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BACKGROUND: In recent years, researchers have made significant strides in understanding the heterogeneity of breast cancer and its various subtypes. However, the wealth of genomic and proteomic data available today necessitates efficient frameworks, instruments, and computational tools for meaningful analysis. Despite its success as a prognostic tool, the PAM50 gene signature's reliance on many genes presents challenges in terms of cost and complexity. Consequently, there is a need for more efficient methods to classify breast cancer subtypes using a reduced gene set accurately. RESULTS: This study explores the potential of achieving precise breast cancer subtype categorization using a reduced gene set derived from the PAM50 gene signature. By employing a "Few-Shot Genes Selection" method, we randomly select smaller subsets from PAM50 and evaluate their performance using metrics and a linear model, specifically the Support Vector Machine (SVM) classifier. In addition, we aim to assess whether a more compact gene set can maintain performance while simplifying the classification process. Our findings demonstrate that certain reduced gene subsets can perform comparable or superior to the full PAM50 gene signature. CONCLUSIONS: The identified gene subsets, with 36 genes, have the potential to contribute to the development of more cost-effective and streamlined diagnostic tools in breast cancer research and clinical settings.

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Gene co-expression networks are a useful tool in the study of interactions that have allowed the visualization and quantification of diverse phenomena, including the loss of co-expression over long distances in cancerous samples. This characteristic, which could be considered fundamental to cancer, has been widely reported in various types of tumors. Since copy number variations (CNVs) have previously been identified as causing multiple genetic diseases, and gene expression is linked to them, they have often been mentioned as a probable cause of loss of co-expression in cancerous networks. In order to carry out a comparative study of the validity of this statement, we took 477 protein-coding genes from chromosome 8, and the CNVs of 101 genes, also protein-coding, belonging to the 8q24.3 region, a cytoband that is particularly active in the appearance of breast cancer. We created CNVS-conditioned co-expression networks of each of the 101 genes in the 8q24.3 region using conditional mutual information. The study was carried out using the four molecular subtypes of breast cancer (Luminal A, Luminal B, Her2, and Basal), as well as a case corresponding to healthy samples. We observed that in all cancer cases, the measurement of the Kolmogorov-Smirnov statistic shows that there are no significant differences between one and other values of the CNVs for any case. Furthermore, the co-expression interactions are stronger in all cancer subtypes than in the control networks. However, the control network presents a homogeneously distributed set of co-expression interactions, while for cancer networks, the highest interactions are more confined to specific cytobands, in particular 8q24.3 and 8p21.3. With this approach, we demonstrate that despite copy number alterations in the 8q24 region being a common trait in breast cancer, the loss of long-distance co-expression in breast cancer is not determined by CNVs.

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Breast cancer is the most common cancer in women. Despite advances in diagnosis and prognosis, distal metastases occur in these patients in up to 15% of cases within 3 years of diagnosis. The main organs in which BC metastasises are the bones, lungs, liver, and brain. Unfortunately, 90% of metastatic patients will die, making this an incurable disease. Researchers are therefore seeking biomarkers for diagnosis and metastasis in different organs. Optimally, such biomarkers should be easy to detect using, preferably, non-invasive methods, such as using miRNA molecules, which are small molecules of about 22 nt that have as their main function the post-transcriptional regulation of genes. Furthermore, due to their uncomplicated detection and reproducibility in the laboratory, they are a tool of complementary interest for diagnosis, prognosis, and treatment. With this in mind, in this review, we focus on describing the most current studies that propose using miRNA independently as a potential biomarker for the diagnosis and prediction of brain, lung, liver, and bone metastases, as well as to open a window of opportunity to deepen this area of study to eventually use miRNAs molecules in clinical practice for the benefit of BC patients.

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BACKGROUND: Triple-negative (TN) breast cancer represents a subtype of breast cancer that does not express estrogen receptor (ER), progesterone receptor (PR), or human epidermal growth factor receptor 2 (HER-2). Clinically, it is characterized by high invasiveness, high metastatic potential, and poor prognosis. Inhibitor of DNA binding 4 (ID4) has been shown to be overexpressed in these tumors acting as an oncogene responsible for many of its aggressive features. CDC42, a plasma membrane-associated small GTPase, can downregulate ID4 gene expression through hypermethylation of its promoter in colorectal adenocarcinomas. Since ID4 acts as an oncogene and is hypomethylated in TN breast tumors, here we asked whether CDC42 could also epigenetically silence ID4 and in doing so revert aggressive features of this tumor type. METHODS: Gene expression was retrieved from TCGA database using UCSC Xena. Association between overall survival (OS) and gene expression was assessed using Kaplan-Meier plotter. In vitro experiments involved ectopic expression of CDC42 in MDA-MB231and in MDA-MB468 breast cancer cell lines. Gene expression was analyzed by qPCR, western blot and inmunofluorescence assays and methylation by MSP, MS-MLPA, or ddMSP. RESULTS: Data mining analysis revealed that CDC42 expression varies among breast cancer subtypes that in the basal-like subtype there is an inverse correlation between CDC42 and ID4 expression and a positive correlation between CDC42 expression and ID4 methylation. In vitro experiments revealed that CDC42 overexpression induced ID4 methylation through the activation of the EZH2 pathway. ID4 silencing produced an increase in BRCA1 expression and a less aggressive phenotype in the tested cell line. CONCLUSION: We show that CDC42 silences ID4 through methylation in TN breast cancer. Given that ID4 acts as an oncogene in these tumors, we think that finding an epigenetic regulator of ID4 contributes to the research and clinical management of TN breast tumors.

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BACKGROUND: The aim of this study was to evaluate the association between metabolic syndrome (MetS) and the immunohistochemical profile of breast cancer (BC) in postmenopausal women. METHODS: This cross-sectional cohort study included 189 women, aged 45 to 75years and amenorrhea >12 months, with newly diagnosed BC and no previous cancer treatment. Clinical, anthropometric and biochemical data were collected, as well as data on BC hormone status (estrogen receptor, ER; progesterone receptor, PR; human epidermal growth factor receptor-2, HER-2), and epithelial proliferative activity (Ki-67). Tumors were divided into 5 subtypes:luminal A, luminal B HER-2 negative, luminal B HER-2 positive, non-luminal HER-2, and triple negative. Women with three or more of the following criteria were diagnosed with MetS: waist circumference ≥88cm; triglycerides ≥150mg/dL; HDL-cholesterol <50mg/dL; blood pressure ≥130/85mmHg; glucose ≥100mg/dL. RESULTS: Sixty-three (33.3%) of the 189 patients had MetS at the time of diagnosis. Women with MetS had a higher frequency of tumors ≤ 2cm than women without MetS (49.2% vs. 31.8%) (P = .038). There were no differences in histological grade, staging, or axillary lymph node metastasis (P > .05). The proportion of PR-positive (P = .006), HER-2-negative (P = .034), and luminal B HER-2-negative (P = .038) tumors was higher among patients with MetS compared to women without MetS (79.4% vs. 61.8%, 89.9% vs. 78.6% and 44.5% vs. 27.8%, respectively). Multivariate analysis adjusted for age, time since menopause and BMI showed a higher risk for luminal B HER-2-negative tumors among women with MetS (OR 2.00, 95% CI 1.03-3.89), obese patients (OR 2.03, 95% CI 1.06-3.90), and women with abdominal obesity (OR 1.96, 95% CI 1.01-4.03). CONCLUSION: In postmenopausal women with newly diagnosed BC, the presence of MetS was associated with smaller tumor size, PR-positive and HER-2-negative status, and the luminal B tumor subtype.

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Breast cancer is a disease that exhibits heterogeneity that goes from the genomic to the clinical levels. This heterogeneity is thought to be captured (at least partially) by the so-called breast cancer molecular subtypes. These molecular subtypes were initially defined based on the unsupervised clustering of gene expression and its correlate with histological, morphological, phenotypic and clinical features already known. Later, a 50-gene signature, PAM50, was defined in order to identify the biological subtype of a given sample within the clinical setting. The PAM50 signature was obtained by the use of unsupervised statistical methods, and therefore no limitation was set on the biological relevance (or lack of) of the selected genes beyond its predictive capacity. An open question that remains is what are the regulatory elements that drive the various expression behaviors of this set of genes in the different molecular subtypes. This question becomes more relevant as the measurement of more biological layers of regulation becomes accessible. In this work, we analyzed the gene expression regulation of the 50 genes in the PAM50 signature, in terms of (a) gene co-expression, (b) transcription factors, (c) micro-RNAs, and (d) methylation. Using data from the Cancer Genome Atlas (TCGA) for the Luminal A and B, Basal, and HER2-enriched molecular subtypes as well as normal tumor adjacent tissue, we identified predictors for gene expression through the use of an elastic net model. We compare and contrast the sets of identified regulators for the gene signature in each molecular subtype, and systematically compare them to current literature. We also identified a unique set of predictors for the expression of genes in the PAM50 signature associated with each of the molecular subtypes. Most selected predictors are exclusive for a PAM50 gene and predictors are not shared across subtypes. There are only 13 coding transcripts and 2 miRNAs selected for the four subtypes. MiR-21 and miR-10b connect almost all the PAM50 genes in all the subtypes and normal tissue, but do it in an exclusive manner, suggesting a cancer switch from miR-10b coordination in normal tissue to miR-21. The PAM50 gene sets of selected predictors that enrich for a function across subtypes, support that different regulatory molecular mechanisms are taking place. With this study we aim to a wider understanding of the regulatory mechanisms that differentiate the expression of the PAM50 signature, which in turn could perhaps help understand the molecular basis of the differences between the molecular subtypes.

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Metabolic deregulation is an emergent hallmark of cancer. Altered patterns of metabolic pathways result in exacerbated synthesis of macromolecules, increased proliferation, and resistance to treatment via alteration of drug processing. In addition, molecular heterogeneity creates a barrier to therapeutic options. In breast cancer, this broad variation in molecular metabolism constitutes, simultaneously, a source of prognostic and therapeutic challenges and a doorway to novel interventions. In this work, we investigated the metabolic deregulation landscapes in breast cancer molecular subtypes. Such landscapes are the regulatory signatures behind subtype-specific metabolic features. n = 735 breast cancer samples of the Luminal A, Luminal B, Her2+, and Basal subtypes, as well as n = 113 healthy breast tissue samples were analyzed. By means of a single-sample-based algorithm, deregulation for all metabolic pathways in every sample was determined. Deregulation levels match almost perfectly with the molecular classification, indicating that metabolic anomalies are closely associated with gene-expression signatures. Luminal B tumors are the most deregulated but are also the ones with higher within-subtype variance. We argued that this variation may underlie the fact that Luminal B tumors usually present the worst prognosis, a high rate of recurrence, and the lowest response to treatment in the long term. Finally, we designed a therapeutic scheme to regulate purine metabolism in breast cancer, independently of the molecular subtype. This scheme is founded on a computational tool that provides a set of FDA-approved drugs to target pathway-specific differentially expressed genes. By providing metabolic deregulation patterns at the single-sample level in breast cancer subtypes, we have been able to further characterize tumor behavior. This approach, together with targeted therapy, may open novel avenues for the design of personalized diagnostic, prognostic, and therapeutic strategies.

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Breast Cancer (BC) is the most commonly diagnosed cancer and is the leading cause of cancer deaths in women. BC is a heterogeneous disease with different clinical and genetic features. According to immunohistochemical markers, BC is subdivided into four main subtypes: luminal A, luminal B, ERBB2 positive and triple negative. Long non-coding RNAs (lncRNAs) are transcripts with more than 200 nucleotides and deregulated lncRNAs are associated with human diseases, including BC. In order to improve BC molecular classification, non-coding RNAs (ncRNAs), including lncRNAs, have been used. In this review, we focus on lncRNAs with differential expression in BC subtypes and how these RNAs may act to contribute to BC heterogeneity. We also emphasize the potential of these lncRNAs as biomarkers.

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Interleukin-12 (IL-12) is an anti-tumor cytokine that promotes biological actions through the IL-12/STAT4 axis. Genetic variation and tumor microenvironment dynamics have been identified as critical elements for impaired immune anti-tumor responses. Breast cancer (BC) is a heterogeneous disease classified at the molecular level in several subtypes, each having unique biological and clinical traits. Despite research identifying the relevance of IL-12 in many cancer types, no studies have assessed the role of the IL-12/STAT4 axis in BC. The goal of this study was to evaluate the correlation of the IL-12/STAT4 signaling axis and BC patients' survival in general and in the context of the BC molecular subtypes. Bioinformatics analyses using TCGA data were completed to evaluate the correlation of the IL-12/STAT4 axis and BC. A high expression of important IL-12/STAT4 axis molecules such as the IL-12 receptor genes (IL12RB1 and IL12RB2), STAT4, IFNG and TBX21 were found to significantly increase BC patients' survival rates, especially in the most aggressive BC subtypes such as the luminal B (LumB), HER-2+ and basal like (BL). A possible relevant role of the IL-12/STAT4 axis in BC is suggested by this bioinformatics-study, which might also be subtype-specific. Further studies such as molecular and tumor microenvironment analyses will be required to clarify better the specific role of the IL-12 /STAT4 axis in BC. The results from these additional analyses may potentially improve IL-12-related immunotherapeutic approaches to BC.

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Interleukin-7 (IL-7) exerts crucial functions on lymphoid cells' development and maintenance. In breast cancer (BC), IL-7 promotes growth of tumor cells in culture through the activation of JAK1/3-STAT5 and PI3K/AKT pathways, and expression of IL-7 signaling components was associated with worst prognosis. AC>T polymorphism (rs6897932; Thr244Ile) at exon 6 of IL-7 receptor alpha (IL-7Rα) gene (IL7RA) shifts the balance between the membrane-bound and soluble IL-7Rα splicing variants and was previously associated with autoimmune diseases, but has not been studied in cancer, including BC, so far. Therefore, the present study aimed to investigate the possible association of this polymorphism with the susceptibility and clinicopathological parameters of BC subgroups. IL7RA Thr244Ile was genotyped through PCR-RFLP in 403 women without neoplasia, no personal history of malignancy or family history of BC and in 338 BC patients with clinicopathological data available. BC patients were stratified according to their positivity for estrogen (ER) and/or progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2). Age-adjusted logistic regression was performed for case-control analyses, and correlations with clinicopathological parameters were assessed through Kendall's Tau-b coefficient. All analyses were two-tailed and had 95% confidence interval. In ER-PR-HER2- BCs, TT genotype was associated with increased susceptibility both in genotypic (TT vs. CC: OR=3.07; CI=1.01-9.38; p=0.05) and recessive (TT vs. CC+CT: OR=3.59; CI=1.19-10.85; p=0.02) models and negatively correlated with disease stage (Tau-b=-0.27; p=0.05). Whereas T allele was positively correlated with histopathological grade (Tau-b=0.29; p=0.03) and lymph node metastasis (Tau-b=0.35; p=0.02) in ER/PR+HER2+BCs and with Ki67 (Tau-b=0.51; p=0.008) in ER-PR-HER2+ subgroup. These data indicate that IL-7Rα is involved in BC, and that IL7RA polymorphism may play distinct roles in breast carcinogenesis according to BC subtype, pointing this genetic variant as an interesting marker for breast carcinogenesis to be validated by further mechanistic and prospective studies with larger samples.

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