miRNA-mRNA integrated analysis reveals roles for miRNAs in primary breast tumors

An overview of extracellular matrix and its remodeling in the development of cancer and metastasis with a glance at therapeutic approaches

The extracellular matrix (ECM) is an inevitable part of tissues able to provide structural support for cells depending on the purpose of tissues and organs. The dynamic characteristics of ECM let this system fluently interact with the extrinsic triggers and get stiffed, remodeled, and/or degraded ending in maintaining tissue homeostasis. ECM could serve as the platform for cancer progression. The dysregulation of biochemical and biomechanical ECM features might take participate in some pathological conditions such as aging, tissue destruction, fibrosis, and particularly cancer. Tumors can reprogram how ECM remodels by producing factors able to induce protein synthesis, matrix proteinase expression, degradation of the basement membrane, growth signals and proliferation, angiogenesis, and metastasis. Therefore, targeting the ECM components, their secretion, and their interactions with other cells or tumors could be a promising strategy in cancer therapies. The present study initially introduces the physiological functions of ECM and then discusses how tumor-dependent dysregulation of ECM could facilitate cancer progression and ends with reviewing the novel therapeutic strategies regarding ECM.

Bayesian Optimized sample-specific Networks Obtained By Omics data (BONOBO)

Gene regulatory networks (GRNs) are effective tools for inferring complex interactions between molecules that regulate biological processes and hence can provide insights into drivers of biological systems. Inferring co-expression networks is a critical element of GRN inference as the correlation between expression patterns may indicate that genes are coregulated by common factors. However, methods that estimate co-expression networks generally derive an aggregate network representing the mean regulatory properties of the population and so fail to fully capture population heterogeneity. To address these concerns, we introduce BONOBO (Bayesian Optimized Networks Obtained By assimilating Omics data), a scalable Bayesian model for deriving individual sample-specific co-expression networks by recognizing variations in molecular interactions across individuals. For every sample, BONOBO assumes a Gaussian distribution on the log-transformed centered gene expression and a conjugate prior distribution on the sample-specific co-expression matrix constructed from all other samples in the data. Combining the sample-specific gene expression with the prior distribution, BONOBO yields a closed-form solution for the posterior distribution of the sample-specific co-expression matrices, thus making the method extremely scalable. We demonstrate the utility of BONOBO in several contexts, including analyzing gene regulation in yeast transcription factor knockout studies, prognostic significance of miRNA-mRNA interaction in human breast cancer subtypes, and sex differences in gene regulation within human thyroid tissue. We find that BONOBO outperforms other sample-specific co-expression network inference methods and provides insight into individual differences in the drivers of biological processes.

Tumor stromal topography promotes chemoresistance in migrating breast cancer cell clusters

Multicellular clustering provides cancer cells with survival advantages and facilitates metastasis. At the tumor migration front, cancer cell clusters are surrounded by an aligned stromal topography. It remains unknown whether aligned stromal topography regulates the resistance of migrating cancer cell clusters to therapeutics. Using a hybrid nanopatterned model to characterize breast cancer cell clusters at the migration front with aligned stromal topography, we demonstrate that topography-induced migrating cancer cell clusters exhibit upregulated cytochrome P450 family 1 (CYP1) drug metabolism and downregulated glycolysis gene signatures, which correlates with unfavorable prognosis. Screening on approved oncology drugs shows that cancer cell clusters on aligned stromal topography are more resistant to diverse chemotherapeutics. Full-dose drug testings further indicate that topography induces drug resistance of hormone receptor-positive breast cancer cell clusters to doxorubicin and tamoxifen and triple-negative breast cancer cell clusters to doxorubicin by activating the aryl hydrocarbon receptor (AhR)/CYP1 pathways. Inhibiting the AhR/CYP1 pathway restores reactive oxygen species-mediated drug sensitivity to migrating cancer cell clusters, suggesting a plausible therapeutic direction for preventing metastatic recurrence.

Analysis of Spatial Molecular Data

Digital analysis of pathology whole-slide images has been recently gaining interest in the context of cancer diagnosis and treatment. In particular, deep learning methods have demonstrated significant potential in supporting pathology analysis, recently detecting molecular traits never before recognized in pathology H&E whole-slide images (WSIs). Alongside these advancements in the digital analysis of WSIs, it is becoming increasingly evident that both spatial and overall tumor heterogeneity may be significant determinants of cancer prognosis and treatment outcome. In this chapter, we describe methods that aim to support these two elements. We describe both an end-to-end deep learning pipeline for producing limited spatial transcriptomics from WSIs with associated bulk gene expression data, as well as an algorithm for quantifying spatial tumor heterogeneity based on the results of this pipeline.

MicroRNAs within the Basal-like signature of Quadruple Negative Breast Cancer impact overall survival in African Americans

We previously found that QNBC tumors are more frequent in African Americans compared to TNBC tumors. To characterize this subtype further, we sought to determine the miRNA-mRNA profile in QNBC patients based on race. Both miRNA and mRNA expression data were analyzed from TCGA and validated using datasets from the METABRIC, TCGA proteomic, and survival analysis by KMPLOT. miRNA-mRNAs which include FOXA1 and MYC (mir-17/20a targets); GATA3 and CCNG2 (mir-135b targets); CDKN2A, CDK6, and B7-H3 (mir-29c targets); and RUNX3, KLF5, IL1-β, and CTNNB1 (mir-375 targets) were correlated with basal-like and immune subtypes in QNBC patients and associated with a worse survival. Thus, QNBC tumors have an altered gene signature implicated in racial disparity and poor survival.

Dual-Channel Heterogeneous Graph Neural Network for Predicting microRNA-Mediated Drug Sensitivity

Many studies have confirmed that microRNAs (miRNAs) are mediated in the sensitivity of tumor cells to anticancer drugs. MiRNAs are emerging as a type of promising therapeutic targets to overcome drug resistance. However, there is limited attention paid to the computational prediction of the associations between miRNAs and drug sensitivity. In this work, we proposed a heterogeneous network-based representation learning method to predict miRNA-drug sensitivity associations (DGNNMDA). An miRNA-drug heterogeneous network was constructed by integrating miRNA similarity network, drug similarity network, and experimentally validated miRNA-drug sensitivity associations. Next, we developed a dual-channel heterogeneous graph neural network model to perform feature propagation among the homogeneous and heterogeneous nodes so that our method can learn expressive representations for miRNA and drug nodes. On two benchmark datasets, our method outperformed other seven competitive methods. We also verified the effectiveness of the feature propagations on homogeneous and heterogeneous nodes. Moreover, we have conducted two case studies to verify the reliability of our methods and tried to reveal the regulatory mechanism of miRNAs mediated in drug sensitivity. The source code and datasets are freely available at https://github.com/19990915fzy/DGNNMDA.

Identifying tumor immunity-associated molecular features in liver hepatocellular carcinoma by multi-omics analysis

Background: Although current immunotherapies have achieved some successes for hepatocellular carcinoma (HCC) patients, their benefits are limited for most HCC patients. Therefore, the identification of biomarkers for promoting immunotherapeutic responses in HCC is urgently needed.

Methods: Using the TCGA HCC cohort, we investigated correlations of various molecular features with antitumor immune signatures (CD8⁺ T cell infiltration and cytolytic activity) and an immunosuppressive signature (PD-L1 expression) in HCC. These molecular features included mRNAs, microRNAs (miRNAs), long non-coding RNAs (lncRNAs), proteins, and pathways.

Results: We found that the mutations of several oncogenes and tumor suppressor genes significantly correlated with reduced antitumor immune signatures, including TTN, CTNNB1, RB1, ZFHX4, and TP53. It indicates that these genes’ mutations may inhibit antitumor immune responses in HCC. Four proteins (Syk, Lck, STAT5, and Caspase-7) had significant positive expression correlations with CD8⁺ T cell enrichment, cytolytic activity, and PD-L1 expression in HCC. It suggests that these proteins’ expression could be useful biomarkers for the response to immune checkpoint inhibitors Similiarly, we identified other types of biomarkers potentially useful for predicting the response to ICIs, including miRNAs (hsa-miR-511-5p, 150-3p, 342-3p, 181a-3p, 625-5p, 4772-3p, 155-3p, 142-5p, 142-3p, 155-5p, 625-3p, 1976, 7702), many lncRNAs, and pathways (apoptosis, cytokine-cytokine receptor interaction, Jak-STAT signaling, MAPK signaling, PI3K-AKT signaling, HIF-1 signaling, ECM receptor interaction, focal adhesion, and estrogen signaling). Further, tumor mutation burden showed no significant correlation with antitumor immunity, while tumor aneuploidy levels showed a significant negative correlation with antitumor immunity.

Conclusion: The molecular features significantly associated with HCC immunity could be predictive biomarkers for immunotherapeutic responses in HCC patients. They could also be potential intervention targets for boosting antitumor immunity and immunotherapeutic responses in HCC.

Nondestructive protein sampling with electroporation facilitates profiling of spatial differential protein expression in breast tumors in vivo

Excision tissue biopsy, while central to cancer treatment and precision medicine, presents risks to the patient and does not provide a sufficiently broad and faithful representation of the heterogeneity of solid tumors. Here we introduce e-biopsy—a novel concept for molecular profiling of solid tumors using molecular sampling with electroporation. As e-biopsy provides access to the molecular composition of a solid tumor by permeabilization of the cell membrane, it facilitates tumor diagnostics without tissue resection. Furthermore, thanks to its non tissue destructive characteristics, e-biopsy enables probing the solid tumor multiple times in several distinct locations in the same procedure, thereby enabling the spatial profiling of tumor molecular heterogeneity.We demonstrate e-biopsy in vivo, using the 4T1 breast cancer model in mice to assess its performance, as well as the inferred spatial differential protein expression. In particular, we show that proteomic profiles obtained via e-biopsy in vivo distinguish the tumors from healthy breast tissue and reflect spatial tumor differential protein expression. E-biopsy provides a completely new molecular sampling modality for solid tumors molecular cartography, providing information that potentially enables more rapid and sensitive detection at lesser risk, as well as more precise personalized medicine.

Elevated miR-29c-5p Expression in Nipple Aspirate Fluid Is Associated with Extremely High Mammographic Breast Density

Simple Summary

High mammographic density is a known risk factor for breast cancer. However, the underlying mechanisms of high mammographic density development and breast cancer are unknown. MicroRNAs are potential biomarkers indicative of carcinogenesis and can be assessed in nipple aspirate fluid. We used nipple aspirate fluid from women with very low and extremely high mammographic density to examine differences in expression of multiple miRNAs between both extremes in the spectrum of mammographic density. We found that hsa-miR-29c-5p was upregulated in an extremely high mammographic density context and potential targets were identified that might provide clues of the relationship between high mammographic density and breast cancer risk. Understanding the relationship between high mammographic density and breast cancer is of great value for early breast cancer diagnosis and treatment. With our research we provide new insight into this relationship and further research could determine the effects of dysregulated hsa-miR-29c-5p on the identified candidate targets.

Abstract

High mammographic density (MD) is associated with an increased risk of breast cancer, however the underlying mechanisms are largely unknown. This research aimed to identify microRNAs (miRNAs) that play a role in the development of extremely dense breast tissue. In the discovery phase, 754 human mature miRNAs were profiled in 21 extremely high MD- and 20 very low MD-derived nipple aspirate fluid (NAF) samples from healthy women. In the validation phase, candidate miRNAs were assessed in a cohort of 89 extremely high MD and 81 very low MD NAF samples from healthy women. Independent predictors of either extremely high MD or miRNA expression were identified by logistic regression and linear regression analysis, respectively. mRNA targets and pathways were identified through miRTarBase, TargetScan, and PANTHER pathway analysis. Statistical analysis identified four differentially expressed miRNAs during the discovery phase. During the validation, linear regression (p = 0.029; fold change = 2.10) and logistic regression (p = 0.048; odds ratio = 1.38) showed that hsa-miR-29c-5p was upregulated in extremely high MD-derived NAF. Identified candidate mRNA targets of hsa-miR-29c-5p are CFLAR, DNMT3A, and PTEN. Further validation and exploration of targets and downstream pathways of has-miR-29c-5p will provide better insight into the processes involved in the development of high MD and in the associated increased risk of breast cancer.

Targeting HIF1-alpha/miR-326/ITGA5 axis potentiates chemotherapy response in triple-negative breast cancer

PURPOSE

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer that is frequently treated with chemotherapy. However, many patients exhibit either de novo chemoresistance or ultimately develop resistance to chemotherapy, leading to significantly high mortality rates. Therefore, increasing the efficacy of chemotherapy has potential to improve patient outcomes.

METHODS

Here, we performed whole transcriptome sequencing (both RNA and small RNA-sequencing), coupled with network simulations and patient survival data analyses to build a novel miRNA-mRNA interaction network governing chemoresistance in TNBC. We performed cell proliferation assay, Western blotting, RNAi/miRNA mimic experiments, FN coating, 3D cultures, and ChIP assays to validate the interactions in the network, and their functional roles in chemoresistance. We developed xenograft models to test the therapeutic potential of the identified key miRNA/proteins in potentiating chemoresponse in vivo. We also analyzed several patient datasets to evaluate the clinical relevance of our findings.

RESULTS

We identified fibronectin (FN1) as a central chemoresistance driver gene. Overexpressing miR-326 reversed FN1-driven chemoresistance by targeting FN1 receptor, ITGA5. miR-326 was downregulated by increased hypoxia/HIF1A and ECM stiffness in chemoresistant tumors, leading to upregulation of ITGA5 and activation of the downstream FAK/Src signaling pathways. Overexpression of miR-326 or inhibition of ITGA5 overcame FN1-driven chemotherapy resistance in vitro by inhibiting FAK/Src pathway and potentiated the efficacy of chemotherapy in vivo. Importantly, lower expression of miR-326 or higher levels of predicted miR-326 target genes was significantly associated with worse overall survival in chemotherapy-treated TNBC patients.

CONCLUSION

FN1 is central in chemoresistance. In chemoresistant tumors, hypoxia and resulting ECM stiffness repress the expression of the tumor suppressor miRNA, miR-326. Hence, re-expression of miR-326 or inhibition of its target ITGA5 reverses FN1-driven chemoresistance making them attractive therapeutic approaches to enhance chemotherapy response in TNBCs.

Association Between RSK2 and Clinical Indexes of Primary Breast Cancer: A Meta-Analysis Based on mRNA Microarray Data

Background: Although ribosomal protein S6 kinases, 90 kDa, polypeptide 3 (RSK2, RPS6KA3) has been reported to play an important role in cancer cell proliferation, invasion, and migration, including breast cancer, its clinical implication in primary breast cancer patients is not well understood, and there were not many studies to explore the relationship between RSK2 and breast cancer on a clinical level.

Methods: A systematic series matrix file search uploaded from January 1, 2008 to November 31, 2017 was undertaken using ArrayExpress and Gene Expression Omnibus (GEO) databases. Search filters were breast cancer, RNA assay, and array assay. Files eligible for inclusion met the following criteria: a) sample capacity is over 100, b) tumor sample comes from unselected patient’s primary breast tumor tissue, and c) expression of RSK2 and any clinical parameters of patients were available from the files. We use median as the cutoff value to assess the association between the expression of RSK2 and the clinical indexes of breast cancer patients.

Finding: The meta-analysis identified 13 series matrix files from GEO database involving 3,122 samples that come from patients’ primary breast cancer tissue or normal tissue. The expression of RSK2 in tumor tissues is lower than that in normal tissues [odds ratio (OR), 0.54; 95% credible interval (CI), 0.44–0.67; Cochran’s Q test p = 0.14; I² = 41.7%]. Patients with a high expression of RSK2 showed more favorable overall survival [hazard ratio (HR), 0.71; 95% CI, 0.49–0.94; Cochran’s Q test p = 0.95; I² = 0.0%] and less potential of distant metastasis (OR, 0.59; 95% CI, 0.41–0.87; Cochran’s Q test p = 0.88; I² = 0.0%) and lymph node infiltration (OR, 0.81; 95% CI, 0.65–0.998; Cochran’s Q test p = 0.09; I² = 42.8%). Besides, the expression of RSK2 in luminal breast cancer is lower than Cochran’s Q test p = 0.06; I² = 63.5%). RSK2 overexpression corresponded with higher histological grade (OR, 1.329; 95% CI, 1.03–1.721; Cochran’s Q test p = 0.69; I² = 0.0%). RSK2 expression is also associated with estrogen receptor (ER) and age.

Conclusion: The meta-analysis provides evidence that RSK2 is a potential biomarker in breast cancer patients. The expression of RSK2 is distinctive in different intrinsic subtypes of breast cancer, indicating that it may play an important role in specific breast cancer. Further study is needed to uncover the mechanism of RSK2 in breast cancer.

Systematic Review Registration: (website), identifier (registration number).

Enhanced Directed Random Walk for the Identification of Breast Cancer Prognostic Markers from Multiclass Expression Data

Artificial intelligence in healthcare can potentially identify the probability of contracting a particular disease more accurately. There are five common molecular subtypes of breast cancer: luminal A, luminal B, basal, ERBB2, and normal-like. Previous investigations showed that pathway-based microarray analysis could help in the identification of prognostic markers from gene expressions. For example, directed random walk (DRW) can infer a greater reproducibility power of the pathway activity between two classes of samples with a higher classification accuracy. However, most of the existing methods (including DRW) ignored the characteristics of different cancer subtypes and considered all of the pathways to contribute equally to the analysis. Therefore, an enhanced DRW (eDRW+) is proposed to identify breast cancer prognostic markers from multiclass expression data. An improved weight strategy using one-way ANOVA (F-test) and pathway selection based on the greatest reproducibility power is proposed in eDRW+. The experimental results show that the eDRW+ exceeds other methods in terms of AUC. Besides this, the eDRW+ identifies 294 gene markers and 45 pathway markers from the breast cancer datasets with better AUC. Therefore, the prognostic markers (pathway markers and gene markers) can identify drug targets and look for cancer subtypes with clinically distinct outcomes.

Identifying miRNA-mRNA Integration Set Associated With Survival Time

In the “personalized medicine” era, one of the most difficult problems is identification of combined markers from different omics platforms. Many methods have been developed to identify candidate markers for each type of omics data, but few methods facilitate the identification of multiple markers on multi-omics platforms. microRNAs (miRNAs) is well known to affect only indirectly phenotypes by regulating mRNA expression and/or protein translation. To take into account this knowledge into practice, we suggest a miRNA-mRNA integration model for survival time analysis, called mimi-surv, which accounts for the biological relationship, to identify such integrated markers more efficiently. Through simulation studies, we found that the statistical power of mimi-surv be better than other models. Application to real datasets from Seoul National University Hospital and The Cancer Genome Atlas demonstrated that mimi-surv successfully identified miRNA-mRNA integrations sets associated with progression-free survival of pancreatic ductal adenocarcinoma (PDAC) patients. Only mimi-surv found miR-96, a previously unidentified PDAC-related miRNA in these two real datasets. Furthermore, mimi-surv was shown to identify more PDAC related miRNAs than other methods because it used the known structure for miRNA-mRNA regularization. An implementation of mimi-surv is available at http://statgen.snu.ac.kr/software/mimi-surv.

Tenascin-C immobilizes infiltrating T lymphocytes through CXCL12 promoting breast cancer progression

Immune checkpoint therapy, where CD8 tumor infiltrating T lymphocytes (TIL) are reactivated, is a promising anti-cancer treatment approach, yet with low response rates. The extracellular matrix, in particular tenascin-C, may generate barriers for TIL. To investigate this possibility, we used a MMTV-NeuNT and syngeneic mammary gland grafting model derived thereof with engineered tenascin-C levels and observed accumulation of CD8 TIL in tenascin-C-rich stroma. Inhibition studies revealed that tenascin-C induced CXCL12 through TLR4. By binding CXCL12, tenascin-C retained CD8 TIL in the stroma. Blockade of CXCR4, the receptor of CXCL12, enhanced macrophage and CD8 TIL infiltration and reduced tumor growth and subsequent metastasis. Retention of CD8 TIL by tenascin-C/CXCL12 was also observed in human breast cancer by tissue staining. Moreover, whereas high CD8 TIL numbers correlated with longer metastasis-free survival, this was not the case when also tenascin-C and CXCL12 levels were high. Altogether, these results may be useful for improving tumor immunity as diagnostic tool and to formulate a future "TIL-matrix-release-and-reactivate" strategy.

Uncovering the roles of microRNAs/lncRNAs in characterising breast cancer subtypes and prognosis

Background

Accurate prognosis and identification of cancer subtypes at molecular level are important steps towards effective and personalised treatments of breast cancer. To this end, many computational methods have been developed to use gene (mRNA) expression data for breast cancer subtyping and prognosis. Meanwhile, microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) have been extensively studied in the last 2 decades and their associations with breast cancer subtypes and prognosis have been evidenced. However, it is not clear whether using miRNA and/or lncRNA expression data helps improve the performance of gene expression based subtyping and prognosis methods, and this raises challenges as to how and when to use these data and methods in practice.

Results

In this paper, we conduct a comparative study of 35 methods, including 12 breast cancer subtyping methods and 23 breast cancer prognosis methods, on a collection of 19 independent breast cancer datasets. We aim to uncover the roles of miRNAs and lncRNAs in breast cancer subtyping and prognosis from the systematic comparison. In addition, we created an R package, CancerSubtypesPrognosis, including all the 35 methods to facilitate the reproducibility of the methods and streamline the evaluation.

Conclusions

The experimental results show that integrating miRNA expression data helps improve the performance of the mRNA-based cancer subtyping methods. However, miRNA signatures are not as good as mRNA signatures for breast cancer prognosis. In general, lncRNA expression data does not help improve the mRNA-based methods in both cancer subtyping and cancer prognosis. These results suggest that the prognostic roles of miRNA/lncRNA signatures in the improvement of breast cancer prognosis needs to be further verified.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04215-3.

Crosstalk between microRNA expression and DNA methylation drives the hormone-dependent phenotype of breast cancer

BACKGROUND

Abnormal DNA methylation is observed as an early event in breast carcinogenesis. However, how such alterations arise is still poorly understood. microRNAs (miRNAs) regulate gene expression at the post-transcriptional level and play key roles in various biological processes. Here, we integrate miRNA expression and DNA methylation at CpGs to study how miRNAs may affect the breast cancer methylome and how DNA methylation may regulate miRNA expression.

METHODS

miRNA expression and DNA methylation data from two breast cancer cohorts, Oslo2 (n = 297) and The Cancer Genome Atlas (n = 439), were integrated through a correlation approach that we term miRNA-methylation Quantitative Trait Loci (mimQTL) analysis. Hierarchical clustering was used to identify clusters of miRNAs and CpGs that were further characterized through analysis of mRNA/protein expression, clinicopathological features, in silico deconvolution, chromatin state and accessibility, transcription factor binding, and long-range interaction data.

RESULTS

Clustering of the significant mimQTLs identified distinct groups of miRNAs and CpGs that reflect important biological processes associated with breast cancer pathogenesis. Notably, two major miRNA clusters were related to immune or fibroblast infiltration, hence identifying miRNAs associated with cells of the tumor microenvironment, while another large cluster was related to estrogen receptor (ER) signaling. Studying the chromatin landscape surrounding CpGs associated with the estrogen signaling cluster, we found that miRNAs from this cluster are likely to be regulated through DNA methylation of enhancers bound by FOXA1, GATA2, and ER-alpha. Further, at the hub of the estrogen cluster, we identified hsa-miR-29c-5p as negatively correlated with the mRNA and protein expression of DNA methyltransferase DNMT3A, a key enzyme regulating DNA methylation. We found deregulation of hsa-miR-29c-5p already present in pre-invasive breast lesions and postulate that hsa-miR-29c-5p may trigger early event abnormal DNA methylation in ER-positive breast cancer.

CONCLUSIONS

We describe how miRNA expression and DNA methylation interact and associate with distinct breast cancer phenotypes.

DDR2 upregulation confers ferroptosis susceptibility of recurrent breast tumors through the Hippo pathway

Recurrent breast cancer presents significant challenges with aggressive phenotypes and treatment resistance. Therefore, novel therapeutics are urgently needed. Here, we report that murine recurrent breast tumor cells, when compared with primary tumor cells, are highly sensitive to ferroptosis. Discoidin Domain Receptor Tyrosine Kinase 2 (DDR2), the receptor for collagen I, is highly expressed in ferroptosis-sensitive recurrent tumor cells and human mesenchymal breast cancer cells. EMT regulators, TWIST and SNAIL, significantly induce DDR2 expression and sensitize ferroptosis in a DDR2-dependent manner. Erastin treatment induces DDR2 upregulation and phosphorylation, independent of collagen I. Furthermore, DDR2 knockdown in recurrent tumor cells reduces clonogenic proliferation. Importantly, both the ferroptosis protection and reduced clonogenic growth may be compatible with the compromised YAP/TAZ upon DDR2 inhibition. Collectively, these findings identify the important role of EMT-driven DDR2 upregulation in recurrent tumors in maintaining growth advantage but activating YAP/TAZ-mediated ferroptosis susceptibility, providing potential strategies to eradicate recurrent breast cancer cells with mesenchymal features.

miRNA normalization enables joint analysis of several datasets to increase sensitivity and to reveal novel miRNAs differentially expressed in breast cancer

Different miRNA profiling protocols and technologies introduce differences in the resulting quantitative expression profiles. These include differences in the presence (and measurability) of certain miRNAs. We present and examine a method based on quantile normalization, Adjusted Quantile Normalization (AQuN), to combine miRNA expression data from multiple studies in breast cancer into a single joint dataset for integrative analysis. By pooling multiple datasets, we obtain increased statistical power, surfacing patterns that do not emerge as statistically significant when separately analyzing these datasets. To merge several datasets, as we do here, one needs to overcome both technical and batch differences between these datasets. We compare several approaches for merging and jointly analyzing miRNA datasets. We investigate the statistical confidence for known results and highlight potential new findings that resulted from the joint analysis using AQuN. In particular, we detect several miRNAs to be differentially expressed in estrogen receptor (ER) positive versus ER negative samples. In addition, we identify new potential biomarkers and therapeutic targets for both clinical groups. As a specific example, using the AQuN-derived dataset we detect hsa-miR-193b-5p to have a statistically significant over-expression in the ER positive group, a phenomenon that was not previously reported. Furthermore, as demonstrated by functional assays in breast cancer cell lines, overexpression of hsa-miR-193b-5p in breast cancer cell lines resulted in decreased cell viability in addition to inducing apoptosis. Together, these observations suggest a novel functional role for this miRNA in breast cancer. Packages implementing AQuN are provided for Python and Matlab: https://github.com/YakhiniGroup/PyAQN.

This work demonstrates a practical approach to the joint-analysis of multiple miRNA expression profiling datasets acquired with different measurement technologies. The use of different platforms in miRNA profiling can lead to major differences in results. In particular, some miRNA species are less amenable to detection and quantification by certain platforms or designs. Our approach, termed AQuN, combines quantile normalization with special attention to missing entities, to normalize miRNA expression across datasets, technologies, designs and platforms. As we show, our proposed approach uncovers patterns of interest that would not have emerged as statistically significant when analyzing the datasets individually or with other standard-practice normalization methods.

Amongst our findings, we noted a previously undocumented miRNA that is significantly over-expressed in samples from estrogen-receptor positive breast cancer patients as compared to samples from estrogen-receptor negative patients. We further investigated this miRNA, hsa-miR-193b-5p, and experimentally show, in cell lines, that its expression level impacts the viability of tumor cells. AQuN is available to the community in the form of Python and Matlab packages. The joint-processed data is also made available for further investigation.

Efficient gene expression signature for a breast cancer immuno-subtype

MOTIVATION AND BACKGROUND

The patient's immune system plays an important role in cancer pathogenesis, prognosis and susceptibility to treatment. Recent work introduced an immune related breast cancer. This subtyping is based on the expression profiles of the tumor samples. Specifically, one study showed that analyzing 658 genes can lead to a signature for subtyping tumors. Furthermore, this classification is independent of other known molecular and clinical breast cancer subtyping. Finally, that study shows that the suggested subtyping has significant prognostic implications.

RESULTS

In this work we develop an efficient signature associated with survival in breast cancer. We begin by developing a more efficient signature for the above-mentioned breast cancer immune-based subtyping. This signature represents better performance with a set of 579 genes that obtains an improved Area Under Curve (AUC). We then determine a set of 193 genes and an associated classification rule that yield subtypes with a much stronger statistically significant (log rank p-value < 2 × 10-4 in a test cohort) difference in survival. To obtain these improved results we develop a feature selection process that matches the high-dimensionality character of the data and the dual performance objectives, driven by survival and anchored by the literature subtyping.

Flavonoid display ability to target microRNAs in cancer pathogenesis

MicroRNAs (miRNAs) are non-coding, conserved, single-stranded nucleotide sequences involved in physiological and developmental processes. Recent evidence suggests an association between miRNAs' deregulation with initiation, promotion, progression, and drug resistance in cancer cells. Besides, miRNAs are known to regulate the epithelial-mesenchymal transition, angiogenesis, autophagy, and senescence in different cancer types. Previous reports proposed that apart from the antioxidant potential, flavonoids play an essential role in miRNAs modulation associated with changes in cancer-related proteins, tumor suppressor genes, and oncogenes. Thus, flavonoids can suppress proliferation, help in the development of drug sensitivity, suppress metastasis and angiogenesis by modulating miRNAs expression. In the present review, we summarize the role of miRNAs in cancer, drug resistance, and the chemopreventive potential of flavonoids mediated by miRNAs. The potential of flavonoids to modulate miRNAs expression in different cancer types demonstrate their selectivity and importance as regulators of carcinogenesis. Flavonoids as chemopreventive agents targeting miRNAs are extensively studied in vitro, in vivo, and pre-clinical studies, but their efficiency in targeting miRNAs in clinical studies is less investigated. The evidence presented in this review highlights the potential of flavonoids in cancer prevention/treatment by regulating miRNAs, although further investigations are required to validate and establish their clinical usefulness.

Spatial transcriptomics inferred from pathology whole-slide images links tumor heterogeneity to survival in breast and lung cancer

Digital analysis of pathology whole-slide images is fast becoming a game changer in cancer diagnosis and treatment. Specifically, deep learning methods have shown great potential to support pathology analysis, with recent studies identifying molecular traits that were not previously recognized in pathology H&E whole-slide images. Simultaneous to these developments, it is becoming increasingly evident that tumor heterogeneity is an important determinant of cancer prognosis and susceptibility to treatment, and should therefore play a role in the evolving practices of matching treatment protocols to patients. State of the art diagnostic procedures, however, do not provide automated methods for characterizing and/or quantifying tumor heterogeneity, certainly not in a spatial context. Further, existing methods for analyzing pathology whole-slide images from bulk measurements require many training samples and complex pipelines. Our work addresses these two challenges. First, we train deep learning models to spatially resolve bulk mRNA and miRNA expression levels on pathology whole-slide images (WSIs). Our models reach up to 0.95 AUC on held-out test sets from two cancer cohorts using a simple training pipeline and a small number of training samples. Using the inferred gene expression levels, we further develop a method to spatially characterize tumor heterogeneity. Specifically, we produce tumor molecular cartographies and heterogeneity maps of WSIs and formulate a heterogeneity index (HTI) that quantifies the level of heterogeneity within these maps. Applying our methods to breast and lung cancer slides, we show a significant statistical link between heterogeneity and survival. Our methods potentially open a new and accessible approach to investigating tumor heterogeneity and other spatial molecular properties and their link to clinical characteristics, including treatment susceptibility and survival.

Differential Network Analysis via Weighted Fused Conditional Gaussian Graphical Model

The development and prognosis of complex diseases usually involves changes in regulatory relationships among biomolecules. Understanding how the regulatory relationships change with genetic alterations can help to reveal the underlying biological mechanisms for complex diseases. Although several models have been proposed to estimate the differential network between two different states, they are not suitable to deal with situations where the molecules of interest are affected by other covariates. Nor can they make use of prior information that provides insights about the structures of biomolecular networks. In this study, we introduce a novel weighted fused conditional Gaussian graphical model to jointly estimate two state-specific biomolecular regulatory networks and their difference between two different states. Unlike previous differential network estimation methods, our model can take into account the related covariates and the prior network information when inferring differential networks. The effectiveness of our proposed model is first evaluated based on simulation studies. Experiment results demonstrate that our model outperforms other state-of-the-art differential networks estimation models in all cases. We then apply our model to identify the differential gene network between two subtypes of glioblastoma based on gene expression and miRNA expression data. Our model is able to discover known mechanisms of glioblastoma and provide interesting predictions.

The regulatory role of microRNA-mRNA co-expression in hepatitis B virus-associated acute liver failure

INTRODUCTION AND OBJECTIVES

Acute liver failure (ALF) is a dramatic disorder requiring intensive care. MicroRNAs (miRNAs) have been identified to play important roles in ALF. This study was performed to identify miRNA-mRNA co-expression network after ALF to investigate the molecule mechanism underlying the pathogenesis of ALF.

MATERIALS AND METHODS

The microarray dataset GSE62030 and GSE62029 were downloaded from Gene Expression Omnibus database. Overlapping differentially expressed miRNAs (DEmiRNAs) and genes (DEGs) were identified in liver tissues from patients with hepatitis B virus (HBV)-associated ALF in comparison with normal tissues from donors. Gene enrichment analysis was performed. Key pathways associated with the DEGs were identified. The miRNA-mRNA regulatory network was constructed.

RESULTS

Total 42 DEmiRNAs and 523 DEGs were identified in liver tissues from patients with HBV-associated ALF. Gene ontology and pathways enrichment analysis showed upregulated DEGs were related to immune responses, inflammation, and infection, and downregulated DEGs were associated with amino acids, secondary metabolites and xenobiotics metabolism. In miRNA-mRNA co-expression network, DEGs were regulated by at least one DEmiRNA and transcription factor. Further analysis showed DEmiRNAs, including has-miR-55-5p, has-miR-193b-5p, has-miR-200b-3p, and has-miR-3175 were associated with amino acid metabolism, drug metabolism and detoxication, and signaling pathways including mitogen-activated protein kinase (MAPK), phosphatidylinositol 3-kinase (PI3K)/AKT, Ras, and Rap1.

CONCLUSIONS

These miRNA-mRNA pairs and changed profiles were associated with and might be responsible for the impairment of detoxification and metabolism induced by HBV-associated ALF.

miRNA551b-3p Activates an Oncostatin Signaling Module for the Progression of Triple-Negative Breast Cancer

Genomic amplification of 3q26.2 locus leads to the increased expression of microRNA 551b-3p (miR551b-3p) in triple-negative breast cancer (TNBC). Our results demonstrate that miR551b-3p translocates to the nucleus with the aid of importin-8 (IPO8) and activates STAT3 transcription. As a consequence, miR551b upregulates the expression of oncostatin M receptor (OSMR) and interleukin-31 receptor-α (IL-31RA) as well as their ligands OSM and IL-31 through STAT3 transcription. We defined this set of genes induced by miR551b-3p as the "oncostatin signaling module," which provides oncogenic addictions in cancer cells. Notably, OSM is highly expressed in TNBC, and the elevated expression of OSM associates with poor outcome in estrogen-receptor-negative breast cancer patients. Conversely, targeting miR551b with anti-miR551b-3p reduced the expression of the OSM signaling module and reduced tumor growth, as well as migration and invasion of breast cancer cells.

AGR3 promotes estrogen receptor-positive breast cancer cell proliferation in an estrogen-dependent manner

Breast cancer is one of the most common malignancies and the leading cause of cancer-associated death among women. Anterior gradient 3 (AGR3) is a cancer-associated gene and is similar to its homologous oncogene AGR2. However, whether AGR3 participates in breast cancer progression remains unclear. The present study aimed to investigate the function of AGR3 in ER-positive breast cancer. In the present study, reverse transcription-quantitative PCR was used to detect AGR3 mRNA expression in breast cancer tissues and cell lines; linear correlation analysis was used to investigate the correlation between AGR3 and estrogen receptor 1 (ESR1) expression in breast cancer via GEO dataset analysis; western blotting was used to assess the levels of AGR3, ER and GAPDH; small interfering (si)RNA transfection was used to knock down AGR3 and ESR1 expression; and finally the Cell Counting Kit-8 assay was used to evaluate cell viability. In the present study, AGR3 expression was markedly increased in estrogen receptor (ER)-positive breast cancer tissues and cell lines compared with that in ER-negative breast cancer. AGR3 expression was upregulated in estrogen-treated T47D cells, whereas 4-hydroxytamoxifen, an inhibitor of estrogen-ER activity in breast cancer cells, downregulated AGR3 expression in T47D cells. Functional assays demonstrated that knockdown of AGR3 using siRNAs inhibited T47D cell proliferation compared with that of the negative control group. Additionally, AGR3 expression was decreased after knocking down ESR1. The present results suggested that AGR3 may serve an important role in estrogen-mediated cell proliferation in breast cancer and that AGR3 knockdown may be a potential therapeutic strategy for ER-positive breast cancer.

MicroRNA-770-5p contributes to podocyte injury via targeting E2F3 in diabetic nephropathy

Diabetic nephropathy (DN) has been identified as the major cause of end-stage renal disease (ESRD) in most developed countries. MicroRNA-770-5p depletion could repress high glucose (HG)-triggered apoptosis in podocytes, and downregulation of E2F transcription factor 3 (E2F3) could facilitate podocyte injury. Nevertheless, whether E2F3 is involved in miR-770-5p knockdown-mediated improvement of DN is still unclear. The expression levels of miR-770-5p and E2F3 were detected in HG-treated podocytes by RT-qPCR. The expression levels of E2F3, apoptosis-related proteins Bcl-2 related X protein (Bax), B-cell lymphoma-2 (Bcl-2), Bad, apoptotic peptidase activating factor 1 (APAF1), C-caspase3, C-caspase7, and C-caspase9 were detected by western blot assay. The effects of miR-770-5p and E2F3 on HG-treated podocytes proliferation and apoptosis were detected by CCK-8 and flow cytometry assays. The interaction between miR-770-5p and E2F3 was predicted by Targetscan, and then verified by the dual-luciferase reporter assay. MiR-770-5p was upregulated and E2F3 was downregulated in HG-treated podocytes. MiR-770-5p inhibited proliferation and promoted apoptosis and E2F3 promoted proliferation and suppressed apoptosis in HG-treated podocytes. E2F3 is a target gene of miR-770-5p and it partially abolished the effect of miR-770-5p in HG-triggered proliferation and apoptosis of podocytes. MiR-770-5p deficiency blocked HG-induced APAF1/caspase9 pathway via targeting E2F3 in podocytes. We firstly confirmed that E2F3 was a target of miR-770-5p in podocytes. These findings suggested that miR-770-5p expedited podocyte injury by targeting E2F3, and the miR-770-5p/E2F3 axis might represent a pathological mechanism of DN progression.

maTE: discovering expressed interactions between microRNAs and their targets

MOTIVATION

Disease is often manifested via changes in transcript and protein abundance. MicroRNAs (miRNAs) are instrumental in regulating protein abundance and may measurably influence transcript levels. miRNAs often target more than one mRNA (for humans, the average is three), and mRNAs are often targeted by more than one miRNA (for the genes considered in this study, the average is also three). Therefore, it is difficult to determine the miRNAs that may cause the observed differential gene expression. We present a novel approach, maTE, which is based on machine learning, that integrates information about miRNA target genes with gene expression data. maTE depends on the availability of a sufficient amount of patient and control samples. The samples are used to train classifiers to accurately classify the samples on a per miRNA basis. Multiple high scoring miRNAs are used to build a final classifier to improve separation.

RESULTS

The aim of the study is to find a set of miRNAs causing the regulation of their target genes that best explains the difference between groups (e.g. cancer versus control). maTE provides a list of significant groups of genes where each group is targeted by a specific miRNA. For the datasets used in this study, maTE generally achieves an accuracy well above 80%. Also, the results show that when the accuracy is much lower (e.g. ∼50%), the set of miRNAs provided is likely not causative of the difference in expression. This new approach of integrating miRNA regulation with expression data yields powerful results and is independent of external labels and training data. Thereby, this approach allows new avenues for exploring miRNA regulation and may enable the development of miRNA-based biomarkers and drugs.

AVAILABILITY AND IMPLEMENTATION

The KNIME workflow, implementing maTE, is available at Bioinformatics online.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

RWRMTN: a tool for predicting disease-associated microRNAs based on a microRNA-target gene network

BACKGROUND

The misregulation of microRNA (miRNA) has been shown to cause diseases. Recently, we have proposed a computational method based on a random walk framework on a miRNA-target gene network to predict disease-associated miRNAs. The prediction performance of our method is better than that of some existing state-of-the-art network- and machine learning-based methods since it exploits the mutual regulation between miRNAs and their target genes in the miRNA-target gene interaction networks.

RESULTS

To facilitate the use of this method, we have developed a Cytoscape app, named RWRMTN, to predict disease-associated miRNAs. RWRMTN can work on any miRNA-target gene network. Highly ranked miRNAs are supported with evidence from the literature. They then can also be visualized based on the rankings and in relationships with the query disease and their target genes. In addition, automation functions are also integrated, which allow RWRMTN to be used in workflows from external environments. We demonstrate the ability of RWRMTN in predicting breast and lung cancer-associated miRNAs via workflows in Cytoscape and other environments.

CONCLUSIONS

Considering a few computational methods have been developed as software tools for convenient uses, RWRMTN is among the first GUI-based tools for the prediction of disease-associated miRNAs which can be used in workflows in different environments.

MiR-18a and miR-18b are expressed in the stroma of oestrogen receptor alpha negative breast cancers

BACKGROUND

Previously, we have shown that miR-18a and miR-18b gene expression strongly correlates with high proliferation, oestrogen receptor -negativity (ER^-), cytokeratin 5/6 positivity and basal-like features of breast cancer.

METHODS

We investigated the expression and localization of miR-18a and -18b in formalin fixed paraffin embedded (FFPE) tissue from lymph node negative breast cancers (n = 40), by chromogenic in situ hybridization (CISH). The expression level and in situ localization of miR-18a and -18b was assessed with respect to the presence of tumour infiltrating lymphocytes (TILs) and immunohistochemical markers for ER, CD4, CD8, CD20, CD68, CD138, PAX5 and actin. Furthermore, in two independent breast cancer cohorts (94 and 377 patients) the correlation between miR-18a and -18b expression and the relative quantification of 22 immune cell types obtained from the CIBERSORT tool was assessed.

RESULTS

CISH demonstrated distinct and specific cytoplasmic staining for both miR-18a and miR-18b, particularly in the intratumoural stroma and the stroma surrounding the tumour margin. Staining by immunohistochemistry revealed some degree of overlap of miR-18a and -18b with CD68 (monocytes/macrophages), CD138 (plasma cells) and the presence of high percentages of TILs. CIBERSORT analysis showed a strong correlation between M1-macrophages and CD4+ memory activated T-cells with mir-18a and -18b.

CONCLUSIONS

Our study demonstrates that miR-18a and miR-18b expression is associated with ER- breast tumours that display a high degree of inflammation. This expression is potentially associated specifically with macrophages. These results suggest that miR-18a and miR-18b may play a role in the systemic immunological response in ER^- tumours.

Identification of miRNA-mRNA associations in hepatocellular carcinoma using hierarchical integrative model

Background

The established role miRNA-mRNA regulation of gene expression has in oncogenesis highlights the importance of integrating miRNA with downstream mRNA targets. These findings call for investigations aimed at identifying disease-associated miRNA-mRNA pairs. Hierarchical integrative models (HIM) offer the opportunity to uncover the relationships between disease and the levels of different molecules measured in multiple omic studies.

Methods

The HIM model we formulated for analysis of mRNA-seq and miRNA-seq data can be specified with two levels: (1) a mechanistic submodel relating mRNAs to miRNAs, and (2) a clinical submodel relating disease status to mRNA and miRNA, while accounting for the mechanistic relationships in the first level.

Results

mRNA-seq and miRNA-seq data were acquired by analysis of tumor and normal liver tissues from 30 patients with hepatocellular carcinoma (HCC). We analyzed the data using HIM and identified 157 significant miRNA-mRNA pairs in HCC. The majority of these molecules have already been independently identified as being either diagnostic, prognostic, or therapeutic biomarker candidates for HCC. These pairs appear to be involved in processes contributing to the pathogenesis of HCC involving inflammation, regulation of cell cycle, apoptosis, and metabolism. For further evaluation of our method, we analyzed miRNA-seq and mRNA-seq data from TCGA network. While some of the miRNA-mRNA pairs we identified by analyzing both our and TCGA data are previously reported in the literature and overlap in regulation and function, new pairs have been identified that may contribute to the discovery of novel targets.

Conclusion

The results strongly support the hypothesis that miRNAs are important regulators of mRNAs in HCC. Furthermore, these results emphasize the biological relevance of studying miRNA-mRNA pairs.

Model-Based Integration Analysis Revealed Presence of Novel Prognostic miRNA Targets and Important Cancer Driver Genes in Triple-Negative Breast Cancers

Background: miRNAs (microRNAs) play a key role in triple-negative breast cancer (TNBC) progression, and its heterogeneity at the expression, pathological and clinical levels. Stratification of breast cancer subtypes on the basis of genomics and transcriptomics profiling, along with the known biomarkers’ receptor status, has revealed the existence of subgroups known to have diverse clinical outcomes. Recently, several studies have analysed expression profiles of matched mRNA and miRNA to investigate the underlying heterogeneity of TNBC and the potential role of miRNA as a biomarker within cancers. However, the miRNA-mRNA regulatory network within TNBC has yet to be understood. Results and Findings: We performed model-based integrated analysis of miRNA and mRNA expression profiles on breast cancer, primarily focusing on triple-negative, to identify subtype-specific signatures involved in oncogenic pathways and their potential role in patient survival outcome. Using univariate and multivariate Cox analysis, we identified 25 unique miRNAs associated with the prognosis of overall survival (OS) and distant metastases-free survival (DMFS) with “risky” and “protective” outcomes. The association of these prognostic miRNAs with subtype-specific mRNA genes was established to investigate their potential regulatory role in the canonical pathways using anti-correlation analysis. The analysis showed that miRNAs contribute to the positive regulation of known breast cancer driver genes as well as the activation of respective oncogenic pathway during disease formation. Further analysis on the “risk associated” miRNAs group revealed significant regulation of critical pathways such as cell growth, voltage-gated ion channel function, ion transport and cell-to-cell signalling. Conclusion: The study findings provide new insights into the potential role of miRNAs in TNBC disease progression through the activation of key oncogenic pathways. The results showed previously unreported subtype-specific prognostic miRNAs associated with clinical outcome that may be used for further clinical evaluation.

Bioinformatic Analysis and in Vitro Validation of Let-7b and Let-7c in Breast Cancer

Members of the let-7 family of miRNAs are well-known with their tumor suppressor properties as they are expressed at low levels in several types of human malignancies. Among them, let-7b and let-7c have gained special attention due their broad significance. Although the role of let-7b and let-7c have been widely reported in various types of cancers, their functional importance and role in oncogenic signaling of breast cancer is poorly investigated. Therefore, in the present study, prognostic and diagnostic significance of let-7b and let-7c in breast cancer and the effects these miRNAs on genes involved in cancer progression were determined by using several bioinformatics analysis and validated in vitro mimic assays, respectively. Using data of TCGA, OncomiR and dbDEMC 2.0, overall expression analysis of let-7b and let-7c was performed. The effect of let-7b and let-7c on genes involved in cancer progression was investigated by mimic transfection assays. We found that both let-7b and let-7c were significantly altered in breast cancer and associated with the clinicopathological findings of patients. Additionally, both let-7b and let-7c significantly altered oncogenic signaling in breast cancer cells. Consequently, both miRNAs might have fundamental roles in breast cancer progression and can be considered as potential targets for breast cancer therapy and diagnosis.

An independent poor-prognosis subtype of breast cancer defined by a distinct tumor immune microenvironment

How mixtures of immune cells associate with cancer cell phenotype and affect pathogenesis is still unclear. In 15 breast cancer gene expression datasets, we invariably identify three clusters of patients with gradual levels of immune infiltration. The intermediate immune infiltration cluster (Cluster B) is associated with a worse prognosis independently of known clinicopathological features. Furthermore, immune clusters are associated with response to neoadjuvant chemotherapy. In silico dissection of the immune contexture of the clusters identified Cluster A as immune cold, Cluster C as immune hot while Cluster B has a pro-tumorigenic immune infiltration. Through phenotypical analysis, we find epithelial mesenchymal transition and proliferation associated with the immune clusters and mutually exclusive in breast cancers. Here, we describe immune clusters which improve the prognostic accuracy of immune contexture in breast cancer. Our discovery of a novel independent prognostic factor in breast cancer highlights a correlation between tumor phenotype and immune contexture.

In breast cancer, the immune infiltration of the tumour associates with clinical outcome. Here, the authors infer immune context based on gene expression data and identify a new independent subtype linked to pro-tumorigenic immune infiltration.

Prediction of clusters of miRNA binding sites in mRNA candidate genes of breast cancer subtypes

The development of breast cancer (BC) subtypes is controlled by distinct sets of candidate genes, and the expression of these genes is regulated by the binding of their mRNAs with miRNAs. Predicting miRNA associations and target genes is thus essential when studying breast cancer. The MirTarget program identifies the initiation of miRNA binding to mRNA, the localization of miRNA binding sites in mRNA regions, and the free energy from the binding of all miRNA nucleotides with mRNA. Candidate gene mRNAs have clusters (miRNA binding sites with overlapping nucleotide sequences). mRNAs of EPOR, MAZ and NISCH candidate genes of the HER2 subtype have clusters, and there are four clusters in mRNAs of MAZ, BRCA2 and CDK6 genes. Candidate genes of the triple-negative subtype are targets for multiple miRNAs. There are 11 sites in CBL mRNA, five sites in MMP2 mRNA, and RAB5A mRNA contains two clusters in each of the three sites. In SFN mRNA, there are two clusters in three sites, and one cluster in 21 sites. Candidate genes of luminal A and B subtypes are targets for miRNAs: there are 21 sites in FOXA1 mRNA and 15 sites in HMGA2 mRNA. There are clusters of five sites in mRNAs of ITGB1 and SOX4 genes. Clusters of eight sites and 10 sites are identified in mRNAs of SMAD3 and TGFB1 genes, respectively. Organizing miRNA binding sites into clusters reduces the proportion of nucleotide binding sites in mRNAs. This overlapping of miRNA binding sites creates a competition among miRNAs for a binding site. From 6,272 miRNAs studied, only 29 miRNAs from miRBase and 88 novel miRNAs had binding sites in clusters of target gene mRNA in breast cancer. We propose using associations of miRNAs and their target genes as markers in breast cancer subtype diagnosis.

Differences in microRNA expression in breast cancer between women of African and European ancestry

Breast cancer is a heterogeneous disease, characterized by molecularly and phenotypically distinct tumor subtypes, linked to disparate clinical outcomes. American women of African ancestry (AA) are more likely than those of European ancestry (EA) to be diagnosed with aggressive, estrogen receptor negative (ER-) or triple negative breast cancer, and to die of this disease. However, the underlying causes of AA predisposition to ER-/triple negative breast cancer are still largely unknown. In this study, we performed high-throughput whole-genome miRNA expression profiling in breast tissue samples from both AA and EA women. A number of differentially expressed miRNAs, i.e., DEmiRs defined as >2-fold change in expression and false discovery rate <0.05, were identified as up- or downregulated by tumor ER status or by ancestry. We found that among 102 ER-subtype-related DEmiRs identified in breast tumors, the majority of these DEmiRs were race specific, with only 23 DEmiRs shared in tumors from both AAs and EAs; this finding indicates that there are unique subsets of miRNAs differentially expressed between ER- and ER positive tumors within AAs versus EAs. Our overall results support the notion that miRNA expression patterns may differ not only by tumor subtype but by ancestry, indicating differences in tumor biology and heterogeneity of breast cancer between AAs and EAs. These results will provide the basis for further functional analysis to elucidate biological differences between AAs and EAs and to help develop targeted treatment strategies to reduce disparities in breast cancer.

A Novel Bioengineered miR-127 Prodrug Suppresses the Growth and Metastatic Potential of Triple-Negative Breast Cancer Cells

miR-127 is downregulated in breast cancer, where it has been shown to suppress the proliferation, migration, and invasion of breast cancer cells. In triple-negative breast cancer (TNBC), miR-127 downregulation correlates with decreased disease-free and overall patient survival. Tumor suppressor miRNAs may hold therapeutic promise but progress has been limited by several factors, including the lability and high cost of miRNA mimics. Here, we take a novel approach to produce a miR-127 prodrug (miR-127^PD), which we demonstrate is processed to mature, functional miR-127-3p in TNBC tumor cells. miR-127^PD decreased the viability and motility of TNBC cells, sensitized TNBC cells to chemotherapy, and restricted the TNBC stem cell population. Furthermore, systemic delivery of miR-127^PD suppressed tumor growth of MDA-MB-231 and MDA-MB-468 TNBC cells and spontaneous metastasis of MDA-MB-231 cells. In addition, CERK, NANOS1, FOXO6, SOX11, SOX12, FASN, and SUSD2 were identified as novel, functionally important targets of miR-127. In conclusion, our study demonstrates that miR-127 functions as a tumor and metastasis suppressor in TNBC and that delivery of miR-127 may hold promise as a novel therapy. SIGNIFICANCE: Exogenous administration of miR-127, which is functionally activated in target cells, inhibits growth and spontaneous metastasis of triple-negative breast cancer.

An integrative bioinformatics analysis identified miR-375 as a candidate key regulator of malignant breast cancer

MicroRNAs (miRNAs) are key regulators that play important biological roles in carcinogenesis and are promising biomarkers for cancer diagnosis and therapy. hsa-miR-375-3p (miR-375) has been suggested to serve as a tumor suppressor or oncogene in various tumor types; however, its specific expression and potential regulatory role in malignant breast cancer remain unclear. In this study, the results from noncoding RNA microarray analysis indicated that the miR-375 expression level is significantly decreased in malignant basal-like breast cancer compared with luminal-like breast cancer. A total of 1895 co-downregulated and 1645 co-upregulated genes were identified in miR-375 mimic-transfected basal-like breast cancer cell lines. Predicted miR-375 targets were obtained from the online databases TargetScan and DIANA-microT-CDS. Combined KEGG enrichment analysis for coregulated genes and predicted miR-375 targets provided information and revealed differences in potential dynamic signaling pathways regulated by miR-375 and also indicated specific regulatory pathways, such as RNA transport and processing, in basal-like breast cancer. Additionally, gene expression microarray analysis accompanied by UALCAN analysis was performed to screen upregulated genes in the basal-like subtype. Four potential key genes, including LDHB, CPNE8, QKI, and EIF5A2, were identified as candidate target genes of miR-375. Therefore, the present study demonstrated that miR-375 may be a potential key regulator and provide a promising direction for diagnostic and therapeutic developments for malignant breast cancer.

Molecular harvesting with electroporation for tissue profiling

Recent developments in personalized medicine are based on molecular measurement steps that guide personally adjusted medical decisions. A central approach to molecular profiling consists of measuring DNA, RNA, and/or proteins in tissue samples, most notably in and around tumors. This measurement yields molecular biomarkers that are potentially predictive of response and of tumor type. Current methods in cancer therapy mostly use tissue biopsy as the starting point of molecular profiling. Tissue biopsies involve a physical resection of a small tissue sample, leading to localized tissue injury, bleeding, inflammation and stress, as well as to an increased risk of metastasis. Here we developed a technology for harvesting biomolecules from tissues using electroporation. We show that tissue electroporation, achieved using a combination of high-voltage short pulses, 50 pulses 500 V cm-1, 30 µs, 1 Hz, with low-voltage long pulses 50 pulses 50 V cm-1, 10 ms, delivered at 1 Hz, allows for tissue-specific extraction of RNA and proteins. We specifically tested RNA and protein extraction from excised kidney and liver samples and from excised HepG2 tumors in mice. Further in vivo development of extraction methods based on electroporation can drive novel approaches to the molecular profiling of tumors and of tumor environment and to related diagnosis practices.

Integrated copy number and miRNA expression analysis in triple negative breast cancer of Latin American patients

Triple negative breast cancer (TNBC), a clinically aggressive breast cancer subtype, affects 15–35% of women from Latin America. Using an approach of direct integration of copy number and global miRNA profiling data, performed simultaneously in the same tumor specimens, we identified a panel of 17 miRNAs specifically associated with TNBC of ancestrally characterized patients from Latin America, Brazil. This panel was differentially expressed between the TNBC and non-TNBC subtypes studied (p ≤ 0.05, FDR ≤ 0.25), with their expression levels concordant with the patterns of copy number alterations (CNAs), present mostly frequent at 8q21.3-q24.3, 3q24-29, 6p25.3-p12.2, 1q21.1-q44, 5q11.1-q22.1, 11p13-p11.2, 13q12.11-q14.3, 17q24.2-q25.3 and Xp22.33-p11.21. The combined 17 miRNAs presented a high power (AUC = 0.953 (0.78–0.99);95% CI) in discriminating between the TNBC and non-TNBC subtypes of the patients studied. In addition, the expression of 14 and 15 of the 17miRNAs was significantly associated with tumor subtype when adjusted for tumor stage and grade, respectively. In conclusion, the panel of miRNAs identified demonstrated the impact of CNAs in miRNA expression levels and identified miRNA target genes potentially affected by both CNAs and miRNA deregulation. These targets, involved in critical signaling pathways and biological functions associated specifically with the TNBC transcriptome of Latina patients, can provide biological insights into the observed differences in the TNBC clinical outcome among racial/ethnic groups, taking into consideration their genetic ancestry.

Morphological and pathological features of basal-like breast cancer

Basal-like breast cancer (BLBC) is characterized by high grade, high mitotic indices, presence of central necrotic or fibrotic zones, and lymphocytic infiltrate. Patients presenting with BLBC have a poor prognosis and a short-term disease-free and overall survival. BLBCs may include different histological types of breast cancers but the most common histological type is represented by invasive ductal carcinomas of no special type (IDC-NST). Typical immunohistochemical markers for these tumors are basal-type cytokeratin markers such as CK5/6, CK14, CK17, but several BLBCs also express luminal-type CKs, such as CK8/18, CK19. Different molecular alterations, including BRCA1 dysfunction, p53 mutations, up-regulation of EGFR, inactivation of PTEN and the aberrant expression of many non-coding RNAs molecules are detected in BLBC cells suggesting the possibility of defining new targeted therapeutic strategies for this tumor type.

FABP7 is a key metabolic regulator in HER2+ breast cancer brain metastasis

Overexpression of human epidermal growth factor receptor 2 (HER2) in breast cancer patients is associated with increased incidence of breast cancer brain metastases (BCBM), but the mechanisms underlying this phenomenon remain unclear. Here, to identify brain-predominant genes critical for the establishment of BCBM, we conducted an in silico screening analysis and identified that increased levels of fatty acid-binding protein 7 (FABP7) correlate with a lower survival and higher incidence of brain metastases in breast cancer patients. We validated these findings using HER2+ BCBM cells compared with parental breast cancer cells. Importantly, through knockdown and overexpression assays, we characterized the role of FABP7 in the BCBM process in vitro and in vivo. Our results uncover a key role of FABP7 in metabolic reprogramming of HER2 + breast cancer cells, supporting a glycolytic phenotype and storage of lipid droplets that enable their adaptation and survival in the brain microenvironment. In addition, FABP7 is shown to be required for upregulation of key metastatic genes and pathways, such as integrins-Src and VEGFA, and for the growth of HER2+ breast cancer cells in the brain microenvironment in vivo. Together, our results support FABP7 as a potential target for the treatment of HER2+ BCBM.

Identification of Methylation Markers and Differentially Expressed Genes with Prognostic Value in Breast Cancer

Breast cancer is one of the most common cancers causing a high mortality worldwide. This study aimed to identify differential methylation and expression genes with prognostic value in breast cancer. DNA methylation and gene expression profiles (GSE60185, GSE42568, GSE21653, GSE58812, and GSE52865) were downloaded from TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus) databases. The differentially expressed genes (DEGs) and differential methylation genes were identified between breast cancer samples and normal samples. Functional analysis was performed using DAVID (Database for Annotation, Visualization, and Integrated Discovery) tool. Furthermore, functional epigenetic modules (FEM) were analyzed to identify critical genes with prognostic values. A large amount of DEGs and aberrant methylation genes were identified between breast cancer samples and normal samples. These genes were mainly associated with several GO (Gene Ontology) terms and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, such as neuroactive ligand-receptor interaction, alcoholism, gamma-aminobutyric acid signaling pathway, and G-protein-coupled receptor signaling pathway. Additionally, 10 DEGs with differential methylation levels were significantly correlated with survival outcomes in breast cancer patients. FEM analysis revealed that several DEGs (e.g., GABRA4, GABRG1, and GABRA1) in module GABRA4 were identified as potential biomarkers in breast cancer patients. Several DEGs identified were associated with breast cancer prognosis. These DEGs might act as prognostic and diagnostic markers in breast cancer.

Carnosine induces intestinal cells to secrete exosomes that activate neuronal cells

Recently, we showed that imidazole dipeptide such as carnosine contained abundantly in chicken breast meat improves brain function in a double-blind randomized controlled trial. However, the underlying molecular mechanisms remain unknown. Here, we investigated whether carnosine activates intestinal epithelial cells and induces the secretion of factors that activate brain function. We focused on exosomes derived from intestinal epithelial cells as mediators of brain-gut interaction. Results showed that exosomes derived from Caco-2 cells treated with carnosine significantly induced neurite growth in SH-SY5Y cells. To clarify the molecular basis of this finding, we performed integrated analysis of microRNAs (miRNAs) with altered expression in exosomes in response to carnosine treatment and mRNAs with altered expression in target cells in response to exosome treatment to identify related miRNAs and their target genes. The combination of miR-6769-5p and its target gene ATXN1 was found to be involved in the exosome-induced activation of neuronal cells.

miR-106b-5p and miR-17-5p could predict recurrence and progression in breast ductal carcinoma in situ based on the transforming growth factor-beta pathway

PURPOSE

Ductal carcinoma in situ (DCIS) is well-known precursor of invasive ductal carcinoma (IDC). Parts of patients show recurrence as DCIS or IDC after local treatment, but there are no established markers predicting relapse. We analyzed changes in miRNA and oncogene expression during DCIS progression/evolution to identify potential markers predicting recurrence.

METHODS

Forty archival tissues diagnosed as primary or recurrent DCIS and DCIS adjacent to IDC were analyzed. MiRNA hierarchical clustering showed up-regulation of miR-17-5p and miR-106b-5p in recurrent DCIS and DCIS adjacent to IDC. Target genes were predicted based on pre-formed miRNA databases and PanCancer Pathway panel. MiRNAs were transfected into MCF-10A and MCF-7 cells; western blot analysis was performed with MCF-7 cell line to evaluate the effects on TGF-β downstream pathway.

RESULTS

miRNA hierarchical clustering showed 17 dysregulated miRNAs, including miR-17-5p and miR-106b-5p. Based on miRNA database and nCounter Pancancer pathway analysis, TGFβRII was selected as target of miR-106b-5p and miR-17-5p. MiR-106b-5p- and miR-17-5p-transfected MCF-7 cells showed decreased expression of TGFβRII, especially in cells transfected with both miRNAs.

CONCLUSION

miR-106b-5p and miR-17-5p might have a role in breast cancer recurrence and progression by suppressing TGF-β activity, leading to early breast cancer carcinogenesis.

Gene co-expression network approach for predicting prognostic microRNA biomarkers in different subtypes of breast cancer

New diagnostic miRNA biomarkers for different types of cancer have been studied extensively, particularly for breast cancer (BC), which is a leading cause of death among women and has many different subtypes. In the present study, a systems biology approach was used to find remarkable and novel miRNA biomarkers for five molecular subtypes of BC: luminal A, luminal B, ERBB2, basal-like and normal-like. The mRNA expression data from the five BC subtypes was used to reconstruct co-expression networks. The important mRNA-miRNA interactions were considered when reconstructing the bipartite networks from which the five bipartite sub-networks were reconstructed for further analysis. The novel biomarkers detected for each subtype are as follows: miRNAs 26b-5p and 124-3p for basal-like, 26b-5p, 124-3p and 5011-5p for ERBB2, 26b-5p and 5011-5p for LumA, 124-3p, 26b-5p and 7-5p for LumB and 26b-5p, 124-3p and 193b-3p for normal-like. The roles of the identified miRNAs in the occurrence or development of each subtype of BC remain unclear and should be investigated in future studies. In addition, the target genes of these miRNAs may be critical to the mechanisms underlying each subtype and should be analyzed as therapeutic targets in future studies.

miR-342-5p as a Potential Regulator of HER2 Breast Cancer Cell Growth

BACKGROUND

HER2 positive Breast Cancers (BC) have aggressive behavior and poor prognosis. Previously, we have identified miR-342-5p as an upstream regulator of HER2 signaling, as well as inhibitor of HER2 positive BC cell line growth.

OBJECTIVE

Here, we aimed to further investigate the molecular mechanisms behind miR-342-5pinduced HER2 pathway deregulation.

METHOD

Two HER2 amplified breast cancer cell lines were transiently transfected with miR-342-5p mimic or negative control, and gene expression was analyzed by Agilent microarrays. Three clinical datasets with BC patients were used to identify correlations between candidate genes and miR-342- 5p, and associations with survival.

RESULTS

Pathway analyses of all deregulated genes revealed a significant suppression of the HER2 downstream pathways ERK/MAPK and SAPK/JNK, whereas the miR-342-5p predicted target genes were enriched for pathways associated with cell motility.Biological functions linked to mitochondrial stability were ranked among the top toxicological functions in both gene lists. Among the most deregulated genes, Cytochrome B5 Reductase 3 (CYB5R3) and Rap Guanine Nucleotide Exchange Factor 6 (RAPGEF6) significantly anticorrelated and correlated, respectively, with miR-342-5p in all three clinical BC datasets. Low CYB5R3 levels and high RAPGEF6 levels were significantly associated with survival, although this was not directly associated with HER2 expression.

CONCLUSION

Our data suggest that miR-342-5p overexpression in HER2 positive BC cell lines elicits broad effects on HER2 downstream signaling, cell motility and mitochondrial stability. Together these effects may render cells less proliferative and more sensitive to cellular stress.