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Capaci V, Kharrat F, Conti A, Salviati E, Basilicata MG, Campiglia P, Balasan N, Licastro D, Caponnetto F, Beltrami AP, Monasta L, Romano F, Di Lorenzo G, Ricci G, Ura B. The Deep Proteomics Approach Identified Extracellular Vesicular Proteins Correlated to Extracellular Matrix in Type One and Two Endometrial Cancer. Int J Mol Sci 2024; 25:4650. [PMID: 38731868 PMCID: PMC11083465 DOI: 10.3390/ijms25094650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 04/19/2024] [Accepted: 04/20/2024] [Indexed: 05/13/2024] Open
Abstract
Among gynecological cancers, endometrial cancer is the most common in developed countries. Extracellular vesicles (EVs) are cell-derived membrane-surrounded vesicles that contain proteins involved in immune response and apoptosis. A deep proteomic approach can help to identify dysregulated extracellular matrix (ECM) proteins in EVs correlated to key pathways for tumor development. In this study, we used a proteomics approach correlating the two acquisitions-data-dependent acquisition (DDA) and data-independent acquisition (DIA)-on EVs from the conditioned medium of four cell lines identifying 428 ECM proteins. After protein quantification and statistical analysis, we found significant changes in the abundance (p < 0.05) of 67 proteins. Our bioinformatic analysis identified 26 pathways associated with the ECM. Western blotting analysis on 13 patients with type 1 and type 2 EC and 13 endometrial samples confirmed an altered abundance of MMP2. Our proteomics analysis identified the dysregulated ECM proteins involved in cancer growth. Our data can open the path to other studies for understanding the interaction among cancer cells and the rearrangement of the ECM.
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Affiliation(s)
- Valeria Capaci
- Institute for Maternal and Child Health—IRCCS Burlo Garofolo, 65/1 Via dell’Istria, 34137 Trieste, Italy (F.K.); (A.C.); (N.B.); (F.R.); (G.D.L.); (G.R.); (B.U.)
| | - Feras Kharrat
- Institute for Maternal and Child Health—IRCCS Burlo Garofolo, 65/1 Via dell’Istria, 34137 Trieste, Italy (F.K.); (A.C.); (N.B.); (F.R.); (G.D.L.); (G.R.); (B.U.)
| | - Andrea Conti
- Institute for Maternal and Child Health—IRCCS Burlo Garofolo, 65/1 Via dell’Istria, 34137 Trieste, Italy (F.K.); (A.C.); (N.B.); (F.R.); (G.D.L.); (G.R.); (B.U.)
| | - Emanuela Salviati
- Department of Pharmacy, University of Salerno, 84084 Salerno, Italy; (E.S.); (P.C.)
| | - Manuela Giovanna Basilicata
- Department of Advanced Medical and Surgical Sciences, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy;
| | - Pietro Campiglia
- Department of Pharmacy, University of Salerno, 84084 Salerno, Italy; (E.S.); (P.C.)
| | - Nour Balasan
- Institute for Maternal and Child Health—IRCCS Burlo Garofolo, 65/1 Via dell’Istria, 34137 Trieste, Italy (F.K.); (A.C.); (N.B.); (F.R.); (G.D.L.); (G.R.); (B.U.)
| | | | - Federica Caponnetto
- Department of Medicine, University of Udine, 33100 Udine, Italy; (F.C.); (A.P.B.)
| | - Antonio Paolo Beltrami
- Department of Medicine, University of Udine, 33100 Udine, Italy; (F.C.); (A.P.B.)
- Azienda Sanitaria Universitaria Friuli Centrale, 33100 Udine, Italy
| | - Lorenzo Monasta
- Institute for Maternal and Child Health—IRCCS Burlo Garofolo, 65/1 Via dell’Istria, 34137 Trieste, Italy (F.K.); (A.C.); (N.B.); (F.R.); (G.D.L.); (G.R.); (B.U.)
| | - Federico Romano
- Institute for Maternal and Child Health—IRCCS Burlo Garofolo, 65/1 Via dell’Istria, 34137 Trieste, Italy (F.K.); (A.C.); (N.B.); (F.R.); (G.D.L.); (G.R.); (B.U.)
| | - Giovanni Di Lorenzo
- Institute for Maternal and Child Health—IRCCS Burlo Garofolo, 65/1 Via dell’Istria, 34137 Trieste, Italy (F.K.); (A.C.); (N.B.); (F.R.); (G.D.L.); (G.R.); (B.U.)
| | - Giuseppe Ricci
- Institute for Maternal and Child Health—IRCCS Burlo Garofolo, 65/1 Via dell’Istria, 34137 Trieste, Italy (F.K.); (A.C.); (N.B.); (F.R.); (G.D.L.); (G.R.); (B.U.)
- Department of Medicine, Surgery and Health Sciences, University of Trieste, 34149 Trieste, Italy
| | - Blendi Ura
- Institute for Maternal and Child Health—IRCCS Burlo Garofolo, 65/1 Via dell’Istria, 34137 Trieste, Italy (F.K.); (A.C.); (N.B.); (F.R.); (G.D.L.); (G.R.); (B.U.)
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Capaci V, Arrigoni G, Monasta L, Aloisio M, Rocca G, Di Lorenzo G, Licastro D, Romano F, Ricci G, Ura B. Phospho-DIGE Identified Phosphoproteins Involved in Pathways Related to Tumour Growth in Endometrial Cancer. Int J Mol Sci 2023; 24:11987. [PMID: 37569364 PMCID: PMC10419128 DOI: 10.3390/ijms241511987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 07/21/2023] [Accepted: 07/24/2023] [Indexed: 08/13/2023] Open
Abstract
Endometrial cancer (EC) is the most common gynecologic malignancy of the endometrium. This study focuses on EC and normal endometrium phosphoproteome to identify differentially phosphorylated proteins involved in tumorigenic signalling pathways which induce cancer growth. We obtained tissue samples from 8 types I EC at tumour stage 1 and 8 normal endometria. We analyzed the phosphoproteome by two-dimensional differential gel electrophoresis (2D-DIGE), combined with immobilized metal affinity chromatography (IMAC) and mass spectrometry for protein and phosphopeptide identification. Quantities of 34 phosphoproteins enriched by the IMAC approach were significantly different in the EC compared to the endometrium. Validation using Western blotting analysis on 13 patients with type I EC at tumour stage 1 and 13 endometria samples confirmed the altered abundance of HBB, CKB, LDHB, and HSPB1. Three EC samples were used for in-depth identification of phosphoproteins by LC-MS/MS analysis. Bioinformatic analysis revealed several tumorigenic signalling pathways. Our study highlights the involvement of the phosphoproteome in EC tumour growth. Further studies are needed to understand the role of phosphorylation in EC. Our data shed light on mechanisms that still need to be ascertained but could open the path to a new class of drugs that could hinder EC growth.
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Affiliation(s)
- Valeria Capaci
- Institute for Maternal and Child Health, IRCCS Burlo Garofolo, 34137 Trieste, Italy; (V.C.); (M.A.); (G.D.L.); (F.R.); (G.R.); (B.U.)
| | - Giorgio Arrigoni
- Department of Biomedical Sciences, University of Padova, 35131 Padova, Italy; (G.A.); (G.R.)
- Proteomics Center, University of Padova and Azienda Ospedaliera di Padova, 35131 Padova, Italy
- CRIBI Biotechnology Center, University of Padova, 35131 Padova, Italy
| | - Lorenzo Monasta
- Institute for Maternal and Child Health, IRCCS Burlo Garofolo, 34137 Trieste, Italy; (V.C.); (M.A.); (G.D.L.); (F.R.); (G.R.); (B.U.)
| | - Michelangelo Aloisio
- Institute for Maternal and Child Health, IRCCS Burlo Garofolo, 34137 Trieste, Italy; (V.C.); (M.A.); (G.D.L.); (F.R.); (G.R.); (B.U.)
| | - Giulia Rocca
- Department of Biomedical Sciences, University of Padova, 35131 Padova, Italy; (G.A.); (G.R.)
- Proteomics Center, University of Padova and Azienda Ospedaliera di Padova, 35131 Padova, Italy
| | - Giovanni Di Lorenzo
- Institute for Maternal and Child Health, IRCCS Burlo Garofolo, 34137 Trieste, Italy; (V.C.); (M.A.); (G.D.L.); (F.R.); (G.R.); (B.U.)
| | | | - Federico Romano
- Institute for Maternal and Child Health, IRCCS Burlo Garofolo, 34137 Trieste, Italy; (V.C.); (M.A.); (G.D.L.); (F.R.); (G.R.); (B.U.)
| | - Giuseppe Ricci
- Institute for Maternal and Child Health, IRCCS Burlo Garofolo, 34137 Trieste, Italy; (V.C.); (M.A.); (G.D.L.); (F.R.); (G.R.); (B.U.)
- Department of Medicine, Surgery and Health Sciences, University of Trieste, 34129 Trieste, Italy
| | - Blendi Ura
- Institute for Maternal and Child Health, IRCCS Burlo Garofolo, 34137 Trieste, Italy; (V.C.); (M.A.); (G.D.L.); (F.R.); (G.R.); (B.U.)
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Boschetti E, Righetti PG. Low-Abundance Protein Enrichment for Medical Applications: The Involvement of Combinatorial Peptide Library Technique. Int J Mol Sci 2023; 24:10329. [PMID: 37373476 DOI: 10.3390/ijms241210329] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 06/09/2023] [Accepted: 06/16/2023] [Indexed: 06/29/2023] Open
Abstract
The discovery of low- and very low-abundance proteins in medical applications is considered a key success factor in various important domains. To reach this category of proteins, it is essential to adopt procedures consisting of the selective enrichment of species that are present at extremely low concentrations. In the past few years pathways towards this objective have been proposed. In this review, a general landscape of the enrichment technology situation is made first with the presentation and the use of combinatorial peptide libraries. Then, a description of this peculiar technology for the identification of early-stage biomarkers for well-known pathologies with concrete examples is given. In another field of medical applications, the determination of host cell protein traces potentially present in recombinant therapeutic proteins, such as antibodies, is discussed along with their potentially deleterious effects on the health of patients on the one hand, and on the stability of these biodrugs on the other hand. Various additional applications of medical interest are disclosed for biological fluids investigations where the target proteins are present at very low concentrations (e.g., protein allergens).
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Romano A, Rižner TL, Werner HMJ, Semczuk A, Lowy C, Schröder C, Griesbeck A, Adamski J, Fishman D, Tokarz J. Endometrial cancer diagnostic and prognostic algorithms based on proteomics, metabolomics, and clinical data: a systematic review. Front Oncol 2023; 13:1120178. [PMID: 37091170 PMCID: PMC10118013 DOI: 10.3389/fonc.2023.1120178] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 03/06/2023] [Indexed: 04/09/2023] Open
Abstract
Endometrial cancer is the most common gynaecological malignancy in developed countries. Over 382,000 new cases were diagnosed worldwide in 2018, and its incidence and mortality are constantly rising due to longer life expectancy and life style factors including obesity. Two major improvements are needed in the management of patients with endometrial cancer, i.e., the development of non/minimally invasive tools for diagnostics and prognostics, which are currently missing. Diagnostic tools are needed to manage the increasing number of women at risk of developing the disease. Prognostic tools are necessary to stratify patients according to their risk of recurrence pre-preoperatively, to advise and plan the most appropriate treatment and avoid over/under-treatment. Biomarkers derived from proteomics and metabolomics, especially when derived from non/minimally-invasively collected body fluids, can serve to develop such prognostic and diagnostic tools, and the purpose of the present review is to explore the current research in this topic. We first provide a brief description of the technologies, the computational pipelines for data analyses and then we provide a systematic review of all published studies using proteomics and/or metabolomics for diagnostic and prognostic biomarker discovery in endometrial cancer. Finally, conclusions and recommendations for future studies are also given.
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Affiliation(s)
- Andrea Romano
- Department of Gynaecology, Maastricht University Medical Centre (MUMC), Maastricht, Netherlands
- GROW – School for Oncology and Reproduction, Maastricht University, Maastricht, Netherlands
- *Correspondence: Andrea Romano, ; Tea Lanišnik Rižner,
| | - Tea Lanišnik Rižner
- Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
- *Correspondence: Andrea Romano, ; Tea Lanišnik Rižner,
| | - Henrica Maria Johanna Werner
- Department of Gynaecology, Maastricht University Medical Centre (MUMC), Maastricht, Netherlands
- GROW – School for Oncology and Reproduction, Maastricht University, Maastricht, Netherlands
| | - Andrzej Semczuk
- Department of Gynaecology, Lublin Medical University, Lublin, Poland
| | | | | | | | - Jerzy Adamski
- Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Dmytro Fishman
- Institute of Computer Science, University of Tartu, Tartu, Estonia
- Quretec Ltd., Tartu, Estonia
| | - Janina Tokarz
- Institute for Diabetes and Cancer, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
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Paleari L. New Strategies for Endometrial Cancer Detection and Management. Int J Mol Sci 2023; 24:ijms24076462. [PMID: 37047434 PMCID: PMC10094696 DOI: 10.3390/ijms24076462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 03/27/2023] [Indexed: 04/01/2023] Open
Abstract
With 400,000 new cases and over 80,000 deaths a year worldwide, endometrial cancer (EC) holds a rather unfortunate record, namely, that of the tumour with the highest increase in incidence, a unique trend among gynaecological cancers [...]
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Abstract
Two-dimensional difference gel electrophoresis (2D-DIGE) is an elegant gel electrophoretic analytical tool for comparative protein assessment. It is based on two-dimensional gel electrophoresis (2D-GE) separation of fluorescently labeled protein extracts. The tagging procedures are designed to not interfere with the chemical properties of proteins with respect to their pI and electrophoretic mobility, once a proper labeling protocol is followed. The use of an internal pooled standard makes 2D-DIGE a highly accurate quantitative method enabling multiple protein samples to be separated on the same two-dimensional gel. Technical limitations of this technique (i.e., underrating of low abundant, high molecular mass and integral membrane proteins) are counterbalanced by the incomparable separation power which allows proteoforms and unknown PTM (posttranslational modification) identification. Moreover, the image matching and cross-gel statistical analysis generates robust quantitative results making data validation by independent technologies successful.
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Affiliation(s)
- Cecilia Gelfi
- Department of Biomedical Sciences for Health, University of Milan, Segrate, Italy
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | - Daniele Capitanio
- Department of Biomedical Sciences for Health, University of Milan, Segrate, Italy.
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A Label-Free Proteomic Approach for the Identification of Biomarkers in the Exosome of Endometrial Cancer Serum. Cancers (Basel) 2022; 14:cancers14246262. [PMID: 36551747 PMCID: PMC9776976 DOI: 10.3390/cancers14246262] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 11/14/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022] Open
Abstract
Endometrial cancers (ECs) are mostly adenocarcinomas arising from the inner part of the uterus. The identification of serum biomarkers, either soluble or carried in the exosome, may be useful in making an early diagnosis. We used label-free quantification mass spectrometry (LFQ-MS)-based proteomics to investigate the proteome of exosomes in the albumin-depleted serum from 12 patients with EC, as compared to 12 healthy controls. After quantification and statistical analysis, we found significant changes in the abundance (p < 0.05) of 33 proteins in EC vs. control samples, with a fold change of ≥1.5 or ≤0.6. Validation using Western blotting analysis in 36 patients with EC as compared to 36 healthy individuals confirmed the upregulation of APOA1, HBB, CA1, HBD, LPA, SAA4, PF4V1, and APOE. A multivariate logistic regression model based on the abundance of these proteins was able to separate the controls from the EC patients with excellent sensitivity levels, particularly for stage 1 ECs. The results show that using LFQ-MS to explore the specific proteome of serum exosomes allows for the identification of biomarkers in EC. These observations suggest that PF4V1, CA1, HBD, and APOE represent biomarkers that are able to reach the clinical stage, after a validation phase.
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Ura B, Capaci V, Aloisio M, Di Lorenzo G, Romano F, Ricci G, Monasta L. A Targeted Proteomics Approach for Screening Serum Biomarkers Observed in the Early Stage of Type I Endometrial Cancer. Biomedicines 2022; 10:biomedicines10081857. [PMID: 36009404 PMCID: PMC9405144 DOI: 10.3390/biomedicines10081857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 07/22/2022] [Accepted: 07/28/2022] [Indexed: 11/16/2022] Open
Abstract
Endometrial cancer (EC) is the most common gynecologic malignancy, and it arises in the inner part of the uterus. Identification of serum biomarkers is essential for diagnosing the disease at an early stage. In this study, we selected 44 healthy controls and 44 type I EC at tumor stage 1, and we used the Immuno-oncology panel and the Target 96 Oncology III panel to simultaneously detect the levels of 92 cancer-related proteins in serum, using a proximity extension assay. By applying this methodology, we identified 20 proteins, associated with the outcome at binary logistic regression, with a p-value below 0.01 for the first panel and 24 proteins with a p-value below 0.02 for the second one. The final multivariate logistic regression model, combining proteins from the two panels, generated a model with a sensitivity of 97.67% and a specificity of 83.72%. These results support the use of the proposed algorithm after a validation phase.
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Affiliation(s)
- Blendi Ura
- Institute for Maternal and Child Health—IRCCS Burlo Garofolo, 34137 Trieste, Italy; (V.C.); (M.A.); (G.D.L.); (F.R.); (G.R.); (L.M.)
- Correspondence:
| | - Valeria Capaci
- Institute for Maternal and Child Health—IRCCS Burlo Garofolo, 34137 Trieste, Italy; (V.C.); (M.A.); (G.D.L.); (F.R.); (G.R.); (L.M.)
| | - Michelangelo Aloisio
- Institute for Maternal and Child Health—IRCCS Burlo Garofolo, 34137 Trieste, Italy; (V.C.); (M.A.); (G.D.L.); (F.R.); (G.R.); (L.M.)
| | - Giovanni Di Lorenzo
- Institute for Maternal and Child Health—IRCCS Burlo Garofolo, 34137 Trieste, Italy; (V.C.); (M.A.); (G.D.L.); (F.R.); (G.R.); (L.M.)
| | - Federico Romano
- Institute for Maternal and Child Health—IRCCS Burlo Garofolo, 34137 Trieste, Italy; (V.C.); (M.A.); (G.D.L.); (F.R.); (G.R.); (L.M.)
| | - Giuseppe Ricci
- Institute for Maternal and Child Health—IRCCS Burlo Garofolo, 34137 Trieste, Italy; (V.C.); (M.A.); (G.D.L.); (F.R.); (G.R.); (L.M.)
- Department of Medicine, Surgery and Health Sciences, University of Trieste, 34129 Trieste, Italy
| | - Lorenzo Monasta
- Institute for Maternal and Child Health—IRCCS Burlo Garofolo, 34137 Trieste, Italy; (V.C.); (M.A.); (G.D.L.); (F.R.); (G.R.); (L.M.)
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