m6A RNA Methylation Regulator YTHDF1 Correlated With Immune Microenvironment Predicts Clinical Outcomes and Therapeutic Efficacy in Breast Cancer.
TL;DR: In this paper, the associations of m6A regulators with breast cancer were analyzed by using gene set enrichment analysis (GSEA) and their expression correlations were analyzed using Spearson test.
Abstract: Objective: Increasing evidence highlights the roles of N6-methyladenosine (m6A) and its regulators in oncogenesis. Herein, this study observed the associations of m6A regulators with breast cancer. Methods: RNA-seq profiles of breast cancer were retrieved from the Cancer Genome Atlas (TCGA) database. The expression of m6A regulators was analyzed in tumor and normal tissues. Their expression correlations were analyzed by Spearson test. Overall survival (OS) analysis of these regulators was then presented. Gene set enrichment analysis (GSEA) was performed in high and low YTHDF1 expression groups. The correlations of YTHDF1 expression with immune cells and tumor mutation burden (TMB) were calculated in breast cancer samples. Somatic variation was assessed in high and low YTHDF1 expression groups. Results: Most of m6A regulators were abnormally expressed in breast cancer compared to normal tissues. At the mRNA levels, there were closely relationships between them. Among them, YTHDF1 up-regulation was significantly related to undesirable prognosis (p = 0.025). GSEA results showed that high YTHDF1 expression was associated with cancer-related pathways. Furthermore, YTHDF1 expression was significantly correlated with T cells CD4 memory activated, NK cells activated, monocytes, and macrophages. There were higher TMB scores in YTHDF1 up-regulation group than its down-regulation group. Missense mutation and non-sense mutation were the most frequent mutation types. Conclusion: Our findings suggested that dysregulated m6A regulator YTHDF1 was predictive of survival outcomes as well as response to immunotherapy of breast cancer, and were closely related to immune microenvironment.
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TL;DR: In this paper , an up-to-date and comprehensive overview of how m6A modifications intrinsically affect immune cells and how alterations in tumor cell modifications extrinsically affect immune cell responses in the tumor microenvironment (TME).
Abstract: N6-methyladenosine (m6A) is the most abundant epigenetic modification of RNA, and its dysregulation drives aberrant transcription and translation programs that promote cancer occurrence and progression. Although defective gene regulation resulting from m6A often affects oncogenic and tumor-suppressing networks, m6A can also modulate tumor immunogenicity and immune cells involved in anti-tumor responses. Understanding this counterintuitive concept can aid the design of new drugs that target m6A to potentially improve the outcomes of cancer immunotherapies. Here, we provide an up-to-date and comprehensive overview of how m6A modifications intrinsically affect immune cells and how alterations in tumor cell m6A modifications extrinsically affect immune cell responses in the tumor microenvironment (TME). We also review strategies for modulating endogenous anti-tumor immunity and discuss the challenge of reshaping the TME. Strategies include: combining specific and efficient inhibitors against m6A regulators with immune checkpoint blockers; generating an effective programmable m6A gene-editing system that enables efficient manipulation of individual m6A sites; establishing an effective m6A modification system to enhance anti-tumor immune responses in T cells or natural killer cells; and using nanoparticles that specifically target tumor-associated macrophages (TAMs) to deliver messenger RNA or small interfering RNA of m6A-related molecules that repolarize TAMs, enabling them to remodel the TME. The goal of this review is to help the field understand how m6A modifications intrinsically and extrinsically shape immune responses in the TME so that better cancer immunotherapy can be designed and developed.
53 citations
TL;DR: In this paper , an up-to-date and comprehensive overview of how m6A modifications intrinsically affect immune cells and how alterations in tumor cell modifications extrinsically affect immune cell responses in the tumor microenvironment (TME).
Abstract: N6-methyladenosine (m6A) is the most abundant epigenetic modification of RNA, and its dysregulation drives aberrant transcription and translation programs that promote cancer occurrence and progression. Although defective gene regulation resulting from m6A often affects oncogenic and tumor-suppressing networks, m6A can also modulate tumor immunogenicity and immune cells involved in anti-tumor responses. Understanding this counterintuitive concept can aid the design of new drugs that target m6A to potentially improve the outcomes of cancer immunotherapies. Here, we provide an up-to-date and comprehensive overview of how m6A modifications intrinsically affect immune cells and how alterations in tumor cell m6A modifications extrinsically affect immune cell responses in the tumor microenvironment (TME). We also review strategies for modulating endogenous anti-tumor immunity and discuss the challenge of reshaping the TME. Strategies include: combining specific and efficient inhibitors against m6A regulators with immune checkpoint blockers; generating an effective programmable m6A gene-editing system that enables efficient manipulation of individual m6A sites; establishing an effective m6A modification system to enhance anti-tumor immune responses in T cells or natural killer cells; and using nanoparticles that specifically target tumor-associated macrophages (TAMs) to deliver messenger RNA or small interfering RNA of m6A-related molecules that repolarize TAMs, enabling them to remodel the TME. The goal of this review is to help the field understand how m6A modifications intrinsically and extrinsically shape immune responses in the TME so that better cancer immunotherapy can be designed and developed.
47 citations
TL;DR: The tumor immune microenvironment was linked to m6A methylation regulators, and changes in their copy number will affect the quantity of tumor-infiltrating immune cells dynamically, and this signature correctly predicted the prognosis of cervical cancer patients.
Abstract: Understanding the role of N6-adenosine methylation (m6A) in the tumor microenvironment (TME) is important since it can contribute to tumor development. However, the research investigating the association between m6A and TME and cervical cancer is still in its early stages. The aim of this study was to discover the possible relationship between m6A RNA methylation regulators, TME, PD-L1 expression levels, and immune infiltration in cervical cancer. We gathered RNA-seq transcriptome data and clinical information from cervical cancer patients using The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. To begin, researchers assessed the differences in m6A regulatory factor expression levels between cervical cancer and normal tissues. Clustering analysis was adapted to assess PD-L1 expression, immunological score, immune cell infiltration, TME, and probable pathways in cervical cancer samples. The majority of m6A regulators were found to be considerably overexpressed in cervical cancer tissues. Using consensus clustering of 21 m6A regulators, we identified two subtypes (clusters 1/2) of cervical cancer, and we found that WHO stage and grade were associated with the subtypes. PD-L1 expression increased dramatically in cervical cancer tissues and was significantly linked to ALKBH5, FTO, METTL3, RBM15B, YTHDF1, YTHDF3, and ZC3H13 expression levels. Plasma cells and regulatory T cells (Tregs) were considerably elevated in cluster 2. Cluster 1 is involved in numerous signature pathways, including basal transcription factors, cell cycle, RNA degradation, and the spliceosome. The prognostic signature-based riskscore (METTL16, YTHDF1, and ZC3H13) was found to be an independent prognostic indicator of cervical cancer. The tumor immune microenvironment (TIME) was linked to m6A methylation regulators, and changes in their copy number will affect the quantity of tumor-infiltrating immune cells dynamically. Overall, our research discovered a powerful predictive signature based on m6A RNA methylation regulators. This signature correctly predicted the prognosis of cervical cancer patients. The m6A methylation regulator could be a critical mediator of PD-L1 expression and immune cell infiltration, and it could have a significant impact on the TIME of cervical cancer.
10 citations
TL;DR: The latest progress in m6A RNA methylation research is reviewed and its regulation on BC occurrence and development is investigated to find out if it affects clinical treatments in elderly patients.
Abstract: N6-methyladenosine (m6A) is a dynamic, reversible post-transcriptional modification, and the most common internal modification of eukaryotic messenger RNA (mRNA). Considerable evidence now shows that m6A alters gene expression, thereby regulating cell self-renewal, differentiation, invasion, and apoptotic processes. M6A methylation disorders are directly related to abnormal RNA metabolism, which may lead to tumor formation. M6A methyltransferase is the dominant catalyst during m6A modification; it removes m6A demethylase, promotes recognition by m6A binding proteins, and regulates mRNA metabolic processes. Bladder cancer (BC) is a urinary system malignant tumor, with complex etiology and high incidence rates. A well-differentiated or moderately differentiated pathological type at initial diagnosis accounts for most patients with BC. For differentiated superficial bladder urothelial carcinoma, the prognosis is normally good after surgery. However, due to poor epithelial cell differentiation, BC urothelial cell proliferation and infiltration may lead to invasive or metastatic BC, which lowers the 5-years survival rate and significantly affects clinical treatments in elderly patients. Here, we review the latest progress in m6A RNA methylation research and investigate its regulation on BC occurrence and development.
9 citations
TL;DR: In this article , a review elucidated the underlying role of RNA methyltransferases (writers), demethylases (erasers), and m 6 A-binding proteins (readers) in therapy resistance.
Abstract: Abstract Cancer drug resistance represents the main obstacle in cancer treatment. Drug-resistant cancers exhibit complex molecular mechanisms to hit back therapy under pharmacological pressure. As a reversible epigenetic modification, N 6 -methyladenosine (m 6 A) RNA modification was regarded to be the most common epigenetic RNA modification. RNA methyltransferases (writers), demethylases (erasers), and m 6 A-binding proteins (readers) are frequently disordered in several tumors, thus regulating the expression of oncoproteins, enhancing tumorigenesis, cancer proliferation, development, and metastasis. The review elucidated the underlying role of m 6 A in therapy resistance. Alteration of the m 6 A modification affected drug efficacy by restructuring multidrug efflux transporters, drug-metabolizing enzymes, and anticancer drug targets. Furthermore, the variation resulted in resistance by regulating DNA damage repair, downstream adaptive response (apoptosis, autophagy, and oncogenic bypass signaling), cell stemness, tumor immune microenvironment, and exosomal non-coding RNA. It is highlighted that several small molecules targeting m 6 A regulators have shown significant potential for overcoming drug resistance in different cancer categories. Further inhibitors and activators of RNA m 6 A-modified proteins are expected to provide novel anticancer drugs, delivering the therapeutic potential for addressing the challenge of resistance in clinical resistance.
7 citations
References
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TL;DR: The Gene Set Enrichment Analysis (GSEA) method as discussed by the authors focuses on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation.
Abstract: Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
34,830 citations
TL;DR: An R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters and can be easily extended to other species and ontologies is presented.
Abstract: Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters The analysis module and visualization module were combined into a reusable workflow Currently, clusterProfiler supports three species, including humans, mice, and yeast Methods provided in this package can be easily extended to other species and ontologies The clusterProfiler package is released under Artistic-20 License within Bioconductor project The source code and vignette are freely available at http://bioconductororg/packages/release/bioc/html/clusterProfilerhtml
16,644 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
TL;DR: An R Bioconductor package, Maftools, is described, which offers a multitude of analysis and visualization modules that are commonly used in cancer genomic studies, including driver gene identification, pathway, signature, enrichment, and association analyses, and is independent of larger alignment files.
Abstract: Numerous large-scale genomic studies of matched tumor-normal samples have established the somatic landscapes of most cancer types. However, the downstream analysis of data from somatic mutations entails a number of computational and statistical approaches, requiring usage of independent software and numerous tools. Here, we describe an R Bioconductor package, Maftools, which offers a multitude of analysis and visualization modules that are commonly used in cancer genomic studies, including driver gene identification, pathway, signature, enrichment, and association analyses. Maftools only requires somatic variants in Mutation Annotation Format (MAF) and is independent of larger alignment files. With the implementation of well-established statistical and computational methods, Maftools facilitates data-driven research and comparative analysis to discover novel results from publicly available data sets. In the present study, using three of the well-annotated cohorts from The Cancer Genome Atlas (TCGA), we describe the application of Maftools to reproduce known results. More importantly, we show that Maftools can also be used to uncover novel findings through integrative analysis.
1,990 citations
Aravind Subramanian1, Rajiv Narayan1, Steven M. Corsello1, Steven M. Corsello2, David Peck1, Ted Natoli1, Xiaodong Lu1, Joshua Gould1, John F. Davis1, Andrew A. Tubelli1, Jacob K. Asiedu1, David L. Lahr1, Jodi E. Hirschman1, Zihan Liu1, Melanie Donahue1, Bina Julian1, Mariya Khan1, David Wadden1, Ian Smith1, Daniel D. Lam1, Arthur Liberzon1, Courtney Toder1, Mukta Bagul1, Marek Orzechowski1, Oana M. Enache1, Federica Piccioni1, Sarah A. Johnson1, Nicholas J. Lyons1, Alice H. Berger2, Alice H. Berger1, Alykhan F. Shamji1, Angela N. Brooks2, Angela N. Brooks1, Anita Vrcic1, Corey Flynn1, Jacqueline Rosains1, David Y. Takeda1, David Y. Takeda2, Roger Hu1, Desiree Davison1, Justin Lamb1, Kristin Ardlie1, Larson Hogstrom1, Peyton Greenside1, Nathanael S. Gray2, Nathanael S. Gray1, Paul A. Clemons1, Serena J. Silver1, Xiaoyun Wu1, Wen-Ning Zhao1, Wen-Ning Zhao2, Willis Read-Button1, Xiaohua Wu1, Stephen J. Haggarty1, Stephen J. Haggarty2, Lucienne Ronco1, Jesse S. Boehm1, Stuart L. Schreiber1, Stuart L. Schreiber2, Stuart L. Schreiber3, John G. Doench1, Joshua A. Bittker1, David E. Root1, Bang Wong1, Todd R. Golub •
TL;DR: The expanded CMap is reported, made possible by a new, low-cost, high-throughput reduced representation expression profiling method that is shown to be highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts.
1,943 citations