Strategies for incorporating alternative splicing variants in thyroid nodule biopsy classification based on expression levels

Background Accurate diagnosis of thyroid nodules remains a challenge, in cases with indeterminate cytology (Bethesda III and IV). Existing molecular tests leave a percentage of these cases unresolved, leading to unnecessary surgeries or delayed treatment. Given that over 90% of genes undergo alternative splicing (AS), this study explores integrating AS data with traditional gene expression profiles for classification. Methods Gene expression data from 335 patients were used. HTA2.0 microarrays were preprocessed using two tools for splicing variant identification: the Transcriptome Analysis Console (TAC), which relies on probe sets and junctions, and EventPointer, which focuses on splicing events. The influence of feature selection, dataset and variant identification was tested in a bootstrap procedure. Modification was introduced to deduplicate features for each gene. Results & conclusions The classification quality was strongly influenced by the processing methodology. While the EventPointer pipeline proved more effective for gene-level features due to a custom chip definition file, TAC-generated variants yielded the best bootstrap-based performance, with an overall classification accuracy of 0.938. The model subsequently underwent patient- and sample-level external validation using independent public microarray dataset. Furthermore, we conducted feature verification using RNA-seq data to confirm cross-platform consistency; however, this specific analysis serves as a technical reassessment rather than a full independent classifier validation. Among the 11 selected isoforms were those corresponding to genes known to be significant in thyroid cancer, such as FN1 and LIPH. In thyroid cancer, certain transcript isoforms may be preferentially expressed. Therefore, diagnostic classifiers might benefit from incorporating alternative splicing variants.