Non-Coding Variants in Cancer: Mechanistic Insights and Clinical Potential for Personalized Medicine

Lange, Marios; Begolli, Rodiola; Giakountis, Antonis

doi:10.3390/ncrna7030047

Cited by 11 publications

(8 citation statements)

References 356 publications

(394 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the case of cancer non-coding SNPs, localization within the UTRs is considered to be especially damaging. The identified variations in these regions are found to perturb the structure of miRNA and thereby the interactions with other miRNAs and gene products [34][35][36]. UTRs maintain post-translation regulation in gene expression, any variation in the UTR region can be linked to severe pathologies [37,38].…”

Section: Discussionmentioning

confidence: 99%

Investigation of UTR Variants by Computational Approaches Reveal Their Functional Significance in PRKCI Gene Regulation

Shah

Khan

Badshah

et al. 2023

Genes

View full text Add to dashboard Cite

Single nucleotide polymorphisms (SNPs) are associated with many diseases including neurological disorders, heart diseases, diabetes, and different types of cancers. In the context of cancer, the variations within non-coding regions, including UTRs, have gained utmost importance. In gene expression, translational regulation is as important as transcriptional regulation for the normal functioning of cells; modification in normal functions can be associated with the pathophysiology of many diseases. UTR-localized SNPs in the PRKCI gene were evaluated using the PolymiRTS, miRNASNP, and MicroSNIper for association with miRNAs. Furthermore, the SNPs were subjected to analysis using GTEx, RNAfold, and PROMO. The genetic intolerance to functional variation was checked through GeneCards. Out of 713 SNPs, a total of thirty-one UTR SNPs (three in 3′ UTR region and twenty-nine in 5′ UTR region) were marked as ≤2b by RegulomeDB. The associations of 23 SNPs with miRNAs were found. Two SNPs, rs140672226 and rs2650220, were significantly linked with expression in the stomach and esophagus mucosa. The 3′ UTR SNPs rs1447651774 and rs115170199 and the 5′ UTR region variants rs778557075, rs968409340, and 750297755 were predicted to destabilize the mRNA structure with substantial change in free energy (∆G). Seventeen variants were predicted to have linkage disequilibrium with various diseases. The SNP rs542458816 in 5′ UTR was predicted to put maximum influence on transcription factor binding sites. Gene damage index(GDI) and loss of function (o:e) ratio values for PRKCI suggested that the gene is not tolerant to loss of function variants. Our results highlight the effects of 3′ and 5′ UTR SNP on miRNA, transcription and translation of PRKCI. These analyses suggest that these SNPs can have substantial functional importance in the PRKCI gene. Future experimental validation could provide further basis for the diagnosis and therapeutics of various diseases.

show abstract

Section: Discussionmentioning

confidence: 99%

Investigation of UTR Variants by Computational Approaches Reveal Their Functional Significance in PRKCI Gene Regulation

Shah

Khan

Badshah

et al. 2023

Genes

View full text Add to dashboard Cite

show abstract

“…There are several factors for the poor performance of predictive computational models of functional non-coding DNA variants, such as the inadequate understanding of the molecular feature of gene regulation, the inaccurate estimation of the DNA variant impact on target gene expression, the significance of DNA sequence conservation in vertebrates on the functional regulatory element, and enrichment of transcription factor binding sites. In cancer diseases, risk prediction based on non-coding variants remains challenging due to the diversity of the non-coding regions among individuals, the inability to distinguish driver and passenger mutations, and the current lack of understanding of the underlying mechanism associated with functional non-coding variants [35]. Hence, proving an underlying biological mechanism for disease development, such as the functional non-coding SNPs.…”

Section: Discussionmentioning

confidence: 99%

Sequence-to-expression approach to identify etiological non-coding DNA variations in P53 and cMYC-driven diseases

Kin

Bhogale

Zhu

et al. 2022

Preprint

View full text Add to dashboard Cite

Disease risk prediction based on DNA sequence and transcriptional profile can improve disease screening, prevention, and potential therapeutic approaches by revealing contributing genetic factors and altered networks. Despite identifying many disease-associated DNA variants through genome-wide association studies, distinguishing deleterious non-coding DNA variations remains poor for most common diseases. We previously reported that non-coding variations disrupting cis-overlapping motifs (CisOMs) of opposing transcription factors significantly affect enhancer activity. Analyzing publicly available ChIP-seq data for P53 and cMYC in human embryonic stem cells and mouse embryonic cells showed that ~344-366 genomic regions are co-occupied by P53 and cMYC. We identified, on average, two CisOMs per region, suggesting that co-occupancy is evolutionarily conserved in vertebrates. Therefore, we designed in vitro experiments to uncover the significance of the co-occupancy and competitive binding and inhibition between P53 and cMYC on target gene expression. We found that treating U2OS cells with doxorubicin increased P53 protein level while reducing cMYC level. In contrast, no change in protein levels was observed in Raji cells. ChIP-seq analysis showed that 16-922 genomic regions were co-occupied by P53 and cMYC before and after treatment, and substitutions of cMYC signals by P53 were detected after doxorubicin treatment in U2OS. Around 187 expressed genes near co-occupied regions were altered at mRNA level according to RNA-seq data. We utilized a computational motif-matching approach to determine that changes in predicted P53 binding affinity by DNA variations in CisOMs of co-occupied elements significantly correlate with alterations in reporter gene expression. We performed a similar analysis using SNPs mapped in CisOMs for P53 and cMYC from ChIP-seq data in U2OS and Raji, and expression of target genes from the GTEx portal. We found a significant correlation between change in motif-predicted cMYC binding affinity by SNPs in CisOMs and gene expression. In conclusion, our study suggests a generally applicable approach to filter etiological non-coding variations associated with P53 and cMYC-dependent diseases.

show abstract

“…Due to the nature of RNAseq platforms only capturing variants in transcribed regions, rarer variants that fall in crucial functional regions such as promoter and enhancer regions go unaccounted for 22 . These rare, non-transcribed variants are mostly linked to increased risk of various diseases and are enriched within expressed quantitative trait loci (eQTLs) [23][24][25] , therefore rendering RNAseq variant calling suboptimal for eQTL analysis. Thus, our study should not be utilized as a one-to-one replacement for conventional genotyping array or whole genome sequencing.…”

Section: Discussionmentioning

confidence: 99%

Direct inference and control of genetic population structure from RNA sequencing data

Fachrul

Karkey

Shakya

et al. 2022

Preprint

View full text Add to dashboard Cite

RNAseq data can be used to infer genetic variants, yet its use for estimating genetic population structure remains underexplored. Here, we construct a freely available computational tool (RGStraP) to estimate RNAseq-based genetic principal components (RG-PCs) and assess whether RG-PCs can be used to control for population structure in gene expression analyses. Using whole blood samples from understudied Nepalese populations, we show that RG-PCs had comparable results to paired array-based genotypes, with high genotype concordance and high correlations of genetic principal components, capturing subpopulations within the dataset. In differential gene expression analysis, we found that inclusion of RG-PCs as covariates reduced test statistic inflation. Our paper demonstrates that genetic population structure can be directly inferred and controlled for using RNAseq data, thus facilitating improved retrospective and future analyses of transcriptomic data.

show abstract

Non-Coding Variants in Cancer: Mechanistic Insights and Clinical Potential for Personalized Medicine

Cited by 11 publications

References 356 publications

Investigation of UTR Variants by Computational Approaches Reveal Their Functional Significance in PRKCI Gene Regulation

Investigation of UTR Variants by Computational Approaches Reveal Their Functional Significance in PRKCI Gene Regulation

Sequence-to-expression approach to identify etiological non-coding DNA variations in P53 and cMYC-driven diseases

Direct inference and control of genetic population structure from RNA sequencing data

Contact Info

Product

Resources

About