Dataset Information

MiRactDB characterizes miRNA-gene relation switch between normal and cancer tissues across pan-cancer.

ABSTRACT: It has been increasingly accepted that microRNA (miRNA) can both activate and suppress gene expression, directly or indirectly, under particular circumstances. Yet, a systematic study on the switch in their interaction pattern between activation and suppression and between normal and cancer conditions based on multi-omics evidences is not available. We built miRactDB, a database for miRNA-gene interaction, at https://ccsm.uth.edu/miRactDB, to provide a versatile resource and platform for annotation and interpretation of miRNA-gene relations. We conducted a comprehensive investigation on miRNA-gene interactions and their biological implications across tissue types in both tumour and normal conditions, based on TCGA, CCLE and GTEx databases. We particularly explored the genetic and epigenetic mechanisms potentially contributing to the positive correlation, including identification of miRNA binding sites in the gene coding sequence (CDS) and promoter regions of partner genes. Integrative analysis based on this resource revealed that top-ranked genes derived from TCGA tumour and adjacent normal samples share an overwhelming part of biological processes, which are quite different than those from CCLE and GTEx. The most active miRNAs predicted to target CDS and promoter regions are largely overlapped. These findings corroborate that adjacent normal tissues might have undergone significant molecular transformations towards oncogenesis before phenotypic and histological change; and there probably exists a small yet critical set of miRNAs that profoundly influence various cancer hallmark processes. miRactDB provides a unique resource for the cancer and genomics communities to screen, prioritize and rationalize their candidates of miRNA-gene interactions, in both normal and cancer scenarios.

SUBMITTER: Tan H

PROVIDER: S-EPMC8138827 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BACKGROUND: In computational biology, a novel knowledge has been obtained mostly by identifying 'intra-relation,' the relation between entities on a specific biological level such as from gene expression or from microRNA (miRNA) and many such researches have been successful. However, intra-relations are not fully explaining complex cancer mechanisms because the inter-relation information between different levels of genomic data is missing, e.g. miRNA and its target genes. The 'inter-relation' between different levels of genomic data can be constructed from biological experimental data as well as genomic knowledge. METHODS: Previously, we have proposed a graph-based framework that integrates with multi-layers of genomic data, copy number alteration, DNA methylation, gene expression, and miRNA expression, for the cancer clinical outcome prediction. However, the limitation of previous work was that we integrated with multi-layers of genomic data without considering of inter-relationship information between genomic features. In this paper, we propose a new integrative framework that combines genomic dataset from gene expression and genomic knowledge from inter-relation between miRNA and gene expression for the clinical outcome prediction as a pilot study. RESULTS: In order to demonstrate the validity of the proposed method, the prediction of short-term/long-term survival for 82 patients in glioblastoma multiforme (GBM) was adopted as a base task. Based on our results, the accuracy of our predictive model increases because of incorporation of information fused over genomic dataset from gene expression and genomic knowledge from inter-relation between miRNA and gene expression. CONCLUSIONS: In the present study, the intra-relation of gene expression was reconstructed from inter-relation between miRNA and gene expression for prediction of short-term/long-term survival of GBM patients. Our finding suggests that the utilization of external knowledge representing miRNA-mediated regulation of gene expression is substantially useful for elucidating the cancer phenotype.

Project description:Background: Sarcoidosis and tuberculosis share similarities in clinical manifestations and histopathological features. We aimed to identify the microRNA (miRNA) profiles of the lymph nodes of individuals with sarcoidosis and of those with tuberculous lymphadenitis to investigate the value of miRNAs in the differential diagnosis of sarcoidosis and tuberculous lymphadenitis. Methods: The miRNA profiles of the lymph nodes of individuals with sarcoidosis, those with tuberculous lymphadenitis (TBLN) and controls were detected by miRNA microarray analysis in the age- and sex-matched development group of the controls (n = 3), patients with TBLN (n = 3) and patients with sarcoidosis (n = 3), and the results were validated by quantitative real-time polymerase chain reaction in the validation group of the controls (n = 30), TBLN (n = 30) and patients with sarcoidosis (n = 31). The relationship between miRNA expression and the clinical parameters of sarcoidosis was analyzed. Results: miR-145, miR-185-5p, miR-301, miR-425-5P, miR-449b and miR-885-5P were differentially expressed between individuals with sarcoidosis and controls (P < 0.0001, P < 0.0001, P = 0.0008, P = 0.0002, P = 0.0018, and P < 0.0001, respectively), and the same six miRNAs were differentially expressed between individuals with tuberculous lymphadenitis and controls (P = 0.0002, P = 0.0004, P = 0.0238, P = 0.0006, P = 0.0149, and P = 0.0045, respectively). miR-185-5p was differentially expressed between individuals with tuberculous lymphadenitis and those with sarcoidosis (P = 0.0101). The area under the receiver operating characteristic curve calculated for miR-185-5p was 0.6860, and the sensitivity and specificity of miR-185-5p for the differential diagnosis of sarcoidosis from TBLN were 61 and 80%, respectively. The levels of miR-145, miR-301, miR-425-5P, and miR-885-5P were positively correlated with CD4+/CD8+ T lymphocytes in bronchoalveolar lavage fluid. Conclusions: miRNAs in lymph nodes show similar expression patterns between individuals with sarcoidosis and those with tuberculous lymphadenitis, which were experimentally selected. miR-185-5p in the lymph nodes can be used as an auxiliary marker for the differential diagnosis of sarcoidosis and tuberculous lymphadenitis.

Project description:BACKGROUND: Precise classification of cancer types is critically important for early cancer diagnosis and treatment. Numerous efforts have been made to use gene expression profiles to improve precision of tumor classification. However, reliable cancer-related signals are generally lacking. METHOD: Using recent datasets on colon and prostate cancer, a data transformation procedure from single gene expression to pair-wise gene expression ratio is proposed. Making use of the internal consistency of each expression profiling dataset this transformation improves the signal to noise ratio of the dataset and uncovers new relevant cancer-related signals (features). The efficiency in using the transformed dataset to perform normal/tumor classification was investigated using feature partitioning with informative features (gene annotation) as discriminating axes (single gene expression or pair-wise gene expression ratio). Classification results were compared to the original datasets for up to 10-feature model classifiers. RESULTS: 82 and 262 genes that have high correlation to tissue phenotype were selected from the colon and prostate datasets respectively. Remarkably, data transformation of the highly noisy expression data successfully led to lower the coefficient of variation (CV) for the within-class samples as well as improved the correlation with tissue phenotypes. The transformed dataset exhibited lower CV when compared to that of single gene expression. In the colon cancer set, the minimum CV decreased from 45.3% to 16.5%. In prostate cancer, comparable CV was achieved with and without transformation. This improvement in CV, coupled with the improved correlation between the pair-wise gene expression ratio and tissue phenotypes, yielded higher classification efficiency, especially with the colon dataset - from 87.1% to 93.5%. Over 90% of the top ten discriminating axes in both datasets showed significant improvement after data transformation. The high classification efficiency achieved suggested that there exist some cancer-related signals in the form of pair-wise gene expression ratio. CONCLUSION: The results from this study indicated that: 1) in the case when the pair-wise expression ratio transformation achieves lower CV and higher correlation to tissue phenotypes, a better classification of tissue type will follow. 2) the comparable classification accuracy achieved after data transformation suggested that pair-wise gene expression ratio between some pairs of genes can identify reliable markers for cancer.

Dataset Information

MiRactDB characterizes miRNA-gene relation switch between normal and cancer tissues across pan-cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets