Construction of lncRNA/Pseudogene-miRNA Network Based on In Silico Approaches for Glycolysis Pathway to Identify Prostate Adenocarcinoma-Related Potential Biomarkers
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Pseudogene
Competing Endogenous RNA
Competing Endogenous RNA
Pseudogene
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Pseudogene
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Pseudogene
Ribosomal protein
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Pseudogene
Competing Endogenous RNA
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Pseudogenes are fossil relatives of genes. Pseudogenes have long been thought of as junk DNAs, since they do not code proteins in normal tissues. Although most of the human pseudogenes do not have noticeable functions, ∼20% of them exhibit transcriptional activity. There has been evidence showing that some pseudogenes adopted functions as lncRNAs and work as regulators of gene expression. Furthermore, pseudogenes can even be reactivated in some conditions, such as cancer initiation. Some pseudogenes are transcribed in specific cancer types, and some are even translated into proteins as observed in several cancer cell lines. All the above have shown that pseudogenes could have functional roles or potentials in the genome. Evaluating the relationships between pseudogenes and their gene counterparts could help us reveal the evolutionary path of pseudogenes and associate pseudogenes with functional potentials. It also provides an insight into the regulatory networks involving pseudogenes with transcriptional and even translational activities.In this study, we develop a novel approach integrating graph analysis, sequence alignment and functional analysis to evaluate pseudogene-gene relationships, and apply it to human gene homologs and pseudogenes. We generated a comprehensive set of 445 pseudogene-gene (PGG) families from the original 3,281 gene families (13.56%). Of these 438 (98.4% PGG, 13.3% total) were non-trivial (containing more than one pseudogene). Each PGG family contains multiple genes and pseudogenes with high sequence similarity. For each family, we generate a sequence alignment network and phylogenetic trees recapitulating the evolutionary paths. We find evidence supporting the evolution history of olfactory family (both genes and pseudogenes) in human, which also supports the validity of our analysis method. Next, we evaluate these networks in respect to the gene ontology from which we identify functions enriched in these pseudogene-gene families and infer functional impact of pseudogenes involved in the networks. This demonstrates the application of our PGG network database in the study of pseudogene function in disease context.
Pseudogene
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Pseudogenes,disabled copies of functional genes,are structurally similar to its parantal genes,but lost their ability to produce proteins.Pseudogene was used to be considered as a typical kind of non-codingjunk DNA.Increasing investigations,however,have shown that pseudogenes play important roles in gene regulation and genome evolution.The progress of research on pseudogenes is summarized in light of the origin and sequence characteristics of pseudogenes,identification of pseudogenes,genomic distribution,behaviour of its molecular evolution and functioning.
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Abstract Colorectal cancer (CRC) is one of the most common and malignant carcinomas. Many long noncoding RNAs (lncRNAs) have been reported to play important roles in the tumorigenesis of CRC by influencing the expression of some mRNAs via competing endogenous RNA (ceRNA) networks and interacting with miRNAs. Pseudogene is one kind of lncRNA and can act as RNA sponges for miRNAs and regulate gene expression via ceRNA networks. However, there are few studies about pseudogenes in CRC. In this study, 31 differentially expressed (DE) pseudogenes, 17 DE miRNAs and 152 DE mRNAs were identified by analyzing the expression profiles of colon adenocarcinoma obtained from The Cancer Genome Atlas. A ceRNA network was constructed based on these RNAs. Kaplan–Meier analysis showed that 7 pseudogenes, 4 miRNAs and 30 mRNAs were significantly associated with overall survival. Then multivariate Cox regression analysis of the ceRNA-related DE pseudogenes was performed and a 5-pseudogene signature with the greatest prognostic value for CRC was identified. Moreover, the results were validated by the Gene Expression Omnibus database, and quantitative real-time PCR in 113 pairs of CRC tissues and colon cancer cell lines. This study provides a pseudogene-associated ceRNA network, 7 prognostic pseudogene biomarkers, and a 5-pseudogene prognostic risk signature that may be useful for predicting the survival of CRC patients.
Competing Endogenous RNA
Pseudogene
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Abstract Colorectal cancer (CRC) is one of the most common and deadly malignant carcinomas. Many long noncoding RNAs (lncRNA) have been reported to play an important role in the tumorigenesis of CRC by interacting with miRNAs and influencing the expression of some mRNAs through a competing endogenous RNA (ceRNA) network. Pseudogenes are one kind of lncRNA and can act as RNA sponges for miRNAs and regulate gene expression via ceRNA networks, but there are few studies about pseudogenes in CRC. In this study, total of 31 differentially expressed (DE) pseudogenes, 17 DE miRNAs and 152 DE mRNAs were identified by analyzing the expression profiles of colon adenocarcinoma (COAD) obtained from The Cancer Genome Atlas (TCGA). And a ceRNA network was constructed based on these RNAs. Kaplan–Meier analysis showed that 7 pseudogenes, 4 miRNAs and 30 mRNAs were significantly associated with overall survival. Then multivariate Cox regression analysis on the ceRNA-related DE pseudogenes was performed and a 5-pseudogene signature with the greatest prognostic value for CRC was identified. What’s more, the results were validated by the Gene Expression Omnibus (GEO) database, and quantitative real‐time PCR (qRT‐PCR) in 113 pairs of CRC tissues. In conclusion, this study provides a pseudogene-associated ceRNA network and 7 prognostic pseudogene biomarkers, and a 5-pseudogene prognostic risk signature that may be useful to predict the survival of CRC patients.
Pseudogene
Competing Endogenous RNA
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Competing Endogenous RNA
Pseudogene
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