Hierarchical identification of qualitative transcriptional signatures for the histopathological diagnosis of lung neuroendocrine tumours
Release date:
2020-12-28
Description:
The treatment for lung neuroendocrine (NE) tumours is histology dependent. However, the histological classification for poorly differentiated tumours remains challenging. In this study, we proposed a hierarchical rank-based method to identify a set of individualised transcriptional signatures for NE subtypes (NEsub-ITSs), based on the 'within-sample' relative gene expression orderings (REOs) of gene-pairs. The accuracy of NEsub-ITSs was more than 87.50% across 20 independent public datasets that included fresh-frozen, formalin-fixed paraffin-embedded (FFPE), small biopsy, micro-dissected, and single-cell samples. NEsub-ITSs also showed good performance in 10 bronchoscopy-derived biopsy samples collected from Harbin Medical University Cancer Hospital. In summary, NEsub-ITSs could accurately distinguish lung NE from non-NE tumours and determine NE subtypes even in clinically challenging samples (FFPE and small biopsy samples) and could be an auxiliary test for the pathological assessment of lung cancer.
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