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

Differential Serum Peptidomics Reveal Multi-Marker Models That Predict Breast Cancer Progression

Here, we assess how the differential expression of low molecular weight serum peptides might predict breast cancer progression with high confidence. We apply an LC/MS-MS-based, unbiased 'omics' analysis of serum samples from breast cancer patients to identify molecules that are differentially expressed in stage I and III breast cancer. Results were generated using standard and machine learning-based analytical workflows. With standard workflow, a discovery study yielded 65 circulating biomarker candidates with statistically significant differential expression. A second study confirmed the differential expression of a subset of these markers. Models based on combinations of multiple biomarkers were generated using an exploratory algorithm designed to generate greater diagnostic power and accuracy than any individual markers. Individual biomarkers and the more complex multi-marker models were then tested in a blinded validation study. The multi-marker models retained their predictive power in the validation study, the best of which attained an AUC of 0.84, with a sensitivity of 43% and a specificity of 88%. One of the markers with m/z 761.38, which was downregulated, was identified as a fibrinogen alpha chain. Machine learning-based analysis yielded a classifier that correctly categorizes every subject in the study and demonstrates parameter constraints required for high confidence in classifier output. These results suggest that serum peptide biomarker models could be optimized to assess breast cancer stage in a clinical setting.

Combining serum peptide signatures with International Federation of Gynecology and Obstetrics (FIGO) risk score to predict the outcomes of patients with gestational trophoblastic neoplasia (GTN) after first-line chemotherapy

Background

Gestational trophoblastic neoplasia (GTN) is a group of clinically rare tumors that develop in the uterus from placental tissue. Currently, its satisfactory curability derives from the timely and accurately classification and refined management for patients. This study aimed to discover biomarkers that could predict the outcomes of GTN patients after first-line chemotherapy.

Methods

A total of 65 GTN patients were included in the study. Patients were divided into the good or poor outcome group and the clinical characteristics of the patients in the two groups were compared. Furthermore, the serum peptide profiles of all patients were uncovered by using weak cation exchange magnetic beads and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Feature peaks were identified by three machine learning algorithms and then models were constructed and compared using five machine learning methods. Additionally, liquid chromatography mass spectrometry was used to identify the feature peptides.

Results

Multivariate logistic regression analysis showed that the International Federation of Gynecology and Obstetrics (FIGO) risk score was associated with poor outcomes. Eight feature peaks (m/z =1287, 2042, 2862, 2932, 2950, 3240, 3277 and 6626) were selected for model construction and validation by the three algorithms. Based on the panel combining FIGO risk score and peptide serum signatures, the neural network (nnet) model showed promising performance in both the training (AUC=0.9635) and validation (AUC=0.8788) cohorts. Peaks at m/z 2042, 2862, 2932, 3240 were identified as the partial sequences of transthyretin, fibrinogen alpha chain (FGA), beta-globin and FGA, respectively.

Conclusion

We combined FIGO risk score and serum peptide signatures using the nnet method to construct the model which can accurately predict outcome of GTN patients after first-line chemotherapy. With this model, patients can be further classified and managed, and those with poor predicted outcomes can be given more attention for developing treatment failure.

Blood-based protein biomarkers in breast cancer

Breast cancer (BCa) is a significant healthcare problem on women worldwide. Thus, early detection is very important to reduce mortality. Furthermore, better BCa prognosis could improve selection of patients eligible for adjuvant therapy. New markers for early diagnosis, accurate prognosis and prediction of response to treatment are necessary to improve BCa care. The present review summarizes important aspects of the potential usefulness of modern technologies, strategies, and scientific findings in proteomic research for discovery of breast cancer-associated blood-based protein biomarkers in the clinic.

Probing the endogenous peptidomes of cancer for biomarkers: A new endeavor

The life span of cancer patients can be prolonged with appropriate therapies if detected early. Mass screening for early detection of cancer, however, requires sensitive and specific biomarkers obtainable from body fluids such as blood or urine. To date, most biomarker discovery programs focus on the proteome rather than the endogenous peptidome. It has been long-established that tumor cells and stromal cells produce tumor resident proteases (TRPs) to remodel the surrounding tumor microenvironment in support of tumor progression. In fact, proteolytic products of TRPs have been shown to correlate with malignant behavior. Being of low molecular weight, these unique peptides can pass through the endothelial barrier of the vasculature into the bloodstream. As such, the cancer peptidome has increasingly become a focus for biomarker discovery. In this review, we discuss on the various aspects of the peptidome in cancer biomarker research.

Selected reaction monitoring for colorectal cancer diagnosis using a set of five serum peptides identified by BLOTCHIP®-MS analysis

BACKGROUND

Colorectal cancer (CRC) is one of the most predominant types of cancer, and it is the fourth most common cause of cancer-related death and it is important to diagnose CRC in early stage to decrease the mortality by CRC. In our previous study, we identified a combination of five peptides as a biomarker candidate to diagnose CRC by BLOTCHIP^®-MS analysis using a set of healthy control subjects and CRC patients (stage II-IV). The aim of the present study was to validate the serum biomarker peptides reported in our previous study using a second cohort and to establish their potential usefulness in CRC diagnosis.

METHODS

A total of 56 patients with CRC (n = 14 each of stages I-IV), 60 healthy controls, and 60 patients with colonic adenoma were included in this study. The five peptides were extracted and analyzed by selected reaction monitoring using ProtoKey^® Colorectal Cancer Risk Test Kit (Protosera, Inc., Amagasaki, Japan).

RESULTS

The results clearly showed that the four CRC groups, stages I-IV, could be sufficiently discriminated from the control group and colonic polyp group. This five-peptide set could identify CRC at each stage compared to the control population in this validation cohort, including those with early-stage disease. The AUC values for each stage of CRC compared to the control population were 0.779, 0.946, 0.852, and 0.973 for stages I, II, III, and IV, respectively.

CONCLUSIONS

In this case-control validation study, we confirmed high diagnostic performance for CRC using five peptides that were identified in our previous study as serum biomarker candidates for the detection of CRC.

Contribution of the plasma and lymph Degradome and Peptidome to the MHC Ligandome

Every biological fluid, blood, interstitial fluid and lymph, urine, saliva, lacrimal fluid, nipple aspirate, and spinal fluid, contains a peptidome-degradome derived from the cellular secretome along with byproducts of the metabolic/catabolic activities of each parenchymal organ. Clement et al. (J Proteomics 78:172-187, 2013), Clement et al. (J Biol Chem 291:5576-5595, 2016), Clement et al. (PLoS One 5:e9863, 2010), Clement et al. (Trends Immunol 32:6-11, 2011), Clement et al. (Front Immunol 4:424, 2013), Geho et al. (Curr Opin Chem Biol 10, 50-55, 2006), Interewicz et al. (Lymphology 37:65‑72, 2004), Leak et al. (Proteomics 4:753‑765, 2004), Popova et al. (PLoS One 9:e110873, 2014), Zhou et al. (Electrophoresis 25:1289‑1298, 2004), D'Alessandro et al. (Shock 42:509‑517, 2014), Dzieciatkowska et al. (Shock 42:485‑498, 2014), Dzieciatkowska et al. (Shock 35:331‑338, 2011), Jordan et al. (J Surg Res 143:130‑135, 2007), Peltz et al. (Surgery 146:347‑357, 2009), Zurawel et al. (Clin Proteomics 8:1, 2011), Ling et al. (Clin Proteomics 6:175‑193, 2010), Sturm et al. (Nat Commun 4:1616, 2013). Over the last decade, qualitative and quantitative analysis of the biological fluids peptidome and degradome have provided a dynamic measurement of tissue homeostasis as well as the tissue response to pathological damage. Proteomic profiling has mapped several of the proteases and resulting degradation by-products derived from cell cycle progression, organ/tissue remodeling and cellular growth, physiological apoptosis, hemostasis, and angiogenesis. Currently, a growing interest lies in the degradome observed during pathological conditions such as cancer, autoimmune diseases, and immune responses to pathogens as a way to exploit biological fluids as liquid biopsies for biomarker discovery Dzieciatkowska et al. (Shock 42:485-498, 2014), Dzieciatkowska et al. (Shock 35:331-338, 2011), Ling et al. (Clin Proteomics 6:175-193, 2010), Ugalde et al. (Methods Mol Biol 622:3-29, 2010), Quesada et al. (Nucleic Acids Res 37:D239‑243, 2009), Cal et al. (Front Biosci 12, 4661-4669, 2007), Shen et al. (PLoS One 5:e13133, 2010a), Antwi et al. (Mol Immunol 46:2931-2937, 2009a), Antwi et al. (J Proteome Res 8:4722‑4731, 2009b), Bedin et al. (J Cell Physiol 231, 915‑925, 2016), Bery et al. (Clin Proteomics 11:13, 2014), Bhalla et al. (Sci Rep 7:1511, 2017), Fan et al. (Diagn Pathol 7:45, 2012a), Fang et al. (Shock 34:291‑298, 2010), Fiedler et al. (Clin Cancer Res 15:3812‑3819, 2009), Fredolini et al. (AAPS J 12:504‑518, 2010), Greening et al. (Enzymes 42:27‑64, 2017), He et al. (PLoS One 8:e63724, 2013), Huang et al. (Int J Gynecol Cancer 28:355‑362, 2018), Hashiguchi et al. (Med Hypotheses 73:760‑763, 2009), Liotta and Petricoin (J Clin Invest 116:26‑30, 2006), Petricoin et al. (Nat Rev Cancer 6:961‑967, 2006), Shen et al. (J Proteome Res 9:2339‑2346, 2010a), Shen et al. (J Proteome Res 5:3154‑3160, 2006), Smith (Clin Proteomics 11:23, 2014), Wang et al. (Oncotarget 8:59376‑59386, 2017), Yang et al. (Clin Exp Med 12:79‑87, 2012a), Yang et al. (J Clin Lab Anal 26:148‑154, 2012b), Yang et al. (Anat Rec (Hoboken) 293:2027‑2033, 2010), Zapico-Muniz et al. (Pancreas 39:1293‑1298, 2010), Villanueva et al. (Mol Cell Proteomics 5:1840‑1852, 2006), Robbins et al. (J Clin Oncol 23:4835‑4837, 2005), Klupczynska et al. (Int J Mol Sci 17:410, 2016). In this review, we focus on the current knowledge of the degradome/peptidome observed in two main biological fluids (plasma and lymph) during physiological and pathological conditions and its importance for immune surveillance.

CancerPDF: A repository of cancer-associated peptidome found in human biofluids

CancerPDF (Cancer Peptidome Database of bioFluids) is a comprehensive database of endogenous peptides detected in the human biofluids. The peptidome patterns reflect the synthesis, processing and degradation of proteins in the tissue environment and therefore can act as a gold mine to probe the peptide-based cancer biomarkers. Although an extensive data on cancer peptidome has been generated in the recent years, lack of a comprehensive resource restrains the facility to query the growing community knowledge. We have developed the cancer peptidome resource named CancerPDF, to collect and compile all the endogenous peptides isolated from human biofluids in various cancer profiling studies. CancerPDF has 14,367 entries with 9,692 unique peptide sequences corresponding to 2,230 unique precursor proteins from 56 high-throughput studies for ~27 cancer conditions. We have provided an interactive interface to query the endogenous peptides along with the primary information such as m/z, precursor protein, the type of cancer and its regulation status in cancer. To add-on, many web-based tools have been incorporated, which comprise of search, browse and similarity identification modules. We consider that the CancerPDF will be an invaluable resource to unwind the potential of peptidome-based cancer biomarkers. The CancerPDF is available at the web address http://crdd.osdd.net/raghava/cancerpdf/.

Plasma Peptidome as a Source of Biomarkers for Diagnosis of Cholangiocarcinoma

Cholangiocarcinoma (CCA) is the bile duct cancer which constitutes one of the important public health problems in Thailand with high mortality rate, especially in the Opisthorchis viverrini (a parasite risk factor for CCA) endemic area of the northeastern region of the country. This study aimed to identify potential biomarkers from the plasma peptidome by CCA patients. Peptides were isolated using 10 kDa cut-off filter column and the flow-through was then used as a peptidome for LC-MS/MS analysis. A total of 209 peptides were obtained. Among these, 15 peptides were concerned with signaling pathways and 12 related to metabolic, regulatory, and biosynthesis of secondary metabolite pathways. Five exclusive peptides were identified as potential biomarkers, i.e. ETS domain-containing transcription factor ERF (P50548), KIAA0220 (Q92617), phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit beta isoform isoform 1 (P42338), LP2209 (Q6XYC0), and casein kinase II subunit alpha (P19784). Three of these biomarkers are signaling related molecules. A combination of these biomarkers for CCA diagnosis is proposed.

Identification of candidate biomarker mass (m/z) ranges in serous ovarian adenocarcinoma using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry profiling

OBJECTIVE

To differentiate plasma from ovarian cancer and healthy individuals using MALDI-TOF mass spectroscopy.

MATERIALS AND METHODS

MALDI-TOF was used to generate profiles of immuno-depleted plasma samples (89 cancers and 199 healthy individuals) that were fractionated using three types of magnetic beads (HIC8, WCX and IMAC-Cu).

RESULTS

Differentially expressed mass ranges showing >1.5-2-fold change in expression from HIC8 (30), WCX (12) and IMAC-Cu (6) fractions were identified. Cross validation and recognition capability scores for the models indicated discrimination between the classes.

CONCLUSIONS

Spectral profiles can differentiate plasma samples of ovarian cancer patients from healthy individuals.

Label-free quantitative mass spectrometry reveals a panel of differentially expressed proteins in colorectal cancer

To identify potential biomarkers involved in CRC, a shotgun proteomic method was applied to identify soluble proteins in three CRCs and matched normal mucosal tissues using high-performance liquid chromatography and mass spectrometry. Label-free protein profiling of three CRCs and matched normal mucosal tissues were then conducted to quantify and compare proteins. Results showed that 67 of the 784 identified proteins were linked to CRC (28 upregulated and 39 downregulated). Gene Ontology and DAVID databases were searched to identify the location and function of differential proteins that were related to the biological processes of binding, cell structure, signal transduction, cell adhesion, and so on. Among the differentially expressed proteins, tropomyosin-3 (TPM3), endoplasmic reticulum resident protein 29 (ERp29), 18 kDa cationic antimicrobial protein (CAMP), and heat shock 70 kDa protein 8 (HSPA8) were verified to be upregulated in CRC tissue and seven cell lines through western blot analysis. Furthermore, the upregulation of TPM3, ERp29, CAMP, and HSPA8 was validated in 69 CRCs byimmunohistochemistry (IHC) analysis. Combination of TPM3, ERp29, CAMP, and HSPA8 can identify CRC from matched normal mucosal achieving an accuracy of 73.2% using IHC score. These results suggest that TPM3, ERp29, CAMP, and HSPA8 are great potential IHC diagnostic biomarkers for CRC.

Comparative serum proteomic analysis of serum diagnosis proteins of colorectal cancer based on magnetic bead separation and maldi-tof mass spectrometry

BACKGROUND

At present, the diagnosis of colorectal cancer (CRC) requires a colorectal biopsy which is an invasive procedure. We undertook this pilot study to develop an alternative method and potential new biomarkers for diagnosis, and validated a set of well-integrated tools called ClinProt to investigate the serum peptidome in CRC patients.

METHODS

Fasting blood samples from 67 patients diagnosed with CRC by histological diagnosis, 55 patients diagnosed with colorectal adenoma by biopsy, and 65 healthy volunteers were collected. Division was into a model construction group and an external validation group randomly. The present work focused on serum proteomic analysis of model construction group by ClinProt Kit combined with mass spectrometry. This approach allowed construction of a peptide pattern able to differentiate the studied populations. An external validation group was used to verify the diagnostic capability of the peptidome pattern blindly. An immunoassay method was used to determine serum CEA of CRC and controls.

RESULTS

The results showed 59 differential peptide peaks in CRC, colorectal adenoma and health volunteers. A genetic algorithm was used to set up the classification models. Four of the identified peaks at m/z 797, 810, 4078 and 5343 were used to construct peptidome patterns, achieving an accuracy of 100% (> CEA, P < 0. 05). Furthermore, the peptidome patterns could differentiate the validation group with high accuracy close to 100%.

CONCLUSIONS

Our results showed that proteomic analysis of serum with MALDI-TOF MS is a fast and reproducible approach, which may provide a novel approach to screening for CRC.

Identification alpha-2-HS-glycoprotein precursor and tubulin beta chain as serology diagnosis biomarker of colorectal cancer

BACKGROUND

Colorectal cancer (CRC) remains a major worldwide cause of cancer-related morbidity and mortality largely due to the insidious onset of the disease. The current clinical procedures utilized for disease diagnosis are invasive, unpleasant, and inconvenient. Hence, the need for simple blood tests that could be used for the early detection is crucial for its ultimate control and prevention.

METHODS

The present work is a case-control study focused on proteomic analysis of serum of healthy volunteers and CRC patients by the ClinProt profiling technology based on mass spectrometry. This approach allowed to identifying a pattern of proteins/peptides able to differentiate the studied populations. Moreover, some of peptides differentially expressed in the serum of patients as compared to healthy volunteers were identified by LTQ Orbitrap XL.

RESULTS

A Quick Classifier Algorithm was used to construct the peptidome patterns (m/z 1208, 1467, 1505, 1618, 1656 and 4215) for the identification of CRC from healthy volunteers with accuracy close to 100% (>CEA, P < 0.05). Peaks at m/z 1505 and 1618 were identified as alpha-2-HS-glycoprotein precursor and tubulin beta chain, respectively.

CONCLUSIONS

Alpha-2-HS-glycoprotein precursor and tubulin beta chain could be involved in the pathogenesis of CRC and perform as potential serology diagnosis biomarker.

VIRTUAL SLIDES

The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/4796578761089186.

Heat shock factor 2 levels are associated with the severity of ulcerative colitis

BACKGROUND AND AIMS

The morbidity of ulcerative colitis (UC) is increasing in China every year. In addition, there is a lack of accurate diagnostic indices with which to evaluate the activity of the disease. The aim of this study was to identify UC-associated proteins as biomarkers for the diagnosis, and objective assessment of disease activity.

METHODS

Differential expression of serum proteins from UC patients compared to normal controls was analyzed by two-dimensional electrophoresis (2-DE) and matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF-MS). The expression of heat shock factor 2(HSF2)in colonic mucosa in Crohn's disease, Behcet's disease, ulcerative colitis, intestinal tuberculosis, infective enteritis, intestinal lymphoma, and normal controls was investigated by immunohistochemistry (IHC). The expression of the HSF2 in colonic mucosa of UC subjects with varying severity of disease was measured by real time-PCR and Western Blots. The expression of HSF2 was inhibited by HSF2 small interfering RNA (siRNA) transfection in Caco-2 cells. The concentrations of HSF2, IL-1β, and TNF-α in serum and IL-1β, and TNF-α in the supernatants of transfected Caco-2 cells were determined by ELISA.

RESULTS

HSF2 was differentially expressed in UC patients compared to normal controls. HSF2 expression was significantly higher in the intestinal mucosa of UC patients compared to other six groups. The results of immunohistochemistry, real time-PCR, Western Blots, and ELISA showed that the expression of HSF2 increased in parallel with the severity of UC. The serum concentration of HSF2 also positively correlated with levels of IL-1β and TNF-α. After down-regulation expression of HSF2 in Caco-2 cells by RNA interference, the productions of IL-1β and TNF-α stimulated by lipopolysaccharide (LPS) increased dramatically.

CONCLUSIONS

HSF2 appears to be a potential novel molecular marker for UC activity, and may provide a basis for studies on the pathogenesis and novel therapeutic targets for UC.

Proteomic approaches in biomarker discovery: new perspectives in cancer diagnostics

Despite remarkable progress in proteomic methods, including improved detection limits and sensitivity, these methods have not yet been established in routine clinical practice. The main limitations, which prevent their integration into clinics, are high cost of equipment, the need for highly trained personnel, and last, but not least, the establishment of reliable and accurate protein biomarkers or panels of protein biomarkers for detection of neoplasms. Furthermore, the complexity and heterogeneity of most solid tumours present obstacles in the discovery of specific protein signatures, which could be used for early detection of cancers, for prediction of disease outcome, and for determining the response to specific therapies. However, cancer proteome, as the end-point of pathological processes that underlie cancer development and progression, could represent an important source for the discovery of new biomarkers and molecular targets for tailored therapies.

Serum peptidome patterns of hepatocellular carcinoma based on magnetic bead separation and mass spectrometry analysis

Background

Hepatocellular carcinoma (HCC) is one of the most common cancers in the world,and the identification of biomarkers for the early detection is a relevant target. The purpose of the study is to discover specific low molecular weight (LMW) serum peptidome biomarkers and establish a diagnostic pattern for HCC.

Methods

We undertook this pilot study using a combined application of magnetic beads with Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) technique and ClinPro Tools v2.2 to detect 32 patients with HCC, 16 patients with chronic hepatitis (CH), 16 patients with liver cirrhosis (LC) and 16 healthy volunteers.

Results

The results showed 49, 33 and 37 differential peptide peaks respectively appeared in HCC, LC and CH groups. A Supervised Neural Network (SNN) algorithm was used to set up the classification model. Eleven of the identified peaks at m/z 5247.62, 7637.05, 1450.87, 4054.21, 1073.37, 3883.64, 5064.37, 4644.96, 5805.51, 1866.47 and 6579.6 were used to construct the peptides patterns. According to the model, we could clearly distinguish between HCC patients and healthy controls as well as between LC or CH patients and healthy controls.

Conclusions

The study demonstrated that a combined application of magnetic beads with MALDI-TOF MB technique was suitable for identification of potential serum biomarkers for HCC and it is a promising way to establish a diagnostic pattern.

Virtual slides

The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1503629821958720.

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.

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.

Magnetic beads-based biological mass spectrometry technology for diagnosis of esophageal carcinoma

AIM: To detect specific protein biomarkers for esophageal cancer by comparing serum proteomic spectra between patients with esophageal cancer and healthy individuals.

METHODS: Serum samples were collected from 63 patients with esophageal cancer and 62 healthy individuals, and randomized into model construction group and validation group. WCX kit and matrix-assisted laser desorption/ ionization time-of-flight mass spectrometry (MALDI-TOF-MS) technology were used to detect serum samples from patients with esophageal cancer and healthy individuals. Flex Analysis software was used to analyze protein peaks for significant difference. Mass spectrometry data obtained were analyzed using ClinPro Tools software. The model was then built using ClinproTools2.2 software and evaluated in a blind test for reliability.

RESULTS: Twenty-four differentially expressed proteins in serum were screened by comparing serum protemic spectra between esophageal cancer patients and healthy individuals, including 12 up-regulated proteins and 9 down-regulated ones. Three proteins (654.74, 1 451.48 and 1 866.67 Dr) were obtained for developing a ClinProt model which was able to classify esophageal cancer patients and healthy individuals with a sensitivity of 93.75% (30/32) and a specificity of 90.00% (27/30). In a double blind validation, the ClinProt model yielded a sensitivity of 93.55% (29/31) and a specificity of 90.32% (28/31).

CONCLUSION: MALDI-TOF-MS combined with WCX kit technology allows directly screening differentially expressed serum protein in esophageal cancer, and these protein markers can be used for diagnosis of early esophageal cancer.

Diagnosis of colorectal cancer by MALDI-TOF-MS combined with WCX magnetic beads

AIM: To explore the difference in serum proteomic spectra between patients with colorectal cancer and healthy individuals, and to identify the specific protein biomarkers.

METHODS: Serum samples collected from 67 colorectal cancer patients and 66 healthy individuals were randomized into model construction group and validation group. Magnetic beads-based weak cation exchange (MB-WCX) kit and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) were used to profile serum proteome. Flex analysis software was used to analyze protein peaks for significant difference. A diagnostic model was then built using ClinproTools2.2software and evaluated blindly for reliability.

RESULTS: Thirty-three differentially expressed proteins in serum were screened between colorectal cancer patients and healthy individuals, including 25 up-regulated proteins and 8 down-regulated ones. Five proteins (759, 3316, 4645, 4248, and 2645 Dr) were used for building a ClinProt model which was able to classify colorectal cancer patients and healthy individuals with a sensitivity of 94.12% (32/34) and a specificity of 96.97% (32/33). A double blind validation test showed that the ClinProt model yielded a sensitivity of 93.94 (31/33) and a specificity of 96.97% (32/33).

CONCLUSION: Combination of MALDI-TOF-MS with WCX kit allows to directly screen proteins differentially expressed in the serum of patients with colorectal cancer, and these proteins can be used to diagnose early-stage colorectal cancer.