Abstract
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.
Highlights
Next-generation genome sequencing is widely used for the identification of genetic variations in cancer genomes [1,2]
By mapping of 29,290 single nucleotide variations (SNVs) to these RefSeq mRNA sequences, we obtained 3646 in 59 untranslated regions (UTRs), 25,627 in 39 UTRs and 17 in both 59 and 39 UTR of overlapping transcripts. These SNVs were further subjected to our comprehensive pipeline (Figure 1) to predict their effect on RNA secondary structure and miRNA target sites, which is described
The lung cancer genome was first sequenced in 2010 [66], which reports that the somatic variants were present in both coding and non-coding (UTR and other non-coding RNAs) transcribed regions, which constitute 0.6% and 0.8% respectively of the total somatic mutations identified (22,910)
Summary
Next-generation genome sequencing is widely used for the identification of genetic variations in cancer genomes [1,2]. Kalari et al [6] performed transcriptome-wide sequencing of NSCLC and identified differentially expressed genes, alternate splicing isoforms and single nucleotide variants (SNV) for tumors with and without KRAS mutations. A network analysis was performed with the genes showing differential expression (374 genes), alternate splicing (259 genes) and SNV-related changes (65 genes) that are differentially present in lung tumor groups with and without KRAS mutations. Genome-wide association studies (GWAS) report that SNVs mostly occur in non-coding regions compared to coding (exonic) regions of RNAs [7]. The effects of rSNVs that are located in untranslated regions (UTRs) of protein-coding genes, need to be analyzed
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