Validation of a network-based strategy for the optimization of combinatorial target selection in breast cancer therapy: siRNA knockdown of network targets in MDA-MB-231 cells as an in vitro model for inhibition of tumor development

Wiring Between Close Nodes in Molecular Networks Evolves More Quickly Than Between Distant Nodes

As species diverge, a wide range of evolutionary processes lead to changes in protein-protein interaction (PPI) networks and metabolic networks. The rate at which molecular networks evolve is an important question in evolutionary biology. Previous empirical work has focused on interactomes from model organisms to calculate rewiring rates, but this is limited by the relatively small number of species and sparse nature of network data across species. We present a proxy for variation in network topology: variation in drug-drug interactions (DDIs), obtained by studying drug combinations (DCs) across taxa. Here, we propose the rate at which DDIs change across species as an estimate of the rate at which the underlying molecular network changes as species diverge. We computed the evolutionary rates of DDIs using previously published data from a high-throughput study in gram-negative bacteria. Using phylogenetic comparative methods, we found that DDIs diverge rapidly over short evolutionary time periods, but that divergence saturates over longer time periods. In parallel, we mapped drugs with known targets in PPI and cofunctional networks. We found that the targets of synergistic DDIs are closer in these networks than other types of DCs and that synergistic interactions have a higher evolutionary rate, meaning that nodes that are closer evolve at a faster rate. Future studies of network evolution may use DC data to gain larger-scale perspectives on the details of network evolution within and between species.

Optimizing therapeutic targets for breast cancer using boolean network models

Studying gene regulatory networks associated with cancer provides valuable insights for therapeutic purposes, given that cancer is fundamentally a genetic disease. However, as the number of genes in the system increases, the complexity arising from the interconnections between network components grows exponentially. In this study, using Boolean logic to adjust the existing relationships between network components has facilitated simplifying the modeling process, enabling the generation of attractors that represent cell phenotypes based on breast cancer RNA-seq data. A key therapeutic objective is to guide cells, through targeted interventions, to transition from the current cancer attractor to a physiologically distinct attractor unrelated to cancer. To achieve this, we developed a computational method that identifies network nodes whose inhibition can facilitate the desired transition from one tumor attractor to another associated with apoptosis, leveraging transcriptomic data from cell lines. To validate the model, we utilized previously published in vitro experiments where the downregulation of specific proteins resulted in cell growth arrest and death of a breast cancer cell line. The method proposed in this manuscript combines diverse data sources, conducts structural network analysis, and incorporates relevant biological knowledge on apoptosis in cancer cells. This comprehensive approach aims to identify potential targets of significance for personalized medicine.

TK1 expression influences pathogenicity by cell cycle progression, cellular migration, and cellular survival in HCC 1806 breast cancer cells

Breast cancer is the most common cancer diagnosis worldwide accounting for 1 out of every 8 cancer diagnoses. The elevated expression of Thymidine Kinase 1 (TK1) is associated with more aggressive tumor grades, including breast cancer. Recent studies indicate that TK1 may be involved in cancer pathogenesis; however, its direct involvement in breast cancer has not been identified. Here, we evaluate potential pathogenic effects of elevated TK1 expression by comparing HCC 1806 to HCC 1806 TK1-knockdown cancer cells (L133). Transcriptomic profiles of HCC 1806 and L133 cells showed cell cycle progression, apoptosis, and invasion as potential pathogenic pathways affected by TK1 expression. Subsequent in-vitro studies confirmed differences between HCC 1806 and L133 cells in cell cycle phase progression, cell survival, and cell migration. Expression comparison of several factors involved in these pathogenic pathways between HCC 1806 and L133 cells identified p21 and AKT3 transcripts were significantly affected by TK1 expression. Creation of a protein-protein interaction map of TK1 and the pathogenic factors we evaluated predict that the majority of factors evaluated either directly or indirectly interact with TK1. Our findings argue that TK1 elevation directly increases HCC 1806 cell pathogenicity and is likely occurring by p21- and AKT3-mediated mechanisms to promote cell cycle arrest, cellular migration, and cellular survival.

A Strategy Utilizing Protein-Protein Interaction Hubs for the Treatment of Cancer Diseases

We describe a strategy for the development of a rational approach of neoplastic disease therapy based on the demonstration that scale-free networks are susceptible to specific attacks directed against its connective hubs. This strategy involves the (i) selection of up-regulated hubs of connectivity in the tumors interactome, (ii) drug repurposing of these hubs, (iii) RNA silencing of non-druggable hubs, (iv) in vitro hub validation, (v) tumor-on-a-chip, (vi) in vivo validation, and (vii) clinical trial. Hubs are protein targets that are assessed as targets for rational therapy of cancer in the context of personalized oncology. We confirmed the existence of a negative correlation between malignant cell aggressivity and the target number needed for specific drugs or RNA interference (RNAi) to maximize the benefit to the patient's overall survival. Interestingly, we found that some additional proteins not generally targeted by drug treatments might justify the addition of inhibitors designed against them in order to improve therapeutic outcomes. However, many proteins are not druggable, or the available pharmacopeia for these targets is limited, which justifies a therapy based on encapsulated RNAi.

The Prognostic and Therapeutic Implications of the Chemoresistance Gene BIRC5 in Triple-Negative Breast Cancer

Chemoresistance affects TNBC patient treatment responses. Therefore, identifying the chemoresistant gene provides a new approach to understanding chemoresistance in TNBC. BIRC5 was examined in the current study as a tool for predicting the prognosis of TNBC patients and assisting in developing alternative therapies using online database tools. According to the examined studies, BIRC5 was highly expressed in 45 to 90% of TNBC patients. BIRC5 is not only abundantly expressed but also contributes to resistance to chemotherapy, anti-HER2 therapy, and radiotherapy. Patients with increased expression of BIRC5 had a median survival of 31.2 months compared to 85.8 months in low-expression counterparts (HR, 1.73; CI, 1.4−2.13; p = 2.5 × 10−7). The overall survival, disease-free survival, relapse-free survival, distant metastasis-free survival, and the complete pathological response of TNBC patients with high expression of BIRC5 who received any chemotherapy (Taxane, Ixabepilone, FAC, CMF, FEC, Anthracycline) and anti-HER2 therapy (Trastuzumab, Lapatinib) did not differ significantly from those patients receiving any other treatment. Data obtained indicate that the BIRC5 promoter region was substantially methylated, and hypermethylation was associated with higher BIRC5 mRNA expression (p < 0.05). The findings of this study outline the role of BIRC5 in chemotherapy-induced resistance of TNBC, further indicating that BIRC5 may serve as a promising prognostic biomarker that contributes to chemoresistance and could be a possible therapeutic target. Meanwhile, several in vitro studies show that flavonoids were highly effective in inhibiting BIRC5 in genetically diverse TNBC cells. Therefore, flavonoids would be a promising strategy for preventing and treating TNBC patients with the BIRC5 molecule.

Methylation status of TK1 correlated with immune infiltrates in prostate cancer

TK1 is overexpressed in numerous cancers and is associated with to a poor prognosis. However, the relationship between methylation status of TK1 and Immune Infiltrates in Prostate Cancer (PCa) is unknown. The goal of this study was to use comprehensive bioinformatic analyses to elucidate the involvement relationship between methylation status of TK1 and Immune Infiltrates in PCa. TK1 mRNA expression and methylation data in PCa were investigated via GEPIA, TIMER, and UALCAN coupled with MEXPRESS data resources. We employed the LinkedOmics data resource to determine the signaling cascades linked to TK1 expression. Single-cell analysis was performed using the CancerSEA data resource. GeneMANIA and CancerSEA were used to analyze the correlation between TK1 and TK1 coexpressed genes. In addition, TIMER and TISIDB were adopted to assess tumor-invading immune cells and immunomodulators. CTD was utilized to detect the drugs acting on TK1. This study found that TK1 was overexpressed in PCa, and its contents were linked to tumor stage and prognosis. Genes co-expressed with TK1 were enriched in cascades involved in the ribosome, cell cycle, oxidative phosphorylation, DNA replication, oocyte meiosis, and the proteasome. The expression of TK1 along with its methylation status was found to be linked to tumor-invading immune cells, as well as PCa immunomodulators. We also examined the prospect of employing TK1 as a possible target for PCa therapy. This work provides the clinical value of TK1 hypermethylation in PCa and highlights new insights into its novel immunomodulatory functions.

Transcript-Targeted Therapy Based on RNA Interference and Antisense Oligonucleotides: Current Applications and Novel Molecular Targets

The development of novel target therapies based on the use of RNA interference (RNAi) and antisense oligonucleotides (ASOs) is growing in an exponential way, challenging the chance for the treatment of the genetic diseases and cancer by hitting selectively targeted RNA in a sequence-dependent manner. Multiple opportunities are taking shape, able to remove defective protein by silencing RNA (e.g., Inclisiran targets mRNA of protein PCSK9, permitting a longer half-life of LDL receptors in heterozygous familial hypercholesteremia), by arresting mRNA translation (i.e., Fomivirsen that binds to UL123-RNA and blocks the translation into IE2 protein in CMV-retinitis), or by reactivating modified functional protein (e.g., Eteplirsen able to restore a functional shorter dystrophin by skipping the exon 51 in Duchenne muscular dystrophy) or a not very functional protein. In this last case, the use of ASOs permits modifying the expression of specific proteins by modulating splicing of specific pre-RNAs (e.g., Nusinersen acts on the splicing of exon 7 in SMN2 mRNA normally not expressed; it is used for spinal muscular atrophy) or by downregulation of transcript levels (e.g., Inotersen acts on the transthryretin mRNA to reduce its expression; it is prescribed for the treatment of hereditary transthyretin amyloidosis) in order to restore the biochemical/physiological condition and ameliorate quality of life. In the era of precision medicine, recently, an experimental splice-modulating antisense oligonucleotide, Milasen, was designed and used to treat an 8-year-old girl affected by a rare, fatal, progressive form of neurodegenerative disease leading to death during adolescence. In this review, we summarize the main transcriptional therapeutic drugs approved to date for the treatment of genetic diseases by principal regulatory government agencies and recent clinical trials aimed at the treatment of cancer. Their mechanism of action, chemical structure, administration, and biomedical performance are predominantly discussed.

A Data Science Approach for the Identification of Molecular Signatures of Aggressive Cancers

The main hallmarks of cancer include sustaining proliferative signaling and resisting cell death. We analyzed the genes of the WNT pathway and seven cross-linked pathways that may explain the differences in aggressiveness among cancer types. We divided six cancer types (liver, lung, stomach, kidney, prostate, and thyroid) into classes of high (H) and low (L) aggressiveness considering the TCGA data, and their correlations between Shannon entropy and 5-year overall survival (OS). Then, we used principal component analysis (PCA), a random forest classifier (RFC), and protein-protein interactions (PPI) to find the genes that correlated with aggressiveness. Using PCA, we found GRB2, CTNNB1, SKP1, CSNK2A1, PRKDC, HDAC1, YWHAZ, YWHAB, and PSMD2. Except for PSMD2, the RFC analysis showed a different list, which was CAD, PSMD14, APH1A, PSMD2, SHC1, TMEFF2, PSMD11, H2AFZ, PSMB5, and NOTCH1. Both methods use different algorithmic approaches and have different purposes, which explains the discrepancy between the two gene lists. The key genes of aggressiveness found by PCA were those that maximized the separation of H and L classes according to its third component, which represented 19% of the total variance. By contrast, RFC classified whether the RNA-seq of a tumor sample was of the H or L type. Interestingly, PPIs showed that the genes of PCA and RFC lists were connected neighbors in the PPI signaling network of WNT and cross-linked pathways.

Comprehensive Analysis of Immune-Related Prognosis of TK1 in Hepatocellular Carcinoma

Increased expression of TK1 is associated with the progression of a variety of tumors. However, the relationship of TK1 expression with immune cell infiltration and its prognostic value in hepatocellular carcinoma (HCC) are still unknown. In this study the TCGA database was used to evaluate TK1 expression and its impact on survival in patients with HCC. Compared with normal tissue, TK1 in the liver tissue of patients with HCC was significantly up-regulated at both the mRNA and protein levels. Furthermore, TK1 expression was significantly related to pathological stage, tumor stage and lymph node metastasis, with high TK1 expression being an unfavorable prognostic factor for HCC. TK1 expression was also significantly associated with the infiltration of B cells, T cells, and dendritic cells in HCC. Single-cell sequencing analysis revealed that TK1 was associated with relatively large changes in T cells, especially gamma-delta T cells. A prognostic risk score based on TK1-related immune genes (CD40LG and TNFRSF4) was established using COX regression analysis. By integrating the immune-related risk score model with clinical features, a nomogram was constructed to predict the survival rate of HCC patients (1 year, 3-year and 5-year AUC of 0.782, 0.783 and 0.771, respectively). Knockdown of the target gene for TK1 was found to have significant anti-apoptosis and pro-proliferation effects on HepG2 cells. The level of TK1 in the serum and liver tissue of patients with HCC was significantly increased relative to healthy controls. These findings highlight the role of TK1 in the tumor immune response of HCC patients and in the proliferation and apoptosis of HepG2 cells. TK1 could therefore be a potential immunotherapy target for HCC patients, while the two immune genes related to TK1 (CD40LG And TNFRSF4) may be promising prognostic biomarkers in HCC.

Data-Driven Modeling of Breast Cancer Tumors Using Boolean Networks

Cancer is a genomic disease involving various intertwined pathways with complex cross-communication links. Conceptually, this complex interconnected system forms a network, which allows one to model the dynamic behavior of the elements that characterize it to describe the entire system’s development in its various evolutionary stages of carcinogenesis. Knowing the activation or inhibition status of the genes that make up the network during its temporal evolution is necessary for the rational intervention on the critical factors for controlling the system’s dynamic evolution. In this report, we proposed a methodology for building data-driven boolean networks that model breast cancer tumors. We defined the network components and topology based on gene expression data from RNA-seq of breast cancer cell lines. We used a Boolean logic formalism to describe the network dynamics. The combination of single-cell RNA-seq and interactome data enabled us to study the dynamics of malignant subnetworks of up-regulated genes. First, we used the same Boolean function construction scheme for each network node, based on canalyzing functions. Using single-cell breast cancer datasets from The Cancer Genome Atlas, we applied a binarization algorithm. The binarized version of scRNA-seq data allowed identifying attractors specific to patients and critical genes related to each breast cancer subtype. The model proposed in this report may serve as a basis for a methodology to detect critical genes involved in malignant attractor stability, whose inhibition could have potential applications in cancer theranostics.

Galaxy and MEAN Stack to Create a User-Friendly Workflow for the Rational Optimization of Cancer Chemotherapy

One aspect of personalized medicine is aiming at identifying specific targets for therapy considering the gene expression profile of each patient individually. The real-world implementation of this approach is better achieved by user-friendly bioinformatics systems for healthcare professionals. In this report, we present an online platform that endows users with an interface designed using MEAN stack supported by a Galaxy pipeline. This pipeline targets connection hubs in the subnetworks formed by the interactions between the proteins of genes that are up-regulated in tumors. This strategy has been proved to be suitable for the inhibition of tumor growth and metastasis in vitro. Therefore, Perl and Python scripts were enclosed in Galaxy for translating RNA-seq data into protein targets suitable for the chemotherapy of solid tumors. Consequently, we validated the process of target diagnosis by (i) reference to subnetwork entropy, (ii) the critical value of density probability of differential gene expression, and (iii) the inhibition of the most relevant targets according to TCGA and GDC data. Finally, the most relevant targets identified by the pipeline are stored in MongoDB and can be accessed through the aforementioned internet portal designed to be compatible with mobile or small devices through Angular libraries.

The role of tumor suppressor short non-coding RNAs on breast cancer

Characterized by remarkable levels of aggression and malignancy, BC remains one of the leading causes of death in females world wide. Accordingly, significant efforts have been made to develop early diagnostic tools, increase treatment efficacy, and improve patient prognosis. Hopefully, many of the molecular mechanisms underlying BC have been detected and show promising targeting potential. In particular, short and long non-coding RNAs (ncRNAs) are a class of endogenous BC controllers and include a number of different species including microRNAs, Piwi-interacting RNAs, small nucleolar RNA, short interfering RNAs, and tRNA-derivatives. In this review, we discuss the tumor suppressing roles of ncRNAs in the context of BC, and the mechanisms by which ncRNAs target tumor hallmarks, including apoptosis, proliferation, invasion, metastasis, epithelial-mesenchymal transition, angiogenesis, and cell cycle progression, in addition to their diagnostic and prognostic significance in cancer treatment.

Thymidine kinase 1 through the ages: a comprehensive review

Proliferation markers, such as proliferating cell nuclear antigen (PCNA), Ki-67, and thymidine kinase 1 (TK1), have potential as diagnostic tools and as prognostic factors in assessing cancer treatment and disease progression. TK1 is involved in cellular proliferation through the recovery of the nucleotide thymidine in the DNA salvage pathway. TK1 upregulation has been found to be an early event in cancer development. In addition, serum levels of TK1 have been shown to be tied to cancer stage, so that higher levels of TK1 indicate a more serious prognosis. As a result of these findings and others, TK1 is not only a potentially viable biomarker for cancer recurrence, treatment monitoring, and survival, but is potentially more advantageous than current biomarkers. Compared to other proliferation markers, TK1 levels during S phase more accurately determine the rate of DNA synthesis in actively dividing tumors. Several reviews of TK1 elaborate on various assays that have been developed to measure levels in the serum of cancer patients in clinical settings. In this review, we include a brief history of important TK1 discoveries and findings, a comprehensive overview of TK1 regulation at DNA to protein levels, and recent findings that indicate TK1’s potential role in cancer pathogenesis and its growing potential as a tumor biomarker and therapeutic target.

Spliceosome-Associated microRNAs Signify Breast Cancer Cells and Portray Potential Novel Nuclear Targets

MicroRNAs (miRNAs) act as negative regulators of gene expression in the cytoplasm. Previous studies have identified the presence of miRNAs in the nucleus. Here we study human breast cancer-derived cell-lines (MCF-7 and MDA-MB-231) and a non-tumorigenic cell-line (MCF-10A) and compare their miRNA sequences at the spliceosome fraction (SF). We report that the levels of miRNAs found in the spliceosome, their identity, and pre-miRNA segmental composition are cell-line specific. One such miRNA is miR-7704 whose genomic position overlaps HAGLR, a cancer-related lncRNA. We detected an inverse expression of miR-7704 and HAGLR in the tested cell lines. Specifically, inhibition of miR-7704 caused an increase in HAGLR expression. Furthermore, elevated levels of miR-7704 slightly altered the cell-cycle in MDA-MB-231. Altogether, we show that SF-miR-7704 acts as a tumor-suppressor gene with HAGLR being its nuclear target. The relative levels of miRNAs found in the spliceosome fractions (e.g., miR-100, miR-30a, and let-7 family) in non-tumorigenic relative to cancer-derived cell-lines was monitored. We found that the expression trend of the abundant miRNAs in SF was different from that reported in the literature and from the observation of large cohorts of breast cancer patients, suggesting that many SF-miRNAs act on targets that are different from the cytoplasmic ones. Altogether, we report on the potential of SF-miRNAs as an unexplored route for cancerous cell state.

Modeling Basins of Attraction for Breast Cancer Using Hopfield Networks

Cancer is a genetic disease for which traditional treatments cause harmful side effects. After two decades of genomics technological breakthroughs, personalized medicine is being used to improve treatment outcomes and mitigate side effects. In mathematical modeling, it has been proposed that cancer matches an attractor in Waddington's epigenetic landscape. The use of Hopfield networks is an attractive modeling approach because it requires neither previous biological knowledge about protein-protein interactions nor kinetic parameters. In this report, Hopfield network modeling was used to analyze bulk RNA-Seq data of paired breast tumor and control samples from 70 patients. We characterized the control and tumor attractors with respect to their size and potential energy and correlated the Euclidean distances between the tumor samples and the control attractor with their corresponding clinical data. In addition, we developed a protocol that outlines the key genes involved in tumor state stability. We found that the tumor basin of attraction is larger than that of the control and that tumor samples are associated with a more substantial negative energy than control samples, which is in agreement with previous reports. Moreover, we found a negative correlation between the Euclidean distances from tumor samples to the control attractor and patient overall survival. The ascending order of each node's density in the weight matrix and the descending order of the number of patients that have the target active only in the tumor sample were the parameters that withdrew more tumor samples from the tumor basin of attraction with fewer gene inhibitions. The combinations of therapeutic targets were specific to each patient. We performed an initial validation through simulation of trastuzumab treatment effects in HER2+ breast cancer samples. For that, we built an energy landscape composed of single-cell and bulk RNA-Seq data from trastuzumab-treated and non-treated HER2+ samples. The trajectory from the non-treated bulk sample toward the treated bulk sample was inferred through the perturbation of differentially expressed genes between these samples. Among them, we characterized key genes involved in the trastuzumab response according to the literature.

Anakoinosis: Correcting Aberrant Homeostasis of Cancer Tissue-Going Beyond Apoptosis Induction

The current approach to systemic therapy for metastatic cancer is aimed predominantly at inducing apoptosis of cancer cells by blocking tumor-promoting signaling pathways or by eradicating cell compartments within the tumor. In contrast, a systems view of therapy primarily considers the communication protocols that exist at multiple levels within the tumor complex, and the role of key regulators of such systems. Such regulators may have far-reaching influence on tumor response to therapy and therefore patient survival. This implies that neoplasia may be considered as a cell non-autonomous disease. The multi-scale activity ranges from intra-tumor cell compartments, to the tumor, to the tumor-harboring organ to the organism. In contrast to molecularly targeted therapies, a systems approach that identifies the complex communications networks driving tumor growth offers the prospect of disrupting or "normalizing" such aberrant communicative behaviors and therefore attenuating tumor growth. Communicative reprogramming, a treatment strategy referred to as anakoinosis, requires novel therapeutic instruments, so-called master modifiers to deliver concerted tumor growth-attenuating action. The diversity of biological outcomes following pro-anakoinotic tumor therapy, such as differentiation, trans-differentiation, control of tumor-associated inflammation, etc. demonstrates that long-term tumor control may occur in multiple forms, inducing even continuous complete remission. Accordingly, pro-anakoinotic therapies dramatically extend the repertoire for achieving tumor control and may activate apoptosis pathways for controlling resistant metastatic tumor disease and hematologic neoplasia.

Signaling Complexity Measured by Shannon Entropy and Its Application in Personalized Medicine

Traditional approaches to cancer therapy seek common molecular targets in tumors from different patients. However, molecular profiles differ between patients, and most tumors exhibit inherent heterogeneity. Hence, imprecise targeting commonly results in side effects, reduced efficacy, and drug resistance. By contrast, personalized medicine aims to establish a molecular diagnosis specific to each patient, which is currently feasible due to the progress achieved with high-throughput technologies. In this report, we explored data from human RNA-seq and protein–protein interaction (PPI) networks using bioinformatics to investigate the relationship between tumor entropy and aggressiveness. To compare PPI subnetworks of different sizes, we calculated the Shannon entropy associated with vertex connections of differentially expressed genes comparing tumor samples with their paired control tissues. We found that the inhibition of up-regulated connectivity hubs led to a higher reduction of subnetwork entropy compared to that obtained with the inhibition of targets selected at random. Furthermore, these hubs were described to be participating in tumor processes. We also found a significant negative correlation between subnetwork entropies of tumors and the respective 5-year survival rates of the corresponding cancer types. This correlation was also observed considering patients with lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) based on the clinical data from The Cancer Genome Atlas database (TCGA). Thus, network entropy increases in parallel with tumor aggressiveness but does not correlate with PPI subnetwork size. This correlation is consistent with previous reports and allowed us to assess the number of hubs to be inhibited for therapy to be effective, in the context of precision medicine, by reference to the 100% patient survival rate 5 years after diagnosis. Large standard deviations of subnetwork entropies and variations in target numbers per patient among tumor types characterize tumor heterogeneity.

Metastatic heterogeneity of breast cancer: Molecular mechanism and potential therapeutic targets

Breast cancer is one of the most common malignancies among women throughout the world and is the major cause of most cancer-related deaths. Several explanations account for the high rate of mortality of breast cancer, and metastasis to vital organs is identified as the principal cause. Over the past few years, intensive efforts have demonstrated that breast cancer exhibits metastatic heterogeneity with distinct metastatic precedence to various organs, giving rise to differences in prognoses and responses to therapy in breast cancer patients. Bone, lung, liver, and brain are generally accepted as the primary target sites of breast cancer metastasis. However, the underlying molecular mechanism of metastatic heterogeneity of breast cancer remains to be further elucidated. Recently, the advent of novel genomic and pathologic approaches as well as technological breakthroughs in imaging analysis and animal modelling have yielded an unprecedented change in our understanding of the heterogeneity of breast cancer metastasis and provided novel insight for establishing more effective therapeutics. This review summarizes recent molecular mechanisms and emerging concepts on the metastatic heterogeneity of breast cancer and discusses the potential of identifying specific molecules against tumor cells or tumor microenvironments to thwart the development of metastatic disease and improve the prognosis of breast cancer patients.

Six novel immunoglobulin genes as biomarkers for better prognosis in triple-negative breast cancer by gene co-expression network analysis

Gene co-expression network analysis (GCNA) can detect alterations in regulatory activities in case/control comparisons. We propose a framework to detect novel genes and networks for predicting breast cancer recurrence. Thirty-four prognosis candidate genes were selected based on a literature review. Four Gene Expression Omnibus Series (GSE) microarray datasets (n = 920) were used to create gene co-expression networks based on these candidates. We applied the framework to four comparison groups according to node (+/−) and recurrence (+/−). We identified a sub-network containing two candidate genes (LST1 and IGHM) and six novel genes (IGHA1, IGHD, IGHG1, IGHG3, IGLC2, and IGLJ3) related to B cell-specific immunoglobulin. These novel genes were correlated with recurrence under the control of node status and were found to function as tumor suppressors; higher mRNA expression indicated a lower risk of recurrence (hazard ratio, HR = 0.87, p = 0.001). We created an immune index score by performing principle component analysis and divided the genes into low and high groups. This discrete index significantly predicted relapse-free survival (RFS) (high: HR = 0.77, p = 0.019; low: control). Public tool KM Plotter and TCGA-BRCA gene expression data were used to validate. We confirmed these genes are correlated with RFS and distal metastasis-free survival (DMFS) in triple-negative breast cancer (TNBC) and general breast cancer.

Combined drug therapeutic strategies for the effective treatment of Triple Negative Breast Cancer

TNBC (Triple Negative Breast Cancer) is a subtype of breast cancer with an aggressive phenotype which shows high metastatic capability and poor prognosis. Owing to its intrinsic properties like heterogeneity, lack of hormonal receptors and aggressive phenotype leave chemotherapy as a mainstay for the treatment of TNBC. Various studies have demonstrated that chemotherapy alone or therapeutic drugs targeting TNBC pathways, epigenetic mechanisms and immunotherapy alone have not shown significant improvement in TNBC patients. On the other hand, a combination of therapeutic drugs or addition of chemotherapy with therapeutic drugs has shown substantial improvement in results and proven to be an effective strategy for TNBC treatment. This review sheds light on effective combinational drug strategies and current clinical trial status of various combinatorial drugs for the treatment of TNBC.

Bioinformatics-based identification of miR-542-5p as a predictive biomarker in breast cancer therapy

Background

Tamoxifen is the first-line hormone therapy for estrogen receptor alpha positive (ERα+) breast cancer. However, about 40% of patients with ERα + breast cancer who receive tamoxifen therapy eventually develop resistance resulting in a poor prognosis. The aim of this study was to mine available data sets in the Gene Expression Omnibus (GEO) database, including in vitro (cell lines) and in vivo (tissue samples), and to identify all miRNAs associated with tamoxifen resistance (TamR) in breast cancer. Secondly, this study aimed to predict the key gene regulatory networks of newly found TamR-related miRNAs and evaluate the potential role of the miRNAs and targets as potential prognosis biomarkers for breast cancer patients.

Result

Microarray data sets from two different studies were used from the GEO database: 1. GSE66607: miRNA of MCF-7 TamR cells; 2. GSE37405: TamR tissues. Differentially expressed microRNAs (miRNAs) were identified in both data sets and 5 differentially expressed miRNAs were found to overlap between the two data sets. Profiles of GSE37405 and data from the Kaplan-Meier Plotter Database (KMPD) along with Gene Expression Profiling Interactive Analysis (GEPIA) were used to reveal the relationship between these 5 miRNAs and overall survival. The results showed that has-miR-542-5p was the only miRNA associated with overall survival of ERα + breast cancer patients who received adjuvant tamoxifen. Targets of has-miR-542-5p were predicted by miRanda and TargetScan, and the mRNA expression of the three 3 target gene, Tyrosine 3-Monooxygenase/Tryptophan 5-Monooxygenase Activation Protein Beta (YWHAB), Lymphocyte Antigen 9 (LY9), and Secreted Frizzled Related Protein 1 (SFRP1) were associated with overall survival in 2 different databases. Copy-number alterations (CNAs) of SFRP1 confer survival disadvantage to breast cancer patients and alter the mRNA expression of SFRP1 in cBioPortal database.

Conclusion

This study indicates that miRNA has-miR-542-5p is associated with TamR and can predict prognosis of breast cancer patients. Furthermore, has-miR-542-5p may be acting through a mechanism involving the target genes YWHAB, LY9, and SFRP1. Overall, has-miR-542-5p is a predictive biomarker and potential target for therapy of breast cancer patients.

Electronic supplementary material

The online version of this article (10.1186/s41065-018-0055-7) contains supplementary material, which is available to authorized users.

Site-Specific Modification Using the 2'-Methoxyethyl Group Improves the Specificity and Activity of siRNAs

Rapid progress has been made toward small interfering RNA (siRNA)-based therapy for human disorders, but rationally optimizing siRNAs for high specificity and potent silencing remains a challenge. In this study, we explored the effect of chemical modification at the cleavage site of siRNAs. We found that modifications at positions 9 and 10 markedly reduced the silencing potency of the unmodified strand of siRNAs but were well tolerated by the modified strand. Intriguingly, addition of the 2′-methoxyethyl (MOE) group at the cleavage site improved both the specificity and silencing activity of siRNAs by facilitating the oriented RNA-induced silencing complex (RISC) loading of the modified strand. Furthermore, we combined MOE modifications at positions 9 and 10 of one strand together with 2′-O-methylation (OMe) at position 14 of the other strand and found a synergistic effect that improved the specificity of siRNAs. The surprisingly beneficial effect of the combined modification was validated using siRNA-targeting endogenous gene intercellular adhesion molecule 1 (ICAM1). We found that the combined modifications eliminated its off-target effects. In conclusion, we established effective strategies to optimize siRNAs using site-specific MOE modifications. The findings may allow the creation of superior siRNAs for therapy in terms of activity and specificity.