Prediction of Melanoma
A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Causes, Screening and Diagnosis".
Deadline for manuscript submissions: 31 January 2026 | Viewed by 1865
Special Issue Editor
Special Issue Information
Dear Colleagues,
In recent years, the management of melanoma has undergone profound changes. Targeted therapy, immunotherapy and combinations of these have revolutionized neo-adjuvant, adjuvant therapy, and the treatment of advanced disease. The roller coaster of therapeutics is gaining new speeds, as trials of personalized mRNA vaccines combined with immunotherapy show promising results in reducing the risk of recurrence, while adoptive cell therapy with tumor-infiltrating lymphocytes (TILs) achieves durable responses in advanced disease. Despite these new and exciting advances that set the paradigm for cancer treatment, melanoma remains the most lethal skin malignancy, accounting for tens thousands of deaths each year. What is more, new treatment modalities come at a substantial financial cost and considerable potential toxicities. It is therefore clinically relevant to enhance our ability to predict disease occurrence and progression and offer a tailor-made approach to our patients.
In this regard, the search for methods improving melanoma prediction and for the discovery and validation of new biomarkers is constantly ongoing. AI-based techniques are being incorporated in non-invasive imaging tools, facilitating prediction through reconstructed deep convolutional neural network (CNN) architecture and optimized algorithms. Some of these models use pathological data from dermatoscopic images and incorporate individual patient characteristics for better prediction. Risk prediction tools for melanoma development, recurrence, progression, therapeutic outcome, and survival are already publicly available. Data from genome-wide association studies (GWASs) and whole-exome sequencing studies (WESs) unravel genetic loci conferring genetic susceptibility, enhancing the effort to stratify disease risk. In addition, transcriptome-wide association studies (TWASs) help to identify prognostic markers for tumorigenesis and progression, while the use of gene expression profiling (GEP) in melanoma is still debated. Circulating tumor DNA (ctDNA) is an emerging biomarker that can serve as a noninvasive liquid biopsy for the evaluation of treatment response and the early detection of disease recurrence. Recently, a novel prognostic model in melanoma based on chemokines-related gene signatures has been developed.
Further research and validation of these new developments are required before they are implemented into routine clinical practice.
Dr. Irene Stefanaki
Guest Editor
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Keywords
- melanoma
- melanoma genetics
- biomarkers
- predictive tools
- risk prediction
- risk factors
- artificial intelligence
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