Overexpression of transposable elements is associated with immune evasion and poor outcome in colorectal cancer
TL;DR: In this article, the authors quantified transposable element (TE) expression and developed a TE expression score that is predictive of prognosis and immune infiltration independent of microsatellite instability status and tumour staging in colorectal cancer.
About: This article is published in European Journal of Cancer.The article was published on 2021-09-04. It has received 7 citations till now. The article focuses on the topics: Immune checkpoint & Microsatellite instability.
Citations
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TL;DR: Improved knowledge of TEs may lead to new potential diagnostic markers of diseases, and the exposure of individuals to stresses and environmental contaminants seems to have a non-negligible impact on the epigenetic derepression and mobility ofTEs, which can lead to the development of diseases.
Abstract: Transposable elements (TEs) are recognized as major players in genome plasticity and evolution. The high abundance of TEs in the human genome, especially the Alu and Long Interspersed Nuclear Element-1 (LINE-1) repeats, makes them responsible for the molecular origin of several diseases. This involves several molecular mechanisms that are presented in this review: insertional mutation, DNA recombination and chromosomal rearrangements, modification of gene expression, as well as alteration of epigenetic regulations. This literature review also presents some of the more recent and/or more classical examples of human diseases in which TEs are involved. Whether through insertion of LINE-1 or Alu elements that cause chromosomal rearrangements, or through epigenetic modifications, TEs are widely implicated in the origin of human cancers. Many other human diseases can have a molecular origin in TE-mediated chromosomal recombination or alteration of gene structure and/or expression. These diseases are very diverse and include hemoglobinopathies, metabolic and neurological diseases, and common diseases. Moreover, TEs can also have an impact on aging. Finally, the exposure of individuals to stresses and environmental contaminants seems to have a non-negligible impact on the epigenetic derepression and mobility of TEs, which can lead to the development of diseases. Thus, improving our knowledge of TEs may lead to new potential diagnostic markers of diseases.
15 citations
TL;DR: In this paper, the authors explored the relationship between PPP1R3G expression and clinical features and employed the Kaplan-Meier curves and Cox regression were employed to investigate the prognostic significance of PPP 1 R3G in lung adenocarcinoma.
Abstract: The protein phosphatase 1 regulatory subunit 3 G (PPP1R3G) participates in many tumor biological processes; however, its effects on lung adenocarcinoma (LUAD) have not been clarified. Therefore, this study aimed to explore the correlation between PPP1R3G and the prognosis and immune invasion of LUAD. We evaluated the relationship between PPP1R3G and LUAD using a wide range of databases and analysis tools, including UALCAN, TIMER, miRDB, The Human Protein Atlas and the MethSurv database. First, we explored the mRNA and protein expression levels of PPP1R3G in LUAD, and results were validated using real-time PCR. Next, we explored the relationship between PPP1R3G expression and clinical features. Finally, Kaplan-Meier curves and Cox regression were employed to investigate the prognostic significance of PPP1R3G in LUAD. In addition, we explored the relationship between the expression of PPP1R3G and immune infiltration using the TIMER database. We analyzed the relationship between PPP1R3G and methylation using MethSurv database. Results showed that PPP1R3G expression in LUAD tissues was higher than that in normal tissues, and high expression was suggestive of a poor prognosis. Moreover, PPP1R3G expression was positively correlated with the immune infiltration of CD4 + T cells, macrophages, neutrophils, and dendritic cells. PPP1R3G copy number variations also demonstrated remarkable associations with the levels of B cells, CD4 + T cells, macrophages, neutrophils, and dendritic cells. Finally, a PPP1R3G-associated regulatory network was constructed. Overall, PPP1R3G might be a poor prognostic biomarker for LUAD and is associated with tumor immune cell infiltration.Abbreviations: LUAD: Lung adenocarcinoma; PPP1R3G: The protein phosphatase 1 regulatory subunit 3G; OS: overall survival; CI: confidence interval; CNV: copy number variance; HR: Hazard Ratio; ROC: receiver operating characteristic curve; AUC: area under the curve; TCGA: The Cancer Genome Atlas.
1 citations
TL;DR: In this article , the authors introduce IRescue (Interspersed Repeats single-cell quantifier), a software that accurately estimates the expression of TE subfamilies at singlecell level, implementing a UMI deduplication algorithm to allocate reads ambiguously mapped on TEs.
Abstract: Transposable elements (TEs) are mobile DNA repeats that contribute to the evolution of eukaryotic genomes. In complex organisms, TE expression is tissue specific. However, their contribution to cellular heterogeneity is still unknown and challenging to investigate in single-cell RNA sequencing (scRNA-seq), due to the ubiquity and homology of TEs in the genome. We introduce IRescue (Interspersed Repeats single-cell quantifier), the first software that accurately estimates the expression of TE subfamilies at single-cell level, implementing a UMI deduplication algorithm to allocate reads ambiguously mapped on TEs, while correcting for UMI sequencing errors. Applying IRescue on simulated datasets and real scRNA-seq of colorectal cancers, we could precisely estimate TE subfamilies expression. We show that IRescue improves the definition of cellular heterogeneity, detecting TE expression signatures and specific TE-containing splicing isoforms.
1 citations
TL;DR: In this paper , DNA methyltransferase inhibitors (DNMTis) have been shown to enhance antitumor immunity by inducing transcription of transposable elements and consequent viral mimicry.
Abstract: Abstract In recent years, the tumour microenvironment (TME) of solid tumours has attracted more and more attention from researchers, especially those non-tumour components such as immune cells. Infiltration of various immune cells causes tumour immune microenvironment (TIME) heterogeneity, and results in different therapeutic effects. Accumulating evidence showed that DNA methylation plays a crucial role in remodelling TIME and is associated with the response towards immune checkpoint inhibitors (ICIs). During carcinogenesis, DNA methylation profoundly changes, specifically, there is a global loss of DNA methylation and increased DNA methylation at the promoters of suppressor genes. Immune cell differentiation is disturbed, and exclusion of immune cells from the TME occurs at least in part due to DNA methylation reprogramming. Therefore, pharmaceutical interventions targeting DNA methylation are promising. DNA methyltransferase inhibitors (DNMTis) enhance antitumor immunity by inducing transcription of transposable elements and consequent viral mimicry. DNMTis upregulate the expression of tumour antigens, mediate immune cells recruitment and reactivate exhausted immune cells. In preclinical studies, DNMTis have shown synergistic effect when combined with immunotherapies, suggesting new strategies to treat refractory solid tumours.
References
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TL;DR: EdgeR as mentioned in this paper is a Bioconductor software package for examining differential expression of replicated count data, which uses an overdispersed Poisson model to account for both biological and technical variability and empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference.
Abstract: Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org).
29,413 citations
TL;DR: The philosophy and design of the limma package is reviewed, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
Abstract: limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
22,147 citations
TL;DR: Preliminary clinical findings with blockers of additional immune-checkpoint proteins, such as programmed cell death protein 1 (PD1), indicate broad and diverse opportunities to enhance antitumour immunity with the potential to produce durable clinical responses.
Abstract: Immune checkpoints refer to the plethora of inhibitory pathways that are crucial to maintaining self-tolerance. Tumour cells induce immune checkpoints to evade immunosurveillance. This Review discusses the progress in targeting immune checkpoints, the considerations for combinatorial therapy and the potential for additional immune-checkpoint targets.
10,602 citations
TL;DR: An analytical strategy for integrating scRNA-seq data sets based on common sources of variation is introduced, enabling the identification of shared populations across data sets and downstream comparative analysis.
Abstract: Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.
7,741 citations
TL;DR: CIBERSORT outperformed other methods with respect to noise, unknown mixture content and closely related cell types when applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen and fixed tissues, including solid tumors.
Abstract: We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content and closely related cell types CIBERSORT should enable large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets (http://cibersortstanfordedu/)
6,967 citations