Characterization of the Biochemical Recurrence Prediction Ability and Progression Correlation of Peroxiredoxins Family in Prostate Cancer Based on Integrating Single-Cell RNA-Seq and Bulk RNA-Seq Cohorts

Abstract

Introduction: The peroxiredoxins (PRDXs) family plays a crucial role in balancing reactive oxygen species (ROS) levels in tumor cells. However, its potential role in prognosis and therapy response of prostate cancer (PCa) remains unknown.

Methods: In this study, we utilized 2 public single-cell RNA datasets and 8 bulk-RNA datasets to investigate the clinical value of six PRDXs family members in PCa. Expression comparison, biochemical recurrence analysis, and therapy response analysis were measured. Pathway enrichments were utilized to predict the potential down-stream pathway it may involve. In vitro experiments were used to validate the function of PRDX5 in the progression of castration-resistant prostate cancer (CRPC) cell lines.

Result: Among the PRDXs family, PRDX5 was most related to the advancement of prostate cancer. A nomogram integrating the expression of PRDX5 with clinical features was developed to better predict clinical outcomes in PCa patients compared to 30 published signatures. Immunohistochemistry was used to verify that PRDX5 expression was higher in advanced levels of PCa tissue. Gene Set Enrichment Analysis (GSEA) and pathway predictive analysis revealed that the PRDX5 related genes were mainly relevant to ROS Pathway, Mitochondria-related functions, cellular respiration, and oxidative phosphorylation. In vitro cell proliferation assays, ROS determination assay, and apoptosis assay together revealed that depletion of PRDX5 induces apoptosis via ROS accumulation in CRPC cells. Moreover, the expression of PRDX5 in CRPC cells also affects the sensitivity to the ARSI therapy.

Conclusion: This study offers new evidence for determining that the expression of PRDX5 is associated with advanced tumor grade, poor prognosis, and suboptimal response to multiple therapies in PCa within the PRDXs family. Last but not least, our study provides new insights into precision medicine in PCa and provides a reference for further research on PRDX5.

Keywords: PRDX5; biochemical recurrence; peroxiredoxins; prostate cancer.

FIGURE 1

The overview flowchart of our study.

FIGURE 2

Landscape of the genome and transcriptome alteration features of the peroxiredoxin family in PCa. (A) The bar chart illustrates the alteration proportion around different public databases. (B) The oncoprint plot depicts the genome alteration frequency of each peroxiredoxin across multiple databases. (C) The CNV mutation frequency of 6 peroxiredoxins in the TCGA database. Green and red are for deletion and amplification, respectively. (D) Circus plot of the human genome shows CNV site of PRDXs in PCa. (E) The box plot displays the gene expression levels of PRDXs between tumor tissue samples and normal tissue samples from the TCGA and GTEx databases. (F–J) The box plot displays the gene expression levels of PRDXs in the TCGA database across different BCR statuses (F), Clinical stages (G), Pathological T stages (H), Gleason scores (I), and Clinical N stages (J). (*p < 0.05, **p < 0.01).

FIGURE 3

Tissue sublocalization expression of peroxiredoxin family in PCa. (A, B) UMAP of GSE141445 and GSE157703 demonstrates the distribution patterns of subgroups in each dataset annotated by cell type annotation and colored accordingly. (C, D) UMAP plot shows the distribution patterns and the relative expression level of PRDX5 in each dataset. (E, F) The heatmap illustrates the expression levels of PRDXs across different cell types in the GSE141445 and GSE157703 datasets. (G) Immunohistochemistry staining of PRDX5 in Human protein atlas database.

FIGURE 4

The BCR‐free survival curves in 5 public databases. (A, B) The Kaplan–Meier curves illustrate the biochemical recurrence status of prostate cancer patients across TCGA (A), CancerMap (B), DKFZ2018 (C), GSE54460 (D), GSE70769 (E), stratified by best cutoff grouping based on PRDXs expression levels.

FIGURE 5

Assessing the predictive significance of PRDXs in conjunction with clinical characteristics in PCa. (A, B) Univariate analyses of PRDXs (A) and clinical characteristics (B) in prostate cancer patients from TCGA. (C) Multivariate analyses of PRDX5 and clinical characteristics for BCR in prostate cancer patients from TCGA. (D) The nomogram and calibrated curve based on PRDX5, Gleason score, and Pathological T stages, along with calibration curves for 3‐year and 5‐year BCR. (E) ROC curves illustrate the predictive performance of Gleason score, Pathological T stages, PRDX5, and the nomogram for 3‐year BCR. (F) ROC curves illustrate the predictive performance of Gleason score, Pathological T stages, PRDX5, and the nomogram for 5‐year BCR.

FIGURE 6

Comparison of Gene Expression‐Based Prognostic Signatures in PCa. (A) The C‐index of PRDX5, PRDX‐Nomo, Gleason score, Pathological_stage, Gleason score + PRDX5, Pathological_stage + PRDX5, and Gleason score + Pathological_stage are presented in CancerMap, GSE70769, TCGA‐PRAD, DKFZ, and GSE54460 datasets. (B) The C‐index of PRDX‐Nomo and 30 published signatures in CancerMap, GSE70769, TCGA‐PRAD, DKFZ, and GSE54460 datasets. (C) The log2(HR) of PRDX‐Nomo and 30 published signatures in CancerMap, GSE70769, TCGA‐PRAD, DKFZ, and GSE54460 datasets.

FIGURE 7

The correlation between the expression of PRDX5 and PAM50 subtypes and response to drug therapy. (A) The box plot displays the expression of PRDX5 across different PAM50 subtypes in TCGA. (B) The Kaplan–Meier curves depict the BCR status of PCa patients in TCGA, stratified by Luminal A, Luminal B, and Basal subtypes. (C) The diagrams display the BCR free survival for PCa patients of two PRDX5 subgroups within each PAM50 subtype. (D) The PRDX5 expression constitution of PCa patients before or after ARSI therapy. (E) The Kaplan–Meier curve of off ARSI time (p = 0.008), and the off ARSI constitution of PCa patients in the two PRDX5 subgroups. (F) The Kaplan–Meier curve of overall survival time (p = 0.083) and outcome constitution (living or deceased) of PCa patients in the two PRDX5 subgroups. (G, H) The lollipop plot illustrates the correlation between multiple drug sensitivities and PRDX5 expression levels in the CTRP (G) and GDSC (H) databases. (I, J) The grouped box plot illustrates the drug sensitivity of nine clinical drugs in two PRDX5 subgroups in the CTRP (I) and GDSC (J) databases.

FIGURE 8

Representative IHC staining for PRDX5 in PCa tumor‐adjacent or tumor tissue in a Tissue microarrays (A) and the staining intensity score was counted and compared (B) (*p < 0.05, **p < 0.01).

FIGURE 9

Biological mechanisms enrichment analysis of PRDX5 in PCa. (A) GSEA plots display functional pathways enriched in differentially expressed genes (DEGs). (B, C) The bubble plots display GO and KEGG pathway enrichment data for related genes (RGs).

FIGURE 10

Depletion of PRDX5 induces apoptosis and affects the sensitivity of ARSI therapy via ROS accumulation. (A) Verification of PRDX5 down‐regulated C4‐2B and 22Rv1cell lines. (B) Colony formation of PRDX5 down‐regulated C4‐2B and 22Rv1cell lines compared with the negative control (si‐NC) group. (C) CCK‐8 assays of PRDX5 down‐regulated C4‐2B cell lines compared with si‐NC group. (D) CCK‐8 assays of PRDX5 down‐regulated 22Rv1 cell lines compared with the si‐NC group. E. Images of cellular ROS levels were captured (scale bar: 250 μm). (F) The ROS levels of 22Rv1 and C4‐2B cells were analyzed by flow cytometry. (G) The apoptosis flow cytometry assays of C4‐2B and 22Rv1 cell lines. (H) Combined application of si‐PRDX5 and NAC was measured by using the CCK‐8 assay. (I) Colony formation of PRDX5 down‐regulated C4‐2B and 22Rv1cell lines comparing with si‐NC under Abiraterone treatment. (J) CCK‐8 assays of PRDX5 down‐regulated C4‐2B and 22Rv1 cell lines compared with si‐NC under Abiraterone treatment. (K) Colony formation of PRDX5 down‐regulated C4‐2B and 22Rv1cell lines compared with si‐NC under Enzalutamide treatment. (L) CCK‐8 assays of PRDX5 down‐regulated C4‐2B and 22Rv1 cell lines comparing with si‐NC under Enzalutamide treatment. (*p < 0.05, **p < 0.01, Abi: Abiraterone, Enz: Enzalutamide).