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Non\u2010invasive approaches such as saliva\u2010based biomarkers could complement current strategies. The aim of this study was to identify and validate a tissue\u2010based gene expression signature for early gastric lesions, explore its potential for MGL prediction, and assess its detectability in saliva.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Three studies were conducted: (1) a retrospective case control study to identify (RNA sequencing with machine learning) and validate (reverse transcription [RT] quantitative polymerase chain reaction [qPCR]) a gene expression signature for early gastric cancer using formalin\u2010fixed paraffin\u2010embedded (FFPE) samples, (2) a retrospective longitudinal study evaluating the ability of the signature to stratify MGL risk, and (3) a prospective study testing the signature in saliva using droplet digital (dd)PCR in patients with gastric lesions and endoscopy\u2010confirmed controls.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A six\u2010gene tissue\u2010based expression signature (\n                      <jats:italic>ADAMTSL1, CCNA2, HSP90AB1, HSPD1, PSAPL1, and SLC5A5<\/jats:italic>\n                      ) robustly discriminated early gastric lesions from non\u2010tumor mucosa (area under the curve (AUC)\u00a0=\u00a00.96 and 95% confidence interval [CI]: 0.94\u20130.99). Models tailored for MGL prediction, incorporating clinical variables, achieved moderate performance (AUC\u00a0=\u00a00.74 and 95% CI: 0.59\u20130.88). In saliva, only the\n                      <jats:italic>SLC5A5<\/jats:italic>\n                      gene showed consistent dysregulation. When combined with age and sex, the model reached an AUC of 0.78 (95% CI: 0.69\u20130.88) for the non\u2010invasive detection of early GC, with a positive predictive value of 0.69 and negative predictive value of 0.81.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      This study presents a validated tissue\u2010based gene signature for early GC detection and exploratory MGL risk stratification. Salivary\n                      <jats:italic>SLC5A5<\/jats:italic>\n                      shows potential as a non\u2010invasive biomarker, though its utility requires further validation in dedicated saliva\u2010based studies.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ueg2.70221","type":"journal-article","created":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T07:11:54Z","timestamp":1779520314000},"source":"Crossref","is-referenced-by-count":0,"title":["Molecular Traces of Gastric Cancer in Saliva: From Tissue Signatures to Salivary\n                    <i>SLC5A5<\/i>\n                    as a Potential Biomarker"],"prefix":"10.1002","volume":"14","author":[{"given":"Catarina","family":"Lopes","sequence":"first","affiliation":[{"name":"Precancerous Lesions and Early Cancer Management Group Research Center of IPO Porto (CI\u2010IPOP)\/CI\u2010IPOP@RISE (Health Research Group) Portuguese Oncology Institute of Porto (IPO Porto)\/Porto 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