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A Robust Discovery Platform for the Identification of Novel Mediators of Melanoma Metastasis
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Ensemble Modeling Approach Targeting Heterogeneous RNA-Seq data: Application to Melanoma Pseudogenes
Enrico Capobianco1, Camilo Valdes2, Samanta Sarti3
1Center for Computational Science, University of Miami, Miami, FL, USA. ecapobianco@med.miami.edu.
Scientific Reports
|December 13, 2017
Summary
This study analyzed skin cutaneous melanoma (SKCM) transcriptomes, identifying key genes and non-coding RNAs. A novel regression approach reconciled RNA-Seq data, highlighting pseudogene associations and the potential role of PINK1 in melanoma.
Area of Science:
- Genomics and Molecular Biology
- Oncology
- Bioinformatics
Background:
- Skin cutaneous melanoma (SKCM) presents a complex transcriptome.
- Understanding non-coding RNAs (ncRNAs) and pseudogenes in SKCM is crucial.
- Existing RNA-Seq analysis methods yield fragmented results.
Purpose of the Study:
- To characterize the transcriptome landscape of SKCM, focusing on pseudogenes.
- To develop a robust method for reconciling diverse RNA-Seq data.
- To identify novel gene targets associated with melanoma.
Main Methods:
- Analysis of 103 primary SKCM tumor samples from The Cancer Genome Atlas (TCGA).
- Application of a novel regression model to ensemble RNA-Seq data for high-confidence gene selection.
- Investigation of pseudogene-ncRNA associations with protein-coding parental genes.
Main Results:
- A reconciled, high-confidence gene set was identified, revealing relevant biological pathways.
- The regression approach effectively integrated and predicted averaged RNA-Seq profiles.
- PINK1 was identified as a validated pseudogene target associated with melanoma and Parkinson's disease.
Conclusions:
- The developed ensemble regression method provides a robust approach for analyzing noisy RNA-Seq data across biological systems.
- Pseudogene-parental gene interactions are significant in SKCM.
- PINK1 emerges as a potential biomarker or therapeutic target in melanoma.
