Identifying potential markers in Breast Cancer subtypes using plasma label-free proteomics

SLPI overexpression in hMSCs could be implicated in the HSC gene expression profile in AML

Acute myeloid leukaemia (AML) is a severe haematological neoplasm that originates from the transformation of haematopoietic stem cells (HSCs) into leukaemic stem cells (LSCs). The bone marrow (BM) microenvironment, particularly that of mesenchymal stromal cells (hMSCs), plays a crucial role in the maintenance of HSCs. In this context, we explored whether alterations in the secretome of hMSCs derived from AML patients (hMSC-AML) could impact HSC gene expression. Proteomic analysis revealed that the secretome of coculture assays with hMSC-AMLs and HSC from healthy donor is altered, with increased levels of secretory leukocyte protease inhibitor (SLPI), a protein associated with important processes for maintenance of the haematopoietic niche that has already been described to be altered in several tumours. Increased SLPI expression was also observed in the BM plasma of AML patients. Transcriptome analysis of HSCs cocultured with hMSC-AML in comparison with HSCs cocultured with hMSC-HD revealed altered expression of SLPI target genes associated with the cell cycle, proliferation, and apoptosis. Important changes were identified, such as increased expression levels of CCNA2, CCNE2, CCND2, CD133 and CDK1 and decreased levels of CDKN2A and IGFBP3, among others. Overall, these findings suggest that the altered secretome of coculture assays with hMSC-AMLs and HSC from healthy donor, particularly increased SLPI expression, can contribute to gene expression changes in HSCs, potentially influencing important molecular mechanisms related to AML development and progression.

Label-free proteomic analysis reveals the hepatoprotective mechanism of gypenosides in liver injury rats

Chronic liver disease, a long-term condition resulting from various causes such as alcohol abuse, metabolic disorders, and viral hepatitis, is becoming a significant global health challenge. Gypenosides (GPs), derived from the traditional Chinese medicine Gynostemma pentaphyllum (Thunb.) Makino, exhibited hepatoprotective properties in recent years, yet the precise therapeutic mechanism remains unclear. In this study, label-free and parallel reaction monitoring (PRM) proteomics were used to elucidate the hepatoprotective mechanism of GPs in liver injury rats. Through label-free proteomics, we identified 2104 differentially expressed proteins (DEPs) associated with liver injury, along with 1974 DEPs related to the effects of GPs. Bioinformatics analysis revealed that GPs primarily restored metabolic processes involving valine, leucine, and isoleucine degradation, as well as propanoate and butanoate metabolism, and steroid hormone biosynthesis during liver injury. Subsequently, overlapping the two groups of DEPs identified 1508 proteins reversed following GPs treatment, with key targets further validated by PRM. Eight target proteins were identified for GPs treatment of liver injury, including Lgals3, Psat1, Phgdh, Cyp3a9, Cyp2c11, Cyp4a2, Glul, and Ces1d. These findings not only elucidated the hepatoprotective mechanism of GPs, but may also serve as potential therapeutic targets of chronic liver disease.

Dynamics and recognition of homeodomain containing protein-DNA complex of IRX4

Iroquois Homeobox 4 (IRX4) belongs to a family of homeobox TFs having roles in embryogenesis, cell specification, and organ development. Recently, large scale genome-wide association studies and epigenetic studies have highlighted the role of IRX4 and its associated variants in prostate cancer. No studies have investigated and characterized the structural aspect of the IRX4 homeodomain and its potential to bind to DNA. The current study uses sequence analysis, homology modeling, and molecular dynamics simulations to explore IRX4 homeodomain-DNA recognition mechanisms and the role of somatic mutations affecting these interactions. Using publicly available databases, gene expression of IRX4 was found in different tissues, including prostate, heart, skin, vagina, and the protein expression was found in cancer cell lines (HCT166, HEK293), B cells, ascitic fluid, and brain. Sequence conservation of the homeodomain shed light on the importance of N- and C-terminal residues involved in DNA binding. The specificity of IRX4 homodimer bound to consensus human DNA sequence was confirmed by molecular dynamics simulations, representing the role of conserved amino acids including R145, A194, N195, S190, R198, and R199 in binding to DNA. Additional N-terminal residues like T144 and G143 were also found to have specific interactions highlighting the importance of N-terminus of the homeodomain in DNA recognition. Additionally, the effects of somatic mutations, including the conserved Arginine (R145, R198, and R199) residues on DNA binding elucidated the importance of these residues in stabilizing the protein-DNA complex. Secondary structure and hydrogen bonding analysis showed the roles of specific residues (R145, T191, A194, N195, R198, and R199) in maintaining the homogeneity of the structure and its interaction with DNA. The differences in relative binding free energies of all the mutants shed light on the structural modularity of this protein and the dynamics behind protein-DNA interaction. We also have predicted that the C-terminal sequence of the IRX4 homeodomain could act as a potential cell-penetrating peptide, emphasizing the role these small peptides could play in targeting homeobox TFs.

Meta-analysis of integrated ChIP-seq and transcriptome data revealed genomic regions affected by estrogen receptor alpha in breast cancer

BACKGROUND

The largest group of patients with breast cancer are estrogen receptor-positive (ER+) type. The estrogen receptor acts as a transcription factor and triggers cell proliferation and differentiation. Hence, investigating ER-DNA interaction genomic regions can help identify genes directly regulated by ER and understand the mechanism of ER action in cancer progression.

METHODS

In the present study, we employed a workflow to do a meta-analysis of ChIP-seq data of ER+ cell lines stimulated with 10 nM and 100 nM of E2. All publicly available data sets were re-analyzed with the same platform. Then, the known and unknown batch effects were removed. Finally, the meta-analysis was performed to obtain meta-differentially bound sites in estrogen-treated MCF7 cell lines compared to vehicles (as control). Also, the meta-analysis results were compared with the results of T47D cell lines for more precision. Enrichment analyses were also employed to find the functional importance of common meta-differentially bound sites and associated genes among both cell lines.

RESULTS

Remarkably, POU5F1B, ZNF662, ZNF442, KIN, ZNF410, and SGSM2 transcription factors were recognized in the meta-analysis but not in individual studies. Enrichment of the meta-differentially bound sites resulted in the candidacy of pathways not previously reported in breast cancer. PCGF2, HNF1B, and ZBED6 transcription factors were also predicted through the enrichment analysis of associated genes. In addition, comparing the meta-analysis results of both ChIP-seq and RNA-seq data showed that many transcription factors affected by ER were up-regulated.

CONCLUSION

The meta-analysis of ChIP-seq data of estrogen-treated MCF7 cell line leads to the identification of new binding sites of ER that have not been previously reported. Also, enrichment of the meta-differentially bound sites and their associated genes revealed new terms and pathways involved in the development of breast cancer which should be examined in future in vitro and in vivo studies.

Brief research report pesticide occupational exposure leads to significant inflammatory changes in normal mammary breast tissue

Studies have documented the high occurrence of several tumors, including female breast cancer, in populations occupationally exposed to pesticides worldwide. It is believed that in addition to direct DNA damage, other molecular alterations that indicate genomic instability are associated, such as epigenetic modifications and the production of inflammation mediators. The present study characterized the profile of inflammatory changes in the breast tissue of women without cancer occupationally exposed to pesticides. In samples of normal breast tissue collected during biopsy and evaluated as negative for cancer by a pathologist, oxidative stress levels were assessed as inflammatory markers through measurements of lipoperoxides and total antioxidant capacity of the sample (TRAP) by high-sensitivity chemiluminescence, as well as levels of nitric oxide (NOx) metabolites. The levels of inflammation-modulating transcription factors PPAR-γ (peroxisome proliferator-activated receptor gamma) and NF-κB (nuclear factor kappa B) were also quantified, in addition to the pro-inflammatory cytokines tumor necrosis factor-alpha (TNF-α) and interleukin 12 (IL-12). The levels of lipoperoxides, TRAP, and NOx were significantly lower in the exposed group. On the other hand, PPAR-γ levels were increased in the breast tissue of exposed women, with no variation in NF-κB. There was also a rise of TNF-α in exposed women samples without significant variations in IL-12 levels. These findings suggest an inflammatory signature of the breast tissue associated with pesticide exposure, which may trigger mechanisms related to mutations and breast carcinogenesis.

Biomarker Reproducibility Challenge: A Review of Non-Nucleotide Biomarker Discovery Protocols from Body Fluids in Breast Cancer Diagnosis

Breast cancer has now become the most commonly diagnosed cancer, accounting for one in eight cancer diagnoses worldwide. Non-invasive diagnostic biomarkers and associated tests are superlative candidates to complement or improve current approaches for screening, early diagnosis, or prognosis of breast cancer. Biomarkers detected from body fluids such as blood (serum/plasma), urine, saliva, nipple aspiration fluid, and tears can detect breast cancer at its early stages in a minimally invasive way. The advancements in high-throughput molecular profiling (omics) technologies have opened an unprecedented opportunity for unbiased biomarker detection. However, the irreproducibility of biomarkers and discrepancies of reported markers have remained a major roadblock to clinical implementation, demanding the investigation of contributing factors and the development of standardised biomarker discovery pipelines. A typical biomarker discovery workflow includes pre-analytical, analytical, and post-analytical phases, from sample collection to model development. Variations introduced during these steps impact the data quality and the reproducibility of the findings. Here, we present a comprehensive review of methodological variations in biomarker discovery studies in breast cancer, with a focus on non-nucleotide biomarkers (i.e., proteins, lipids, and metabolites), highlighting the pre-analytical to post-analytical variables, which may affect the accurate identification of biomarkers from body fluids.

Clinical applications of plasma proteomics and peptidomics: Towards precision medicine

In the context of precision medicine, disease treatment requires individualized strategies based on the underlying molecular characteristics to overcome therapeutic challenges posed by heterogeneity. For this purpose, it is essential to develop new biomarkers to diagnose, stratify, or possibly prevent diseases. Plasma is an available source of biomarkers that greatly reflects the physiological and pathological conditions of the body. An increasing number of studies are focusing on proteins and peptides, including many involving the Human Proteome Project (HPP) of the Human Proteome Organization (HUPO), and proteomics and peptidomics techniques are emerging as critical tools for developing novel precision medicine preventative measures. Excitingly, the emerging plasma proteomics and peptidomics toolbox exhibits a huge potential for studying pathogenesis of diseases (e.g., COVID-19 and cancer), identifying valuable biomarkers and improving clinical management. However, the enormous complexity and wide dynamic range of plasma proteins makes plasma proteome profiling challenging. Herein, we summarize the recent advances in plasma proteomics and peptidomics with a focus on their emerging roles in COVID-19 and cancer research, aiming to emphasize the significance of plasma proteomics and peptidomics in clinical applications and precision medicine.

Targeted proteomics using parallel reaction monitoring confirms salivary proteins indicative of metastatic triple-negative breast cancer

Triple-negative breast cancer (TNBC) is the most aggressive subtype due to the absence of hormonal receptors. Our study aimed to identify and determine the effectiveness of salivary proteins as candidate markers for metastatic TNBC subtype using parallel reaction monitoring mass spectrometry (PRM-MS). Three salivary proteins (lipocalin-1, SMR3B, and plastin-2) that showed significant differential expression in label-free quantitation (LFQ) between TNBC (N = 6) and health subjects (HS; N = 6) were selected for further validation. The developed PRM assay was used to quantify peptides GLST and NNLE (lipocalin-1), VYAL and MINL (Plastin-2) and GPYP, and IPPP (SMR3B) on a different cohort of TNBC patients (N = 20) and HS (N = 20) for evaluating their discriminating performances. Quantitative validation using PRM correlated well with the LFQ results, and 5 peptides from three proteins showed a similar up-or down-regulation. Subsequently, these proteins were validated by Western blot analysis. Compared to one protein's performance as an individual marker, the five-signature panel with salivary GLST, VYAL, MINL, GPYP, and IPPP achieved better performance in differentiating aggressive TNBC and HS with sensitivity (80%) and specificity (95%). Targeted proteomic analysis of the prioritized proteins highlights a peptide-based signature in saliva as the potential predictor to distinguish between TNBC and HS. SIGNIFICANCE OF THE STUDY: This study was designed to identify and quantify potential markers in saliva from the triple-negative breast cancer (TNBC) patients using parallel reaction monitoring assay. Three salivary proteins, Lipocalin-1 (LCN-1), Submaxillary androgen-regulated protein 3B (SMR3B), and Plastin-2 (LCP-1) selected in the discovery-phase were further quantified by targeted proteomics and Western blots. The salivary proteins successfully differentiated TNBC patients from healthy subjects with a sensitivity (80%) and specificity (95%).

High-Resolution Secretome Analysis of Chemical Hypoxia Treated Cells Identifies Putative Biomarkers of Chondrosarcoma

Chondrosarcoma is the second most common bone tumor, accounting for 20% of all cases. Little is known about the pathology and molecular mechanisms involved in the development and in the metastatic process of chondrosarcoma. As a consequence, there are no approved therapies for this tumor and surgical resection is the only treatment currently available. Moreover, there are no available biomarkers for this type of tumor, and chondrosarcoma classification relies on operator-dependent histopathological assessment. Reliable biomarkers of chondrosarcoma are urgently needed, as well as greater understanding of the molecular mechanisms of its development for translational purposes. Hypoxia is a central feature of chondrosarcoma progression. The hypoxic tumor microenvironment of chondrosarcoma triggers a number of cellular events, culminating in increased invasiveness and migratory capability. Herein, we analyzed the effects of chemically-induced hypoxia on the secretome of SW 1353, a human chondrosarcoma cell line, using high-resolution quantitative proteomics. We found that hypoxia induced unconventional protein secretion and the release of proteins associated to exosomes. Among these proteins, which may be used to monitor chondrosarcoma development, we validated the increased secretion in response to hypoxia of glyceraldehyde 3-phosphate dehydrogenase (GAPDH), a glycolytic enzyme well-known for its different functional roles in a wide range of tumors. In conclusion, by analyzing the changes induced by hypoxia in the secretome of chondrosarcoma cells, we identified molecular mechanisms that can play a role in chondrosarcoma progression and pinpointed proteins, including GAPDH, that may be developed as potential biomarkers for the diagnosis and therapeutic management of chondrosarcoma.

Breast cancer in the era of integrating “Omics” approaches

Worldwide, breast cancer is the leading cause of cancer-related deaths in women. Breast cancer is a heterogeneous disease characterized by different clinical outcomes in terms of pathological features, response to therapies, and long-term patient survival. Thus, the heterogeneity found in this cancer led to the concept that breast cancer is not a single disease, being very heterogeneous both at the molecular and clinical level, and rather represents a group of distinct neoplastic diseases of the breast and its cells. Indubitably, in the past decades we witnessed a significant development of innovative therapeutic approaches, including targeted and immunotherapies, leading to impressive results in terms of increased survival for breast cancer patients. However, these multimodal treatments fail to prevent recurrence and metastasis. Therefore, it is urgent to improve our understanding of breast tumor and metastasis biology. Over the past few years, high-throughput “omics” technologies through the identification of novel biomarkers and molecular profiling have shown their great potential in generating new insights in the study of breast cancer, also improving diagnosis, prognosis and prediction of response to treatment. In this review, we discuss how the implementation of “omics” strategies and their integration may lead to a better comprehension of the mechanisms underlying breast cancer. In particular, with the aim to investigate the correlation between different “omics” datasets and to define the new important key pathway and upstream regulators in breast cancer, we applied a new integrative meta-analysis method to combine the results obtained from genomics, proteomics and metabolomics approaches in different revised studies.

The protein-protein interaction network of intestinal gastric cancer patients reveals hub proteins with potential prognostic value

BACKGROUND

Gastric cancer (GC) is the third leading cause of cancer worldwide. According to the Lauren classification, gastric adenocarcinoma is divided into two subtypes: diffuse and intestinal. The development of intestinal gastric cancer (IGC) can take years and involves multiple factors.

OBJECTIVE

To investigate the protein profile of tumor samples from patients with IGC in comparison with adjacent nontumor tissue samples.

METHODS

We used label-free nano-LC-MS/MS to identify proteins from the tissues samples. The results were analyzed using MetaCore™ software to access functional enrichment information. Protein-protein interactions (PPI) were predicted using STRING analysis. Hub proteins were determined using the Cytoscape plugin, CytoHubba. Survival analysis was performed using KM plotter. We identified 429 differentially expressed proteins whose pathways and processes were related to protein folding, apoptosis, and immune response.

RESULTS

The PPI network of these proteins showed enrichment modules related to the regulation of cell death, immune system, neutrophil degranulation, metabolism of RNA and chromatin DNA binding. From the PPI network, we identified 20 differentially expressed hub proteins, and assessed the prognostic value of the expression of genes that encode them. Among them, the expression of four hub genes was significantly associated with the overall survival of IGC patients.

CONCLUSIONS

This study reveals important findings that affect IGC development based on specific biological alterations in IGC patients. Bioinformatics analysis showed that the pathogenesis of IGC patients is complex and involves different interconnected biological processes. These findings may be useful in research on new targets to develop novel therapies to improve the overall survival of patients with IGC.

Identification and Characterization of Alternatively Spliced Transcript Isoforms of IRX4 in Prostate Cancer

Alternative splicing (AS) is tightly regulated to maintain genomic stability in humans. However, tumor growth, metastasis and therapy resistance benefit from aberrant RNA splicing. Iroquois-class homeodomain protein 4 (IRX4) is a TALE homeobox transcription factor which has been implicated in prostate cancer (PCa) as a tumor suppressor through genome-wide association studies (GWAS) and functional follow-up studies. In the current study, we characterized 12 IRX4 transcripts in PCa cell lines, including seven novel transcripts by RT-PCR and sequencing. They demonstrate unique expression profiles between androgen-responsive and nonresponsive cell lines. These transcripts were significantly overexpressed in PCa cell lines and the cancer genome atlas program (TCGA) PCa clinical specimens, suggesting their probable involvement in PCa progression. Moreover, a PCa risk-associated SNP rs12653946 genotype GG was corelated with lower IRX4 transcript levels. Using mass spectrometry analysis, we identified two IRX4 protein isoforms (54.4 kDa, 57 kDa) comprising all the functional domains and two novel isoforms (40 kDa, 8.7 kDa) lacking functional domains. These IRX4 isoforms might induce distinct functional programming that could contribute to PCa hallmarks, thus providing novel insights into diagnostic, prognostic and therapeutic significance in PCa management.

PUM1 and RNase P genes as potential cell-free DNA markers in breast cancer

BACKGROUND

Cell-free DNA (cfDNA) is used in clinical research to identify biomarkers for diagnosis of and follow-up on cancer. Here, we propose a fast and innovative approach using traditional housekeeping genes as cfDNA targets in a copy number analysis. We focus on the application of highly sensitive technology such as digital PCR (dPCR) to differentiate breast cancer (BC) patients and controls by quantifying regions of PUM1 and RPPH1 (RNase P) in plasma samples.

METHODS

We conducted a case-control study with 82 BC patients and 82 healthy women. cfDNA was isolated from plasma using magnetic beads and quantified by spectrophotometry to estimate total cfDNA. Then, both PUM1 and RPPH1 genes were specifically quantified by dPCR. Data analysis was calibrated using a reference genomic DNA in different concentrations.

RESULTS

We found RNase P and PUM1 values were correlated in the patient group (intraclass correlation coefficient [ICC] = 0.842), but they did not have any correlation in healthy women (ICC = 0.519). In dPCR quantification, PUM1 showed the capacity to distinguish early-stage patients and controls with good specificity (98.67%) and sensitivity (100%). Conversely, RNase P had lower cfDNA levels in triple-negative BC patients than luminal subtypes (p < 0.025 for both), confirming their utility for patient classification.

CONCLUSION

We propose the PUM1 gene as a cfDNA marker for early diagnosis of BC and RNase P as a cfDNA marker related to hormonal status and subtype classification in BC. Further studies with larger sample sizes are warranted.

Comparative Analysis of Systemic and Tumor Microenvironment Proteomes From Children With B-Cell Acute Lymphocytic Leukemia at Diagnosis and After Induction Treatment

Among the childhood diseases, B-cell acute lymphocytic leukemia (B-ALL) is the most frequent type of cancer. Despite recent advances concerning disease treatment, cytotoxic chemotherapy remains the first line of treatment in several countries, and the modifications induced by such drugs in the organism are still poorly understood. In this context, the present study provided a comparative high-throughput proteomic analysis of the cumulative changes induced by chemotherapeutic drugs used in the induction phase of B-ALL treatment in both peripheral blood (PB) and bone marrow compartment (BM) samples. To reach this goal, PB and BM plasma samples were comparatively analyzed by using label-free proteomics at two endpoints: at diagnosis (D0) and the end of the cumulative induction phase treatment (D28). Proteomic data was available via ProteomeXchange with identifier PXD021584. The resulting differentially expressed proteins were explored by bioinformatics approaches aiming to identify the main gene ontology processes, pathways, and transcription factors altered by chemotherapy, as well as to understand B-ALL biology in each compartment at D0. At D0, PB was characterized as a pro-inflammatory environment, with the involvement of several downregulated coagulation proteins as KNG, plasmin, and plasminogen. D28 was characterized predominantly by immune response-related processes and the super expression of the transcription factor IRF3 and transthyretin. RUNX1 was pointed out as a common transcription factor found in both D0 and D28. We chose to validate the proteins transthyretin and interferon-gamma (IFN-γ) by commercial kits and expressed the results as PB/BM ratios. Transthyretin ratio was augmented after induction chemotherapy, while IFN-γ was reduced at the end of the treatment. Considering that most of these proteins were not yet described in B-ALL literature, these findings added to understanding disease biology at diagnosis and highlighted a possible role for transthyretin and IFN-γ as mechanisms related to disease resolution.

Identifying four DNA methylation gene sites signature for predicting prognosis of osteosarcoma

BACKGROUND

Osteosarcoma (OS) is a common malignant bone tumor in children and adolescents. DNA methylation plays a crucial role in the prognosis prediction of cancer. Identification of novel DNA methylation sites biomarkers could be beneficial for the prognosis of OS patients. In this study, we aim to find an efficient methylated site model for predicting survival in OS.

METHODS

DNA methylation data were downloaded from the Cancer Genome Atlas database (TCGA) and the GEO database. Cox proportional hazard regression and random survival forest algorithm (RSFVH) were applied to identify DNA methylated site signature in the samples randomly assigned to the training subset and the other samples as the test subset. By randomizing 71 clinical samples into two individual groups and a series of statistical analyses between the two groups, a DNA methylation signature is verified.

RESULTS

This signature comprises four methylation sites (cg04533248, cg12401425, cg13997435, and cg15075357) associated with the patient training group from the univariate Cox proportional hazards regression analysis, RSFVH, and multivariate Cox regression analysis. Kaplan-Meier survival curves showed the OS patients in the high-risk group have a poor 5-year overall survival compared with the low-risk group, and this finding was identified in the test data set. A ROC analysis was performed in the current research. The results revealed that this signature was an independent predictor of patient survival by investigating the AUC of the four methylation sites signature in the training data set (AUC =0.861) and test data set, respectively (AUC =0.920). The nomogram described in the current study placed a great guiding value for predicting 1-, 2-, 3-year survival of the OS by combining age, gender, grade, and TNM stage as covariates with the RS of patients' methylation related signatures.

CONCLUSIONS

Our study proved that this signature might be a powerful prognostic tool for survival rate evaluation and guide tailored therapy for OS patients.

Toxicoproteomics Disclose Pesticides as Downregulators of TNF-α, IL-1β and Estrogen Receptor Pathways in Breast Cancer Women Chronically Exposed

Deleterious effects have been widely associated with chronic pesticide exposure, including cancer development. In spite of several known consequences that pesticides can trigger in the human body, few is known regarding its impact on breast cancer women that are chronically exposed to such substances during agricultural work lifelong. In this context, the present study performed a high-throughput toxicoproteomic study in association with a bioinformatics-based design to explore new putative processes and pathways deregulated by chronic pesticide exposure in breast cancer patients. To reach this goal, we analyzed comparatively non-depleted plasma samples from exposed (n = 130) and non-occupationally exposed (n = 112) women diagnosed with breast cancer by using a label-free proteomic tool. The list of proteins differentially expressed was explored by bioinformatics and the main pathways and processes further investigated. The toxicoproteomic study revealed that women exposed to pesticides exhibited mainly downregulated events, linked to immune response, coagulation and estrogen-mediated events in relation to the unexposed ones. Further investigation shown that the identified deregulated processes and pathways correlated with significant distinct levels tumor necrosis factor alpha and interleukin 1 beta in the blood, and specific clinicopathological characteristics pointed out by bioinformatics analysis as adipose-trophic levels, menopause and intratumoral clots formation. Altogether, these findings reinforce pesticides as downregulators of several biological process and highlight that these compounds can be linked to poor prognosis 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.

Proteomics biomarkers for solid tumors: Current status and future prospects

Cancer is a heterogeneous multifactorial disease, which continues to be one of the main causes of death worldwide. Despite the extensive efforts for establishing accurate diagnostic assays and efficient therapeutic schemes, disease prevalence is on the rise, in part, however, also due to improved early detection. For years, studies were focused on genomics and transcriptomics, aiming at the discovery of new tests with diagnostic or prognostic potential. However, cancer phenotypic characteristics seem most likely to be a direct reflection of changes in protein metabolism and function, which are also the targets of most drugs. Investigations at the protein level are therefore advantageous particularly in the case of in-depth characterization of tumor progression and invasiveness. Innovative high-throughput proteomic technologies are available to accurately evaluate cancer formation and progression and to investigate the functional role of key proteins in cancer. Employing these new highly sensitive proteomic technologies, cancer biomarkers may be detectable that contribute to diagnosis and guide curative treatment when still possible. In this review, the recent advances in proteomic biomarker research in cancer are outlined, with special emphasis placed on the identification of diagnostic and prognostic biomarkers for solid tumors. In view of the increasing number of screening programs and clinical trials investigating new treatment options, we discuss the molecular connections of the biomarkers as well as their potential as clinically useful tools for diagnosis, risk stratification and therapy monitoring of solid tumors.

Differential protein expression of hippocampal cells associated with heavy metals (Pb, As, and MeHg) neurotoxicity: Deepening into the molecular mechanism of neurodegenerative diseases

Chronic exposure to heavy metals such as Pb, As, and MeHg can be associated with an increased risk of developing neurodegenerative diseases. Our in vitro bioassays results showed the potency of heavy metals in the order of Pb < As < MeHg on hippocampal cells. The main objective of this study was combining in vitro label free proteomics and systems biology approach for elucidating patterns of biological response, discovering underlying mechanisms of Pb, As, and MeHg toxicity in hippocampal cells. The omics data was refined by using different filters and normalization and multilevel analysis tools were employed to explore the data visualization. The functional and pathway visualization was performed by using Gene ontology and PathVisio tools. Using these all integrated approaches, we identified significant proteins across treatments within the mitochondrial dysfunction, oxidative stress, ubiquitin proteome dysfunction, and mRNA splicing related to neurodegenerative diseases. The systems biology analysis revealed significant alterations in proteins implicated in Parkinson's disease (PD) and Alzheimer's disease (AD). The current proteomics analysis of three metals support the insight into the proteins involved in neurodegeneration and the altered proteins can be useful for metal-specific biomarkers of exposure and its adverse effects.

SIGNIFICANCE

The proteomics techniques have been claimed to be more sensitive than the conventional toxicological assays, facilitating the measurement of responses to heavy metals (Pb, As, and MeHg) exposure before obvious harm has occurred demonstrating their predictive value. Also, proteomics allows for the comparison of responses between Pb, As, and MeHg metals, permitting the evaluation of potency differences hippocampal cells of the brain. Hereby, the molecular information provided by pathway and gene functional analysis can be used to develop a more thorough understanding of each metal mechanism at the protein level for different neurological adverse outcomes (e.g. Parkinson's disease, Alzheimer's diseases). Efforts are put into developing proteomics based toxicity testing methods using in vitro models for improving human risk assessment. Some of the key proteins identified can also potentially be used as biomarkers in epidemiologic studies. These heavy metal response patterns shed new light on the mechanisms of mRNA splicing, ubiquitin pathway role in neurodegeneration, and can be useful for the development of molecular biomarkers of heavy metals exposure.

γ-Secretase Inhibition Induces Muscle Hypertrophy in a Notch-Independent Mechanism

A wide variety of cellular processes and signaling events are regulated by the proteolytic enzyme γ-secretase. Notch-1 is one of the substrates of γ-secretase and its role in the regulation of muscle differentiation has been well described. Importantly, besides Notch-1, a number of proteins have been identified to undergo proteolysis by γ-secretase. To date, the specific role of γ-secretase during embryonic skeletal muscle differentiation has not been studied. Therefore, we address this question through the analysis of in vitro grown chick myogenic cells during the formation of multinucleated myotubes. The γ-secretase inhibitor DAPT (N-N[-(3,5-Difluorophenacetyl-l-alanyl)]-S-328 phenylglycine-t-butyl-ester) induces muscle hypertrophy. Knockdown of Notch-1 using siRNA specific to chick shows no significant effect in myotube size, suggesting that γ-secretase-dependent effects on muscle hypertrophy in chick myogenic cells are Notch-1-independent. We also investigate the effects of γ-secretase inhibition in the whole proteomic profile of chick myogenic cells. We identified 276 differentially expressed proteins from Label-free proteomic approach. Data overview of interaction network obtained from STRING show that after γ-secretase inhibition cells exhibited imbalance in protein metabolism, cytoskeleton/adhesion, and Sonic Hedgehog signaling. The collection of these results provides new insights into the role of γ-secretase in skeletal muscle hypertrophy.

Advancement of mass spectrometry-based proteomics technologies to explore triple negative breast cancer

Understanding the complexity of cancer biology requires extensive information about the cancer proteome over the course of the disease. The recent advances in mass spectrometry-based proteomics technologies have led to the accumulation of an incredible amount of such proteomic information. This information allows us to identify protein signatures or protein biomarkers, which can be used to improve cancer diagnosis, prognosis and treatment. For example, mass spectrometry-based proteomics has been used in breast cancer research for over two decades to elucidate protein function. Breast cancer is a heterogeneous group of diseases with distinct molecular features that are reflected in tumour characteristics and clinical outcomes. Compared with all other subtypes of breast cancer, triple-negative breast cancer is perhaps the most distinct in nature and heterogeneity. In this review, we provide an introductory overview of the application of advanced proteomic technologies to triple-negative breast cancer research.

Clinical proteomics in cancer: Where we are

Proteomics has emerged as a promising field in the post-genomic era. Notwithstanding the great advances provided by gene expression analysis in cancer, the lack of a correlation between gene expression and protein levels has highlighted the need for a proteomic focus on cancer. Although the increasing knowledge regarding cancer biology, a reliable marker to improve diagnosis, prognosis and treatment for cancer patients is not a reality at present. In this review, we address the main considerations regarding proteomics-based studies and their clinical applications on cancer research, highlighting some considerations related to strengths and limitations of proteomics-based studies and its application to clinical practice.