Identification of plasma lipid biomarkers for prostate cancer by lipidomics and bioinformatics

Abstract

Background: Lipids have critical functions in cellular energy storage, structure and signaling. Many individual lipid molecules have been associated with the evolution of prostate cancer; however, none of them has been approved to be used as a biomarker. The aim of this study is to identify lipid molecules from hundreds plasma apparent lipid species as biomarkers for diagnosis of prostate cancer.

Methodology/principal findings: Using lipidomics, lipid profiling of 390 individual apparent lipid species was performed on 141 plasma samples from 105 patients with prostate cancer and 36 male controls. High throughput data generated from lipidomics were analyzed using bioinformatic and statistical methods. From 390 apparent lipid species, 35 species were demonstrated to have potential in differentiation of prostate cancer. Within the 35 species, 12 were identified as individual plasma lipid biomarkers for diagnosis of prostate cancer with a sensitivity above 80%, specificity above 50% and accuracy above 80%. Using top 15 of 35 potential biomarkers together increased predictive power dramatically in diagnosis of prostate cancer with a sensitivity of 93.6%, specificity of 90.1% and accuracy of 97.3%. Principal component analysis (PCA) and hierarchical clustering analysis (HCA) demonstrated that patient and control populations were visually separated by identified lipid biomarkers. RandomForest and 10-fold cross validation analyses demonstrated that the identified lipid biomarkers were able to predict unknown populations accurately, and this was not influenced by patient's age and race. Three out of 13 lipid classes, phosphatidylethanolamine (PE), ether-linked phosphatidylethanolamine (ePE) and ether-linked phosphatidylcholine (ePC) could be considered as biomarkers in diagnosis of prostate cancer.

Conclusions/significance: Using lipidomics and bioinformatic and statistical methods, we have identified a few out of hundreds plasma apparent lipid molecular species as biomarkers for diagnosis of prostate cancer with a high sensitivity, specificity and accuracy.

Figure 1. Effect of multiple individual lipid species in diagnosis of prostate cancer.

The points indicated by the two head arrows are the predictive powers of top 15 plasma apparent lipid species when they are used together in diagnosis of prostate cancer. Using top 15 plasma apparent lipid species has the highest sensitivity (93.6%), the highest specificity (90.1%), and higher accuracy (ROC Area, 97.3%) in the diagnosis of prostate cancer as compared with using any other combination of different numbers.

Figure 2. Comparison of Principal Component Analysis (PCA) with 390 and 15 selected plasma apparent lipid species.

A: The first component in PCA cross all 390 detected plasma apparent lipid species accounts for 28.3% of the overall variance; B: The first component in PCA cross 15 selected plasma apparent lipid biomarkers accounts for 86.9% of the overall variance.

Figure 3. Mass spectra of phosphocholine-containing lipids (Pre-184 positive mode, including biomarker species.

A: Spectra of 15 selected apparent lipid species in a representative patient with prostate cancer. B: Spectra of 15 selected apparent lipid species in a representative male control. Spectral intensities were normalized to that of internal standard LPC(13∶0). The intensities of phosphocholine-containing internal standards (I.S.) are indicated in green. The intensities of the identified biomarkers are shown in red. Internal standards and biomarkers (15 selected apparent lipid species) are labeled.