Despite the overall success of prostate specific antigen (PSA) in screening and detection of prostate cancer (PCa), its use has been limited due to the lack of specificity. The principal driving goal currently within PCa research is to identify non-invasive biomarker(s) for early detection of aggressive tumors with greater sensitivity and specificity than PSA. In this study, we focused on identification of non-invasive biomarkers in urine with higher specificity than PSA. We tested urine samples from PCa and benign prostatic hyperplasia (BPH) patients by 2-D DIGE coupled with MS and bioinformatics analysis. Statistically significant (p < 0.05), 1.8 fold variation or more in abundance, showed 41 spots, corresponding to 23 proteins. The Ingenuity Pathway Analysis showed significant association with the Acute Phase Response Signaling pathway. Nine proteins with differential abundances were included in this pathway: AMBP, APOA1, FGA, FGG, HP, ITIH4, SERPINA1, TF and TTR. The expression pattern of 4 acute phase response proteins differed from the defined expression in the canonical pathway. The urine levels of TF, AMPB and HP were measured by immunoturbidimetry in an independent validation set. The concentration of AMPB in urine was significantly higher in PCa while levels of TF and HP were opposite (p < 0.05). The AUC for the individual proteins ranged from 0.723 to 0.754. The combination of HP and AMBP yielded the highest accuracy (AUC = 0.848), greater than PSA. The proposed biomarker set is quickly quantifiable and economical with potential to improve the sensitivity and specificity of PCa detection.

Detecting prostate cancer (PCa) using non-invasive diagnostic markers still remains a challenge. The aim of this study was the identification of urine proteins that are sufficiently sensitive and specific to detect PCa in the early stages. Comparative proteomics profiling of urine from patients with PCa, benign prostate hyperplasia, bladder cancer, and renal cancer, coupled with bioinformatics analysis, were performed. Statistically significant difference in abundance showed 20 and 85 proteins in the 2-D DIGE/MS and label-free LC-MS/MS experiments, respectively. In silico analysis indicated activation, binding, and cell movement of subset of immune cells as the top affected cellular functions in PCa, together with the down-regulation of Acute Phase Response Signaling and Liver X Receptor/ Retinoid X Receptor (LXR/RXR) activation pathways. The most promising biomarkers were 35, altered in PCa when compared to more than one group. Half of these have confirmed localization in normal or PCa tissues. Twenty proteins (CD14, AHSG, ENO1, ANXA1, CLU, COL6A1, C3, FGA, FGG, HPX, PTGDS, S100A9, LMAN2, ITIH4, ACTA2, GRN, HBB, PEBP1, CTSB, SPP1) are oncogenes, tumor suppressors, and multifunctional proteins with highly confirmed involvement in PCa, while 9 (AZU1, IGHG1, RNASE2, PZP, REG1A, AMY1A, AMY2A, ACTG2, COL18A1) have been associated with different cancers, but not with PCa so far, and may represent novel findings. LC-MS/MS data are available via ProteomeXchange with identifier PXD008407.

The high recurrence rate and invasive diagnostic and monitoring methods in bladder cancer (BCa) clinical management require the development of new non-invasive molecular tools for early detection, particularly for low-grade and low-stage BCa as well as for risk stratification. By using an in-solution digestion method and label-free data-independent LC-MS/MS coupled with ion mobility, we profiled the BCa tissues from initiation to advanced stages and confidently identified and quantified 1619 proteins (≥2 peptides). A statistically significant difference in abundance (Anova ≤ 0.05) showed 494 proteins. Significant correlation with stage with steady up or down with BCa stages showed 15 proteins. Testing of NNMT, GALK1, and HTRA1 in urine samples showed excellent diagnostic potential for NNMT and GALK1 with AUC of 1.000 (95% CI: 1.000–1.000; p < 0.0001) and 0.801 (95% CI: 0.655–0.947; p < 0.0001), respectively. NNMT and GALK1 also showed very good potential in discriminating non-invasive low-grade from invasive high-grade BCa with AUC of 0.763 (95% CI: 0.606–0.921; p = 0.001) and 0.801 (95% CI: 0.653–0.950; p < 0.0001), respectively. The combination of NNMT and GALK1 increased prognostic accuracy (AUC = 0.813). Our results broaden the range of potential novel candidates for non-invasive BCa diagnosis and prognosis.

Background/Objectives: Expanded carrier screening (ECS) panels often rely on pan-ethnic databases, such as the ACMG Tier 3 panel, but their ability to capture population-specific reproductive risk is uncertain. We characterized pathogenic/likely pathogenic (P/LP) variants and at-risk-couple (ARC) probabilities in an underrepresented population from North Macedonia to inform ECS gene prioritization. Methods: NGS data from 1438 subjects of Macedonian, Albanian, and other local ancestry, referred for rare-disease diagnostics, were analyzed across 1795 candidate autosomal recessive/X-linked genes. P/LP variants were compared with gnomAD exomes v2.1.1 non-Finnish European frequencies (Fisher’s exact test). Gene-specific ARC probabilities ranked genes and generated cumulative ARC curves per subgroup. Results: We identified 2007 P/LP variants across 1096 genes. One hundred variants in 89 genes reached an allele frequency ≥ 1/200 in the Macedonian or Albanian subgroups; over one-third were absent from candidate ECS panels, mostly linked to severe or variable phenotypes. ARC saturated rapidly: genes with carrier counts ≥ 1/100 (ACMG Tier 2) accounted for over 85% of total ARC, and with ≥1/200 (ACMG Tier 3) for more than 93%. Total ARC among autosomal recessive panel genes (X-linked genes analyzed separately) was 3.42%, rising to 5.02% in the Albanian subgroup. Excluding top hypomorphic variants reduced ARC by 34–37%, highlighting variant-specific penetrance. The highest-ARC genes diverged between subgroups, with limited overlap with pan-ethnic panels. Conclusions: Population-specific carrier frequencies influence gene prioritization and estimated reproductive-risk probabilities for ECS. Population-tailored ECS panels that incorporate local carrier-frequency data, including embryonic/fetal-lethal genes, and account for hypomorphic alleles to avoid overestimating reproductive risk probabilities could improve the identification of couples at increased reproductive risk, particularly in underrepresented populations.