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In the last decade, the development of radiogenomics research has produced a significant amount of papers describing relations between imaging features and several molecular 'omic signatures arising from next-generation sequencing technology and their potential role in the integrated diagnostic field. The most vulnerable point of many of these studies lies in the poor number of involved patients. In this scenario, a leading role is played by The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA), which make available, respectively, molecular 'omic data and linked imaging data. In this review, we systematically collected and analyzed radiogenomic studies based on TCGA-TCIA data. We organized literature per tumor type and molecular 'omic data in order to discuss salient imaging genomic associations and limitations of each study. Finally, we outlined the potential clinical impact of radiogenomics to improve the accuracy of diagnosis and the prediction of patient outcomes in oncology.
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http://dx.doi.org/10.3390/ijms20236033 | DOI Listing |
J Allergy Clin Immunol
September 2025
National Heart and Lung Institute, Imperial College London, London, United Kingdom; Frankland and Kay Allergy Centre, UK NIHR Imperial Biomedical Research Centre, United Kingdom.
Recent advancements in genomics and "omic" technologies have ushered in a transformative era referred to as personalized or precision medicine. This innovative approach considers the unique genetic profiles of individuals, along with a range of variability factors, to devise tailored disease treatments and prevention strategies that cater to the distinct needs of each patient. Although the terms personalized medicine and precision medicine are frequently utilized interchangeably, it is essential to delineate the subtle distinctions between them.
View Article and Find Full Text PDFAn exciting feature of nanopore sequencing is its ability to record multi-omic information on the same sequenced DNA molecule. Well-trained models allow the detection of nucleotide-specific molecular signatures through changes in ionic current as DNA molecules translocate through the nanopore. Thus, naturally occurring DNA modifications, such as DNA methylation and hydroxymethylation, may be recorded simultaneously with the genetic sequence.
View Article and Find Full Text PDFWorld J Gastroenterol
August 2025
Department of Spine Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an 710054, Shaanxi Province, China.
Dyslipidemia, a complex disorder characterized by systemic lipid profile abnormalities, affects more than half of adults globally and constitutes a major modifiable risk factor for atherosclerotic cardiovascular disease. Mounting evidence has established the gut microbiota (GM) as a pivotal metabolic modulator that is correlated with atherogenic lipid profiles through dietary biotransformation, immunometabolic regulation, and bioactive metabolite signaling. However, the host-microbe interactions that drive dyslipidemia pathogenesis involve complex gene-environment crosstalk spanning epigenetic modifications to circadian entrainment.
View Article and Find Full Text PDFMol Oncol
September 2025
Division of Oncology, Department of Clinical Sciences Lund, Lund University, Sweden.
Squamous cell lung carcinoma (SqCC) is the second most common histological subtype of lung cancer. Besides tumor-initiating and promoting DNA, RNA, and epigenetic alterations, aberrant cell metabolism is a hallmark of carcinogenesis. This study aimed to identify SqCC-specific key regulators that could eventually be used as new anticancer targets.
View Article and Find Full Text PDFFront Oncol
August 2025
Department of Imaging, Yantaishan Hospital, Yantai, Shangdong, China.
This systematic review evaluates the integration of radiomics, artificial intelligence (AI), and molecular signatures for diagnosing and prognosticating bone and soft tissue tumors (BSTTs). Following PRISMA 2020 guidelines, we analyzed 24 studies from 1,141 initial records across PubMed, Scopus, Web of Science, and Google Scholar. Our findings reveal that while radiomics-AI pipelines are well-developed for BSTT assessment - particularly using MRI (72% of studies) and CT (25%) with machine learning classifiers like random forests (42%) and CNNs (17%) - molecular data integration remains virtually absent.
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