Effectiveness of the Combined Evaluation of KLK3 Genetics and Free-to-Total Prostate Specific Antigen Ratio for Prostate Cancer Diagnosis

KLK3 SNP-SNP interactions for prediction of prostate cancer aggressiveness

Risk classification for prostate cancer (PCa) aggressiveness and underlying mechanisms remain inadequate. Interactions between single nucleotide polymorphisms (SNPs) may provide a solution to fill these gaps. To identify SNP-SNP interactions in the four pathways (the angiogenesis-, mitochondria-, miRNA-, and androgen metabolism-related pathways) associated with PCa aggressiveness, we tested 8587 SNPs for 20,729 cases from the PCa consortium. We identified 3 KLK3 SNPs, and 1083 (P < 3.5 × 10-9) and 3145 (P < 1 × 10-5) SNP-SNP interaction pairs significantly associated with PCa aggressiveness. These SNP pairs associated with PCa aggressiveness were more significant than each of their constituent SNP individual effects. The majority (98.6%) of the 3145 pairs involved KLK3. The 3 most common gene-gene interactions were KLK3-COL4A1:COL4A2, KLK3-CDH13, and KLK3-TGFBR3. Predictions from the SNP interaction-based polygenic risk score based on 24 SNP pairs are promising. The prevalence of PCa aggressiveness was 49.8%, 21.9%, and 7.0% for the PCa cases from our cohort with the top 1%, middle 50%, and bottom 1% risk profiles. Potential biological functions of the identified KLK3 SNP-SNP interactions were supported by gene expression and protein-protein interaction results. Our findings suggest KLK3 SNP interactions may play an important role in PCa aggressiveness.

Identification of potential biomarkers and pivotal biological pathways for prostate cancer using bioinformatics analysis methods

Background

Prostate cancer (PCa) is a common urinary malignancy, whose molecular mechanism has not been fully elucidated. We aimed to screen for key genes and biological pathways related to PCa using bioinformatics method.

Methods

Differentially expressed genes (DEGs) were filtered out from the GSE103512 dataset and subjected to the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The protein–protein interactions (PPI) network was constructed, following by the identification of hub genes. The results of former studies were compared with ours. The relative expression levels of hub genes were examined in The Cancer Genome Atlas (TCGA) and Oncomine public databases. The University of California Santa Cruz Xena online tools were used to study whether the expression of hub genes was correlated with the survival of PCa patients from TCGA cohorts.

Results

Totally, 252 (186 upregulated and 66 downregulated) DEGs were identified. GO analysis enriched mainly in “oxidation-reduction process” and “positive regulation of transcription from RNA polymerase II promoter”; KEGG pathway analysis enriched mostly in “metabolic pathways” and “protein digestion and absorption.” Kallikrein-related peptidase 3, cadherin 1 (CDH1), Kallikrein-related peptidase 2 (KLK2), forkhead box A1 (FOXA1), and epithelial cell adhesion molecule (EPCAM) were identified as hub genes from the PPI network. CDH1, FOXA1, and EPCAM were validated by other relevant gene expression omnibus datasets. All hub genes were validated by both TCGA and Oncomine except KLK2. Two additional top DEGs (ABCC4 and SLPI) were found to be associated with the prognosis of PCa patients.

Conclusions

This study excavated the key genes and pathways in PCa, which might be biomarkers for diagnosis, prognosis, and potential therapeutic targets.

Search for genetic factor association with cancer-free prostate-specific antigen level elevation on the basis of a genome-wide association study in the Korean population

We investigated the genetic markers associated with elevated serum prostate-specific antigen (sPSA) levels to improve the predictive power of sPSA in screening for prostate cancer. A genome-wide association study was carried out among 4124 healthy Korean male adults using the Affymetrix Axiom Customized Biobank Genotyping Arrays for sPSA levels. A subgroup analysis for increased sPSA levels who underwent a prostate biopsy (n=64) was also carried out. We detected 11 single nucleotide polymorphisms (SNPs) near the Solute carrier family 45member 3, AGAP7P, MSMB, LOC101929917, and KLK3 genes associated with sPSA levels. The top SNP associated with the log of the sPSA levels was rs72434280 in the Solute carrier family 45 member 3 gene (P value, discovery set=2.98×10, replication set=7.31×10). A case-control study utilizing available biopsy reports (49 patients with normal biopsies vs. 15 patients with biopsies indicating cancer) for the sPSA more than 3 ng/ml group was carried out for the respective SNPs after adjusting for age. Only the SNPs near the KLK3 gene were associated with prostate cancer. In the model of the predictive elevation of sPSA level, adding the genetic risk score [area under the curve (AUC)=0.697] to age and BMI (AUC=0.602) significantly improved the results of the AUC (P<0.0001). We found seven SNPs associated with elevated prostate-specific antigen levels in healthy Korean men. Four SNPs were a novel marker in the Korean population. In men with increased prostate-specific antigen levels, genotyping SNP related to cancer-free elevation of sPSA level could be informative to decide the indication of prostate biopsy.

Discovery of Bladder Cancer-related Genes Using Integrative Heterogeneous Network Modeling of Multi-omics Data

In human health, a fundamental challenge is the identification of disease-related genes. Bladder cancer (BC) is a worldwide malignant tumor, which has resulted in 170,000 deaths in 2010 up from 114,000 in 1990. Moreover, with the emergence of multi-omics data, more comprehensive analysis of human diseases become possible. In this study, we propose a multi-step approach for the identification of BC-related genes by using integrative Heterogeneous Network Modeling of Multi-Omics data (iHNMMO). The heterogeneous network model properly and comprehensively reflects the multiple kinds of relationships between genes in the multi-omics data of BC, including general relationships, unique relationships under BC condition, correlational relationships within each omics and regulatory relationships between different omics. Besides, a network-based propagation algorithm with resistance is utilized to quantize the relationships between genes and BC precisely. The results of comprehensive performance evaluation suggest that iHNMMO significantly outperforms other approaches. Moreover, further analysis suggests that the top ranked genes may be functionally implicated in BC, which also confirms the superiority of iHNMMO. In summary, this study shows that disease-related genes can be better identified through reasonable integration of multi-omics data.

Integrative genomic analysis for the discovery of biomarkers in prostate cancer

Genome-wide association studies (GWAS) have achieved great success in identifying single nucleotide polymorphisms (SNPs, herein called genetic variants) and genes associated with risk of developing prostate cancer. However, GWAS do not typically link the genetic variants to the disease state or inform the broader context in which the genetic variants operate. Here, we present a novel integrative genomics approach that combines GWAS information with gene expression data to infer the causal association between gene expression and the disease and to identify the network states and biological pathways enriched for genetic variants. We identified gene regulatory networks and biological pathways enriched for genetic variants, including the prostate cancer, IGF-1, JAK2, androgen, and prolactin signaling pathways. The integration of GWAS information with gene expression data provides insights about the broader context in which genetic variants associated with an increased risk of developing prostate cancer operate.

Suitability of quality control materials for prostate-specific antigen (PSA) measurement: inter-method variability of common tumor marker control materials

BACKGROUND

Quality control materials with minimal inter-assay differences and clinically relevant proportions of different molecular forms of the analyte are needed to optimize intra- and inter-laboratory accuracy and precision.

METHODS

We assessed if clinically relevant total prostate-specific antigen (tPSA) levels were present in seven commercially available Multi Constituent Tumor Marker Controls (MC-TMC). Further, we determined the concentration of free PSA (fPSA) and calculated the percentage of free PSA (%fPSA) in all materials. Finally, we determined variability of TMC materials across several commonly used PSA platforms.

RESULTS

All MC-TMC materials contained at least one concentration of tPSA in normal and pathologic range. Control materials varied in the amount of fPSA and %fPSA, with most controls consisting of fPSA only and only one MC-TMC containing medically relevant levels of around 35% fPSA. Only a minority of MC-TMC materials showed minimal variability across four PSA methods while the majority of PSA controls showed wide inter-method differences.

CONCLUSIONS

Use of many commercially available controls for PSA could lead to biased PSA measurements because they contain medically irrelevant proportions of fPSA and show significant variation among different PSA assay platforms.