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Multi-Omics Integration Identifies Critical Molecular Drivers and Alternative Splicing Events in Small Cell Lung Cancer II

GSE330548 Mus musculus Expression profiling by high throughput sequencing 6 samples Submitted 2026/05/14 Platform GPL24247
Summary
Small cell lung cancer (SCLC) remains a highly aggressive malignancy and a therapeutic challenge in clinical oncology. Here, we conducted an integrated multi-omics analysis of treatment-naïve human SCLC tissues along with paired adjacent tissues (NAT, n = 12), including transcriptomic, proteomic, phosphoproteomic, and glycoproteomic profiling to identify novel prognostic markers and therapeutic targets. Integration of these datasets revealed significant enrichment of RNA splicing pathway in SCLC. We systematically characterized aberrant alternative splicing events (AASEs), identifying 6,972 events with exon skipping representing the most prevalent type. These AASEs were regulated by RNA-binding proteins and predominantly enriched in pathways related to tumor proliferation and metastasis. We further identified four splicing-related proteins (THOC1, MAGOHB, RBM8A, and TXNL4A) that were markedly overexpressed in SCLC, correlated with proliferation, and predicted poor prognosis. Functional validation showed that THOC1 knockdown suppressed tumor growth in vivo, potentially through enhanced anti-tumor immunity. Additionally, we present the first N-glycoproteomic atlas of SCLC implicating glycosylated CEACAM proteins as potential biomarkers and revealing aberrantly glycosylated sites and proteins associated with SCLC. Our study unveils novel molecular features underlying SCLC pathogenesis and provides insights for developing therapeutic strategies targeting RNA splicing and protein glycosylation.
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Direct links to NCBI, no account and no request form: the whole study as GSE330548_RAW.tar, processed values as the series matrix, the supplementary file directory, and per-sample supplementary files for any of the 6 samples. Raw sequencing reads are also available from ENA.

Also filed as BioProject PRJNA1464579 and SRA study SRP699321. Searching any of these in the dataset finder brings you back here.

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