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Revealing nuclear receptor hub modules from Basal-like breast cancer expression networks.

ABSTRACT: Nuclear receptors are a class of transcriptional factors. Together with their co-regulators, they regulate development, homeostasis, and metabolism in a ligand-dependent manner. Their ability to respond to environmental stimuli rapidly makes them versatile cellular components. Their coordinated activities regulate essential pathways in normal physiology and in disease. Due to their complexity, the challenge remains in understanding their direct associations in cancer development. Basal-like breast cancer is an aggressive form of breast cancer that often lacks ER, PR and Her2. The absence of these receptors limits the treatment for patients to the non-selective cytotoxic and cytostatic drugs. To identify potential drug targets it is essential to identify the most important nuclear receptor association network motifs in Basal-like subtype progression. This research aimed to reveal the transcriptional network patterns, in the hope to capture the underlying molecular state driving Basal-like oncogenesis. In this work, we illustrate a multidisciplinary approach of integrating an unsupervised machine learning clustering method with network modelling to reveal unique transcriptional patterns (network motifs) underlying Basal-like breast cancer. The unsupervised clustering method provides a natural stratification of breast cancer patients, revealing the underlying heterogeneity in Basal-like. Identification of gene correlation networks (GCNs) from Basal-like patients in both the TCGA and METABRIC databases revealed three critical transcriptional regulatory constellations that are enriched in Basal-like. These represent critical NR components implicated in Basal-like breast cancer transcription. This approach is easily adaptable and applicable to reveal critical signalling relationships in other diseases.

SUBMITTER: Hsu SN

PROVIDER: S-EPMC8221501 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Although molecular signatures based on transcript expression in breast cancer samples have provided new insights into breast cancer classification and prognosis, there are acknowledged limitations in current signatures. To provide rational, pathway-based signatures of disrupted physiology in cancer tissues that may be relevant to prognosis, this study has directly quantitated changed gene expression, between normal breast and cancer tissue, as a basis for signature development. The nuclear receptor (NR) family of transcription factors, and their coregulators, are fundamental regulators of every aspect of metazoan life, and were rigorously quantified in normal breast tissues and ER? positive and ER? negative breast cancers. Coregulator expression was highly correlated with that of selected NR in normal breast, particularly from postmenopausal women. These associations were markedly decreased in breast cancer, and the expression of the majority of coregulators was down-regulated in cancer tissues compared with normal. While in cancer the loss of NR-coregulator associations observed in normal breast was common, a small number of NR (Rev-ERB?, GR, NOR1, LRH-1 and PGR) acquired new associations with coregulators in cancer tissues. Elevated expression of these NR in cancers was associated with poorer outcome in large clinical cohorts, as well as suggesting the activation of ER? -related, but ER?-independent, pathways in ER? negative cancers. In addition, the combined expression of small numbers of NR and coregulators in breast cancer was identified as a signature predicting outcome in ER? negative breast cancer patients, not linked to proliferation and with predictive power superior to existing signatures containing many more genes. These findings highlight the power of predictive signatures derived from the quantitative determination of altered gene expression between normal breast and breast cancers. Taken together, the findings of this study identify networks of NR-coregulator associations active in normal breast but disrupted in breast cancer, and moreover provide evidence that signatures based on NR networks disrupted in cancer can provide important prognostic information in breast cancer patients.

Project description:MotivationProtein phosphorylation is a ubiquitous mechanism of post-translational modification that plays a central role in cellular signaling. Phosphorylation is particularly important in the context of cancer, as downregulation of tumor suppressors and upregulation of oncogenes by the dysregulation of associated kinase and phosphatase networks are shown to have key roles in tumor growth and progression. Despite recent advances that enable large-scale monitoring of protein phosphorylation, these data are not fully incorporated into such computational tasks as phenotyping and subtyping of cancers.ResultsWe develop a network-based algorithm, CoPPNet, to enable unsupervised subtyping of cancers using phosphorylation data. For this purpose, we integrate prior knowledge on evolutionary, structural and functional association of phosphosites, kinase-substrate associations and protein-protein interactions with the correlation of phosphorylation of phosphosites across different tumor samples (a.k.a co-phosphorylation) to construct a context-specific-weighted network of phosphosites. We then mine these networks to identify subnetworks with correlated phosphorylation patterns. We apply the proposed framework to two mass-spectrometry-based phosphorylation datasets for breast cancer (BC), and observe that (i) the phosphorylation pattern of the identified subnetworks are highly correlated with clinically identified subtypes, and (ii) the identified subnetworks are highly reproducible across datasets that are derived from different studies. Our results show that integration of quantitative phosphorylation data with network frameworks can provide mechanistic insights into the differences between the signaling mechanisms that drive BC subtypes. Furthermore, the reproducibility of the identified subnetworks suggests that phosphorylation can provide robust classification of disease response and markers.Availability and implementationCoPPNet is available at http://compbio.case.edu/coppnet/.Supplementary informationSupplementary data are available at Bioinformatics online.

Project description:Background:Basal-like breast cancer (BLBC) is the most aggressive subtype of breast cancer (BC) and links to poor outcomes. As the molecular mechanism of BLBC has not yet been completely discovered, identification of key pathways and hub genes of this disease is an important way for providing new insights into exploring the mechanisms of BLBC initiation and progression. Objective:The aim of this study was to identify potential gene signatures of the development and progression of the BLBC via bioinformatics analysis. Methods and results:The differential expressed genes (DEGs) including 40 up-regulated and 21 down-regulated DEGs were identified between GSE25066 and GSE21422 microarrays, and these DEGs were significantly enriched in the terms related to oncogenic or suppressive roles in BLBC progression. In addition, KEGG pathway and GSEA (Gene Set Enrichment Analysis) enrichment analyses were performed for DEGs between the basal type and non-basal-type breast cancer from GSE25066 microarray. These DEGs were enriched in pathways such as cell cycle, cytokine-cytokine receptor interaction, chemokine signaling pathway, central carbon metabolism signaling and TNF signaling pathway. Moreover, the protein-protein interaction (PPI) network was constructed with those 61 DEGs using the Cytoscape software, and the biological significance of putative modules was established using MCODE. The module 1 was found to be closely related with a term of mitosis regulation and enriched in cell cycle pathway, and thus confirmed the pathological characteristic of BLBC with a high mitotic index. Furthermore, prediction values of the top 10 hub genes such as CCNB2, BUB1, NDC80, CENPE, KIF2C, TOP2A, MELK, TPX2, CKS2 and KIF20A were validated using Oncomine and Kaplan-Meier plotter. Conclusion:Our results suggest the intriguing possibility that the hub genes and modules in the PPI network contributed to in-depth knowledge about the molecular mechanism of BLBC, paving a way for more accurate discovery of potential treatment targets for BLBC patients.

Project description:Rationale: Basal-like breast cancer (BLBC) is associated with high grade, distant metastasis, and poor prognosis; however, the mechanism underlying aggressiveness of BLBC is still unclear. Emerging evidence has suggested that phospholipid scramblase 1 (PLSCR1) is involved in tumor progression. Here, we aimed to study the possible involvement and molecular mechanisms of PLSCR1 contributing to the aggressive behavior of BLBC. Methods: The potential functions of PLSCR1 in breast cancer cells were assessed by Western blotting, colony formation, migration and invasion, Cell Counting Kit-8 assay, mammosphere formation and flow cytometry. The relationship between nuclear translocation of PLSCR1 and transactivation of STAT1 was examined by immunostaining, co-IP, ChIP, and quantitative reverse transcription PCR. The effect of PLSCR1 expression on BLBC cells was determined by in vitro and in vivo tumorigenesis and a lung metastasis mouse model. Results: Compared to other subtypes, PLSCR1 was considerably increased in BLBC. Phosphorylation of PLSCR1 at Tyr 69/74 contributed to the nuclear translocation of this protein. PLSCR1 was enriched in the promoter region of STAT1 and enhanced STAT3 binding to the STAT1 promoter, resulting in transactivation of STAT1; STAT1 then enhanced cancer stem cell (CSC)-like properties that promoted BLBC progression. The knockdown of PLSCR1 led to significant inhibitory effects on proliferation, migration, invasion, tumor growth and lung metastasis of BLBC cells. Clinically, high PLSCR1 expression was strongly correlated with large tumor size, high grade, metastasis, chemotherapy resistance, and poor survival, indicating poor prognosis in breast cancer patients. Conclusions: Our data show that overexpression and nuclear translocation of PLSCR1 provide tumorigenic and metastatic advantages by activating STAT1 signaling in BLBC. This study not only reveals a critical mechanism of how PLSCR1 contributes to BLBC progression, but also suggests potential prognostic indicators and therapeutic targets for this challenging disease.

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Revealing nuclear receptor hub modules from Basal-like breast cancer expression networks.

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