EccBase: A high-quality database for exploration and characterization of extrachromosomal circular DNAs in cancer

Extrachromosomal Circular DNA in Cancer: Mechanisms and Clinical Applications

Extrachromosomal circular DNA (eccDNA) has emerged as a critical area of cancer research due to its ubiquitous presence in tumour cells and significant role in tumorigenesis, progression and drug resistance. Recent studies demonstrate that eccDNA promotes cancer progression by influencing genomic instability, amplifying oncogenes, regulating gene expression and enhancing tumour cell adaptability to adverse conditions. While the precise mechanisms underlying eccDNA formation and its biological functions remain unclear, its potential applications in cancer diagnosis, prognosis and targeted therapy are gaining increasing recognition. This review summarises the latest advancements in eccDNA research, highlighting its potential as both a biomarker and a therapeutic target. Additionally, it emphasises the translational potential of eccDNA in clinical diagnostics and personalised treatment strategies, offering new perspectives for future cancer research and innovative therapies.

Exploring the potential of extrachromosomal DNA as a novel oncogenic driver

Extrachromosomal DNA (ecDNA) is a form of circular DNA mostly found in tumor cells. Unlike the typical chromosomal DNA, ecDNA is circular, self-replicating, and carries complete or partial gene fragments. Although ecDNA occurrence remains a rare event in cancer, recent studies have shown that oncogene amplification on ecDNA is widespread throughout many types of cancer, implying that ecDNA plays a central role in accelerating tumor evolution. ecDNA has also been associated with increased tumor mutation burden, chromosomal instability, and even tumor microenvironment remodeling. ecDNA may be crucial in influencing tumor heterogeneity, drug sensitivity, oncogenic senescence, and tumor immunogenicity, leading to a worsening prognosis for tumor patients. In this way, several clinical trials have been conducted to investigate the importance of ecDNA in clinical treatment. In this review, we summarize the biogenesis, characteristics, and current research methods of ecDNA, discuss the vital role of ecDNA-caused tumor heterogeneity in cancers, and highlight the potential role of ecDNA in cancer therapy.

scEccDNAdb: an integrated single-cell eccDNA resource for human and mouse

Extrachromosomal circular DNA (eccDNA), an extrachromosomal circular structured DNA, is extensively found in eukaryotes. Investigating eccDNA at the single-cell level is crucial for understanding cellular heterogeneity, evolution, development, and specific cellular functions. However, high-throughput identification methods for single-cell eccDNA are complex, and the lack of mature, widely applicable technologies has resulted in limited resources. To address this gap, we built scEccDNAdb, a database based on single-cell whole-genome sequencing data. It contains 3 195 464 single-cell eccDNA entries from human and mouse samples, with annotations including oncogenes, typical enhancers, super-enhancers, CCCTC-binding factor-binding sites, single nucleotide polymorphisms, chromatin accessibility, expression quantitative trait loci, transcription factor binding sites, motifs, and structural variants. Additionally, it provides nine online analysis and visualization tools, which enable the creation of publication-quality figures through user-uploaded files. Overall, scEccDNAdb is a comprehensive database for analyzing single-cell eccDNA data across diverse cell types, tissues, and species. Database URL: https://lcbb.swjtu.edu.cn/scEccDNAdb/.

ECGA: A web server to explore and analyze extrachromosomal gene in cancer

Circular extrachromosomal DNA (ecDNA) plays a crucial role in the onset, progression, and evolution of many types of cancers, with dysregulated gene expression driven by ecDNA as a key mechanism. Although database resources for ecDNA are now available, a sophisticated web application dedicated to ecDNA gene analysis remains absent. Therefore, we developed ecDNA gene analyzer (ECGA). ECGA catalogues 23,274 unique ecDNA genes of 27 cancers across 27 tissues. ECGA also offers five specialized analysis tools: (1) 'Venn analysis' looks for overlaps between a given gene list and ecDNA genes; (2) 'Enrichment analysis' performs over-representation analysis and gene set enrichment analysis of input gene list within predefined ecDNA gene sets; (3) 'Target discovery' identifies upregulated ecDNA genes as targets by comparing with reference expression in normal samples; (4) 'DE analysis' finds differentially expressed ecDNA genes; (5) 'Signature discovery' discerns ecDNA gene signatures capable of classifying samples into phenotypic groups, and it is accompanied by 'Signature validation' for model test on unseen data. In summary, ECGA emerges as an indispensable platform in cancer genetics, bridging gaps in basic research, medical reporting, and pharmaceutical development, and propelling ecDNA research forward. ECGA is freely available at https://www.zhounan.org/ecga/.

Comparative analysis of methodologies for detecting extrachromosomal circular DNA

Extrachromosomal circular DNA (eccDNA) is crucial in oncogene amplification, gene transcription regulation, and intratumor heterogeneity. While various analysis pipelines and experimental methods have been developed for eccDNA identification, their detection efficiencies have not been systematically assessed. To address this, we evaluate the performance of 7 analysis pipelines using seven simulated datasets, in terms of accuracy, identity, duplication rate, and computational resource consumption. We also compare the eccDNA detection efficiency of 7 experimental methods through twenty-one real sequencing datasets. Here, we show that Circle-Map and Circle_finder (bwa-mem-samblaster) outperform the other short-read pipelines. However, Circle_finder (bwa-mem-samblaster) exhibits notable redundancy in its outcomes. CReSIL is the most effective pipeline for eccDNA detection in long-read sequencing data at depths higher than 10X. Moreover, long-read sequencing-based Circle-Seq shows superior efficiency in detecting copy number-amplified eccDNA over 10 kb in length. These results offer valuable insights for researchers in choosing the suitable methods for eccDNA research.

Bioinformatics advances in eccDNA identification and analysis

Extrachromosomal circular DNAs (eccDNAs) are a unique class of chromosome-originating circular DNA molecules, which are closely linked to oncogene amplification. Due to recent technological advances, particularly in high-throughput sequencing technology, bioinformatics methods based on sequencing data have become primary approaches for eccDNA identification and functional analysis. Currently, eccDNA-relevant databases incorporate previously identified eccDNA and provide thorough functional annotations and predictions, thereby serving as a valuable resource for eccDNA research. In this review, we collected around 20 available eccDNA-associated bioinformatics tools, including identification tools and annotation databases, and summarized their properties and capabilities. We evaluated some of the eccDNA detection methods in simulated data to offer recommendations for future eccDNA detection. We also discussed the current limitations and prospects of bioinformatics methodologies in eccDNA research.

Decoding the genomic enigma: Approaches to studying extrachromosomal circular DNA

Extrachromosomal circular DNA (eccDNA), a pervasive yet enigmatic component of the eukaryotic genome, exists autonomously from its chromosomal counterparts. Ubiquitous in eukaryotes, eccDNA plays a critical role in the orchestration of cellular processes and the etiology of diseases, particularly cancers. However, the full scope of its influence on health and disease remains elusive, presenting a rich vein of research yet to be mined. Unraveling the complexities of eccDNA necessitates a distillation of methodologies - from biogenesis to functional analysis - a landscape we overview in this study with precision and clarity. Here, we systematically outline cutting-edge methodologies from high-throughput sequencing and bioinformatics to experimental validations, showcasing the intricate world of eccDNAs. We combed through a treasure trove of auxiliary research resources and analytical tools. Moreover, we chart a course for future inquiry, illuminating the horizon with potential groundbreaking strategies for designing eccDNA research projects and pioneering new methodological frontiers.

Characterization, biogenesis model, and current bioinformatics of human extrachromosomal circular DNA

Human extrachromosomal circular DNA, or eccDNA, has been the topic of extensive investigation in the last decade due to its prominent regulatory role in the development of disorders including cancer. With the rapid advancement of experimental, sequencing and computational technology, millions of eccDNA records are now accessible. Unfortunately, the literature and databases only provide snippets of this information, preventing us from fully understanding eccDNAs. Researchers frequently struggle with the process of selecting algorithms and tools to examine eccDNAs of interest. To explain the underlying formation mechanisms of the five basic classes of eccDNAs, we categorized their characteristics and functions and summarized eight biogenesis theories. Most significantly, we created a clear procedure to help in the selection of suitable techniques and tools and thoroughly examined the most recent experimental and bioinformatics methodologies and data resources for identifying, measuring and analyzing eccDNA sequences. In conclusion, we highlighted the current obstacles and prospective paths for eccDNA research, specifically discussing their probable uses in molecular diagnostics and clinical prediction, with an emphasis on the potential contribution of novel computational strategies.

Long extrachromosomal circular DNA identification by fusing sequence-derived features of physicochemical properties and nucleotide distribution patterns

Long extrachromosomal circular DNA (leccDNA) regulates several biological processes such as genomic instability, gene amplification, and oncogenesis. The identification of leccDNA holds significant importance to investigate its potential associations with cancer, autoimmune, cardiovascular, and neurological diseases. In addition, understanding these associations can provide valuable insights about disease mechanisms and potential therapeutic approaches. Conventionally, wet lab-based methods are utilized to identify leccDNA, which are hindered by the need for prior knowledge, and resource-intensive processes, potentially limiting their broader applicability. To empower the process of leccDNA identification across multiple species, the paper in hand presents the very first computational predictor. The proposed iLEC-DNA predictor makes use of SVM classifier along with sequence-derived nucleotide distribution patterns and physicochemical properties-based features. In addition, the study introduces a set of 12 benchmark leccDNA datasets related to three species, namely Homo sapiens (HM), Arabidopsis Thaliana (AT), and Saccharomyces cerevisiae (SC/YS). It performs large-scale experimentation across 12 benchmark datasets under different experimental settings using the proposed predictor, more than 140 baseline predictors, and 858 encoder ensembles. The proposed predictor outperforms baseline predictors and encoder ensembles across diverse leccDNA datasets by producing average performance values of 81.09%, 62.2% and 81.08% in terms of ACC, MCC and AUC-ROC across all the datasets. The source code of the proposed and baseline predictors is available at https://github.com/FAhtisham/Extrachrosmosomal-DNA-Prediction . To facilitate the scientific community, a web application for leccDNA identification is available at https://sds_genetic_analysis.opendfki.de/iLEC_DNA/.