Prediction of prostate cancer using hair trace element concentration and support vector machine method

• Published in: Biological trace element research Vol. 116; no. 3; pp. 257 - 271
• Main Authors: Guo, Jingkang, Deng, Wenhua, Zhang, Liecheng, Li, Chonghe, Wu, Ping, Mao, Peiling
• Format: Journal Article
• Language: English
• Published: United States Springer Nature B.V 01.06.2007
• Subjects: Data Interpretation, Statistical; Decision Support Techniques; Hair - metabolism; Humans; Male; Mass spectrometry; Mass Spectrometry - methods; Medical research; Metals - analysis; Models, Statistical; Prostate - metabolism; Prostate cancer; Prostatic Neoplasms - metabolism; Risk; Trace elements; Trace Elements - analysis; Vector space
• ISSN: 0163-4984; 1559-0720
• DOI: 10.1007/BF02698010

A change in the normal concentration of essential trace elements in the human body might lead to major health disturbances. In this study, hair samples were collected from 115 human subject, including 55 healthy people and 60 patients with prostate cancer. The concentrations of 20 trace elements (TEs) in these samples were measured by inductively coupled plasma-mass spectrometry. Asupport vector machine was used to investigate the relationship between TEs and prostate cancer. It is found that, among the 20 TEs, 10 (Mg P, K, Ca, Cr, Mn, Fe. Cu, Zn, and Se) are related to the risk of prostate cancer. These 10 TEs were used to build the prediction model for prostate cancer. The model obtained can satisfactorily distinguish the healthy samples from the cancer samples. Furthermore, the cross-validation by leaving-one method proved that the prediction ability of this model reaches as high as 95.8%. It is practical to predict the risk of prostate cancer using this model in the clinics.