Ensemble Modeling Approach Targeting Heterogeneous RNA-Seq data: Application to Melanoma Pseudogenes

Abstract

We studied the transcriptome landscape of skin cutaneous melanoma (SKCM) using 103 primary tumor samples from TCGA, and measured the expression levels of both protein coding genes and non-coding RNAs (ncRNAs). In particular, we emphasized pseudogenes potentially relevant to this cancer. While cataloguing the profiles based on the known biotypes, all the employed RNA-Seq methods generated just a small consensus of significant biotypes. We thus designed an approach to reconcile the profiles from all methods following a simple strategy: we selected genes that were confirmed as differentially expressed by the ensemble predictions obtained in a regression model. The main advantages of this approach are: 1) Selection of a high-confidence gene set identifying relevant pathways; 2) Use of a regression model whose covariates embed all method-driven outcomes to predict an averaged profile; 3) Method-specific assessment of prediction power and significance. Furthermore, the approach can be generalized to any biological system for which noisy RNA-Seq profiles are computed. As our analyses concerned bio-annotations of both high-quality protein coding genes and ncRNAs, we considered the associations between pseudogenes and parental genes (targets). Among the candidate targets that were validated, we identified PINK1, which is studied in patients with Parkinson and cancer (especially melanoma).

Non-Coding RNAs in Cutaneous Melanoma Development, Progression and Dissemination

“…Among more than 3,000 miRNAs [90] and 24,793 annotated lncRNAs that have been identified within the human genome [91], only a few show deregulations in cutaneous melanoma (Table 1). Nevertheless, we are only at the beginning of our understanding and identification of ncRNAs; recent computational analyses using high-throughput expression data, including microarrays and RNA sequencing, have helped us identify deregulated mRNAs, lncRNAs and pseudogenes in melanoma [92,93]. Consequently, the growing body of evidence strongly suggests a role of pseudogene transcription as a source of endogenous siRNAs that either bind to their cognate wildtype (WT) genes or act as sponges by binding miRNAs targeting their WT genes.…”

Network assessment of demethylation treatment in melanoma: Differential transcriptome-methylome and antigen profile signatures

“…Here, the observed correlative or causative dynamics call for further investigation, and single cancers already represent highly complex systems. For instance, melanoma involves multiple signaling pathways, cell cycle regulators and apoptotic control mechanisms whose dynamics can be studied at a genomic network scale [15]. Interestingly, while the genetic predisposition explains no more than 10% of melanoma cases, and BRAF is the most frequently mutated gene (in up to 70% of cases), the role of epigenetics and epigenetically driven regulatory networks in both initiation and progression of melanoma remain to be clarified.…”