PseudoFuN: Deriving functional potentials of pseudogenes from integrative relationships with genes and microRNAs across 32 cancers

Abstract

Background Long thought “relics” of evolution, not until recently have pseudogenes been of medical interest regarding regulation in cancer. Often, these regulatory roles are a direct by-product of their close sequence homology to protein-coding genes. Novel pseudogene-gene (PGG) functional associations can be identified through the integration of biomedical data, such as sequence homology, functional pathways, gene expression, pseudogene expression, and microRNA expression. However, not all of the information has been integrated, and almost all previous pseudogene studies relied on 1:1 pseudogene–parent gene relationships without leveraging other homologous genes/pseudogenes. Results We produce PGG families that expand beyond the current 1:1 paradigm. First, we construct expansive PGG databases by (i) CUDAlign graphics processing unit (GPU) accelerated local alignment of all pseudogenes to gene families (totaling 1.6 billion individual local alignments and >40,000 GPU hours) and (ii) BLAST-based assignment of pseudogenes to gene families. Second, we create an open-source web application (PseudoFuN [Pseudogene Functional Networks]) to search for integrative functional relationships of sequence homology, microRNA expression, gene expression, pseudogene expression, and gene ontology. We produce four “flavors” of CUDAlign-based databases (>462,000,000 PGG pairwise alignments and 133,770 PGG families) that can be queried and downloaded using PseudoFuN. These databases are consistent with previous 1:1 PGG annotation and also are much more powerful including millions of de novo PGG associations. For example, we find multiple known (e.g., miR-20a - PTEN - PTENP1 ) and novel (e.g., miR-375 - SOX15 - PPP4R1L ) microRNA-gene-pseudogene associations in prostate cancer. PseudoFuN provides a “one stop shop” for identifying and visualizing thousands of potential regulatory relationships related to pseudogenes in The Cancer Genome Atlas cancers. Conclusions Thousands of new PGG associations can be explored in the context of microRNA-gene-pseudogene co-expression and differential expression with a simple-to-use online tool by bioinformaticians and oncologists alike.

Pseudogene-gene functional networks are prognostic of patient survival in breast cancer

“…4c). GPS2 and GPS2P1 are uncorrelated in normal tissue but are correlated in primary tumor samples [21] (Fig. 4d, e) achieving a significantly higher correlation in tumor tissue (Fisher's r to z transformation p-value < 0.0001).…”

“…The top genes and pseudogenes that were identified in the earlier analysis were used as features in the subsequent pseudogene-gene integrative models. The pseudogenegene functional network edge file was obtained from the BlastDB database (a flavor of the pseudogene-gene family database) in the PseudoFuN website [21]. The edge between a gene and a pseudogene indicates that the gene and pseudogene were contained in the same homology network and were used to identify the possible interactions between genes and pseudogenes in the model.…”

“…Aside from identifying multiple individual genes and pseudogenes that are prognostic of patient survival, we leveraged prior knowledge of possible pseudogene-gene interactions from PseudoFuN [21] to identify prognostic interactions between genes and pseudogenes. In the case of GPS2-GPS2P1 there is clear evidence that the protein is expressed in normal breast tissue, that GPS2 and GPS2P1 are positively correlated in tumor samples (i.e., positive correlation is indicative of ceRNA interaction), there is evidence of miRNA targeting GPS2, and there are external experimental findings showing GPS2 miRNA regulation causes breast cancer cell proliferation by reducing GPS2 protein levels.…”

“…For this purpose, we use the idea of pseudogenegene networks, which are networks of pseudogenes and genes that have high sequence homology and as a result may be functionally related [20]. These functional relationships are taken from the Pseudogene Functional Network (PseudoFuN) database [21] so that pseudogenes and genes could be mapped back to prospective regulatory partners.…”

Deep learning-based cancer survival prognosis from RNA-seq data: approaches and evaluations

“…With the high prevalence of neural networks and Deep Learning-based algorithms in the Computational Biology, it is clear that the advantages of optimization in a highly non-linear space are welcomed improvements in biomedicine [1][2][3][4][5][6][7]. In Bioinformatics, significant effort has been committed to harnessing transcriptomic data for multiple analyses [7][8][9][10][11][12][13] especially cancer survival prognosis [14,15]. Faraggi and Simon [16] was the first study to use clinical information to predict prostate cancer survival through an artificial neural network model.…”