Dataset Information

Evolution and genomic signatures of spontaneous somatic mutation in Drosophila intestinal stem cells.

ABSTRACT: Spontaneous mutations can alter tissue dynamics and lead to cancer initiation. Although large-scale sequencing projects have illuminated processes that influence somatic mutation and subsequent tumor evolution, the mutational dynamics operating in the very early stages of cancer development are currently not well understood. To explore mutational processes in the early stages of cancer evolution, we exploited neoplasia arising spontaneously in the Drosophila intestine. Analysing whole-genome sequencing data with a dedicated bioinformatic pipeline, we found neoplasia formation to be driven largely through the inactivation of Notch by structural variants, many of which involve highly complex genomic rearrangements. The genome-wide mutational burden in neoplasia was found to be similar to that of several human cancers. Finally, we identified genomic features associated with spontaneous mutation, and defined the evolutionary dynamics and mutational landscape operating within intestinal neoplasia over the short lifespan of the adult fly. Our findings provide unique insight into mutational dynamics operating over a short timescale in the genetic model system, Drosophila melanogaster.

SUBMITTER: Riddiford N

PROVIDER: S-EPMC8327918 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Evolution and genomic signatures of spontaneous somatic mutation in <i>Drosophila</i> intestinal stem cells.

Riddiford Nick N Siudeja Katarzyna K van den Beek Marius M Boumard Benjamin B Bardin Allison J AJ

Genome research 20210624 8

Spontaneous mutations can alter tissue dynamics and lead to cancer initiation. Although large-scale sequencing projects have illuminated processes that influence somatic mutation and subsequent tumor evolution, the mutational dynamics operating in the very early stages of cancer development are currently not well understood. To explore mutational processes in the early stages of cancer evolution, we exploited neoplasia arising spontaneously in the <i>Drosophila</i> intestine. Analysing whole-gen ...[more]

PMID: 34168010

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Project description:Background: Lung adenocarcinoma (LUAD) is a highly heterogeneous tumor with substantial somatic mutations and genome instability, which are emerging hallmarks of cancer. Long non-coding RNAs (lncRNAs) are promising cancer biomarkers that are reportedly involved in genomic instability. However, the identification of genome instability-related lncRNAs (GInLncRNAs) and their clinical significance has not been investigated in LUAD. Methods: We determined GInLncRNAs by combining somatic mutation and transcriptome data of 457 patients with LUAD and probed their potential function using co-expression network and Gene Ontology (GO) enrichment analyses. We then filtered GInLncRNAs by Cox regression and LASSO regression to construct a genome instability-related lncRNA signature (GInLncSig). We subsequently evaluated GInLncSig using correlation analyses with mutations, external validation, model comparisons, independent prognostic significance analyses, and clinical stratification analyses. Finally, we established a nomogram for prognosis prediction in patients with LUAD and validated it in the testing set and the entire TCGA dataset. Results: We identified 161 GInLncRNAs, of which seven were screened to develop a prognostic GInLncSig model (LINC01133, LINC01116, LINC01671, FAM83A-AS1, PLAC4, MIR223HG, and AL590226.1). GInLncSig independently predicted the overall survival of patients with LUAD and displayed an improved performance compared to other similar signatures. Furthermore, GInLncSig was related to somatic mutation patterns, suggesting its ability to reflect genome instability in LUAD. Finally, a nomogram comprising the GInLncSig and tumor stage exhibited improved robustness and clinical practicability for predicting patient prognosis. Conclusion: Our study identified a signature for prognostic prediction in LUAD comprising seven lncRNAs associated with genome instability, which may provide a useful indicator for clinical stratification management and treatment decisions for patients with LUAD.

Project description:BackgroundRenal cell carcinoma (RCC) is a malignant tumor with high morbidity and mortality. It is characterized by a large number of somatic mutations and genomic instability. Long non-coding RNAs (lncRNAs) are widely involved in the expression of genomic instability in renal cell carcinoma. But no studies have identified the genome instability-related lncRNAs (GInLncRNAs) and their clinical significances in RCC.MethodsClinical data, gene expression data and mutation data of 943 RCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Based on the mutation data and lncRNA expression data, GInLncRNAs were screened out. Co-expression analysis, Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted to explore their potential functions and related signaling pathways. A prognosis model was further constructed based on genome instability-related lncRNAs signature (GInLncSig). And the efficiency of the model was verified by receiver operating characteristic (ROC) curve. The relationships between the model and clinical information, prognosis, mutation number and gene expression were analyzed using correlation prognostic analysis. Finally, the prognostic model was verified in clinical stratification according to TCGA dataset.ResultsA total of 45 GInLncRNAs were screened out. Functional analysis showed that the functional genes of these GInLncRNAs were mainly enriched in chromosome and nucleoplasmic components, DNA binding in molecular function, transcription and complex anabolism in biological processes. Univariate and Multivariate Cox analyses further screened out 11 GInLncSig to construct a prognostic model (AL031123.1, AC114803.1, AC103563.7, AL031710.1, LINC00460, AC156455.1, AC015977.2, 'PRDM16-dt', AL139351.1, AL035661.1 and LINC01606), and the coefficient of each GInLncSig in the model was calculated. The area under the curve (AUC) value of the ROC curve was 0.770. Independent analysis of the model showed that the GInLncSig model was significantly correlated with the RCC patients' overall survival. Furthermore, the GInLncSig model still had prognostic value in different subgroups of RCC patients.ConclusionOur study preliminarily explored the relationship between genomic instability, lncRNA and clinical characteristics of RCC patients, and constructed a GInLncSig model consisted of 11 GInLncSig to predict the prognosis of patients with RCC. At the same time, our study provided theoretical support for the exploration of the formation and development of RCC.

Dataset Information

Evolution and genomic signatures of spontaneous somatic mutation in Drosophila intestinal stem cells.

Publications

Evolution and genomic signatures of spontaneous somatic mutation in <i>Drosophila</i> intestinal stem cells.

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