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Roccuzzo G, Bongiovanni E, Tonella L, Pala V, Marchisio S, Ricci A, Senetta R, Bertero L, Ribero S, Berrino E, Marchiò C, Sapino A, Quaglino P, Cassoni P. Emerging prognostic biomarkers in advanced cutaneous melanoma: a literature update. Expert Rev Mol Diagn 2024; 24:49-66. [PMID: 38334382 DOI: 10.1080/14737159.2024.2314574] [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: 08/05/2023] [Accepted: 02/01/2024] [Indexed: 02/10/2024]
Abstract
INTRODUCTION Over the past two years, the scientific community has witnessed an exponential growth in research focused on identifying prognostic biomarkers for melanoma, both in pre-clinical and clinical settings. This surge in studies reflects the need of developing effective prognostic indicators in the field of melanoma. AREAS COVERED The aim of this work is to review the scientific literature on the most recent findings on the development or validation of prognostic biomarkers in melanoma, in the attempt of providing both clinicians and researchers with an updated broad synopsis of prognostic biomarkers in cutaneous melanoma. EXPERT OPINION While the field of prognostic biomarkers in melanoma appears promising, there are several complexities and limitations to address. The interdependence of clinical, histological, and molecular features requires accurate classification of different biomarker families. Correlation does not imply causation, and adjustments for confounding factors are often overlooked. In this scenario, large-scale studies based on high-quality clinical trial data can provide more reliable evidence. It is essential to avoid oversimplification by focusing on a single biomarker, as the interactions among multiple factors contribute to define the disease course and patient's outcome. Furthermore, implementing well-supported evidence in real-life settings can help advance prognostic biomarker research in melanoma.
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Affiliation(s)
- Gabriele Roccuzzo
- Department of Medical Sciences, Section of Dermatology, University of Turin, Turin, Italy
| | - Eleonora Bongiovanni
- Department of Medical Sciences, Section of Dermatology, University of Turin, Turin, Italy
| | - Luca Tonella
- Department of Medical Sciences, Section of Dermatology, University of Turin, Turin, Italy
| | - Valentina Pala
- Department of Medical Sciences, Section of Dermatology, University of Turin, Turin, Italy
| | - Sara Marchisio
- Pathology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Alessia Ricci
- Pathology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Rebecca Senetta
- Pathology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Luca Bertero
- Pathology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Simone Ribero
- Department of Medical Sciences, Section of Dermatology, University of Turin, Turin, Italy
| | - Enrico Berrino
- Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
- Department of Medical Sciences, University of Turin, Turin, Italy
| | - Caterina Marchiò
- Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
- Department of Medical Sciences, University of Turin, Turin, Italy
| | - Anna Sapino
- Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
- Department of Medical Sciences, University of Turin, Turin, Italy
| | - Pietro Quaglino
- Department of Medical Sciences, Section of Dermatology, University of Turin, Turin, Italy
| | - Paola Cassoni
- Pathology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
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Identification of Alternative Splicing in Proteomes of Human Melanoma Cell Lines without RNA Sequencing Data. Int J Mol Sci 2023; 24:ijms24032466. [PMID: 36768787 PMCID: PMC9916885 DOI: 10.3390/ijms24032466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/06/2023] [Accepted: 01/13/2023] [Indexed: 01/31/2023] Open
Abstract
Alternative splicing is one of the main regulation pathways in living cells beyond simple changes in the level of protein expression. Most of the approaches proposed in proteomics for the identification of specific splicing isoforms require a preliminary deep transcriptomic analysis of the sample under study, which is not always available, especially in the case of the re-analysis of previously acquired data. Herein, we developed new algorithms for the identification and validation of protein splice isoforms in proteomic data in the absence of RNA sequencing of the samples under study. The bioinformatic approaches were tested on the results of proteome analysis of human melanoma cell lines, obtained earlier by high-resolution liquid chromatography and mass spectrometry (LC-MS). A search for alternative splicing events for each of the cell lines studied was performed against the database generated from all known transcripts (RefSeq) and the one composed of peptide sequences, which included all biologically possible combinations of exons. The identifications were filtered using the prediction of both retention times and relative intensities of fragment ions in the corresponding mass spectra. The fragmentation mass spectra corresponding to the discovered alternative splicing events were additionally examined for artifacts. Selected splicing events were further validated at the mRNA level by quantitative PCR.
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