Integrated microRNA and mRNA Signatures Associated with Survival in Triple Negative Breast Cancer II
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE45498 | GEO | 2021/02/01
REPOSITORIES: GEO
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Project description:Triple negative breast cancer (TNBC) includes basal and non-basal subclasses. To further stratify TNBC we determined microRNA (miRNA) and mRNA expression profiles, linked specific miRNA signatures to patient survival and used miRNA/mRNA anti-correlations to identify TNBC subclasses associated with expression of canonical signal pathways
2013-04-19 | GSE41970 | GEO
Project description:Triple negative breast cancer (TNBC) includes basal and non-basal subclasses. To further stratify TNBC we determined microRNA (miRNA) and mRNA expression profiles, linked specific miRNA signatures to patient survival and used miRNA/mRNA anti-correlations to identify TNBC subclasses associated with expression of canonical signal pathways RNA was isolated from formalin-fixed paraffin-embedded tissue cores of 165 primary tumors, 59 adjacent normal and 54 lymph node metastatic samples and expression of 664 miRNAs and 230 cancer-associated mRNAs was assessed for each sample using the nanoString nCounter platform. Kaplan-Meier distant-disease free and overall survival curves were compared using the log-rank test. Cox proportional hazard regression and risk score analysis were used to identify miRNAs for classification of patients with significantly different prognoses.
2013-04-19 | E-GEOD-41970 | biostudies-arrayexpress
Project description:Triple-negative breast cancer (TNBC) is regarded as the most aggressive subtype of breast cancer lacking targeted therapies. This is attributed to its high heterogeneity that complicates elucidation of its molecular aberrations. Here, we report identification of specific proteome expression profiles pertaining to two TNBC subclasses, basal A and basal B, through in-depth proteomics analysis of breast cancer cells. We observed that kinases and proteases displayed unique expression patterns within the subclasses, similar to whole proteome clusters. Systematic analyses of protein-protein interaction and co-regulation networks of these kinases and proteases unraveled dysregulated pathways and plausible targets for each TNBC subclass. Among these, we identified kinases AXL, PEAK1 and TGFBR2, and proteases FAP, UCHL1 and MMP2/14, as specific targets for basal B subclass, which represents the more aggressive TNBC cell lines. Our study thus highlights intricate mechanisms and distinct targets within TNBC, and emphasizes that these have to be exploited in a subclass-specific manner rather than a one-for-all TNBC therapy.
2020-03-03 | PXD017025 | Pride
Project description:Triple negative breast cancer (TNBC) represents a challenging tumor type due to their poor prognosis and limited treatment options. It is well recognize that clinical and molecular heterogeneity of TNBC is driven in part by post-transcriptional regulators such as miRNAs. To stratify TNBCs, we profiled 1050 miRNAs in 132 adjuvant TNBC tumors and 40 tumors from other immunophenotypes using an Affymetrix microarray platform. A NMF clustering analysis allowed us to identify 4 TNBC subtypes featuring unique miRNA expression patterns, disease free and overall survival rates and particular gene ontology enrichments (performed with GSEA algorithm). Our agglomerative approach was cross-validated by using two other clustering algorithms (k-means and consensus clustering). TNBC miRNAs subgroups were also correlated with the Lehmann intrinsic subtypes, finding a significant enrichment of immmnomodulartory and basal 1 subtypes in our high risk miRNA subgroups. 3 cell line models (MDA MB 468, MDA MB 231 and HS578T) were classified according to our miRNA signature, recapitulating two different miRNA subgroups. The TNBC tumors were compared against other phenotypes identifying differentially expressed miRNAs that together with the altered miRNAs within the subgroups allowed us to define interesting miRNAs for further functional analysis.
2018-08-31 | GSE86278 | GEO
Project description:Triple negative breast cancer (TNBC) represents a challenging tumor type due to their poor prognosis and limited treatment options. It is well recognize that clinical and molecular heterogeneity of TNBC is driven in part by post-transcriptional regulators such as miRNAs. To stratify TNBCs, we profiled 1050 miRNAs in 132 adjuvant TNBC tumors and 40 tumors from other immunophenotypes using an Affymetrix microarray platform. A NMF clustering analysis allowed us to identify 4 TNBC subtypes featuring unique miRNA expression patterns, disease free and overall survival rates and particular gene ontology enrichments (performed with GSEA algorithm). Our agglomerative approach was cross-validated by using two other clustering algorithms (k-means and consensus clustering). TNBC miRNAs subgroups were also correlated with the Lehmann intrinsic subtypes, finding a significant enrichment of immmnomodulartory and basal 1 subtypes in our high risk miRNA subgroups. 3 cell line models (MDA MB 468, MDA MB 231 and HS578T) were classified according to our miRNA signature, recapitulating two different miRNA subgroups. The TNBC tumors were compared against other phenotypes identifying differentially expressed miRNAs that together with the altered miRNAs within the subgroups allowed us to define interesting miRNAs for further functional analysis.
2018-08-16 | GSE86281 | GEO
Project description:Triple negative breast cancer (TNBC) represents a challenging tumor type due to their poor prognosis and limited treatment options. It is well recognize that clinical and molecular heterogeneity of TNBC is driven in part by post-transcriptional regulators such as miRNAs. To stratify TNBCs, we profiled 1050 miRNAs in 132 adjuvant TNBC tumors and 40 tumors from other immunophenotypes using an Affymetrix microarray platform. A NMF clustering analysis allowed us to identify 4 TNBC subtypes featuring unique miRNA expression patterns, disease free and overall survival rates and particular gene ontology enrichments (performed with GSEA algorithm). Our agglomerative approach was cross-validated by using two other clustering algorithms (k-means and consensus clustering). TNBC miRNAs subgroups were also correlated with the Lehmann intrinsic subtypes, finding a significant enrichment of immmnomodulartory and basal 1 subtypes in our high risk miRNA subgroups. 3 cell line models (MDA MB 468, MDA MB 231 and HS578T) were classified according to our miRNA signature, recapitulating two different miRNA subgroups. The TNBC tumors were compared against other phenotypes identifying differentially expressed miRNAs that together with the altered miRNAs within the subgroups allowed us to define interesting miRNAs for further functional analysis.
2018-08-16 | GSE86277 | GEO
Project description:Several studies have shown that negative and positive miRNA-mRNA correlations are symmetrically distributed. While negative correlations are consistent with a faster degradation of miRNA targets, the presence of positive correlations suggests bidirectional interactions between the two classes of molecules. However, a comprehensive study of miRNA and mRNA correlations is lacking. A homogeneous map of miRNA and mRNA relationships was obtained by multidimensional scaling (MDS) applied to a single matrix including both heterologous (miRNA-mRNA) and homologous (miRNA-miRNA and mRNA-mRNA) correlations.
2015-10-08 | GSE62030 | GEO
Project description:Several studies have shown that negative and positive miRNA-mRNA correlations are symmetrically distributed. While negative correlations are consistent with a faster degradation of miRNA targets, the presence of positive correlations suggests bidirectional interactions between the two classes of molecules. However, a comprehensive study of miRNA and mRNA correlations is lacking. A homogeneous map of miRNA and mRNA relationships was obtained by multidimensional scaling (MDS) applied to a single matrix including both heterologous (miRNA-mRNA) and homologous (miRNA-miRNA and mRNA-mRNA) correlations.
2015-10-08 | GSE62029 | GEO
Project description:Triple negative breast cancer (TNBC) represents a challenging tumor type due to their poor prognosis and limited treatment options. It is well recognize that clinical and molecular heterogeneity of TNBC is driven in part by mRNA and lncRNAs. To stratify TNBCs, we profiled mRNAs and lncRNA in 158 adjuvant TNBC tumors using an Affymetrix microarray platform. Lehmann clustering analysis allowed us to identify TNBC subtypes featuring unique lncRNA expression patterns, disease free and overall survival rates and particular gene ontology enrichments (performed with GSEA algorithm).
2018-08-31 | GSE86946 | GEO
Project description:Triple negative breast cancer (TNBC) represents a challenging tumor type due to their poor prognosis and limited treatment options. It is well recognize that clinical and molecular heterogeneity of TNBC is driven in part by mRNA and lncRNAs. To stratify TNBCs, we profiled mRNAs and lncRNA in 158 adjuvant TNBC tumors using an Affymetrix microarray platform. Lehmann clustering analysis allowed us to identify TNBC subtypes featuring unique lncRNA expression patterns, disease free and overall survival rates and particular gene ontology enrichments (performed with GSEA algorithm).
2018-08-31 | GSE86945 | GEO