Transcriptome-Wide Analysis of UTRs in Non-Small Cell Lung Cancer Reveals Cancer-Related Genes with SNV-Induced Changes on RNA Secondary Structure and miRNA Target Sites

Transcriptome-Wide Analysis of UTRs in Non-Small Cell Lung Cancer Reveals Cancer-Related Genes with SNV-Induced Changes on RNA Secondary Structure and miRNA Target Sites

Traditional mutation assessment methods generally focus on predicting disruptive changes in protein-coding regions rather than non-coding regulatory regions like untranslated regions (UTRs) of mRNAs. The UTRs, however, are known to have many sequence and structural motifs that can regulate translati...

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Main Authors: Sabarinathan, Radhakrishnan, Wenzel, Anne, Novotny, Peter, Tang, Xiaojia, Kalari, Krishna R., Gorodkin, Jan
Format: Online
Language:English
Published: Public Library of Science 2014
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3885406/
id pubmed-3885406
recordtype oai_dc
spelling pubmed-38854062014-01-10 Transcriptome-Wide Analysis of UTRs in Non-Small Cell Lung Cancer Reveals Cancer-Related Genes with SNV-Induced Changes on RNA Secondary Structure and miRNA Target Sites Sabarinathan, Radhakrishnan Wenzel, Anne Novotny, Peter Tang, Xiaojia Kalari, Krishna R. Gorodkin, Jan Research Article Traditional mutation assessment methods generally focus on predicting disruptive changes in protein-coding regions rather than non-coding regulatory regions like untranslated regions (UTRs) of mRNAs. The UTRs, however, are known to have many sequence and structural motifs that can regulate translational and transcriptional efficiency and stability of mRNAs through interaction with RNA-binding proteins and other non-coding RNAs like microRNAs (miRNAs). In a recent study, transcriptomes of tumor cells harboring mutant and wild-type KRAS (V-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog) genes in patients with non-small cell lung cancer (NSCLC) have been sequenced to identify single nucleotide variations (SNVs). About 40% of the total SNVs (73,717) identified were mapped to UTRs, but omitted in the previous analysis. To meet this obvious demand for analysis of the UTRs, we designed a comprehensive pipeline to predict the effect of SNVs on two major regulatory elements, secondary structure and miRNA target sites. Out of 29,290 SNVs in 6462 genes, we predict 472 SNVs (in 408 genes) affecting local RNA secondary structure, 490 SNVs (in 447 genes) affecting miRNA target sites and 48 that do both. Together these disruptive SNVs were present in 803 different genes, out of which 188 (23.4%) were previously known to be cancer-associated. Notably, this ratio is significantly higher (one-sided Fisher's exact test p-value = 0.032) than the ratio (20.8%) of known cancer-associated genes (n = 1347) in our initial data set (n = 6462). Network analysis shows that the genes harboring disruptive SNVs were involved in molecular mechanisms of cancer, and the signaling pathways of LPS-stimulated MAPK, IL-6, iNOS, EIF2 and mTOR. In conclusion, we have found hundreds of SNVs which are highly disruptive with respect to changes in the secondary structure and miRNA target sites within UTRs. These changes hold the potential to alter the expression of known cancer genes or genes linked to cancer-associated pathways. Public Library of Science 2014-01-08 /pmc/articles/PMC3885406/ /pubmed/24416147 http://dx.doi.org/10.1371/journal.pone.0082699 Text en © 2014 Sabarinathan et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
repository_type Open Access Journal
institution_category Foreign Institution
institution US National Center for Biotechnology Information
building NCBI PubMed
collection Online Access
language English
format Online
author Sabarinathan, Radhakrishnan
Wenzel, Anne
Novotny, Peter
Tang, Xiaojia
Kalari, Krishna R.
Gorodkin, Jan
spellingShingle Sabarinathan, Radhakrishnan
Wenzel, Anne
Novotny, Peter
Tang, Xiaojia
Kalari, Krishna R.
Gorodkin, Jan
Transcriptome-Wide Analysis of UTRs in Non-Small Cell Lung Cancer Reveals Cancer-Related Genes with SNV-Induced Changes on RNA Secondary Structure and miRNA Target Sites
author_facet Sabarinathan, Radhakrishnan
Wenzel, Anne
Novotny, Peter
Tang, Xiaojia
Kalari, Krishna R.
Gorodkin, Jan
author_sort Sabarinathan, Radhakrishnan
title Transcriptome-Wide Analysis of UTRs in Non-Small Cell Lung Cancer Reveals Cancer-Related Genes with SNV-Induced Changes on RNA Secondary Structure and miRNA Target Sites
title_short Transcriptome-Wide Analysis of UTRs in Non-Small Cell Lung Cancer Reveals Cancer-Related Genes with SNV-Induced Changes on RNA Secondary Structure and miRNA Target Sites
title_full Transcriptome-Wide Analysis of UTRs in Non-Small Cell Lung Cancer Reveals Cancer-Related Genes with SNV-Induced Changes on RNA Secondary Structure and miRNA Target Sites
title_fullStr Transcriptome-Wide Analysis of UTRs in Non-Small Cell Lung Cancer Reveals Cancer-Related Genes with SNV-Induced Changes on RNA Secondary Structure and miRNA Target Sites
title_full_unstemmed Transcriptome-Wide Analysis of UTRs in Non-Small Cell Lung Cancer Reveals Cancer-Related Genes with SNV-Induced Changes on RNA Secondary Structure and miRNA Target Sites
title_sort transcriptome-wide analysis of utrs in non-small cell lung cancer reveals cancer-related genes with snv-induced changes on rna secondary structure and mirna target sites
description Traditional mutation assessment methods generally focus on predicting disruptive changes in protein-coding regions rather than non-coding regulatory regions like untranslated regions (UTRs) of mRNAs. The UTRs, however, are known to have many sequence and structural motifs that can regulate translational and transcriptional efficiency and stability of mRNAs through interaction with RNA-binding proteins and other non-coding RNAs like microRNAs (miRNAs). In a recent study, transcriptomes of tumor cells harboring mutant and wild-type KRAS (V-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog) genes in patients with non-small cell lung cancer (NSCLC) have been sequenced to identify single nucleotide variations (SNVs). About 40% of the total SNVs (73,717) identified were mapped to UTRs, but omitted in the previous analysis. To meet this obvious demand for analysis of the UTRs, we designed a comprehensive pipeline to predict the effect of SNVs on two major regulatory elements, secondary structure and miRNA target sites. Out of 29,290 SNVs in 6462 genes, we predict 472 SNVs (in 408 genes) affecting local RNA secondary structure, 490 SNVs (in 447 genes) affecting miRNA target sites and 48 that do both. Together these disruptive SNVs were present in 803 different genes, out of which 188 (23.4%) were previously known to be cancer-associated. Notably, this ratio is significantly higher (one-sided Fisher's exact test p-value = 0.032) than the ratio (20.8%) of known cancer-associated genes (n = 1347) in our initial data set (n = 6462). Network analysis shows that the genes harboring disruptive SNVs were involved in molecular mechanisms of cancer, and the signaling pathways of LPS-stimulated MAPK, IL-6, iNOS, EIF2 and mTOR. In conclusion, we have found hundreds of SNVs which are highly disruptive with respect to changes in the secondary structure and miRNA target sites within UTRs. These changes hold the potential to alter the expression of known cancer genes or genes linked to cancer-associated pathways.
publisher Public Library of Science
publishDate 2014
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3885406/
_version_ 1612045728792707072

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