Similar Datasets
Project description:The colorectal cancer (CRC) cell line pair SW480/SW620 is an accepted model to study CRC progression and metastasis formation. Studying miRNA expression differences might allow to uncover molecular mechanisms that underlie metastasis initiation Both cell lines are derived from the same patient, but at different stages of the disease. They might show differences in tumor-initiating cell (TIC) potential.
2018-04-12 | GSE89522 | GEO
Project description:The colorectal cancer (CRC) cell line pair SW480/SW620 is an accepted model to study CRC progression and metastasis formation. Studying gene expression differences might allow to uncover molecular mechanisms that underlie metastasis initiation Both cell lines are derived from the same patient, but at different stages of the disease. They might show differences in tumor-initiating cell (TIC) potential.
2018-04-12 | GSE89523 | GEO
Project description:Identification of differentially expressed miRNAs between SW480 and SW620 spheroid cultures
| PRJNA352464 | ENA
Project description:Microarray-based gene expression analysis identified microRNAs and mRNAs differentially expressed in 5 glioblastoma spheroid cultures upon ATRA treatment. In this study, a set of 5 glioblastoma spheroid cultures was used to acquire microRNA expression profiles, leading to the identification of differentially expressed microRNAs between untreated and ATRA-treated cultures. In this study, a set of 5 glioblastoma spheroid cultures was used to acquire expression profiles of a total of 16,651 transcripts, leading to the identification of differentially expressed genes between untreated and ATRA-treated cultures by Significance Analysis of Microarray data.
| S-ECPF-GEOD-17227 | biostudies-other
Project description:Retinoblastoma (RB) is a common pediatric cancer. The study aimed to uncover the mechanisms of RB progression and identify novel therapeutic biomarkers.The miRNA expression profile GSE7072, which includes three RB samples and three healthy retina samples, was used. After data normalization using the preprocessCore package, differentially expressed miRNAs (DE-miRs) were selected by the limma package. The targets of the DE-miRs were predicted based on two databases, followed by construction of the miRNA-target network. Pathway enrichment analysis was conducted for the targets of the DE-miRNAs using DAVID. The CTD database was used to predict RB-related genes, followed by clustering analysis using the pvclust package. The correlation network of DE-miRs was established. MiRNA expression was validated in another data set, GSE41321.In total, 24 DE-miRs were identified whose targets were correlated with the cell cycle pathway. Among them, hsa-miR-373, hsa-miR-125b, and hsa-miR-181a were highlighted in the miRNA-target regulatory network; 14 DE-miRs, including hsa-miR-373, hsa-miR-125b, hsa-miR-18a, hsa-miR-25, hsa-miR-20a, and hsa-let-7 (a, b, c), were shown to distinguish RB from healthy tissue. In addition, hsa-miR-25, hsa-miR-18a, and hsa-miR-20a shared the common target BCL2L11; hsa-let-7b and hsa-miR-125b targeted the genes CDC25A, CDK6, and LIN28A. Expression of three miRNAs in GSE41321 was consistent with that in GSE7072.Several critical miRNAs were identified in RB progression. Hsa-miR-373 might regulate RB invasion and metastasis, hsa-miR-181a might involve in the CDKN1B-mediated cell cycle pathway, and hsa-miR-125b and hsa-let-7b might serve as tumor suppressors by coregulating CDK6, CDC25A, and LIN28A. The miRNAs hsa-miR-25, hsa-miR-18a, and hsa-miR-20a might exert their function by coregulating BCL2L1.
| S-EPMC4688417 | biostudies-literature
Project description:The microRNAs, also known as miRNAs, are the class of small noncoding RNAs. They repress the expression of a gene posttranscriptionally. In effect, they regulate expression of a gene or protein. It has been observed that they play an important role in various cellular processes and thus help in carrying out normal functioning of a cell. However, dysregulation of miRNAs is found to be a major cause of a disease. Various studies have also shown the role of miRNAs in cancer and the utility of miRNAs for the diagnosis of cancer and other diseases. Unlike with mRNAs, a modest number of miRNAs might be sufficient to classify human cancers. However, the absence of a robust method to identify differentially expressed miRNAs makes this an open problem. In this regard, this paper presents a novel approach for in silico identification of differentially expressed miRNAs from microarray expression data sets. It integrates judiciously the theory of rough sets and merit of the so-called B.632+ bootstrap error estimate. While rough sets select relevant and significant miRNAs from expression data, the B.632+ error rate minimizes the variability and bias of the derived results. The effectiveness of the proposed approach, along with a comparison with other related approaches, is demonstrated on several miRNA microarray expression data sets, using the support vector machine.
| S-EPMC3790281 | biostudies-literature
Project description:Patient-derived TIC cultures T6 and T18 were used to study the effect of normoxic vs. hypoxic culture conditions The main focus of the study was the identification and validation of new hypoxia-responsive miRNAs and target genes in such colon TICs
2019-09-17 | GSE120424 | GEO
Project description:The differential ribosome-bound lncRNAs between SW620 and SW480 were investigated by RNA-seq.
2020-05-26 | GSE139407 | GEO
Project description:BACKGROUND: The miRNAs, a class of short approximately 22-nucleotide non-coding RNAs, often act post-transcriptionally to inhibit mRNA expression. In effect, they control gene expression by targeting mRNA. They also help in carrying out normal functioning of a cell as they play an important role in various cellular processes. However, dysregulation of miRNAs is found to be a major cause of a disease. It has been demonstrated that miRNA expression is altered in many human cancers, suggesting that they may play an important role as disease biomarkers. Multiple reports have also noted the utility of miRNAs for the diagnosis of cancer. Among the large number of miRNAs present in a microarray data, a modest number might be sufficient to classify human cancers. Hence, the identification of differentially expressed miRNAs is an important problem particularly for the data sets with large number of miRNAs and small number of samples. RESULTS: In this regard, a new miRNA selection algorithm, called ?HEM, is presented based on rough hypercuboid approach. It selects a set of miRNAs from a microarray data by maximizing both relevance and significance of the selected miRNAs. The degree of dependency of sample categories on miRNAs is defined, based on the concept of hypercuboid equivalence partition matrix, to measure both relevance and significance of miRNAs. The effectiveness of the new approach is demonstrated on six publicly available miRNA expression data sets using support vector machine. The.632+ bootstrap error estimate is used to minimize the variability and biasedness of the derived results. CONCLUSIONS: An important finding is that the ?HEM algorithm achieves lowest B.632+ error rate of support vector machine with a reduced set of differentially expressed miRNAs on four expression data sets compare to some existing machine learning and statistical methods, while for other two data sets, the error rate of the ?HEM algorithm is comparable with the existing techniques. The results on several microarray data sets demonstrate that the proposed method can bring a remarkable improvement on miRNA selection problem. The method is a potentially useful tool for exploration of miRNA expression data and identification of differentially expressed miRNAs worth further investigation.
| S-EPMC3844490 | biostudies-literature
Project description:BackgroundTuberculosis pleural effusion (TPE) and malignant pleural effusion (MPE) are very common clinical complications. Considering the totally different prognosis and clinical treatment of TPE and MPE, the accurate and non-invasive diagnosis are very critical for patients with pleural effusion to initiate efficient management and treatment. However, effective clinical biomarkers were rarely explored to distinguish benign from MPE. The purpose of this study is to identify potential miRNAs which can probably be used to differentiate malignant pleural effusion from TPE.ResultsA total of 23 significantly differentially expressed miRNAs were identified in MPE, with 18 up-expressed and 5 down-expressed. And the target genes of the miRNAs mainly involved in the biology process of nervous system, cancer, immune system and metabolic process etc. Three high confident target genes, AGO4, FGF9 and LEF1 can be regulated by miR-195-5p, miR-182-5p and miR-34a-5p respectively. And these genes participate in the canonical pathway of regulation of the Epithelial-Mesenchymal and the biological functions of apoptosis, growth of tumor and cell proliferation of tumor cell lines. Further, RT-PCR validation results based on 64 collected individuals showed that the expression levels of the three miRNAs were 2-5 times higher in MPE samples, which were consistent with the microarray results. In addition, ROC curve analysis demonstrated that the combination of the three miRNAs can achieve higher AUC of 0.93 (p-value< 0.0001) to differentiate MPE from TPE.ConclusionsThe identified miR-195-5p, miR-182-5p and miR-34a-5p can become potential diagnostic biomarkers for MPE with further evidences.
| S-EPMC7045593 | biostudies-literature