Challenges and prospects in utilizing technologies for gene fusion analysis in cancer diagnostics

Gene fusions are vital biomarkers for tumor diagnosis and drug development, with precise detection becoming increasingly important. The team led by Yudong Wang, Da Han and Ping Song from Shanghai Jiao Tong Univesity explores the links between gene fusions and common tumors, systematically evaluating detection technologies like fluorescence in situ hybridization (FISH), polymerase chain reaction (PCR), immunohistochemistry (IHC), electrochemiluminescence (ECL), and next-generation sequencing (NGS).

FISH is the gold standard for DNA-level rearrangements, while PCR and NGS are widely used, with PCR confirming known fusions and NGS offering comprehensive genome-wide detection. Bioinformatic tools like STAR-Fusion, FusionCatcher, and Arriba are assessed for diagnostic accuracy. The review highlights how artificial intelligence (AI), particularly deep learning (DL) technologies like convolutional neural networks (CNNs) and recurrent neural networks (RNNs), is transforming gene fusion research by accurately detecting and annotating genes from genomic data, eliminating biases.

Continuous advancements in technology are significantly enhancing our ability to perform gene fusion detection and related analyses. With the development of technology, we have gained increasingly comprehensive insight into the genesis, functions, and connections of gene fusions with various diseases. This progress not only boosts the accuracy of gene fusion detection but also amplifies its clinical significance. Consequently, ongoing technological innovations are opening up new vistas for a deeper understanding of gene fusions and broadening their potential clinical applications.