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Next-Generation Sequencing Approaches for the Study of Hereditary Tumors

A special issue of Current Issues in Molecular Biology (ISSN 1467-3045). This special issue belongs to the section "Molecular Medicine".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 713

Special Issue Editor


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Guest Editor
Biosciences Laboratory, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) “Dino Amadori”, 47014 Meldola, Italy
Interests: hereditary cancer; next-generation sequencing; cancer genetics; cancer epigenetics; molecular characterization

Special Issue Information

Dear Colleagues,

The global increase in cancer incidence and mortality is due to many reasons, in particular to environmental and behavioral factors associated with socioeconomic development. However, genetics is one of the main factors that can influence the development of cancer. Germline mutations in highly penetrant cancer susceptibility genes are observed in 5%–10% of all cancers. The main genetic syndromes are characterized by an increased risk of developing breast, ovarian, colorectal, gastric, pancreatic, melanoma, endometrial and prostate cancers; however, there are also other rare disorders linked to a predisposition to rare tumors. Hereditary cancers have been studied for a long time using different techniques. Traditional methods for the detection of mutations in cancer predisposition genes have been combined over time with new molecular techniques, such as next-generation sequencing. As a result of these advances, the number of genes involved in cancer predisposition has dramatically increased in recent years, highlighting the need for faster and more accurate methodologies. The aim of this collection is to provide an overview of the different molecular approaches that can be used in the field of research and diagnostics to study different forms of hereditary cancer.

Dr. Gianluca Tedaldi
Guest Editor

Manuscript Submission Information

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Keywords

  • hereditary tumors
  • cancer predisposition
  • next-generation sequencing
  • cancer genetics
  • cancer risk

Published Papers (1 paper)

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Review

15 pages, 1257 KiB  
Review
Radiogenomics and Texture Analysis to Detect von Hippel–Lindau (VHL) Mutation in Clear Cell Renal Cell Carcinoma
by Federico Greco, Valerio D’Andrea, Bruno Beomonte Zobel and Carlo Augusto Mallio
Curr. Issues Mol. Biol. 2024, 46(4), 3236-3250; https://doi.org/10.3390/cimb46040203 - 08 Apr 2024
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Abstract
Radiogenomics, a burgeoning field in biomedical research, explores the correlation between imaging features and genomic data, aiming to link macroscopic manifestations with molecular characteristics. In this review, we examine existing radiogenomics literature in clear cell renal cell carcinoma (ccRCC), the predominant renal cancer, [...] Read more.
Radiogenomics, a burgeoning field in biomedical research, explores the correlation between imaging features and genomic data, aiming to link macroscopic manifestations with molecular characteristics. In this review, we examine existing radiogenomics literature in clear cell renal cell carcinoma (ccRCC), the predominant renal cancer, and von Hippel–Lindau (VHL) gene mutation, the most frequent genetic mutation in ccRCC. A thorough examination of the literature was conducted through searches on the PubMed, Medline, Cochrane Library, Google Scholar, and Web of Science databases. Inclusion criteria encompassed articles published in English between 2014 and 2022, resulting in 10 articles meeting the criteria out of 39 initially retrieved articles. Most of these studies applied computed tomography (CT) images obtained from open source and institutional databases. This literature review investigates the role of radiogenomics, with and without texture analysis, in predicting VHL gene mutation in ccRCC patients. Radiogenomics leverages imaging modalities such as CT and magnetic resonance imaging (MRI), to analyze macroscopic features and establish connections with molecular elements, providing insights into tumor heterogeneity and biological behavior. The investigations explored diverse mutations, with a specific focus on VHL mutation, and applied CT imaging features for radiogenomic analysis. Moreover, radiomics and machine learning techniques were employed to predict VHL gene mutations based on CT features, demonstrating promising results. Additional studies delved into the relationship between VHL mutation and body composition, revealing significant associations with adipose tissue distribution. The review concludes by highlighting the potential role of radiogenomics in guiding targeted and selective therapies. Full article
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