Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1041452 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Tumour DNA contains thousands of somatic single nucleotide variants (SNVs) in non-protein-coding elements, yet their functional significance remains poorly understood. Amongst the most highly mutated elements are long noncoding RNAs (lncRNAs), functional transcripts with known roles in carcinogenesis. To search for driver mutations in lncRNAs, we apply an integrative driver discovery algorithm to SNVs from 2583 primary tumours and 3527 metastases to reveal 54 potential “driver lncRNAs”. Our algorithm confirms a particularly high mutation rate in the iconic cancer lncRNA, NEAT1, which has been ascribed by recent studies to passenger effects. We directly test the functionality of NEAT1 SNVs using in cellulo mutagenesis, identifying discrete regions where mutations reproducibly increase cell proliferation in diverse cell backgrounds, both cancerous and normal. In particular, mutations in the 5’ region alter ribonucleoprotein assembly and boost the population of subnuclear paraspeckles, thus mechanistically linking mutations to cellular proliferation. We then used RNA-pull down followed by mass spectrometry to identify the protein interactor changing between the wild type and mutant form of NEAT1.
2023-07-20 | PXD034007 | Pride
Project description:Integration of multi-omic data for the purposes of biomarker discovery can provide novel and robust panels across multiple biological compartments. Appropriate analytical methods are key to ensuring accurate and meaningful outputs in the multi-omic setting. Here, we extensively profile the proteome and transcriptome of patient pancreatic cyst fluid (PCF) (n=32) and serum (n=68), before integrating matched omic and biofluid data, to identify biomarkers of pancreatic cancer risk. Differential expression analysis, feature reduction, multi-omic data integration, unsupervised hierarchical clustering, principal component analysis, spearman correlations and leave-one-out cross-validation were performed using RStudio and CombiROC software. An 11-feature multi-omic panel in PCF [PIGR, S100A8, REG1A, LGALS3, TCN1, LCN2, PRSS8, MUC6, SNORA66, miR-216a-5p, miR-216b-5p] generated an AUC=0.80. A 13-feature multi-omic panel in serum [SHROOM3, IGHV3-72, IGJ, IGHA1, PPBP, APOD, SFN, IGHG1, miR-197-5p, miR-6741-5p, miR-3180, miR-3180-3p, miR-6782-5p] produced an AUC=0.824. Integration of the strongest performing biomarkers generated a 10-feature cross-biofluid multi-omic panel [S100A8, LGALS3, SNORA66, miR-216b-5p, IGHV3-72, IGJ, IGHA1, PPBP, miR-3180, miR-3180-3p] with an AUC=0.970. Multi-omic profiling provides an abundance of potential biomarkers. Integration of data from different omic compartments, and across biofluids, produced a biomarker panel that performs with high accuracy, showing promise for the risk stratification of these patients.
2025-01-06 | GSE280772 | GEO
Project description:Integration of multi-omic data for the purposes of biomarker discovery can provide novel and robust panels across multiple biological compartments. Appropriate analytical methods are key to ensuring accurate and meaningful outputs in the multi-omic setting. Here, we extensively profile the proteome and transcriptome of patient pancreatic cyst fluid (PCF) (n=32) and serum (n=68), before integrating matched omic and biofluid data, to identify biomarkers of pancreatic cancer risk. Differential expression analysis, feature reduction, multi-omic data integration, unsupervised hierarchical clustering, principal component analysis, spearman correlations and leave-one-out cross-validation were performed using RStudio and CombiROC software. An 11-feature multi-omic panel in PCF [PIGR, S100A8, REG1A, LGALS3, TCN1, LCN2, PRSS8, MUC6, SNORA66, miR-216a-5p, miR-216b-5p] generated an AUC=0.80. A 13-feature multi-omic panel in serum [SHROOM3, IGHV3-72, IGJ, IGHA1, PPBP, APOD, SFN, IGHG1, miR-197-5p, miR-6741-5p, miR-3180, miR-3180-3p, miR-6782-5p] produced an AUC=0.824. Integration of the strongest performing biomarkers generated a 10-feature cross-biofluid multi-omic panel [S100A8, LGALS3, SNORA66, miR-216b-5p, IGHV3-72, IGJ, IGHA1, PPBP, miR-3180, miR-3180-3p] with an AUC=0.970. Multi-omic profiling provides an abundance of potential biomarkers. Integration of data from different omic compartments, and across biofluids, produced a biomarker panel that performs with high accuracy, showing promise for the risk stratification of these patients.
2025-01-06 | GSE280768 | GEO
Project description:The unexplained association between infection and autoimmune disease is strongest for hepatitis C virus-induced cryoglobulinemic vasculitis (HCV-CV). We traced the evolution of the pathogenic rheumatoid factor (RF) autoantibodies in four HCV-CV patients by deep single cell multi-omic analysis, revealing three sources of B cell somatic mutation converged to drive accumulation of a large disease causing clone. A sensitive method for quantifying low affinity binding revealed three recurring heavy/light chain combinations created byV(D)Jrecombination bound self IgG but not viral E2 antigen. Whole genome sequencing revealed accumulation of thousands of somatic mutations, at levels comparable to CLL and normal memory B cells, but with 1-2 corresponding to driver mutations found recurrently in B cell leukemia/lymphoma.V(D)Jhypermutation created autoantibodies with compromised solubility in complex with self-IgG. In this virus-induced autoimmune disease, infection promotes a perfect storm of somatic mutagenesis in the descendants of a single B cell
2025-01-05 | GSE269333 | GEO
Project description:Comprehensive multi-omic profiling of somatic mutations in malformations of cortical development
| PRJNA901943 | ENA
Project description:Thousands of noncoding somatic Single Nucleotide Variants (SNVs) of unknown function are reported in tumors. Partitioning the genome according to cistromes, reveals the enrichment of somatic SNVs in prostate tumors as opposed to adjacent normal tissue cistromes of master transcription regulators, including AR, FOXA1 and HOXB13. This parallels enrichment of prostate cancer genetic predispositions over these transcription regulators’ tumor cistromes, exemplified at the 8q24 locus harboring both risk-variants and somatic SNVs in cis-regulatory elements, upregulating MYC expression and altering the binding of transcription regulators to DNA. However, Massively-Parallel Reporter Assays reveal that few SNVs can alter the transactivation potential of individual CREs. Instead, SNVs accumulate, similarly to inherited riskvariants, in cistromes of master transcription regulators required for prostate cancer development. Significance Difficulties in inferring the biological significance of noncoding mutations have limited their inclusion in precision genomics medicine pipelines. Most attempts to delineate a role for noncoding mutations relied on detecting evidence for positive selection within individual CREs, such as reported for the TERT gene promoter. By considering the enrichment of noncoding mutations in cistromes as opposed to individual CREs, we reveal their specificity towards master transcription regulators that promote prostate cancer development, a feature shared with inherited risk-variants. Overall, our work provides a blueprint for the functional interpretation of noncoding mutations in genomic tests relying on defining cis-regulatory units according to cistrome-partitioning to identify cancer driver transcription regulators.
2019-12-01 | GSE137527 | GEO
Project description:Background: Primary knee osteoarthritis (KOA) is a heterogeneous disease with clinical and molecular contributors. Biofluids contain microRNAs and metabolites that can be measured by omic technologies. Deep learning captures complex non-linear associations within multimodal data but, to date, has not been used for multi-omic-based endotyping of KOA patients. We developed a novel multimodal deep learning framework for clustering of multi-omic data from three subject-matched biofluids to identify distinct KOA endotypes and classify one-year post-total knee arthroplasty (TKA) pain/function responses. Materials and Methods: In 414 KOA patients, subject-matched plasma, synovial fluid and urine were analyzed by microRNA sequencing or metabolomics. Integrating 4 high-dimensional datasets comprising metabolites from plasma (n=151 features), along with microRNAs from plasma (n=421), synovial fluid (n=930), or urine (n=1225), a multimodal deep learning variational autoencoder architecture with K-means clustering was employed. Features influencing cluster assignment were identified and pathway analyses conducted. An integrative machine learning framework combining 4 molecular domains and a clinical domain was then used to classify WOMAC pain/function responses post-TKA within each cluster. Findings: Multimodal deep learning-based clustering of subjects across 4 domains yielded 3 distinct patient clusters. Feature signatures comprising microRNAs and metabolites across biofluids included 30, 16, and 24 features associated with Clusters 1-3, respectively. Pathway analyses revealed distinct pathways associated with each cluster. Integration of 4 multi-omic domains along with clinical data improved response classification performance, with Cluster 3 achieving AUC=0·879 for subject pain response classification and Cluster 2 reaching AUC=0·808 for subject function response, surpassing individual domain classifications by 12% and 15% respectively. Interpretation: We have developed a deep learning-based multimodal clustering model capable of integrating complex multi-fluid, multi-omic data to assist in KOA patient endotyping and test outcome response to TKA surgery.
2024-12-29 | GSE222979 | GEO
Project description:The Hippo pathway is a commonly altered signaling pathway involved in cancer initiation and progression; however, exactly how this pathway becomes dysregulated to promote human cancer development has not been fully understood. In this study, we systematically analyzed the Hippo somatic mutations derived from human cancer genome and functionally annotated their roles in targeting the Hippo pathway. We identified a total of 85 driver missense mutations for the major Hippo pathway genes and elucidated the mechanisms by which these mutations altered their functions in the Hippo pathway. Through these analyses, we revealed zinc-finger domain (ZNF) as an integral structure required for MOB1 function, whose driver mutations promoted head and neck cancer development. Moreover, we discovered that the schwannoma/meningioma-derived NF2 driver mutations gained an oncogenic role by activating the VANGL-JNK pathway. Taken together, our study offers a rich somatic mutation resource for further investigating the Hippo pathway in human cancer, providing a molecular basis for the development of Hippo-related personalized cancer therapy.
2024-10-18 | PXD049472 | Pride