Detection of miRNA regulatory effect on triple negative breast cancer transcriptome

E2F5 Targeted by Let-7d-5p Facilitates Cell Proliferation, Metastasis and Immune Escape in Gallbladder Cancer

BACKGROUND

Gallbladder cancer (GBC) remains a serious cause of cancer-related mortality across the globe. E2F5 has been identified to as a known oncogene in various cancers. However, the special functions of E2F5 have not been investigated in GBC.

AIMS

To explore the regulatory functions of E2F5 and its related molecular regulatory mechanism in GBC progression.

METHODS

The expression of genes were examined through qRT-PCR, western blot and IHC assay. The cell proliferation was assessed through CCK-8 and EDU assays. The cytotoxicity was tested through LDH assay. The percentage of CD8+ T cells and cell apoptosis were evaluated through flow cytometry. The binding ability was detected through luciferase reporter assay. The tumor growth was assessed through in vivo assays.

RESULTS

In this study, it was demonstrated that E2F5 expression was evaluated in GBC, and resulted into poor prognosis. Bioinformatics analysis revealed E2F5 as a target for let-7d-5p, which when overexpressed, suppressed the metastasis and proliferation of GBC through the downregulation of E2F5. It was discovered that E2F5 activates JAK2/STAT3 signaling which is suppressed by let-7d-5p, implicating this pathway as one of the effectors of the oncogenic effects of ESF5 in GBC. E2F5 had been confirmed to aggravate tumor growth in vivo.

CONCLUSION

E2F5 targeted by let-7d-5p facilitated cell proliferation, metastasis and immune escape in GBC through the JAK2/STAT3 pathway.

Identification of mRNA and non-coding RNA hubs using network analysis in organ tropism regulated triple negative breast cancer metastasis

Triple negative breast cancer (TNBC) is aggressive in nature, resistant to conventional therapy and often ends in organ specific metastasis. In this study, publicly available datasets were used to identify miRNA, mRNA and lncRNA hubs. Using validated mRNA-miRNA, mRNA-mRNA and lncRNA-miRNA interaction information obtained from various databases, RNA interaction networks for TNBC and its subtype specific as well as organ tropism regulated metastasis were generated. Further, miRNA-mRNA-lncRNA triad classification was performed using social network analysis from subnetworks and visualized using Cytoscape. Survival analysis of the RNA hubs, oncoprint analysis for mRNAs and pathway analysis of the lncRNAs were also performed. Results indicated that two lncRNAs (NEAT1 and CASC7) and four miRNAs (hsa-miR-106b-5p, hsa-miR-148a-3p, hsa-miR-25-3p and hsa-let-7i-5p) were common between hubs identified in TNBC and TNBC associated metastasis. The exclusive hubs for TNBC associated metastasis were hsa-miR-200b-3p, SP1, HSPA4 and RAB1B. HMGA1 was the top ranked hub in mesenchymal subtype associated lung metastasis, while hsa-miR-27a-3p was identified as the top ranked hub mRNA in luminal androgen receptor subtype associated bone metastasis. When lncRNA associated pathway analysis was performed, Hs Cytoplasmic Ribosomal Protein pathway was found to be the most significant and among the selected hubs, CTNND1, SON and hsa-miR-29c emerged as TNBC survival markers. TP53, FOXA1, MTDH and HDGF were found as the top ranked mRNAs in oncoprint analysis. The pipeline proposed for the first time in this study with validated RNA interaction data integration and graph-based learning for miRNA-mRNA-lncRNA triad classification from RNA hubs may aid experimental cost reduction and its successful execution will allow it to be extended to other diseases too.

A panel of eight microRNAs is a good predictive parameter for triple-negative breast cancer relapse

Rationale: Triple-negative breast cancer (TNBC), which has the highest recurrence rate and shortest survival time of all breast cancers, is in urgent need of a risk assessment method to determine an accurate treatment course. Recently, miRNA expression patterns have been identified as potential biomarkers for diagnosis, prognosis, and personalized therapy. Here, we investigate a combination of candidate miRNAs as a clinically applicable signature that can precisely predict relapse in TNBC patients after surgery. Methods: Four total cohorts of training (TCGA_TNBC and GEOD-40525) and validation (GSE40049 and GSE19783) datasets were analyzed with logistic regression and Gaussian mixture analyses. We established a miRNA signature risk model and identified an 8-miRNA signature for the prediction of TNBC relapse. Results: The miRNA signature risk model identified ten candidate miRNAs in the training set. By combining 8 of the 10 miRNAs (miR-139-5p, miR-10b-5p, miR-486-5p, miR-455-3p, miR-107, miR-146b-5p, miR-324-5p and miR-20a-5p), an accurate predictive model of relapse in TNBC patients was established and was highly correlated with prognosis (AUC of 0.80). Subsequently, this 8-miRNA signature prognosticated relapse in the two validation sets with AUCs of 0.89 and 0.90. Conclusion: The 8-miRNA signature predictive model may help clinicians provide a prognosis for TNBC patients with a high risk of recurrence after surgery and provide further personalized treatment to decrease the chance of relapse.

Three-microRNA expression signature predicts survival in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a specific type of breast cancer with poor overall survival (OS) time. Previous studies revealed that microRNAs (miRNAs/miRs) serve important roles in the pathogenesis, progression and prognosis of TNBC. The present study analyzed the miRNA expression and clinical data of patients with TNBC downloaded from The Cancer Genome Atlas. A total of 194 differentially expressed miRNAs were identified between TNBC and matched normal tissues using the cut-off criteria of P<0.05 and |log2 fold change|>2. Of these miRNAs, 65 were downregulated and 129 were upregulated. Using Kaplan-Meier survival analysis, a total of 77 miRNAs that were closely associated with OS time were identified (P<0.05). The intersection of the 77 miRNAs and 194 differentially expressed miRNAs revealed six miRNAs. Log-rank tests based on survival curves were performed and two miRNAs were eliminated. The prognostic value of the remaining four miRNAs was evaluated with a Cox proportional hazards model using multiple logistic regression with forward stepwise selection of variables. Three miRNAs (miR-21-3p, miR-659-5p and miR-200b-5p) were subsequently identified as independent risk factors associated with OS time in the model. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses revealed that the target genes of these three miRNAs were mainly involved in ‘cell protein metabolism’, ‘RNA transcriptional regulation’, ‘cell migration’, ‘MAPK signaling pathway’, ‘ErbB signaling pathway’, ‘prolactin signaling pathway’ and ‘adherens junctions’. Taken together, the results obtained in the present study suggested that the three-miRNA signature may serve as a prognostic biomarker for patients with TNBC.

Identification of microRNA clusters cooperatively acting on epithelial to mesenchymal transition in triple negative breast cancer

MicroRNAs play important roles in many biological processes. Their aberrant expression can have oncogenic or tumor suppressor function directly participating to carcinogenesis, malignant transformation, invasiveness and metastasis. Indeed, miRNA profiles can distinguish not only between normal and cancerous tissue but they can also successfully classify different subtypes of a particular cancer. Here, we focus on a particular class of transcripts encoding polycistronic miRNA genes that yields multiple miRNA components. We describe 'clustered MiRNA Master Regulator Analysis (ClustMMRA)', a fully redesigned release of the MMRA computational pipeline (MiRNA Master Regulator Analysis), developed to search for clustered miRNAs potentially driving cancer molecular subtyping. Genomically clustered miRNAs are frequently co-expressed to target different components of pro-tumorigenic signaling pathways. By applying ClustMMRA to breast cancer patient data, we identified key miRNA clusters driving the phenotype of different tumor subgroups. The pipeline was applied to two independent breast cancer datasets, providing statistically concordant results between the two analyses. We validated in cell lines the miR-199/miR-214 as a novel cluster of miRNAs promoting the triple negative breast cancer (TNBC) phenotype through its control of proliferation and EMT.

Metformin reverses PARP inhibitors-induced epithelial-mesenchymal transition and PD-L1 upregulation in triple-negative breast cancer

Poly (ADP-ribose) polymerase (PARP) inhibitors have emerged as promising targeted therapies for BRCA-mutated cancers by blocking repair of DNA double-strand breaks. However, resistance to PARP inhibitors (PARPi) has been described in some patients lowering the overall response rates. To investigate the underlying mechanisms of PARPi resistance, we developed the adaptive resistant clones in triple-negative breast cancer cell lines. We identified epithelial-mesenchymal transition (EMT) and upregulation of programmed death-ligand 1 (PD-L1) in resistant cells and further demonstrated the important role of Akt S473 phosphorylation in PARPi resistance. In addition, PARPi mediated EMT is independent of PD-L1 upregulation. Blocking the p-Akt S473 axis by metformin reversed EMT and PD-L1 expression which sensitized PARPi-resistant cells to cytotoxic T cells. Thus, a combination of metformin and PARP inhibitors may be a promising therapeutic strategy to increase the efficacy of PARP inhibitors and tumor sensitivity to immunotherapy.

LRP5 regulates the expression of STK40, a new potential target in triple-negative breast cancers

Triple-negative breast cancers (TNBCs) account for a large proportion of breast cancer deaths, due to the high rate of recurrence from residual, resistant tumor cells. New treatments are needed, to bypass chemoresistance and improve survival. The WNT pathway, which is activated in TNBCs, has been identified as an attractive pathway for treatment targeting. We analyzed expression of the WNT coreceptors LRP5 and LRP6 in human breast cancer samples. As previously described, LRP6 was overexpressed in TNBCs. However, we also showed, for the first time, that LRP5 was overexpressed in TNBCs too. The knockdown of LRP5 or LRP6 decreased tumorigenesis in vitro and in vivo, identifying both receptors as potential treatment targets in TNBC. The apoptotic effect of LRP5 knockdown was more robust than that of LRP6 depletion. We analyzed and compared the transcriptomes of cells depleted of LRP5 or LRP6, to identify genes specifically deregulated by LRP5 potentially implicated in cell death. We identified serine/threonine kinase 40 (STK40) as one of two genes specifically downregulated soon after LRP5 depletion. STK40 was found to be overexpressed in TNBCs, relative to other breast cancer subtypes, and in various other tumor types. STK40 depletion decreased cell viability and colony formation, and induced the apoptosis of TNBC cells. In addition, STK40 knockdown impaired growth in an anchorage-independent manner in vitro and slowed tumor growth in vivo. These findings identify the largely uncharacterized putative protein kinase STK40 as a novel candidate treatment target for TNBC.

Identification of differentially expressed genes regulated by molecular signature in breast cancer-associated fibroblasts by bioinformatics analysis

OBJECTIVE

Breast cancer is a severe risk to public health and has adequately convoluted pathogenesis. Therefore, the description of key molecular markers and pathways is of much importance for clarifying the molecular mechanism of breast cancer-associated fibroblasts initiation and progression. Breast cancer-associated fibroblasts gene expression dataset was downloaded from Gene Expression Omnibus database.

METHODS

A total of nine samples, including three normal fibroblasts, three granulin-stimulated fibroblasts and three cancer-associated fibroblasts samples, were used to identify differentially expressed genes (DEGs) between normal fibroblasts, granulin-stimulated fibroblasts and cancer-associated fibroblasts samples. The gene ontology (GO) and pathway enrichment analysis was performed, and protein-protein interaction (PPI) network of the DEGs was constructed by NetworkAnalyst software.

RESULTS

Totally, 190 DEGs were identified, including 66 up-regulated and 124 down-regulated genes. GO analysis results showed that up-regulated DEGs were significantly enriched in biological processes (BP), including cell-cell signalling and negative regulation of cell proliferation; molecular function (MF), including insulin-like growth factor II binding and insulin-like growth factor I binding; cellular component (CC), including insulin-like growth factor binding protein complex and integral component of plasma membrane; the down-regulated DEGs were significantly enriched in BP, including cell adhesion and extracellular matrix organization; MF, including N-acetylgalactosamine 4-sulfate 6-O-sulfotransferase activity and calcium ion binding; CC, including extracellular space and extracellular matrix. WIKIPATHWAYS analysis showed the up-regulated DEGs were enriched in myometrial relaxation and contraction pathways. WIKIPATHWAYS, REACTOME, PID_NCI and KEGG pathway analysis showed the down-regulated DEGs were enriched endochondral ossification, TGF beta signalling pathway, integrin cell surface interactions, beta1 integrin cell surface interactions, malaria and glycosaminoglycan biosynthesis-chondroitin sulfate/dermatan sulphate. The top 5 up-regulated hub genes, CDKN2A, MME, PBX1, IGFBP3, and TFAP2C and top 5 down-regulated hub genes VCAM1, KRT18, TGM2, ACTA2, and STAMBP were identified from the PPI network, and subnetworks revealed these genes were involved in significant pathways, including myometrial relaxation and contraction pathways, integrin cell surface interactions, beta1 integrin cell surface interaction. Besides, the target hsa-mirs for DEGs were identified. hsa-mir-759, hsa-mir-4446-5p, hsa-mir-219a-1-3p and hsa-mir-26a-5p were important miRNAs in this study.

CONCLUSIONS

We pinpoint important key genes and pathways closely related with breast cancer-associated fibroblasts initiation and progression by a series of bioinformatics analysis on DEGs. These screened genes and pathways provided for a more detailed molecular mechanism underlying breast cancer-associated fibroblasts occurrence and progression, holding promise for acting as molecular markers and probable therapeutic targets.

ROMA: Representation and Quantification of Module Activity from Target Expression Data

In many analyses of high-throughput data in systems biology, there is a need to quantify the activity of a set of genes in individual samples. A typical example is the case where it is necessary to estimate the activity of a transcription factor (which is often not directly measurable) from the expression of its target genes. We present here ROMA (Representation and quantification Of Module Activities) Java software, designed for fast and robust computation of the activity of gene sets (or modules) with coordinated expression. ROMA activity quantification is based on the simplest uni-factor linear model of gene regulation that approximates the expression data of a gene set by its first principal component. The proposed algorithm implements novel functionalities: it provides several method modifications for principal components computation, including weighted, robust and centered methods; it distinguishes overdispersed modules (based on the variance explained by the first principal component) and coordinated modules (based on the significance of the spectral gap); finally, it computes statistical significance of the estimated module overdispersion or coordination. ROMA can be applied in many contexts, from estimating differential activities of transcriptional factors to finding overdispersed pathways in single-cell transcriptomics data. We describe here the principles of ROMA providing several practical examples of its use. ROMA source code is available at https://github.com/sysbio-curie/Roma.

Preface: BITS2014, the annual meeting of the Italian Society of Bioinformatics

This Preface introduces the content of the BioMed Central journal Supplements related to BITS2014 meeting, held in Rome, Italy, from the 26th to the 28th of February, 2014.