ArticleThe Prostate2022
Protein signatures to distinguish aggressive from indolent prostate cancer.
Article in The Prostate, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 16 citations in OpenAlex.
- An Integrated Cell-Death Program Defines Aggressive Prostate Cancer by Coupling Ezrin to the Hippo-YAP/TAZ Axis.Annals of surgical oncology · 2026Article
- FTO inhibition attenuates renal fibrosis by downregulating ferroptosis activator ACSL4 and profibrotic factor TGFBI.iScience · 2025Article
- Article
- Establishing and Characterizing the Molecular Profiles, Cellular Features, and Clinical Utility of a Patient-Derived Xenograft Model Using Benign Prostatic Tissues.Laboratory investigation; a journal of technical methods and pathology · 2024Article
- Heme Oxygenase-1 and Prostate Cancer: Function, Regulation, and Implication in Cancer Therapy.International journal of molecular sciences · 2024Review
- Proteomic-based stratification of intermediate-risk prostate cancer patients.Life science alliance · 2024Article
- The Potential of Extracellular Matrix- and Integrin Adhesion Complex-Related Molecules for Prostate Cancer Biomarker Discovery.Biomedicines · 2023Review
- Applying recommended definition of aggressive prostate cancer: a validation study using high-quality data from the Cancer Registry of Norway.Acta oncologica (Stockholm, Sweden) · 2023Article
- Review
- The Association between Cyclin Dependent Kinase 2 Associated Protein 1 (CDK2AP1) and Molecular Subtypes of Lethal Prostate Cancer.International journal of molecular sciences · 2022Article
- From Omics to Multi-Omics Approaches for In-Depth Analysis of the Molecular Mechanisms of Prostate Cancer.International journal of molecular sciences · 2022Review
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundDistinguishing men with aggressive from indolent prostate cancer is critical to decisions in the management of clinically localized prostate cancer. Molecular signatures of aggressive disease could help men overcome this major clinical challenge by reducing unnecessary treatment and allowing more appropriate treatment of aggressive disease.
methodsWe performed a mass spectrometry-based proteomic analysis of normal and malignant prostate tissues from 22 men who underwent surgery for prostate cancer. Prostate cancer samples included Grade Groups (3-5), with 8 patients experiencing recurrence and 14 without evidence of recurrence with a mean of 6.8 years of follow-up. To better understand the biological pathways underlying prostate cancer aggressiveness, we performed a systems biology analysis and gene enrichment analysis. Proteins that distinguished recurrent from nonrecurrent cancer were chosen for validation by immunohistochemical analysis on tissue microarrays containing samples from a larger cohort of patients with recurrent and nonrecurrent prostate cancer.
resultsIn all, 24,037 unique peptides (false discovery rate < 1%) corresponding to 3,313 distinct proteins were identified with absolute abundance ranges spanning seven orders of magnitude. Of these proteins, 115 showed significantly (p < 0.01) different levels in tissues from recurrent versus nonrecurrent cancers. Analysis of all differentially expressed proteins in recurrent and nonrecurrent cases identified several protein networks, most prominently one in which approximately 24% of the proteins in the network were regulated by the YY1 transcription factor (adjusted p < 0.001). Strong immunohistochemical staining levels of three differentially expressed proteins, POSTN, CALR, and CTSD, on a tissue microarray validated their association with shorter patient survival.
conclusionsThe protein signatures identified could improve understanding of the molecular drivers of aggressive prostate cancer and be used as candidate prognostic biomarkers.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.