Abstract
SummaryBackgroundExitron is a new type of non-canonical alternative splicing. Accumulating evidence implies exitron may have pathological function and contribute to another source of anti-tumor immunogenicity in various cancers. Its role in gastric cancer remains poorly understood. Large-scale, multi-omics analysis could comprehensively characterize the landscape of exitrons in gastric cancer, reveal undiscovered mechanism and hopefully identify molecular biomarkers for predicting immunotherapy response.MethodsWe collected datasets from five studies for analysis. RNA sequencing was used for exitron identification. Somatic mutations were detected by whole exome sequencing. Neopeptides were confirmed by proteome mass spectrometry.Findings42174 gastric cancer-specific exitrons (GCSEs) were identified in 632 patients. GCSEs were clinically relevant to gender, age, Lauren type, tumor stage and prognosis. Tissue specificity test and pathogenic exitron prediction revealed their unique functional impact. GCSEs were mutually exclusive with mutations and demonstrated both unique and complementary function against TP53 mutation in gastric cancer. We further established splicing regulatory network to reveal upstream regulation of exitron splicing. We also evaluated the immunogenicity and diagnostic potential of GCSEs. Evidence of GCSEs-derived neopeptide expression was validated by whole proteome mass spectrometry. PD-1 and Siglecs were significantly increased in high neoantigen load patients. But exitron-related biomarkers failed to predict immunotherapy response, possibly due to small sample size and insufficient sequencing depth.InterpretationThe present study provided a comprehensive multidimensional landscape of gastric cancer exitrons and underscores insights into underexplored mechanism in gastric cancer pathology.FundingThe Guangdong Provincial Key Laboratory of Precision Medicine for Gastroinstestinal Cancer (2020B121201004), the Guangdong Provincial Major Talents Project (No. 2019JC05Y361) and National Natural Science Foundation of China (grant number:82172960 and 81872013).
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