プロテオミクスとバイオインフォマティクスのジャーナル
オープンアクセス

概要

Plasma Anti-Glycan Antibody Profiles Associated with Nickel level in Urine

Marko Vuskovic, Anna-Maria Barbuti, Emma Goldsmith-Rooney, Laura Glassman, Nicolai Bovin, Harvey Pass, Kam-Meng Tchou-Wong, Meichi Chen, Bing Yan, Jingping Niu, Qingshan Qu, Max Costa and Margaret Huflejt

Nickel (Ni) compounds are widely used in industrial and commercial products including household and cooking utensils, jewelry, dental appliances and implants. Occupational exposure to nickel is associated with an increased risk for lung and nasal cancers, is the most common cause of contact dermatitis and has an extensive effect on the immune system. The purpose of this study was two-fold: (i) to evaluate immune response to the occupational exposure to nickel measured by the presence of anti-glycan antibodies (AGA) using a new biomarker-discovery platform based on printed glycan arrays (PGA), and (ii) to evaluate and compile a sequence of bioinformatics and statistical methods which are specifically relevant to PGA-derived information and to identification of putative “Ni toxicity signature”. The PGAs are similar to DNA microarrays, but contain deposits of various carbohydrates (glycans) instead of spotted DNAs. The study uses data derived from a set of 89 plasma specimens and their corresponding demographic information.

The study population includes three subgroups: subjects directly exposed to Nickel that work in a refinery, subjects environmentally exposed to Nickel that live in a city where the refinery is located and subjects that live in a remote location. The paper describes the following sequence of nine data processing and analysis steps: (1) Analysis of inter-array reproducibility based on benchmark sera; (2) Analysis of intra-array reproducibility; (3) Screening of data - rejecting glycans which result in low intra-class correlation coefficient (ICC), high coefficient of variation and low fluorescent intensity; (4) Analysis of inter-slide bias and choice of data normalization technique; (5) Determination of discriminatory subsamples based on multiple bootstrap tests; (6) Determination of the optimal signature size (cardinality of selected feature set) based on multiple cross-validation tests; (7) Identification of the top discriminatory glycans and their individual performance based on nonparametric univariate feature selection; (8) Determination of multivariate performance of combined glycans; (9) Establishing the statistical significance of multivariate performance of combined glycan signature.

The above analysis steps have delivered the following results: inter-array reproducibility ρ=0.920 ± 0.030; intraarray reproducibility ρ=0.929 ± 0.025; 249 out of 380 glycans passed the screening at ICC>80%, glycans in selected signature have ICC ≥ 88.7%; optimal signature size (after quantile normalization)=3; individual significance for the signature glycans p=0.00015 to 0.00164, individual AUC values 0.870 to 0.815; observed combined performance for three glycans AUC=0.966, p=0.005, CI=[0.757, 0947]; specifity=94.4%, sensitivity=88.9%; predictive (crossvalidated) AUC value 0.836.