A comprehensive framework for analysis of microRNA sequencing data in metastatic colorectal cancer.
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ABSTRACT:
SUBMITTER: Hoye E
PROVIDER: S-EPMC8759566 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:miRNAs play important roles in the regulation of gene expression. The rapidly developing field of microRNA sequencing (miRNA-seq; small RNA-seq) needs comprehensive, robust, user-friendly and standardized bioinformatics tools to analyze these large datasets. We present miRge 2.0, in which multiple enhancements were made towards these goals. RESULTS:miRge 2.0 has become more comprehensive with increased functionality including a novel miRNA detection method, A-to-I editing analysis, integrated standardized GFF3 isomiR reporting, and improved alignment to miRNAs. The novel miRNA detection method uniquely uses both miRNA hairpin sequence structure and composition of isomiRs resulting in higher specificity for potential miRNA identification. Using known miRNA data, our support vector machine (SVM) model predicted miRNAs with an average Matthews correlation coefficient (MCC) of 0.939 over 32 human cell datasets and outperformed miRDeep2 and miRAnalyzer regarding phylogenetic conservation. The A-to-I editing detection strongly correlated with a reference dataset with adjusted R2?=?0.96. miRge 2.0 is the most up-to-date aligner with custom libraries to both miRBase v22 and MirGeneDB v2.0 for 6 species: human, mouse, rat, fruit fly, nematode and zebrafish; and has a tool to create custom libraries. For user-friendliness, miRge 2.0 is incorporated into bcbio-nextgen and implementable through Bioconda. CONCLUSIONS:miRge 2.0 is a redesigned, leading miRNA RNA-seq aligner with several improvements and novel utilities. miRge 2.0 is freely available at: https://github.com/mhalushka/miRge .
| S-EPMC6112139 | biostudies-literature
Project description:BackgroundmiRNAs play a key role in normal physiology and various diseases. miRNA profiling through next generation sequencing (miRNA-seq) has become the main platform for biological research and biomarker discovery. However, analyzing miRNA sequencing data is challenging as it needs significant amount of computational resources and bioinformatics expertise. Several web based analytical tools have been developed but they are limited to processing one or a pair of samples at time and are not suitable for a large scale study. Lack of flexibility and reliability of these web applications are also common issues.ResultsWe developed a Comprehensive Analysis Pipeline for microRNA Sequencing data (CAP-miRSeq) that integrates read pre-processing, alignment, mature/precursor/novel miRNA detection and quantification, data visualization, variant detection in miRNA coding region, and more flexible differential expression analysis between experimental conditions. According to computational infrastructure, users can install the package locally or deploy it in Amazon Cloud to run samples sequentially or in parallel for a large number of samples for speedy analyses. In either case, summary and expression reports for all samples are generated for easier quality assessment and downstream analyses. Using well characterized data, we demonstrated the pipeline's superior performances, flexibility, and practical use in research and biomarker discovery.ConclusionsCAP-miRSeq is a powerful and flexible tool for users to process and analyze miRNA-seq data scalable from a few to hundreds of samples. The results are presented in the convenient way for investigators or analysts to conduct further investigation and discovery.
| S-EPMC4070549 | biostudies-literature
Project description:Tools for microRNA (miR) sequencing data analyses are broadly used in biomedical research. However, the complexity of computational approaches still remains a challenge for biologists with scarce experience in data analytics and bioinformatics. Here, we present miRGalaxy, a Galaxy-based framework for comprehensive analysis of miRs and their sequence variants-miR isoforms (isomiRs). Though isomiRs are commonly reported in deep-sequencing experiments, their detailed structure complexity and specific differential expression (DE) remain not fully examined by the majority of the available analysis tools. miRGalaxy encompasses biologist-user-friendly tools and workflows dedicated to the analysis of the isomiR-ome and its complex behavior in various biological samples. miRGalaxy is developed as a modular, accessible, redistributable, shareable, and user-friendly framework for scientists working with small RNA (sRNA)-seq data. Due to its modular workflow, advanced users can customize the steps and tools for their needs. In addition, the framework provides an analysis report where the significant output results are summarized in charts and visualizations. miRGalaxy can be accessed via preconfigured Docker image flavor and a Toolshed installation if the user already has a running Galaxy instance. Over the last decade, studies on the expression of miRs and isomiRs in normal and deregulated tissues have led to the discovery of their potential as diagnostic biomarkers. The detection of miRs in biofluids further expanded the exploration of the miR repertoire as a source of liquid biopsy biomarkers. Here we show the miRGalaxy framework application for in-depth analysis of the sRNA-seq data from two different biofluids, milk and plasma, to identify, annotate, and discover specific differentially expressed miRs and isomiRs.
| S-EPMC8616193 | biostudies-literature
Project description:BackgroundColorectal cancer (CRC) is the third most common cancer in the world, and most of them are adenocarcinomas. CRC could be classified as colon adenocarcinoma (COAD) and rectum adenocarcinoma (READ) according to the original tumorigenesis position. Increasing evidences indicated that microRNAs (miRNAs) play an important role in the occurrence of multiple tumors.MethodsIn this study, we firstly downloaded miRNA (COAD, 8 controls vs. 455 tumors; READ, 3 controls vs. 161 tumors) and mRNA (COAD, 41 controls vs. 478 tumors; READ, 10 controls vs. 166 tumors) data from The Cancer Genome Atlas (TCGA) database and then used DESeq2, RegParallel, miRDB, TargetScanHuman 7.2, DAVID 6.8, STRING, and Cytoscape software to identify the potential prognosis biomarkers.ResultsWe identified 175 differential expression miRNAs (DEMs) and 3747 differential expression genes (DEGs) in COAD and 184 DEMs and 3928 DEGs in READ. And then, we obtained 21 (13 in COAD and 8 in READ) DEMs associated with the survival rates, which correlated with 440 (217 in COAD and 223 in READ) overlapping DEGs. Through survival analysis for those overlapping DEGs, we found 11 (8 in COAD and 3 in READ) overlapping DGEs associated with survival rates of patients, which were correlated with 9 (7 in COAD and 2 in READ) DEMs significantly.ConclusionIn this study, we found several candidate prognostic biomarkers which have been identified in various cancers and also found several new prognosis biomarkers of COAD and READ. In conclusion, this analysis based on theoretical knowledge and clinical outcomes we have done needs further confirmation by more researches.
| S-EPMC7501550 | biostudies-literature
Project description:This study was to identify microRNAs responsible for colorectal cancer
2017-08-15 | GSE93377 | GEO
Project description:Considering the recent increase in the number of colorectal cancer (CRC) cases in South Korea, we aimed to clarify the molecular characteristics of CRC unique to the Korean population. To gain insights into the complexities of CRC and promote the exchange of critical data, RNA-sequencing analysis was performed to reveal the molecular mechanisms that drive the development and progression of CRC; this analysis is critical for developing effective treatment strategies. We performed RNA-sequencing analysis of CRC and adjacent normal tissue samples from 214 Korean participants (comprising a total of 381 including 169 normal and 212 tumor samples) to investigate differential gene expression between the groups. We identified 19,575 genes expressed in CRC and normal tissues, with 3,830 differentially expressed genes (DEGs) between the groups. Functional annotation analysis revealed that the upregulated DEGs were significantly enriched in pathways related to the cell cycle, DNA replication, and IL-17, whereas the downregulated DEGs were enriched in metabolic pathways. We also analyzed the relationship between clinical information and subtypes using the Consensus Molecular Subtype (CMS) classification. Furthermore, we compared groups clustered within our dataset to CMS groups and performed additional analysis of the methylation data between DEGs and CMS groups to provide comprehensive biological insights from various perspectives. Our study provides valuable insights into the molecular mechanisms underlying CRC in Korean patients and serves as a platform for identifying potential target genes for this disease. The raw data and processed results have been deposited in a public repository for further analysis and exploration.
| S-EPMC11004400 | biostudies-literature
Project description:Colorectal cancer (CRC) is one of the leading causes of cancer related deaths and the search for prognostic biomarkers that might improve treatment decisions is warranted. MicroRNAs (miRNAs) are short non-coding RNA molecules involved in regulating gene expression and have been proposed as possible biomarkers in CRC. In order to characterize the miRNA transcriptome, a large cohort including 88 CRC tumors with long-term follow-up was deep sequenced. 523 mature miRNAs were expressed in our cohort, and they exhibited largely uniform expression patterns across tumor samples. Few associations were found between clinical parameters and miRNA expression, among them, low expression of miR-592 and high expression of miR-10b-5p and miR-615-3p were associated with tumors located in the right colon relative to the left colon and rectum. High expression of miR-615-3p was also associated with poorly differentiated tumors. No prognostic biomarker candidates for overall and metastasis-free survival were identified by applying the LASSO method in a Cox proportional hazards model or univariate Cox. Examination of the five most abundantly expressed miRNAs in the cohort (miR-10a-5p, miR-21-5p, miR-22-3p, miR-143-3p and miR-192-5p) revealed that their collective expression represented 54% of the detected miRNA sequences. Pathway analysis of the target genes regulated by the five most highly expressed miRNAs uncovered a significant number of genes involved in the CRC pathway, including APC, TGF? and PI3K, thus suggesting that these miRNAs are relevant in CRC.
| S-EPMC3688869 | biostudies-literature
Project description:MicroRNAs and tRFs are classes of small non-coding RNAs, known for their roles in translational regulation of genes. Advances in next-generation sequencing (NGS) have enabled high-throughput small RNA-seq studies, which require robust alignment pipelines. Our laboratory previously developed miRge and miRge2.0, as flexible tools to process sequencing data for annotation of miRNAs and other small-RNA species and further predict novel miRNAs using a support vector machine approach. Although miRge2.0 is a leading analysis tool in terms of speed with unique quantifying and annotation features, it has a few limitations. We present miRge3.0 that provides additional features along with compatibility to newer versions of Cutadapt and Python. The revisions of the tool include the ability to process Unique Molecular Identifiers (UMIs) to account for PCR duplicates while quantifying miRNAs in the datasets, correct erroneous single base substitutions in miRNAs with miREC and an accurate mirGFF3 formatted isomiR tool. miRge3.0 also has speed improvements benchmarked to miRge2.0, Chimira and sRNAbench. Finally, miRge3.0 output integrates into other packages for a streamlined analysis process and provides a cross-platform Graphical User Interface (GUI). In conclusion miRge3.0 is our third generation small RNA-seq aligner with improvements in speed, versatility and functionality over earlier iterations.
| S-EPMC8294687 | biostudies-literature
Project description:This study aimed to investigate the mechanisms underlying the development of the androgen-independent phenotype in prostate cancer. Methylation patterns were detected in androgen-independent and androgen-dependent lymph node carcinoma of the prostate (LNCaP) prostate carcinoma cells based on methylated DNA immunoprecipitation-bisulfite sequencing data and differentially methylated regions (DMRs) were identified. Differentially expressed genes (DEGs) and micro RNAs (miRNAs) with DMRs (named MDEGs and MDEmiRNAs) were identified by combining transcriptome and methylation data, and transcription factor (TF)-DEGs with DMRs in promoter (PMDEGs) and MDEmiRNA-MDEGs networks were constructed. Furthermore, a time-course analysis of gene transcription during androgen deprivation was performed based on microarray data and DMRs, MDEGs, and DEmiRNAs were validated. In total, 18,447 DMRs, 3369 MDEGs, 850 PMDEGs, and 1 MDEmiRNA (miR-429) were identified. A TF-target network (94 PMDEGs and 5 TFs) and a miRNA-target network (172 MDEGs and miR-429) were constructed. Based on the time-course analysis of genes in the networks, NEDD4L and PBX3 were targeted by SOX5, while GNAQ, ANLN, and KIF11 were targeted by miR-429. The expression levels of these genes and miR-429 were confirmed by quantitative real-time polymerase chain reaction. Additionally, 109 DMRs were confirmed using additional public datasets. The regulatory pathways SOX5-NEDD4L/PBX3, miR429-GNAQ/ANLN-RHOA, and miR429-ANLN-KIF11 may participate in the progression of the androgen-independent phenotype in prostate cancer.
| S-EPMC5793184 | biostudies-literature
Project description:Tumors rich in stroma are associated with advanced stage and poor prognosis in colorectal adenocarcinoma (CRC). Abundance of stromal cells also has implications for genomic analysis of patient tumors as it may prevent detection of somatic mutations. As part of our efforts to interrogate stroma-cancer cell interactions and to identify actionable therapeutic targets in metastatic CRC, we aimed to determine the proportion of stroma embedded in hepatic CRC metastases by performing computational tumor purity analysis based on whole exome sequencing data (WES). Unlike previous studies focusing on histopathologically prescreened samples, we used an unbiased in-house collection of tumor specimens. WES from CRC liver metastasis samples were utilized to evaluate stromal content and to assess the performance of three in silico tumor purity tools, ABSOLUTE, Sequenza and PureCN. Matching tumor derived organoids were analyzed as a high purity control as they are enriched in cancer cells. Computational purity estimates were compared to those from a histopathological assessment conducted by a board-certified pathologist. According to all computational methods, metastatic specimens had a median tumor purity of 30% whereas the organoids were enriched for cancer cells with a median purity estimate of 94%. In line with this, variant allele frequencies (VAFs) of oncogenes and tumor suppressor genes were undetectable or low in most patient tumors, but higher in matching organoid cultures. Positive correlation was observed between VAFs and in silico tumor purity estimates. Sequenza and PureCN produced concordant results whereas ABSOLUTE yielded lower purity estimates for all samples. Our data shows that unbiased sample selection combined with molecular, computational, and histopathological tumor purity assessment is critical to determine the level of stroma embedded in metastatic colorectal adenocarcinoma.
| S-EPMC10079084 | biostudies-literature