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Systematic Profile Analysis of Prognostic Alternative Messenger RNA Splicing Signatures and Splicing Factors in Head and Neck Squamous Cell Carcinoma. | LitMetric

Systematic Profile Analysis of Prognostic Alternative Messenger RNA Splicing Signatures and Splicing Factors in Head and Neck Squamous Cell Carcinoma.

DNA Cell Biol

3 Shandong Provincial Key Laboratory of Oral Tissue Regeneration, Department of Oral and Maxillofacial Surgery, School of Stomatology, Shandong University, Jinan, China.

Published: July 2019

AI Article Synopsis

  • Head and neck squamous cell carcinoma (HNSC) is a prevalent cancer associated with high mortality rates and challenging prognosis, highlighting the need for better predictive markers.
  • Researchers analyzed RNA sequencing data from HNSC patients to uncover 4,626 survival-related alternative splicing (AS) events across 3,280 genes, primarily identifying exon skipping as the most common splicing event.
  • Prognostic models derived from these AS events demonstrated strong predictive capability, with an area under the curve of 0.765, allowing for patient stratification into high- and low-risk categories, while revealing important splicing regulatory networks.

Article Abstract

Head and neck squamous cell carcinoma (HNSC) is a common malignancy with high mortality and poor prognosis. Alternative splicing (AS) is a transcriptional regulation mechanism that generates multiple transcripts from same genes, and aberrant AS signatures of cancers can be predictive for prognosis. We identified the survival-related AS events and splicing factors (SFs) from the RNA sequencing data and the corresponding clinical information of an HNSC cohort downloaded from The Cancer Genome Atlas (TCGA) and SpliceSeq. The independent prognostic predictors were assessed by Cox proportional regression analysis, and the regulatory network of SFs and AS events was analyzed by Spearman's test and constructed. A total of 4626 survival-related AS events in 3280 genes were identified, and most were protective factors. Among the different types of splicing events, exon skip was the most frequent. The prognostic models were constructed for each type of AS, and the area under the curve of the receiver operating characteristic curve of the combined prognostic model was 0.765, indicating good predictive performance. Finally, a correlation network between SF and AS events was constructed. We identified prognostic predictors based on AS events that stratified HNSC patients into the high- and low-risk groups, and revealed splicing networks that provide insights into the underlying mechanisms.

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Source
http://dx.doi.org/10.1089/dna.2019.4644DOI Listing

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