Molecular Changes in Tissue Proteome during Prostate Cancer Development: Proof-of-Principle Investigation

Abstract

(1) Background: Prostate cancer (PCa) is characterized by high heterogeneity. The aim of this study was to investigate molecular alterations underlying PCa development based on proteomics data. (2) Methods: Liquid chromatography coupled to tandem mass spectrometry was conducted for 22 fresh-frozen tissue specimens from patients with benign prostatic hyperplasia (BPH, n = 5) and PCa (n = 17). Mann Whitney test was used to define significant differences between the two groups. Association of protein abundance with PCa progression was evaluated using Spearman correlation, followed by verification through investigating the Prostate Cancer Transcriptome Atlas. Functional enrichment and interactome analysis were carried out using Metascape and String. (3) Results: Proteomics analysis identified 1433 proteins, including 145 proteins as differentially abundant between patients with PCa and BPH. In silico analysis revealed alterations in several pathways and hallmarks implicated in metabolism and signalling, represented by 67 proteins. Among the latter, 21 proteins were correlated with PCa progression at both the protein and mRNA levels. Interactome analysis of these 21 proteins predicted interactions between Myc proto-oncogene (MYC) targets, protein processing in the endoplasmic reticulum, and oxidative phosphorylation, with MYC targets having a central role. (4) Conclusions: Tissue proteomics allowed for characterization of proteins and pathways consistently affected during PCa development. Further validation of these findings is required.

Keywords: drug target; personalized medicine; prostate cancer; proteomics; tissue.

Figure 1

Tissue proteome characterization: (A) Boxplots representing the number of proteins identified in prostate tissue samples from patients with benign prostatic hyperplasia (BPH) and prostate cancer (PCa). (B) Graphical representation of the correlation matrix for normalized protein abundances across the individual samples: the Spearman Rho correlation coefficient is colour coded. All relationships were significant at p < 0.05. (C) The data completeness plot reflects representation of the number of proteins identified in the specific number of samples. The number of proteins identified in more than 30%, 60% and 90% of samples are indicated. (D) Graphical representation of protein rank against the average protein abundance (log10) calculated based on all analysed samples (n = 22): Ten proteins with the highest abundance are highlighted in red. Abbreviations: ALB—serum albumin, ACTB—actin, cytoplasmic 1, ACTG2—actin, gamma-enteric smooth muscle, BPH—benign prostatic hyperplasia, COL6A3—collagen alpha-3(VI) chain, DES—desmin, FLNA—filamin-A, HBB—haemoglobin subunit beta, HIST1H2AH—histone H2A type 1-H, MYH11—myosin-11, PCa—prostate cancer and TAGLN—transgelin.

Figure 2

Protein differences between patients with PCa and BPH: (A) Volcano plot. Proteins that were identified only in one of the two groups were not plotted. The latter covers 217 and 50 proteins found solely in the case (PCa) and the control (BPH) groups, respectively. Differentially abundant proteins (significant change in the abundance (p < 0.05), detected in more than 30% of samples) are shown in red. (B) Distribution of protein classes of differentially expressed proteins: Protein classes were defined according to the Panther Classification System. Information on the protein class was available for 88 out of 145 differentially abundant proteins. (C) Graphical representation of the enrichment analysis based on Molecular Signatures Database (MSigDB) hallmark gene set ontology: the ten most significantly enriched terms are presented. p-values were calculated using the Banjamini–Hochberg procedure. Abbreviations: BPH—benign prostatic hyperplasia, MSigDB—Molecular Signatures Database, MYC—Myc proto-oncogene and PCa—prostate cancer.

Figure 3

Protein–protein interaction network and associated pathways/hallmarks: Interaction network of proteins associated with progression at protein and mRNA levels. Nodes (proteins) connecting all three pathways/hallmarks are highlighted in red, while those nodes connecting between two pathways/hallmarks are marked in purple. Disconnected nodes in the network are not shown. Colour clouds represent proteins belonging to the indicated pathway/hallmark. Abbreviations: ACACA—acetyl-CoA carboxylase 1, ALDH1B1—aldehyde dehydrogenase X, mitochondrial, ALDH6A1—methylmalonate-semialdehyde dehydrogenase (acylating), AKR1B1—aldo-keto reductase family 1 member B1, ARF1—ADP-ribosylation factor 1, EPRS1—bifunctional glutamate/proline–tRNA ligase, ER—endoplasmic reticulum, GRHPR—glyoxylate reductase/hydroxypyruvate reductase, HSP90AB1—heat shock protein HSP 90-beta, LAMP1—lysosome-associated membrane glycoprotein 1, MYC— Myc proto-oncogene, P4HB—protein disulphide-isomerase, PA2G4—proliferation-associated protein 2G4, RACK1—receptor of activated protein C kinase 1, RPN1—dolichyl-diphosphooligosaccharide–protein glycosyltransferase subunit 1, SEC61A1—protein transport protein Sec61 subunit alpha isoform 1 and SLC25A6—ADP/ATP translocase 3.

Figure 4

Boxplots representing distribution of the normalized protein abundance according to the disease progression. Abbreviations: BPH—benign prostatic hyperplasia, EPRS1—bifunctional glutamate/proline–tRNA ligase, GS—Gleason score, HSP90AB1—heat shock protein HSP 90-beta, PA2G4—proliferation-associated protein 2G4, RACK1—receptor of activated protein C kinase 1, RPN1—dolichyl-diphosphooligosaccharide–protein glycosyltransferase subunit 1, SEC61A1—protein transport protein Sec61 subunit alpha isoform 1 and SLC25A6—ADP/ATP translocase 3.