{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T15:18:03Z","timestamp":1778167083537,"version":"3.51.4"},"posted":{"date-parts":[[2025,12,27]]},"group-title":"Health Informatics","reference-count":42,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,12,27]],"date-time":"2025-12-27T00:00:00Z","timestamp":1766793600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,5,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                <jats:p>\n                  Identification of minimally invasive biomarkers of cancer-associated cachexia may help to recognize high risk patients for progression to more severe cachectic stages. We developed a machine learning-based\n                  <jats:underline>Mo<\/jats:underline>\n                  del for\n                  <jats:underline>Ca<\/jats:underline>\n                  chectic\n                  <jats:underline>P<\/jats:underline>\n                  atients\n                  <jats:underline>S<\/jats:underline>\n                  tratification (MoCaPS) to determine the sets of blood biomarkers that differentiate between noncachectic (NCa), precachectic (PCa), or cachectic (Ca) patients. The model was applied to data collected from treatment-na\u00efve patients with pancreatic ductal adenocarcinoma through the Florida Pancreas Collaborative multi-institutional cohort study and biobanking initiative. Cachexia status of all participants was classified according to modified criteria by Vigano and colleagues. The MoCaPS model pipeline was designed to work effectively with datasets of moderate size to robustly select predictive data features, and to efficiently handle data imbalance. MoCaPS identified between 4 and 5 biomarkers out of 37 candidates that distinguished precachectic and cachectic stages, and demonstrated accuracies near or greater than 75% for predictors of NCa, PCa, and Ca.\n                <\/jats:p>","DOI":"10.64898\/2025.12.23.25342866","type":"posted-content","created":{"date-parts":[[2025,12,27]],"date-time":"2025-12-27T12:00:12Z","timestamp":1766836812000},"source":"Crossref","is-referenced-by-count":1,"title":["MoCaPS: A Machine Learning Model for Stratification of Cancer-Associated Cachexia Based on Blood Biomarkers"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1569-4351","authenticated-orcid":false,"given":"Kayode D.","family":"Olumoyin","sequence":"first","affiliation":[{"name":"Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Magaret","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA"},{"name":"Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Evan W.","family":"Davis","sequence":"additional","affiliation":[{"name":"Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA"},{"name":"Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jennifer B.","family":"Permuth","sequence":"additional","affiliation":[{"name":"Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA"},{"name":"Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Katarzyna A.","family":"Rejniak","sequence":"additional","affiliation":[{"name":"Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA"},{"name":"University of South Florida, Morsani College of Medicine, Department of Oncologic Sciences, Tampa, FL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2026050707550845000_2025.12.23.25342866v2.1","doi-asserted-by":"crossref","first-page":"17105","DOI":"10.1038\/nrdp.2017.105","article-title":"Cancer-associated cachexia","volume":"4","year":"2018","journal-title":"Nat Rev Dis Primers"},{"key":"2026050707550845000_2025.12.23.25342866v2.2","doi-asserted-by":"publisher","DOI":"10.1080\/01635581.2015.1073753"},{"key":"2026050707550845000_2025.12.23.25342866v2.3","doi-asserted-by":"crossref","first-page":"1057930","DOI":"10.3389\/fonc.2022.1057930","article-title":"Review of the endocrine organ-like tumor hypothesis of cancer cachexia in pancreatic ductal adenocarcinoma","volume":"12","year":"2022","journal-title":"Front Oncol"},{"key":"2026050707550845000_2025.12.23.25342866v2.4","doi-asserted-by":"crossref","first-page":"1362244","DOI":"10.3389\/fonc.2024.1362244","article-title":"Leveraging real-world data to predict cancer cachexia stage, quality of life, and survival in a racially and ethnically diverse multi-institutional cohort of treatment-naive patients with pancreatic ductal adenocarcinoma","volume":"14","year":"2024","journal-title":"Front Oncol"},{"issue":"5","key":"2026050707550845000_2025.12.23.25342866v2.5","doi-asserted-by":"crossref","first-page":"1378","DOI":"10.1016\/j.clnu.2016.09.008","article-title":"Use of routinely available clinical, nutritional, and functional criteria to classify cachexia in advanced cancer patients","volume":"36","year":"2017","journal-title":"Clin Nutr"},{"key":"2026050707550845000_2025.12.23.25342866v2.6","doi-asserted-by":"publisher","DOI":"10.1080\/01635581.2013.755554"},{"key":"2026050707550845000_2025.12.23.25342866v2.7","doi-asserted-by":"publisher","DOI":"10.1016\/j.pccm.2024.02.003"},{"key":"2026050707550845000_2025.12.23.25342866v2.8","doi-asserted-by":"crossref","unstructured":"Cao, Z. , et al., Biomarkers for Cancer Cachexia: A Mini Review. Int J Mol Sci, 2021. 22(9).","DOI":"10.3390\/ijms22094501"},{"key":"2026050707550845000_2025.12.23.25342866v2.9","doi-asserted-by":"publisher","DOI":"10.1016\/j.ctrv.2012.08.003"},{"issue":"2","key":"2026050707550845000_2025.12.23.25342866v2.10","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1002\/jcsm.12251","article-title":"Circulating monocyte chemoattractant protein-1 (MCP-1) is associated with cachexia in treatment-naive pancreatic cancer patients","volume":"9","year":"2018","journal-title":"J Cachexia Sarcopenia Muscle"},{"issue":"4","key":"2026050707550845000_2025.12.23.25342866v2.11","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1097\/SPC.0000000000000384","article-title":"Targeting the divergent TGFbeta superfamily cytokine MIC-1\/GDF15 for therapy of anorexia\/cachexia syndromes","volume":"12","year":"2018","journal-title":"Curr Opin Support Palliat Care"},{"issue":"1","key":"2026050707550845000_2025.12.23.25342866v2.12","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1038\/s43856-025-01277-9","article-title":"Race-based differences in serum biomarkers for cancer-associated cachexia in a diverse cohort of patients with pancreatic ductal adenocarcinoma","volume":"6","year":"2025","journal-title":"Communications Medicine"},{"key":"2026050707550845000_2025.12.23.25342866v2.13","doi-asserted-by":"crossref","first-page":"927312","DOI":"10.3389\/fbinf.2022.927312","article-title":"A review of feature selection methods for machine learning-based disease risk prediction","volume":"2","year":"2022","journal-title":"Front. Bioinform"},{"key":"2026050707550845000_2025.12.23.25342866v2.14","doi-asserted-by":"publisher","DOI":"10.1007\/s12021-013-9204-3"},{"key":"2026050707550845000_2025.12.23.25342866v2.15","doi-asserted-by":"crossref","unstructured":"Permuth, J.B. , et al., The Florida Pancreas Collaborative Next-Generation Biobank: Infrastructure to Reduce Disparities and Improve Survival for a Diverse Cohort of Patients with Pancreatic Cancer. Cancers (Basel), 2021. 13(4).","DOI":"10.3390\/cancers13040809"},{"key":"2026050707550845000_2025.12.23.25342866v2.16","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","year":"2011","journal-title":"Journal of Machine Learning Research"},{"issue":"9","key":"2026050707550845000_2025.12.23.25342866v2.17","first-page":"1","article-title":"Efficient Multiple Imputation for Diverse Data in Python and R: MIDASpy and rMIDAS","volume":"107","year":"2023","journal-title":"Journal of Statistical Software"},{"issue":"1","key":"2026050707550845000_2025.12.23.25342866v2.18","first-page":"763","article-title":"Feature selection using mutual information based uncertainty measures for tumor classification","volume":"24","year":"2014","journal-title":"Biomed Mater Eng"},{"key":"2026050707550845000_2025.12.23.25342866v2.19","unstructured":"Borboudakis, G. and I. Tsamardinos , Forward-Backward Selection with Early Dropping. Journal of Machine Learning Research, 2019. 20."},{"key":"2026050707550845000_2025.12.23.25342866v2.20","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-10-213"},{"key":"2026050707550845000_2025.12.23.25342866v2.21","doi-asserted-by":"publisher","DOI":"10.1021\/ci034160g"},{"key":"2026050707550845000_2025.12.23.25342866v2.22","first-page":"211","article-title":"Tree induction vs. logistic regression: a learning-curve analysis","volume":"4","year":"2003","journal-title":"Journal of Machine Learning Research"},{"key":"2026050707550845000_2025.12.23.25342866v2.23","doi-asserted-by":"publisher","DOI":"10.1186\/1472-6947-12-8"},{"key":"2026050707550845000_2025.12.23.25342866v2.24","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1000173"},{"key":"2026050707550845000_2025.12.23.25342866v2.25","doi-asserted-by":"publisher","DOI":"10.1001\/JAMA.2016.7653"},{"key":"2026050707550845000_2025.12.23.25342866v2.26","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2021.05.055"},{"key":"2026050707550845000_2025.12.23.25342866v2.27","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"2026050707550845000_2025.12.23.25342866v2.28","doi-asserted-by":"crossref","unstructured":"Chicco, D. and G. Jurman , The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics, 2020. 21(1): p.","DOI":"10.1186\/s12864-019-6413-7"},{"key":"2026050707550845000_2025.12.23.25342866v2.29","doi-asserted-by":"crossref","unstructured":"Yu, S. and J.C. Principe , Simple Stopping Criteria for Information Theoretic Feature Selection. Entropy (Basel), 2019. 21(1).","DOI":"10.3390\/e21010099"},{"key":"2026050707550845000_2025.12.23.25342866v2.30","doi-asserted-by":"publisher","DOI":"10.1007\/s00520-024-08833-4"},{"issue":"5","key":"2026050707550845000_2025.12.23.25342866v2.31","doi-asserted-by":"crossref","first-page":"1229","DOI":"10.1093\/ajcn\/nqac251","article-title":"Identifying cancer cachexia in patients without weight loss information: machine learning approaches to address a real-world challenge","volume":"116","year":"2022","journal-title":"American Journal of Clinical Nutrition"},{"issue":"3","key":"2026050707550845000_2025.12.23.25342866v2.32","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/j.ajcnut.2025.01.006","article-title":"Early identification of potentially reversible cancer cachexia using explainable machine learning driven by body weight dynamics: a multicenter cohort study","volume":"121","year":"2025","journal-title":"Am J Clin Nutr"},{"key":"2026050707550845000_2025.12.23.25342866v2.33","doi-asserted-by":"publisher","DOI":"10.1016\/S1470-2045(10)70218-7"},{"key":"2026050707550845000_2025.12.23.25342866v2.34","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.2014.56.1894"},{"key":"2026050707550845000_2025.12.23.25342866v2.35","doi-asserted-by":"publisher","DOI":"10.5555\/2999611.2999712"},{"key":"2026050707550845000_2025.12.23.25342866v2.36","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.69.066138"},{"issue":"2","key":"2026050707550845000_2025.12.23.25342866v2.37","first-page":"108714","article-title":"Ovarian cancer detection from mutual information-ranked clinical biomarkers using an explainable attention-based residual multilayer perceptron","volume":"120","year":"2025","journal-title":"Comput Biol Chem"},{"key":"2026050707550845000_2025.12.23.25342866v2.38","doi-asserted-by":"crossref","first-page":"e43665","DOI":"10.2196\/43665","article-title":"Decision of the Optimal Rank of a Nonnegative Matrix Factorization Model for Gene Expression Data Sets Utilizing the Unit Invariant Knee Method: Development and Evaluation of the Elbow Method for Rank Selection","volume":"4","year":"2023","journal-title":"JMIR Bioinform Biotechnol"},{"issue":"8","key":"2026050707550845000_2025.12.23.25342866v2.39","doi-asserted-by":"crossref","first-page":"2119","DOI":"10.1002\/jum.14909","article-title":"Feasibility of a Support Vector Machine Classifier for Myofascial Pain Syndrome: Diagnostic Case-Control Study","volume":"38","year":"2019","journal-title":"J Ultrasound Med"},{"key":"2026050707550845000_2025.12.23.25342866v2.40","doi-asserted-by":"publisher","DOI":"10.1109\/72.788646"},{"key":"2026050707550845000_2025.12.23.25342866v2.41","doi-asserted-by":"publisher","DOI":"10.1162\/NECO_a_00434"},{"key":"2026050707550845000_2025.12.23.25342866v2.42","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0177678"}],"container-title":[],"original-title":[],"link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.64898\/2025.12.23.25342866","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T14:55:22Z","timestamp":1778165722000},"score":1,"resource":{"primary":{"URL":"http:\/\/medrxiv.org\/lookup\/doi\/10.64898\/2025.12.23.25342866"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,27]]},"references-count":42,"URL":"https:\/\/doi.org\/10.64898\/2025.12.23.25342866","relation":{},"subject":[],"published":{"date-parts":[[2025,12,27]]},"subtype":"preprint"}}