Classification of follicular cell-derived thyroid cancer by global RNA profiling.
The incidence of thyroid cancer is increasing worldwide and thyroid nodules are a frequent clinical finding. Diagnosing follicular cell-derived cancers is, however, challenging both histopathologically and especially cytopathologically. The advent of high-throughput molecular technologies has prompted many researchers to explore the transcriptome and, in recent years, also the miRNome in order to generate new molecular classifiers capable of classifying thyroid tumours more accurately than by conventional cytopathological and histopathological methods. This has led to a number of molecular classifiers that may differentiate malignant from benign thyroid nodules. Molecular classification models based on global RNA profiles from fine-needle aspirations are currently being evaluated; results are preliminary and lack validation in prospective clinical trials. There is no doubt that molecular classification will not only contribute to our biological insight but also improve clinical and pathological examinations, thus advancing thyroid tumour diagnosis and ultimately preventing superfluous surgery. This review evaluates the status of classification and biological insights gained from molecular profiling of follicular cell-derived thyroid cancers.
Centre for Genomic Medicine, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark. maria.rossing@rh.regionh.dk
Maria
M
Rossing
D018263:Adenocarcinoma, Follicular / Q000145:classification* / Q000235:genetics*; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D012313:RNA
D012313:RNA