Analyzing the differentially expressed genes and pathway cross-talk in aggressive breast cancer

Exploring the tumor microenvironment of breast cancer to develop a prognostic model and predict immunotherapy responses

Breast cancer is the most prevalent malignancy in women and exhibits significant heterogeneity. The tumor microenvironment (TME) plays a critical role in tumorigenesis, progression, and response to therapy. However, its impact on the prognosis and immunotherapy responses is incompletely understood. Using public databases, we conducted a comprehensive investigation of transcriptome and single-cell sequencing data. After performing immune infiltration analysis, we conducted consensus clustering, weighted gene co-expression network analysis (WGCNA), Cox regression, and least absolute shrinkage and selection operator (Lasso) regression to identify independent prognostic genes in breast cancer. Subsequently, we developed a prognostic model for patients with breast cancer. Tumor Immune Dysfunction and Exclusion (TIDE) values were used to assess patient's responsiveness to breast cancer. Based on single-cell RNA-sequencing data, we identified various cell types through cluster analysis and investigated the expression of prognostic model genes in each cell type. The drug sensitivity of targeted therapeutic agents for breast cancer treatment was analyzed in different cell types. We identified 12 independent prognostic genes associated with breast cancer and used these genes to construct a prognostic model. The prognostic model accurately discriminated between patients classified as high- and low-risk, providing precise prognostic predictions for individual patients. Additionally, our model exhibited a robust capacity to predict the immunotherapeutic response in breast cancer patients. Our investigation revealed a notable association between the proportion of endothelial cells (ECs) and patient prognosis in breast cancer. A prognostic model for breast cancer was formulated that showed close associations between prognosis and response to immunotherapy. For patients predicted by our model to not respond effectively to immunotherapeutic agents, it may be considered to combine immunotherapeutic agents with targeted therapeutic agents identified through our drug sensitivity analysis, which could potentially enhance treatment efficacy.

SPARC: a potential target for functional nanomaterials and drugs

Secreted protein acidic and rich in cysteine (SPARC), also termed osteonectin or BM-40, is a matricellular protein which regulates cell adhesion, extracellular matrix production, growth factor activity, and cell cycle. Although SPARC does not perform a structural function, it, however, modulates interactions between cells and the surrounding extracellular matrix due to its anti-proliferative and anti-adhesion properties. The overexpression of SPARC at sites, including injury, regeneration, obesity, cancer, and inflammation, reveals its application as a prospective target and therapeutic indicator in the treatment and assessment of disease. This article comprehensively summarizes the mechanism of SPARC overexpression in inflammation and tumors as well as the latest research progress of functional nanomaterials in the therapy of rheumatoid arthritis and tumors by manipulating SPARC as a new target. This article provides ideas for using functional nanomaterials to treat inflammatory diseases through the SPARC target. The purpose of this article is to provide a reference for ongoing disease research based on SPARC-targeted therapy.

Insights into the Relationship between Pentraxin-3 and Cancer

Although cancer can be cured if detected early and treated effectively, it is still a leading cause of death worldwide. Tumor development can be limited by an appropiate immune response, but it can be promoted by chronic extensive inflammation through metabolic dysregulation and angiogenesis. In the past decade, numerous efforts have been made in order to identify novel candidates with predictive values in cancer diagnostics. In line with this, researchers have investigated the involvement of pentraxin-3 (PTX-3) in cellular proliferation and immune escape in various types of cancers, although it has not been clearly elucidated. PTX-3 is a member of the long pentraxin subfamily which plays an important role in regulating inflammation, innate immunity response, angiogenesis, and tissue remodeling. Increased synthesis of inflammatory biomarkers and activation of different cellular mechanisms can induce PTX-3 expression in various types of cells (neutrophils, monocytes, lymphocytes, myeloid dendritic cells, fibroblasts, and epithelial cells). PTX-3 has both pro- and anti-tumor functions, thus dual functions in oncogenesis. This review elucidates the potential usefulness of PTX-3 as a serum biomarker in cancer. While future investigations are needed, PTX-3 is emerging as a promising tool for cancer's diagnosis and prognosis, and also treatment monitoring.

Pinpointing Cancer Sub-Type Specific Metabolic Tasks Facilitates Identification of Anti-cancer Targets

Metabolic reprogramming is one of the hallmarks of tumorigenesis. Understanding the metabolic changes in cancer cells may provide attractive therapeutic targets and new strategies for cancer therapy. The metabolic states are not the same in different cancer types or subtypes, even within the same sample of solid tumors. In order to understand the heterogeneity of cancer cells, we used the Pareto tasks inference method to analyze the metabolic tasks of different cancers, including breast cancer, lung cancer, digestive organ cancer, digestive tract cancer, and reproductive cancer. We found that cancer subtypes haves different propensities toward metabolic tasks, and the biological significance of these metabolic tasks also varies greatly. Normal cells treat metabolic tasks uniformly, while different cancer cells focus on different pathways. We then integrated the metabolic tasks into the multi-objective genome-scale metabolic network model, which shows higher accuracy in the in silico prediction of cell states after gene knockout than the conventional biomass maximization model. The predicted potential single drug targets could potentially turn into biomarkers or drug design targets. We further implemented the multi-objective genome-scale metabolic network model to predict synthetic lethal target pairs of the Basal and Luminal B subtypes of breast cancer. By analyzing the predicted synthetic lethal targets, we found that mitochondrial enzymes are potential targets for drug combinations. Our study quantitatively analyzes the metabolic tasks of cancer and establishes cancer type-specific metabolic models, which opens a new window for the development of specific anti-cancer drugs and provides promising treatment plans for specific cancer subtypes.

Component Identification of Phenolic Acids in Cell Suspension Cultures of Saussureainvolucrata and Its Mechanism of Anti-Hepatoma Revealed by TMT Quantitative Proteomics

Saussurea involucrata (S. involucrata) had been reported to have anti-hepatoma function. However, the mechanism is complex and unclear. To evaluate the anti-hepatoma mechanism of S. involucrata comprehensively and make a theoretical basis for the mechanical verification of later research, we carried out this work. In this study, the total phenolic acids from S. involucrata determined by a cell suspension culture (ESPI) was mainly composed of 4,5-dicaffeoylquinic acid, according to the LC-MS analysis. BALB/c nude female mice were injected with HepG2 cells to establish an animal model of liver tumor before being divided into a control group, a low-dose group, a middle-dose group, a high-dose group, and a DDP group. Subsequently, EPSI was used as the intervention drug for mice. Biochemical indicators and differences in protein expression determined by TMT quantitative proteomics were used to resolve the mechanism after the low- (100 mg/kg), middle- (200 mg/kg), and high-dose (400 mg/kg) interventions for 24 days. The results showed that EPSI can not only limit the growth of HepG2 cells in vitro, but also can inhibit liver tumors significantly with no toxicity at high doses in vivo. Proteomics analysis revealed that the upregulated differentially expressed proteins (DE proteins) in the high-dose group were over three times that in the control group. ESPI affected the pathways significantly associated with the protein metabolic process, metabolic process, catalytic activity, hydrolase activity, proteolysis, endopeptidase activity, serine-type endopeptidase activity, etc. The treatment group showed significant differences in the pathways associated with the renin-angiotensin system, hematopoietic cell lineage, etc. In conclusion, ESPI has a significant anti-hepatoma effect and the potential mechanism was revealed.

Prediagnostic circulating metabolites in female breast cancer cases with low and high mammographic breast density

Mammographic breast density (MBD) is a strong independent risk factor for breast cancer (BC). We designed a matched case-case study in the EPIC Florence cohort, to evaluate possible associations between the pre-diagnostic metabolomic profile and the risk of BC in high- versus low-MBD women who developed BC during the follow-up. A case-case design with 100 low-MBD (MBD ≤ 25%) and 100 high-MDB BC cases (MBD > 50%) was performed. Matching variables included age, year and type of mammographic examination. ¹H NMR metabolomic spectra were available for 87 complete case-case sets. The conditional logistic analyses showed an inverse association between serum levels of alanine, leucine, tyrosine, valine, lactic acid, pyruvic acid, triglycerides lipid main fraction and 11 VLDL lipid subfractions and high-MBD cases. Acetic acid was directly associated with high-MBD cases. In models adjusted for confounding variables, tyrosine remained inversely associated with high-MBD cases while 3 VLDL subfractions of free cholesterol emerged as directly associated with high-MBD cases. A pathway analysis showed that the "phenylalanine, tyrosine and tryptophan pathway" emerged and persisted after applying the FDR procedure. The supervised OPLS-DA analysis revealed a slight but significant separation between high- and low-MBD cases. This case-case study suggested a possible role for pre-diagnostic levels of tyrosine in modulating the risk of BC in high- versus low-MBD women. Moreover, some differences emerged in the pre-diagnostic concentration of other metabolites as well in the metabolomic fingerprints among the two groups of patients.

Circulating nuclear factor-kappa B mediates cancer-associated inflammation in human breast and colon cancer

BACKGROUND

Inflammation is recognized as a hallmark feature of cancer development and progression. The aim of our study was to investigate the significance of serum nuclear factor kappa-B (NF-κB) levels as a circulating marker in the monitoring of inflammation in breast and colon cancer; to show the relationship between NF-κB with inflammatory parameters as tumour necrosis factor-α (TNF-α), soluble TNF-related apoptosis-inducing ligand (sTRAIL), interleukin-6 (IL-6), pentraxin-3 (PTX-3), procalcitonin (PCT), and C-reactive protein (CRP) levels.

METHODS

Serum NF-κB, TNF-α, sTRAIL, IL-6, PTX-3, PCT, and serum CRP levels were measured using enzyme-linked immunosorbent assay (ELISA) in 40 patients with breast cancer, 40 patients with colon cancer and 30 healthy controls.

RESULTS

The serum NF-κB, TNF-α, IL-6, PTX-3, PCT, and serum CRP concentration was significantly higher, and the serum sTRAIL concentration was significantly lower in the patients with breast and colon cancer than in healthy controls. NF-κB was positively correlated with CRP and negatively correlated with sTRAIL.

CONCLUSIONS

These results suggest that increased NF-κB may decrease the clinical efficacy of sTRAIL in solid tumour cells. There is a relationship between inflammation and carcinogenesis so that the development of cancer occurs with chronic inflammation in breast and colon. The study results have shown that colon and breast cancer patients have increased systemic inflammation, as measured by increased circulating cytokines, and acute-phase proteins, or by abnormalities in circulating cells. NF-κB may combine with other markers of the systemic inflammatory response in prognostic scores in cancer. In addition to surgical resection of the tumour, and conventional radio and chemotherapy for cancer treatment, the use of sTRAIL or other agonists for cancer therapy appeared a new potential therapy.

Cancer-Associated Stemness and Epithelial-to-Mesenchymal Transition Signatures Related to Breast Invasive Carcinoma Prognostic

Simple Summary

Breast cancer is one of the most common oncological diseases in women, as its incidence is rapidly growing. In this study, we have investigated the mechanism of epithelial-to-mesenchymal transition (EMT) and cancer stem cells (CSCs), demonstrating presence of an interconnectedness between them. This interconnectedness plays important roles in patient prognostic, as well as in diagnostic and therapeutic targets. It is identified that there is a common signature between CSCs and EMT, and this is represented by ALDH1A1, SFRP1, miR-139, miR-21, and miR-200c. This finding will provide a better understanding of this mechanism, and will facilitate the development of novel treatment options.

Abstract

Breast cancer is one of the most common oncological diseases in women, as its incidence is rapidly growing, rendering it unpredictable and causing more harm than ever before on an annual basis. Alterations of coding and noncoding genes are related to tumorigenesis and breast cancer progression. In this study, several key genes associated with epithelial-to-mesenchymal transition (EMT) and cancer stem cell (CSC) features were identified. EMT and CSCs are two key mechanisms responsible for self-renewal, differentiation, and self-protection, thus contributing to drug resistance. Therefore, understanding of the relationship between these processes may identify a therapeutic vulnerability that can be further exploited in clinical practice, and evaluate its correlation with overall survival rate. To determine expression levels of altered coding and noncoding genes, The Cancer Omics Atlas (TCOA) are used, and these data are overlapped with a list of CSCs and EMT-specific genes downloaded from NCBI. As a result, it is observed that CSCs are reciprocally related to EMT, thus identifying common signatures that allow for predicting the overall survival for breast cancer genes (BRCA). In fact, common CSCs and EMT signatures, represented by ALDH1A1, SFRP1, miR-139, miR-21, and miR-200c, are deemed useful as prognostic biomarkers for BRCA. Therefore, by mapping changes in gene expression across CSCs and EMT, suggesting a cross-talk between these two processes, we have been able to identify either the most common or specific genes or miRNA markers associated with overall survival rate. Thus, a better understanding of these mechanisms will lead to more effective treatment options.

Integrating Two-Dimensional Gas and Liquid Chromatography-Mass Spectrometry for Untargeted Colorectal Cancer Metabolomics: A Proof-of-Principle Study

Untargeted metabolomics is expected to lead to a better mechanistic understanding of diseases and thus applications of precision medicine and personalized intervention. To further increase metabolite coverage and achieve high accuracy of metabolite quantification, the present proof-of-principle study was to explore the applicability of integration of two-dimensional gas and liquid chromatography-mass spectrometry (GC × GC-MS and 2DLC-MS) platforms to characterizing circulating polar metabolome extracted from plasma collected from 29 individuals with colorectal cancer in comparison with 29 who remained cancer-free. After adjustment of multiple comparisons, 20 metabolites were found to be up-regulated and 8 metabolites were found to be down-regulated, which pointed to the dysregulation in energy metabolism and protein synthesis. While integrating the GC × GC-MS and 2DLC-MS data can dramatically increase the metabolite coverage, this study had a limitation in analyzing the non-polar metabolites. Given the small sample size, these results need to be validated with a larger sample size and with samples collected prior to diagnostic and treatment. Nevertheless, this proof-of-principle study demonstrates the potential applicability of integration of these advanced analytical platforms to improve discrimination between colorectal cancer cases and controls based on metabolite profiles in future studies.

PTX3 modulates the immunoflogosis in tumor microenvironment and is a prognostic factor for patients with clear cell renal cell carcinoma

Pentraxin-3 (PTX3) belongs to the pentraxine family, innate immune regulators involved in angiogenesis, proliferation and immune escape in cancer. Here, we evaluated PTX3 tissue expression and serum levels as biomarkers of clear cell renal cell carcinoma (ccRCC) and analyzed the possible role of complement system activation on tumor site. A 10-year retrospective cohort study including patients undergoing nephrectomy for ccRCC was also performed. PTX3 expression was elevated in both neoplastic renal cell lines and tissues, while it was absent in both normal renal proximal tubular cells (HK2) and normal renal tissues. Analysis of complement system activation on tumor tissues showed the co-expression of PTX3 with C1q, C3aR, C5R1 and CD59, but not with C5b-9 terminal complex. RCC patients showed higher serum PTX3 levels as compared to non-neoplastic patients (p<0.0001). Higher PTX3 serum levels were observed in patients with higher Fuhrman grade (p<0.01), lymph node (p<0.0001), and visceral metastases (p<0.001). Patients with higher PTX3 levels also showed significantly lower survival rates (p=0.002). Our results suggest that expression of PTX3 can affect the immunoflogosis in the ccRCC microenvironment, by activating the classical pathway of CS (C1q) and releasing pro-angiogenic factors (C3a, C5a). The up-regulation of CD59 also inhibits the complement-mediated cellular lysis.

Recognition of early and late stages of bladder cancer using metabolites and machine learning

INTRODUCTION

Bladder cancer (BCa) is one of the most common and aggressive cancers. It is the sixth most frequently occurring cancer in men and its rate of occurrence increases with age. The current method of BCa diagnosis includes a cystoscopy and biopsy. This process is expensive, unpleasant, and may have severe side effects. Recent growth in the power and accessibility of machine-learning software has allowed for the development of new, non-invasive diagnostic methods whose accuracy and sensitivity are uncompromising to function.

OBJECTIVES

The goal of this research was to elucidate the biomarkers including metabolites and corresponding genes for different stages of BCa, show their distinguishing and common features, and create a machine-learning model for classification of stages of BCa.

METHODS

Sets of metabolites for early and late stages, as well as common for both stages were analyzed using MetaboAnalyst and Ingenuity® Pathway Analysis (IPA®) software. Machine-learning methods were utilized in the development of a binary classifier for early- and late-stage metabolites of BCa. Metabolites were quantitatively characterized using EDragon 1.0 software. The two modeling methods used are Multilayer Perceptron (MLP) and Stochastic Gradient Descent (SGD) with a logistic regression loss function.

RESULTS

We explored metabolic pathways related to early-stage BCa (Galactose metabolism and Starch and sucrose metabolism) and to late-stage BCa (Glycine, serine, and threonine metabolism, Arginine and proline metabolism, Glycerophospholipid metabolism, and Galactose metabolism) as well as those common to both stages pathways. The central metabolite impacting the most cancerogenic genes (AKT, EGFR, MAPK3) in early stage is D-glucose, while late-stage BCa is characterized by significant fold changes in several metabolites: glycerol, choline, 13(S)-hydroxyoctadecadienoic acid, 2'-fucosyllactose. Insulin was also seen to play an important role in late stages of BCa. The best performing model was able to predict metabolite class with an accuracy of 82.54% and the area under precision-recall curve (PRC) of 0.84 on the training set. The same model was applied to three separate sets of metabolites obtained from public sources, one set of the late-stage metabolites and two sets of the early-stage metabolites. The model was better at predicting early-stage metabolites with accuracies of 72% (18/25) and 95% (19/20) on the early sets, and an accuracy of 65.45% (36/55) on the late-stage metabolite set.

CONCLUSION

By examining the biomarkers present in the urine samples of BCa patients as compared with normal patients, the biomarkers associated with this cancer can be pinpointed and lead to the elucidation of affected metabolic pathways that are specific to different stages of cancer. Development of machine-learning model including metabolites and their chemical descriptors made it possible to achieve considerable accuracy of prediction of stages of BCa.

The Long Pentraxin PTX3 as a Link Between Innate Immunity, Tissue Remodeling, and Cancer

The innate immune system comprises a cellular and a humoral arm. Humoral pattern recognition molecules include complement components, collectins, ficolins, and pentraxins. These molecules are involved in innate immune responses by recognizing microbial moieties and damaged tissues, activating complement, exerting opsonic activity and facilitating phagocytosis, and regulating inflammation. The long pentraxin PTX3 is a prototypic humoral pattern recognition molecule that, in addition to providing defense against infectious agents, plays several functions in tissue repair and regulation of cancer-related inflammation. Characterization of the PTX3 molecular structure and biochemical properties, and insights into its interactome and multiple roles in tissue damage and remodeling support the view that microbial and matrix recognition are evolutionarily conserved functions of humoral innate immunity molecules.

Differentially expressed gene profile and relevant pathways of the traditional Chinese medicine cinobufotalin on MCF‑7 breast cancer cells

Cinobufotalin is a chemical compound extracted from the skin of dried bufo toads that may have curative potential for certain malignancies through different mechanisms; however, these mechanisms remain unexplored in breast cancer. The aim of the present study was to investigate the antitumor mechanism of cinobufotalin in breast cancer by using microarray data and in silico analysis. The microarray data set GSE85871, in which cinobufotalin exerted influences on the MCF‑7 breast cancer cells, was acquired from the Gene Expression Omnibus database, and the differentially expressed genes (DEGs) were analyzed. Subsequently, protein interaction analysis was conducted, which clarified the clinical significance of core genes, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were used to analyze cinobufotalin‑related pathways. The Connectivity Map (CMAP) database was used to select existing compounds that exhibited curative properties similar to those of cinobufotalin. A total of 1,237 DEGs were identified from breast cancer cells that were treated with cinobufotalin. Two core genes, SRC proto‑oncogene non‑receptor tyrosine kinase and cyclin‑dependent kinase inhibitor 2A, were identified as serving a vital role in the onset and development of breast cancer, and their expression levels were markedly reduced following cinobufotalin treatment as detected by the microarray of GSE85871. It also was revealed that the 'neuroactive ligand‑receptor interaction' and 'calcium signaling' pathways may be crucial for cinobufotalin to perform its functions in breast cancer. Conducting a matching search in CMAP, miconazole and cinobufotalin were indicated to possessed similar molecular mechanisms. In conclusion, cinobufotalin may serve as an effective compound for the treatment of a subtype of breast cancer that is triple positive for the presence of estrogen, progesterone and human epidermal growth factor receptor‑2 receptors, and its mechanism may be related to different pathways. In addition, cinobufotalin is likely to exert its antitumor influences in a similar way as miconazole in MCF‑7 cells.

Developing DNA methylation-based prognostic biomarkers of acute myeloid leukemia

Acute myeloid leukemia (AML) is a heterogeneous clonal neoplasm characterized by complex genomic alterations. The incidence of AML increases with age, and most cases experience serious illness and poor prognosis. To explore the relationship between abnormal DNA methylation and the occurrence and development of AML based on the Gene Expression Database (GEO), this study extracted data related to methylation in AML and identified a methylated CpG site that was significantly different in terms of expression and distribution between the primary cells of AML patients, and hematopoietic stem/progenitor cells from normal bone marrow. To further investigate the differences caused by the dysfunction of methylation sites, bioinformatics analysis was used to screen methylation-related biomarkers, and the potential prognostic genes were selected by univariate and multivariate Cox proportional hazards regressions. Finally, five independent prognostic indicators were identified. In addition, these results provide new insight into the molecular mechanisms of methylation.

pETM: a penalized Exponential Tilt Model for analysis of correlated high-dimensional DNA methylation data

Motivation

DNA methylation plays an important role in many biological processes and cancer progression. Recent studies have found that there are also differences in methylation variations in different groups other than differences in methylation means. Several methods have been developed that consider both mean and variance signals in order to improve statistical power of detecting differentially methylated loci. Moreover, as methylation levels of neighboring CpG sites are known to be strongly correlated, methods that incorporate correlations have also been developed. We previously developed a network-based penalized logistic regression for correlated methylation data, but only focusing on mean signals. We have also developed a generalized exponential tilt model that captures both mean and variance signals but only examining one CpG site at a time.

Results

In this article, we proposed a penalized Exponential Tilt Model (pETM) using network-based regularization that captures both mean and variance signals in DNA methylation data and takes into account the correlations among nearby CpG sites. By combining the strength of the two models we previously developed, we demonstrated the superior power and better performance of the pETM method through simulations and the applications to the 450K DNA methylation array data of the four breast invasive carcinoma cancer subtypes from The Cancer Genome Atlas (TCGA) project. The developed pETM method identifies many cancer-related methylation loci that were missed by our previously developed method that considers correlations among nearby methylation loci but not variance signals.

Availability and Implementation

The R package 'pETM' is publicly available through CRAN: http://cran.r-project.org .

Contact

sw2206@columbia.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Identification of Shared Molecular Signatures Indicate the Susceptibility of Endometriosis to Multiple Sclerosis

Women with endometriosis (EMS) appear to be at a higher risk of developing other autoimmune diseases predominantly multiple sclerosis (MS). Though EMS and MS are evidently diverse in their phenotype, they are linked by a common autoimmune condition or immunodeficiency which could play a role in the expansion of endometriosis and possibly increase the risk of developing MS in women with EMS. However, the common molecular links connecting EMS with MS are still unclear. We conducted a meta-analysis of microarray experiments focused on EMS and MS with their respective controls. The GEO2R web application discovered a total of 711 and 1516 genes that are differentially expressed across the experimental conditions in EMS and MS, respectively with 129 shared DEGs between them. The functional enrichment analysis of DEGs predicts the shared gene expression signatures as well as the overlapping biological processes likely to infer the co-occurrence of EMS with MS. Network based meta-analysis unveiled six interaction networks/crosstalks through overlapping edges between commonly dysregulated pathways of EMS and MS. The PTPN1, ERBB3, and CDH1 were observed to be the highly ranked hub genes connected with disease-related genes of both EMS and MS. Androgen receptor (AR) and nuclear factor-kB p65 (RelA) were observed to be the most enriched transcription factor in the upstream of shared down-regulated and up-regulated genes, respectively. The two disease sample sets compared through crosstalk interactions between shared pathways revealed commonly up- and down-regulated expressions of 10 immunomodulatory proteins as probable linkers between EMS and MS. This study pinpoints the number of shared genes, pathways, protein kinases, and upstream regulators that may help in the development of biomarkers for diagnosis of MS and endometriosis at the same time through improved understanding of shared molecular signatures and crosstalk.

Inhibition of mitochondrial 2-oxoglutarate dehydrogenase impairs viability of cancer cells in a cell-specific metabolism-dependent manner

2-Oxoglutarate dehydrogenase (OGDH) of the tricarboxylic acid (TCA) cycle is often implied to be inactive in cancer, but this was not experimentally tested. We addressed the question through specific inhibition of OGDH by succinyl phosphonate (SP). SP action on different cancer cells was investigated using indicators of cellular viability and reactive oxygen species (ROS), metabolic profiling and transcriptomics. Relative sensitivity of various cancer cells to SP changed with increasing SP exposure and could differ in the ATP- and NAD(P)H-based assays. Glioblastoma responses to SP revealed metabolic sub-types increasing or decreasing cellular ATP/NAD(P)H ratio under OGDH inhibition. Cancer cell homeostasis was perturbed also when viability indicators were SP-resistant, e.g. in U87 and N2A cells. The transcriptomics database analysis showed that the SP-sensitive cells, such as A549 and T98G, exhibit the lowest expression of OGDH compared to other TCA cycle enzymes, associated with higher expression of affiliated pathways utilizing 2-oxoglutarate. Metabolic profiling confirmed the dependence of cellular SP reactivity on cell-specific expression of the pathways. Thus, oxidative decarboxylation of 2-oxoglutarate is significant for the interdependent homeostasis of NAD(P)H, ATP, ROS and key metabolites in various cancer cells. Assessment of cell-specific responses to OGDH inhibition is of diagnostic value for anticancer strategies.

Identification of miRNA-mRNA Network Associated with Acute Myeloid Leukemia Survival

Background

Acute myeloid leukemia (AML) is a common hematologic malignancy of adults. The pathophysiological mechanism of AML is not well understood. The purpose of this study was to examine the crucial miRNAs and mRNAs associated with AML survival.

Material/Methods

The full clinical dataset of miRNA and mRNA expression profiling of AML patients was downloaded from The Cancer Genome Atlas database. Univariate Cox regression analysis was performed to obtain those miRNAs and mRNAs associated with AML survival. A miRNA-mRNA interaction network was constructed. The underlying functions of mRNAs were predicted through Kyoto Encyclopedia of Genes and Genomes (KEEG) pathway enrichment. The expression levels of miRNAs and mRNAs were detected by quantitative real-time polymerase chain reaction (qRT-PCR).

Results

Fourteen miRNAs and 830 mRNAs associated with AML survival were identified. Of the 14 miRNAs, hsa-mir-425, hsa-mir-1201, and hsa-mir-1978 were identified as risk factors and the other 11 miRNAs were identified as protective factors of AML survival. For target-genes of miRNAs, GTSF1, RTN4R, and CD44 were the top risk factor target-genes associated with AML survival. An interaction network was constructed that including 607 miRNA-target gene pairs associated with AML survival. Target-genes associated with AML survival were significantly enriched in several pathways including pancreatic secretion, calcium signaling pathway, natural killer cell mediated cytotoxicity, and Alzheimer’s disease. The qRT-PCR results were consistent with our bioinformatics analyses.

Conclusions

The miRNA hsa-mir-425 was identified as the top risk factor miRNA of AML survival and CD44 was identified as one of the top three risk factor target-genes associated with AML survival. Both hsa-mir-425 and CD44 may play key roles in progression and development of AML through calcium signaling pathway and natural killer cell mediated cytotoxicity.

A network-based pathway-expanding approach for pathway analysis

BACKGROUND

Pathway analysis combining multiple types of high-throughput data, such as genomics and proteomics, has become the first choice to gain insights into the pathogenesis of complex diseases. Currently, several pathway analysis methods have been developed to study complex diseases. However, these methods did not take into account the interaction between internal and external genes of the pathway and between pathways. Hence, these approaches still face some challenges. Here, we propose a network-based pathway-expanding approach that takes the topological structures of biological networks into account.

RESULTS

First, two weighted gene-gene interaction networks (tumor and normal) are constructed integrating protein-protein interaction(PPI) information, gene expression data and pathway databases. Then, they are used to identify significant pathways through testing the difference of topological structures of expanded pathways in the two weighted networks. The proposed method is employed to analyze two breast cancer data. As a result, the top 15 pathways identified using the proposed method are supported by biological knowledge from the published literatures and other methods. In addition, the proposed method is also compared with other methods, such as GSEA and SPIA, and estimated using the classification performance of the top 15 expanded pathways.

CONCLUSIONS

A novel network-based pathway-expanding approach is proposed to avoid the limitations of existing pathway analysis approaches. Experimental results indicate that the proposed method can accurately and reliably identify significant pathways which are related to the corresponding disease.

Inflammatory F2-isoprostane, prostaglandin F2α, pentraxin 3 levels and breast cancer risk: The Swedish Mammography Cohort

INTRODUCTION

Breast cancer is a common cancer among women. Identifying cellular participation of F₂-isoprostane, prostaglandin F_2α (PGF_2α) and pentraxin 3 (PTX3) in cancer we evaluated whether their prediagnostic systemic levels that originate from different inflammatory pathways were associated with breast cancer risk.

METHODS

Seventy-eight breast cancer cases diagnosed after blood collection and 797 controls from the Swedish Mammography Cohort were analysed for urinary F₂-isoprostane, PGF_2α and plasma PTX3 levels.

RESULTS

None of the biomarkers investigated were significantly associated with breast cancer risk. However, there was the suggestion of an inverse association with PTX3 with multivariable adjusted ORs (95% CI) of 0.56 (95% CI=0.29-1.06) and 0.67 (95% CI=0.35-1.28) for the second and third tertiles, respectively (p_trend=0.20). No associations were observed between F₂-isoprostane (OR=0.87; 95% CI=0.48-1.57; p_trend=0.67) and PGF_2α metabolite (OR=1.03; 95% CI=0.56-1.88; p_trend=0.91) comparing the top to bottom tertiles.

CONCLUSIONS

The systemic levels of F₂-isoprostane, PGF_2α and PTX3 witnessed in women who later developed breast cancer may not provide prognostic information regarding tumor development in spite of their known involvement in situ cellular context. These observations may indicate that other mechanisms exist in controlling cellular formation of F₂-isoprostane, PGF_2α and PTX3 and their systemic availability in breast cancer patients.

Nonessential amino acid metabolism in breast cancer

Interest in studying cancer metabolism has risen in recent years, as it has become evident that the relationship between cancer and metabolic pathways could reveal novel biomarkers and therapeutic targets. Metabolic starvation therapy is particularly promising due to its low toxicity. Nonessential amino acids are promising metabolites for such therapy because they become essential in many tumor cells, including breast cancer cells. This review will focus on four nonessential amino acid metabolism pathways: glutamine-glutamate, serine-glycine, cysteine, and arginine-proline metabolism. Recent studies of these amino acids have revealed metabolic enzymes that have the potential to be effective as cancer therapy targets or biomarkers for response to metabolic starvation therapy. The review will also discuss features of nonessential amino acid metabolism that merit further investigation to determine their relevancy to breast cancer treatment.