Proteomic analysis for finding serum pathogenic factors and potential biomarkers in multiple myeloma

Serum NMR-Based Metabolomics Profiling Identifies Lipoprotein Subfraction Variables and Amino Acid Reshuffling in Myeloma Development and Progression

Multiple myeloma (MM) is an incurable hematological cancer. It is preceded by monoclonal gammopathy of uncertain significance (MGUS)-an asymptomatic phase. It has been demonstrated that early detection increases the 5-year survival rate. However, blood-based biomarkers that enable early disease detection are lacking. Metabolomic and lipoprotein subfraction variable profiling is gaining traction to expand our understanding of disease states and, more specifically, for identifying diagnostic markers in patients with hematological cancers. This study aims to enhance our understanding of multiple myeloma (MM) and identify candidate metabolites, allowing for a more effective preventative treatment. Serum was collected from 25 healthy controls, 20 patients with MGUS, and 30 patients with MM. 1H-NMR (Nuclear Magnetic Resonance) spectroscopy was utilized to evaluate serum samples. The metabolite concentrations were examined using multivariate, univariate, and pathway analysis. Metabolic profiles of the MGUS patients revealed lower levels of alanine, lysine, leucine but higher levels of formic acid when compared to controls. However, metabolic profiling of MM patients, compared to controls, exhibited decreased levels of total Apolipoprotein-A1, HDL-4 Apolipoprotein-A1, HDL-4 Apolipoprotein-A2, HDL Free Cholesterol, HDL-3 Cholesterol and HDL-4 Cholesterol. Lastly, metabolic comparison between MGUS to MM patients primarily indicated alterations in lipoproteins levels: Total Cholesterol, HDL Cholesterol, HDL Free Cholesterol, Total Apolipoprotein-A1, HDL Apolipoprotein-A1, HDL-4 Apolipoprotein-A1 and HDL-4 Phospholipids. This study provides novel insights into the serum metabolic and lipoprotein subfraction changes in patients as they progress from a healthy state to MGUS to MM, which may allow for earlier clinical detection and treatment.

A Comparison of Different Sample Processing Protocols for MALDI Imaging Mass Spectrometry Analysis of Formalin-Fixed Multiple Myeloma Cells

Sample processing of formalin-fixed specimens constitutes a major challenge in molecular profiling efforts. Pre-analytical factors such as fixative temperature, dehydration, and embedding media affect downstream analysis, generating data dependent on technical processing rather than disease state. In this study, we investigated two different sample processing methods, including the use of the cytospin sample preparation and automated sample processing apparatuses for proteomic analysis of multiple myeloma (MM) cell lines using imaging mass spectrometry (IMS). In addition, two sample-embedding instruments using different reagents and processing times were considered. Three MM cell lines fixed in 4% paraformaldehyde were either directly centrifuged onto glass slides using cytospin preparation techniques or processed to create paraffin-embedded specimens with an automatic tissue processor, and further cut onto glass slides for IMS analysis. The number of peaks obtained from paraffin-embedded samples was comparable between the two different sample processing instruments. Interestingly, spectra profiles showed enhanced ion yield in cytospin compared to paraffin-embedded samples along with high reproducibility compared to the sample replicate.

Advancements in Oncoproteomics Technologies: Treading toward Translation into Clinical Practice

Proteomics continues to forge significant strides in the discovery of essential biological processes, uncovering valuable information on the identity, global protein abundance, protein modifications, proteoform levels, and signal transduction pathways. Cancer is a complicated and heterogeneous disease, and the onset and progression involve multiple dysregulated proteoforms and their downstream signaling pathways. These are modulated by various factors such as molecular, genetic, tissue, cellular, ethnic/racial, socioeconomic status, environmental, and demographic differences that vary with time. The knowledge of cancer has improved the treatment and clinical management; however, the survival rates have not increased significantly, and cancer remains a major cause of mortality. Oncoproteomics studies help to develop and validate proteomics technologies for routine application in clinical laboratories for (1) diagnostic and prognostic categorization of cancer, (2) real-time monitoring of treatment, (3) assessing drug efficacy and toxicity, (4) therapeutic modulations based on the changes with prognosis and drug resistance, and (5) personalized medication. Investigation of tumor-specific proteomic profiles in conjunction with healthy controls provides crucial information in mechanistic studies on tumorigenesis, metastasis, and drug resistance. This review provides an overview of proteomics technologies that assist the discovery of novel drug targets, biomarkers for early detection, surveillance, prognosis, drug monitoring, and tailoring therapy to the cancer patient. The information gained from such technologies has drastically improved cancer research. We further provide exemplars from recent oncoproteomics applications in the discovery of biomarkers in various cancers, drug discovery, and clinical treatment. Overall, the future of oncoproteomics holds enormous potential for translating technologies from the bench to the bedside.

Multimerin-1 and cancer: a review

Multimerin-1 (MMRN1) is a platelet protein with a role in haemostasis and coagulation. It is also present in endothelial cells (ECs) and the extracellular matrix (ECM), where it may be involved in cell adhesion, but its molecular functions and protein–protein interactions in these cellular locations have not been studied in detail yet. In recent years, MMRN1 has been identified as a differentially expressed gene (DEG) in various cancers and it has been proposed as a possible cancer biomarker. Some evidence suggest that MMRN1 expression is regulated by methylation, protein interactions, and non-coding RNAs (ncRNAs) in different cancers. This raises the questions if a functional role of MMRN1 is being targeted during cancer development, and if MMRN1’s differential expression pattern correlates with cancer progression. As a result, it is timely to review the current state of what is known about MMRN1 to help inform future research into MMRN1’s molecular mechanisms in cancer.

Liquid biopsy: an evolving paradigm for the biological characterisation of plasma cell disorders

Liquid biopsies-a source of circulating cell-free nucleic acids, proteins and extracellular vesicles-are currently being explored for the quantitative and qualitative characterisation of the tumour genome and as a mode of non-invasive therapeutic monitoring in cancer. Emerging data suggest that liquid biopsies might offer a potentially simple, non-invasive, repeatable strategy for diagnosis, prognostication and therapeutic decision making in a genetically heterogeneous disease like multiple myeloma (MM), with particular applicability in subsets of patients where conventional markers of disease burden may be less informative. In this review, we describe the emerging utility of the evaluation of circulating tumour DNA, extracellular RNA, cell-free proteins and metabolites and extracellular vesicles in MM.

Next-Generation Biomarkers in Multiple Myeloma: Understanding the Molecular Basis for Potential Use in Diagnosis and Prognosis

Multiple myeloma (MM) is considered to be the second most common blood malignancy and it is characterized by abnormal proliferation and an accumulation of malignant plasma cells in the bone marrow. Although the currently utilized markers in the diagnosis and assessment of MM are showing promising results, the incidence and mortality rate of the disease are still high. Therefore, exploring and developing better diagnostic or prognostic biomarkers have drawn global interest. In the present review, we highlight some of the recently reported and investigated novel biomarkers that have great potentials as diagnostic and/or prognostic tools in MM. These biomarkers include angiogenic markers, miRNAs as well as proteomic and immunological biomarkers. Moreover, we present some of the advanced methodologies that could be utilized in the early and competent diagnosis of MM. The present review also focuses on understanding the molecular concepts and pathways involved in these biomarkers in order to validate and efficiently utilize them. The present review may also help in identifying areas of improvement for better diagnosis and superior outcomes of MM.

Serum metabolomic profiling correlated with ISS and clinical outcome for multiple myeloma patients treated with high-dose melphalan and autologous stem cell transplantation

The metabolome, which is the final down-stream global product of metabolic processes in organisms, is not sufficiently described in multiple myeloma (MM) patients. The aim of this study was, therefore, to study the serum metabolomic profile using proton nuclear magnetic resonance (¹H-NMR) spectroscopy, and its relationship to clinical characteristics and patient outcome. Serum samples, which were taken at diagnosis, from 201 MM patients who underwent high-dose melphalan followed by autologous stem cell transplantation as the first-line therapy, were analyzed. We found that the metabolomic profile differed between patients with different MM International Staging System (ISS) stages. The profile revealed increased levels of cholesterol, phospholipids, high-density lipoprotein, low-density lipoprotein, apolipoproteins A1 and A2, valine, and leucine in ISS I patients compared with ISS III patients. The metabolomic profile also differed between patients with IgA and IgG paraproteins, predominantly because of higher levels of high- and low-density lipoprotein subfractions in IgA patients. The exact pathway of metabolism leading to accumulation of these metabolites is still elusive, but this study indicates an area of interest for further investigation in the search for new therapy targets and prognostic markers for this disease.

Proteomic Alterations in Multiple Myeloma: A Comprehensive Study Using Bone Marrow Interstitial Fluid and Serum Samples

Multiple myeloma (MM) is a plasma cell-associated cancer and exists as the second most common hematological malignancy worldwide. Although researchers have been working on MM, a comprehensive quantitative Bone Marrow Interstitial Fluid (BMIF) and serum proteomic analysis from the same patients’ samples is not yet reported. The present study involves the investigation of alterations in the BMIF and serum proteome of MM patients compared to controls using multipronged quantitative proteomic approaches viz., 2D-DIGE, iTRAQ, and SWATH-MS. A total of 279 non-redundant statistically significant differentially abundant proteins were identified by the combination of three proteomic approaches in MM BMIF, while in the case of serum 116 such differentially abundant proteins were identified. The biological context of these dysregulated proteins was deciphered using various bioinformatic tools. Verification experiments were performed in a fresh independent cohort of samples using immunoblotting and mass spectrometry based SRM assays. Thorough data evaluation led to the identification of a panel of five proteins viz., haptoglobin, kininogen 1, transferrin, and apolipoprotein A1 along with albumin that was validated using ELISA in a larger cohort of serum samples. This panel of proteins could serve as a useful tool in the diagnosis and understanding of the pathophysiology of MM in the future.

Analysis of Multimerin 1 (MMRN1) expression in ovarian cancer

Ovarian cancer, the most lethal gynecological cancer, is the fifth most common cause of cancer-related deaths in women. A cost-effective and non-invasive early screening method for ovarian cancer is required to reduce the high mortality rate. Saliva is a clinically informative unique fluid, which is useful for novel approaches to prognosis, clinical diagnosis, and monitoring for non-invasive detection of disease. Multimerin1 (MMRN1) is a di-sulfide linked homo-polymeric glycoprotein from EMILIN family. Altered expression of MMRN1 has been reported in hepatocellular carcinoma, cervical cancer, and ovarian cancer. But, its role in epithelial ovarian cancer (EOC) is not clear and well documented. In this study, expression of Multimerin 1 was validated in saliva and tissues of EOC patients and age-matched controls by western blotting, ELISA, RT-PCR, and immunohistochemistry. Significant over expression of MMRN1 was observed by western blot and ELISA in saliva samples of EOC patients. The average concentration of MMRN1 in the saliva of healthy controls was 28.7 pg/ml (SE ± 1.76), 42.53 pg/ml (SE ± 4.06) in low grade and 52.91 pg/ml (SE ± 4.24) with p < 0.01 in high-grade EOC. Upregulated cytoplasmic expression of MMRN1 was observed in EOC tissue by immunohistochemistry. Our results suggest that MMRN1 expression is associated with EOC progression and MMRN1 may be potential biomarker candidates for early-stage EOC detection however further experiments are required in a large cohort to establish this proposition. Also, saliva can be explored as a novel medium for ovarian cancer diagnosis.

Challenges and Opportunities in Clinical Applications of Blood-Based Proteomics in Cancer

Simple Summary

The traditional approach in identifying cancer related protein biomarkers has focused on evaluation of a single peptide/protein in tissue or circulation. At best, this approach has had limited success for clinical applications, since multiple pathological tumor pathways may be involved during initiation or progression of cancer which diminishes the significance of a single candidate protein/peptide. Emerging sensitive proteomic based technologies like liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics can provide a platform for evaluating serial serum or plasma samples to interrogate secreted products of tumor–host interactions, thereby revealing a more “complete” repertoire of biological variables encompassing heterogeneous tumor biology. However, several challenges need to be met for successful application of serum/plasma based proteomics. These include uniform pre-analyte processing of specimens, sensitive and specific proteomic analytical platforms and adequate attention to study design during discovery phase followed by validation of discovery-level signatures for prognostic, predictive, and diagnostic cancer biomarker applications.

Abstract

Blood is a readily accessible biofluid containing a plethora of important proteins, nucleic acids, and metabolites that can be used as clinical diagnostic tools in diseases, including cancer. Like the on-going efforts for cancer biomarker discovery using the liquid biopsy detection of circulating cell-free and cell-based tumor nucleic acids, the circulatory proteome has been underexplored for clinical cancer biomarker applications. A comprehensive proteome analysis of human serum/plasma with high-quality data and compelling interpretation can potentially provide opportunities for understanding disease mechanisms, although several challenges will have to be met. Serum/plasma proteome biomarkers are present in very low abundance, and there is high complexity involved due to the heterogeneity of cancers, for which there is a compelling need to develop sensitive and specific proteomic technologies and analytical platforms. To date, liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics has been a dominant analytical workflow to discover new potential cancer biomarkers in serum/plasma. This review will summarize the opportunities of serum proteomics for clinical applications; the challenges in the discovery of novel biomarkers in serum/plasma; and current proteomic strategies in cancer research for the application of serum/plasma proteomics for clinical prognostic, predictive, and diagnostic applications, as well as for monitoring minimal residual disease after treatments. We will highlight some of the recent advances in MS-based proteomics technologies with appropriate sample collection, processing uniformity, study design, and data analysis, focusing on how these integrated workflows can identify novel potential cancer biomarkers for clinical applications.

Identification of potential cervical cancer serum biomarkers in Thai patients

Cervical cancer is one of the most common causes of cancer-associated mortality in females worldwide. Serum biomarkers are important tools for diagnosis, disease staging, monitoring treatment and detecting recurrence in different types of cancer. However, only a small number of established biomarkers have been used for clinical diagnosis of cervical cancer. Therefore, the identification of minimally invasive, sensitive and highly specific biomarkers for detection of cervical cancer may improve outcomes. In the present pilot study, changes in disease-relevant proteins in 31 patients with cervical cancer were compared with 16 healthy controls. The Human 14 Multiple Affinity Removal system was used to deplete the 14 most abundant serum proteins to decrease sample complexity and to enrich proteins that exhibited decreased levels of abundance in the serum samples. Immunoaffinity-depleted serum samples were analyzed by in-gel digestion, followed by liquid chromatography mass spectrometry analysis and data processing. Automated quantitative western blot assays and receiver operating characteristic (ROC) curves were used to evaluate the differential protein expression levels between the two groups. Capillary electrophoresis-based western blot analysis was performed to quantitatively determine serum levels of the candidate biomarkers. Significantly increased levels of α-1-antitrypsin (A1AT) and pyrroline-5-carboxylate reductase 2 (PYCR2) were detected, whereas the levels of transthyretin (TTR), apolipoprotein A-I (ApoA-I), vitamin D binding protein (VDBP) and multimerin-1 (MMRN1) were significantly decreased in patients with cervical cancer compared with the healthy controls. ROC curve analysis indicated that the sensitivity and specificity was improved through the combination of the 6 candidate biomarkers. In summary, the results demonstrated that 6 candidate biomarkers (A1AT, PYCR2, TTR, ApoA-I, VDBP and MMRN1) exhibited significantly different expression between serum samples from healthy controls and patients with cervical cancer. These proteins may represent potential biomarkers for distinguishing patients with cervical cancer from healthy controls and for differentiation of patient subgroups.

Emerging biomarkers in Multiple Myeloma: A review

Multiple Myeloma (MM) is the second most common hematological malignancy after non-Hodgkin lymphoma and is manifested by uncontrolled proliferation and accumulation of abnormal plasma cells in the bone marrow (BM). The incidence along with deaths associated with MM is on rise due to lack of an effective diagnosis at an early stage. The identification of MM decades ago marks the adoption of certain conventional markers such as plasma cell percentage in BM, serum protein electrophoresis for M-band and urinary Bence-Jones protein. This was then followed by utilization of β2 microglobulin and serum albumin for determining the staging of MM. The need for a better diagnostic or prognostic marker prompts researchers and hence, certain novel markers have been tested which includes extracellular matrix proteins, angiogenic factors, telomeres and telomerase along with the immune markers. Nowadays, proteomic and genomic studies are being performed to identify novel diagnostic and/or prognostic markers for MM. Followed by this, comes the emerging concept of liquid biopsy which allows easy and non-invasive detection of the disease. The liquid biopsy comprises of circulatory tumor cells along with the nucleic acids (microRNAs and cell-free DNA) released from the tumor cells in peripheral circulation which could be a true representation of BM. This review, hence, summarizes the emerging biomarkers involved in the diagnosis and prognosis of MM.

The impact of the sepsis on female urogenital system: the role of pregabalin

PURPOSE

The aim of the study was to investigate the oxidative damage and inflammatory effects of sepsis on the urogenital system in the Lipopolysaccharide (LPS)-induced sepsis model and ameliorating role of Pregabalin (PGB).

METHODS

Twenty-four female Wistar Albino rats (12 months old) were divided into 3 groups as follows: Sepsis group (Group S) (5 mg/kg LPS, i.p, single dose); Sepsis+ PGB group (Group SP) (5 mg/kg LPS, i.p, single dose and 30 mg/kg PGB); Control group (Group C) (0.1 ml/oral and i.p. saline, single dose), 6 h after LPS administration, the animals were killed. Subsequently, analyses of urogenital tissue oxidant/antioxidant status, histopathological and immunohistochemical analyses were performed.

RESULTS

Total oxidative status (TOS) and oxidative stress index (OSI) values in the urogenital tissues were increased in Group S (Total anti-oxidative status (TAS) decreased) compared to the Control group (p < 0.05). PGB improved these values (p < 0.05). The immunohistochemical markers [Caspase-3, granulocyte colony-stimulating factor (G-CSF), interleukin-6 (IL-6), Serum Amyloid A (SAA) and inducible nitric oxide synthase (iNOS)] were significantly increased in Group S except for bladder (p < 0.001). Statistically significant immunohistochemical positiveness was found only for IL-6 in urinary bladder, though all the others values were negative. With the administration of PGB (Group SP), the expressions of these immunoreactions were markedly decreased (p < 0.001).

CONCLUSIONS

These findings demonstrated that sepsis caused oxidative stress and inflammation in the urogenital tissues. We have revealed that PGB ameliorated tissue damage caused by sepsis.

Identification of High Serum Apolipoprotein A1 as a Favorable Prognostic Indicator in Patients with Multiple Myeloma

This study is to explore the prognostic significance of serum lipid profiles in patients with multiple myeloma (MM). The study retrospectively enrolled 307 MM patients in Zhongshan Hospital, Shanghai, China, from 2007 to 2016. We evaluated the prognostic significance of the pre-diagnostic serum lipid profile [cholesterol, triglyceride, low-density lipoprotein (LDL), high-density lipoprotein (HDL), Apolipoprotein A1 (Apo A1) and Apolipoprotein B (Apo B)]. Prognostic factors identified through univariate and multivariate analysis were used to construct a new model based on Lasso Cox regression. Results indicated that lipid levels showed significant difference between ISS stages: Apo A1, Apo B, Cholesterol and LDL levels were lower in late ISS stage. However, only Apo A1 showed statistically significance in overall survival (OS), progression free survival (PFS) and cause specific survival (CSS) (P=0.038, P=0.028, P=0.011) in univariate Cox regression. Patients with higher Apo A1 displayed longer OS (median OS, 67 months vs. 30 months; P<0.001). Also, Apo A1 was revealed to be an independent prognostic indicator through multivariate analysis. Combining the Apo A1 level, Zhongshan Score model was constructed with Lasso regression for prognosis prediction. This model exhibited higher accuracy than International Staging System (ISS) and Durie and Salmon (DS) system. In conclusion, among all the serum lipid profiles, serum Apo A1 is a powerful prognostic indicator for patients with MM.

Rapid discrimination of multiple myeloma patients by artificial neural networks coupled with mass spectrometry of peripheral blood plasma

Multiple myeloma (MM) is a highly heterogeneous disease of malignant plasma cells. Diagnosis and monitoring of MM patients is based on bone marrow biopsies and detection of abnormal immunoglobulin in serum and/or urine. However, biopsies have a single-site bias; thus, new diagnostic tests and early detection strategies are needed. Matrix-Assisted Laser Desorption/Ionization Time-of Flight Mass Spectrometry (MALDI-TOF MS) is a powerful method that found its applications in clinical diagnostics. Artificial intelligence approaches, such as Artificial Neural Networks (ANNs), can handle non-linear data and provide prediction and classification of variables in multidimensional datasets. In this study, we used MALDI-TOF MS to acquire low mass profiles of peripheral blood plasma obtained from MM patients and healthy donors. Informative patterns in mass spectra served as inputs for ANN that specifically predicted MM samples with high sensitivity (100%), specificity (95%) and accuracy (98%). Thus, mass spectrometry coupled with ANN can provide a minimally invasive approach for MM diagnostics.

Variability of serum novel serum peptide biomarkers correlates with the disease states of multiple myeloma

Background

The bone marrow microenvironment provides an optimal substrate for multiple myeloma (MM) initiation and progression. The soluble component of MM niche is dynamic with the disease states of MM. We formerly employed proteomic profiling to construct a MM model. Four peptides constituting the model were selected by supervised neural network algorithm (SNN).

Methods

62 Newly diagnosed (ND) MM and 64 healthy controls (HCs) were picked up for validating the distinguishing capability of the SNN model. Nano-liquid chromatography-electrospray ionization-tandem mass spectrometry was used for peptide identification. MM in different disease states and HCs were choosed for peptides relative intensities comparison. Western blot and ELISA were employed to validate the variability.

Results

The sensitivity and specificity of the independent testing data set for blind validation were 93.55% and 92.19%. The relative intensities of three out of the four peptides were increased in ND and refractory and relapse patients but decreased to that level of HCs in complete remission and very good partial remission patients. Relative intensity of the remaining peptide was negatively associated with MM remission. The peptides sequencing results showed that they were derived from dihydropyrimidinase-like 2, fibrinogen alpha chain, platelet factor 4 and alpha-fetoprotein.

Conclusions

The potential value of the four peptides in monitoring MM treatment response was arised from their close correlation with MM disease states.

Urinary biomarkers for the diagnosis of cervical cancer by quantitative label-free mass spectrometry analysis

Due to the invasive procedure associated with Pap smears for diagnosing cervical cancer and the conservative culture of developing countries, identifying less invasive biomarkers is of great interest. Quantitative label-free mass spectrometry was performed to identify potential biomarkers in the urine samples of patients with cervical cancer. This technique was used to study the differential expression of urinary proteomes between normal individuals and cancer patients. The alterations in the levels of urinary proteomes in normal and cancer patients were analyzed by Progenesis label-free software and the results revealed that 60 proteins were upregulated while 73 proteins were downregulated in patients with cervical cancer. This method could enrich high molecular weight proteins from 100 kDa. The protein-protein interactions were obtained by Search Tool for the Retrieval of Interacting Genes/Proteins analysis and predicted the biological pathways involving various functions including cell-cell adhesion, blood coagulation, metabolic processes, stress response and the regulation of morphogenesis. Two notable upregulated urinary proteins were leucine-rich α-2-glycoprotein (LRG1) and isoform-1 of multimerin-1 (MMRN1), while the 3 notable downregulated proteins were S100 calcium-binding protein A8 (S100A8), serpin B3 (SERPINB3) and cluster of differentiation-44 antigen (CD44). The validation of these 5 proteins was performed by western blot analysis and the biomarker sensitivity of these proteins was analyzed individually and in combination with receiver operator characteristic curve (ROC) analysis. Quantitative mass spectrometry analysis may allow for the identification of urinary proteins of high molecular weight. The proteins MMRN1 and LRG1 were presented, for the first time, to be highly expressed urinary proteins in cervical cancer. ROC analysis revealed that LRG1 and SERPINB3 could be individually used, and these 5 proteins could also be combined, to detect the occurrence of cervical cancer.

Differential expression of serum proteins in multiple myeloma

The exact cause instigating multiple myeloma (MM) has not been fully elucidated, and the disease has a median survival of 6 months without any treatment. To identify potential biomarkers of MM, serum proteins reflecting alteration in their proteomes were analyzed in 6 patients with MM compared with 6 healthy controls using two-dimensional electrophoresis (2-DE) and matrix-assisted laser desorption/ionization time-of flight mass spectrometry. The most notable differentially expressed proteins were validated by immunoblotting and changes in mRNA expression were evaluated by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). A total of 11 differentially expressed protein spots were found. The expression levels of 7 proteins [Immunoglobulin heavy constant µ; proto-oncogene diffuse B-cell lymphoma (DBL2); 26S protease regulatory subunit 4 (P26s4); serum albumin; haptoglobin; and two unknown proteins with isoelectronic point (pI) of 6.41 and molecular weight of 35.4 kDa, and pI of 8.05 and molecular weight of 27.4 kDa, respectively] were downregulated in MM compared with healthy controls. Expression of gel actin-related protein 2/3 complex subunit 1A (ARPC1A); immunoglobulin heavy constant γ 1; fibrinogen α chain (FGA) fragment D; and zinc finger protein 70 were increased in serum of MM patients. Protein expressions of ARPC1A, FGA, P26s4 and DBL2 were measured by immunoblotting in an independent cohort of 12 MM patients and 10 healthy controls. RT-qPCR analysis demonstrated that ARPC1A expression only mimicked protein expression, whereas FGA, PSMC1 (encoding P26s4) and MCF2 (encoding DBL2) did not exhibit significant changes in mRNA expression between control and MM samples. These proteins represent putative serological biomarkers for patients with MM.

The impact of gallic acid on the methotrexate-induced kidney damage in rats

Prolonged use of an antineoplastic agent methotrexate (MTX), can cause numerous side effects such as nephrotoxicity. The aim of this study was to examine the effects of MTX on kidneys and demonstrate the protective effects of gallic acid (GA). Twenty-four, male, rats distributed into three groups. Each groups consisted eight rats and only saline was administered to the control group. The MTX group received a single dose (20 mg/kg) MTX intraperitoneally. The MTX + GA group received same dose MTX and 100 mg/kg GA orally during the 7 days. Renal functions, oxidative stress markers, histopathological and immunohistochemical changes were evaluated at the end of the experiment. Blood urea nitrogen, creatinine, uric acid levels and tissue oxidative stress markers, total oxidant status and oxidative stress index levels significantly increased and total antioxidant status levels significantly decreased in MTX group compared with the control group. At the histopathological examination hemorrhages, tubular cell necrosis, glomerulosclerosis, inflammatory cell infiltrations and proteinous materials in tubules were noticed in MTX group. Immunohistochemical examination revealed that increased expressions of serum amyloid A (SAA), tumor necrosis factor alpha (TNF-α), prostaglandin E2 (PGE-2) and C-reactive protein (CRP) in tubular epithelial cells of kidneys in this group. There were no immunoreaction with SAA and CRP, only small number of PGE-2 and TNF-α positive tubular epithelial cells were observed in MTX + GA group. In conclusion, all evidence suggested that oxidative stress caused MTX-induced nephrotoxicity and GA prevent the kidney from the nephrotoxicity due to its antioxidant and anti-inflammatory activities.

The impact of methotrexate on lung inflammatory and apoptotic pathway biomarkers-The role of gallic acid

BACKGROUNDS

The aim of this study was to investigate the effects of methotrexate (MTX) on the lung via inflammatory and apoptotic pathway biomarkers and the role of gallic acid (GA).

METHODS

In this study, twenty four male Wistar-Albino rats weighing 300-350g were divided into 3 groups as follows; Control group (0.1ml/oral saline, for 7 days+2nd day i.p.). MTX group (20mg/kg, single dose, on 2nd day). MTX+GA group (15mg/kg, orally, for 7 days). Comet analysis, oxidant-antioxidant status, IMA were conducted. Histopathological analyses were evaluated.

RESULTS

Comet assay on the blood, TOS and OSI values in the lung were increased in the group II compared with the control group (p<0.05). GA significantly reduced the comet score and IMA levels in the blood, TOS and OSI values in the lung tissue in group III compared with group II (p<0.05). Immunohistochemically PGE₂, TNF-α, CRP, serum SAA, Caspase 3 and Caspase 9 expressions significantly increased in group II compared with the control group (p<0.001) and GA treatment ameliorated these parameters significantly in group III compared with group II (p<0.001).

CONCLUSIONS

MTX caused oxidative stress and DNA damage in the blood tissue and caused oxidative damage, inflammation and apoptosis in the lung tissue.

Towards Stratified Medicine in Plasma Cell Myeloma

Plasma cell myeloma is a clinically heterogeneous malignancy accounting for approximately one to 2% of newly diagnosed cases of cancer worldwide. Treatment options, in addition to long-established cytotoxic drugs, include autologous stem cell transplant, immune modulators, proteasome inhibitors and monoclonal antibodies, plus further targeted therapies currently in clinical trials. Whilst treatment decisions are mostly based on a patient’s age, fitness, including the presence of co-morbidities, and tumour burden, significant scope exists for better risk stratification, sub-classification of disease, and predictors of response to specific therapies. Clinical staging, recurring acquired cytogenetic aberrations, and serum biomarkers such as β-2 microglobulin, and free light chains are in widespread use but often fail to predict the disease progression or inform treatment decision making. Recent scientific advances have provided considerable insight into the biology of myeloma. For example, gene expression profiling is already making a contribution to enhanced understanding of the biology of the disease whilst Next Generation Sequencing has revealed great genomic complexity and heterogeneity. Pathways involved in the oncogenesis, proliferation of the tumour and its resistance to apoptosis are being unravelled. Furthermore, knowledge of the tumour cell surface and its interactions with bystander cells and the bone marrow stroma enhance this understanding and provide novel targets for cell and antibody-based therapies. This review will discuss the development in understanding of the biology of the tumour cell and its environment in the bone marrow, the implementation of new therapeutic options contributing to significantly improved outcomes, and the progression towards more personalised medicine in this disorder.