{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T20:05:46Z","timestamp":1789589146600,"version":"build-2803163510"},"reference-count":49,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T00:00:00Z","timestamp":1786924800000},"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>Metabolomic cancer classifiers trained on mixed-smoking cohorts may embed tobacco exposure signal within their predictions, degrading performance in never-smokers, a population in which lung adenocarcinoma is frequently diagnosed. We developed a two-stage framework that (i) decomposes a shared 129-metabolite panel from a combined lung\u2013breast cancer cohort (n=1038) into cancer-specific (Signal C), smoking-specific (Signal S), and shared (Signal S\u2229C) components using two-way analysis of variance with Benjamini\u2013Hochberg correction, and (ii) applies multiple feature-selection strategies to identify the minimal Signal C subset that surpasses the all-metabolite baseline for never-smoker cancer detection. Two-way ANOVA partitioned 54 of 129 metabolites as cancer-specific (Signal C) and 57 as smoking-specific (Signal S), suggesting that nearly half of the shared panel is influenced by tobacco exposure. A model of 19 Signal C metabolites, selected by composite rank aggregation across four feature-selection methods and trained with gradient-boosted trees, achieved a never-smoker area under the receiver operating characteristic curve (AUC) of 0.907 on pooled out-of-fold predictions (0.910 as a mean across folds) against an all-metabolite baseline of 0.895, using 85% fewer metabolite measurements. The signal decomposition is a reproducible and interpretable way to identify metabolites whose case\u2013control differences are not attributable to tobacco exposure, and it permits a substantial reduction in panel size.<\/jats:p>","DOI":"10.3390\/ijms27167350","type":"journal-article","created":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T16:35:08Z","timestamp":1786984508000},"page":"7350","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Smoking-Stratified Signal Decomposition and Feature Selection for Never-Smoker Cancer Classification in a Combined Lung\u2013Breast Metabolomics Cohort"],"prefix":"10.3390","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2979-5199","authenticated-orcid":false,"given":"Bharadwaj","family":"Popuri","sequence":"first","affiliation":[{"name":"Department of Data Science, Harrisburg University of Science and Technology, Harrisburg, PA 17101, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-5261-9967","authenticated-orcid":false,"given":"Jean-Fran\u00e7ois","family":"Haince","sequence":"additional","affiliation":[{"name":"BioMark Diagnostic Solutions Inc., Quebec, QC G1P4P5, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rashid A.","family":"Bux","sequence":"additional","affiliation":[{"name":"BioMark Diagnostics Inc., Richmond, BC V6X2W2, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5854-9482","authenticated-orcid":false,"given":"Guoyu","family":"Huang","sequence":"additional","affiliation":[{"name":"BioMark Diagnostic Solutions Inc., Quebec, QC G1P4P5, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8307-2760","authenticated-orcid":false,"given":"Paramjit S.","family":"Tappia","sequence":"additional","affiliation":[{"name":"Department of Food & Human Nutritional Sciences, Faculty of Agricultural & Food Sciences, University of Manitoba, Winnipeg, MB R3E 0T6, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bram","family":"Ramjiawan","sequence":"additional","affiliation":[{"name":"Department of Food & Human Nutritional Sciences, Faculty of Agricultural & Food Sciences, University of Manitoba, Winnipeg, MB R3E 0T6, Canada"},{"name":"Department of Pharmacology & Therapeutics, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB R3E 0T6, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7869-1900","authenticated-orcid":false,"given":"Maria","family":"Vaida","sequence":"additional","affiliation":[{"name":"Department of Data Science, Harrisburg University of Science and Technology, Harrisburg, PA 17101, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Aslam, M.A., Iqbal, H., Ilyas, K., Rehman, K., Hussain, A., Akash, M.S.H., Shahid, M., and Chen, S. 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