{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T10:02:49Z","timestamp":1789552969756,"version":"build-2803163510"},"reference-count":16,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T00:00:00Z","timestamp":1789516800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Mol. Sci.","IJMS"],"abstract":"<jats:p>Cancer remains one of the most formidable challenges in contemporary medicine, due to not only its substantial and growing global burden but also the profound biological heterogeneity that characterizes individual tumors [...]<\/jats:p>","DOI":"10.3390\/ijms27188242","type":"journal-article","created":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T09:06:37Z","timestamp":1789549597000},"page":"8242","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Special Issue \u201cMolecular Biomarkers in Cancers: Advances and Challenges\u201d"],"prefix":"10.3390","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8307-2760","authenticated-orcid":false,"given":"Paramjit S.","family":"Tappia","sequence":"first","affiliation":[{"name":"Asper Clinical Research Institute, St. Boniface Hospital, Winnipeg, MB R2H 2A6, Canada"},{"name":"Department of Food and Human Nutritional Sciences, Faculty of Agricultural and Food Science, University of Manitoba, Winnipeg, MB R3T 2N2, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bram","family":"Ramjiawan","sequence":"additional","affiliation":[{"name":"Asper Clinical Research Institute, St. Boniface Hospital, Winnipeg, MB R2H 2A6, Canada"},{"name":"Department of Pharmacology and Therapeutics, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0T6, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,16]]},"reference":[{"key":"ref_1","first-page":"229","article-title":"Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"74","author":"Bray","year":"2024","journal-title":"CA Cancer J. Clin."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1617","DOI":"10.1016\/j.cell.2024.02.041","article-title":"Cancer biomarkers: Emerging trends and clinical implications for personalized treatment","volume":"187","author":"Passaro","year":"2024","journal-title":"Cell"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1007\/s13402-023-00912-8","article-title":"Omics-based molecular classifications empowering in precision oncology","volume":"47","author":"Zhou","year":"2024","journal-title":"Cell. Oncol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1038\/s41571-025-01074-2","article-title":"Clinical relevance of extracellular vesicles in cancer-therapeutic and diagnostic potential","volume":"22","author":"Greening","year":"2025","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"0930","DOI":"10.34133\/research.0930","article-title":"Multi-omics meets premalignancy: Paving the way for early prevention of cancer","volume":"8","author":"Zhang","year":"2025","journal-title":"Research"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4792","DOI":"10.1016\/j.csbj.2025.10.067","article-title":"Comprehensive serum glycopeptide spectrum analysis with machine learning for non-invasive early detection of gastrointestinal cancers","volume":"27","author":"Hisamatsu","year":"2025","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"155567","DOI":"10.1016\/j.prp.2024.155567","article-title":"Emerging methods and techniques for cancer biomarker discovery","volume":"262","author":"Dakal","year":"2024","journal-title":"Pathol. Res. Pract."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, L., Shen, H., and Zhang, N. (2025). Advances in extracellular vesicle (EV) biomarkers for precision diagnosis and therapeutic in colorectal cancer. Front. Oncol., 15.","DOI":"10.3389\/fonc.2025.1581015"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.1007\/s11033-026-12661-6","article-title":"Recent advancements in exosomal content analysis: The future of liquid biopsy","volume":"53","author":"Banerjee","year":"2026","journal-title":"Mol. Biol. Rep."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1038\/s41698-026-01285-5","article-title":"Liquid biopsies in precision oncology for older adults with cancer","volume":"10","author":"Zhao","year":"2026","journal-title":"npj Precis. Oncol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1038\/s41698-026-01337-w","article-title":"Integrating liquid biopsy and mutational signatures to advance precision oncology","volume":"10","author":"Carrasco","year":"2026","journal-title":"npj Precis Oncol."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Popuri, B., Haince, J.F., Bux, R.A., Huang, G., Tappia, P.S., Ramjiawan, B., and Vaida, M. (2026). Smoking-stratified signal decomposition and feature selection for never-smoker cancer classification in a combined lung-breast metabolomics cohort. Int. J. Mol. Sci., 27.","DOI":"10.3390\/ijms27167350"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wu, J., Haince, J.F., Bux, R.A., Huang, G., Tappia, P.S., Ramjiawan, B., and Vaida, M. (2026). SEFA: Semantic embedding-based feature augmentation of biomedical language-model embeddings improves interpretable metabolomic prediction of lung cancer. Biomedicines, 14.","DOI":"10.20944\/preprints202606.1685.v1"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Himdiat, E., Haince, J.F., Bux, R.A., Huang, G., Tappia, P.S., Ramjiawan, B., and Vaida, M. (2026). Translational impact of machine learning-driven predictive modeling with pathway-based plasma metabolomic biomarkers for lung cancer detection. Front. Oncol., 15.","DOI":"10.3389\/fonc.2025.1718863"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Vaida, M., Wu, J., Himdiat, E., Haince, J.F., Bux, R.A., Huang, G., Tappia, P.S., Ramjiawan, B., and Ford, W.R. (2025). M-GNN: A Graph Neural Network framework for lung cancer detection using metabolomics and heterogeneous graph modeling. Int. J. Mol. Sci., 26.","DOI":"10.20944\/preprints202503.1832.v1"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Arumalla, K.K., Haince, J.F., Bux, R.A., Huang, G., Tappia, P.S., Ramjiawan, B., Ford, W.R., and Vaida, M. (2024). Metabolomics-based machine learning models accurately predict breast cancer estrogen receptor status. Int. J. Mol. Sci., 25.","DOI":"10.20944\/preprints202410.1558.v1"}],"container-title":["International Journal of Molecular Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1422-0067\/27\/18\/8242\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T09:08:24Z","timestamp":1789549704000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1422-0067\/27\/18\/8242"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,16]]},"references-count":16,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["ijms27188242"],"URL":"https:\/\/doi.org\/10.3390\/ijms27188242","relation":{},"ISSN":["1422-0067"],"issn-type":[{"value":"1422-0067","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,16]]}}}