Integrative Bioinformatics Analysis Reveals CHEK1 and UBE2C as Luminal A Breast Cancer Subtype Biomarkers
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ABSTRACT:
SUBMITTER: Yu D
PROVIDER: S-EPMC9322798 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundBreast 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.MethodsTumor tissue from 425 patients with primary breast cancer from the Oslo2 study was cut and blended, and divided into fractions for DNA, RNA and protein isolation and metabolomics, allowing the acquisition of 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.ResultsBased on DNA copy number data, tumors were categorized 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 tumors were classified into ten subtypes using integrative clustering. The final consensus clustering that incorporated all 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; 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 significantly different outcomes, and in another a trend was observed.ConclusionsThe six integrated subtypes identified confirm the heterogeneity of breast cancer and show that finer subdivisions of subtypes are evident. Increasing knowledge of the heterogeneity of the luminal A subtype may add pivotal information to guide therapeutic choices, evidently bringing us closer to improved treatment for this largest subgroup of breast cancer.
| S-EPMC5372339 | biostudies-literature
Project description:Different breast cancer (BC) subtypes have unique gene expression patterns, but their regulatory mechanisms have yet to be fully elucidated. We hypothesized that the top upregulated (Yin) and downregulated (Yang) genes determine the fate of cancer cells. To reveal the regulatory determinants of these Yin and Yang genes in different BC subtypes, we developed a lasso regression model integrating DNA methylation (DM), copy number variation (CNV) and microRNA (miRNA) expression of 391 BC patients, coupled with miRNA-target interactions and transcription factor (TF) binding sites. A total of 25, 20, 15 and 24 key regulators were identified for luminal A, luminal B, Her2-enriched, and triple negative (TN) subtypes, respectively. Many of the 24 TN regulators were found to regulate the PPARA and FOXM1 pathways. The Yin Yang gene expression mean ratio (YMR) and combined risk score (CRS) signatures built with either the targets of or the TN regulators were associated with the BC patients' survival. Previously, we identified FOXM1 and PPARA as the top Yin and Yang pathways in TN, respectively. These two pathways and their regulators could be further explored experimentally, which might help to identify potential therapeutic targets for TN.
| S-EPMC6521146 | biostudies-literature
Project description:Ubiquitin-Conjugating Enzyme E2 C (UBE2C) is a gene that encodes protein. Disorders associated with UBE2C include methotrexate-related lymphatic hyperplasia and complement component 7 deficiency. The encoded protein is necessary for the destruction of mitotic cell cyclins and cell cycle progression, and may be involved in cancer progression. In this paper, on the basis of public databases, we study the expression differential mechanism of gene expression of UBE2C in various tumors and the performance of prognosis, clinical features, immunity, methylation, etc.
| S-EPMC9091452 | biostudies-literature
Project description:The molecular classification of human breast tumors has afforded insights into subtype specific biological processes, patient prognosis and response to therapies. However, using current methods roughly one quarter of breast tumors cannot be classified into one or another molecular subtype. To explore the possibility that the unclassifiable samples might comprise one or more novel subtypes we employed a collection of publically available breast tumor datasets with accompanying clinical information to assemble 1,593 transcript profiles: 25% of these samples could not be assigned to one of the current molecular subtypes of breast cancer. All of the unclassifiable samples could be grouped into a new molecular subtype, which we termed "luminal-like". We also identified the luminal-like subtype in an independent collection of tumor samples (NKI295). We found that patients harboring tumors of the luminal-like subtype have a better prognosis than those with basal-like breast cancer, a similar prognosis to those with ERBB2+, luminal B or claudin-low tumors, but a worse prognosis than patients with luminal A or normal-like breast tumors. Our findings suggest the occurrence of another molecular subtype of breast cancer that accounts for the vast majority of previously unclassifiable breast tumors.
| S-EPMC4116208 | biostudies-literature
Project description:Luminal B breast tumors have aggressive clinical and biological features, and constitute the most heterogeneous molecular subtype, both clinically and molecularly. Unfortunately, the immunohistochemistry correlate of the luminal B subtype remains still imprecise, and it has now become of paramount importance to define a classification scheme capable of segregating luminal tumors into clinically meaningful subgroups that may be used clinically to guide patient management. With the aim of unraveling the DNA methylation profiles of the luminal subtypes currently being most used in the clinical setting, we have quantified the DNA methylation level of 27,578 CpG sites in 17 luminal B (ER+, Ki67 ≥ 20 % or PgR < 20 % and HER2-), 8 luminal A (ER+ and Ki67 > 20 %) and 4 luminal B-HER2+ (ER+ and HER2+) breast cancer samples by using the Illumina Infinium methylation microarray approach. Unsupervised hierarchical clustering revealed that DNA methylation stratifies luminal B samples in two categories with differing epigenetic and clinical features. One subgroup of luminal B samples showed a methylator phenotype and clustered with the lumB-HER tumors, while the other showed less methylated events, clustered with the luminal A. A 3 CpG marker panel capable of discriminating methylator versus non-methylator luminal B samples was identified and further validated in an independent cohort of patients. Our results provide evidence that DNA methylation and, more specifically, a panel of 3 CpG markers, enables the stratification of luminal B samples in two categories with differing epigenetic and clinical features and support the utilization of this panel for therapeutic stratification of patients with luminal breast cancer.
| S-EPMC4870487 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below.
2017-03-01 | GSE81002 | 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 | 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:Muscle invasive bladder cancer (MIBC) is a heterogeneous disease with a high recurrence rate and poor clinical outcomes. Molecular subtype provides a new framework for the study of MIBC heterogeneity. Clinically, MIBC can be classified as basal and luminal subtypes; they display different clinical and pathological characteristics, but the molecular mechanism is still unclear. Lipidomic and metabolomic molecules have recently been considered to play an important role in the genesis and development of tumors, especially as potential biomarkers. Their different expression profiles in basal and luminal subtypes provide clues for the molecular mechanism of basal and luminal subtypes and the discovery of new biomarkers. Herein, we stratified MIBC patients into basal and luminal subtypes using a MIBC classifier based on transcriptome expression profiles. We qualitatively and quantitatively analyzed the lipids and metabolites of basal and luminal MIBC subtypes and identified their differential lipid and metabolite profiles. Our results suggest that free fatty acids (FFAs) and sulfatides (SLs), which are closely associated with immune and stromal cell types, can contribute to the diagnosis of basal and luminal subtypes of MIBC. Moreover, we showed that glycerophosphocholine (GCP)/imidazoles and nucleosides/imidazoles ratios can accurately distinguish the basal and luminal tumors. Overall, by integrating transcriptomic, lipidomic, and metabolomic data, our study reveals specific biomarkers to differentially diagnose basal and luminal MIBC subtypes and may provide a basis for precision therapy of MIBC.
| S-EPMC8415304 | biostudies-literature
Project description:The recent dramatic increase in breast cancer incidence across China with progressive urbanization and economic development has signaled the urgent need for molecular and clinical detailing of breast cancer in the Chinese population. Our analyses of a unique transethnic collection of breast cancer frozen specimens from Shanghai Fudan Cancer Center (Chinese Han) profiled simultaneously with an analogous Caucasian Italian series revealed consistent transcriptomic data lacking in batch effects. The prevalence of Luminal A subtype was significantly lower in Chinese series, impacting the overall prevalence of estrogen receptor (ER)-positive disease in a large cohort of Chinese/Caucasian patients. Unsupervised and supervised comparison of gene and microRNA (miRNA) profiles of Chinese and Caucasian samples revealed extensive similarity in the comprehensive taxonomy of transcriptional elements regulating breast cancer biology. Partition of gene expression data using gene lists relevant to breast cancer as "intrinsic" and "extracellular matrix" genes identified Chinese and Caucasian subgroups with equivalent global gene and miRNA profiles. These findings indicate that in the Chinese and Caucasian groups, breast neoplasia and the surrounding stromal characteristics undergo the same differentiation and molecular processes. Transcriptional similarity across transethnic cohorts may simplify translational medicine approaches and clinical management of breast cancer patients worldwide.
| S-EPMC4529340 | biostudies-literature