MicroRNA-related sequence variations in human cancers.
Ontology highlight
ABSTRACT:
SUBMITTER: Wojcicka A
PROVIDER: S-EPMC3953544 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:MicroRNAs (miRNAs) are small regulatory RNAs that act by blocking the translation and increasing the degradation of target transcripts. MiRNAs play a critical role in many biological processes including development and differentiation and many studies have shown that major changes in miRNA levels occur in cancer. Since miRNAs degrade target messages, we used this property to develop a novel computational method aimed at determining the actual biological activity of miRNAs using variations in gene expression. Using the method described here, we quantified miRNA activity in papillary thyroid carcinoma and breast cancer, and found a strong and distinctive signal of increased global miRNA activity, embedded in the pertaining gene expression measurements. Interestingly, we found that in these two cancers, miRNA activity is globally increased, and is associated with a global downregulation of miRNA target genes. This downregulation of miRNA regulated genes is particularly noticeable for genes carrying multiple target sites for miRNAs. Among the miRNA-repressed genes, we found a significant enrichment of known tumor suppressors, thereby suggesting that the increased miRNA activity was indeed tumorigenic.
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Project description:It is commonly believed that similarities between the sequences of two proteins infer similarities between their structures. Sequence alignments reliably recognize pairs of protein of similar structures provided that the percentage sequence identity between their two sequences is sufficiently high. This distinction, however, is statistically less reliable when the percentage sequence identity is lower than 30% and little is known then about the detailed relationship between the two measures of similarity. Here, we investigate the inverse correlation between structural similarity and sequence similarity on 12 protein structure families. We define the structure similarity between two proteins as the cRMS distance between their structures. The sequence similarity for a pair of proteins is measured as the mean distance between the sequences in the subsets of sequence space compatible with their structures. We obtain an approximation of the sequence space compatible with a protein by designing a collection of protein sequences both stable and specific to the structure of that protein. Using these measures of sequence and structure similarities, we find that structural changes within a protein family are linearly related to changes in sequence similarity.
| S-EPMC2692051 | biostudies-literature
Project description:Naturally occurring microRNAs (miRNAs), small non-coding RNAs of 19 to 24 nucleotides (nt), are encoded in the genomes of invertebrates, vertebrates, and plants. miRNAs act as regulators of gene expression during development and differentiation at the transcriptional, posttranscriptional, and/or translational levels, although most target genes are still elusive. Many miRNAs are conserved in sequence between distantly related organisms, suggesting that these molecules participate in essential processes. In this review, we present principles related to the basic and translational research that has emerged in the last decade, a period that can be truly considered the "miRNA revolution" in molecular oncology. These principles include the regulation mechanism of miRNA expression, functions of miRNAs in cancers, diagnostic values and therapeutic potentials of miRNAs. Furthermore, we present a compendium of information about the main miRNAs that have been identified in the last several years as playing important roles in cancers. Also, we orient the reader to several additional reviews that may provide a deeper understanding of this new and exciting field of research.
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Project description:BackgroundAutophagy is a stress response mechanism that causes cellular components to degrade. Its defects were associated with multiple pathologies, including cancers. Thyroid cancer is known to be the most prevalent form of malignant neoplasm among endocrine tumors. The aim of the study was to seek and comprehensively explore the role of autophagy related genes and proteins play in thyroid cancers through bioinformatics analysis with their detection in the tissue samples.MethodsBioinformatics analysis was performed to investigate autophagy related proteins and genes involvement in thyroid cancer progression. The experimental verification was done in cancer samples of one hundred and three patients with thyroid pathology included in the study. The miR-125blevel was detected by PCR in real time.Results and discussionThe bioinformatics analysis verified the miR-125b as a regulatory mechanism in autophagy. Its expression in patients with PTC was reduced by 6.75 times in cancer patients compared to the patients with benign tumors. The BRAFV600E mutations were associated with a decrease in hsa-miR-125b expression by 12.67 times compared to tumors with the wild-type gene.ConclusionsOur findings revealed involvement of the autophagy related proteins in cancer progression. The significant mechanisms of regulation are non-coding RNA sequences implicated in a variety of oncogenic processes. We found that miR-125b is a potential maker in thyroid cancer invasion, BRAV600E mutational status and risk of recurrence.
| S-EPMC10048312 | biostudies-literature
Project description:BackgroundWith the recent growth of information on sequence variations in the human genome, predictions regarding the functional effects and relevance to disease phenotypes of coding sequence variations are becoming increasingly important. The aims of this study were to catalog protein-coding sequence variations (CVs) occurring in genetic variation databases and to use bioinformatic programs to analyze CVs. In addition, we aim to provide insight into the functionality of the reference databases.Methodology and findingsTo catalog CVs on a genome-wide scale with regard to protein function and disease, we investigated three representative databases; the Human Gene Mutation Database (HGMD), the Single Nucleotide Polymorphisms database (dbSNP), and the Haplotype Map (HapMap). Using these three databases, we analyzed CVs at the protein function level with bioinformatic programs. We proposed a combinatorial approach using the Support Vector Machine (SVM) to increase the performance of the prediction programs. By cataloging the coding sequence variations using these databases, we found that 4.36% of CVs from HGMD are concurrently registered in dbSNP (8.11% of CVs from dbSNP are concurrent in HGMD). The pattern of substitutions and functional consequences predicted by three bioinformatic programs was significantly different among concurrent CVs, and CVs occurring solely in HGMD or in dbSNP. The experimental results showed that the proposed SVM combination noticeably outperformed the individual prediction programs.ConclusionsThis is the first study to compare human sequence variations in HGMD, dbSNP and HapMap at the genome-wide level. We found that a significant proportion of CVs in HGMD and dbSNP overlap, and we emphasize the need to use caution when interpreting the phenotypic relevance of these concurrent CVs. Combining bioinformatic programs can be helpful in predicting the functional consequences of CVs because it improved the performance of functional predictions.
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Project description:MicroRNAs are small non-coding RNAs that play critical roles in the regulatory mechanisms involving cell differentiation, proliferation, apoptosis and tumorigenesis. Recent research efforts have been conducted to apply these discoveries into clinical functions, including the early diagnosis and therapeutic outcome of patients with cancer. Previous studies have shown that microRNA-149 (miR-149) is dysregulated in various human cancers and exerts its effects on tumorigenesis and tumour progression. In this review, we summarized the potential roles of miR-149 dysregulation and its target genes during tumorigenesis and clinical treatment of human cancers.
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Project description:MicroRNAs (miRNAs) are endogenous short non-coding RNA molecules that regulate gene expression by repressing translation or cleaving RNA transcripts in a sequence-specific manner. A growing body of evidence suggests that miRNAs are aberrantly expressed in many human cancers and that they play significant roles in the initiation, development and metastasis of human cancers. Genome-wide miRNA expression signatures provide information on the aberrant expression of miRNAs in cancers rapidly and precisely. Recently, studies from our group and others revealed that microRNA-1 (miR-1), microRNA-133a (miR-133a), microRNA-133b?(miR-133b) and microRNA-206 (miR-206) are frequently downregulated in various types of cancers. Interestingly, miR-1-1/miR-133a-2,?miR-1-2/miR-133a-1, and miR-206/miR-133b form clusters in three different chromosomal regions of the human genome - 20q13.33, 18q11.2 and 6p12.1, respectively. Here we review highlights of recent findings on the aberrant expression and functional significance of?the miR-1/miR-133a and miR-206/miR-133b clusters in human cancers.
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Project description:The complex regulatory network between microRNAs and gene expression remains unclear domain of active research. We proposed to address in part this complex regulation with a novel approach for the genome-wide identification of biomodules derived from paired microRNA and mRNA profiles, which could reveal correlations associated with a complex network of de-regulation in human cancer. Two published expression datasets for 68 samples with 11 distinct types of epithelial cancers and 21 samples of normal tissues were used, containing microRNA expression (Lu et al. Nature Letters 2005) and gene expression (Ramaswarmy et al. PNAS 2001) profiles, respectively. As results, the microRNA expression used jointly with mRNA expression can provide better classifiers of epithelial cancers against normal epithelial tissue than either dataset alone (p=1×10(-10), F-Test). We identified a combination of six microRNA-mRNA biomodules that optimally classified epithelial cancers from normal epithelial tissue (total accuracy = 93.3%; 95% confidence intervals: 86% - 97%), using penalized logistic regression (PLR) algorithm and three-fold cross-validation. Three of these biomodules are individually sufficient to cluster epithelial cancers from normal tissue using mutual information distance. The biomodules contain 10 distinct microRNAs and 98 distinct genes, including well known tumor markers such as miR-15a, miR-30e, IRAK1, TGFBR2, DUSP16, CDC25B and PDCD2. In addition, there is a significant enrichment (Fisher's exact test p=3×10(-10)) between putative microRNA-target gene pairs reported in five microRNA target databases and the inversely correlated micro-RNA-mRNA pairs in the biomodules. Further, microRNAs and genes in the biomodules were found in abstracts mentioning epithelial cancers (Fisher Exact Test, unadjusted p<0.05). Taken together, these results strongly suggest that the discovered microRNA-mRNA biomodules correspond to regulatory mechanisms common to human epithelial cancer samples. In conclusion, we developed and evaluated a novel comprehensive method to systematically identify, on a genome scale, microRNA-mRNA expression biomodules common to distinct cancers of the same tissue. These biomodules also comprise novel microRNA and genes as well as an imputed regulatory network, which may accelerate the work of cancer biologists as large regulatory maps of cancers can be drawn efficiently for hypothesis generation.
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