Refinement of breast cancer molecular classification by miRNA expression profiles
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ABSTRACT:
PROVIDER: PRJNA544113 | ENA |
REPOSITORIES: ENA
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Project description:Background: Accurate classification of breast cancer using gene expression profiles has contributed to a better understanding of the biological mechanisms behind the disease and has paved the way for better prognostication and treatment prediction. Results: We found that miRNA profiles largely recapitulate intrinsic subtypes. In the case of HER2-enriched tumors a small set of miRNAs including the HER2-encoded mir-4728 identifies the group with very high specificity. We also identified differential expression of the miR-99a/let-7c/miR-125b miRNA cluster as a marker for separation of the Luminal A and B subtypes. High expression of this miRNA cluster is linked to better overall survival among patients with Luminal A tumors. Correlation between the miRNA cluster and their precursor LINC00478 is highly significant suggesting that its expression could help improve the accuracy of present day’s signatures. Conclusions: We show here that miRNA expression can be translated into mRNA profiles and that the inclusion of miRNA information facilitates the molecular diagnosis of specific subtypes, in particular the clinically relevant sub-classification of luminal tumors.
2019-06-20 | GSE131599 | GEO
Project description: Not available
| S-EPMC6580620 | biostudies-literature
Project description:Lung adenocarcinoma (LUAD) is one of the most common pathological and histological subtypes of primary lung cancer, with high morbidity and mortality. MicroRNAs (miRNAs) are endogenous small non-coding RNAs that regulate the expression of genes at post-transcriptional level. It was reported that A-to-I miRNA editing was decreased in tumors, suggesting the potential value of miRNA editing in cancer classification. However, existing miRNA-based cancer classification models mainly used the frequencies of miRNAs. In order to validate the contribution of miRNA editing information in cancer classification, we extracted three types of miRNA features, including the abundances of original miRNAs, the abundances of edited miRNAs, and the editing levels of miRNA editing sites. Our results show that four classification algorithms selected, i.e., kNN, C4.5, RF and SVM, generally had better performances on all features than on the abundances of miRNAs alone. Since the number of features were large, we used three feature selection (FS) methods to further improve the classification models. One of the FS methods, the DFL algorithm, selected only three features, i.e., the frequencies of hsa-miR-135b-5p, hsa-miR-210-3p and hsa-miR-182 48u (an edited miRNA), from 316 training samples. And all of the four classification algorithms achieved 100% accuracy on these three features for 79 independent testing samples. These results indicate that the additional information of miRNA editing are useful in improving the classification of LUAD samples. And the three miRNAs selected by DFL potentially represent an effective molecular signature for LUAD diagnosis.
2024-02-22 | GSE244311 | GEO
Project description:miRBind: a Deep Learning Method for miRNA Binding Classification
| PRJNA903902 | ENA
Project description:Consensus molecular classification of triple-negative breast cancers
| PRJNA933542 | ENA
Project description:In this study gene expression profiles for 307 cases of advanced bladder cancers were compared to molecular phenotype at the tumor cell level. TUR-B tissue for RNA extraction was macrodissected from the close vicinity of the tissue sampled for immunohistochemistry to ensure high-quality sampling and to minimize the effects of intra-tumor heterogeneity. Despite excellent agreement between gene expression values and IHC-score at the single marker level, broad differences emerge when samples are clustered at the global mRNA versus tumor cell (IHC) levels. Classification at the different levels give different results in a systematic fashion, which implicates that analysis at both levels is required for optimal subtype-classification of bladder cancer.
2017-03-13 | GSE83586 | GEO
Project description:Cervical cancers is the second most malignancy in women. It has been clinically important histological variants such as squamous cell carcinoma (SCC) and adenocarcinoma (AC) and adenosquamous carcinomas (ASC). It has been postulated that AC and ASC has a worse prognosis than pure SCC. However, many of the mixed or other types confuses its diagnosis and aggressive/resistant behavior of some tumors has resulted in debate for prognostic role of empirical pathological classification. In addition, the prognosis of adenosquamous carcinoma is still under debate. To establish a novel molecular classification of cervical cancer, we investigated intrinsic characteristics using expression profile.
2010-09-28 | GSE20167 | GEO
Project description:Molecular refinement of pilocytic astrocytoma in adult patients
| PRJNA1023582 | ENA
Project description:Pediatric acute myeloid leukemia (AML) is a heterogeneous disease with respect to biology as well as outcome. In this study, we investigated whether known biological subgroups of pediatric AML are reflected by a common microRNA (miRNA) expression pattern. We assayed 665 miRNAs in 165 pediatric AML samples. First, unsupervised clustering was performed to identify patient clusters with common miRNA expression profiles. Our analysis unraveled 14 clusters, seven of which had a known (cyto-)genetic denominator. Finally, a robust classifier was constructed to discriminate six known molecular aberration groups: 11q23-rearrangements, t(8;21)(q22;q22), inv(16)(p13q22), t(15;17)(q21;q22), NPM1 and CEBPA mutations. The classifier achieved accuracies of 89%, 95%, 95%, 98%, 91% and 96%, respectively. Although lower sensitivities were obtained for the NPM1 and CEBPA (32% and 66%), relatively high sensitivities (84%-94%) were attained for the rest. Specificity was high in all groups (87%-100%). Due to a robust double-loop cross validation procedure we employed, the classifier only used expression of 47 miRNAs to generate the aforementioned accuracies. To validate the 47 miRNA signatures, we applied them to a publicly available adult AML dataset. Despite partial overlap of miRNA platforms and known molecular differences between pediatric and adult AML, the signatures performed reasonably well. This corroborates our claim that the reported miRNA signatures are not dominated by sample size bias in the pediatric AML dataset. We conclude that cytogenetic subtypes of pediatric AML have distinct miRNA expression patterns. Note that, reproducibility of the miRNA signatures in adult dataset suggests that the respective aberrations have a similar biology both in pediatric and adult AML
2017-04-01 | GSE94066 | GEO
Project description:miRNA expression profiles of multiple myeloma
| PRJNA668113 | ENA