Additional file 2: of Comparison of pre-processing methods for multiplex bead-based immunoassays

Supplementary table. Detailed results for the evaluation criteria skewness, tail length and coefficient of variation. (PDF 54 kb

Additional file 3: of Comparison of pre-processing methods for multiplex bead-based immunoassays

Supplementary material. Datasets, which support the conclusions of this article. (ZIP 58 kb

Additional file 1: of Comparison of pre-processing methods for multiplex bead-based immunoassays

Supplementary figures. All results for the QQ-, Mean-SD, Bland-Altman and Volcano plots. (PDF 5133 kb

Quantification of corresponding autoantigen levels in prostate tissue.

<p><b>A</b> Immunohistochemical stainings of representative tissue microarray spots from high and low inflammation patient cohorts. SPAST, STX18 and SPOP are expressed in the epithelium of benign (BE) and cancer (CA) areas of both cohorts. Quantitative analysis was performed using the HistoQuest immunohistochemistry analysis software (TissueGnostics). A score was calculated by multiplying staining intensity and percentage of positively stained cells. n = 25 per group. *P<0.05, Mann-Whitney Test. Bar, 100μm. <b>B</b> Quantification of the pan-lymphocyte marker CD45 (PTPRC) and autoantigen mRNA levels in high and low inflammation patient groups. n = 25 per group. ***P<0.001, Mann-Whitney Test. <b>C</b> Electropherograms of transcriptome (5’-3’) and exome (3’-5’) sequencing results depicting wild-type and D130H SPOP mutation sequences. High resolution melting curves for mutated and wild-type DNA: The purple melting curve of the sample consists of 50% mutant and 50% wild-type DNA.</p

Chronic prostatic inflammation induces elevated autoantibody levels.

<p><b>A</b> Flow chart of the strategy used for the detection and cross-validation of autoantibody (AAB) signatures associated with chronic prostatic inflammation. Radical prostatectomy specimens were classified into two (high/low inflammation) groups based on the extent of immune cell infiltrations in the whole prostate. The corresponding pre-surgery blood serum samples were analyzed for autoantibodies (AAB) using a planar protein array (screening, n = 70). A cross-validation study testing the robustness of the identified AAB panel was based on the Luminex-bead protein array technology (cross-validation, n = 63). Statistical comparison of the serum autoantibody profiles in the low and high inflammation groups was used to identify and validate differentially abundant AABs. The prostate tissue expression patterns and the expression in different prostate cancer progression stages were established for three selected corresponding autoantigens (AAGs). <b>B</b> Bar chart for positively classified observations of the 15 most differentially detected autoantibodies in the high inflammation group compared to the low inflammation group. Data are expressed as percentage of total number of positive samples in each group. <b>C</b> Calculation of the fold change for each autoantibody revealed a significant increase of 165 antigens in high inflammation prostate cancer (upper right panel, p<0.05, fold change>2, Mann-Whitney Test) and a decrease of only one (upper left panel). <b>D</b> Graphical representation of the ten top ranked functional clusters assigned for inflammation associated autoantibodies using the DAVID functional annotation tool. The bar size corresponds to the percentage of identified corresponding genes related to a specific functional category (P<0.05).</p

Clinical and pathological cohort characteristics.

<p>Clinical and pathological cohort characteristics.</p

Functional annotation of the top biomarker candidates.

<p>Functional annotation of the top biomarker candidates.</p

Candidate autoantigens selected for analysis of their tissue expression pattern.

<p>Candidate autoantigens selected for analysis of their tissue expression pattern.</p