Early diagnosis of nasopharyngeal carcinoma based on machine learning modelling and blood plasma metallomics analysis.

Current diagnosis of nasopharyngeal carcinoma (NPC) mainly relies on detection of plasma Epstein-Barr virus DNA or nasal endoscopy. However, trace metals or other elements may have critical roles in the pathophysiology of NPC. In this pilot study, blood plasma samples from 93 NPC patients and 30 healthy control were prepared by alkali dilution method, and metal contents were analysed quantitatively by inductively coupled plasma-mass spectrometry. We then built six machine learning (ML) algorithms based on the element concentrations in blood plasma and evaluated the predictive performance by the area under the receiver operating characteristic curve (AUC). SHapley Additive exPlanations was employed to interpret the prediction results and explain the contribution of each variable to the model. Compared to the healthy control group, patients with NPC were characterised by increased tin (p < 0.01) and reduced in nickel and iron (p < 0.05), phosphorus (p < 0.01), magnesium, manganese, cobalt, zinc, strontium, molybdenum, antimony, barium, thallium and lead (p < 0.001) concentrations in the plasma. Among the ML models, the bagging model demonstrated the most promising performance in discriminating NPC patients with AUC of 0.999 in testing sets. We further recruited 15 patients with esophageal squamous cell carcinoma (ESCC), 15 non-cancer patients, and used them as blind testing samples. The model can successfully identify NPC patients from those samples with AUC and specificity of 0.857 and 0.800. In summary, the present pilot study highlights the use of metallomics analysis combined with machine learning in NPC identification, especially in early-stage cancer prediction.