{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T01:19:30Z","timestamp":1791335970419,"version":"4.3.1"},"reference-count":40,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cancer Medicine"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>\n                      Breast cancer (BC) is one of the most common types of cancer incidence and mortality rates among women all over the world. Limitations in existing diagnostic methods, especially in low\u2010 and middle\u2010income countries, require novel, noninvasive biomarkers. A complex and modifiable ecosystem, the gut microbiome has become a potential origin of such biomarkers because it has a systemic influence on host immunity, metabolism, and regulation of hormones. This study explores the gut microbiome alterations associated with BC and evaluates machine learning (ML) models to predict disease status based on 16S rRNA gene sequencing data of Ghanaian cohort (520\n                      <jats:sc>BC<\/jats:sc>\n                      patients, 442 healthy controls).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Following high\u2010quality sequence processing with QIIME2 and DADA2, alpha and beta diversity analyses showed a lower microbial richness and different community structures in BC patients (PERMANOVA,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.001). Random Forest\u2010based feature selection was used to find the top 20 discriminative genera, which were used to train 10 supervised ML algorithms. Ensemble models, particularly Random Forest, achieved the highest performance in predictive models (mean across 100 folds: AUC\u2009=\u20090.78, accuracy\u2009=\u20090.70), indicating strong discriminative potential. The results of SHAP analysis provided clear pictures of how specific microbes influence BC risk. We identified several risk\u2010associated genera, notably\n                      <jats:italic>Bacteroides<\/jats:italic>\n                      ,\n                      <jats:italic>Lachnoclostridium<\/jats:italic>\n                      ,\n                      <jats:italic>Parasutterella<\/jats:italic>\n                      , and\n                      <jats:italic>Tannerellaceae<\/jats:italic>\n                      , whereas\n                      <jats:italic>Coprococcus<\/jats:italic>\n                      ,\n                      <jats:italic>Romboutsia<\/jats:italic>\n                      ,\n                      <jats:italic>Ruminococcus<\/jats:italic>\n                      , and\n                      <jats:italic>Prevotella<\/jats:italic>\n                      were associated with lower BC risk.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study reveals a clear shift in the gut microbiome community structure among BC patients. We identified specific bacterial genera that act as either elevated or decreased disease risk. These findings not only highlight the power of machine learning models for noninvasive risk prediction but also provide a foundation for novel microbiome\u2010based diagnostic strategies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/cam4.72008","type":"journal-article","created":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T06:24:46Z","timestamp":1780381486000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Identification of Key Microbial Signatures Associated With Breast Cancer: Machine Learning\u2010Based Approaches Using Gut Microbiome Data"],"prefix":"10.1002","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-4274-1272","authenticated-orcid":false,"given":"Md. Tanvir","family":"Ahmed","sequence":"first","affiliation":[{"name":"Department of Genetic Engineering and Biotechnology Shahjalal University of Science and Technology  Sylhet Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samriddha","family":"Majumdar","sequence":"additional","affiliation":[{"name":"Department of Genetic Engineering and Biotechnology Shahjalal University of Science and Technology  Sylhet Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdullah Al","family":"Noman","sequence":"additional","affiliation":[{"name":"Department of Computer Science Engineering Shahjalal University of Science and Technology  Sylhet Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md. Jahangir","family":"Alam","sequence":"additional","affiliation":[{"name":"Department of Genetic Engineering and Biotechnology Shahjalal University of Science and Technology  Sylhet Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md. Jahangir","family":"Alam","sequence":"additional","affiliation":[{"name":"Department of Genetic Engineering and Biotechnology Shahjalal University of Science and Technology  Sylhet Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,1]]},"reference":[{"key":"e_1_2_14_2_1","volume-title":"International Agency for Research on Cancer. Global Cancer Observatory","author":"GLOBOCAN","year":"2022"},{"key":"e_1_2_14_3_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21660"},{"key":"e_1_2_14_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/cncr.22655"},{"key":"e_1_2_14_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1470\u20102045(12)70425\u20104"},{"key":"e_1_2_14_6_1","doi-asserted-by":"publisher","DOI":"10.7326\/0003\u20104819\u2010151\u201010\u2010200911170\u201000009"},{"key":"e_1_2_14_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/nm.4517"},{"key":"e_1_2_14_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/sj.embor.7400731"},{"key":"e_1_2_14_9_1","doi-asserted-by":"publisher","DOI":"10.1210\/endocr\/bqac177"},{"key":"e_1_2_14_10_1","doi-asserted-by":"publisher","DOI":"10.3390\/cells12151945"},{"key":"e_1_2_14_11_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41587\u2010019\u20100209\u20109"},{"key":"e_1_2_14_12_1","doi-asserted-by":"publisher","DOI":"10.3389\/frmbi.2024.1394719"},{"key":"e_1_2_14_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/s43705\u2010022\u201000182\u20109"},{"key":"e_1_2_14_14_1","doi-asserted-by":"publisher","DOI":"10.3389\/fmicb.2023.1261889"},{"key":"e_1_2_14_15_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12934\u2010022\u201001973\u20104"},{"key":"e_1_2_14_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.crbiot.2024.100211"},{"key":"e_1_2_14_17_1","doi-asserted-by":"publisher","DOI":"10.1080\/19490976.2024.2375679"},{"key":"e_1_2_14_18_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btu170"},{"key":"e_1_2_14_19_1","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.3869"},{"key":"e_1_2_14_20_1","doi-asserted-by":"publisher","DOI":"10.1186\/2047\u2010217X\u20102\u201016"},{"key":"e_1_2_14_21_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1442\u20109993.2001.01070.pp.x"},{"key":"e_1_2_14_22_1","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gks1219"},{"key":"e_1_2_14_23_1","doi-asserted-by":"publisher","DOI":"10.1186\/1471\u20102105\u20107\u201091"},{"key":"e_1_2_14_24_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_2_14_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0031\u20103203(96)00142\u20102"},{"key":"e_1_2_14_26_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007515423169"},{"key":"e_1_2_14_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/pgec.1965.264137"},{"key":"e_1_2_14_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_2_14_29_1","unstructured":"A. V.Dorogush V.Ershov andA.Gulin \u201cCatBoost: Gradient Boosting With Categorical Features Support (Version 1) \u201darXiv(2018) https:\/\/doi.org\/10.48550\/ARXIV.1810.11363."},{"key":"e_1_2_14_30_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994\u2010006\u20106226\u20101"},{"key":"e_1_2_14_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1967.1053964"},{"key":"e_1_2_14_32_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1958.tb00292.x"},{"key":"e_1_2_14_33_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1469\u20101809.1936.tb02137.x"},{"key":"e_1_2_14_34_1","unstructured":"S. M.LundbergandS.\u2010I.Lee \u201cA Unified Approach to Interpreting Model Predictions \u201dAdvances in Neural Information Processing Systems(2017): 30."},{"key":"e_1_2_14_35_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256\u2010019\u20100138\u20109"},{"key":"e_1_2_14_36_1","doi-asserted-by":"publisher","DOI":"10.1002\/ijc.33473"},{"key":"e_1_2_14_37_1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers15020443"},{"key":"e_1_2_14_38_1","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2023.1289753"},{"key":"e_1_2_14_39_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13099\u2010017\u20100208\u20107"},{"key":"e_1_2_14_40_1","doi-asserted-by":"publisher","DOI":"10.3390\/biology11050757"},{"key":"e_1_2_14_41_1","doi-asserted-by":"publisher","DOI":"10.3892\/ijo.2024.5632"}],"container-title":["Cancer Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/cam4.72008","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/cam4.72008","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/cam4.72008","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T10:04:56Z","timestamp":1784196296000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/cam4.72008"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":40,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["10.1002\/cam4.72008"],"URL":"https:\/\/doi.org\/10.1002\/cam4.72008","archive":["Portico"],"relation":{},"ISSN":["2045-7634","2045-7634"],"issn-type":[{"value":"2045-7634","type":"print"},{"value":"2045-7634","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"2026-01-07","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-05-27","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-06-01","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e72008"}}