Peptidomics and Machine Learning-based Evaluation of Noncoding RNA-Derived Micropeptides in Breast Cancer: Expression Patterns and Functional/Therapeutic Insights.

Breast cancer is a highly heterogeneous disease characterized by different subtypes arising from molecular alterations that give the disease different phenotypes, clinical behaviors, and prognostic. The noncoding RNA (ncRNA)-derived micropeptides (MPs) represent a novel layer of complexity in cancer study once they can be biologically active and can present potential as biomarkers and also in therapeutics. However, few large-scale studies address the expression of these peptides at the peptidomics level or evaluate their functions and potential in peptide-based therapeutics for breast cancer. In this study, we propose deepening the landscape of ncRNA-derived MPs in breast cancer subtypes and advance the comprehension of the relevance of these molecules to the disease. First, we constructed a 16,349 unique putative MP sequence data set by integrating 2 previously published lists of predicted ncRNA-derived MPs. We evaluated its expression on high-throughput mass spectrometry data of breast tumor samples from different subtypes. Next, we applied several machine and deep learning tools, such as AntiCP 2.0, MULocDeep, PEPstrMOD, Peptipedia, and PreAIP, to predict its functions, cellular localization, tertiary structure, physicochemical features, and other properties related to therapeutics. We identified 58 peptides expressed on breast tissue, including 27 differentially expressed MPs in tumor compared with nontumor samples and MPs exhibiting tumor or subtype specificity. These peptides presented physicochemical features compatible with the canonical proteome and were predicted to influence the tumor immune environment and participate in cell communication, metabolism, and signaling processes. In addition, some MPs presented potential as anticancer, antiinflammatory, and antiangiogenic molecules. Our data demonstrate that MPs derived from ncRNAs have expression patterns associated with specific breast cancer subtypes and tumor specificity, thus highlighting their potential as biomarkers for molecular classification. We also reinforce the relevance of MPs as biologically active molecules that play a role in breast tumorigenesis, besides their potential in peptide-based therapeutics.

Biomarker potential of the LEF1/TCF family members in breast cancer: Bioinformatic investigation on expression and clinical significance.

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.

Integrated analysis of label-free quantitative proteomics and bioinformatics reveal insights into signaling pathways in male breast cancer.

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.

Stress-induced phosphoprotein 1: how does this co-chaperone influence the metastasis steps?

In several cancer types, metastasis is associated with poor prognosis, survival, and quality of life, representing a life risk more significant than the primary tumor itself. Metastasis is a multi-step process that spreads tumor cells from primary sites to surrounding or distant organs, originating secondary tumors. The interconnected steps that drive metastasis depend of several capabilities that enable cells to detach from the primary tumor, acquire motility and migrate through the basal membrane; invade and spread through the vascular system, and finally settle and originate a new tumor. Recently, stress-induced phosphoprotein 1 (STIP1) has emerged as a protein capable of driving tumor cells through these metastasis steps by mediating several biological processes and signaling pathways. This protein is mainly known for its function as a co-chaperone, acting as a scaffold for the interaction of its client heat-shock proteins Hsp70/90 chaperones; however, it is also known that STIP1 can act independently of chaperones to activate downstream phosphorylation pathways. The over-expression of STIP1 has been reported across various cancer types, identifying it as a potential biomarker for predicting patient prognosis and monitoring the progression of metastasis. Here, we present a discussion on how this co-chaperone mediates the initial steps of metastasis (cell adhesion loss, epithelial-to-mesenchymal transition, and angiogenesis), highlighting the biological mechanisms in which STIP1 plays a vital role, also presenting an overview of the current knowledge regarding its clinical relevance.

High-throughput proteomics of breast cancer subtypes: Biological characterization and multiple candidate biomarker panels to patients' stratification.

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.

Comprehensive analysis of the large and small ribosomal proteins in breast cancer: Insights on proteomic and transcriptomic expression patterns, regulation, mutational landscape, and prognostic significance.

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.

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.

Proteins play an essential role in the biological processes associated with cancer. Their altered expression levels can deregulate critical cellular pathways and interactive networks. In this study, the mass spectrometry-based label-free quantification followed by functional annotation was performed to investigate the most significant deregulated proteins among tissues of primary breast tumor (PT) and axillary metastatic lymph node (LN) and corresponding non-tumor tissues contralateral (NCT) and adjacent (ANT) from patients diagnosed with invasive ductal carcinoma. A total of 462 proteins was observed as differentially expressed (DEPs) among the groups analyzed. A high level of similarity was observed in the proteome profile of both non-tumor breast tissues and DEPs (n = 12) were mainly predicted in the RNA metabolism. The DEPs among the malignant and non-tumor breast tissues [n = 396 (PTxNCT) and n = 410 (LNxNCT)] were related to pathways of the LXR/RXR, NO, eNOS, eIF2 and sirtuins, tumor-related functions, fatty acid metabolism and oxidative stress. Remarkable similarity was observed between both malignant tissues, which the DEPs were related to metastatic capabilities. Altogether, our findings revealed differential proteomic profiles that affected cancer associated and interconnected signaling processes. Validation studies are recommended to demonstrate the potential of individual proteins and/or pathways as biological markers in breast cancer. SIGNIFICANCE: The proteomic analysis of this study revealed high similarity in the proteomic profile of the contralateral and adjacent non-tumor breast tissues. Significant differences were identified among the proteome of the malignant and non-tumor tissue groups of the same patients, providing relevant insights into the hallmarks, signaling pathways, biological functions, and interactive protein networks that act during tumorigenesis and breast cancer progression. These proteins are suggested as targets of relevant interest to be explored as potential biological markers related to tumor development and metastatic progression in the breast cancer disease.

High-throughput mass spectrometry and bioinformatics analysis of breast cancer proteomic data.

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].

Comparative Proteomics of Tumor and Paired Normal Breast Tissue Highlights Potential Biomarkers in Breast Cancer.

BACKGROUND/AIM: Breast cancer is the most common type of cancer among women worldwide, and about 57,000 new cases are expected for the Brazilian population in 2015. Elucidation of protein expression and modification is essential for the biological understanding, early diagnosis and therapeutics of breast cancer. The main objectives of the study are comparison between the proteome of tumor and paired non-tumor breast cancer tissues, describing all identified proteins, highlighting the ones most differentially expressed and comparing the data with existing literature. MATERIALS AND METHODS: The five paired samples from patients with invasive ductal carcinoma were analyzed by 2-DE and MS. RESULTS: We collected 161 identified spots corresponding to 110 distinct proteins. Forty-three differentially-expressed spots were common to at least two samples, and the ten proteins with the highest-fold changes were CASPE, ENOG, TPM1, CAPG, VIME, TPM3, TRFE, PDIA6, WDR61 and PDIA3. Metabolic enzymes and proteins with binding functions were the most representative functional classes of proteins with increased and decreased expression in tumor tissue respectively. CONCLUSION: Taking the fold change as a parameter, we point to future targets to be studied by functional methods in a search for biomarkers for initiation and progress of breast cancer.