ArticleOncogene2025
Global profiling of alternative splicing in non-small cell lung cancer reveals novel histological and population differences.
Article in Oncogene, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- The identification of RNA splicing aberrations and their potential regulatory network in Chinese non-small cell lung cancer.Respiratory research · 2026Article
- Systematic Pathway Screening via Integrated Machine Learning Identifies FOXO-Mediated Transcription Signature for Robust Immunotherapy Response Prediction in Non-Small Cell Lung Cancer.Human mutation · 2026Article
- Integrated transcriptomic and functional analysis reveals overlapping pathways in lung adenocarcinoma and chronic obstructive pulmonary disease.Hereditas · 2025Article
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Authors and funding
8 authors.
Funding
Abstract
Lung cancer is one of the most frequently diagnosed cancers in the US. African-American (AA) men are more likely to develop lung cancer with higher incidence and mortality rates than European-American (EA) men. Herein, we report high-confidence alternative splicing (AS) events from high-throughput, high-depth total RNA sequencing of lung tumors and non-tumor adjacent tissues (NATs) in two independent cohorts of patients with adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC). We identified novel AS biomarkers with notable differential percent spliced in (PSI) values between lung tumors and NATs enriched in the AA and EA populations, which were associated with oncogenic signaling pathways. We also uncovered tumor subtype- and population-specific AS events associated with cell surface proteins and cancer driver genes. We highlighted significant AS events in SYNE2 specific to LUAD in both populations, as well as those in CD44 from EAs and TMBIM6 from AAs specific to LUAD. Here, we also present the validation of cancer signatures based on direct high-throughput reverse transcription-PCR. Our large survey of lung tumors presents a rich data resource that may help to understand molecular subtypes of lung tumor between AAs and EAs and reveal new therapeutic vulnerabilities that potentially advance health equity.
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