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. 2022 Apr 15;14(8):2001.
doi: 10.3390/cancers14082001.

Application of Proteogenomics to Urine Analysis towards the Identification of Novel Biomarkers of Prostate Cancer: An Exploratory Study

Tânia Lima  1   2 , António S Barros  3 , Fábio Trindade  3 , Rita Ferreira  4 , Adelino Leite-Moreira  3 , Daniela Barros-Silva  2 , Carmen Jerónimo  2   5   6 , Luís Araújo  7 , Rui Henrique  2   5   6 , Rui Vitorino  1   3   4 , Margarida Fardilha  1
Affiliations

Application of Proteogenomics to Urine Analysis towards the Identification of Novel Biomarkers of Prostate Cancer: An Exploratory Study

Tânia Lima et al. Cancers (Basel). .

Abstract

To identify new protein targets for PCa detection, first, a shotgun discovery experiment was performed to characterize the urinary proteome of PCa patients. This revealed 18 differentially abundant urinary proteins in PCa patients. Second, selected targets were clinically tested by immunoblot, and the soluble E-cadherin fragment was detected for the first time in the urine of PCa patients. Third, the proteogenome landscape of these PCa patients was characterized, revealing 1665 mutant protein isoforms. Statistical analysis revealed 6 differentially abundant mutant protein isoforms in PCa patients. Analysis of the likely effects of mutations on protein function and PPIs involving the dysregulated mutant protein isoforms suggests a protective role of mutations HSPG2*Q1062H and VASN*R161Q and an adverse role of AMBP*A286G and CD55*S162L in PCa patients. This work originally characterized the urinary proteome, focusing on the proteogenome profile of PCa patients, which is usually overlooked in the analysis of PCa and body fluids. Combined analysis of mass spectrometry data using two different software packages was performed for the first time in the context of PCa, which increased the robustness of the data analysis. The application of proteogenomics to urine proteomic analysis can be very enriching in mutation-related diseases such as cancer.

Keywords: biomarker; human; immunoblot; label-free quantitation; prostate cancer; proteogenome; proteome; urine.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Exploratory analysis of proteome data from MaxQuant. (A) Principal Component Analysis of the urine proteome of the two groups. (B) The heatmap of proteins identified in all individuals. Samples are represented in columns and proteins in rows. Proteins whose gene name is not available are indicated by their UniProt accession number. The dashed line on the heatmap indicates the two clusters of proteins.
Figure 2
Figure 2
Exploratory analysis of proteome data from Proteome discoverer. (A) Principal Component Analysis of the urine proteome of the two groups. (B) The heatmap of proteins identified in all individuals. Samples are represented in columns and proteins in rows.
Figure 3
Figure 3
Urinary protein levels of the candidate targets for PCa in the discovery group (using MS) and in the testing group (using immunoblot and immunoassay). MS: mass spectrometry.
Figure 4
Figure 4
Exploratory analysis of proteogenome data from Pinnacle. (A) Principal Component Analysis of the urine proteogenome of the two groups. (B) The heatmap of mutant proteins identified in all individuals. Samples are represented in columns and proteins in rows. Proteins are identified by their gene name, and the mutation identified. The dashed line on the heatmap indicates the two clusters of proteins.
Figure 5
Figure 5
PPI network of 86 mutated proteins with known expression in the prostate.
See this image and copyright information in PMC

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