ArticleCommunications medicine2026
Multidimensional proteomics and explainable AI feature selection identify cross-platform lung cancer molecular signature in blood plasma.
Article in Communications medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Multidimensional proteomics and explainable AI feature selection identify cross-platform lung cancer molecular signature in blood plasma.Communications medicine · 2026Article
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLung cancer is the leading cause of cancer mortality worldwide despite the availability of low-dose computed tomography (LDCT) for screening in high-risk populations.
methodsTo develop an approach and identify blood-based protein signatures for lung cancer that can be deployed across platforms, we combined data-independent acquisition mass-spectrometry (DIA-MS) and proximity extension assay (PEA) with explainable artificial intelligence (XAI)-led machine learning (ML) for plasma-based biomarker discovery. Using a cohort of 490 lung cancer patients and 124 matched controls, ML models were trained to predict lung cancer and XAI was used to characterise networks of model-consistent features. We then introduced a DNA-aptamer based proteomic approach to assess cross-platform concordance and define a cross-platform signature. This signature was subsequently evaluated using an external cohort.
resultsHere we show that ML models achieve an AUROC of 0.91 [95% CI: 0.88-0.93] and 0.97 [95% CI: 0.92-0.98] in DIA-MS and PEA, respectively, using a 80/20% train/holdout split. XAI further characterises networks of model-consistent features related to chemotaxis, cell adhesion, wound healing and immune response. Introduction of the DNA-aptamer proteomic approach identifies a cross-platform signature, with performances of 0.88 [95% CI: 0.80-0.90] and 0.88 [95% CI: 0.81-0.95] in DIA-MS and PEA, respectively. Assessment of this signature in an external cohort separates lung cancer from control cases.
conclusionsThis study develops an approach combining multi-dimensional proteomics with XAI-ML and demonstrates the characterisation of cross-platform biomarker signatures for lung cancer.
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Registered trials
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