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Identification of prothymosin alpha (PTMA) as a biomarker for esophageal squamous cell carcinoma (ESCC) by label-free quantitative proteomics and Quantitative Dot Blot (QDB)

  • 1,
  • 1,
  • 2,
  • 3,
  • 1,
  • 1,
  • 1,
  • 1,
  • 1,
  • 1,
  • 1,
  • 4,
  • 1, 5,
  • 1, 6,
  • 7Email author and
  • 1Email author
Contributed equally
Clinical Proteomics201916:12

https://doi.org/10.1186/s12014-019-9232-6

  • Received: 21 December 2018
  • Accepted: 25 March 2019
  • Published:

Abstract

Background

Esophageal cancer (EC) is one of the malignant tumors with a poor prognosis. The early stage of EC is asymptomatic, so identification of cancer biomarkers is important for early detection and clinical practice.

Methods

In this study, we compared the protein expression profiles in esophageal squamous cell carcinoma (ESCC) tissues and adjacent normal esophageal tissues from five patients through high-resolution label-free mass spectrometry. Through bioinformatics analysis, we found the differentially expressed proteins of ESCC. To perform the rapid identification of biomarkers, we adopted a high-throughput protein identification technique of Quantitative Dot Blot (QDB). Meanwhile, the QDB results were verified by classical immunohistochemistry.

Results

In total 2297 proteins were identified, out of which 308 proteins were differentially expressed between ESCC tissues and normal tissues. By bioinformatics analysis, the four up-regulated proteins (PTMA, PAK2, PPP1CA, HMGB2) and the five down-regulated proteins (Caveolin, Integrin beta-1, Collagen alpha-2(VI), Leiomodin-1 and Vinculin) were selected and validated in ESCC by Western Blot. Furthermore, we performed the QDB and IHC analysis in 64 patients and 117 patients, respectively. The PTMA expression was up-regulated gradually along the progression of ESCC, and the PTMA expression ratio between tumor and adjacent normal tissue was significantly increased along with the progression. Therefore, we suggest that PTMA might be a potential candidate biomarker for ESCC.

Conclusion

In this study, label-free quantitative proteomics combined with QDB revealed that PTMA expression was up-regulated in ESCC tissues, and PTMA might be a potential candidate for ESCC. Since Western Blot cannot achieve rapid and high-throughput screening of mass spectrometry results, the emergence of QDB meets this demand and provides an effective method for the identification of biomarkers.

Keywords

  • Esophageal squamous cell carcinoma (ESCC)
  • Label-free quantitative proteomics
  • Prothymosin alpha (PTMA)
  • Quantitative Dot Blot (QDB)

Introduction

Esophageal cancer (EC) is one of the malignant tumors with a 5-year survival incidence of 20.9% [1, 2]. EC is ranked as the eighth most common malignant tumor with the sixth highest mortality rate worldwide. There are two histological subtypes of EC: esophageal squamous cell carcinoma (ESCC) and esophageal adeno carcinoma (EAC). ESCC often occurs in the top or middle of the esophagus, and starts in the flat thin cells that make up the lining of the esophagus. Meanwhile, EAC is most common in the lower portion of the esophagus, and starts in the glandular cells that are responsible for the production of fluids such as mucus. China is a high-risk area for EC, and more than 90% of cases are esophageal squamous cell carcinoma (ESCC) [35]. Moreover, most of the patients exhibit locally advanced or metastatic EC at the time of being diagnosed [6, 7]. Therefore, it is urgent to discover biomarkers for early clinical diagnosis to improve survival.

Esophageal cancer biomarkers have been found in saliva, blood, and urine. Sedighi et al. showed that the serum level of Matric metalloproteinase (MMP)-13 in ESCC patients were significantly higher than in the control group, and suggested that the MMP-13 was associated with increasing ESCC invasion, lymph node involvement and decreased survival rates [8]. In saliva, the miRNAs (miR-10b*, miR-144 and miR-451) were identified up-regulated expression in EC, which possessed discriminatory ability of detecting EC [9]. Although these biomarkers contribute to the early diagnosis and prognosis of EC, the EC biomarker is still in the stage of exploration and verification, with limitations of specificity and low sensitivity.

Proteomic technologies have been applied to understand tumor pathogenesis, and to discover novel targets for cancer therapy or prognosis. Combining MS-based proteomic data with integrative bioinformatics can predict protein signal network and identify more clinical relevant molecules [1012]. To date, quantitative proteomic methods have been applied in the study of various cancer, such as breast cancer, lung cancer, pancreatic cancer and gastric cancer [13]. Mass spectrometric identification of differentially expressed proteins has been a highly successful approach for finding novel cancer-specific biomarkers [14]. For more than a decade, attempts have been made to uncover valid biomarkers for the diagnosis of EC. Currently, various molecules have been identified as closely correlated with ESCC, such as transgelin (TAGLN) and proteasome activator 28-beta subunit (PA28β) [15], pituitary tumor transforming gene (PTTG) [6], transglutaminase 3 (TGM) by proteomics [2]. However, the number of proteins identified was limited in these studies and they did not provide validation of the suggested biomarkers. Therefore, it is still necessary to perform further in-depth proteomics to explore novel candidate biomarkers for EC, and to validate the findings with orthogonal techniques.

Differential proteins obtained from mass spectrometry are commonly identified by Western Blot. However, it couldn’t meet the requirements for high-throughput analysis, due to the complicated processing steps and the requirements for large amount of total protein. Recently, Quantitative Dot Blot (QDB) technology developed by our team achieves high-throughput quantitative detection with the same principle of traditional Western Blot. In addition, QDB technology has the advantages of less sample consumption, short time consumption and low cost [16]. The experiment has been successfully applied to the detection of biomarker of papillary thyroid carcinoma. With its accuracy and reliability, the QDB is a very effective method for protein detection.

The aim of this study was to investigate the protein expression profiles in ESCC tissues and adjacent normal esophageal tissues with a label-free quantitative proteomics approach through nano-liquid chromatography coupled with tandem mass spectrometry (Nano-LC–MS/MS). The differentially expressed proteins were selected and their expression trends were validated in ESCC by Western Blot, then high-throughput protein screening was achieved by QDB, and the results of QDB were verified by classical IHC experiment. This research provides a new methodological strategy for validation and identification ESCC biomarkers by combining quantitative proteomic with QDB.

Materials and methods

Tissue samples

The five patients for LC/MS analysis were all male, with the average age of 61. Samples of ESCC tissues and adjacent normal esophageal tissues were taken for mass spectrometry analysis. The 64 pairs of matched ESCC and adjacent normal tissue samples for QDB were based on a clear pathological diagnosis, which included 35 men and 29 women, with an age range of 46–73 years (mean 61 years). The above samples were obtained at the Affiliated Yantai Hospital of Binzhou Medical University. All data were obtained from patient medical records. All specimens were quickly rinsed and then frozen immediately in liquid nitrogen and then stored at − 80 °C until further processing. The tissue microarrays (TMA) (ES701 and ES1922) for immunohistochemistry analysis were purchased from the alenabio company, the total sample size reached 117 pairs after removing duplicates in two arrays (n = 14). This study was approved by the Human Research Ethics Committee of Binzhou Medical University.

Reagents

Rabbit anti-PPP1CA (CSB-PA030161) and rabbit anti-PAK2 (CSB-PA622641DSR1HU) were purchased from CUSABIO (Wuhan, China). Rabbit anti-PTMA (YN2871) and rabbit anti-HMGB-2 (YT2187) were purchased from ImmunoWay Biotechnology Company (USA). The antibody of Caveolin (AF0126), Integrin beta-1 (AF5379), Collagen alpha-2(VI) (DF3552), Leiomodin-1 (DF12160) and Vinculin (AF5122) were purchased from Affinity Biosciences (USA). Mouse anti-GAPDH monoclonal antibody (sc-32233) was purchased from Santa Cruz Biotechnology (Dallas, TX, USA). Goat anti-rabbit (127,760) and goat anti-mouse (124,227) secondary antibodies were purchased from ZSGB-BIO (Beijing, China).

Sample preparation

The 5 pairs of clinical samples were homogenized and broken with lysis buffer containing 9 M Urea, 20 mM HEPES, and protease inhibitor cocktail. The samples were centrifuged at 12,000×g for 10 min at 4 °C and supernatants retained. Then 20 μg of total protein were digested using the way of in-solution digestion. Firstly, the samples were reduced with 50 mM dithiothreitol (DTT) at 50 °C for 15 min, then alkylated with 50 mM iodoacetamide (IAA) for 15 min in darkness, and then diluted 4 times with digestion buffer (50 mM NH4HCO3, pH 8.0). The proteins were digested by Trypsin with a final concentration of 5% (w/w), then incubated at 37 °C overnight. The reaction was stopped by diluting the sample 1:1 with trifluoroacetic acid (TFA) in acetonitrile (ACN) and Milli-Q water (1/5/94 v/v). Finally, peptides were desalted using Pierce C18 Spin Columns and dried completely in a vacuum centrifuge.

LC–MS/MS

The peptides were dissolved in 20 μL 0.5% TFA in 5% ACN and analyzed using QExactive Plus Orbitrap™ mass spectrometer (Thermo Fisher Scientific, Bremen, Germany) coupled with the liquid chromatography system (EASY-nLC 1000, Thermo Fisher Scientific, Bremen, Germany). A 85-min LC gradient was applied, with a binary mobile phase system of buffer A (0.1% formic acid) and buffer B (80% acetonitrile with 0.1% formic acid) at a flow rate of 250 nL/min. In MS analysis, peptides were loaded onto the 2 cm EASY-column precolumn (1D 100 μm, 5 μm, C18, Thermo Fisher Scientific), and eluted at a 10 cm EASY-column analytical column (1D 75 μm, 3 μm, C18, Thermo Fisher Scientific). For information data dependent analysis (DDA), full scan MS spectra were executed in the m/z range 150–2000 at a resolution of 70,000. The peptides elution was performed with a linear gradient from 4 to 100% ACN at the speed 250 nL/min in 90 min. Then the top 10 precursors were dissociated into fragmentation spectra by high collision dissociation (HCD) in positive ion mode.

Proteomic data processing

The acquired data were analyzed by using Maxquant (version 1.5.0.1) against the UniProt Homo sapiens database. The searching parameters were set as maximum 10 and 5 ppm error tolerance for the survey scan and MS/MS analysis, respectively. The enzyme was trypsin, and two missed cuts were allowed. The max number of modifications per peptide is 5. Using the Label-free quantification (LFQ), the LFQ minimum ratio count was set to 2. The FDR (false discovery rate) was set to 1% for the peptide spectrum matches (PSMs) and protein quantitation. Gene ontology and protein class analysis were performed with the PANTHER system (http://pantherdb.org/). Meanwhile, the heat map of significantly different proteins was screened by using Morpheus (https://software.broadinstitute.org/morpheus). The protein–protein interaction analysis of the differently expressed proteins was performed by STRING (https://string-db.org/).

Western blot (WB)

Tissues lysates were prepared by using highly efficient RIPA lysis buffer including PMSF (Phenylmethanesulfonyl fluoride). The total proteins were quantified by BCA protein assay kit and then separated by sodium dodesyl sulphate–polyacrylamide gel electrophoresis (SDS-PAGE). Equal amounts of protein were separated by 6%, 15% and 12% SDS-PAGE, respectively. Subsequently, proteins were transferred to a PVDF membrane and then blocked with TBS (pH 7.4) containing 0.05% Tween 20 and 5% nonfat milk. Next, the membranes were incubated with rabbit anti-PTMA (1:1000), rabbit anti-HMGB-2 (1:500), rabbit anti- PPP1CA (1:1000), rabbit anti-PAK2 (1:1000), and mouse anti-GAPDH (1:1000) antibodies at 4 °C overnight, respectively. The other five antibodies (Caveolin, Integrin beta-1, Collagen alpha-2(VI), Leiomodin-1 and Vinculin) were diluted in a ratio of 1:200. After washing, membranes were incubated with goat anti-rabbit (1:2000) and goat anti-mouse (1:2000) secondary antibodies at room temperature for 1 h. The ECL system was used to detect protein expression.

QDB

The total proteins were quantified by BCA protein assay kit and then validated by Quantitative Dot Blot (QDB). Firstly, we determined the linear range of PTMA of the QDB analysis, through the testing of series of concentrations including 0, 0.25, 0.5, 1, 2 and 4 μg/μL. After that, equal amounts of protein were loaded. The sample was incubated at 37 °C for 15 min or until the membrane was completely dried. To block the plate, the QDB plate was dipped in 20% methanol. The plate was then washed with TBST, followed by 5% fat-free milk under constant shaking at room temperature for 1 h. After washing with TBST, the QDB plate was placed in a 96 well plate and 100 μL of primary antibodies was separately added to each individual well and shaken overnight at 4 °C. After washing the QDB plate, 100 μL of the secondary antibody was added to each well and incubated for 1 h at room temperature with shaking. Samples were washed with TBST and detected with the ECL substrate using a Tecan Infiniti 200 pro microplate reader. For each sample, a triplicate measurement was performed, and the average value was obtained. The relative quantitation of each PTMA protein in the lysates was then calculated.

Immunohistochemistry (IHC)

The PTMA expression was detected by IHC in tissue microarrays (TMA) (ES701, ES1922). Firstly, the tissue microarrays were heated at 60 °C for 30 min, then deparaffinized and hydrated with xylol and gradient alcohol, respectively. Next, the antigen retrieval was accomplished by boiling the TMAs for 10 min in citrate buffer (0.01 M, pH 6.0). After cooling at room temperature, the microarrays were treated with 3% hydrogen peroxide for 30 min at 37 °C. The samples were blocked with bovine serum albumin for 30 min at 37 °C, then the PTMA antibody (YN2871, ImmunoWay; dilution 1:50) were incubated overnight at 4 °C in a moist chamber. After using the Histostain-SP (Streptavidin–Peroxidase) kit (SP-0023) as the secondary antibody following the recommendation from the manufacture, operation manual, the samples were washed with PBS (0.01 M, pH 7.2–7.4). Finally, the immunoreactivity was detected by DAB Horseradish Peroxidase Color Development Kit.

Statistics analysis

The WB data was analyzed by means and standard deviation for four independent experiments. The other data was compared between esophageal cancer tissues and adjacent normal esophageal tissues using the two-tailed paired Student’s t test. All statistical analyses were performed by using the statistical software SPSS v20.0 (Chicago, Illinois, USA). P < 0.05 was considered statistically significant.

Results

Identification of differently expressed proteins

The clinical information of the five patients was summarized in Table 1. The five pairs of cancer tissues and adjacent normal tissues were analyzed by label-free mass spectrometry. Total 2297 proteins were identified and 308 proteins with significant differences were selected. Among these proteins, 102 proteins were expressed only in ESCC tissues (Table 2), 155 proteins were significantly up-regulated (Table 3) and 40 proteins were down-regulated in ESCC tissues (Table 4) (P < 0.05). Using the PANTHER classification system, we analyzed the biological significance of these proteins including the cellular component, molecular function and biological process (Fig. 1). The majority of proteins belonged to cell part proteins (37.3%) and organelle proteins (30.1%), possessed the ability of binding (41.8%) and catalytic activity (25.8%), and involved in the cellular process (29.6%), metabolic process (20.2%), cellular component organization or biogenesis (16.3%).
Table 1

The clinical features of ESCC patients for mass spectrometry

No.

Gender

Age

Organ/anatomic site

Grade

TNM

1

Male

69

Mid-thoracic esophagus

II

T2N0MO

2

Male

61

esophagus

I

T1N0M0

3

Male

59

Middle-lower esophagus

II

T1N0M0

4

Male

52

Mid-thoracic esophagus

III

T3N0M0

5

Male

64

Middle segment of esophagus

II

T2N1M1

Table 2

List of 102 proteins that were uniquely identified in ESCC tissues

Protein IDs

Protein names

P30050

60S ribosomal protein L12

P25788

Proteasome subunit alpha type-3

Q15254

Prothymosin alpha

P12956

X-ray repair cross-complementing protein 6

O15371

Eukaryotic translation initiation factor 3 subunit D

Q59FF0

Staphylococcal nuclease domain-containing protein 1

Q06323

Proteasome activator complex subunit 1

Q15366

Poly(rC)-binding protein 2;Poly(rC)-binding protein 3

Q99729

Heterogeneous nuclear ribonucleoprotein A/B

P62273

40S ribosomal protein S29

O15144

Actin-related protein 2/3 complex subunit 2

Q07955

Serine/arginine-rich splicing factor 1

Q13838

Spliceosome RNA helicase DDX39B

Q14666

Keratin, type I cytoskeletal 17

P00491

Purine nucleoside phosphorylase

P13667

Protein disulfide-isomerase A4

P49755

Transmembrane emp24 domain-containing protein 10

P34932

Heat shock 70 kDa protein 4

P62750

60S ribosomal protein L23a

Q9BRL6

Serine/arginine-rich splicing factor 2

P26583

High mobility group protein B2

O60716

Catenin delta-1

Q13151

Heterogeneous nuclear ribonucleoprotein A0

P62244

40S ribosomal protein S15a

Q8TBK5

60S ribosomal protein L6

P39656

Dolichyl-diphosphooligosaccharide–protein glycosyltransferase 48 kDa subunit

Q53GA7

Tubulin alpha-1C chain

Q92598

Heat shock protein 105 kDa

Q92928

Ras-related protein Rab-1B

Q59F66

Probable ATP-dependent RNA helicase DDX17

P46782

40S ribosomal protein S5

P78417

Glutathione S-transferase omega-1

P23526

Adenosylhomocysteinase

P62081

40S ribosomal protein S7

P11413

Glucose-6-phosphate 1-dehydrogenase

P67809

Nuclease-sensitive element-binding protein 1

Q08211

ATP-dependent RNA helicase A

P17980

26S protease regulatory subunit 6A

Q59EG8

26S proteasome non-ATPase regulatory subunit 2

P27695

DNA-(apurinic or apyrimidinic site) lyase, mitochondrial

P61019

Ras-related protein Rab-2A

P28066

Proteasome subunit alpha type

P49588

Alanine–tRNA ligase, cytoplasmic

O14818

Proteasome subunit alpha type

Q8NB80

Serine/arginine-rich splicing factor 7

Q86UE4

Protein LYRIC

P83731

60S ribosomal protein L24

B4DDM6

Mitotic checkpoint protein BUB3

P20618

Proteasome subunit beta type

P31942

Heterogeneous nuclear ribonucleoprotein H3

Q13177

Serine/threonine-protein kinase PAK 2

P53621

Coatomer subunit alpha;Xenin;Proxenin

Q04760

Lactoylglutathione lyase

Q99439

Calponin;Calponin-2

P62266

40S ribosomal protein S23

P62857

40S ribosomal protein S28

O43852

Calumenin

Q567R6

Single-stranded DNA-binding protein

P22234

Multifunctional protein ADE2

P62195

26S protease regulatory subunit 8

P98179

RNA-binding protein 3

P46781

40S ribosomal protein S9

Q96FW1

Ubiquitin thioesterase OTUB1

O14979

Heterogeneous nuclear ribonucleoprotein D-like

P51571

Translocon-associated protein subunit delta

P05455

Lupus La protein

Q96AE4

Far upstream element-binding protein 1

P17844

Probable ATP-dependent RNA helicase DDX5

P52597

Heterogeneous nuclear ribonucleoprotein F

P60866

40S ribosomal protein S20

Q13148

TAR DNA-binding protein 43

P62136

Serine/threonine-protein phosphatase PP1-alpha catalytic subunit

P07602

Prosaposin

P62633

Cellular nucleic acid-binding protein

Q6FI03

Ras GTPase-activating protein-binding protein 1

P51572

B-cell receptor-associated protein 31

P27635

60S ribosomal protein L10

Q09028

Histone-binding protein RBBP4

Q9UMS4

Pre-mRNA-processing factor 19

P62318

Small nuclear ribonucleoprotein Sm D3

Q15056

Eukaryotic translation initiation factor 4H

P38159

RNA-binding motif protein, X chromosome

Q1KMD3

Heterogeneous nuclear ribonucleoprotein U-like protein 2

P17987

T-complex protein 1 subunit alpha

Q13263

Transcription intermediary factor 1-beta

P29590

Protein PML

Q92499

ATP-dependent RNA helicase DDX1

P51858

Hepatoma-derived growth factor

P60468

Protein transport protein Sec61 subunit beta

Q13185

Chromobox protein homolog 3

P55209

Nucleosome assembly protein 1-like 1

P50454

Serpin H1

P42704

Leucine-rich PPR motif-containing protein, mitochondrial

P61204

ADP-ribosylation factor 1;ADP-ribosylation factor 3

Q9HB71

Calcyclin-binding protein

P11166

Solute carrier family 2, facilitated glucose transporter member 1

Q9Y265

RuvB-like 1

P62807

Histone H2B

Q9UK76

Hematological and neurological expressed 1 protein

P12004

Proliferating cell nuclear antigen

P43243

Matrin-3

P62333

26S protease regulatory subunit 10B

Table 3

List of 155 proteins that were overexpressed in ESCC tissues

IDs

Log ratio

P value

Protein names

P60842

7.814

0.000

Eukaryotic initiation factor 4A-I

P23396

6.277

0.000

40S ribosomal protein S3

P52272

7.623

0.000

Heterogeneous nuclear ribonucleoprotein M

P43686

10.195

0.000

26S protease regulatory subunit 6B

P14866

8.871

0.000

Heterogeneous nuclear ribonucleoprotein L

P53675

5.484

0.001

Clathrin heavy chain;Clathrin heavy chain 1

P84090

11.171

0.001

Enhancer of rudimentary homolog

P22392

12.881

0.001

Nucleoside diphosphate kinase

Q01105

7.330

0.001

Protein SET;Protein SETSIP

P84103

7.084

0.001

Serine/arginine-rich splicing factor 3

P07900

9.462

0.001

Heat shock protein HSP 90-alpha

Q01518

2.076

0.001

Adenylyl cyclase-associated protein

Q15233

22.489

0.001

Non-POU domain-containing octamer-binding protein

P51149

7.249

0.001

Ras-related protein Rab-7a

Q05CK9

9.797

0.001

Heterogeneous nuclear ribonucleoprotein Q

P10809

9.235

0.001

60 kDa heat shock protein, mitochondrial

P68371

1.935

0.001

Tubulin beta-4B chain

P37802

3.333

0.001

Transgelin-2

P62826

6.962

0.002

GTP-binding nuclear protein Ran

P25398

4.816

0.002

40S ribosomal protein S12

P57723

4.611

0.002

Poly(rC)-binding protein 1

Q12906

28.577

0.002

Interleukin enhancer-binding factor 3

P08865

5.309

0.002

40S ribosomal protein SA

P63244

6.237

0.002

Guanine nucleotide-binding protein subunit beta-2-like 1

P14314

14.510

0.002

Glucosidase 2 subunit beta

P60900

9.105

0.002

Proteasome subunit alpha type

P06748

12.711

0.002

Nucleophosmin

P05388

8.012

0.002

60S acidic ribosomal protein P0

P46940

3.595

0.003

Ras GTPase-activating-like protein IQGAP1

P61978

10.444

0.003

Heterogeneous nuclear ribonucleoprotein K

P05141

2.807

0.003

ADP/ATP translocase 2

Q6LDX7

13.007

0.003

Tyrosine-protein kinase receptor

Q99623

14.381

0.003

Prohibitin-2

P06733

2.361

0.003

Alpha-enolase

P13639

5.459

0.003

Elongation factor 2

Q15084

43.388

0.003

Protein disulfide-isomerase A6

Q96DV6

3.944

0.003

40S ribosomal protein S6

Q66K53

9.606

0.003

HNRPA3 protein

P15880

4.502

0.003

40S ribosomal protein S2

P39019

5.898

0.004

40S ribosomal protein S19

P63104

2.043

0.004

14-3-3 protein zeta/delta

P22626

6.638

0.004

Heterogeneous nuclear ribonucleoproteins A2/B1

P30101

6.086

0.005

Protein disulfide-isomerase

P25786

8.420

0.005

Proteasome subunit alpha type-1

P11940

12.404

0.006

Polyadenylate-binding protein

P16401

4.877

0.006

Histone H1.5

P07237

5.704

0.006

Protein disulfide-isomerase

Q16777

10.160

0.006

Histone H2A type 2-C;Histone H2A type 2-A

P05386

5.889

0.006

60S acidic ribosomal protein P1

P31948

11.491

0.006

Stress-induced-phosphoprotein 1

P31946

2.156

0.007

14-3-3 protein beta/alpha

P68104

2.558

0.007

Elongation factor 1-alpha

P00338

1.590

0.007

L-lactate dehydrogenase

Q14103

6.189

0.007

Heterogeneous nuclear ribonucleoprotein D0

P38646

10.649

0.007

Stress-70 protein, mitochondrial

P26641

19.766

0.007

Elongation factor 1-gamma

O75347

4.168

0.008

Tubulin-specific chaperone A

P09429

5.878

0.008

High mobility group protein B1

P62942

7.427

0.008

Peptidyl-prolyl cis–trans isomerase FKBP1A

Q9NUV1

7.289

0.008

Cytosolic non-specific dipeptidase

P11021

7.467

0.008

78 kDa glucose-regulated protein

P11142

2.320

0.008

Heat shock cognate 71 kDa protein

P02533

5.320

0.008

Keratin, type I cytoskeletal 14

P30040

6.657

0.008

Endoplasmic reticulum resident protein 29

P50990

11.713

0.008

T-complex protein 1 subunit theta

P46783

9.508

0.008

40S ribosomal protein S10

P31943

14.091

0.008

Heterogeneous nuclear ribonucleoprotein H

P19338

13.679

0.009

Nucleolin

P14625

13.173

0.009

Endoplasmin

Q92597

4.464

0.009

Protein NDRG1

P26599

19.501

0.009

Polypyrimidine tract-binding protein 1

P68363

2.317

0.009

Tubulin alpha-1B chain

P61604

9.723

0.009

10 kDa heat shock protein, mitochondrial

P08238

8.920

0.009

Heat shock protein HSP 90-beta

Q00839

15.338

0.009

Heterogeneous nuclear ribonucleoprotein U

P04843

64.275

0.009

Dolichyl-diphosphooligosaccharide–protein glycosyltransferase subunit 1

P09651

10.489

0.010

Heterogeneous nuclear ribonucleoprotein A1

P22314

3.758

0.010

Ubiquitin-like modifier-activating enzyme 1

P30085

3.180

0.010

UMP-CMP kinase

P23246

39.026

0.011

Splicing factor, proline- and glutamine-rich

P29692

13.726

0.011

Elongation factor 1-delta

P27797

7.508

0.011

Calreticulin

Q06830

1.788

0.011

Peroxiredoxin-1

P84243

2.541

0.012

Histone H3

P05023

15.342

0.012

Sodium/potassium-transporting ATPase subunit alpha-1

Q14974

3.995

0.014

Importin subunit beta-1

P30154

2.882

0.014

Serine/threonine-protein phosphatase 2A

P49448

5.013

0.015

Glutamate dehydrogenase

P20700

14.379

0.015

Lamin-B1

P55072

6.054

0.016

Transitional endoplasmic reticulum ATPase

P35579

8.278

0.016

Myosin-9

P40227

8.241

0.016

T-complex protein 1 subunit zeta

P13010

223.628

0.017

X-ray repair cross-complementing protein 5

Q03252

12.919

0.017

Lamin-B2

P27824

9.105

0.017

Calnexin

P02545

1.376

0.017

Prelamin-A/C;Lamin-A/C

P67936

10.102

0.017

Tropomyosin alpha-4 chain

P04908

2.018

0.018

Histone H2A

P13797

5.684

0.019

Plastin-3

P52907

3.377

0.019

F-actin-capping protein subunit alpha-1

P63241

4.197

0.019

Eukaryotic translation initiation factor 5A

P62491

3.628

0.019

Ras-related protein Rab-11A;Ras-related protein Rab-11B

P45880

2.304

0.020

Voltage-dependent anion-selective channel protein 2

P05387

4.257

0.020

60S acidic ribosomal protein P2

Q5SRT3

3.484

0.021

Chloride intracellular channel protein

P07437

3.687

0.021

Tubulin beta chain

P23284

8.401

0.022

Peptidyl-prolyl cis–trans isomerase

P18124

5.442

0.022

60S ribosomal protein L7

P07355

1.909

0.022

Annexin;Annexin A2

P46777

12.124

0.023

60S ribosomal protein L5

Q99714

1.923

0.023

3-hydroxyacyl-CoA dehydrogenase type-2

O75531

9.745

0.024

Barrier-to-autointegration factor

Q14697

21.165

0.025

Neutral alpha-glucosidase AB

P62263

6.347

0.025

40S ribosomal protein S14

P0DMV9

2.049

0.026

Heat shock 70 kDa protein 1B

P29034

6.458

0.026

Protein S100-A2

P62888

2.893

0.026

60S ribosomal protein L30

Q6IBT3

23.335

0.027

T-complex protein 1 subunit eta

P47756

2.818

0.027

F-actin-capping protein subunit beta

P35222

7.555

0.028

Catenin beta-1

P07339

5.983

0.029

Cathepsin D

Q86SZ7

4.151

0.029

Proteasome activator complex subunit 2

P15311

3.903

0.029

Ezrin;Tyrosine-protein kinase receptor

P59665

4.537

0.029

Neutrophil defensin 1

P09960

5.492

0.030

Leukotriene A-4 hydrolase

P63220

4.048

0.030

40S ribosomal protein S21

Q16658

114.974

0.031

Fascin

P07954

5.399

0.032

Fumarate hydratase, mitochondrial

P54819

4.652

0.034

Adenylate kinase 2, mitochondrial

P07737

1.223

0.034

Profilin-1

P63313

5.261

0.034

Thymosin beta-10

P21796

3.716

0.034

Voltage-dependent anion-selective channel protein 1

P61247

12.449

0.035

40S ribosomal protein S3a

P14618

1.508

0.035

Pyruvate kinase

P61626

4.029

0.036

Lysozyme;Lysozyme C

Q15181

8.459

0.037

Inorganic pyrophosphatase

P27348

3.220

0.037

14-3-3 protein theta

P49411

14.069

0.037

Elongation factor Tu, mitochondrial

P05164

10.019

0.037

Myeloperoxidase

P61160

5.976

0.038

Actin-related protein 2

Q04917

4.768

0.039

14-3-3 protein eta

P62805

1.761

0.039

Histone H4

P26373

3.700

0.040

60S ribosomal protein L13

Q14204

2.799

0.041

Cytoplasmic dynein 1 heavy chain 1

P56537

7.504

0.041

Eukaryotic translation initiation factor 6

P08708

10.144

0.042

40S ribosomal protein S17

P15153

2.613

0.042

Ras-related C3 botulinum toxin substrate 2

P31949

2.100

0.045

Protein S100

P36952

6.679

0.046

Serpin B5

Q15149

4.694

0.047

Plectin

P46779

6.182

0.048

60S ribosomal protein L28

Q59FH0

5.442

0.048

Histone H2A

P62937

1.778

0.049

Peptidyl-prolyl cis–trans isomerase

P07741

5.077

0.049

Adenine phosphoribosyltransferase

P62269

3.688

0.050

40S ribosomal protein S18

Table 4

List of 40 proteins that were low-expressed in ESCC tissues

IDs

Log ratio

P value

Protein names

P55268

0.078

0.001

Laminin subunit beta-2

Q13361

0.000

0.001

Microfibrillar-associated protein 5

O95682

0.000

0.001

Tenascin-X

P12277

0.024

0.001

Creatine kinase B-type

P20774

0.018

0.002

Mimecan

P06396

0.501

0.002

Gelsolin

O75106

0.000

0.002

Membrane primary amine oxidase

P60660

0.260

0.002

Myosin light polypeptide 6

P51884

0.118

0.003

Lumican

P35555

0.183

0.003

Fibrillin-1

Q5U0D2

0.081

0.004

Transgelin

P35749

0.029

0.004

Myosin-11

P51888

0.032

0.004

Prolargin

P24844

0.033

0.005

Myosin regulatory light polypeptide 9

P17661

0.063

0.005

Desmin

P98160

0.213

0.006

Basement membrane-specific heparan sulfate proteoglycan core protein

P12109

0.299

0.006

Collagen alpha-1(VI) chain

Q07507

0.084

0.006

Dermatopontin

P11047

0.209

0.006

Laminin subunit gamma-1

Q6ZN40

0.114

0.006

CDNA FLJ16459 fis

P18206

0.259

0.008

Vinculin

Q14112

0.065

0.010

Nidogen-2

P21291

0.086

0.011

Cysteine and glycine-rich protein 1

P68032

0.312

0.011

Actin, alpha cardiac muscle 1

Q9NZN4

0.000

0.012

EH domain-containing protein 2

P07585

0.087

0.012

Decorin

Q15746

0.021

0.014

Myosin light chain kinase, smooth muscle

Q9Y490

0.318

0.015

Talin-1

P12110

0.223

0.016

Collagen alpha-2(VI) chain

P21810

0.235

0.020

Biglycan

Q93052

0.048

0.021

Lipoma-preferred partner

P30086

0.507

0.021

Phosphatidylethanolamine-binding protein 1

P62736

0.043

0.022

Actin, aortic smooth muscle

Q96AC1

0.029

0.023

Fermitin family homolog 2

Q6NZI2

0.213

0.025

Polymerase I and transcript release factor

Q59F18

0.000

0.027

Smoothelin isoform b variant

O14558

0.000

0.027

Heat shock protein beta-6

Q13642

0.004

0.028

Four and a half LIM domains protein 1

P12111

0.321

0.031

Collagen alpha-3(VI) chain

P29536

0.000

0.032

Leiomodin-1

P05556

0.416

0.033

Integrin beta-1

Q15124

0.000

0.033

Phosphoglucomutase-like protein 5

P21333

0.213

0.033

Filamin-A

Q53GG5

0.013

0.036

PDZ and LIM domain protein 3

P01009

0.429

0.037

Alpha-1-antitrypsin;Short peptide from AAT

P43121

0.000

0.038

Cell surface glycoprotein MUC18

P52943

0.210

0.041

Cysteine-rich protein 2

P08294

0.000

0.043

Extracellular superoxide dismutase [Cu–Zn]

P56539

0.155

0.043

Caveolin

O15061

0.000

0.045

Synemin

Q9NR12

0.044

0.047

PDZ and LIM domain protein 7

Fig. 1
Fig. 1

Classification of identified proteins by gene ontology based on their a molecular function, b biological process and c cellular component. The analysis of proteins were performed via the PANTHER (http://pantherdb.org/)

Bioinformatics analysis of differentially expressed proteins

A volcano plot was generated based on the differential expression ratio and P value (Fig. 2a). Moreover, the heat map of significantly different proteins was shown in Fig. 2b by using Morpheus (https://software.broadinstitute.org/morpheus). Further protein–protein interaction analysis of the differently expressed proteins was performed by STRING, the result was shown in Fig. 3. Out of the four proteins selected for next analysis, the PPI network analysis revealed that PTMA was a valid target of c-myc transcriptional activation, while PPP1CA was involved in down-regulation of TGF-beta receptor signaling. PAK2 plays a role in apoptosis and activation of Rac, while HMGB2 is participating in chromatin regulation and retinoblastoma in cancer. Above mentioned, all these four proteins were associated with the occurrence and development of cancer. Bioinformatics analysis of the four genes from TCGA database revealed that the four genes up-regulated in gene level in EC tissue (Fig. 4). Whether these four genes can be used as biomarkers of esophageal cancer remains to be further studied.
Fig. 2
Fig. 2

Analysis of protein differential expression. a Volcano plot graph illustrating the differential abundant proteins in the quantitative analysis. The − log10 (P value) was plotted against the log2 (ratio cancer/normal). The red dots represented proteins up-regulated in cancer samples, green dots corresponded to proteins down-regulated in cancer samples. b The heat map of significantly different proteins was shown between cancer tissues and adjacent normal tissues. The analysis was achieved by using Morpheus (https://software.broadinstitute.org/morpheus)

Fig. 3
Fig. 3

Protein-protein interaction network of the differently expressed proteins was identified by STRING. Four proteins were selected for further study with filled red circles (https://string-db.org/)

Fig. 4
Fig. 4

The expression of PTMA, PAK2, PPP1CA and HMGB2 in ESCC based on major cancer stages. In the TCGA databases, the four genes were up-regulated in EC patients (P < 0.001). (http://ualcan.path.uab.edu/analysis.html)

Validation of differentially expressed proteins by Western Blot

To further validate the LC–MS/MS results, we evaluated the four up-regulated proteins (PTMA, PAK2, PPP1CA, HMGB2) and the five down-regulated proteins [Caveolin, Integrin beta-1, Collagen alpha-2(VI), Leiomodin-1 and Vinculin] with Western Blot on the same samples. Compared with adjacent normal tissues, the protein expression of PTMA, PAK2, PPP1CA, HMGB2 were up-regulated (Fig. 5a, b), and the protein expression of Caveolin, Integrin beta-1, Collagen alpha-2(VI), Leiomodin-1, Vinculin were down-regulated in ESCC tissues from four pairs of samples (Fig. 5c, d). The results showed that the trends expression of these proteins were consistent with the LC–MS results.
Fig. 5
Fig. 5

The differentially expressed proteins were validated by Western Blot. Compared with adjacent normal tissues, the protein expression of PTMA, PAK2, PPP1CA, HMGB2 were up-regulated (a, b), and the protein expression of Caveolin, Integrin beta-1, Collagen alpha-2(VI), Leiomodin-1, Vinculin were down-regulated in ESCC tissues from four pairs of samples (c, d). Representative immunoblot images (a, c) and histograms (mean ± SD; b, d).The experiments were repeated at least three times, N represented normal tissues and T represented tumor tissues

Validation of PTMA involved in ESCC by QDB and IHC

In order to validate the proteins identified by mass spectrometric, the QDB technique was applied in a larger set of samples. We collected the samples of 64 patients, and the relevant clinical information was summarized in Table 5. In the analysis of 64 patient samples, we found that 53 out of 64 esophageal cancer tissues showed higher PTMA expression than in the normal tissues (P < 0.001) (Fig. 6). This trend was in accordance with the previous data. To further validate the QDB results, we performed the tissue microarray analysis by IHC. The results showed that among 117 pairs of tissues, the high expression rate of PTMA in tumor tissues was 98% (115/117). A significant overexpression of PTMA was found in tumor tissues in contrast to adjacent normal tissues (P < 0.01) (Fig. 7). The sample information in the chip is summarized in Tables 6 and 7. We further evaluated the expression pattern of PTMA with the progression, and analyzed the PTMA expression trend in the different tumor Grades. The results revealed that the PTMA expression was up-regulated gradually along the progression of ESCC (Fig. 8). The PTMA expression ratio between tumor and adjacent normal tissue was significantly increased along with the progression (P < 0.05). So we can suspect that PTMA might be participating in the development of esophageal cancer.
Table 5

The clinical features of ESCC patients for QDB analysis

No.

Gender

Age

Organ/anatomic site

Grade

TNM

1

Male

69

esophagus

II

T1N0M0

2

Male

61

esophagus

I

T0N0M0

3

Male

59

esophagus

II

T3N0M0

4

Female

65

esophagus

I

T0N0M0

5

Male

52

esophagus

II–III

T3N0M0

6

Female

73

esophagus

I–II

T1N0M0

7

Male

46

esophagus

I

T0N0M0

8

Male

64

Lower segment of esophagus

II

T3N2M0

9

Male

57

Mid-thoracic esophagus

II

T3N0M0

10

Male

54

Mid-thoracic esophagus

II–III

T3N0M0

11

Male

72

Mid-thoracic esophagus

II

T3N3M0

12

Male

66

Mid-thoracic esophagus

II

T3N3M0

13

Male

62

Middle-lower esophagus

II

T1N0M0

14

Male

60

esophagus

II

T3N0M0

15

Female

60

esophagus

II

T3N0M0

16

Male

64

esophagus

II

T3N0M0

17

Female

58

Lower thoracic esophagus

III

T3N0M0

18

Male

53

esophagus

II

T3N0M0

19

Male

65

Lower thoracic esophagus

II–III

T3N0M0

20

Female

60

Mid-thoracic esophagus

I–III

T3N0M0

21

Male

69

Middle-lower esophagus

II

T3N3M0

22

Female

66

esophagus

II–III

T3N2M0

23

Female

67

Lower segment of esophagus

II–III

T3N3M1

24

Male

67

Mid-thoracic esophagus

III

T3N1M0

25

Female

55

Mid-thoracic esophagus

II

T2N1M0

26

Female

61

Mid-thoracic esophagus

I–II

T1N2M0

27

Male

68

esophagus

II–III

T3N2M0

28

Female

48

Mid-thoracic esophagus

I–II

T3N0M0

29

Female

63

Mid-thoracic esophagus

II

T1N1M0

30

Male

70

Lower segment of esophagus

II

T2N1M0

31

Female

59

Mid-thoracic esophagus

III

T3N1M0

32

Female

48

Mid-thoracic esophagus

II

T3N0M0

33

Female

53

Mid-thoracic esophagus

II

T3N2M1

34

Female

58

Lower thoracic esophagus

I-II

T3N0M0

35

Male

62

Mid-thoracic esophagus

II

T2N0M0

36

Female

59

esophagus

II

T3N1M1

37

Female

57

esophagus

II

T3N0M0

38

Female

57

Lower thoracic esophagus

II

T3N1M1

39

Female

62

Mid-thoracic esophagus

I–II

T3N0M0

40

Female

69

Mid-thoracic esophagus

II–III

T3N1M1

41

Female

61

Mid-thoracic esophagus

II

T3N2M1

42

Female

67

Mid-thoracic esophagus

II

T2N0M0

43

Female

47

Mid-thoracic esophagus

II

T2N0M0

44

Female

69

Lower thoracic esophagus

III

T2N2M1

45

Male

66

esophagus

II

T3N0M0

46

Male

72

Mid-thoracic esophagus

II

T3N0M0

47

Female

69

Mid-thoracic esophagus

II–III

T3N0M0

48

Female

73

Mid-thoracic esophagus

I

T1N0M0

49

Male

62

esophagus

II

T3N0M0

50

Male

58

esophagus

II

T2N0M0

51

Male

56

Lower segment of esophagus

II

T1N0M0

52

Male

56

Middle-lower esophagus

II

T3N0M0

53

Male

56

Middle-lower esophagus

II

T3N0M0

54

Male

55

esophagus

I–II

T3N0M0

55

Female

61

esophagus

I–II

T3N0M0

56

Female

71

Middle-lower esophagus

I–II

T1N0M0

57

Male

61

esophagus

II–III

T3N3M1

58

Male

62

Upper thoracic esophagus

III

T3N0M0

59

Male

67

Mid-thoracic esophagus

I

T1N0M0

60

Male

65

esophagus

I

T3N0M0

61

Male

58

esophagus

II–III

T2N1M1

62

Male

49

Lower segment of esophagus

I

T1N0M0

63

Female

66

esophagus

III

T3N1M1

64

Male

70

esophagus

I

T1N0M0

Fig. 6
Fig. 6

The relative PTMA expression was tested by QDB in ESCC and adjacent normal tissues from 64 esophageal cancer patients. a The differential expression of PTMA was shown in each pair of tissues. b The PTMA expression was up-regulated in esophageal cancer tissues from the average of 64 pairs of tissues

Fig. 7
Fig. 7

The relative PTMA expression was tested by IHC in ESCC and adjacent normal tissues among 117 pairs of tissues (× 200). a The expression of PTMA in adjacent normal tissues were presented. b The expression of PTMA in esophageal cancer were up-regulated. c The gray-scale analysis of immunohistochemical results (P < 0.001)

Table 6

The 35 pairs samples in tissue microarrays (TMA) (ES701) for immunohistochemistry analysis

No.

Gender

Age

Organ/anatomic site

Grade

TNM

1

Male

60

Esophagus

II

T3N1M0

2

Male

60

Esophagus

3

Male

44

Esophagus

I

T3N1M0

4

Male

44

Esophagus

5

Male

50

Esophagus

I

T3N2M0

6

Male

50

Esophagus

7

Male

53

Esophagus

I

T3N0M0

8

Male

53

Esophagus

9

Male

64

Esophagus

I

T3N1M0

10

Male

64

Esophagus

11

Male

69

Esophagus

I

T3N0M0

12

Male

69

Esophagus

13

Male

59

Esophagus

I

T3N0M0

14

Male

59

Esophagus

15

Male

60

Esophagus

I

T3N1M0

16

Male

60

Esophagus

17

Male

72

Esophagus

I

T3N1M0

18

Male

72

Esophagus

19

Female

60

Esophagus

I

T3N1M0

20

Female

60

Esophagus

21

Female

75

Esophagus

III

T3N0M0

22

Female

75

Esophagus

23

Male

57

Esophagus

II

T3N1M0

24

Male

57

Esophagus

25

Female

54

Esophagus

II

T3N1M0

26

Female

54

Esophagus

27

Male

45

Esophagus

III

T3N0M0

28

Male

45

Esophagus

29

Male

52

Esophagus

II

T3N0M0

30

Male

52

Esophagus

31

Male

68

Esophagus

T3N0M0

32

Male

68

Esophagus

33

Male

67

Esophagus

I

T3N0M0

34

Male

67

Esophagus

35

Male

55

Esophagus

I

T3N0M0

36

Male

55

Esophagus

37

Male

71

Esophagus

I

T3N1M0

38

Male

71

Esophagus

39

Male

63

Esophagus

III

T3N1M0

40

Male

63

Esophagus

41

Male

67

Esophagus

III

T3N1M0

42

Male

67

Esophagus

43

Male

57

Esophagus

III

T3N0M0

44

Male

57

Esophagus

45

Male

63

Esophagus

III

T3N0M0

46

Male

63

Esophagus

47

Male

57

Esophagus

III

T3N1M0

48

Male

57

Esophagus

49

Male

58

Esophagus

III

T3N1M0

50

Male

58

Esophagus

51

Male

53

Esophagus

II

T3N1M0

52

Male

53

Esophagus

53

Male

49

Esophagus

I

T3N1M0

54

Male

49

Esophagus

55

Male

68

Esophagus

III

T3N1M0

56

Male

68

Esophagus

57

Male

48

Esophagus

III

T3N0M0

58

Male

48

Esophagus

59

Female

58

Esophagus

II

T3N1M0

60

Female

58

Esophagus

61

Male

44

Esophagus

III

T3N1M0

62

Male

44

Esophagus

63

Male

63

Esophagus

II

T3N1M0

64

Male

63

Esophagus

65

Male

68

Esophagus

III

T3N1M0

66

Male

68

Esophagus

67

Female

68

Esophagus

III

T3N1M0

68

Female

68

Esophagus

69

Male

62

Esophagus

III

T2M1N1B

70

Male

62

Esophagus

Table 7

The 96 pairs samples in tissue microarrays (TMA) (ES1922) for immunohistochemistry analysis

No.

Gender

Age

Organ/anatomic site

Grade

TNM

1

Male

58

Esophagus

I

T3N0M0

2

Male

58

Esophagus

3

Male

68

Esophagus

I

T3N1M0

4

Male

68

Esophagus

5

Male

52

Esophagus

I

T1N0M0

6

Male

52

Esophagus

7

Female

66

Esophagus

I

T3N0M0

8

Female

66

Esophagus

9

Male

72

Esophagus

I

T3N1M0

10

Male

72

Esophagus

11

Male

67

Esophagus

I

T3N0M0

12

Male

67

Esophagus

13

Male

66

Esophagus

I

T3N1M0

14

Male

66

Esophagus

15

Male

55

Esophagus

I

T3N1M0

16

Male

55

Esophagus

17

Male

67

Esophagus

I

T3N1M0

18

Male

67

Esophagus

19

Female

71

Esophagus

I

T3N0M0

20

Female

71

Esophagus

21

Male

69

Esophagus

I

T3N0M0

22

Male

69

Esophagus

23

Male

68

Esophagus

I

T3N0M0

24

Male

68

Esophagus

25

Male

44

Esophagus

I

T3N1M0

26

Male

44

Esophagus

27

Female

63

Esophagus

I

T2N0M0

28

Female

63

Esophagus

29

Female

54

Esophagus

I

T3N1M0

30

Female

54

Esophagus

31

Male

60

Esophagus

I

T2N0M0

32

Male

60

Esophagus

33

Female

68

Esophagus

II

T3N0M0

34

Female

68

Esophagus

35

Male

49

Esophagus

I

T3N1M0

36

Male

49

Esophagus

37

Male

61

Esophagus

I

T3N0M0

38

Male

61

Esophagus

39

Female

69

Esophagus

I

T3N1M0

40

Female

69

Esophagus

41

Male

49

Esophagus

I

T3N1M0

42

Male

49

Esophagus

43

Male

68

Esophagus

I

T3N0M0

44

Male

68

Esophagus

45

Male

66

Esophagus

II

T3N0M0

46

Male

66

Esophagus

47

Male

53

Esophagus

II

T3N1M0

48

Male

53

Esophagus

49

Female

58

Esophagus

I

T3N0M0

50

Female

58

Esophagus

51

Male

63

Esophagus

I

T3N0M0

52

Male

63

Esophagus

53

Female

68

Esophagus

I

T2N0M0

54

Female

68

Esophagus

55

Female

68

Esophagus

I

T3N0M0

56

Female

68

Esophagus

57

Male

58

Esophagus

I

T3N0M0

58

Male

58

Esophagus

59

Female

60

Esophagus

I

T3N0M0

60

Female

60

Esophagus

61

Male

70

Esophagus

II

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Fig. 8
Fig. 8

The PTMA expression was up-regulated gradually along the progression of ESCC. a The PTMA expression trend at the different Grades in QDB samples. b The PTMA expression trend at the different Grades in IHC samples. I, II, III represented ESCC Grade I, Grade II and Grade III respectively. (*P < 0.05)

Discussions

At present, most patients with esophageal cancer are diagnosed at the late and advanced stages [17]. It is thus urgent to reveal biomarkers related to the progression of esophageal cancer for early diagnosis. Recently, several biomarkers were identified in EC detection, diagnosis, treatment and prognosis. For example, the epidermal growth factor receptor (EGFR), vascular endothelial growth factor (VEGF) and estrogen receptor (ER) were important detection factors for immunohistochemistry in EC [1820]. In blood, the serum p53 antibody had a potential diagnostic value for EC, however, the detection was limited by its low sensitivity [21]. Therefore, we need to discover and verify more biomarker candidates for the prediction, diagnosis, treatment and prognosis of esophageal cancer.

Mass spectrometry is an effective method for finding distinct molecular regulators, between normal tissues and cancer tissues [22]. In current study, we proposed a significant proteomics profiling difference including 308 proteins. However, compare to previous tissue-based ESCC proteomics study, a poor overlap of proteome profiling was noticed. There are several potential reasons. First, like many other cancers, ESCC is a heterogeneous cancer with different gene expression profiles from different populations [23]. Recently, the whole-genome sequencing revealed the diverse models of structural variations in ESCC, which indicted the biological differences among patients [24]. Therefore, the proteome variation may be a consequence of distinct molecular signatures that exist in ESCC. Another reasons could be related to the different experiment design, some of studies pooled several individual samples into a sample pooling, which would also lead to potential difference compare to our individual analysis [25]. The difference of data analysis method would be another reason too, most of the labeled-based MS approach selected the expression fold change as the major criteria. In our study, with a label-free approach, we proposed paired Student’s t-test significance as the main criteria. Such difference could lead to a different proteome profiling. The poor overlap indicated the importance of large-scale validation of biomarker. Thus we suggest in future studies, the proposed novel biomarker should be validated in a larger population no less than 100 samples. Besides TMA, our group recently developed QDB as a novel fast and accurate validation approach, which can easily validate biomarkers up to thousand samples [16].

Human prothymosin-α (PTMA) is a 109 amino acid protein belonged to the α-thymosin family, which is ubiquitously distributed in mammalian blood, tissues and especially abundant in lymphoid cells. However, its role still remains elusive. The growing evidences suggested that PTMA being an important immune mediator as well as a biomarker might eventually become a new therapeutic target or diagnostic method in several diseases such as cancer and inflammation [26]. So we focused on the possibility of PTMA as a biomarker of ESCC.

The proteomic studies show that PTMA exerts multifunction in nuclear and cytoplasmic. In proliferating cells, PTMA mainly locates in nuclear depending on the C-terminus signal sequence, but this protein can be transferred from the nucleus into the cytoplasmic during the cell extraction process [27, 28]. PTMA may mediate the chromatin activity by participated the nuclear-protein complex. In cytoplasmic, the function of PTMA is related to the state of phosphorylation, for example, the Thr7 is the only residue phosphorylated in carcinogenic lymphocytes while the Thr12 or Thr13 phosphorylated in normal lymphocytes [29, 30]. The co-immunoprecipitation experiments shows that PTMA interact with SET, ANP32A and ANP32B to form the complex, which is related to the cell proliferation, membrane trafficking, proteolytic processing and so on [3133].

PTMA is known to play an important role in cell growth, proliferation, apoptosis and so on [34, 35]. Recent studies have confirmed that overexpression of PTMA is involved in the development of various malignancies, including colorectal, bladder, lung, and liver cancer [3638]. In vivo tumorigenesis, the PTMA expression promotes the transplant tumor growth in mice and speeds up their death. Meanwhile, the PTMA interacts with TRIM21 directly to regulate the Nrf2 expression through p62/Keap1 signaling in human bladder cancer [39]. In the patients with squamous cell carcinoma (SCC), adenosquamous cell carcinoma (ASC) and adenocarcinoma (AC) of the gallbladder, the positive expression of PTMA may be associated with the tumorigenesis, tumor progression and prognosis in gallbladder tumor. In addition, the high expression of PTMA may be as an indicator in the prevention and early diagnosis of gallbladder tumor [40]. In addition to inducing cancer, Wang et al. discovered that PTMA as a new autoantigen regulated oral submucous fibroblast proliferation and extracellular matrix using human proteome microarray analysis. In addition, PTMA knockdown reversed TGFβ1-induced fibrosis process through reducing the protein levels of collagen I, α-SMA and MMP [34]. However, there have been no evidences that PTMA participates in the pathogenesis of esophageal cancer.

Our mass spectrometry results showed that PTMA expression was up-regulated in ESCC tissues, and if the result was universal, it would provide a good biomarker for the diagnosis of ESCC. The traditional Western Blot is tedious, laborious and time-consuming for hundreds and thousands of large samples tests. In order to verify the results of mass spectrometry, we adopted the QDB technology invented recently, which was capable of high-throughput identification of target proteins from the perspective of biological experiments compared with Western Blot. QDB performed an affordable method for high-throughput immunoblot analysis and achieved relative or absolute quantification. In addition, the QDB needs less sample consumption, and the data can be conveniently read by a microplate reader. In HEK293 cells, the QDB successfully compared the levels of relative p65 levels between Luciferase and p65 clones in 71 pairs of samples. We have confirmed the accuracy and reliability of QDB from both cells and tissues [16]. As above mentioned, QDB is a convenient, reliable and affordable method. In our study, we confirmed that 53 out of 64 tested ESCC tissues had higher PTMA expression by the QDB, and the results were identified by classical IHC methods in 117 pairs of samples.

In this study, we included both explore experiment and validation experiment, using early and late stage samples. The results from explore experiment indicated that PTMA was overexpressed in all stages. We further evaluated the expression pattern of PTMA with the progression, and analyzed the PTMA expression trend in the different Grades. The results revealed that the PTMA expression was up-regulated gradually along the progression of ESCC, and the PTMA expression ratio between tumor and adjacent normal tissue was significantly increased along with the progression. As it is almost impossible to obtain the extreme early stage (such as the stage without any symptom, or the stage prior to Grade I), but from the trend between Grade I and III, we can suspect the expression ratio of PTMA would be a potential indicator for the progression, even in the early diagnosis.

Conclusions

In our research, we used label-free quantitative proteomics to detect differentially expressed protein profiles in ESCC tissues compared to control tissues. In total 2297 proteins were identified and 308 proteins with significant differences were selected for study. Based on in-depth bioinformatic analysis, the four up-regulated proteins [PTMA, PAK2, PPP1CA, HMGB2) and the five down-regulated proteins Caveolin, Integrin beta-1, Collagen alpha-2(VI), Leiomodin-1 and Vinculin] were selected and validated in ESCC by Western Blot. Furthermore, we performed the QDB and IHC analysis in 64 patients and 117 patients, respectively. The PTMA expression was up-regulated gradually along the progression of ESCC, and the PTMA expression ratio between tumor and adjacent normal tissue was significantly increased along with the progression. Therefore, the PTMA is suggested as a candidate biomarker for ESCC. Our research also presents a new methodological strategy for the identification and validation of novel cancer biomarkers by combining quantitative proteomic with QDB.

Notes

Declarations

Authors’ contributions

JM and LW conceived the experiments; YPZ, XYQ, CCY, YZ and XXL performed the experiments; CCY, SJY, YXX and CHY collected the clinical materials; JM and CZ analyzed the protein data; WGJ, GT and JDZ conducted the statistical analysis; XRL and JB modified the paper. All authors read and approved the final manuscript.

Competing interests

All authors declare that they have no competing interests. Jiandi Zhang declares competing interests, and he has filed patent applications. Jiandi Zhang is the founders of Yantai Zestern Biotechnique Co. LTD, a startup company with interest to commercialize the QDB technique and QDB plate.

Availability of data and materials

The data will be made available upon publication.

Consent for publication

Not applicable.

Ethics approval and consent to participate

The study was approved by the Human Research Ethics Committee of Binzhou Medical University (2016-37).

Funding

This work is supported by the National Natural Science Foundation of China (81670855, 31671139) for sample collection and publication charges, Key Research and Development Plan of Shandong Province (2016GSF201100, 2017GSF218113, 2018GSF118131, 2018GSF118183) for MS experiments and IHC TMA analysis, Yantai science and technology plan (2017WS102) and Doctoral fund of Shandong Natural Science Foundation (ZR2017BC063) for antibody consumption, BZMC Scientific Research Foundation (BY2017KYQD08) for QDB analysis, Scientific Research Foundation for Returned Overseas Chinese Scholars of the Education Office of Heilongjiang Province (LC2009C21) for interpretation of data, Development Plan of Traditional Chinese Medicine Science in Shandong Province (2017-237) for general lab facility.

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Authors’ Affiliations

(1)
Precision Medicine Research Center, Binzhou Medical University, No. 346 Guanhai Rd., Laishan District, Yantai, 264003, Shandong Province, People’s Republic of China
(2)
Department of Anesthesiology, The Affiliated Yantai Yuhuangding Hospital of Qing Dao University, No. 20 Yudong Rd., Zhifu District, Yantai, S264009, Shandong, People’s Republic of China
(3)
Department of Ultrasound, Yantai Affiliated Hospital of Binzhou Medical University, No. 717 Jinfu Rd., Muping District, Binzhou, 264100, Shandong Province, People’s Republic of China
(4)
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, 510060, People’s Republic of China
(5)
Department of Chemistry, BMC, Uppsala University, PO Box 599, Husargatan 3, 75124 Uppsala, Sweden
(6)
Yantai Zestern Biotechnique Co. LTD, 39 Keji Ave. Bioasis, Yantai, People’s Republic of China
(7)
Department of Thoracic Surgery, The First Affiliated Hospital of Harbin Medical University, No. 23, Youzheng Street, Nangang District, Harbin, 150000, Heilongjiang Province, People’s Republic of China

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