Serum Levels of an Isoform of Apolipoprotein A-II as a Potential Marker for Prostate Cancer

Cardiovascular Complications in Patients with Prostate Cancer: Potential Molecular Connections

Cardiovascular diseases (CVDs) and complications are often seen in patients with prostate cancer (PCa) and affect their clinical management. Despite acceptable safety profiles and patient compliance, androgen deprivation therapy (ADT), the mainstay of PCa treatment and chemotherapy, has increased cardiovascular risks and metabolic syndromes in patients. A growing body of evidence also suggests that patients with pre-existing cardiovascular conditions show an increased incidence of PCa and present with fatal forms of the disease. Therefore, it is possible that a molecular link exists between the two diseases, which has not yet been unraveled. This article provides insight into the connection between PCa and CVDs. In this context, we present our findings linking PCa progression with patients' cardiovascular health by performing a comprehensive gene expression study, gene set enrichment (GSEA) and biological pathway analysis using publicly available data extracted from patients with advanced metastatic PCa. We also discuss the common androgen deprivation strategies and CVDs most frequently reported in PCa patients and present evidence from various clinical trials that suggest that therapy induces CVD in PCa patients.

Cellular, Molecular and Proteomic Characteristics of Early Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) accounts for the majority of primary liver cancers. Early detection/diagnosis is vital for the prognosis of HCC, whereas diagnosis at late stages is associated with very low survival rate. Early diagnosis is based on 6-month surveillance of the patient and the use of at least two imaging modalities. The aim of this study was to investigate diagnostic markers for the detection of early HCC based on proteome analysis, microRNAs (miRNAs) and circulating tumor cells (CTCs) in the blood of patients with cirrhosis or early or advanced HCC. We studied 89 patients with HCC, of whom 33 had early HCC and 28 were cirrhotic. CTCs were detected by real-time quantitative reverse transcription PCR and immunofluorescence using the markers epithelial cell adhesion molecule (EPCAM), vimentin, alpha fetoprotein (aFP) and surface major vault protein (sMVP). Expression of the five most common HCC-involved miRNAs (miR-122, miR-200a, miR-200b, miR-221, miR-222) was examined in serum using quantitative real time PCR (qRT-PCR). Finally, patient serum was analyzed via whole proteome analysis (LC/MS). Of 53 patients with advanced HCC, 27 (51%) had detectable CTCs. Among these, 10/27 (37%) presented evidence of mesenchymal or intermediate stage cells (vimentin and/or sMVP positive). Moreover, 5/17 (29%) patients with early HCC and 2/28 (7%) cirrhotic patients had detectable CTCs. Patients with early or advanced HCC exhibited a significant increase in miR-200b when compared to cirrhotic patients. Our proteome analysis indicated that early HCC patients present a significant upregulation of APOA2, APOC3 proteins when compared to cirrhotic patients. When taken in combination, this covers the 100% of the patients with early HCC. miR-200b, APOA2 and APOC3 proteins are sensitive markers and can be potentially useful in combination for the early diagnosis of HCC.

Construction of 2DE Patterns of Plasma Proteins: Aspect of Potential Tumor Markers

The use of tumor markers aids in the early detection of cancer recurrence and prognosis. There is a hope that they might also be useful in screening tests for the early detection of cancer. Here, the question of finding ideal tumor markers, which should be sensitive, specific, and reliable, is an acute issue. Human plasma is one of the most popular samples as it is commonly collected in the clinic and provides noninvasive, rapid analysis for any type of disease including cancer. Many efforts have been applied in searching for “ideal” tumor markers, digging very deep into plasma proteomes. The situation in this area can be improved in two ways—by attempting to find an ideal single tumor marker or by generating panels of different markers. In both cases, proteomics certainly plays a major role. There is a line of evidence that the most abundant, so-called “classical plasma proteins”, may be used to generate a tumor biomarker profile. To be comprehensive these profiles should have information not only about protein levels but also proteoform distribution for each protein. Initially, the profile of these proteins in norm should be generated. In our work, we collected bibliographic information about the connection of cancers with levels of “classical plasma proteins”. Additionally, we presented the proteoform profiles (2DE patterns) of these proteins in norm generated by two-dimensional electrophoresis with mass spectrometry and immunodetection. As a next step, similar profiles representing protein perturbations in plasma produced in the case of different cancers will be generated. Additionally, based on this information, different test systems can be developed.

Apolipoprotein A-II, a Player in Multiple Processes and Diseases

Apolipoprotein A-II (apoA-II) is the second most abundant apolipoprotein in high-density lipoprotein (HDL) particles, playing an important role in lipid metabolism. Human and murine apoA-II proteins have dissimilar properties, partially because human apoA-II is dimeric whereas the murine homolog is a monomer, suggesting that the role of apoA-II may be quite different in humans and mice. As a component of HDL, apoA-II influences lipid metabolism, being directly or indirectly involved in vascular diseases. Clinical and epidemiological studies resulted in conflicting findings regarding the proatherogenic or atheroprotective role of apoA-II. Human apoA-II deficiency has little influence on lipoprotein levels with no obvious clinical consequences, while murine apoA-II deficiency causes HDL deficit in mice. In humans, an increased plasma apoA-II concentration causes hypertriglyceridemia and lowers HDL levels. This dyslipidemia leads to glucose intolerance, and the ensuing high blood glucose enhances apoA-II transcription, generating a vicious circle that may cause type 2 diabetes (T2D). ApoA-II is also used as a biomarker in various diseases, such as pancreatic cancer. Herein, we provide a review of the most recent findings regarding the roles of apoA-II and its functions in various physiological processes and disease states, such as cardiovascular disease, cancer, amyloidosis, hepatitis, insulin resistance, obesity, and T2D.

The issues and challenges with cancer biomarkers

A biomarker is a measurable indicator used to distinguish precisely/objectively either normal biological state/pathological condition/response to a specific therapeutic intervention. The use of novel molecular biomarkers within evidence-based medicine may improve the diagnosis/treatment of disease, improve health outcomes, and reduce the disease's socio-economic impact. Presently cancer biomarkers are the backbone of therapy, with greater efficacy and better survival rates. Cancer biomarkers are extensively used to treat cancer and monitor the disease's progress, drug response, relapses, and drug resistance. The highest percent of all biomarkers explored are in the domain of cancer. Extensive research using various methods/tissues is carried out for identifying biomarkers for early detection, which has been mostly unsuccessful. The quantitative/qualitative detection of various biomarkers in different tissues should ideally be done in accordance with qualification rules laid down by the Early Detection Research Network (EDRN), Program for the Assessment of Clinical Cancer Tests (PACCT), and National Academy of Clinical Biochemistry. Many biomarkers are presently under investigation, but lacunae lie in the biomarker's sensitivity and specificity. An ideal biomarker should be quantifiable, reliable, of considerable high/low expression, correlate with the outcome progression, cost-effective, and consistent across gender and ethnic groups. Further, we also highlight that these biomarkers' application remains questionable in childhood malignancies due to the lack of reference values in the pediatric population. The development of a cancer biomarker stands very challenging due to its complexity and sensitivity/resistance to the therapy. In past decades, the cross-talks between molecular pathways have been targeted to study the nature of cancer. To generate sensitive and specific biomarkers representing the pathogenesis of specific cancer, predicting the treatment responses and outcomes would necessitate inclusion of multiple biomarkers.

Apolipoproteins and cancer

The role of apolipoproteins in cardiovascular disease has been well investigated, but their participation in cancer has only been explored in a few published studies which showed a close link with certain kinds of cancer. In this review, we focused on the function of different kinds of apolipoproteins in cancers, autophagy, oxidative stress, and drug resistance. The potential application of apolipoproteins as biomarkers for cancer diagnosis and prognosis was highlighted, together with an investigation of their potential as drug targets for cancer treatment. Many important roles of apolipoproteins and their mechanisms in cancers were reviewed in detail and future perspectives of apolipoprotein research were discussed.

The Need for Review and Understanding of SELDI/MALDI Mass Spectroscopy Data Prior to Analysis

Multiple studies have reported that surface enhanced laser desorption/ionization time of flight mass spectroscopy (SELDI-TOF-MS) is useful in the early detection of disease based on the analysis of bodily fluids. Use of any multiplex mass spectroscopy based approach as in the analysis of bodily fluids to detect disease must be analyzed with great care due to the susceptibility of multiplex and mass spectroscopy methods to biases introduced via experimental design, patient samples, and/or methodology. Specific biases include those related to experimental design, patients, samples, protein chips, chip reader and spectral analysis. Contributions to biases based on patients include demographics (e.g., age, race, ethnicity, sex), homeostasis (e.g., fasting, medications, stress, time of sampling), and site of analysis (hospital, clinic, other). Biases in samples include conditions of sampling (type of sample container, time of processing, time to storage), conditions of storage, (time and temperature of storage), and prior sample manipulation (freeze thaw cycles). Also, there are many potential biases in methodology which can be avoided by careful experimental design including ensuring that cases and controls are analyzed randomly. All the above forms of biases affect any system based on analyzing multiple analytes and especially all mass spectroscopy based methods, not just SELDI-TOF-MS. Also, all current mass spectroscopy systems have relatively low sensitivity compared with immunoassays (e.g., ELISA). There are several problems which may be unique to the SELDI-TOF-MS system marketed by Ciphergen®. Of these, the most important is a relatively low resolution (±0.2%) of the bundled mass spectrometer which may cause problems with analysis of data. Foremost, this low resolution results in difficulties in determining what constitutes a “peak” if a peak matching approach is used in analysis. Also, once peaks are selected, the peaks may represent multiple proteins. In addition, because peaks may vary slightly in location due to instrumental drift, long term identification of the same peaks may prove to be a challenge. Finally, the Ciphergen® system has some “noise” of the baseline which results from the accumulation of charge in the detector system. Thus, we must be very aware of the factors that may affect the use of proteomics in the early detection of disease, in determining aggressive subsets of cancers, in risk assessment and in monitoring the effectiveness of novel therapies.

Identification of extracellular matrix protein 1 as a potential plasma biomarker of ESCC by proteomic analysis using iTRAQ and 2D-LC-MS/MS

PURPOSE

This study was aimed to conduct a proteomics profiling analysis on plasma obtained from ESCC patients with the goal of identifying appropriate plasma protein biomarkers in the progression of ESCC.

EXPERIMENTAL DESIGN

Plasma from 28 ESCC patients and 28 healthy controls (HC) were analyzed by iTRAQ combined with 2D-LC-MS/MS. ProteinPilot software was used to identify the differentially expressed plasma proteins in ESCC compared to HC. Western blot was performed to verify the expression of selected proteins in 37 independent ESCC patients and 37 HC. Transwell and MTT assays were used to detect the biological function of ECM1 protein in vitro.

RESULTS

Nineteen (four upregulated and fifteen downregulated) proteins were identified as differentially expressed between ESCC and HC (p <0.05). Biological functions of these proteins are involved in cell adhesion, cell apoptosis and metabolic processes, visual perception and immune response. Of these, extracellular matrix 1 (ECM1) and lumican (LUM) were selected further confirmation by Western blot (p <0.05), which were consistent with the iTRAQ results. Furthermore, the migration ability of EC9706 cell line after overexpressing ECM1 was increased significantly (p <0.05). The proliferation ability of HUVEC cell was enhanced when treated with the culture supernatants of EC9706 overexpressed ECM1(p <0.05).

CONCLUSION AND CLINICAL RELEVANCE

This proteome analysis indicate that ECM1 is a potential novel plasma protein biomarker for the detection of primary ESCC and evaluation of neoplasms progression.

Label-Free Proteome Analysis of Plasma from Patients with Breast Cancer: Stage-Specific Protein Expression

Breast cancer is one of the most commonly diagnosed types of cancer among women. Breast cancer mortality rates remain high probably because its diagnosis is hampered by inaccurate detection methods. Since changes in protein expression as well as modifications in protein glycosylation have been frequently reported in cancer development, the aim of this work was to study the differential expression as well as modifications of glycosylation of proteins from plasma of women with breast cancer at different stages of disease (n = 30) compared to healthy women (n = 10). A proteomics approach was used that depleted albumin and IgG from plasma followed by glycoprotein enrichment using immobilized Moraceae lectin (frutalin)-affinity chromatography and data-independent label-free mass spectrometric analysis. Data are available via ProteomeXchange with identifier PXD003106. As result, 57,016 peptides and 4,175 proteins among all samples were identified. From this, 40 proteins present in unbound (PI—proteins that did not interact with lectin) and bound (PII—proteins that interacted with lectin) fractions were differentially expressed. High levels of apolipoprotein A-II were detected here that were elevated significantly in the early and advanced stages of the disease. Apolipoprotein C-III was detected in both fractions, and its level was increased slightly in the PI fraction of patients with early-stage breast cancer and expressed at higher levels in the PII fraction of patients with early and intermediate stages. Clusterin was present at higher levels in both fractions of patients with early and intermediate stages of breast cancer. Our findings reveal a correlation between alterations in protein glycosylation, lipid metabolism, and the progression of breast cancer.

Discovery of distinct protein profiles for polycystic ovary syndrome with and without insulin resistance by surface-enhanced laser adsorption/ionization time of flight mass spectrometry

OBJECTIVE

To screen the serum protein expression profiles in patients having polycystic ovary syndrome (PCOS) with or without insulin resistance (IR) and search for discriminatory proteins.

DESIGN

Cross-sectional study.

SETTING

Reproductive Center of Peking University Third Hospital.

PATIENT(S)

Thirty patients with PCOS with IR, 30 patients with PCOS without IR, and 30 control individuals.

INTERVENTION(S)

Fasting serum samples.

MAIN OUTCOME MEASURE(S)

Serum protein peak spectrum.

RESULT(S)

There were 27 differential protein peaks between patients with PCOS and IR and controls, 17 between patients with PCOS without IR and controls, and 19 between patients with PCOS and IR and patients without IR. Marker proteins from differentially expressed proteins were screened out with use of a support vector machine and were used to establish three diagnostic models for PCOS IR, PCOS non-IR, and IR, respectively.

CONCLUSION(S)

There were statistically significantly different serum proteomic patterns in different types of PCOS. With use of ProteinChip combined with the support vector machine, computer diagnostic models for PCOS with and without IR were set up quickly and efficiently. These discriminatory proteins may help us understand the proteomic changes in serum and find out potential biomarkers of PCOS and IR.

A list of candidate cancer biomarkers for targeted proteomics

We have compiled from literature and other sources a list of 1261 proteins believed to be differentially expressed in human cancer. These proteins, only some of which have been detected in plasma to date, represent a population of candidate plasma biomarkers that could be useful in early cancer detection and monitoring given sufficiently sensitive specific assays. We have begun to prioritize these markers for future validation by frequency of literature citations, both total and as a function of time. The candidates include proteins involved in oncogenesis, angiogenesis, development, differentiation, proliferation, apoptosis, hematopoiesis, immune and hormonal responses, cell signaling, nucleotide function, hydrolysis, cellular homing, cell cycle and structure, the acute phase response and hormonal control. Many have been detected in studies of tissue or nuclear components; nevertheless we hypothesize that most if not all should be present in plasma at some level. Of the 1261 candidates only 9 have been approved as "tumor associated antigens" by the FDA. We propose that systematic collection and large-scale validation of candidate biomarkers would fill the gap currently existing between basic research and clinical use of advanced diagnostics.

Deconvolution filters to enhance resolution of dense time-of-flight survey spectra in the time-lag optimization range

By applying time-domain filters to time-of-flight (TOF) mass spectrometry signals, we have simultaneously smoothed and narrowed spectra resulting in improved resolution and increased signal-to-noise ratios. This filtering procedure has an advantage over detailed curve fitting of spectra in the case of large dense spectra, when neither the location nor the number of mass peaks is known a priori. This time series method is directly applicable in the time lag optimization range, where point density per peak is constant. We present a systematic methodology to optimize the filters according to any desired figure of merit, illustrating the procedure by optimizing the signal-to-noise per unit bandwidth of matrix-assisted laser desorption/ionization (MALDI) data. We also introduce a nonlinear filter that reduces the spurious structure that often accompanies deconvolution filters. The net result of the application of these filters is that we can identify new structures in dense MALDI-TOF data, clearly showing small adducts to heavy biomolecules.