Identification of candidate biomarkers in ovarian cancer serum by depletion of highly abundant proteins and differential in‐gel electrophoresis

Ovarian cancer is the fifth leading cause of cancer death for women in the US, yet survival rates are over 90% when it is diagnosed at an early stage, highlighting the need for biomarkers for early detection. To enhance the discovery of tumor‐specific proteins that could represent novel serum biomarkers for ovarian cancer, we depleted serum of highly abundant proteins which can mask the detection of proteins present in serum at low concentrations. Three commercial immunoaffinity columns were used in parallel to deplete the highly abundant proteins in serum from 60 patients with serous ovarian carcinoma and 60 non‐cancer controls. Medium and low abundance serum proteins from each serum pool were then evaluated by the quantitative proteomic technique of differential in‐gel electrophoresis. The number of protein spots that were elevated in ovarian cancer sera by at least twofold ranged from 36 to 248, depending upon the depletion and separation methods. From the 33 spots picked for MS analysis, nine different proteins were identified, including the novel candidate ovarian cancer biomarkers leucine‐rich α2 glycoprotein‐1 and ficolin 3. Western blotting validated the relative increases in serum protein levels for three of the proteins identified, demonstrating the utility of this approach for the identification of novel serum biomarkers for ovarian cancer.     

Ovarian Cancer: Biomarker Proteomic Diagnosis in Progress

Ovarian cancer lacks clear syndromes at an early stage and could result in serious problem in woman's health status. The current diagnostic approach relies on physical examination, ultrasound examination, and blood test for CA125. These approaches could not diagnose early stage ovarian cancer with high sensitivity and specificity. The present paper reviewed the efforts in screening the proteomic biomarkers for ovarian cancer. The sources of biomarkers were discussed. Then, the current techniques in proteomics were introduced. Finally, the biomarkers for ovarian cancer were summarized.     

Expanded metabolomics approach to profiling endogenous carbohydrates in the serum of ovarian cancer patients

We applied hydrophilic interaction liquid chromatography coupled with tandem mass spectrometry to the quantitative analysis of serum from 58 women, including ovarian cancer patients, ovarian benign tumor patients, and healthy controls. All of these ovarian cancer and ovarian benign tumor patients have elevated cancer antigen 125, which makes them clinically difficult to differentiate the malignant from the benign. All of the 16 endogenous carbohydrates were quantitatively detected in the human sera, of which, eight endogenous carbohydrates were significantly different (P‐value < 0.05) between the ovarian cancer and healthy control. According to the receiver operating characteristic curve analysis, arabitol was the most potentially specific biomarker for discriminating ovarian cancer from healthy control, having an area under the curve of 0.911. A panel of metabolite markers composed of maltose, maltotriose, raffinose, and mannitol was selected, which was able to discriminate the ovarian cancer from the benign ovarian tumor counterparts, with an area under concentration‐time curve value of 0.832. Endogenous carbohydrates in the expanded metabolomics approach after the global metabolic profiling are characterized and are potential biomarkers for the early diagnosis of ovarian cancer.     

AGR2, a mucinous ovarian cancer marker, promotes cell proliferation and migration

Ovarian cancer is a leading cause of death in women. Early detection of ovarian cancer is essential to decrease mortality. However, the early diagnosis of ovarian cancer is difficult due to a lack of clinical symptoms and suitable molecular diagnostic markers. Thus, identification of meaningful tumor biomarkers with potential clinical application is clearly needed. To search for a biomarker for the early detection of ovarian cancer, we identified human anterior gradient 2 (AGR2) from our systematic analysis of paired normal and ovarian tumor tissue cDNA microarray. We noted a marked overexpression of AGR2 mRNA and protein in early stage mucinous ovarian tumors compared to normal ovarian tissues and serous type ovarian tumors by Western blot analysis and immunohistochemistry. To further elucidate the role of AGR2 in ovarian tumorigenesis, stable 2774 human ovarian cancer cell lines overexpressing AGR2 were established. Forced expression of AGR2 in 2774 cells enhanced the growth and migration of ovarian cancer cells. AGR2 protein was detected in the serum of mucinous ovarian cancer patients by Western blot and ELISA analysis. Thus, AGR2 is a potential biomarker for the diagnosis of mucinous ovarian cancer and an ELISA assay may facilitate the early detection of mucinous ovarian cancer using patient serum.     

Plasmonic Gold Chip for Multiplexed Detection of Ovarian Cancer Biomarker in Urine

Human epididymal protein 4(HE4), carbohydrate antigen 125 (CA125) and osteopontin(OPN) are three key biomarkers in detecting ovarian cancer. To explore the diagnosis value of combined detection of these three biomarkers for ovarian cancer, we developed a multiplexed assay on a plasmonic gold(pGOLD) platform for measuring HE4, CA125 and OPN in urine. The receiver operator characteristic(ROC) curve was drawn, and the diagnosis values of each biomarker alone or in combination for ovarian cancer were evaluated. In the analysis to distinguish ovarian cancer from other gynecological cancers, ovarian cysts and healthy people, the sensitivities of HE4, CA125 and OPN were 72.55%, 52.82% and 68.63%, the specificity values were 95.06%, 87.65% and 90.12%, while the areas under the curve(AUC) were 0.85, 0.75 and 0.77, respectively. The sensitivity and specificity for combination detection of the three markers were 90.20% and 80.25%. The detection methods of HE4, CA125 and OPN based on plasma fluorescence enhanced chip showed good analytic and diagnostic performance, and provided a non-invasive method for the diagnosis of ovarian cancer.     

Protein complexes/aggregates as potential cancer biomarkers revealed by a nanoparticle aggregation immunoassay

Protein–protein interactions and protein complex/aggregate formation play an essential role in almost all biological functions and activities. Through a nanoparticle aggregation immunoassay, we discovered that some proteins are substantially more complexed/aggregated in cancer tissues than normal tissues. This study examined four biomarkers proteins, CA125, CEA (carcinoembryonic antigen), CA19-9 and PAP (prostatic acid phosphatase) in ovarian, colon and prostate tissue lysates. The most exciting results were observed from the PAP assay of prostate tissues: prostate cancer can be clearly distinguished from normal prostate and prostate with benign conditions such as BPH (benign prostate hyperplasia) based on the complex/aggregation level of PAP in prostate tissue lysates. The complex/aggregate level of a protein can be potential biomarkers for cancer detection and diagnosis.     

Quantitative Mass Spectrometry-Based Proteomics for Biomarker Development in Ovarian Cancer

Ovarian cancer is the most lethal gynecologic malignancy among women. Approximately 70–80% of patients with advanced ovarian cancer experience relapse within five years and develop platinum-resistance. The short life expectancy of patients with platinum-resistant or platinum-refractory disease underscores the need to develop new and more effective treatment strategies. Early detection is a critical step in mitigating the risk of disease progression from early to an advanced stage disease, and protein biomarkers have an integral role in this process. The best biological diagnostic tool for ovarian cancer will likely be a combination of biomarkers. Targeted proteomics methods, including mass spectrometry-based approaches, have emerged as robust methods that can address the chasm between initial biomarker discovery and the successful verification and validation of these biomarkers enabling their clinical translation due to the robust sensitivity, specificity, and reproducibility of these versatile methods. In this review, we provide background information on the fundamental principles of biomarkers and the need for improved treatment strategies in ovarian cancer. We also provide insight into the ways in which mass spectrometry-based targeted proteomics approaches can provide greatly needed solutions to many of the challenges related to ovarian cancer biomarker development.     

Identification of Peptide tumor markers in a tumor graft model in immunodeficient mice

The medical demand for useful biomarkers is large and still increasing. This is especially true for cancer, because for this disease adequate diagnostic markers with high specificity and sensitivity are still lacking. Despite advances in imaging technologies for early detection of cancer, peptidomic multiplex techniques evolved in recent years will provide new opportunities for detection of low molecular weight (LMW) proteome biomarker (peptides) by mass spectrometry. Improvements in peptidomics research were made based on separation of peptides and/or proteins by their physico-chemical properties in combination with mass spectrometric detection, respectively identification, and sophisticated bioinformatic tools for data analysis. To evaluate the potential of serological tumor marker detection by differential peptide display (DPD) we analyzed plasma samples from a tumor graft model. After subcutaneous injection of HCT-116 cells in immunodeficient mice and their growth to a palpable tumor, plasma samples were analyzed by DPD. The comparison of obtained mass spectrometric data allows discovery of tumor specific peptides which fit well into the biological context of cancer pathogenesis and show a strong correlation to tumor growth. The identified peptides indicate events associated with hyper-proliferation and dedifferentiation of cells from an epithelial origin, which are typical characteristics of human carcinomas. We conclude that these findings are a "proof of principle" to detect differentially expressed, tumor-related peptides in plasma of tumor-bearing mice.     

超高效液相色谱-串联质谱法鉴定猪肉的特异性肽生物标志物

以亲缘关系较近的猪、牛和羊3个物种的肌肉组织为研究对象,采用超高效液相色谱-串联质谱(UPLC-MS),筛选并确认了猪物种肉特异性肽生物标志物.3种纯肉样品经蛋白质提取、胰蛋白酶消化和UPLC-TripleTOF-MS分离鉴定,得到的总离子流图谱(TIC)与Uniprot蛋白质数据库对比分析,筛选出3个物种肉的3种高丰度同源蛋白和8种潜在的肽生物标志物;潜在的肽生物标志物经QTRAP-MS质谱的多反应模式(MRM)分析,最终确认了猪物种肉的5种肽生物标志物,其中3种肽生物标志物未见文献报道.     

In vivo magnetic resonance spectroscopy for ovarian cancer diagnostics: quantification by the fast Padé transform

Early-stage ovarian cancer has an excellent prognosis, but due mainly to late detection, ovarian cancer remains a major cause of cancer deaths among women. In vivo magnetic resonance spectroscopy (MRS) would be an excellent candidate for early ovarian cancer detection, being non-invasive, surpassing anatomic imaging to identify metabolic features of cancer, and free of ionizing radiation. However, the present meta-analysis of 13 studies indicates that with conventional Fourier-based processing, in vivo MRS insufficiently distinguished 134 cancerous from 114 benign ovarian lesions. The fast Padé transform (FPT), an advanced signal processor with high-resolution and parametric (quantification-equipped) capabilities is best qualified for MRS time signals from the ovary, as demonstrated in our earlier proof-of-concept studies. We now apply the FPT to MRS time signals encoded in vivo on a 3 T scanner, echo time of 30 ms, from a borderline serous cystic ovarian tumor. The FPT-produced total shape spectrum was better resolved than with Fourier processing. Spectra averaging through the FPT generated a denoised total shape spectrum. Subsequent parametric analysis reconstructed dense component spectra in the “usual” mode: absorption and dispersion components mixed and “ersatz” mode: reconstructed phases set to zero, thus eliminating interference effects. Numerous metabolites, including potential cancer biomarkers, were identified and quantified by the FPT, including isoleucine, valine, lipids, lactate, alanine, lysine, N-acetyl aspartate, N-acetylneuraminic acid, glutamine, choline, phosphocholine, myoinositol. Many of these are difficult or impossible to detect with Fourier plus fitting techniques for in vivo MRS of the ovary. These Padé-generated results are promising, overcoming major barriers hindering MRS from becoming a key method for non-invasively assessing ovarian lesions.     

Proteome profile of the MCF7 cancer cell line: a mass spectrometric evaluation

The development of novel proteomic technologies that will enable the discovery of disease specific biomarkers is essential in the clinical setting to facilitate early diagnosis and increase survivability rates. We are reporting a shotgun two-dimensional (2D) strong cationic exchange/reversed-phase liquid chromatography/electrospray ionization tandem mass spectrometry (SCX/RPLC/ESI-MS/MS) protocol for the analysis of proteomic constituents in cancerous cells. The MCF7 breast cancer cell line was chosen as a model system. A series of optimization steps were performed to improve the LC/MS experimental setup, sample preparation, data acquisition and database search protocols, and a data filtering strategy was developed to enable confident identification of a large number of proteins and potential biomarkers. This research has resulted in the identification of >2000 proteins using multiple filtering and p-value sorting. Approximately 1600-1900 proteins had p < 0.001, and, of these, approximately 60% were matched by >or=2 unique peptides. Alternatively, >99% of the proteins identified by >or=2 unique peptides had p < 0.001. When searching the data against a reversed database of proteins, the rate of false positive identifications was 0.1% at the peptide level and 0.4% at the protein level. The typical reproducibility in detecting overlapping proteins across replicate runs exceeded 90% for proteins matched by >or=2 unique peptides. According to their biological function, approximately 200 proteins were involved in cancer-relevant cellular processes, and over 25 proteins were previously described in the literature as putative cancer biomarkers, as they were found to be differentially expressed between normal and cancerous cell states. Among these, biomarkers such PCNA, cathepsin D, E-cadherin, 14-3-3-sigma, antigen Ki-67, TP53RK, and calreticulin were identified. These data were generated by subjecting to MS analysis approximately 42 microg of sample, analyzing 16 SCX peptide fractions, and interpreting approximately 55,000 MS2 spectra. Total MS time required for analysis was 40 h.     

2-D difference gel electrophoresis of the lung squamous cell carcinoma versus normal sera demonstrates consistent alterations in the levels of ten specific proteins

Most lung cancers are diagnosed too late for curative treatment to be possible, therefore early detection is crucial. Serum proteins are a rich source of biomarkers and have the potential to be used as diagnostic and prognostic indicators for lung cancer. In order to examine differences in serum levels of specific proteins associated with human lung squamous carcinoma, immunodepletion of albumin and five other high-abundant serum proteins followed by 2-D difference gel electrophoresis (DIGE) analysis and subsequent MS was used to generate a panel of proteins found to be differentially expressed between the cancer and normal samples. Proteins found to have increased abundance levels in squamous cell carcinoma sera compared to normal sera included apolipoprotein A-IV precursor, chain F; human complement component C3c, haptoglobin, serum amyloid A protein precursor and Ras-related protein Rab-7b. Proteins found to have lower abundance levels in squamous cell carcinoma sera compared to normal sera included alpha-2-HS glycoprotein, hemopexin precursor, proapolipoprotein, antithrombin III and SP40; 40. The data presented here demonstrate that high-abundant protein removal combined with 2-D DIGE is a powerful strategy for the discovery of potential biomarkers. The identification of lung cancer-specific biomarkers is crucial to early detection, which in turn could lead to a dramatic increase in survival rates.     

A new cytotoxic limonoid from Odontadenia macrantha from the Suriname rainforest

Bioassay‐guided fractionation of the methanol extract of Odontadenia macrantha afforded a new limonoid, odontadenin A (1) and two known triterpenoids, lupeol (2) and α‐amyrin (3). The structure of 1 was established on the basis of 1D and 2D NMR and high‐resolution fast atom bombardment mass spectrometric data. The new compound was found to possess moderate cytotoxicity against A2780, the ovarian cancer cell line. Copyright © 2003 John Wiley & Sons, Ltd.     

Multivariate analyses for biomarkers hunting and validation through on-tissue bottom-up or in-source decay in MALDI-MSI: application to prostate cancer

The large amount of data generated using matrix-assisted laser desorption/ionization mass spectrometric imaging (MALDI-MSI) poses a challenge for data analysis. In fact, generally about 1.10⁸–1.10⁹ values (m/z, I) are stored after a single MALDI-MSI experiment. This imposes processing techniques using dedicated informatics tools to be used since manual data interpretation is excluded. This work proposes and summarizes an approach that utilizes a multivariable analysis of MSI data. The multivariate analysis, such as principal component analysis–symbolic discriminant analysis, can remove and highlight specific m/z from the spectra in a specific region of interest. This approach facilitates data processing and provides better reproducibility, and thus, broadband acquisition for MALDI-MSI should be considered an effective tool to highlight biomarkers of interest. Additionally, we demonstrate the importance of the hierarchical classification of biomarkers by analyzing studies of clusters obtained either from digested or undigested tissues and using bottom-up and in-source decay strategies for in-tissue protein identification. This provides the possibility for the rapid identification of specific markers from different histological samples and their direct localization in tissues. We present an example from a prostate cancer study using formalin-fixed paraffin-embedded tissue.     

Electrochemical Biosensing in Cancer Diagnostics and Follow‐up

In cancer, screening and early detection are critical for the success of the patient's treatment and to increase the survival rate. The development of analytical tools for non‐invasive detection, through the analysis of cancer biomarkers, is imperative for disease diagnosis, treatment and follow‐up. Tumour biomarkers refer to substances or processes that, in clinical settings, are indicative of the presence of cancer in the body. These biomarkers can be detected using biosensors, that, because of their fast, accurate and point of care applicability, are prominent alternatives to the traditional methods. Moreover, the constant innovations in the biosensing field improve the determination of normal and/or elevated levels of tumour biomarkers in patients’ biological fluids (such as serum, plasma, whole blood, urine, etc.). Although several biomarkers (DNA, RNA, proteins, cells) are known, the detection of proteins and circulating tumour cells (CTCs) are the most commonly reported due to their approval as tumour biomarkers by the specialized entities and commonly accepted for diagnosis by medical and clinical teams. Therefore, electrochemical immunosensors and cytosensors are vastly described in this review, because of their fast, simple and accurate detection, the low sample volumes required, and the excellent limits of detection obtained. The biosensing strategies reported for the six most commonly diagnosed cancers (lung, breast, colorectal, prostate, liver and stomach) are summarized and the distinct phases of the sensors’ constructions (surface modification, antibody immobilization, immunochemical interactions, detection approach) and applications are discussed.     

Proteomics analysis of human breast milk by two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) coupled with mass spectrometry to assess breast cancer risk

Breast cancer (BC) is one of the most common cancers and one of the most common causes for cancer-related mortality. Discovery of protein biomarkers associated with cancer is considered important for early diagnosis and prediction of the cancer risk. Protein biomarkers could be investigated by large-scale protein investigation or proteomics, using mass spectrometry (MS)-based techniques. Our group applies MS-based proteomics to study the protein pattern in human breast milk from women with BC and controls and investigates the alterations and dysregulations of breast milk proteins in comparison pairs of BC versus control. These dysregulated proteins might be considered potential future biomarkers of BC. Identification of potential biomarkers in breast milk may benefit young women without BC, but who could collect the milk for future assessment of BC risk. Previously we identified several dysregulated proteins in different sets of human breast milk samples from BC patients and controls using gel-based protein separation coupled with MS. Here, we performed 2D-PAGE coupled with nano-liquid chromatography–tandem MS (nanoLC-MS/MS) in a small-scale study on a set of six human breast milk pairs (three BC samples vs. three controls) and we identified several dysregulated proteins that have potential roles in cancer progression and might be considered potential BC biomarkers in the future.     

Murine fecal proteomics: A model system for the detection of potential biomarkers for colorectal cancer

Tumor related products shed into the feces offer a potential source of biomarkers for the detection of colorectal cancer (CRC). Using SDS-PAGE followed by nanoflow reversed-phased LC–MS/MS to analyse fecal samples from Apc^Min/+ mice (that develop spontaneous multiple intestinal neoplasia with age) we have identified 336 proteins (115 proteins of murine origin, 201 from fecal bacteria, 18 associated with food intake and 2 of apparent parasitic origin). 75% of the murine proteins identified in this study are predicted to be extracellular or associated with the cell plasma membrane. Of these proteins, a number of the murine homologues of colorectal cancer associated proteins (CCAP) such as hemoglobin, haptoglobin, hemopexin, alpha-2-macroglobulin and cadherin-17 have been identified, demonstrating the potential of fecal proteomics for detecting potential biomarkers and paving the way for subsequent MS/MS based biomarker studies on similar human samples.     

Prerequisites for peptidomic analysis of blood samples: I. Evaluation of blood specimen qualities and determination of technical performance characteristics

Proteomics studies aiming at a detailed analysis of proteins, and peptidomics, aiming at the analysis of the low molecular weight proteome (peptidome) offer a promising approach to discover novel biomarkers valuable for different crucial steps in detection, prevention and treatment of disease. Much emphasis has been given to the analysis of blood, since this source would by far offer the largest number of meaningful biomarker applications. Blood is a complex liquid tissue that comprises cells and extra-cellular fluid. The choice of suitable specimen collection is crucial to minimize artificial occurring processes during specimen collection and preparation (e.g. cell lysis, proteolysis). After specimen collection, sample preparation for peptidomics is carried out by physical methods (filtration, gel-chromatography, precipitation) which allow for separation based on molecular size, with and without immunodepletion of major abundant proteins. Differential Peptide Display (DPD) is an offline-coupled combination of Reversed-Phase-HPLC and MALDI mass spectrometry in combination with in-house developed data display and analysis tools. Identifications of peptides are carried out by additional mass spectrometric methods (e.g. online LC-ESI-MS/MS). In the work presented here, insights into semi-quantitative mass spectrometric profiling of plasma peptides by DPD are given. This includes proper specimen selection (plasma vs. serum), sample preparation, especially peptide extraction, the determination of sensitivity (i.e. by establishing detection limits of exogenously spiked peptides), the reproducibility for individual as well as for all peptides (Coefficient of Variation calculations) and quantification (correlation between signal intensity and concentration). Finally, the implications for clinical peptidomics are discussed.     

Can glycoprofiling be helpful in detecting prostate cancer?

Glycans are chains of carbohydrates attached to proteins (glycoproteins and proteoglycans) or lipids (glycolipids). Glycosylation is a post-translational modification and glycans have a wide range of functions in the human body including involvement in oncological diseases. Change in a glycan structure can not only indicate the presence of a pathological process but, more importantly, in some cases also its stage. Thus, a glycan analysis has the potential to be an effective and reliable tool in cancer diagnostics. Lectins are proteins responsible for natural biorecognition of glycans; even carbohydrate moieties still attached to proteins or whole cells can be recognised by lectins, which makes them an ideal candidate for designing label-free biosensors for glycan analysis. This review seeks to summarise evidence that the glycoprofiling of biomarkers by lectin-based biosensors can be of significant help in detecting prostate cancer.