Proteomic landscape profiling of primary prostate cancer reveals a 16-protein panel for prognosis prediction

Abstract

Prostate cancer (PCa) is the most common malignant tumor in men. Currently, there are few prognosis indicators for predicting PCa outcomes and guiding treatments. Here, we perform comprehensive proteomic profiling of 918 tissue specimens from 306 Chinese patients with PCa using data-independent acquisition mass spectrometry (DIA-MS). We identify over 10,000 proteins and define three molecular subtypes of PCa with significant clinical and proteomic differences. We develop a 16-protein panel that effectively predicts biochemical recurrence (BCR) for patients with PCa, which is validated in six published datasets and one additional 99-biopsy-sample cohort by targeted proteomics. Interestingly, this 16-protein panel effectively predicts BCR across different International Society of Urological Pathology (ISUP) grades and pathological stages and outperforms the D'Amico risk classification system in BCR prediction. Furthermore, double knockout of NUDT5 and SEPTIN8, two components from the 16-protein panel, significantly suppresses the PCa cells to proliferate, invade, and migrate, suggesting the combination of NUDT5 and SEPTIN8 may provide new approaches for PCa treatment.

Keywords: BCR-free survival; DIA-MS; NUDT5 and SEPTIN8; prognosis prediction; prostate cancer; proteomics.

Graphical abstract

Figure 1

Overview of the PCSHA cohort analysis (A) The graphic illustrates the workflow encompassing tissue sample punching, sample information annotation, batch design, and subsequent proteomic data analysis. GS1 denoted a more aggressive tumor region, whereas GS2 represented a less malignant region. (B) Heatmap displaying the relative protein expression levels of 10,071 proteins from 918 prostate tissue samples (N = 918). The gray bricks represented benign tissue. BE, preoperative endocrine therapy, PE, postoperative endocrine therapy; PR, postoperative radiotherapy. The patients were graded following the ISUP guidance. (C) Boxplots representing the intra- and inter-individual correlation comparisons based on proteomic data. In the boxplot, the middle bar represents the average value, and the box represents the interquartile range. (D) Volcano plot showing 1,784 differentially expressed proteins between tumor and benign samples (Welch’s t test, B-H adjusted p value < 0.05, fold change > 1.5). (E) Pathways enriched (p value < 0.05) by these differentially expressed proteins in Figure 1D using Metascape. Blue and orange pathways were enriched by 61 downregulated proteins and by 2,009 upregulated proteins in the tumor samples, respectively. See also Table 1, Figure S1, and Table S1. Patient information, related to Figure 1, Table S2. Protein quantification matrix, related to Figure 1, Table S3. Pathway enrichment from differentially expressed proteins between tumor and benign samples, related to Figure 1.

Figure 2

Dysregulated proteins across different ISUP grades (A) 12 different clusters identified by mFuzz clustering across different ISUP grades in the tumor samples. (B) Normalized protein expression across five ISUP grades in clusters 1, 2, 3, and 4. (C) Normalized average protein expression of all the proteins in each cluster across five ISUP grades. (D) Pathway enrichment from all the proteins in each cluster across five ISUP grades (p < 0.05, Metascape). BE, preoperative endocrine therapy; PE, postoperative endocrine therapy; PR, postoperative radiotherapy. (E and F) Networks based on the enriched pathways obtained using the most significantly dysregulated proteins from each cluster (one-way ANOVA p < 0.01). Orange points (E) show the proteins from cluster 1 and the blue ones (F) show the proteins from cluster 4.

Figure 3

Proteomic pathway-based stratification of the patients with PCa associated with their prognosis (A) The heatmap shows the normalized enrichment scores of the three patient subtypes (CPC1, N = 126; CPC2, N = 57; and CPC3, N = 43) using NMF analysis. The associations of proteomic subtypes with clinical characteristics (pathological stages, PSA, biochemical recurrence [BCR], and ISUP grade) are annotated. (B) Kaplan-Meier curves of the BCR-free survival of each patient subtype from our cohort (log rank p = 0.0012). (C) Distribution of the clinical indexes in the three patient subtypes. (D) Relative expression of the dysregulated druggable proteins in the three patient subtypes. One-way ANOVA, B-H adjusted p value: ∗ < 0.05; ∗∗ < 0.01; ∗∗∗ < 0.001; ∗∗∗∗ < 0.0001. BE, preoperative endocrine therapy; PE, postoperative endocrine therapy; PR, postoperative radiotherapy. See also Figure S2.

Figure 4

Identification and validation of a panel of 16 proteins for BCR prediction (A) Workflow for the development and validation of the 16-protein panel. (B) Heatmap showing the relative abundance of 16 proteins in two different risk groups. The associations of the two risk groups with clinical characteristics were tested by Fisher’s exact test. The differential expression of each protein between the high- and low-risk groups was tested by Welch’s t test, B-H adjusted p value: ∗ < 0.05; ∗∗ < 0.01; ∗∗∗ < 0.001; ∗∗∗∗ < 0.0001. (C–F) Kaplan-Meier plot for BCR-free survival (left panel) and ROC curves (right panel) of the 16-protein model in the PCSHA validation dataset (C), TCGA dataset (D), the MSKCC dataset (E), and the CPGEA dataset (F) for BCR-free survival based on our 16-protein prediction model, with a 1-year predictive power (yellow), a 2-year predictive power (blue), and a 3-year predictive power (red). (G–I) Kaplan-Meier plots and ROC curves of BCR-free survival based on the 16-protein prediction model in different ISUP grades using the PCSHA validation set (G), TCGA dataset (H), and the MSKCC dataset (I). See also Figures S3 and S4 and Table S4.

Figure 5

Comparing the predictive performance of the 16-protein model and clinicopathological characteristics (A) Forest plot showing the BCR-free prognostic score for each clinical parameter in a multivariate Cox regression analysis. The middle points indicate the hazard ratios. The endpoints represent the lower or upper 95% confidence intervals. BE, preoperative endocrine therapy; PE, postoperative endocrine therapy; PR, postoperative radiotherapy. (B) ROC curves of the 16-protein panel and clinicopathological characteristics (PSA level, Gleason score, pathology stage, and D’Amico) at 1 (upper panel), 2 (middle panel), and 3 (lower panel) years in the PCSHA validation dataset. (C–E) Sankey plots showing the patients with PCa overlapping among the 16-protein panel prediction system, the recurrence status, and the ISUP grade using the PCSHA validation dataset (C), TCGA dataset (D), and the MSKCC dataset (E). See also Figure S5 and Table S4.

Figure 6

Validation of the 16-protein panel for BCR prediction in prostate cancer patients using biopsy samples (A and B) Kaplan-Meier plots for BCR-free survival based on D’Amico (A) and pathological stage (B) in the PCSHA-biopsy dataset (N = 99). (C) The peak groups of the unique peptides and the protein abundance between high- and low-risk patients. (D) ROC curves (right panel) of the 16-protein model and clinicopathological characteristics (PSA level, ISUP grade based on biopsy and surgical samples, pathological stage, and D’Amico) in the PCSHA-biopsy test set. (E) Kaplan-Meier plots of BCR-free survival based on the 16-protein prediction model in the PCSHA-biopsy set. See also Figure S6 and Tables S5 and S6.

Figure 7

Validation of potential synthetic targets (A and B) Immunohistochemical (IHC) staining showing the protein expression of SEPTIN8 and NUDT5 in benign and tumor samples of low-risk and high-risk PCa patients (A), and the statistics for the IHC intensity score of 69 patients with PCa were shown in (B). (C and D) Western blot showing the efficacy of the single (C) or double (D) knockdown of SEPTIN8 or NUDT5 in PC-3 cells. (E and F) Cell proliferation assay of the single (E) or double (F) knockout of SEPTIN8 or NUDT5 knockdown PC-3 cells using a ZenCell Owl live-cell imaging system. (G and H) Cell proliferation assay of SEPTIN8-knockout (KO), NUDT5 KO, and double KO (dKO) (SEPTIN8/NUDT5-dKO) using EdU staining method, and the statistics for the percentage of EdU-positive cells were shown in (H). (I and J) Wound healing assay of dKO in PC-3 cell line, and the statistics were shown in (J). (K) Bar chart shows the statistics of the wound healing assay for NUDT5 or SEPTIN8 single knockout cells. (L and M) Invasion assay of SEPTIN8/NUDT5-dKO in PC-3 cell line using transwell, and the statistics were shown in (M). Experiments were repeated in triplicates. Data are represented as mean ± SD. The p values are calculated by Welch’s t test, p value: ∗ < 0.05, ∗∗∗ < 0.001. See also Figure S7.