Application of serum protein fingerprint in diagnosis of papillary thyroid carcinoma

Integrated proteomic and metabolomic analysis of plasma reveals regulatory pathways and key elements in thyroid cancer

Thyroid cancer (TC) is the most common endocrine malignancy with increasing incidence in recent years. Fine-needle aspiration biopsy (FNAB), as a gold standard for the initial evaluation of thyroid nodules, fails to cover all the cytopathologic conditions resulting in overdiagnosis. There is an urgent need for a better classification of thyroid cancer from benign thyroid nodules (BTNs). Here, data independent acquisition (DIA)-based proteomics and untargeted metabolomics in plasma samples of 10 patients with TC and 15 patients with BTNs were performed. Key proteins and metabolites were identified specific to TC, and an independent cohort was used to validate the potential biomarkers using enzyme-linked immunosorbent assay (ELISA). In total, 1429 proteins and 1172 metabolites were identified. Principal component analysis showed a strong overlap at the proteomic level and a significant discrimination at the metabolomic level between the two groups, indicating a more drastic disturbance in the metabolome of thyroid cancer. Integrated analysis of proteomics and metabolomics shows glycerophospholipid metabolism and arachidonic acid metabolism as key regulatory pathways. Furthermore, a multi-omics biomarker panel was developed consisting of LCAT, GPX3 and leukotriene B4. Based on the AUC value for the discovery set, the classification performance was 0.960. The AUC value of the external validation set was 0.930. Altogether, our results will contribute to the clinical application of potential biomarkers in the diagnosis of thyroid cancer.

Novel circulating protein biomarkers for thyroid cancer determined through data-independent acquisition mass spectrometry

Background

Distinguishing between different types of thyroid cancers (TC) remains challenging in clinical laboratories. As different tumor types require different clinical interventions, it is necessary to establish new methods for accurate diagnosis of TC.

Methods

Proteomic analysis of the human serum was performed through data-independent acquisition mass spectrometry for 29 patients with TC (stages I–IV): 13 cases of papillary TC (PTC), 10 cases of medullary TC (MTC), and six cases follicular TC (FTC). In addition, 15 patients with benign thyroid nodules (TNs) and 10 healthy controls (HCs) were included in this study. Subsequently, 17 differentially expressed proteins were identified in 291 patients with TC, including 247 with PTC, 38 with MTC, and six with FTC, and 69 patients with benign TNs and 176 with HC, using enzyme-linked immunosorbent assays.

Results

In total, 517 proteins were detected in the serum samples using an Orbitrap Q-Exactive-plus mass spectrometer. The amyloid beta A4 protein, apolipoprotein A-IV, gelsolin, contactin-1, gamma-glutamyl hydrolase, and complement factor H-related protein 1 (CFHR1) were selected for further analysis. The median serum CFHR1 levels were significantly higher in the MTC and FTC groups than in the PTC and control groups (P < 0.001). CFHR1 exhibited higher diagnostic performance in distinguishing patients with MTC from those with PTC (P < 0.001), with a sensitivity of 100.0%, specificity of 85.08%, area under the curve of 0.93, and detection cut-off of 0.92 ng/mL.

Conclusion

CFHR1 may serve as a novel biomarker to distinguish PTC from MTC with high sensitivity and specificity.

Multi-omics Signatures and Translational Potential to Improve Thyroid Cancer Patient Outcome

Recent advances in high-throughput molecular and multi-omics technologies have improved our understanding of the molecular changes associated with thyroid cancer initiation and progression. The translation into clinical use based on molecular profiling of thyroid tumors has allowed a significant improvement in patient risk stratification and in the identification of targeted therapies, and thereby better personalized disease management and outcome. This review compiles the following: (1) the major molecular alterations of the genome, epigenome, transcriptome, proteome, and metabolome found in all subtypes of thyroid cancer, thus demonstrating the complexity of these tumors and (2) the great translational potential of multi-omics studies to improve patient outcome.

Comparative analysis of the serum proteome profiles of thyroid cancer: An initial focus on the lipid profile

The serum lipid profile and clinical outcomes of cancer patients are commonly correlated in a wide range of carcinomas. However, few studies have investigated the serum lipid profile of patients with thyroid cancer (TC). The present study therefore aimed to analyze the lipid profiles of patients with TC. The serum proteomes of 31 participants with stage I-IV TC were screened using Orbitrap Q Exactive Plus. Analytical data collected between November 1, 2013 and November 11, 2018 from the laboratory information system included the total cholesterol (CHO), triglyceride (TG), high-density lipoprotein-cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein A1 (ApoA1), lipoprotein (a) and apolipoprotein B (ApoB) levels that were used to validate the screening results. A total of 3875 outpatients were enrolled in this study. A number of 17 differentially expressed proteins were identified. An Ingenuity pathway analysis identified activation of the liver X receptor/retinoid X receptor (LXR/RXR) activation, which is a crucial pathway involved in lipid metabolism. The results demonstrated that the total CHO levels were significantly different between patients with TC and control groups, both in men and women. In women, the levels of TG, HDL-C, Apo A1 and LDL-C/HDL-C were significantly different between patients with TC and control groups (all P<0.05). Higher concentrations of TG and LDL-C/HDL-C were observed in the cancer group compared with the control group. However, lower levels of Apo A1 and HDL-C were observed in women from the cancer group compared with the control group. The results from the present study revealed the presence of a disordered lipid profile in patients with TC. The molecular mechanism underlying the association between lipid metabolism and cancer requires further investigation and may be used to develop novel diagnostic biomarkers and therapeutic targets in human cancers.

Serum protein expression patterns in detecting a new viral protein in HBeAg-negative chronic hepatitis B

We analysed the changes in viral protein expression in HBeAg-negative chronic hepatitis B (CHB). In total, 160 samples were obtained from individuals infected by hepatitis B virus (HBV) and divided into four groups. Group A included 71 cases of hepatitis B e antigen (HBeAg)-negative CHB, Group B included 58 cases of inactive seroconverters and Group C included 31 cases of HBeAg-positive CHB. Group D included 22 normal healthy individuals as a control. All serum samples were examined using surface enhance laser desorption/ionization time of flight-mass spectrometry (SELDI-TOF-MS). The results indicated that a peak with 4140 m/z increased markedly in Group A at 1295.55 ± 745.87, which was significantly different from that in Group B at 896.99 ± 534.86 (P = 0.013). This peak indicated a close relationship with HBV DNA replication and may contribute to pathogenesis of HBeAg-negative chronic hepatitis.

Identification of Kininogen 1 as a Serum Protein Marker of Colorectal Adenoma in Patients with a Family History of Colorectal Cancer

The serum protein markers of colorectal adenoma in patients with a family history of colorectal cancer have been rarely reported. Serum samples from colorectal adenoma patients with or without a family history of colorectal cancer and healthy controls were profiled using Matrix-Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry (MALDI-TOF-MS). The model to distinguish colorectal adenoma patients with a family history of colorectal cancer from atypical hereditary colorectal families (CRA-H) and sporadic colorectal adenoma patients without a family history of colorectal cancer (CRA-S) was established with 85.0% accuracy. The model distinguishing CRA-H from healthy individuals was established with 90.0% specificity and 86.7% sensitivity. Additionally, five peaks (2202, 5821, 3260, 2480, and 2218) showing differential expression in advanced colorectal adenoma patients with a family history of colorectal cancer were selected. The protein Kininogen 1 (KNG1) was identified in colorectal adenoma patients and validated using Western Blotting. KNG1 may be a biomarker for colorectal adenoma patients with a family history of colorectal cancer.

Identification of MST1 as a potential early detection biomarker for colorectal cancer through a proteomic approach

Colorectal cancer (CRC) is a common malignant neoplasm worldwide. It is important to identify new biomarkers for the early detection of CRC. In this study, magnetic beads and the Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) platform were used to analyse CRC and healthy control (HC) serum samples. The CRC diagnosis pattern was established to have a specificity of 94.7% and sensitivity of 92.3% in a blind test. The candidate biomarker serine/threonine kinase 4 (STK4, also known as MST1) was identified by Tandem mass spectrometry (MS/MS) and verified with western blotting and enzyme-linked immunosorbent assay (ELISA). The results indicated that there was a higher concentration of MST1 in HC subjects than stage I CRC patients for the early detection of CRC and a lower concentration in stage IV patients than in other CRC patients. The sensitivity and specificity of MST1 combined with carcinoembryonic antigen (CEA) and faecal occult blood test (FOBT) in diagnosis of colorectal cancer were 92.3% and 100%, respectively. Additionally, low MST1 expression was associated with the poor prognosis. These results illustrate that MST1 is a potential biomarker for early detection, prognosis and prediction of distant metastasis of CRC.

The proteomic 2D-DIGE approach reveals the protein voltage-dependent anion channel 2 as a potential therapeutic target in epithelial thyroid tumours

We investigated the role of VDAC2 in human epithelial thyroid tumours using proteomic 2D-DIGE analysis and qRT-PCR. We found a significant up-regulation of VDAC2 in thyroid tumours and in thyroid tumour cell lines (TPC-1 and CAL-62). We did not detect overexpression of VDAC2 in a normal thyroid cell line (Nthy-ori 3-1). Silico analysis revealed that two proteins, BAK1 and BAX, had a strong relationship with VDAC2. BAK1 gene expression showed down-regulation in thyroid tumours (follicular and papillary tumours) and in TPC-1 and CAL-62 cell lines. Transient knockdown of VDAC2 in TPC-1 and CAL-62 promoted upregulation of the BAK1 gene and protein expression, and increased susceptibility to sorafenib treatment. Overexpression of the BAK1 gene in CAL-62 showed lower sorafenib sensitivity than VDAC2 knockdown cells. We propose the VDAC2 gene as a novel therapeutic target in these tumours.

Serum profiling by mass spectrometry combined with bioinformatics for the biomarkers discovery in diffuse large B-cell lymphoma

The aim of this study was to identify potential serum biomarkers of diffuse large B-cell lymphoma (DLBCL) and to detect DLBCL therapy response biomarkers. DLBCL serum proteomic analysis was performed using the CM10 ProteinChip mass spectrometry (SELDI-TOF-MS) approach combined with bioinformatics. A total of 178 samples were analyzed in this study from untreated early stage DLBCL patients (38), patients with inflammatory lymphadenopathy (13), healthy donors (35), post-treatment non-relapsed DLBCL patients (53), and relapsed DLBCL patients (39). Model 1 formed by nine protein peaks (m/z: 6443, 5913, 6198, 4098, 7775, 9293, 5946, 5977, and 4628) could be used to distinguish DLBCL patients from healthy individuals with an accuracy of 95.89% (70/73). The diagnostic pattern constructed using the support vector machine including the nine proteins of model 1, showed a maximum Youden's Index. Model 2 formed by three protein peaks (m/z: 3942, 6639, and 4121) could be used to distinguish DLBCL patients from those with inflammatory lymphadenopathy with an accuracy of 94.12% (48/51). Model 3 formed by six protein peaks could distinguish patients with inflammatory lymphadenopathy from healthy individuals with an accuracy of 97.92% (47/48). Model 4 could be used to distinguish non-relapsed DLBCL patients from relapsed DLBCL patients with an accuracy of 84.78% (78/92). The four patterns were validated by leave-one-out cross-validation. These data demonstrate that the CM10 ProteinChip and SELDI-TOF-MS approach combined with bioinformatics can be used effectively to screen for the differential protein expression profiles of DLBCL patients and to predict the response to therapy.

Evaluation of serum diagnosis of pancreatic cancer by using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry

Proteomic methods have been widely used in disease marker discovery research. The aim of this study was to discover potential biomarkers for pancreatic cancer (PCa) using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). Crude serum samples from 132 patients with PCa and 67 healthy controls (HCs) were analyzed in duplicate using SELDI. Support vector machine (SVM) analysis of the spectra was used to generate a predictive algorithm based on proteins that were maximally differentially expressed between patients with PCa and the HCs in the training cohort. This algorithm was tested using leave-one-out cross-validation in the test cohort. From the 4 significant peaks in the training cohort, a classifier for separating patients with PCa from HCs was developed. The classifier was challenged with all samples achieving 96.67% sensitivity and 100% specificity in the training cohort and 93.1% sensitivity and 78.57% specificity in the test cohort. Additionally, the classifier correctly classified 12/12 stage Ia and 13/16 stage IIa PCa cases. The combination of the SELDI panel and CA19-9 was superior to CA19-9 alone in distinguishing individuals with PCa from the healthy subject group. These results suggest that high-throughput proteomic profiling has the capacity to provide new biomarkers for the early detection and diagnosis of PCa.

Identification of a new protein biomarker for colorectal cancer diagnosis

As one of the most common cancers, colorectal cancer (CRC) is a major public health issue worldwide. Thus, the identification of novel biomarkers to aid in the early diagnosis of CRC is crucial. The aim of the present study was to identify a novel protein biomarker for CRC, and to identify its structure. In this study, a total of 99 serum samples from 73 CRC patients and 26 healthy controls were collected and analyzed by SELDI-TOF-MS. The biomarkers were separated using HPLC and detected with MALDI-TOF-MS. The qualified peaks were ranked by p-value of non-parametric tests and the top 10 peaks displaying significant differences were selected. Among the 10 protein biomarkers, the concentration of a 3.9‑kDa protein in the serum of the CRC patients was much lower than that in the healthy controls. Therefore, the 3.9‑kDa protein was selected as a biomarker for CRC and its separation and purification were performed. The structure of the 3.9-kDa protein biomarker was determined by LC-MS/MS, and was confirmed to be a fragment of serine/theonine kinase 4 (MST1/STK4). The 3.9‑kDa protein biomarker had high sensitivity and specificity for CRC, and its potential clinical application warrants further investigation.

Proteomic profiling of follicular and papillary thyroid tumors

OBJECTIVE

Thyroid proteomics is a new direction in thyroid cancer research aiming at etiological understanding and biomarker identification for improved diagnosis.

METHODS

Two-dimensional electrophoresis was applied to cytosolic protein extracts from frozen thyroid samples (ten follicular adenomas, nine follicular carcinomas, ten papillary carcinomas, and ten reference thyroids). Spots with differential expression were revealed by image and multivariate statistical analyses, and identified by mass spectrometry.

RESULTS

A set of 25 protein spots significant for discriminating between the sample groups was identified. Proteins identified for nine of these spots were studied further including 14-3-3 protein beta/alpha, epsilon, and zeta/delta, peroxiredoxin 6, selenium-binding protein 1, protein disulfide-isomerase precursor, annexin A5 (ANXA5), tubulin alpha-1B chain, and α1-antitrypsin precursor. This subset of protein spots carried the same predictive power in differentiating between follicular carcinoma and adenoma or between follicular and papillary carcinoma, as compared with the larger set of 25 spots. Protein expression in the sample groups was demonstrated by western blot analyses. For ANXA5 and the 14-3-3 proteins, expression in tumor cell cytoplasm was demonstrated by immunohistochemistry both in the sample groups and an independent series of papillary thyroid carcinomas.

CONCLUSION

The proteins identified confirm previous findings in thyroid proteomics, and suggest additional proteins as dysregulated in thyroid tumors.

Serum proteomic analysis revealed diagnostic value of hemoglobin for nonalcoholic fatty liver disease

BACKGROUND & AIMS

Nonalcoholic fatty liver disease (NAFLD) is one of the most common chronic liver diseases worldwide. The two linked studies presented herein aimed to identify and verify new biomarkers for NAFLD.

METHODS

First, 70 serum samples were analyzed using proteomics approaches to identify potential biomarkers for NAFLD. Second, a total of 6944 initial NAFLD-free subjects were followed up for 3 years to evaluate the predictive value of hemoglobin for NAFLD.

RESULTS

In the first study, 20 differentially expressed protein peaks (11 up-regulated and nine down-regulated) were observed in NAFLD patients upon comparison to the controls. With the aid of bioinformatic tools, we established a biomarker pattern for NAFLD with a sensitivity of 89% and a specificity of 83%. Further analysis suggested a protein peak to be hemoglobin subunit alpha. In the second study, prospective analysis showed that subjects with higher baseline hemoglobin levels were associated with higher incidence of NAFLD. Cox proportional hazards regression analyses showed that the age, gender, and body mass index adjusted hazard ratio (95% CI) for subjects with baseline hemoglobin level in quintile 2, 3, 4, and 5 vs. quintile 1 was 1.36 (1.02-1.81), 1.66 (1.23-2.25), 1.76 (1.28-2.41), and 1.83 (1.33-2.53), respectively.

CONCLUSIONS

Our study showed that serum hemoglobin may have significant predictive value for NAFLD.

[Proteomic technologies for identification of serum potential biomarkers of autoimmune demyelinating polyneuropathies]

Time-of-flight MALDI mass spectrometry (MALDI-TOF-MS) profiling of blood serum of patients with Guillain-Barré syndrome (GBS, 36 samples), chronic inflammatory demyelinating polyneuropathy (CIDP, 24 samples) and practically healthy donors (HD) (35 samples) was carried out in order to identify potential biomarkers of autoimmune demyelinating polyneuropathies (ADP). To simplify the peptide-protein mixture of serum prior to MALDI-TOF-MS analysis samples were pre-fractionated on magnetic microparticles with a weak cation-exchange (MB-WCX) surface. Comparative analysis of mass spectrometric data using the classification algorithms (genetic and neural network-controlled) revealed a characteristic set of peaks, agreed change area with a high specificity and sensitivity of the differentiated mass spectrometry profiles of the blood serum of patients with DPNP and healthy donors (for GBS values of these characteristics reached 100 and 100, and for CIDP 94.1 and 100% respectively). Comparative analysis of mass spectrometric profiles of serum samples obtained from patients with GBS and CIDP, allowed to build a classification model to differentiate these diseases from each other, with a specificity of 88.9 and a sensitivity of 80%.

Detection of renal allograft dysfunction with characteristic protein fingerprint by serum proteomic analysis

This study aimed to diagnose renal allograft dysfunction with specific biomarkers by serum proteomic analysis. Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) and bioinformatics (support vector machine and leave-one cross validation) were used to analyze serum proteome. Enrolled patients included 38 biopsy-proved acute rejection (BPAR), 10 acute tubular necrosis (ATN), 24 subclinical rejection (SCR) and 29 stable control recipients verified by protocol biopsy. A characteristic protein profile can be detected in each renal allograft dysfunction group. BPAR patients were differentiated from stable patients with markers of 9710.1, 4971, 6675.5, 8563.8, 6709.2, 9319 and 4476.7 Da with high sensitivity and specificity. ATN can be clearly distinguished from BPAR and stable control. Subclinical rejection differentiated from stable control with markers of 9193.1, 2759.1, 8464.6 Da. The independent blind test yielded with high specificity and sensitivity for each group. Serum proteome analysis by SELDI-TOF MS combined with bioinformatics in renal allograft dysfunction is valuable and promising. Specific markers were detected in each group. Identification of these proteins may prove useful as diagnostic markers for allograft dysfunction and better to elucidate the mechanism of acute rejection.

New method for peptide desorption from abundant blood proteins for plasma/serum peptidome analyses by mass spectrometry

This report describes a new method for desorption of low-molecular weight (LMW) peptides from abundant blood proteins for use in subsequent mass spectrometry analyses. Heating of diluted blood serum to 98°C for 15min resulted in dissociation of LMW peptides from the most abundant blood proteins. Application of blood plasma/serum fractionation using magnetic beads with a functionalized surface followed by heating of the resultant fractions significantly increases the number of LMW peptides detected by MALDI-TOF MS, enhances the general reproducibility of mass spectrometry profiles and considerably increases the number of identified blood serum peptides by LC-MS/MS using an Agilent 6520 Accurate-Mass Q-TOF.

Analysis of urinary proteomic patterns for type 2 diabetic nephropathy by ProteinChip

OBJECTIVE

To detect urinary proteomic profiling of patients with type 2 diabetes by using ProteinChip array technology, for searching new potential biomarkers in early diagnosis of type 2 diabetic nephropathy (T2DN).

METHODS

A total of 95 urine samples from type 2 diabetic patients with normoalbuminuria (DM, n=30), microalbuminuria (DNl, n=25) and macroalbuminuria (DN2, n=20), and healthy controls (n=20) were analyzed by SELDI-TOF-MS (the surface-enhanced laser desorption/ionization time-of-flight mass spectrometry) technology combined with bioinformatics tools.

RESULTS

Over 300 proteins or peptides from 1 to 80 kDa were obtained using ProteinChip. About 40 of them with the m/z values from 2008.78 to 79176.55 Da were significantly differentiated between type 2 diabetic patients and control subjects. Four proteins of mass 2797.03, 4545.77, 4984.03 and 9083.71 Da were selected as the potential biomarkers for T2DN with sensitivity of 88% and specificity of 96.7%.

CONCLUSION

ProteinChip technology can help to discover new biomarkers and provide a novel non-invasive tool to early diagnosis of T2DN.

The evolution of biomarkers in thyroid cancer-from mass screening to a personalized biosignature

Thyroid cancer is the most common malignancy of the endocrine system. The diagnosis of thyroid nodules, made by neck examination and ultrasonography, is a common event occurring in over 50% of the patient population over the age of 50. Yet, only 5% of these patients will be diagnosed with cancer. Fine needle aspiration biopsy is the gold standard for diagnosing thyroid nodules. However, 10–15% of these biopsies are inconclusive, ultimately requiring a diagnostic thyroid lobectomy. Consequently, research in thyroid biomarkers has become an area of active interest. In the 40 years since calcitonin was first described as the biomarker for medullary thyroid cancer, new biomarkers in thyroid cancer have been discovered. Advances in genomic and proteomic technologies have defined many of these novel thyroid biomarkers. The purpose of this article is to provide a comprehensive literature review of how these biomarkers have evolved from simple screening tests into a complex array of multiple markers to help predict the malignant potential and genetic signature of thyroid neoplasms.

Elevated levels of the acute-phase serum amyloid are associated with heightened lung cancer risk

BACKGROUND

The authors investigated whether early stage lung cancer could be identified by proteomic analyses of plasma.

METHODS

For the first case-control study, plasma samples from 52 patients with lung cancer and from a group of 51 controls were analyzed by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. In a second case-control study, a classifier of 4 markers (mass-to-charge ratio, 11,681, 6843, 5607, and 8762) also was tested for validation on plasma from 16 consecutive patients with screen-detected cancer versus 406 healthy individuals. The most relevant marker was identified, and an enzyme-linked immunosorbent assay-based analysis revealed that signal intensity was correlated with concentration.

RESULTS

The classifier had a sensitivity of 94.23% and a specificity of 76.47% in the first study but lost predictive value in the second study. Nevertheless, the 11,681 cluster, which was identified as serum amyloid protein A (SAA), resulted in a multiple logistic regression model that indicated a strong association with lung cancer. When both studies were considered as a together, the odds ratio (OR) for an SAA intensity > or =0.5 was 10.27 (95% confidence interval [CI], 4.64-22.74), whereas an analysis restricted to stage I cancers (TNM classification) revealed an OR of 8.45 (95% CI, 2.76-25.83) for T1 lung cancer and 21.22 (95% CI, 5.62-80.14) for T2 lung cancer.

CONCLUSIONS

SAA levels were predictive of an elevated risk of lung cancer, supporting the general view that inflammation is implicated in lung cancer development.

Diagnosis of C4d+ Renal Allograft Acute Humoral Rejection by Urine Protein Fingerprint Analysis

This study aimed to develop urine protein fingerprint models for the diagnosis of acute rejection (AR) and complement split product positive (C4d+) acute humoral rejection (AHR) following renal allograft transplantation. Urine samples from 101 renal transplant recipients were analysed by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry combined with bioinformatics. The patients comprised 36 with stable allograft function (stable group), 10 with acute tubular necrosis (ATN) and 55 with AR (20 with C4d- acute cellular rejection [ACR] and 15 with C4d+ AHR). The ATN group was differentiated from the stable group with a sensitivity and specificity of 100% (pattern 1). The stable group was differentiated from the AR group with a specificity of 86.4% and a sensitivity of 85.4% (pattern 2). The C4d- ACR subgroup was differentiated from the C4d+ AHR subgroup with a specificity and sensitivity of 95% and 80%, respectively (pattern 3). It is concluded that urine protein fingerprint analysis can provide a noninvasive tool to diagnose AR and C4d+ AHR.

Mining novel biomarkers for prognosis of gastric cancer with serum proteomics

Background

Although gastric caner (GC) remains the second cause of cancer-related death, useful biomarkers for prognosis are still unavailable. We present here the attempt of mining novel biomarkers for GC prognosis by using serum proteomics.

Methods

Sera from 43 GC patients and 41 controls with gastritis as Group 1 and 11 GC patients as Group 2 was successively detected by Surface Enhanced Laser Desorption/ionization Time of Flight Mass Spectrometry (SELDI-TOF-MS) with Q10 chip. Peaks were acquired by Ciphergen ProteinChip Software 3.2.0 and analyzed by Zhejiang University-ProteinChip Data Analysis System (ZJU-PDAS). CEA level were evaluated by chemiluminescence immunoassay.

Results

After median follow-up periods of 33 months, Group 1 with 4 GC patients lost was divided into 20 good-prognosis GC patients (overall survival more than 24 months) and 19 poor-prognosis GC patients (no more than 24 months). The established prognosis pattern consisted of 5 novel prognosis biomarkers with 84.2% sensitivity and 85.0% specificity, which were significantly higher than those of carcinoembryonic antigen (CEA) and TNM stage. We also tested prognosis pattern blindly in Group 2 with 66.7% sensitivity and 80.0% specificity. Moreover, we found that 4474-Da peak elevated significantly in GC and was associated with advanced stage (III+IV) and short survival (p < 0.03).

Conclusion

We have identified a number of novel biomarkers for prognosis prediction of GC by using SELDI-TOF-MS combined with sophisticated bioinformatics. Particularly, elevated expression of 4474-Da peak showed very promising to be developed into a novel biomarker associated with biologically aggressive features of GC.

Proteomics in thyroid tumor research

BACKGROUND

In recent years, "OMICS" technologies have paved novel ways for the broad-scale identification of molecular signatures and signaling pathways specific to tumorigenesis. Related to this are high hopes for the discovery of biomarkers facilitating diagnosis and prognosis of cancer as well as the option for pathway-targeted tumor treatment. Among the different OMICS methods, the potential of proteomics is just beginning to emerge, and according to the current literature, the proteome is to date the most feasible tool to reflect tumor biology.

OBJECTIVE

In this review we discuss the application of proteomics to the field of thyroid tumor research.

CONTEXT

First, we provide an overview of different methods for protein expression profiling and then discuss specific requirements and challenges of thyroid proteomics. Furthermore, we summarize results of published proteomics studies on human thyroid tumors and finally explore perspectives of thyroid proteomics, which, combined with mRNA expression profiling and traditional biochemical methods, is increasingly contributing to an improved understanding of thyroid tumorigenesis and may in the future open novel avenues in thyroid cancer therapy.

Study of distinct protein profiles for early diagnosis of NSCLC using LCM and SELDI-TOF-MS

No biomarker has been available to detect early lung cancer so far. The aim of this study is to screen biomarker patterns for early diagnosis of non-small cell lung cancer (NSCLC) using laser capture microdissection (LCM) and surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF-MS). The 3 groups of the interested cells from 13 NSCLC tissues, 11 normal lung tissues (out of the 13 NSCLC patients), and 6 benign lung diseased tissues (BLD) were successfully separated by LCM, respectively, and the homogeneities of each type of the cell populations in the three groups were estimated to be over 95%. One-hundred- and twenty-three M/Z peaks were found in the NSCLCs and normal lungs, and between the two groups the relative intensity of 98 M/Z peaks was significantly different (P < 0.05) using SELDI-TOF-MS. The diagnostic pattern constructed using support vector machine (SVM) including three proteins, M/Z 4282, 3201, and 4252 Da, respectively, showed maximum Youden Index (YI). The pattern was validated by leave-one-out cross validation (LOOCV) and the results showed that the sensitivity was 100.0%, specificity 90.9%, and positive predictive value (PPV) 92.9%. In the NSCLCs and BLDs 188 M/Z peaks were determined and 54 showed statistically difference (P < 0.05). The sensitivity, specificity, and PPV of the diagnostic pattern consisting of two proteins, M/Z 3204 and 3701 Da, were all 100.0%. So, by using LCM we have successfully purified the interested cells and solved the problem of heterogeneity of lung cancer tissue. SELDI protein chip coupled with SVM could effectively screen the differentially expressional protein profiles and eventually establish biomarker patterns with high sensitivity and specificity.

Applications of SELDI-MS technology in oncology

Considerable interest, speculation and controversy have been generated utilising surface-enhanced laser desorption/ionization in conjunction with mass spectrometry (SELDI-MS) for the diagnosis, prognosis and therapeutic monitoring of cancer and offers an attractive approach to cancer biomarker discovery from tissues and biological fluids. This technology utilises a combination of mass spectrometry and chromatography to facilitate protein profiling of complex biological mixtures. Compared to some other more traditional proteomic platforms, such as 2D polyacrylamide gel electrophoresis, it has a high-throughput capability and can resolve low-mass proteins. However, a considerable number of challenging issues related to the design of studies, including reproducibility, sensitivity, specificity, variation in sample collection, processing and storage, have been reported as problematic with this technology; albeit some of these concerns could perhaps also be lauded against other proteomic approaches that have attempted to address complex protein mixtures, such as plasma. Applications, successes and limitations of SELDI-MS in both clinical and basic science arenas will be reviewed in this article.

Diagnosis of renal allograft subclinical rejection by urine protein fingerprint analysis

AIMS

This study aimed to find new biomarkers and establish urine protein fingerprint model for diagnosis of renal allograft subclinical rejection (SCR).

METHODS

A total of 73 urine samples were analyzed by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) combined with bioinformatics tools.

RESULTS

Firstly, 22 urine samples from recipients of stable graft function proved by protocol biopsies and 27 from subclinical rejection gruop were analyzed by SELDI-TOF-MS and Zhejiang University Cancer Institute-ProteinChip Data Analysis System (ZUCI-PDAS). The diagnostic pattern comprised of 4 biomarkers could differentiate SCR group from stable group with sensitivity of 81.5% and specificity of 81.8%. The remaining 14 samples from stable group and 10 samples from SCR were analyzed on the second day as an independent test set. The independent tests yielded a specificity of 71.4% and sensitivity of 90%.

CONCLUSIONS

Urine protein fingerprint analysis by SELDI-TOF-MS combined with bioinformatics can help to discover new biomarkers and provide a non-invasive tool to diagnosis of SCR.