Identification of microRNAs involved in pathways characterizing the expression subtypes of NSCLC
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ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Ann Rita Halvorsen
PROVIDER: E-MTAB-7954 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
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Project description:microRNA dysregulation is a common feature of cancer cells, but the complex roles of microRNAs in cancer are not fully elucidated. Here we used functional genomics to identify oncogenic microRNAs in non-small cell lung cancer and to evaluate their impact on response to EGFR targeting therapy. Our data demonstrate that microRNAs with an AAGUGC-motif in their seed-sequence increase both cancer cell proliferation and sensitivity to EGFR inhibitors. Global transcriptomics, proteomics and target prediction resulted in the identification of several tumor suppressors involved in the G1/S transition as targets of AAGUGC-microRNAs. The clinical implications of our findings were evaluated by analysis of public domain data supporting the link between this microRNA seed-family, their tumor suppressor targets and cancer cell proliferation. In conclusion we propose that AAGUGC-microRNAs are an integral part of an oncogenic signaling network, and that these findings have potential therapeutic implications, especially in selecting patients for EGFR-targeting therapy.
2017-03-31 | MSV000080829 | MassIVE
Project description:Accurate characterization and understanding of the breast cancer subtypes is of crucial clinical importance to the heterogeneity of this disease. Several layers of information, including immunohistochemical markers, mRNA and microRNA expression profiles, and pathway analysis have been used for such purpose in several studies. However, a comprehensively integrative approach is currently missing. This paper provides microRNA and mRNA expression profiles, characterizing four breast tumor subtypes, as defined by four immunohistochemical markers. The defined sets of features were validated in two independent data sets at multiple levels, including unsupervised clustering and supervised classification. Moreover, the gene expression signatures of the tumor subtypes were screened by in-depth analysis of 12 cancer core pathways. We successfully identified and validated a novel breast cancer subtypes gene expression signature composed of 976 mRNAs and 69 miRNAs. Luminal and non-luminal tumors are shown to significantly differ both at the mRNA and miRNA levels. HER2 positive tumors are more closely related to triple negative tumors by mRNA profiling than by miRNA expression. Closely related miRNAs sharing the same targets may exert opposite roles during tumor progression. Besides the core cancer pathways, other pathways such as those controling biomass synthesis are shown to be important to enable the core basal subtype with additional progressive nature compared with the other triple negative tumors. Some therapeutic strategies are proposed for breast cancer treatment, including the combined blockage of MAPK/ERK and PI3K/Pten signalings for tumors with poor clinical outcome, and targeting Wnt and JAK/STAT and/or Hedgehog, depending on tumor subtypes, together with conventional chemotherapy with the purpose of achieving an eradicative outcome. The pathway analysis also reveals that the clinical strategy and pivotal targets need to be tuned according to different tumor subtypes. This study is the first attempt to elucidate breast cancer subtypes by combining microRNA and mRNA expression, immunohistochemical markers, and cancer core pathways. The results can avail the functional studies of the etiology of breast cancer and translated for clinical use given their intrinsic link in terms of immunohistochemistry and survival. This submission consists of microRNA profiles of 115 breast cancer tumors of several subtypes only.
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Project description:Identification of microRNAs involved in pathways characterizing the expression subtypes of NSCLC
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2020-09-18 | PXD020795 | Pride
Project description:microRNA dysregulation is a common feature of cancer cells, but the complex roles of microRNAs in cancer are not fully elucidated. Here we used functional genomics to identify oncogenic microRNAs in non-small cell lung cancer and to evaluate their impact on response to EGFR targeting therapy. Our data demonstrate that microRNAs with an AAGUGC-motif in their seed-sequence increase both cancer cell proliferation and sensitivity to EGFR inhibitors. Global transcriptomics, proteomics and target prediction resulted in the identification of several tumor suppressors involved in the G1/S transition as targets of AAGUGC-microRNAs. The clinical implications of our findings were evaluated by analysis of public domain data supporting the link between this microRNA seed-family, their tumor suppressor targets and cancer cell proliferation. In conclusion we propose that AAGUGC-microRNAs are an integral part of an oncogenic signaling network, and that these findings have potential therapeutic implications, especially in selecting patients for EGFR-targeting therapy.
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Project description:Background: Breast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from five different molecular levels in order to identify integrated subtypes. Methods: Tumor tissue from 425 primary breast cancer patients of the Oslo2 study was cut and blended before being divided into fractions for DNA-, RNA- and protein isolation, and metabolomics, allowing for representative and comparable molecular data. Patients were stratified into groups based on their tumor characteristics from five different molecular levels, using various clustering methods. Finally, all previously identified and newly determined subgroups were combined in a multilevel classification using a “cluster-of-clusters” approach with consensus clustering. Results: Based on DNA copy number data, tumors were scored into three groups according to the complex arm aberration index. mRNA expression profiles divided tumors into five molecular subgroups according to PAM50 subtyping, and clustering based on microRNA expression revealed four subgroups. Reverse-phase protein array data divided tumors into five subgroups. Hierarchical clustering of tumor metabolic profiles revealed three clusters. Combining DNA copy number and mRNA expression classified tumors into seven clusters based on pathway activity levels, and using integrative clustering, tumors were classified into ten subtypes. The final consensus clustering incorporating all the aforementioned subtypes revealed six major groups. Five corresponded well with the mRNA subtypes, while a sixth group resulted from a split of the luminal A subtype where these tumors belonged to distinct microRNA clusters. Gain-of-function studies using MCF-7 cells showed that microRNAs differentially expressed between the luminal A clusters were important for cancer cell survival. These microRNAs were used to validate the split in luminal A tumors in four independent breast cancer cohorts. In two cohorts the microRNAs divided tumors into subgroups with significant different outcome, and in another a trend was observed. Conclusions: The herein identified six integrated subtypes confirm the heterogeneity of breast cancer and show that finer subdivisions of subtypes are evident. Increasing the knowledge of the luminal A subtype heterogeneity may add pivotal information guiding therapeutic choices, evidently bringing us closer to more personalized treatment for this largest subgroup of breast cancer.
2017-03-01 | GSE80999 | GEO
Project description:Background: Breast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from five different molecular levels in order to identify integrated subtypes. Methods: Tumor tissue from 425 primary breast cancer patients of the Oslo2 study was cut and blended before being divided into fractions for DNA-, RNA- and protein isolation, and metabolomics, allowing for representative and comparable molecular data. Patients were stratified into groups based on their tumor characteristics from five different molecular levels, using various clustering methods. Finally, all previously identified and newly determined subgroups were combined in a multilevel classification using a “cluster-of-clusters” approach with consensus clustering. Results: Based on DNA copy number data, tumors were scored into three groups according to the complex arm aberration index. mRNA expression profiles divided tumors into five molecular subgroups according to PAM50 subtyping, and clustering based on microRNA expression revealed four subgroups. Reverse-phase protein array data divided tumors into five subgroups. Hierarchical clustering of tumor metabolic profiles revealed three clusters. Combining DNA copy number and mRNA expression classified tumors into seven clusters based on pathway activity levels, and using integrative clustering, tumors were classified into ten subtypes. The final consensus clustering incorporating all the aforementioned subtypes revealed six major groups. Five corresponded well with the mRNA subtypes, while a sixth group resulted from a split of the luminal A subtype where these tumors belonged to distinct microRNA clusters. Gain-of-function studies using MCF-7 cells showed that microRNAs differentially expressed between the luminal A clusters were important for cancer cell survival. These microRNAs were used to validate the split in luminal A tumors in four independent breast cancer cohorts. In two cohorts the microRNAs divided tumors into subgroups with significant different outcome, and in another a trend was observed. Conclusions: The herein identified six integrated subtypes confirm the heterogeneity of breast cancer and show that finer subdivisions of subtypes are evident. Increasing the knowledge of the luminal A subtype heterogeneity may add pivotal information guiding therapeutic choices, evidently bringing us closer to more personalized treatment for this largest subgroup of breast cancer.
2017-03-01 | GSE81000 | GEO
Project description:Accurate characterization and understanding of the breast cancer subtypes is of crucial clinical importance to the heterogeneity of this disease. Several layers of information, including immunohistochemical markers, mRNA and microRNA expression profiles, and pathway analysis have been used for such purpose in several studies. However, a comprehensively integrative approach is currently missing. This paper provides microRNA and mRNA expression profiles, characterizing four breast tumor subtypes, as defined by four immunohistochemical markers. The defined sets of features were validated in two independent data sets at multiple levels, including unsupervised clustering and supervised classification. Moreover, the gene expression signatures of the tumor subtypes were screened by in-depth analysis of 12 cancer core pathways. We successfully identified and validated a novel breast cancer subtypes gene expression signature composed of 976 mRNAs and 69 miRNAs. Luminal and non-luminal tumors are shown to significantly differ both at the mRNA and miRNA levels. HER2 positive tumors are more closely related to triple negative tumors by mRNA profiling than by miRNA expression. Closely related miRNAs sharing the same targets may exert opposite roles during tumor progression. Besides the core cancer pathways, other pathways such as those controling biomass synthesis are shown to be important to enable the core basal subtype with additional progressive nature compared with the other triple negative tumors. Some therapeutic strategies are proposed for breast cancer treatment, including the combined blockage of MAPK/ERK and PI3K/Pten signalings for tumors with poor clinical outcome, and targeting Wnt and JAK/STAT and/or Hedgehog, depending on tumor subtypes, together with conventional chemotherapy with the purpose of achieving an eradicative outcome. The pathway analysis also reveals that the clinical strategy and pivotal targets need to be tuned according to different tumor subtypes. This study is the first attempt to elucidate breast cancer subtypes by combining microRNA and mRNA expression, immunohistochemical markers, and cancer core pathways. The results can avail the functional studies of the etiology of breast cancer and translated for clinical use given their intrinsic link in terms of immunohistochemistry and survival.
2013-06-01 | GSE43040 | GEO