ArticleFrontiers in genetics2020
Alternative Splicing Events and Splicing Factors Are Prognostic in Adrenocortical Carcinoma.
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- HnRNPR-mediated UPF3B mRNA splicing drives hepatocellular carcinoma metastasis.Journal of advanced research · 2025Article
- SNRPE is Associated with ERK/mTOR Signaling Activation and Reduced Autophagy to Promote Lung Adenocarcinoma Cell Proliferation.OncoTargets and therapy · 2025Article
- SF3B4 Depletion Retards the Growth of A549 Non-Small Cell Lung Cancer Cells via UBE4B-Mediated Regulation of p53/p21 and p27 Expression.Molecules and cells · 2022Article
- Overexpressed SNRPB/D1/D3/E/F/G correlate with poor survival and immune infiltration in hepatocellular carcinoma.American journal of translational research · 2022Article
- Significance of Spliceosome-Related Genes in the Prediction of Prognosis and Treatment Strategies for Lung Adenocarcinoma.BioMed research international · 2022Article
- Multi-omics integration of methyltransferase-like protein family reveals clinical outcomes and functional signatures in human cancer.Scientific reports · 2021Article
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Authors and funding
4 authors.
Funding
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Abstract
Alternative splicing is involved in the pathogenesis of human diseases, including cancer. Here, we investigated the potential application of alternative splicing events (ASEs) and splicing factors (SFs) in the prognosis of adrenocortical carcinoma (ACC). Transcriptome data from 79 ACC cases were downloaded from The Cancer Genome Atlas database, and percent spliced-in values of seven splicing types were downloaded from The Cancer Genome Atlas SpliceSeq database. By the univariate Cox regression analysis, 1,839 survival-related ASEs were identified. Prognostic indices based on seven types of survival-related ASEs were calculated by multivariate Cox regression analysis. Survival curves and receiver operating characteristic curves were used to assess the diagnostic value of the prognostic model. Independent prognosis analysis identified several ASEs (e.g., THNSL2| 54469| ME) that could be used as biomarkers to predict the prognosis of patients with ACC accurately. By analyzing the co-expression correlation between SFs and ASEs, 188 highly correlated interactions were established. From the protein interaction network, we finally screened six hub SFs, including YBX1, SART1, PRCC, SNRPG, SNRPE, and SF3B4, whose expression levels were significantly related to the overall survival and prognosis of ACC. Our findings provide a reliable model for predicting the prognosis of ACC patients based on aberrant alternative splicing patterns.
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