Similar Datasets
Project description:Most clinical diagnostic settings and genomic research focus almost exclusively on coding regions and essential splice sites, mostly ignoring non-coding variants. Indeed, the investigation of intronic mis-splicing variants interpreting mechanisms to disease-associated splicing events requires both genomic and transcriptomic data. Unfortunately, there are not many datasets where both are available, leading to the understanding of intronic variants in diseases full of gaps. In this study, we present for the first time a full-length single nuclei RNA-sequencing (snRNA-Seq) approach improving the proper investigation of pathogenic mis-splicing intronic variants in pancreatic cancer showing its contribution to abnormal splicing changes and their transcriptional effects. Finally, we discuss the demands of further machine learning-based methods to process the growing number of single cell data and to enhance the precision and recall.
2024-05-10 | GSE229006 | GEO
Project description:Most clinical diagnostic settings and genomic research focus almost exclusively on coding regions and essential splice sites, mostly ignoring non-coding variants. Indeed, the investigation of intronic mis-splicing variants interpreting mechanisms to disease-associated splicing events requires both genomic and transcriptomic data. Unfortunately, there are not many datasets where both are available, leading to the understanding of intronic variants in diseases full of gaps. In this study, we present for the first time a full-length single nuclei RNA-sequencing (snRNA-Seq) approach improving the proper investigation of pathogenic mis-splicing intronic variants in pancreatic cancer showing its contribution to abnormal splicing changes and their transcriptional effects. Finally, we discuss the demands of further machine learning-based methods to process the growing number of single cell data and to enhance the precision and recall.
2024-05-08 | GSE228844 | GEO
Project description:New strategies for the identification of intronic variants related to splicing events in pancreas cancer
| PRJNA952548 | ENA
Project description:New strategies for the identification of intronic variants related to splicing events in pancreas cancer [iCELL8]
| PRJNA952549 | ENA
Project description:New strategies for the identification of intronic variants related to splicing events in pancreas cancer [RNA-Seq]
| PRJNA951721 | ENA
Project description:FBN1 encodes fibrillin 1, a key structural component of the extracellular matrix, and its variants are associated with a wide range of hereditary connective tissues disorders, such as Marfan syndrome (MFS) and mitral valve-aorta-skeleton-skin (MASS) syndrome. Interpretations of the genomic data and possible genotype-phenotype correlations in FBN1 are complicated by the high rate of intronic variants of unknown significance. Here, we report two unrelated individuals with the FBN1 deep intronic variants c.6872-24T>A and c.7571-12T>A, clinically associated with MFS and MASS syndrome, respectively. The individual carrying the c.6872-24T>A variant is positive for aortic disease. Both individuals lacked ectopia lentis. In silico analysis and subsequent mRNA study by RT-PCR demonstrated the effect of the identified variant on the splicing process in both cases. The c.6872-24T>A and c.7571-12T>A variants generate the retention of intronic nucleotides and lead to the introduction of a premature stop codon. This study enlarges the mutation spectrum of FBN1 and points out the importance of intronic sequence analysis and the need for integrative functional studies in FBN1 diagnostics.
| S-EPMC6627396 | biostudies-literature
Project description:Previous studies revealed that alternative splicing (AS) events and gene variants played key roles in reproduction; however, their location and distribution in hypothalamic fecundity-related genes in sheep without the FecB mutation remain largely unknown. Therefore, in this study, we described the hypothalamic AS events and variants in differentially expressed genes (DEGs) in Small-tailed Han sheep without the FecB mutation at polytocous sheep in the follicular phase vs. monotocous sheep in the follicular phase (PF vs. MF) and polytocous sheep in the luteal phase vs. monotocous sheep in the luteal phase (PL vs. ML) via an RNA-seq study for the first time. We found 39 DEGs with AS events (AS DEGs) in PF vs. MF, while 42 AS DEGs were identified in PL vs. ML. No DEGs with single nucleotide polymorphisms (SNPs) were observed in PF vs. MF, but five were identified in PL vs. ML. We also performed a correlation analysis of transcriptomics and proteomics, and the results suggested several key DEGs/differentially expressed proteins (DEPs), such as LGALS3 in PF vs. MF and aspartoacylase (ASPA) and transthyretin (TTR) in PL vs. ML, could be candidate genes influencing ovine litter size. In addition, further analyses suggested that AS events, SNPs and miRNA-binding sites existed in key DEGs/DEPs, such as ASPA and TTR. All in all, this study provides a new insight into ovine and even other mammalian reproduction.
| S-EPMC7698220 | biostudies-literature
Project description:The contribution of deep intronic splice-altering variants to hereditary breast and ovarian cancer (HBOC) is unknown. Current computational in silico tools to predict spliceogenic variants leading to pseudoexons have limited efficiency. We assessed the performance of the SpliceAI tool combined with ESRseq scores to identify spliceogenic deep intronic variants by affecting cryptic sites or splicing regulatory elements (SREs) using literature and experimental datasets. Our results with 233 published deep intronic variants showed that SpliceAI, with a 0.05 threshold, predicts spliceogenic deep intronic variants affecting cryptic splice sites, but is less effective in detecting those affecting SREs. Next, we characterized the SRE profiles using ESRseq, showing that pseudoexons are significantly enriched in SRE-enhancers compared to adjacent intronic regions. Although the combination of SpliceAI with ESRseq scores (considering ∆ESRseq and SRE landscape) showed higher sensitivity, the global performance did not improve because of the higher number of false positives. The combination of both tools was tested in a tumor RNA dataset with 207 intronic variants disrupting splicing, showing a sensitivity of 86%. Following the pipeline, five spliceogenic deep intronic variants were experimentally identified from 33 variants in HBOC genes. Overall, our results provide a framework to detect deep intronic variants disrupting splicing.
| S-EPMC8268271 | biostudies-literature
Project description:BackgroundEndometrial cancer (EC) is one of the most common female malignant tumors. The immunity is believed to be associated with EC patients' survival, and growing studies have shown that aberrant alternative splicing (AS) might contribute to the progression of cancers.MethodsWe downloaded the clinical information and mRNA expression profiles of 542 tumor tissues and 23 normal tissues from The Cancer Genome Atlas (TCGA) database. ESTIMATE algorithm was carried out on each EC sample, and the OS-related different expressed AS (DEAS) events were identified by comparing the high and low stromal/immune scores groups. Next, we constructed a risk score model to predict the prognosis of EC patients. Finally, we used unsupervised cluster analysis to compare the relationship between prognosis and tumor immune microenvironment.ResultsThe prognostic risk score model was constructed based on 16 OS-related DEAS events finally identified, and then we found that compared with high-risk group the OS in the low-risk group was notably better. Furthermore, according to the results of unsupervised cluster analysis, we found that the better the prognosis, the higher the patient's ESTIMATE score and the higher the infiltration of immune cells.ConclusionsWe used bioinformatics to construct a gene signature to predict the prognosis of patients with EC. The gene signature was combined with tumor microenvironment (TME) and AS events, which allowed a deeper understanding of the immune status of EC patients, and also provided new insights for clinical patients with EC.
| S-EPMC8116885 | biostudies-literature
Project description:The use of multigene panel testing for patients with a predisposition to breast/ovarian cancer is increasing as the identification of variants is useful for diagnosis and disease management. We identified pathogenic and likely pathogenic (P/LP) variants of high-and moderate-risk genes using a 23-gene germline cancer panel in 518 patients with hereditary breast and ovarian cancers (HBOC). The frequency of P/LP variants was 12.4% (64/518) for high- and moderate-penetrant genes, namely, BRCA2 (5.6%), BRCA1 (3.3%), CHEK2 (1.2%), MUTYH (0.8%), PALB2 (0.8%), MLH1 (0.4%), ATM (0.4%), BRIP1 (0.4%), TP53 (0.2%), and PMS2 (0.2%). Five patients possessed two P/LP variants in BRCA1/2 and other genes. We also compared the results from in silico splicing predictive tools and exon splicing patterns from patient samples by analyzing RT-PCR product sequences in six P/LP intronic variants and two intronic variants of unknown significance (VUS). Altered transcriptional fragments were detected for P/LP intronic variants in BRCA1, BRIP1, CHEK2, PARB2, and PMS2. Notably, we identified an in-frame deletion of the BRCA1 C-terminal (BRCT) domain by exon skipping in BRCA1 c.5152+6T>C-as known VUS-indicating a risk for HBOC. Thus, exon splicing analysis can improve the identification of veiled intronic variants that would aid decision making and determination of hereditary cancer risk.
| S-EPMC7540976 | biostudies-literature