Serum Mass Spectrometry Proteomics and Protein Set Identification in Response to FOLFOX-4 in Drug-Resistant Ovarian Carcinoma.

FOLFOX-4 cancer molecular pathways mass spectrometry proteomics network enrichment analysis ovarian cancer protein panel serum samples time lapse detection

Journal

Cancers
ISSN: 2072-6694
Titre abrégé: Cancers (Basel)
Pays: Switzerland
ID NLM: 101526829

Informations de publication

Date de publication:
08 Jan 2023
Historique:
received: 07 12 2022
revised: 30 12 2022
accepted: 04 01 2023
entrez: 21 1 2023
pubmed: 22 1 2023
medline: 22 1 2023
Statut: epublish

Résumé

Ovarian cancer is a highly lethal gynecological malignancy. Drug resistance rapidly occurs, and different therapeutic approaches are needed. So far, no biomarkers have been discovered to predict early response to therapies in the case of multi-treated ovarian cancer patients. The aim of our investigation was to identify a protein panel and the molecular pathways involved in chemotherapy response through a combination of studying proteomics and network enrichment analysis by considering a subset of samples from a clinical setting. Differential mass spectrometry studies were performed on 14 serum samples from patients with heavily pretreated platinum-resistant ovarian cancer who received the FOLFOX-4 regimen as a salvage therapy. The serum was analyzed at baseline time (T0) before FOLFOX-4 treatment, and before the second cycle of treatment (T1), with the aim of understanding if it was possible, after a first treatment cycle, to detect significant proteome changes that could be associated with patients responses to therapy. A total of 291 shared expressed proteins was identified and 12 proteins were finally selected between patients who attained partial response or no-response to chemotherapy when both response to therapy and time dependence (T0, T1) were considered in the statistical analysis. The protein panel included APOL1, GSN, GFI1, LCATL, MNA, LYVE1, ROR1, SHBG, SOD3, TEC, VPS18, and ZNF573. Using a bioinformatics network enrichment approach and metanalysis study, relationships between serum and cellular proteins were identified. An analysis of protein networks was conducted and identified at least three biological processes with functional and therapeutic significance in ovarian cancer, including lipoproteins metabolic process, structural component modulation in relation to cellular apoptosis and autophagy, and cellular oxidative stress response. Five proteins were almost independent from the network (LYVE1, ROR1, TEC, GFI1, and ZNF573). All proteins were associated with response to drug-resistant ovarian cancer resistant and were mechanistically connected to the pathways associated with cancer arrest. These results can be the basis for extending a biomarker discovery process to a clinical trial, as an early predictive tool of chemo-response to FOLFOX-4 of heavily treated ovarian cancer patients and for supporting the oncologist to continue or to interrupt the therapy.

Identifiants

pubmed: 36672361
pii: cancers15020412
doi: 10.3390/cancers15020412
pmc: PMC9856519
pii:
doi:

Types de publication

Journal Article

Langues

eng

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Auteurs

Domenico D'Arca (D)

Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy.

Leda Severi (L)

Department Life Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125 Modena, Italy.

Stefania Ferrari (S)

Department Life Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125 Modena, Italy.

Luca Dozza (L)

Seràgnoli Institute of Hematology, Department of Experimental, Diagnostic and Specialty Medicine, Bologna University School of Medicine, S. Orsola Malpighi Hospital, 40138 Bologna, Italy.

Gaetano Marverti (G)

Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy.

Fulvio Magni (F)

Department of Medicine and Surgery, Clinical Proteomics and Metabolomics Unit, University of Milano-Bicocca, 20126 Vedano al Lambro, Italy.

Clizia Chinello (C)

Department of Medicine and Surgery, Clinical Proteomics and Metabolomics Unit, University of Milano-Bicocca, 20126 Vedano al Lambro, Italy.

Lisa Pagani (L)

Department of Medicine and Surgery, Clinical Proteomics and Metabolomics Unit, University of Milano-Bicocca, 20126 Vedano al Lambro, Italy.

Lorenzo Tagliazucchi (L)

Department Life Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125 Modena, Italy.
Clinical and Experimental Medicine (CEM) Doctorate School, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy.

Marco Villani (M)

Department of Physics, Informatics and Mathematics, Modena and Reggio Emilia University, Via Campi 213/A, 41125 Modena, Italy.

Gianluca d'Addese (G)

Department of Physics, Informatics and Mathematics, Modena and Reggio Emilia University, Via Campi 213/A, 41125 Modena, Italy.

Isabella Piga (I)

Department of Medicine and Surgery, Clinical Proteomics and Metabolomics Unit, University of Milano-Bicocca, 20126 Vedano al Lambro, Italy.

Vincenza Conteduca (V)

IRCCS Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST), 47014 Meldola, Italy.

Lorena Rossi (L)

IRCCS Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST), 47014 Meldola, Italy.

Giorgia Gurioli (G)

IRCCS Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST), 47014 Meldola, Italy.

Ugo De Giorgi (U)

IRCCS Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST), 47014 Meldola, Italy.

Lorena Losi (L)

Department Life Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125 Modena, Italy.

Maria Paola Costi (MP)

Department Life Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125 Modena, Italy.

Classifications MeSH