Association of zinc level and polymorphism in <i>MMP-7</i> gene with prostate cancer in Polish population

<div><p>Introduction</p><p>Prostate cancer is one of the most commonly diagnosed malignancies among men in Western populations. Evidence reported in the literature suggests that zinc may be related to prostate cancer. In this study we evaluated the association of serum zinc levels and polymorphisms in genes encoding zinc-dependent proteins with prostate cancer in Poland.</p><p>Methods</p><p>The study group consisted of 197 men affected with prostate cancer and 197 healthy men. Serum zinc levels were measured and 5 single nucleotide polymorphisms in <i>MMP-1</i>, <i>MMP-2</i>, <i>MMP-7</i>, <i>MMP-13</i>, <i>MT2A</i> genes were genotyped.</p><p>Results</p><p>The mean serum zinc level was higher in prostate cancer patients than in healthy controls (898.9±12.01 μg/l vs. 856.6±13.05 μg/l, p<0.01). When compared in quartiles a significant association of higher zinc concentration with the incidence of prostate cancer was observed. The highest OR (OR = 4.41, 95%CI 2.07–9.37, p<0.01) was observed in 3^rd quartile (>853.0–973.9 μg/l). Among five analyzed genetic variants, rs11568818 in <i>MMP-7</i> appeared to be correlated with 2-fold increased prostate cancer risk (OR = 2.39, 95% CI = 1.19–4.82, p = 0.015).</p><p>Conclusion</p><p>Our results suggest a significant correlation of higher serum zinc levels with the diagnosis of prostate cancer. The polymorphism rs11568818 in <i>MMP-7</i> gene was also associated with an increased prostate cancer risk in Poland.</p></div

Single-Patient Molecular Testing with NanoString nCounter Data Using a Reference-Based Strategy for Batch Effect Correction

<div><p>A major weakness in many high-throughput genomic studies is the lack of consideration of a clinical environment where one patient at a time must be evaluated. We examined generalizable and platform-specific sources of variation from NanoString gene expression data on both ovarian cancer and Hodgkin lymphoma patients. A reference-based strategy, applicable to single-patient molecular testing is proposed for batch effect correction. The proposed protocol improved performance in an established Hodgkin lymphoma classifier, reducing batch-to-batch misclassification while retaining accuracy and precision. We suggest this strategy may facilitate development of NanoString and similar molecular assays by accelerating prospective validation and clinical uptake of relevant diagnostics.</p></div

PVCA and PCA plots of the Hodgkin Lymphoma clinical samples.

<p>We considered the PVCA plot (A) of the HL clinical samples run in different batches. The percentages represent the variability explained by each factor and first order interaction between factors. The PCA plot (B) provides a two-dimensional summary of the pairwise plot of the first three principal components, which represent 49% of the variability in the data. HL1, HL2, and HL3 label each of unique CodeSets corresponding to the HL gene list.</p

Impact of BE on downstream analysis, illustrated using a HL prognostic model.

<p>The x and y axes correspond to risk scores obtained in HL1 and HL2 respectively. The dashed line represents the identity line, and the solid line represents the best linear fit. The horizontal line indicates the threshold used for prediction. The results in (A) correspond to scores not corrected for BE, and in (B) scores are corrected using 3 reference samples that were run in both CodeSets.</p

Concordance between duplicates of HL clinical samples obtained from two CodeSets, after adjusting using different methods.

<p>Concordance between duplicates of HL clinical samples obtained from two CodeSets, after adjusting using different methods.</p

Percentage of genes detected as a function of Signal to Noise Ratio by cohort.

<p>Each point on the plot represents a NanoString nCounter unique run (duplicates and triplicates included where available). The zoomed in section illustrates how the selected cut-off excludes samples that have low signal to noise and low % genes detected. HL: Hodgkin lymphoma clinical samples, OC: ovarian cancer clinical samples, OVCL: ovarian cancer cell lines, HLO: oligonucleotides corresponding to the HL CodeSet, OVO: oligonucleotides corresponding to the OC CodeSet.</p

PVCA of the OC clinical samples after adjusting batch effect using different methods.

<p>We consider the PVCA plot of the OC clinical samples run in different batches after adjusting BE with different methods. In each plot, percentages represent the variability explained by each factor and first order interaction between factors.</p

Cohort Description.

<p>Cohort Description.</p

Percentage of genes detected above the limit of detection (LOD) by cohort.

<p>Each point on the boxplot represents a NanoString nCounter unique run (duplicates and triplicates included where available). The colored boxes represent the distribution of the percentage of genes detected in a particular cohort. The white line indicates the median. A cutoff of 50% was used for Cell Lines and clinical samples, and 95% was used for oligonucleotide samples. HL: Hodgkin lymphoma clinical samples, OC: ovarian cancer clinical samples, OVCL: ovarian cancer cell lines, HLO: oligonucleotides corresponding to the HL CodeSet, OVO: oligonucleotides corresponding to the OC CodeSet.</p

PVCA and PCA plots of the ovarian cancer clinical samples.

<p>We considered the PVCA plot (A) of the OC clinical samples run in different batches. The percentages represent the variability explained by each factor and first order interaction between factors. The PCA plot (B) provides a two-dimensional summary of the pairwise plot of the first three principal components, which represent 40% of the variability in the data. CS1, CS2, and CS3 label each of unique CodeSets corresponding to the OC gene list.</p