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Findings show that integrating multi-omics data—especially combining CA19-9 with circulating tumor DNA and metabolomic profiles—improves diagnostic accuracy while microbiome composition and genomic alterations refine risk and prognosis. Clinical adoption requires further verification, standardization, and prospective 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Getsina *, Nikolay Tsyba and Ekaterina Chernevskaya   \nAcademic Editors: Maria Mesuraca, Maria Concetta Faniello and Barbara Quaresima  \nReceived: 27 May 2026  \nRevised: 25 June 2026  \nAccepted: 28 June 2026  \nPublished: 29 June 2026  \nCopyright: © 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.  \nFederal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, 25-2 Petrovka Str., 107031 Moscow, Russia; [tsyba77@gmail.com](tsyba77@gmail.com) (N.T.); [echernevskaya@fnkcrr.ru](echernevskaya@fnkcrr.ru) (E.C.)  \n* [Correspondence: mgetsina@mail.ru](Correspondence: mgetsina@mail.ru)  \nAbstract  \nPancreatic cancer is among the most aggressive malignancies, and late diagnosis remains a key challenge. For a systematic review of pancreatic cancer diagnosis and prognosis, Scopus and Web of Science databases were used for the period from 2016 to 2026 . The search query included the following keywords and their combinations: pancreatic cancer, diagnosis, early detection, prognosis, biomarkers, metabolomic profiling, CA19-9, microbiome, metagenomic changes, circulating tumor DNA, genomic analysis. Inclusion criteria included only articles published in English. Exclusion criteria included case reports and studies that did not examine pancreatic cancer. Our analysis demonstrates that integrating multi-omics data, particularly combining traditional CA19-9 with circulating tumor DNA (ctDNA) and metabolomic profiles (lipids, amino acids, carbohydrates), significantly improves diagnostic accuracy. Microbiome composition and genomic alterations further refine risk stratification and prognostic assessment. The synergistic use of these biomarkers may facilitate the development of screening, early diagnosis, risk stratification, and treatment optimization. However, the introduction of new diagnostic approaches into clinical practice requires additional verification, standardization and prospective clinical studies.  \nKeywords: pancreatic cancer; microbiome; metabolites; metagenomic changes; diagnostic markers  \n1. Introduction  \nPancreatic cancer is among the most aggressive malignancies. Despite advances in clinical care and a deeper understanding of the disease’s biology, five-year survival remains low—less than 9%[1–3] . Since pancreatic ductal adenocarcinoma (PDAC) accounts for 95% of pancreatic cancer cases, the obtained statistical data can be extrapolated to pancreatic cancer in general. Global trends indicate an increase in the incidence of PDAC, although prevalence rates vary significantly by region. In countries with a low human incidence index, the incidence rate is approximately five times higher than in North America: standard rates are 8.5 cases per 100,000 people in economically developed Europe, 8.0 in North America, and only 1.3 in Southeast Asia [4] . The incidence rate of PDAC in Russia was approximately 6.8 per 100,000 people [5] .  \nPDAC incidence can increase at any age, but peak incidence occurs between 60 and 80 years of age. Late diagnosis remains a key problem in PDAC: on average, approximately 10 years pass from the onset of mutation to the formation of the primary tumor, and another 10 years are required for the disease to progress to death [6,7] . However, this timeline represents a generalized approximation; substantial variability exists across  \npatients due to differences in molecular pathogenesis, host factors, and environmental influences. Therefore, these estimates should be interpreted with caution.  \nCurrently, treatment methods remain chemotherapy and surgery. Still, their effectiveness is limited by several factors: 80–85% of patients are already inoperable at the time of diagnosis, and there is a high risk of postoperative complicat","cbCaigzGCAAE5EcA","https://ap.wps.com/l/cbCaigzGCAAE5EcA","pdf",988959,22,"English","# Introduction\n## Background and survival rates\n## Incidence and age distribution\n## Challenges of late diagnosis\n## Current treatment limitations\n## Role of metabolomics\n## Microbiome and metabolic network changes\n## Metabolomic signatures and differential markers\n## Personalized medicine applications","[{\"question\":\"What time period and databases were used for the review?\",\"answer\":\"The review searched Scopus and Web of Science for the period from 2016 to 2026.\"},{\"question\":\"Which biomarker combinations are highlighted as improving diagnostic accuracy?\",\"answer\":\"The analysis emphasizes integrating multi-omics data, particularly combining traditional CA19-9 with circulating tumor DNA (ctDNA) and metabolomic profiles, such as lipids, amino acids, and carbohydrates.\"},{\"question\":\"Why does the article argue that new diagnostic approaches need further work before routine clinical use?\",\"answer\":\"It notes that introducing new approaches into clinical practice requires additional verification, standardization, and prospective clinical studies.\"}]","Modern Approaches to Diagnosis and Evaluation of Survival Prognosis in Patients with Pancreatic Cancer | PDF",1790096659,55]