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The LEF1/TCF transcription factor family is related to the development of diverse tissue types, including the mammary tissue, and dysregulation of its expression and function has been described to favor breast tumorigenesis. However, the clinical and biological relevance of this gene family in breast cancer is still poorly understood. Here, we used bioinformatics approaches aiming to reduce this gap. We investigated its expression patterns in molecular and immune breast cancer subtypes; its correlation with immune cell infiltration, and its prognostic values in predicting outcomes. Also, through regulons construction, we determined the genes whose expression is influenced by these transcription factors, and the pathways in which they are involved. We found that LEF1 and TCF3 are over-expressed in breast tumors regarding non-tumor samples, while TCF4 and TCF7 are down-expressed, with the gene's methylation status being associated with its expression dysregulation. All four transcription factors presented significance at the diagnostic and prognostic levels. LEF1, TCF4, and TCF7 presented a significant correlation with immune cell infiltration, being associated with the immune subtypes of less favorable outcomes. Altogether, this research contributes to a more accurate understanding of the expression and clinical and biomarker significance of the LEF1/TCF transcription factors in breast cancer.

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BACKGROUND AND AIMS: The actual classification of breast tumors in subtypes represents an attempt to stratify patients into clinically cohesive groups, nevertheless, clinicians still lack reproducible and reliable protein biomarkers for breast cancer subtype discrimination. In this study, we aimed to access the differentially expressed proteins between these tumors and its biological implications, contributing to the subtype's biological and clinical characterization, and with protein panels for subtype discrimination. METHODS: In our study, we applied high-throughput mass spectrometry, bioinformatic, and machine learning approaches to investigate the proteome of different breast cancer subtypes. RESULTS: We identified that each subtype depends on different protein expression patterns to sustain its malignancy, and also alterations in pathways and processes that can be associated with each subtype and its biological and clinical behaviors. Regarding subtype biomarkers, our panels achieved performances with at least 75% of sensibility and 92% of specificity. In the validation cohort, the panels obtained acceptable to outstanding performances (AUC = 0.740 to 1.00). CONCLUSIONS: In general, our results expand the accuracy of breast cancer subtypes' proteomic landscape and improve the understanding of its biological heterogeneity. In addition, we identified potential protein biomarkers for the stratification of breast cancer patients, improving the repertoire of reliable protein biomarkers. SIGNIFICANCE: Breast cancer is the most diagnosed cancer type worldwide and the most lethal cancer in women. As a heterogeneous disease, breast cancer tumors can be classified into four major subtypes, each presenting particular molecular alterations, clinical behaviors, and treatment responses. Thus, a pivotal step in patient management and clinical decisions is accurately classifying breast tumor subtypes. Currently, this classification is made by the immunohistochemical detection of four classical markers (estrogen receptor, progesterone receptor, HER2 receptor, and the Ki-67 index); however, it is known that these markers alone do not fully discriminate the breast tumor subtypes. Also, the poor understanding of the molecular alterations of each subtype leads to a challenging decision-making process regarding treatment choice and prognostic determination. This study, through high-throughput label-free mass-spectrometry data acquisition and downstream bioinformatic analysis, advances in the proteomic discrimination of breast tumors and achieves an in-depth characterization of the subtype's proteomes. Here, we indicate how the variations in the subtype's proteome can influence the tumor's biological and clinical differences, highlighting the variation in the expression pattern of oncoproteins and tumor suppressor proteins between subtypes. Also, through our machine-learning approach, we propose multi-protein panels with the potential to discriminate the breast cancer subtypes. Our panels achieved high classification performance in our cohort and in the independent validation cohort, demonstrating their potential to improve the current tumor discrimination system as complements to the classical immunohistochemical classification.

Assuntos

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Metastasis is a multi-step process that leads to the dissemination of tumor cells to new sites and, consequently, to multi-organ neoplasia. Although most lethal breast cancer cases are related to metastasis occurrence, little is known about the dysregulation of each step, and clinicians still lack reliable therapeutic targets for metastasis impairment. To fill these gaps, we constructed and analyzed gene regulatory networks for each metastasis step (cell adhesion loss, epithelial-to-mesenchymal transition, and angiogenesis). Through topological analysis, we identified E2F1, EGR1, EZH2, JUN, TP63, and miR-200c-3p as general hub-regulators, FLI1 for cell-adhesion loss specifically, and TRIM28, TCF3, and miR-429 for angiogenesis. Applying the FANMOD algorithm, we identified 60 coherent feed-forward loops regulating metastasis-related genes associated with distant metastasis-free survival prediction. miR-139-5p, miR-200c-3p, miR-454-3p, and miR-1301-3p, among others, were the FFL's mediators. The expression of the regulators and mediators was observed to impact overall survival and to go along with metastasis occurrence. Lastly, we selected 12 key regulators and observed that they are potential therapeutic targets for canonical and candidate antineoplastics and immunomodulatory drugs, like trastuzumab, goserelin, and calcitriol. Our results highlight the relevance of miRNAs in mediating feed-forward loops and regulating the expression of metastasis-related genes. Altogether, our results contribute to understanding the multi-step metastasis complexity and identifying novel therapeutic targets and drugs for breast cancer management.

Assuntos

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Several evidence has demonstrated the involvement of the ribosomal proteins (RPs) in many malignancies, however, the function and clinical relevance of the RPs in breast cancer remains unclear. The present study aims to contribute to the understanding of the role of the RPs in breast tumorigenesis and its clinical implications in the field of biomarker discovery and outcome prediction. We investigated the proteomic and transcriptomic expression of the RPs in non-tumor and tumor tissues of different breast cancer subtypes, and integrated bioinformatics approaches and online databases to comprehensively evaluate the potential functions, regulatory networks, mutational landscape, and prognostic values of the ribosomal proteins in breast cancer. Our results show that 33 RPs have deregulated expression in breast cancer and its subtypes and that 26 RPs have potential as prognostic markers in a subtype-dependent way, with mutations in RP genes being frequent in breast tumors and related to overall survival and relapse-free status. Our RP gene regulatory network indicates the transcription factors MYC, ETS1, and SPI1, and the miRNAs has-let-7c-5p, has-mir-20b-5p, and has-mir-4668-3p as regulators of the RPs expression in breast cancer. The RPs were associated with several clinicopathological parameters of breast cancer and predicted to be involved in ribosomal-independent mechanisms such as regulation of the SLITS-ROBO pathway. This study comprehensively investigated the ribosomal proteins in breast cancer, suggesting that the RPs have clinical potential as biomarkers of diagnostic and prognostic, also providing an in-depth view of the RPs significance in breast cancer.

Assuntos

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Breast cancer (BC) is a heterogeneous disease, and establishing biomarkers is essential to patient management. We previously described that extracellular vesicle-derived miRNAs (EV-miRNAs) miR-142-5p, miR-150-5p, miR-320a, and miR-4433b-5p in serum discriminated BC from control samples, either alone or combined in a panel. Using these previously described markers, we intend to evaluate whether the same markers identified in EVs are also potential biomarkers in tissue and serum. Expression analysis using RT-qPCR was performed using serum of 67 breast cancer patients (BC-S), 19 serum controls (CT), 83 fresh tumor tissues (BC-T), and 29 adjacent nontumor tissue samples (NT). In addition, analysis from The Cancer Genome Atlas (TCGA) data (832 BC-T and 136 NT) was performed. In all comparisons, we found concordant high expression levels of miR-320a and miR-4433b-5p in BC-S compared to CT in both EVs and cell-free miRNAs (cf-miRNAs). Although miR-150-5p and miR-142-5p were not found to be differentially expressed in serum, panels including these miRNAs improved sensitivity and specificity, supporting our previous findings in EVs. Fresh tissue and data from the TCGA database had, in most comparisons, an opposite behavior when compared to serum and EVs: lower levels of all miRNAs in BC-T than those in NT samples. TCGA analyses revealed reduced expression levels of miR-150-5p and miR-320a-3p in BC-T than those in NT samples and the overexpression of miR-142-5p in BC-T, unlike our RT-qPCR results from tissue in the Brazilian cohort. The fresh tissue analysis showed that all miRNAs individually could discriminate between BC-T and NT in the Brazilian cohort, with high sensitivity and sensibility. Furthermore, combining panels showed higher AUC values and improved sensitivity and specificity. In addition, lower levels of miR-320a-3p in serum were associated with poor overall survival in BC Brazilian patients. In summary, we observed that miR-320a and miR-4433b-5p distinguished BC from controls with high specificity and sensibility, regardless of the sample source. In addition, lower levels of miR-150-5p and higher levels of miR-142-5p were statistically significant biomarkers in tissue, according to TCGA. When combined in panels, all combinations could distinguish BC patients from controls. These results highlight a potential application of these miRNAs as BC biomarkers.

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Breast cancer (BC) is a heterogeneous disease, and it is the leading cause of death among women. NORAD and HCG11 are highly similar lncRNAs that present binding sites for PUMILIO proteins. PUMILIO acts on hundreds of mRNA targets, contributing to the modulation of gene expression. We analyzed the expression levels of NORAD and HCG11 in the BC subtypes luminal A (LA) and basal-like (BL), and the regulatory networks associated with these lncRNAs. In the analysis of TCGA cohort (n=329) and Brazilian BC samples (n=44), NORAD was up-regulated in LA while HCG11 was up-regulated in BL subtype. An increased expression of NORAD is associated with reduced disease-free survival in basal-like patients (p = 0.002), which suggests that its prognostic value could be different in specific subtypes. The biological pathways observed for the HCG11 network are linked to the epithelial-to-mesenchymal transition; while NORAD associated pathways appear to be related to luminal epithelial cell transformation. NORAD and HCG11 regulons respectively present 36% and 21.5% of PUMILIO targets, which suggests that these lncRNAs act as a decoy for PUMILIO. These lncRNAs seem to work as players in the differentiation process that drives breast cells to acquire distinct phenotypes related to a specific BC subtype.

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Male breast cancer (MBC) is a rare malignancy that accounts for about 1.8% of all breast cancer cases. In contrast to the high number of the "omics" studies in breast cancer in women, only recently molecular approaches have been performed in MBC research. High-throughput proteomics based methodologies are promisor strategies to characterize the MBC proteomic signatures and their association with clinico-pathological parameters. In this study, the label-free quantification-mass spectrometry and bioinformatics approaches were applied to analyze the proteomic profiling of a MBC case using the primary breast tumor and the corresponding axillary metastatic lymph nodes and adjacent non-tumor breast tissues. The differentially expressed proteins were identified in the signaling pathways of granzyme B, sirtuins, eIF2, actin cytoskeleton, eNOS, acute phase response and calcium and were connected to the upstream regulators MYC, PI3K SMARCA4 and cancer-related chemical drugs. An additional proteomic comparative analysis was performed with a primary breast tumor of a female patient and revealed an interesting set of proteins, which were mainly involved in cancer biology. Together, our data provide a relevant data source for the MBC research that can help the therapeutic strategies for its management.

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MicroRNAs (miRNAs) play an essential role in gene expression and affect the development of tumours, including breast cancer (BC). Polymorphisms in miRNA genes can affect the interaction of miRNAs with their target messenger RNA by interfering, creating or disrupting target sites. The single nucleotide polymorphism (SNP) rs2910164, located in the seed region of miR146a, was shown to be associated with BC among different populations. In the present study, we investigated whether rs2910164 is associated with BC in 326 patients and 411 controls from a Brazilian population of predominantly European ancestry. The presence of the allele rs2910164*C was associated with an increased risk of BC (OR=1.4, 95% CI=1.03-1.85, p = 0.03). We also analysed publicly available RNA-seq data to evaluate if miR146a is differentially expressed in different subtypes of BC. Genotyping was performed by polymerase chain reaction with sequence-specific primers (PCR-SSP). By leveraging public data from TCGA database, we analysed 461 patients and found that miR146a is significantly more expressed in BC than in non-tumor tissue (1.47 fold, p = 0.02) and is expressed to a greater degree in aggressive BC subtypes.

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Data present here describe a comparative proteomic analysis among the malignant [primary breast tumor (PT) and axillary metastatic lymph nodes (LN)], and the non-tumor [contralateral (NCT) and adjacent (ANT)] breast tissues. Protein identification and quantification were performed through label-free mass spectrometry using a nano-liquid chromatography coupled to an electrospray ionization-mass spectrometry (nLC-ESI-MS/MS). The mass spectrometry proteomic data have been deposited to the ProteomeXchange Consortium via PRIDE partner repository with the dataset identifier PXD012431. A total of 462 differentially expressed proteins was identified among these tissues and was analyzed in six groups' comparisons (named NCTxANT, PTxNCT, PTxANT, LNxNCT, LNxANT and PTxLN). Proteins at 1.5 log2 fold change were submitted to the Ingenuity® Pathway Analysis (IPA) software version 2.3 (QIAGEN Inc.) to identify biological pathways, disease and function annotation, and interaction networks related to cancer biology. The detailed data present here provides information about the proteome alterations and their role on breast tumorigenesis. This information can lead to novel biological insights on cancer research. For further interpretation of these data, please see our research article 'Quantitative label-free mass spectrometry using contralateral and adjacent breast tissues reveal differentially expressed proteins and their predicted impacts on pathways and cellular functions in breast cancer' [2].

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Copy number alterations (CNAs) are a frequent feature in human breast cancer, and one of the hallmarks of genomic instability. The FOSL1, GSTP1 and CCND1 genes are located at 11q13, a cytoband commonly affected by CNA in breast cancer, with relevant function in progression and invasion. Our main goal was to analyze CNAs of these genes and determine their association with breast cancer subtypes. Seventy-three cases of invasive breast tumors [52 Luminal, 7 HER2+ and 14 triple negative (TNBC) subtypes] were analyzed by TaqMan assays. CNAs were observed for all genes, with gains more frequently observed. Gains of the FOSL1 gene were observed in 71% of the cases. This gene was the only one with a statistically significant difference (p<0.001) among tumor subtypes, with increased copy number in TNBC compared to luminal and HER2+. No significant association of CNA and clinical and histopathological parameters from the patients was observed. Additional studies in larger breast cancer patient cohorts based on more refined molecular subtypes are necessary to confirm the observed association of FOSL1 gain with aggressive breast tumors phenotypes.