic capabilities.https://doi.org/10.1038/s41598-025-96864-0Early screeningSalivaomicsLiquid biopsiesBiomarkersBioinformatics |
| spellingShingle | Catarina Lopes Andreia Brandão Manuel R. Teixeira Mário Dinis-Ribeiro Carina Pereira Saliva-derived transcriptomic signature for gastric cancer detection using machine learning and leveraging publicly available datasets Scientific Reports Early screening Salivaomics Liquid biopsies Biomarkers Bioinformatics |
| title | Saliva-derived transcriptomic signature for gastric cancer detection using machine learning and leveraging publicly available datasets |
| title_full | Saliva-derived transcriptomic signature for gastric cancer detection using machine learning and leveraging publicly available datasets |
| title_fullStr | Saliva-derived transcriptomic signature for gastric cancer detection using machine learning and leveraging publicly available datasets |
| title_full_unstemmed | Saliva-derived transcriptomic signature for gastric cancer detection using machine learning and leveraging publicly available datasets |
| title_short | Saliva-derived transcriptomic signature for gastric cancer detection using machine learning and leveraging publicly available datasets |
| title_sort | saliva derived transcriptomic signature for gastric cancer detection using machine learning and leveraging publicly available datasets |
| topic | Early screening Salivaomics Liquid biopsies Biomarkers Bioinformatics |
| url | https://doi.org/10.1038/s41598-025-96864-0 |
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