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Hamed MA, Wasinger V, Wang Q, Graham P, Malouf D, Bucci J, Li Y. Prostate cancer-derived extracellular vesicles metabolic biomarkers: Emerging roles for diagnosis and prognosis. J Control Release 2024; 371:126-145. [PMID: 38768661 DOI: 10.1016/j.jconrel.2024.05.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 04/23/2024] [Accepted: 05/17/2024] [Indexed: 05/22/2024]
Abstract
Prostate cancer (PCa) is a global health concern, ranking as the most common cancer among men in Western countries. Traditional diagnostic methods are invasive with adverse effects on patients. Due to the heterogeneous nature of PCa and their multifocality, tissue biopsies often yield false-negative results. To address these challenges, researchers are exploring innovative approaches, particularly in the realms of proteomics and metabolomics, to identify more reliable biomarkers and improve PCa diagnosis. Liquid biopsy (LB) has emerged as a promising non-invasive strategy for PCa early detection, biopsy selection, active surveillance for low-risk cases, and post-treatment and progression monitoring. Extracellular vesicles (EVs) are lipid-bilayer nanovesicles released by all cell types and play an important role in intercellular communication. EVs have garnered attention as a valuable biomarker resource in LB for PCa-specific biomarkers, enhancing diagnosis, prognostication, and treatment guidance. Metabolomics provides insight into the body's metabolic response to both internal and external stimuli, offering quantitative measurements of biochemical alterations. It excels at detecting non-genetic influences, aiding in the discovery of more accurate cancer biomarkers for early detection and disease progression monitoring. This review delves into the potential of EVs as a resource for LB in PCa across various clinical applications. It also explores cancer-related metabolic biomarkers, both within and outside EVs in PCa, and summarises previous metabolomic findings in PCa diagnosis and risk assessment. Finally, the article addresses the challenges and future directions in the evolving field of EV-based metabolomic analysis, offering a comprehensive overview of its potential in advancing PCa management.
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Affiliation(s)
- Mahmoud Assem Hamed
- St George and Sutherland Clinical Campuses, School of Clinical Medicine, UNSW Sydney, Kensington, NSW 2052, Australia; Cancer Care Centre, St George Hospital, Kogarah, NSW 2217, Australia
| | - Valerie Wasinger
- Bioanalytical Mass Spectrometry Facility, Mark Wainwright Analytical Centre, UNSW Sydney, Kensington, NSW 2052, Australia
| | - Qi Wang
- St George and Sutherland Clinical Campuses, School of Clinical Medicine, UNSW Sydney, Kensington, NSW 2052, Australia; Cancer Care Centre, St George Hospital, Kogarah, NSW 2217, Australia
| | - Peter Graham
- St George and Sutherland Clinical Campuses, School of Clinical Medicine, UNSW Sydney, Kensington, NSW 2052, Australia; Cancer Care Centre, St George Hospital, Kogarah, NSW 2217, Australia
| | - David Malouf
- Department of Urology, St, George Hospital, Kogarah, NSW 2217, Australia
| | - Joseph Bucci
- St George and Sutherland Clinical Campuses, School of Clinical Medicine, UNSW Sydney, Kensington, NSW 2052, Australia; Cancer Care Centre, St George Hospital, Kogarah, NSW 2217, Australia
| | - Yong Li
- St George and Sutherland Clinical Campuses, School of Clinical Medicine, UNSW Sydney, Kensington, NSW 2052, Australia; Cancer Care Centre, St George Hospital, Kogarah, NSW 2217, Australia.
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Gao Y, Kim H, Kitata RB, Lin TT, Swensen AC, Shi T, Liu T. Multiplexed quantitative proteomics in prostate cancer biomarker development. Adv Cancer Res 2024; 161:31-69. [PMID: 39032952 DOI: 10.1016/bs.acr.2024.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/23/2024]
Abstract
Prostate cancer (PCa) is the most common non-skin cancer among men in the United States. However, the widely used protein biomarker in PCa, prostate-specific antigen (PSA), while useful for initial detection, its use alone cannot detect aggressive PCa and can lead to overtreatment. This chapter provides an overview of PCa protein biomarker development. It reviews the state-of-the-art liquid chromatography-mass spectrometry-based proteomics technologies for PCa biomarker development, such as enhancing the detection sensitivity of low-abundance proteins through antibody-based or antibody-independent protein/peptide enrichment, enriching post-translational modifications such as glycosylation as well as information-rich extracellular vesicles, and increasing accuracy and throughput using advanced data acquisition methodologies. This chapter also summarizes recent PCa biomarker validation studies that applied those techniques in diverse specimen types, including cell lines, tissues, proximal fluids, urine, and blood, developing novel protein biomarkers for various clinical applications, including early detection and diagnosis, prognosis, and therapeutic intervention of PCa.
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Affiliation(s)
- Yuqian Gao
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, United States
| | - Hyeyoon Kim
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, United States
| | - Reta Birhanu Kitata
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, United States
| | - Tai-Tu Lin
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, United States
| | - Adam C Swensen
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, United States
| | - Tujin Shi
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, United States
| | - Tao Liu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, United States.
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Yu Q, Xie T, Zhang Y, Pan T, Tan Y, Qin H, Yan S. Exploration of SERPINA family functions and prognostic value in breast cancer based on transcriptome and in vitro analysis. ENVIRONMENTAL TOXICOLOGY 2024; 39:1951-1967. [PMID: 38069587 DOI: 10.1002/tox.24079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 11/23/2023] [Accepted: 11/28/2023] [Indexed: 03/09/2024]
Abstract
Breast cancer poses a significant risk to women worldwide, yet specific role of SERPINA gene family in breast cancer remains unclarified. Data were collected from online databases. SERPINA family gene expression was presented, and prognosis value was evaluated. Multi-omics methods were employed to explore the SERPINA-related biological processes, followed by comprehensive analyses of their roles in breast cancer. Single-cell data were analyzed to characterize the SERPINA family gene expression in different cell clusters. We selected SERPINA5 as the target gene. Via pan-cancer analysis, SERPINA5 was also investigated in various cancers. The experimental validation was conducted in MDA-MB-231 cell line eventually. SERPINA family showed differential expression in breast cancer, which were mainly expressed in myeloid cells, epithelial cells, and dendritic cells. SERPINA5 expression was upregulated in breast cancer, which was associated with a better prognosis. Immune infiltration illustrated the positive correlativity between SERPINA5 intensity and eosinophilic recruitment. Pan-cancer analysis indicated the function of SERPINA5 as a potential biomarker in other cancers. Finally, experimental validation demonstrated that SERPINA5 contributes to lower invasion and metastatic potential of breast cancer cells. With bioinformatics analysis, the significant role SERPINA family genes functioned in breast cancer was comprehensively explored, with SERPINA5 emerging as a key gene in suppressing breast cancer progression.
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Affiliation(s)
- Qiyi Yu
- School of Life Science and Technology, Jiangsu Key Laboratory of Carcinogenesis and Intervention, School of Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Tianyuan Xie
- School of Life Science and Technology, Jiangsu Key Laboratory of Carcinogenesis and Intervention, School of Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Yidong Zhang
- School of Life Science and Technology, Jiangsu Key Laboratory of Carcinogenesis and Intervention, School of Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Tianyue Pan
- School of Life Science and Technology, Jiangsu Key Laboratory of Carcinogenesis and Intervention, School of Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Yongmei Tan
- School of Life Science and Technology, Jiangsu Key Laboratory of Carcinogenesis and Intervention, School of Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Hai Qin
- Department of Clinical Laboratory, Beijing Jishuitan Hospital Guizhou Hospital, Guiyang, China
| | - Simin Yan
- Department of Pharmacy, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
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Samaržija I. The Potential of Extracellular Matrix- and Integrin Adhesion Complex-Related Molecules for Prostate Cancer Biomarker Discovery. Biomedicines 2023; 12:79. [PMID: 38255186 PMCID: PMC10813710 DOI: 10.3390/biomedicines12010079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 12/16/2023] [Accepted: 12/26/2023] [Indexed: 01/24/2024] Open
Abstract
Prostate cancer is among the top five cancer types according to incidence and mortality. One of the main obstacles in prostate cancer management is the inability to foresee its course, which ranges from slow growth throughout years that requires minimum or no intervention to highly aggressive disease that spreads quickly and resists treatment. Therefore, it is not surprising that numerous studies have attempted to find biomarkers of prostate cancer occurrence, risk stratification, therapy response, and patient outcome. However, only a few prostate cancer biomarkers are used in clinics, which shows how difficult it is to find a novel biomarker. Cell adhesion to the extracellular matrix (ECM) through integrins is among the essential processes that govern its fate. Upon activation and ligation, integrins form multi-protein intracellular structures called integrin adhesion complexes (IACs). In this review article, the focus is put on the biomarker potential of the ECM- and IAC-related molecules stemming from both body fluids and prostate cancer tissue. The processes that they are involved in, such as tumor stiffening, bone turnover, and communication via exosomes, and their biomarker potential are also reviewed.
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Affiliation(s)
- Ivana Samaržija
- Laboratory for Epigenomics, Division of Molecular Medicine, Ruđer Bošković Institute, 10000 Zagreb, Croatia
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Su KYC, Reynolds JA, Reed R, Da Silva R, Kelsall J, Baricevic-Jones I, Lee D, Whetton AD, Geifman N, McHugh N, Bruce IN. Proteomic analysis identifies subgroups of patients with active systemic lupus erythematosus. Clin Proteomics 2023; 20:29. [PMID: 37516862 PMCID: PMC10385905 DOI: 10.1186/s12014-023-09420-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 07/17/2023] [Indexed: 07/31/2023] Open
Abstract
OBJECTIVE Systemic lupus erythematosus (SLE) is a clinically and biologically heterogenous autoimmune disease. We aimed to investigate the plasma proteome of patients with active SLE to identify novel subgroups, or endotypes, of patients. METHOD Plasma was collected from patients with active SLE who were enrolled in the British Isles Lupus Assessment Group Biologics Registry (BILAG-BR). The plasma proteome was analysed using a data-independent acquisition method, Sequential Window Acquisition of All theoretical mass spectra mass spectrometry (SWATH-MS). Unsupervised, data-driven clustering algorithms were used to delineate groups of patients with a shared proteomic profile. RESULTS In 223 patients, six clusters were identified based on quantification of 581 proteins. Between the clusters, there were significant differences in age (p = 0.012) and ethnicity (p = 0.003). There was increased musculoskeletal disease activity in cluster 1 (C1), 19/27 (70.4%) (p = 0.002) and renal activity in cluster 6 (C6) 15/24 (62.5%) (p = 0.051). Anti-SSa/Ro was the only autoantibody that significantly differed between clusters (p = 0.017). C1 was associated with p21-activated kinases (PAK) and Phospholipase C (PLC) signalling. Within C1 there were two sub-clusters (C1A and C1B) defined by 49 proteins related to cytoskeletal protein binding. C2 and C6 demonstrated opposite Rho family GTPase and Rho GDI signalling. Three proteins (MZB1, SND1 and AGL) identified in C6 increased the classification of active renal disease although this did not reach statistical significance (p = 0.0617). CONCLUSIONS Unsupervised proteomic analysis identifies clusters of patients with active SLE, that are associated with clinical and serological features, which may facilitate biomarker discovery. The observed proteomic heterogeneity further supports the need for a personalised approach to treatment in SLE.
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Affiliation(s)
- Kevin Y C Su
- Rheumatology Research Group, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK
- Rheumatology Department, Sandwell and West Birmingham NHS Trust, Birmingham, UK
| | - John A Reynolds
- Rheumatology Research Group, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK.
- Rheumatology Department, Sandwell and West Birmingham NHS Trust, Birmingham, UK.
| | - Rachel Reed
- Stoller Biomarker Discovery Centre, Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Rachael Da Silva
- Stoller Biomarker Discovery Centre, Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Janet Kelsall
- Stoller Biomarker Discovery Centre, Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Ivona Baricevic-Jones
- Stoller Biomarker Discovery Centre, Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - David Lee
- Stoller Biomarker Discovery Centre, Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Anthony D Whetton
- Stoller Biomarker Discovery Centre, Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Nophar Geifman
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Neil McHugh
- Department of Pharmacy and Pharmacology, University of Bath, Bath, UK
| | - Ian N Bruce
- Centre for Epidemiology Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, The University of Manchester, Manchester, UK
- NIHR Manchester Biomedical Research Centre, Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
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