Nai-Jun Fan

Serum peptidome patterns of breast cancer based on magnetic bead separation and mass spectrometry analysis

BackgroundBreast cancer is one of the most common cancers in the world, and the identification of biomarkers for the early detection of breast cancer is a relevant target. The present study aims to determine serum peptidome patterns for screening of breast cancer.MethodsThe present work focused on the serum proteomic analysis of 36 healthy volunteers and 37 breast cancer patients using a ClinProt Kit combined with mass spectrometry (MS). This approach allows the determination of peptidome patterns that are able to differentiate the studied populations. An independent group of sera (36 healthy volunteers and 37 breast cancer patients) was used to verify the diagnostic capabilities of the peptidome patterns blindly. An immunoassay method was used to determine the serum mucin 1 (CA15-3) of validation group samples.ResultsS upport Vector Machine (SVM) Algorithm was used to construct the peptidome patterns for the identification of breast cancer from the healthy volunteers. Three of the identified peaks at m/z 698, 720 and 1866 were used to construct the peptidome patterns with 91.78% accuracy. Furthermore, the peptidome patterns could differentiate the validation group achieving a sensitivity of 91.89% (34/37) and a specitivity of 91.67% (33/36) (> CA 15–3, P < 0.05).ConclusionsThese results suggest that the ClinProt Kit combined with MS shows great potentiality for the diagnosis of breast cancer.Virtual slidesThe virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1501556838687844

Identification of the up-regulation of TP-alpha, collagen alpha-1(VI) chain, and S100A9 in esophageal squamous cell carcinoma by a proteomic method

Esophageal squamous cell carcinoma (ESCC) is one of the most common primary malignant tumor of digestive tract. However, the early diagnosis and molecular mechanisms that underlie tumor formation and progression have been progressed less. To identify new biomarkers for ESCC, we performed a comparative proteomic research. Isobaric tags for relative and absolute quantitation-based proteomic method was used to screen biomarkers between ESCC and normal. 802 non-redundant proteins were identified, 39 of which were differentially expressed with 1.5-fold difference (29 up-regulated and 10 down-regulated). Through Swiss-Prot and GO database, the location and function of differential proteins were analyzed, which are related to the biological processes of binding, cell structure, signal transduction, cell adhesion, etc. Among the differentially expressed proteins, TP-alpha, collagen alpha-1(VI) chain and S100A9 were verified to be upregulated in 77.19%, 75.44% and 59.65% of ESCC by immunohistochemistry and western-blot. Diagnostic value of these three proteins was validated. These results provide new insights into ESCC biology and potential diagnostic and therapeutic biomarkers, which suggest that TP-alpha, collagen alpha-1(VI) chain and S100A9 are potential biomarkers of ESCC, and may play an important role in tumorigenesis and development of ESCC.

Discovery and verification of gelsolin as a potential biomarker of colorectal adenocarcinoma in a Chinese population: Examining differential protein expression using an iTRAQ labelling-based proteomics approach

OBJECTIVE To identify and validate potential biomarkers of colorectal adenocarcinoma using a proteomic approach. METHODS Multidimensional liquid chromatography⁄mass spectrometry was used to analyze biological samples labelled with isobaric mass tags for relative and absolute quantitation to identify differentially expressed proteins in human colorectal adenocarcinoma and paired normal mucosa for the discovery of cancerous biomarkers. Cancerous and noncancerous samples were compared using online and offline separation. Protein identification was performed using mass spectrometry. The downregulation of gelsolin protein in colorectal adenocarcinoma samples was confirmed by Western blot analysis and validated using immunohistochemistry. RESULTS A total of 802 nonredundant proteins were identified in colorectal adenocarcinoma samples, 82 of which fell outside the expression range of 0.8 to 1.2, and were considered to be potential cancer-specific proteins. Immunohistochemistry revealed a complete absence of gelsolin expression in 86.89% of samples and a reduction of expression in 13.11% of samples, yielding a sensitivity of 86.89% and a specificity of 100% for distinguishing colorectal adenocarcinoma from normal tissue. CONCLUSIONS These findings suggest that decreased expression of gelsolin is a potential biomarker of colorectal adenocarcinoma.

Serum Peptidome Patterns of Colorectal Cancer Based on MagneticBead Separation and MALDI-TOF Mass Spectrometry Analysis

Background. Colorectal cancer (CRC) is one of the most common cancers in the world, identification of biomarkers for early detection of CRC represents a relevant target. The present study aims to determine serum peptidome patterns for CRC diagnosis. Methods. The present work focused on serum proteomic analysis of 32 health volunteers and 38 CRC by ClinProt Kit combined with mass spectrometry. This approach allowed the construction of a peptide patterns able to differentiate the studied populations. An independent group of serum (including 33 health volunteers, 34 CRC, 16 colorectal adenoma, 36 esophageal carcinoma, and 31 gastric carcinoma samples) was used to verify the diagnostic and differential diagnostic capability of the peptidome patterns blindly. An immunoassay method was used to determine serum CEA of CRC and controls. Results. A quick classifier algorithm was used to construct the peptidome patterns for identification of CRC from controls. Two of the identified peaks at m/z 741 and 7772 were used to construct peptidome patterns, achieving an accuracy close to 100% (>CEA, P < 0.05). Furthermore, the peptidome patterns could differentiate validation group with high accuracy. Conclusions. These results suggest that the ClinProt Kit combined with mass spectrometry yields significantly higher accuracy for the diagnosis and differential diagnosis of CRC.

Tubulin Beta Chain, Filamin A Alpha Isoform 1, and Cytochrome b-c1 Complex Subunit 1 As Serological Diagnostic Biomarkers of Esophageal Squamous Cell Carcinoma: A Proteomics Study

Despite the major advances in diagnosis and treatment, esophageal squamous cell carcinoma (ESCC) remains a major life-threatening disease. Early diagnosis is critical for guiding the therapeutic management of ESCC. This case-control study focused on the proteomic analysis of serum of healthy volunteers and ESCC patients using the ClinProt profiling technology based on mass spectrometry. A total of 80 healthy volunteers and 119 ESCC patients were enrolled. We identified a pattern of proteins/peptides (including m/z 1867, 2700, and 2094) and differentiated ESCC patients from healthy volunteers with sensitivity and specificity close to 100%. Using mass spectrometry (LTQ orbitrap XL), tubulin beta chain, filamin A alpha isoform 1, and cytochrome b-c1 complex subunit 1 were identified as the three differentially expressed proteins/peptides in the patient serum. These three dysregulated proteins/peptides could be involved in the pathogenesis of ESCC and may serve as putative serological diagnostic biomarkers of ESCC. We suggest that further proteomics and multi-omics research are warranted to identify novel post-genomics diagnostics that can in the future pave the way for personalized medicine for patients with ESCC, a cancer for which we currently lack an integrated battery of diagnostics in the field of oncology.

Serum peptidome patterns for early screening of esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is one of the most common cancers in the world. Early diagnosis is critical for guiding the therapeutic management of ESCC. The present study aims to determine serum peptidome patterns for diagnosing ESCC. To identify novel peptidome patterns for diagnosing ESCC, sera from 31 healthy volunteers and 32 ESCC patients were subjected to a comparative proteomic analysis using a ClinProt™ Kit combined with mass spectrometry (MS). This approach enables the determination of peptidome patterns that can differentiate between ESCC sera and sera from healthy volunteers. For further validation, the diagnostic and differential diagnostic capabilities of the peptidome patterns were verified blindly by using an independent group of sera, consisting of sera from 31 ESCC patients, 33 healthy volunteers, 38 colorectal patients, and 36 gastric cancer patients. A Quick Classifier Algorithm was used to construct the peptidome patterns for the identification of ESCC from the control samples. Five of the identified peaks at mass to charge ratios 759, 786, 1,866, 3,316, and 6,634 were used to construct the peptidome patterns with almost 100% accuracy. Furthermore, the peptidome patterns could also differentiate the validation group with high accuracy. These results suggest that the ClinProt™ Kit combined with MS achieves significantly high accuracy for ESCC diagnosis and differential diagnosis.

Identification of tubulin beta chain, thymosin beta-4-like protein 3, and cytochrome b–c1 complex subunit 1 as serological diagnostic biomarkers of gastric cancer

OBJECTIVE Despite major advances in its diagnosis and treatment, gastric cancer (GC) remains a major life-threatening disease. Treatment of the disease is further aggravated by the lack of diagnostic biomarkers that can aid in the early detection of GC and promote its favorable prognosis. The present work aims to identify novel diagnostic biomarkers for GC. DESIGN AND METHODS The present work is a case-control study that focuses on proteomic analysis of serum from healthy volunteers and GC patients using ClinProt profiling technology based on mass spectrometry. A pattern of proteins/peptides with the ability to differentiate the studied populations was identified. Deregulated proteins/peptides differentially expressed in the serum of patients compared with healthy volunteers were identified by mass spectroscopy. RESULTS A pattern of proteins/peptides consisting of four protein/peptide peaks at m/z 1467, 1867, 2701, and 2094 was identified. These protein/peptide peaks were able to differentiate the studied populations with close to 100% sensitivity and specificity. Three of the deregulated proteins/peptides at m/z 1867, 2701, and 2094 were identified by mass spectroscopy (LTQ Orbitrap XL) as tubulin beta chain, thymosin beta-4-like protein 3, and cytochrome b-c₁ complex subunit 1, respectively. CONCLUSIONS The pattern of proteins/peptides identified in the present work shows great potential for GC diagnosis. Deregulated proteins of tubulin beta chain, thymosin beta-4-like protein 3, and cytochrome b-c₁ complex subunit 1 may be involved in the pathogenesis of GC and serve as potential serological diagnostic biomarkers.

Identification of Regional Lymph Node Involvement of Colorectal Cancer by Serum SELDI Proteomic Patterns

Background. To explore the application of serum proteomic patterns for the preoperative detection of regional lymph node involvement of colorectal cancer (CRC). Methods. Serum samples were applied to immobilized metal affinity capture ProteinChip to generate mass spectra by Surface-Enhanced Laser Desorption/ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS). Proteomic spectra of serum samples from 70 node-positive CRC patients and 75 age- and gender-matched node-negative CRC patients were employed as a training set, and a classification tree was generated by using Biomarker Pattern Software package. The validity of the classification tree was then challenged with a blind test set including another 65 CRC patients. Results. The software identified an average of 46 mass peaks/spectrum and 5 of the identified peaks at m/z 3,104, 3,781, 5,867, 7,970, and 9,290 were used to construct the classification tree. The classification tree separated effectively node-positive CRC patients from node-negative CRC patients, achieving a sensitivity of 94.29% and a specificity of 100.00%. The blind test challenged the model independently with a sensitivity of 91.43% a specificity of 96.67%. Conclusions. The results indicate that SELDI-TOF-MS can correctly distinguish node-positive CRC patients from node-negative ones and show great potential for preoperative screening for regional lymph node involvement of CRC.

Association of single-nucleotide polymorphisms in the RAGE gene and its gene- environment interactions with diabetic nephropathy in Chinese patients with type 2 diabetes

Aims To investigate the association of several single nucleotide polymorphisms (SNPs) within RAGE gene and additional gene- smoking interaction with diabetic nephropathy (DN) risk in Chinese patients with type 2 diabetes mellitus (T2DM). Methods A total of 865 participants (570 males, 295 females) were selected, including 430 T2DM complicated DN patients and 435 controls (T2DM patients without DN). Generalized multifactor dimensionality reduction (GMDR) was used to screen the best interaction combination among SNPs and smoking. Logistic regression was performed to investigate impact of 4 SNPs within RAGE gene, additional gene- smoking interaction on DN risk. Results DN risk was significantly higher in carriers with the C allele of rs1800625 than those with TT genotype, adjusted OR (95%CI) =1.57 (1.16-2.17), and higher in carriers with the T allele of rs184003 than those with GG genotype, adjusted OR (95%CI) = 1.64 (1.21-2.12). GMDR model indicated a significant two-locus model (p=0.0010) involving rs1800625 and smoking, the cross-validation consistency of this two- locus model was 10/ 10, and the testing accuracy was 60.72%. We also conducted stratified analysis for the significant models in the GMDR analysis by using logistic regression. We found that current smokers with rs1800625- TC or CC genotype have the highest DN risk, compared with never- smokers with rs1800625- TT genotype, OR (95%CI) = 2.92 (1.94 -3.96), after covariates adjustment. Conclusions We found that the C allele of rs1800625 and the T allele of rs184003 within RAGE gene, interaction between rs1800625 and smoking were all associated with increased DN risk.AIMS To investigate the association of several single nucleotide polymorphisms (SNPs) within RAGE gene and additional gene- smoking interaction with diabetic nephropathy (DN) risk in Chinese patients with type 2 diabetes mellitus (T2DM). METHODS A total of 865 participants (570 males, 295 females) were selected, including 430 T2DM complicated DN patients and 435 controls (T2DM patients without DN). Generalized multifactor dimensionality reduction (GMDR) was used to screen the best interaction combination among SNPs and smoking. Logistic regression was performed to investigate impact of 4 SNPs within RAGE gene, additional gene- smoking interaction on DN risk. RESULTS DN risk was significantly higher in carriers with the C allele of rs1800625 than those with TT genotype, adjusted OR (95%CI) =1.57 (1.16-2.17), and higher in carriers with the T allele of rs184003 than those with GG genotype, adjusted OR (95%CI) = 1.64 (1.21-2.12). GMDR model indicated a significant two-locus model (p=0.0010) involving rs1800625 and smoking, the cross-validation consistency of this two- locus model was 10/ 10, and the testing accuracy was 60.72%. We also conducted stratified analysis for the significant models in the GMDR analysis by using logistic regression. We found that current smokers with rs1800625- TC or CC genotype have the highest DN risk, compared with never- smokers with rs1800625- TT genotype, OR (95%CI) = 2.92 (1.94 -3.96), after covariates adjustment. CONCLUSIONS We found that the C allele of rs1800625 and the T allele of rs184003 within RAGE gene, interaction between rs1800625 and smoking were all associated with increased DN risk.

Identification of Differentially Expressed Proteins of Normal and Cancerous Human Colorectal Tissues by Liquid Chromatograph-Mass Spectrometer Based on iTRAQ Approach

Liquid chromatograph-mass spectrometer (LC/MS) based labeled with isobaric mass tags for relative and absolute quantitation (iTRAQ) analyses were performed to identify differentially expressed proteins from normal and cancerous human colorectal tissues. Around 802 proteins were identified, 68 proteins of which were defined as differentially expressed proteins. Bioinformatics analysis indicated that these differentially expressed proteins correlated with several specific cellular processes and pathways which have relationships with pathological changes of colorectal cancer (CRC). EHD2 were selected to verify its expression patterns and localization using western blotting and immunohistochemistry respectively. LC/MS-based iTRAQ proteomic approach would provide new information about the character of CRC.