Comparative biocomputational analysis of the splicing landscape across lung neuroendocrine neoplasms unveils a new actionable molecular layer

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Introduction: Dysregulation of alternative splicing has emerged as a new, overarching hallmark of cancer, which may arise from mutations and/or altered expression of specific components of the splicing machinery. To date, no studies have been reported comparing the spliceosomic landscape, i.e. the status of the splicing machinery and alternative splicing patterns, among the different histological types of pulmonary neuroendocrine neoplasms (LungNENs), namely typical and atypical pulmonary carcinoids, Large Cell Neuroendocrine Carcinoma (LCNEC) and Small Cell Lung Cancer (SCLC).

Aim(s): Thus, here we aimed to assess the spliceosomic landscape in LungNENs and explore their potential dysregulation in relation with their pathophysiology.

Materials and methods: A set of 284 LungNENs samples (164 carcinoids, 69 LCNEC and 51 SCLC) with RNA-seq data was used in this study. Briefly, gene expression was calculated using DESeq-2 and alternative splicing events were quantified using SUPPA2. Dimensionality reduction techniques like Principal Component Analysis (PCA) or Uniform Manifold Approximation and Projection (UMAP) were used to cluster the samples according to their splicing pattern.

Conference:

Presenting Author: Blázquez-Encinas R

Authors: Blázquez-Encinas R, García-Vioque V, Sexton-Oates A, Moreno-Montilla M, Alors-Pérez E,

Keywords: Neuroendocrine, NEN, Lung, Splicing,