Functional annotation of noncoding variants and prioritization of cancer-associated lncRNAs in lung cancer

Abstract

Abstract. Multiple computational tools have been widely applied to the detection of coding driver mutations in cancer; however, the prioritization of pathogenic non-coding variants remains a difficult and demanding task. The present study was performed to distinguish non-coding disease-causing mutations from neutral ones, and to prioritize potential cancer-associated long non-coding RNAs (lncRNAs) with a logistic regression model in lung cancer. A logistic regression model was constructed, combining 19,153 disease-associated ClinVar and Human Gene Mutation Database pathogenic variants as the response variable and non-coding features as the predictor variable. Validation of the model was conducted with genome-wide association study (GWAS) disease-or trait-associated single nucleotide polymorphisms (SNPs) and recurrent somatic mutations. High scoring regions were characterized with respect to their distribution in various features and gene classes; potential cancer-associated lncRNA candidates were prioritized, combining the fraction of high-scoring regions and average score predicted by the logistic regression model. H3K79me2 was the most negative factor that contributed to the model, while conserved regions were most positively informative to the model. The area under the receiver operating characteristic curve of the model was 0.89. The model assigned a significantly higher score to GWAS SNPs and recurrent somatic mutations compared with neutral SNPs (mean, 5.9012 vs. 5.5238; P<0.001, Mann-Whitney U test) and non-recurrent mutations (mean, 5.4677 vs. 5.2277, P<0.001, Mann-Whitney U test), respectively. It was observed that regions, including splicing sites and untranslated regions, and gene classes, including cancer genes and cancer-associated lncRNAs, had an increased enrichment of high-scoring regions. In total, 2,679 cancer-associated lncRNAs were determined and characterized. A total of 104 of these lncRNAs were differentially expressed between lung cancer and normal specimens. The logistic regression model is a useful and efficient scoring system to prioritize non-coding pathogenic variants and lncRNAs, and may provide the basis for detecting non-coding driver lncRNAs in lung cancer.

Prioritization of non-coding disease-causing variants and long non-coding RNAs in liver cancer

“…Cancer lncRNAs, consisting of 25 lncRNAs, are a curation of mammalian long non-coding transcripts that have been experimentally shown to be associated with a variety of cancer types (22). A list of cancer census genes was obtained from the current release of catalogue of somatic mutations in cancer (COSMIC) v71 (34).…”

“…In a previous study, we successfully constructed a logistic regression model to score the functionalities of non-coding variants using an array of lung cancer-specific features (22). Considering the function impact of non-coding variants is largely feature and cancer type-dependent, in the present study, another logistic regression model was created based on a liver cancer-specific feature annotation to interpret the function information of non-coding pathogenic variants.…”

The long non-coding RNA SNHG5 regulates gefitinib resistance in lung adenocarcinoma cells by targetting miR-377/CASP1 axis

“…Long non-coding RNAs (lncRNAs) are a class of mRNA-like transcripts ranging from 200 bp to 100 kbp, which were regarded as transcription noise in the human genome, due to their lack of capability of protein translation [ 6 ]. LncRNAs emerged rapidly as a diverse group of essential regulators of genetic information flow that interact with the epigenetic, transcriptional, and post-transcriptional pathways of cell proliferation, differentiation, and survival [ 7 ].…”

Noncoding RNAs in Lung Cancer Angiogenesis

“…As a result of the limited success of the classical antiangiogenic therapies targeting VEGF and its related receptors [35,36], researchers have deepened their knowledge by analyzing the expression of ncRNAs sequences in this pathology (Figure 4) [37,38]. The mechanism of lung cancer angiogenesis is far from being completely deciphered and implicit the process of therapeutic inhibition via ncRNAs remains to be further investigated.…”

“…The mechanism of lung cancer angiogenesis is far from being completely deciphered and implicit the process of therapeutic inhibition via ncRNAs remains to be further investigated. Targeting ncRNAs will enable a more precise treatment and will avoid compensatory mechanisms retrieved in lung cancer [2,37,38]. Physiologic and Pathologic Angiogenesis -Signaling Mechanisms and Targeted Therapy…”