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Article

The Deep Proteomics Approach Identified Extracellular Vesicular Proteins Correlated to Extracellular Matrix in Type One and Two Endometrial Cancer

by
Valeria Capaci
1,†,
Feras Kharrat
1,†,
Andrea Conti
1,
Emanuela Salviati
2,
Manuela Giovanna Basilicata
3,
Pietro Campiglia
2,
Nour Balasan
1,
Danilo Licastro
4,
Federica Caponnetto
5,
Antonio Paolo Beltrami
5,6,
Lorenzo Monasta
1,*,
Federico Romano
1,
Giovanni Di Lorenzo
1,
Giuseppe Ricci
1,7 and
Blendi Ura
1
1
Institute for Maternal and Child Health—IRCCS Burlo Garofolo, 65/1 Via dell’Istria, 34137 Trieste, Italy
2
Department of Pharmacy, University of Salerno, 84084 Salerno, Italy
3
Department of Advanced Medical and Surgical Sciences, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy
4
AREA Science Park, Basovizza, 34149 Trieste, Italy
5
Department of Medicine, University of Udine, 33100 Udine, Italy
6
Azienda Sanitaria Universitaria Friuli Centrale, 33100 Udine, Italy
7
Department of Medicine, Surgery and Health Sciences, University of Trieste, 34149 Trieste, Italy
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Int. J. Mol. Sci. 2024, 25(9), 4650; https://doi.org/10.3390/ijms25094650
Submission received: 7 March 2024 / Revised: 19 April 2024 / Accepted: 20 April 2024 / Published: 24 April 2024
(This article belongs to the Special Issue Integration of Meta-Multi-Omics Data: Unraveling Impact and Therapeutic Potential)
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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.
Keywords: proteomics; mass spectrometry; extracellular matrix proteomics; mass spectrometry; extracellular matrix

Share and Cite

MDPI and ACS Style

Capaci, V.; Kharrat, F.; Conti, A.; Salviati, E.; Basilicata, M.G.; Campiglia, P.; Balasan, N.; Licastro, D.; Caponnetto, F.; Beltrami, A.P.; et al. 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. https://doi.org/10.3390/ijms25094650

AMA Style

Capaci V, Kharrat F, Conti A, Salviati E, Basilicata MG, Campiglia P, Balasan N, Licastro D, Caponnetto F, Beltrami AP, et al. The Deep Proteomics Approach Identified Extracellular Vesicular Proteins Correlated to Extracellular Matrix in Type One and Two Endometrial Cancer. International Journal of Molecular Sciences. 2024; 25(9):4650. https://doi.org/10.3390/ijms25094650

Chicago/Turabian Style

Capaci, Valeria, Feras Kharrat, Andrea Conti, Emanuela Salviati, Manuela Giovanna Basilicata, Pietro Campiglia, Nour Balasan, Danilo Licastro, Federica Caponnetto, Antonio Paolo Beltrami, and et al. 2024. "The Deep Proteomics Approach Identified Extracellular Vesicular Proteins Correlated to Extracellular Matrix in Type One and Two Endometrial Cancer" International Journal of Molecular Sciences 25, no. 9: 4650. https://doi.org/10.3390/ijms25094650

APA Style

Capaci, V., Kharrat, F., Conti, A., Salviati, E., Basilicata, M. G., Campiglia, P., Balasan, N., Licastro, D., Caponnetto, F., Beltrami, A. P., Monasta, L., Romano, F., Di Lorenzo, G., Ricci, G., & Ura, B. (2024). The Deep Proteomics Approach Identified Extracellular Vesicular Proteins Correlated to Extracellular Matrix in Type One and Two Endometrial Cancer. International Journal of Molecular Sciences, 25(9), 4650. https://doi.org/10.3390/ijms25094650

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