Literature DB >> 31886120

Identification and Verification of the Main Differentially Expressed Proteins in Gastric Cancer via iTRAQ Combined with Liquid Chromatography-Mass Spectrometry.

Zhihua Gao1, Jiabao Wang2, Yuru Bai3, Jun Bao4, Erqing Dai5.   

Abstract

BACKGROUND: To find the potential intersections between the differentially expressed proteins and abnormally expressed genes in gastric cancer (GC) patients.
METHODS: Gastric cancer tissue and adjacent normal mucosa tissue were used for iTRAQ analysis. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and protein-protein interaction (PPI) analysis were used to evaluate gene function. Western blotting and immunohistochemistry (IHC) were applied to verify the protein expression.
RESULTS: A total of 2770 proteins were identified, of which 147 proteins were upregulated and 159 proteins were downregulated. GO analysis revealed that the differentially expressed genes were mainly enriched for the terms "cellular process," "binding," and "cell." The results of the KEGG analysis showed that the most abundantly enriched proteins were involved in the "focal adhesion" pathway. The results of the PPI analysis showed that VCAM1 was located at the center of the PPI network. Western blotting and IHC analysis demonstrated that VCAM1, FLNA, VASP, CAV1, PICK1, and COL4A2 were differentially expressed in GC and adjacent normal tissues, which was consistent with the results of the iTRAQ analysis.
CONCLUSION: In conclusion, 6 highly differentially expressed proteins were identified as novel differentially expressed proteins in human GC. This exploratory research may provide useful information for the treatment of gastric cancer in the clinic.
Copyright © 2019 Zhihua Gao et al.

Entities:  

Mesh:

Substances:

Year:  2019        PMID: 31886120      PMCID: PMC6914953          DOI: 10.1155/2019/5310684

Source DB:  PubMed          Journal:  Anal Cell Pathol (Amst)        ISSN: 2210-7177            Impact factor:   2.916


1. Introduction

Gastric cancer (GC) is a malignant tumor originating from the gastric mucosa. It is one of the most common digestive tract tumors. China is ranked as one of countries with a high incidence of gastric cancer. There are approximately 400,000 new cases of gastric cancer diagnosed in China each year, and the death toll is approximately 350,000, which accounts for 40% of the total number of GC cases worldwide [1-3]. The early diagnosis and treatment rate of gastric cancer in China is low, and the significantly low rate of 10% is far lower than that in Japan and South Korea [4, 5]. The death rate for gastric cancer ranks third among the rates for malignant tumors. The early diagnosis and treatment rates of gastric cancer in China are relatively low, and the diagnosis of gastric cancer is made mostly in the advanced stages, resulting in a high mortality rate for gastric cancer. The early diagnosis of gastric cancer is an important step to improve the clinical curative effects of GC treatment and to save lives. Surgery is the main treatment method for gastric cancer. Chemotherapy is the main treatment method for patients who miss the opportunity for surgery or for patients with recurrence and metastatic GC after surgery. Drug resistance (or drug insensitivity) could lead to the failure of chemotherapy, which is one of the major problems that plagues most patients during treatment [6-8]. Multidrug resistance (MDR) is the main reason for the failure of chemotherapy in gastric cancer [9]. The screening of MDR-related molecules for gastric cancer and potential markers to predict the extent of drug resistance are fundamental for the improvement of drug therapy and drug development processes. With the rapid development of genomics and proteomics, screening of the tumor target is no longer limited to subtractive hybridization and gene chip methods, and proteomics has become a new method that is used for screening tumor-related targets. One of the hot topics in proteomics research is the use of differential screening to explore the differentially expressed proteins in experimental cells (tissues) and control cells (tissues). Using this method, we explored the mediators of the upstream and downstream molecular pathways and elucidated the factors involved in the occurrence and development of disease. The use of isobaric tags for relative and absolute quantitation with iTRAQ technology is a novel proteomics quantitative research technique used to conduct quantitative analysis in different samples simultaneously [10, 11]. iTRAQ could screen for differential proteins with good quantitative effects and high repeatability. It has become an effective method for screening differentially expressed proteins in cancer research. In this study, we examined the differentially expressed proteins in gastric cancer tissues and normal gastric mucosa using iTRAQ technology to explore the mechanism of gastric cancer. In this study, tumor gene detection was carried out in patients to determine the potential intersections between the differentially expressed proteins and the abnormally expressed genes based on a literature search and clinical medication analysis results. This exploratory research could provide useful information for the treatment of gastric cancer in the clinic.

2. Methods

2.1. Clinical Samples

A total of 240 GC patients were recruited from the Affiliated Hospital of the Logistics Institute of the Chinese People's Armed Police Forces between October 2014 and September 2016. All patients were diagnosed with gastric cancer by pathological examination. All patients underwent surgical resection without any prior treatment. The flow chart showing the process of the recruitment of the study participants is shown in Figure 1. After obtaining informed consent, 6 gene detections were carried out to search for the potential intersections with abnormally expressed genes and proteins in these GC patients, and the gene detection results were also used for the individually targeted treatment of the patients. The study was approved and registered with the Ethics Committee of the Affiliated Hospital of the Logistics Institute of the Chinese People's Armed Police Forces in September 2014. The Ethics Committee approved the data collection and the related screening, treatment, and follow-up of these patients. Written informed consent was obtained from all subjects. All work was undertaken according to the provisions of the Declaration of Helsinki.
Figure 1

STARD flowchart of the process used to recruit the study participants.

2.2. Sample Collection and Protein Extraction

The gastric cancer tissue and adjacent normal gastric mucosa tissue were resected from GC patients. The normal gastric mucosa tissue was obtained 10-15 cm away from the tumor center and pathologically confirmed as normal gastric mucosa. The partially resected tissue was fixed with 4% formaldehyde, and the rest was stored in liquid nitrogen immediately prior to protein extraction and other follow-up analyses. For protein extraction, the thawed tissue (150 mg) was cut into pieces with scissors. Six hundred microliters of RIPA lysis buffer (Thermo Fischer Scientific, Waltham, MA, USA) and 10 μL PMSF (Thermo) were added to the tissues. The tissue was ground on ice. The suspension was mixed and processed by a homogenizer (24 × 2, 6.0 M/S, MP FastPrep-24, MP Biomedicals, Santa Ana, CA, USA) twice for 60 s. The suspension was treated by ultrasound (80 W, 10 s, 16 times) on ice and then placed in a boiling water bath for 10-15 min, followed by centrifugation at 14,000 g for 15 min. The suspension was filtered through a 0.22 μm filter membrane, and the filtrate was collected. The protein quantitation of each specimen was performed by the BCA method. In the GC group or the normal control group, the samples were mixed according to the principle that the protein extracted from each specimen was added to the same amount of protein; finally, the total protein samples of the GC group and the normal control group were obtained. The total protein samples of the GC group and normal control group were collected and stored at -80°C.

2.3. ITRAQ Labeling

The mixed protein was reduced by alkylation and processed by enzymolysis. The sample (100 μg) was labeled with iTRAQ reagents (CIEX, Framingham, MA, USA) for 2 h. The iTRAQ-labeled samples were reconstituted in 4 mL buffer A (10 mM KH2PO4 in 25% acetonitrile at pH 3.0) and loaded onto a 5 μm particle size, 4.6 × 250 mm Ultremex SCX column (Phenomenex). The samples were eluted at a rate of 1 mL/min with a gradient consisting of 100% buffer A from 0 min to 25 min, 0%–10% buffer B (10 mM KH2PO4 in 25% acetonitrile/500 mM KCl at pH 3.0) from 25 min to 32 min, 10%–20% buffer B from 32 min to 42 min, 20%–45% buffer B from 42 min to 47 min, 45%–100% buffer B from 47 min to 52 min, and 100% buffer B from 52 min to 60 min. Then, the system was equilibrated with buffer A for 10 min prior to the next injection. The absorbance at 214 nm was monitored during the elution, and fractions were collected every 1 min. After lyophilization, a C18 cartridge was used for desalting.

2.4. nanoLC-MALDI-TOF/TOF MS/MS Assay

All samples were analyzed using the Easy nLC HPLC system (Thermo Fisher) combined with a Q Exactive mass spectrometer (Thermo Fisher). The samples were treated with a Thermo Scientific EASY Column SC200 (10 cm × 75 μm, 3 μm C18-A2) for 60 min with a gradient consisting of 0%-35% buffer B (84% acetonitrile/0.1% formic acid) from 0 min to 50 min, 35%-100% buffer B from 50 min to 55 min, and 100% buffer B from 55 min to 60 min. Buffer A was a 0.1% formic acid solution. The sample was chromatographically analyzed by mass spectrometry using a Q Exactive mass spectrometer. The analysis time was 60 min, and the detection mode was positive ion mode. The parent ion scanning ranged from 300 m/z to 1800 m/z. The primary mass spectrometer resolution was 70,000 at 200 m/z. The AGC (automatic gain control) target was 1e6, and the maximum IT was 50 ms. The dynamic exclusion was 60.0 s. The mass-to-charge ratio of the polypeptide and polypeptide fragments was determined using the following parameters: 20 fragments were acquired after each full scan, the MS2 activation type was HCD, the isolation window was 2 m/z, the secondary mass spectrometer resolution was 17,500 at 200 m/z, the normalized collision energy was 30 eV, and the underfill was 0.1%. Mascot 2.2 and Proteome Discoverer 1.4 software were used for the data analysis.

2.5. Gene Ontology (GO), Protein-Protein Interaction (PPI), and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Analyses

The differentially expressed genes were annotated using the Database for Annotation, Visualization, and Integrated Discovery (DAVID; http://david.ncifcrf.gov) (version 6.7), and the enriched biological metabolic pathways were determined using the Kyoto Encyclopedia of Genes and Genomes (KEGG) (http://www.genome.jp/kegg/). A P value < 0.05 was considered to indicate a significant correlation. The PPI network was assessed using the Search Tool for the Retrieval of Interacting Genes database (STRING, https://string-db.org/) and visualized using Cytoscape software according to the previous reference [12-14].

2.6. Western Blotting

Total proteins were extracted using RIPA lysis buffer (Pierce, Invitrogen, Gaithersburg, MD, USA). The concentration of the extracted protein was determined by a BCA assay. The total protein was separated by 12% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), followed by transfer to a PVDF membrane (EMD Millipore, Billerica, MA, USA), which was blocked with 5% skim milk for 1 h. The primary antibodies, anti-VCAM1 (1 : 1000 dilution, Cell Signaling Technology, MA, USA), anti-VASP (1 : 1000 dilution, Cell Signaling Technology), anti-PICK1 (1 : 1000 dilution, Cell Signaling Technology), anti-FLNA (1 : 1000 dilution, Cell Signaling Technology), anti-COL4A2 (1 : 1000 dilution, Cell Signaling Technology), anti-CAV1 (1 : 1000 dilution, Cell Signaling Technology), and anti-GAPDH (1 : 1000 dilution, Cell Signaling Technology), were added and incubated with the membranes at 4°C overnight. Then, the membranes were washed with PBS buffer and incubated with anti-rabbit IgG antibody (1 : 10,000 dilution, Cell Signaling Technology) at 37°C for 45 min. An imaging system (Odyssey, LI-COR Biosciences, Lincoln, NE, USA) was used for the semiquantitative analysis. GAPDH was used as an internal control.

2.7. Immunohistochemistry (IHC) Assay

GC and control samples were fixed with 4% formaldehyde solution and embedded in paraffin. Then, the sections (5 μm thickness) were incubated with 3% H2O2 for 10 min at room temperature to eliminate endogenous peroxidase activity. The sections were blocked with 10% goat serum at room temperature for 10 min. Then, the sections were incubated with primary antibodies, including anti-CAV1 (1 : 1000 dilution, Cell Signaling Technology), anti-VASP (1 : 1000 dilution, Cell Signaling Technology), and anti-VCAM1 (1 : 1000 dilution, Cell Signaling Technology, MA, USA), at 37°C for 2 h. After the application of the secondary antibody (anti-rabbit IgG antibody, 1 : 10,000 dilution, Cell Signaling Technology), the sections were incubated at 37°C for 30 min. Subsequently, the DAB Plus Substrate Chromogen mixture was added, and the sections were incubated for 10 min. The Human Protein Atlas (http://www.proteinatlas.org/) was used to validate the expression of the six genes in GC tissue.

2.8. Statistical Analysis

SPSS 22.0 statistical software was used for the statistical analysis. The values were expressed as the mean ± standard deviation (SD) and compared using Student's t test or the Wilcoxon/Mann-Whitney rank sum test. P < 0.05 was considered to indicate a statistically significant difference.

3. Results

3.1. iTRAQ and GO Analysis

Compared to adjacent normal tissues, 2770 proteins were differentially expressed in tumor tissues, of which 147 were upregulated by more than 1.2-fold (P < 0.05) and 159 were downregulated by more than 0.8-fold (P < 0.05). The top 50 proteins upregulated by more than 1.2-fold and downregulated by more than 0.8-fold are shown in Tables 1 and 2, respectively. Subsequently, GO analysis was applied to analyze the differentially expressed genes. The differentially expressed genes were enriched in various molecular functions (MF), biological processes (BP), and cellular component terms (CC) (Figure 2). “Cellular process,” “binding,” and “cell” were the most enriched terms in BP, MF, and CC, respectively (Figure 2).
Table 1

The top 50 proteins with upregulation multiples greater than 1.2-fold.

NAccessionDescriptionUp fold P value
1A0A087X079Ig gamma-1 chain C region OS=Homo sapiens GN=IGHG1 PE=1 SV=1 (A0A087X079_HUMAN)4.6831841.29E-25
2O14604Thymosin beta-4, Y-chromosomal OS=Homo sapiens GN=TMSB4Y PE=1 SV=3 (TYB4Y_HUMAN)3.0160638.25E-14
3P31327Carbamoyl-phosphate synthase (ammonia), mitochondrial OS=Homo sapiens GN=CPS1 PE=1 SV=2 (CPSM_HUMAN)2.7212311.33E-11
4A1A5C5RRBP1 protein OS=Homo sapiens GN=RRBP1 PE=2 SV=1 (A1A5C5_HUMAN)2.5475322.67E-10
5P25815Protein S100-P OS=Homo sapiens GN=S100P PE=1 SV=2 (S100P_HUMAN)2.5286053.71E-10
6P0DMU9Cancer/testis antigen family 45 member A10 OS=Homo sapiens GN=CT45A10 PE=2 SV=1 (CT45A_HUMAN)2.4861127.72E-10
Q86XP6Gastrokine-2 OS=Homo sapiens GN=GKN2 PE=1 SV=2 (GKN2_HUMAN)2.2838842.51E-08
8B3KY79cDNA FLJ46620 fis, clone TLUNG2000654, highly similar to keratin, type II cytoskeletal 7 OS=Homo sapiens PE=2 SV=1 (B3KY79_HUMAN)2.2152528.09E-08
9A8K2T5cDNA FLJ77047, highly similar to Homo sapiens zinc finger protein 217 (ZNF217), mRNA (fragment) OS=Homo sapiens PE=2 SV=1 (A8K2T5_HUMAN)2.2092098.97E-08
10B4E2T6cDNA FLJ58231, highly similar to NMDA receptor-regulated protein 1 OS=Homo sapiens PE=2 SV=1 (B4E2T6_HUMAN)2.0455481.42E-06
11D6CHE9Proteinase 3 OS=Homo sapiens GN=PRTN3 PE=2 SV=1 (D6CHE9_HUMAN)2.0387241.59E-06
12H0YJL6Ena/VASP-like protein (fragment) OS=Homo sapiens GN=EVL PE=1 SV=1 (H0YJL6_HUMAN)2.0367081.64E-06
13Q5T619Zinc finger protein 648 OS=Homo sapiens GN=ZNF648 PE=2 SV=1 (ZN648_HUMAN)2.0357271.67E-06
14Q658S4Putative uncharacterized protein DKFZp666N164 (fragment) OS=Homo sapiens GN=DKFZp666N164 PE=2 SV=1 (Q658S4_HUMAN)1.9907093.53E-06
15B2RBL3Thymidine phosphorylase OS=Homo sapiens PE=2 SV=1 (B2RBL3_HUMAN)1.9884533.66E-06
16P98088Mucin-5AC OS=Homo sapiens GN=MUC5AC PE=1 SV=4 (MUC5A_HUMAN)1.9814624.11E-06
17Q9HD89Resistin OS=Homo sapiens GN=RETN PE=1 SV=1 (RETN_HUMAN)1.9491746.99E-06
18B4DLS7cDNA FLJ53454, highly similar to interferon-induced protein with tetratricopeptide repeats 3 OS=Homo sapiens PE=2 SV=1 (B4DLS7_HUMAN)1.9112311.30E-05
19P06702Protein S100-A9 OS=Homo sapiens GN=S100A9 PE=1 SV=1 (S10A9_HUMAN)1.8829362.06E-05
20C9JKF7Lymphocyte-specific protein 1 (fragment) OS=Homo sapiens GN=LSP1 PE=1 SV=1 (C9JKF7_HUMAN)1.8780332.23E-05
21P52566Rho GDP-dissociation inhibitor 2 OS=Homo sapiens GN=ARHGDIB PE=1 SV=3 (GDIR2_HUMAN)1.8438843.86E-05
22P16402Histone H1.3 OS=Homo sapiens GN=HIST1H1D PE=1 SV=2 (H13_HUMAN)1.8343764.49E-05
23Q7Z351Putative uncharacterized protein DKFZp686N02209 OS=Homo sapiens GN=DKFZp686N02209 PE=2 SV=1 (Q7Z351_HUMAN)1.826815.07E-05
24D3DP16Fibrinogen gamma chain, isoform CRA_a OS=Homo sapiens GN=FGG PE=4 SV=1 (D3DP16_HUMAN)1.8187055.77E-05
25P40261Nicotinamide N-methyltransferase OS=Homo sapiens GN=NNMT PE=1 SV=1 (NNMT_HUMAN)1.7926048.72E-05
26B7Z747cDNA FLJ51120, highly similar to matrix metalloproteinase-9 (EC 3.4.24.35) OS=Homo sapiens PE=2 SV=1 (B7Z747_HUMAN)1.7563010.000154
27I1VZV6Hemoglobin alpha 1 OS=Homo sapiens GN=HBA1 PE=3 SV=1 (I1VZV6_HUMAN)1.7551440.000157
28P02792Ferritin light chain OS=Homo sapiens GN=FTL PE=1 SV=2 (FRIL_HUMAN)1.750210.000169
29F5H5I5ATP-binding cassette sub-family B member 9 (fragment) OS=Homo sapiens GN=ABCB9 PE=4 SV=1 (F5H5I5_HUMAN)1.7460980.000181
30Q6P4A8Phospholipase B-like 1 OS=Homo sapiens GN=PLBD1 PE=1 SV=2 (PLBL1_HUMAN)1.7321040.000224
31H0YJG9Dehydrogenase/reductase SDR family member 2, mitochondrial (fragment) OS=Homo sapiens GN=DHRS2 PE=1 SV=1 (H0YJG9_HUMAN)1.7279960.000239
32C9JZJ5Melanoma-associated antigen 4 (fragment) OS=Homo sapiens GN=MAGEA4 PE=1 SV=7 (C9JZJ5_HUMAN)1.7170630.000283
33P20591Interferon-induced GTP-binding protein Mx1 OS=Homo sapiens GN=MX1 PE=1 SV=4 (MX1_HUMAN)1.7051170.00034
34Q400J6Arylamine N-acetyltransferase (fragment) OS=Homo sapiens GN=NAT1 PE=2 SV=1 (Q400J6_HUMAN)1.69830.000377
35P80723Brain acid soluble protein 1 OS=Homo sapiens GN=BASP1 PE=1 SV=2 (BASP1_HUMAN)1.6966990.000386
36P05109Protein S100-A8 OS=Homo sapiens GN=S100A8 PE=1 SV=1 (S10A8_HUMAN)1.6849610.000462
37B2MUD5Neutrophil elastase (fragment) OS=Homo sapiens GN=ELA2 PE=4 SV=1 (B2MUD5_HUMAN)1.6813830.000487
38P23381Tryptophan-tRNA ligase, cytoplasmic OS=Homo sapiens GN=WARS PE=1 SV=2 (SYWC_HUMAN)1.6702880.000576
39Q86YQ1Hemoglobin alpha-2 (fragment) OS=Homo sapiens GN=HBA2 PE=3 SV=1 (Q86YQ1_HUMAN)1.6698020.000581
40A0A0G2JMH6HLA class II histocompatibility antigen, DR alpha chain OS=Homo sapiens GN=HLA-DRA PE=1 SV=1 (A0A0G2JMH6_HUMAN)1.6632350.000641
41B4DVG3cDNA FLJ53104, moderately similar to Homo sapiens N-acetylneuraminate pyruvate lyase (dihydrodipicolinate synthase) (NPL), mRNA OS=Homo sapiens PE=2 SV=1 (B4DVG3_HUMAN)1.6623570.000649
42A0A140VJJ6Testicular tissue protein Li 70 OS=Homo sapiens PE=2 SV=1 (A0A140VJJ6_HUMAN)1.6534890.000742
43B4E0J9cDNA FLJ57348, highly similar to Homo sapiens hexokinase domain containing 1 (HKDC1), mRNA OS=Homo sapiens PE=2 SV=1 (B4E0J9_HUMAN)1.6532610.000744
44U6FVB0Tyrosine-protein kinase receptor OS=Homo sapiens GN=CD74-Ntrk1 fusion gene PE=2 SV=1 (U6FVB0_HUMAN)1.6165270.001283
45A2MYE1A30 (fragment) OS=Homo sapiens PE=4 SV=1 (A2MYE1_HUMAN)1.5993010.001651
46B4DVC2cDNA FLJ51332, highly similar to HLA class II histocompatibility antigen, DMbeta chain OS=Homo sapiens PE=2 SV=1 (B4DVC2_HUMAN)1.5929260.001811
47Q8IZI0Hemoglobin beta chain variant Hb-I_Toulouse (fragment) OS=Homo sapiens GN=HBB PE=3 SV=1 (Q8IZI0_HUMAN)1.5924520.001823
48Q6P1N7TAPBP protein OS=Homo sapiens GN=TAPBP PE=1 SV=1 (Q6P1N7_HUMAN)1.5842380.002054
49B4DNT5cDNA FLJ60316, highly similar to Apolipoprotein-L1 OS=Homo sapiens PE=2 SV=1 (B4DNT5_HUMAN)1.5774750.002264
50O15451Proline and glutamic acid-rich nuclear protein isoform (fragment) OS=Homo sapiens PE=2 SV=2 (O15451_HUMAN)1.5769060.002282
Table 2

The top 50 proteins with downregulation folds less than 0.8-fold.

nAccessionDescriptionDown fold P value
1O60844Zymogen granule membrane protein 16 OS=Homo sapiens GN=ZG16 PE=1 SV=2 (ZG16_HUMAN)0.1989593.21E-19
2E1CKY7Protein phosphatase 1 regulatory subunit 12B OS=Homo sapiens GN=sm-M20 PE=1 SV=1 (E1CKY7_HUMAN)0.2542362.81E-14
3Q9BYX7Putative beta-actin-like protein 3 OS=Homo sapiens GN=POTEKP PE=5 SV=1 (ACTBM_HUMAN)0.2606898.13E-14
4P51911Calponin-1 OS=Homo sapiens GN=CNN1 PE=1 SV=2 (CNN1_HUMAN)0.2623191.06E-13
5B7Z9B7cDNA FLJ54732, moderately similar to sorbin and SH3 domain-containing protein 1 OS=Homo sapiens PE=2 SV=1 (B7Z9B7_HUMAN)0.3155021.42E-10
6B4E1Q7cDNA FLJ57294, highly similar to lipoamide acyltransferase component of branched-chain alpha-keto acid dehydrogenase complex, mitochondrial (EC 2.3.1.168) OS=Homo sapiens PE=2 SV=1 (B4E1Q7_HUMAN)0.3398321.93E-09
7Q9BTA4Epididymis secretory protein Li 286 (fragment) OS=Homo sapiens GN=HEL-S-286 PE=2 SV=1 (Q9BTA4_HUMAN)0.3423042.47E-09
8A5Z217Mutant desmin OS=Homo sapiens PE=2 SV=1 (A5Z217_HUMAN)0.3821638.56E-08
9A5A3E0POTE ankyrin domain family member F OS=Homo sapiens GN=POTEF PE=1 SV=2 (POTEF_HUMAN)0.3879631.35E-07
10Q96JG9Zinc finger protein 469 OS=Homo sapiens GN=ZNF469 PE=2 SV=3 (ZN469_HUMAN)0.3953972.38E-07
11P08217Chymotrypsin-like elastase family member 2A OS=Homo sapiens GN=CELA2A PE=1 SV=1 (CEL2A_HUMAN)0.3988663.08E-07
12P07098Gastric triacylglycerol lipase OS=Homo sapiens GN=LIPF PE=1 SV=1 (LIPG_HUMAN)0.4054734.96E-07
13B7Z6U8cDNA FLJ53665, highly similar to four and a half LIM domains protein 1 OS=Homo sapiens PE=2 SV=1 (B7Z6U8_HUMAN)0.4083926.09E-07
14P12277Creatine kinase B-type OS=Homo sapiens GN=CKB PE=1 SV=1 (KCRB_HUMAN)0.4090146.36E-07
15P0CG38POTE ankyrin domain family member I OS=Homo sapiens GN=POTEI PE=3 SV=1 (POTEI_HUMAN)0.4097726.71E-07
16B7Z7M8cDNA FLJ60950, highly similar to hydroxymethylglutaryl-CoA synthase, mitochondrial (EC 2.3.3.10) OS=Homo sapiens PE=2 SV=1 (B7Z7M8_HUMAN)0.4199181.33E-06
17P35749Myosin-11 OS=Homo sapiens GN=MYH11 PE=1 SV=3 (MYH11_HUMAN)0.4207751.41E-06
18O15061Synemin OS=Homo sapiens GN=SYNM PE=1 SV=2 (SYNEM_HUMAN)0.4300912.57E-06
19P68032Actin, alpha cardiac muscle 1 OS=Homo sapiens GN=ACTC1 PE=1 SV=1 (ACTC_HUMAN)0.4374794.06E-06
20P26678Cardiac phospholamban OS=Homo sapiens GN=PLN PE=1 SV=1 (PPLA_HUMAN)0.4402924.81E-06
21Q15124Phosphoglucomutase-like protein 5 OS=Homo sapiens GN=PGM5 PE=1 SV=2 (PGM5_HUMAN)0.4428455.60E-06
22B4DTX5cDNA FLJ60072, highly similar to Homo sapiens sorbin and SH3 domain containing 1 (SORBS1), transcript variant 6, mRNA OS=Homo sapiens PE=2 SV=1 (B4DTX5_HUMAN)0.4440076.00E-06
23B3KW93Sodium/potassium-transporting ATPase subunit alpha OS=Homo sapiens PE=2 SV=1 (B3KW93_HUMAN)0.4469797.14E-06
24F8VPF3Myosin light polypeptide 6 (fragment) OS=Homo sapiens GN=MYL6 PE=1 SV=1 (F8VPF3_HUMAN)0.4488227.94E-06
25Q9NR12PDZ and LIM domain protein 7 OS=Homo sapiens GN=PDLIM7 PE=1 SV=1 (PDLI7_HUMAN)0.4550341.13E-05
26Q01995Transgelin OS=Homo sapiens GN=TAGLN PE=1 SV=4 (TAGL_HUMAN)0.4567781.24E-05
27A0A024R5W6Tropomyosin 1 (alpha), isoform CRA_a OS=Homo sapiens GN=TPM1 PE=3 SV=1 (A0A024R5W6_HUMAN)0.4584741.37E-05
28Q63ZY3KN motif and ankyrin repeat domain-containing protein 2 OS=Homo sapiens GN=KANK2 PE=1 SV=1 (KANK2_HUMAN)0.4611421.58E-05
29E9PIE4Mitochondrial carrier homolog 2 (fragment) OS=Homo sapiens GN=MTCH2 PE=1 SV=7 (E9PIE4_HUMAN)0.4620791.66E-05
30Q16853Membrane primary amine oxidase OS=Homo sapiens GN=AOC3 PE=1 SV=3 (AOC3_HUMAN)0.4654862.00E-05
31Q15746Myosin light chain kinase, smooth muscle OS=Homo sapiens GN=MYLK PE=1 SV=4 (MYLK_HUMAN)0.4668252.14E-05
32B3KUD6cDNA FLJ39634 fis, clone SMINT2002689, highly similar to SMOOTHELIN OS=Homo sapiens PE=2 SV=1 (B3KUD6_HUMAN)0.4671992.19E-05
33A0A024R5N4WD repeat domain 71, isoform CRA_a OS=Homo sapiens GN=WDR71 PE=4 SV=1 (A0A024R5N4_HUMAN)0.4676612.24E-05
34P68133Actin, alpha skeletal muscle OS=Homo sapiens GN=ACTA1 PE=1 SV=1 (ACTS_HUMAN)0.4678652.26E-05
35K7EM16Vasodilator-stimulated phosphoprotein (fragment) OS=Homo sapiens GN=VASP PE=1 SV=1 (K7EM16_HUMAN)0.4718062.78E-05
36B4DWU6cDNA FLJ51361, highly similar to keratin, type II cytoskeletal 6A OS=Homo sapiens PE=2 SV=1 (B4DWU6_HUMAN)0.4742483.15E-05
37Q14315Filamin-C OS=Homo sapiens GN=FLNC PE=1 SV=3 (FLNC_HUMAN)0.4788643.98E-05
38O75795UDP-glucuronosyltransferase 2B17 OS=Homo sapiens GN=UGT2B17 PE=1 SV=1 (UDB17_HUMAN)0.4865475.80E-05
39B2RTX2Palladin, cytoskeletal associated protein OS=Homo sapiens GN=PALLD PE=2 SV=1 (B2RTX2_HUMAN)0.4917377.42E-05
40G3 V144SH3 and PX domain-containing protein 2B OS=Homo sapiens GN=SH3PXD2B PE=1 SV=1 (G3 V144_HUMAN)0.4928267.81E-05
41Q05682Caldesmon OS=Homo sapiens GN=CALD1 PE=1 SV=3 (CALD1_HUMAN)0.4984780.000101
42Q99795Cell surface A33 antigen OS=Homo sapiens GN=GPA33 PE=1 SV=1 (GPA33_HUMAN)0.4999290.000108
43P21333Filamin-A OS=Homo sapiens GN=FLNA PE=1 SV=4 (FLNA_HUMAN)0.5023170.000121
44B7Z964Sarcolemmal membrane-associated protein OS=Homo sapiens GN=SLMAP PE=1 SV=1 (B7Z964_HUMAN)0.5033730.000127
45A0A142CHG9GO2-q chimeric G-protein OS=Homo sapiens PE=2 SV=1 (A0A142CHG9_HUMAN)0.5034630.000127
46A9LSU1Type IV collagen alpha 1 (fragment) OS=Homo sapiens PE=2 SV=1 (A9LSU1_HUMAN)0.5044290.000133
47B3KM36cDNA FLJ10153 fis, clone HEMBA1003417, highly similar to BAG family molecular chaperone regulator 2 OS=Homo sapiens PE=2 SV=1 (B3KM36_HUMAN)0.5093950.000165
48Q9UMK6Dystrophin (fragment) OS=Homo sapiens GN=DMD PE=4 SV=1 (Q9UMK6_HUMAN)0.5179950.000238
49P10645Chromogranin-A OS=Homo sapiens GN=CHGA PE=1 SV=7 (CMGA_HUMAN)0.5283930.000364
50A8K2W3cDNA FLJ78516 OS=Homo sapiens PE=2 SV=1 (A8K2W3_HUMAN)0.5309590.000403
Figure 2

GO analysis of the differentially expressed genes. The differentially expressed proteins were enriched in molecular function (MF), biological process (BP), and cellular component (CC) terms.

3.2. KEGG Pathway and Protein-Protein Interaction (PPI) Analysis

The KEGG analysis revealed differential protein enrichment in 41 KEGG metabolic pathways (Table 3). The top 20 metabolic pathways are shown in Figure 3(a), and the most abundantly enriched protein was involved in the “focal adhesion” pathway. The differentially expressed proteins involved in “focal adhesion” pathways included COL6A3, MYLK, VASP, FLNC, FLNA, ACTN2, PARVA, ACTN1, ITGA5, CAV1, VCL, PICK1, COL4A2, and ITGA1. The detailed information about these 14 proteins is listed in Table 4. In addition, the results of the PPI analysis showed that VCAM1 was located at the center of the PPI network (Figure 3(b)).
Table 3

Differential protein enrichment in 41 KEGG metabolic pathways.

nMap IDMap nameNumber of protein P value
1map04672Intestinal immune network for IgA production57.81E-05
2map05321Inflammatory bowel disease (IBD)50.000244
3map05140Leishmaniasis70.001049
4map04670Leukocyte transendothelial migration110.001234
5map04510Focal adhesion140.001851
6map05310Asthma40.003388
7map05164Influenza A100.004899
8map05144Malaria50.005231
9map04514Cell adhesion molecules (CAMs)80.007151
10map05145Toxoplasmosis80.007151
11map00982Drug metabolism-cytochrome P45060.007464
12map04973Carbohydrate digestion and absorption40.009718
13map04972Pancreatic secretion70.010504
14map05143African trypanosomiasis50.010539
15map04530Tight junction90.013667
16map04975Fat digestion and absorption40.014458
17map04960Aldosterone-regulated sodium reabsorption40.014458
18map05204Chemical carcinogenesis60.015534
19map04974Protein digestion and absorption60.01907
20map05150Staphylococcus aureus infection60.01907
21map00983Drug metabolism-other enzymes40.020278
22map05332Graft-versus-host disease40.020278
23map04978Mineral absorption40.020278
24map04933AGE-RAGE signaling pathway in diabetic complications60.023035
25map05416Viral myocarditis60.023035
26map05202Transcriptional misregulation in cancer50.023177
27map05323Rheumatoid arthritis50.023177
28map04261Adrenergic signaling in cardiomyocytes70.02526
29map05330Allograft rejection40.027148
30map05320Autoimmune thyroid disease40.027148
31map05146Amoebiasis80.029455
32map04612Antigen processing and presentation70.033293
33map04940Type I diabetes mellitus40.034998
34map04666Fc gamma R-mediated phagocytosis60.03738
35map05412Arrhythmogenic right ventricular cardiomyopathy (ARVC)50.0413
36map04971Gastric acid secretion50.0413
37map04640Hematopoietic cell lineage40.043728
38map04727GABAergic synapse40.043728
39map04145Phagosome110.043902
40map00980Metabolism of xenobiotics by cytochrome P45050.048381
41map04970Salivary secretion50.048381
Figure 3

KEGG pathway and PPI analysis of the differentially expressed proteins. (a) The top 20 KEGG pathways with differentially expressed protein enrichment. (b) VCAM1 was located at the center of the PPI network.

Table 4

The 14 differentially expressed proteins enriched in “focal adhesion” pathway.

nAccessionGNDescription P value
1P12111COL6A3CO6A3_HUMANCollagen alpha-3 (VI) chain OS=Homo sapiens GN=COL6A3 PE=1 SV=50.0196709
2Q15746MYLKMYLK_HUMANMyosin light chain smooth muscle OS=Homo sapiens GN=MYLK PE=1 SV=42.14273E-05
3K7EM16VASPVASP_HUMANVasodilator-stimulated phospho OS=Homo sapiens GN=VASP PE=1 SV=30.000027793
4Q14315FLNCFLNC_HUMANFilamin-C OS=Homo sapiens GN=FLNC PE=1 SV=30.000039772
5P21333FLNAFLNA_HUMANFilamin-A OS=Homo sapiens GN=FLNA PE=1 SV=40.000120726
6B7Z2N5ACTN2ACTN2_HUMANAlpha-actinin-2 OS=Homo sapiens GN=ACTN2 PE=1 SV=10.000524345
7B7Z952PARVAPARVA_HUMANAlpha-parvin OS=Homo sapiens GN=PARVA PE=1 SV=10.00113605
8P12814ACTN1ACTN1_HUMANAlpha-actinin-1 OS=Homo sapiens GN=ACTN1 PE=1 SV=20.00271591
9A8K6A5ITGA5ITA5_HUMANIntegrin alpha-5 OS=Homo sapiens GN=ITGA5 PE=1 SV=20.0030417
10Q03135CAV1CAV1_HUMANCaveolin-1 OS=Homo sapiens GN=CAV1 PE=1 SV=40.0047552
11A0A024QZN4VCLVINC_HUMANVinculin OS=Homo sapiens GN=VCL PE=1 SV=40.0063742
12A0A169TED2PRKCAKPCA_HUMAN kinase C alpha type OS=Homo sapiens GN=PRKCA PE=1 SV=40.0136269
13P08572COL4A2CO4A2_HUMANCollagen alpha-2 (IV) chain OS=Homo sapiens GN=COL4A2 PE=1 SV=40.0140035
14B4DTY8ITGA1ITA1_HUMANIntegrin alpha-1 OS=Homo sapiens GN=ITGA1 PE=1 SV=20.029126

3.3. Verification of the Differentially Expressed Proteins Involved in the “Focal Adhesion” Pathway and Located at the Center of the PPI Network

Western blot assays were performed to measure the expression levels of VCAM1 and 5 other proteins (FLNA, VASP, CAV1, PICK1, and COL4A2) enriched in the “focal adhesion” pathway in GC and adjacent normal tissues. As shown in Figure 4, the results of Western blotting were consistent with the trends revealed by the iTRAQ assay. Furthermore, the expression levels of VASP (highest enrichment in the “focal adhesion” pathway), VCAM1 (located at the center of the PPI network) and CAV1 (related to the metastasis, proliferation, and aggregation of GC cells) were detected by IHC. The results showed that compared with their expression levels in adjacent normal tissues, CAV1 and VASP were downregulated in GC tissues (P < 0.001, Figure 5), while VCAM1 was upregulated in GC tissues (P < 0.001, Figure 5). For validation of the identified differentially expressed proteins, the Human Protein Atlas database was searched to analyze the expression of VCAM1, FLNA, VASP, CAV1, PICK1, and COL4A2 in GC and adjacent normal tissues. As shown in Figure 6, the trends were consistent with the results of Western blotting and iTRAQ analysis. We speculated that the downregulation of COL4A2 outside the cell may downregulate the expression of CAV1 in the cell membrane through cell signaling, thereby affecting the intracellular expression of FLNA, VASP, and PICK1. We generated a simple activity flowchart of these proteins in Figure 7.
Figure 4

Western blotting was performed to measure the levels of VCAM1, FLNA, VASP, CAV1, PICK1, and COL4A2 in GC and adjacent normal tissues. ∗∗P < 0.01.

Figure 5

The expression levels of CAV1, VASP, and VCAM1 in GC and adjacent normal tissues were verified by IHC assays. ∗∗∗P < 0.001.

Figure 6

The protein levels of VCAM1, FLNA, VASP, CAV1, PICK1, and COL4A2 in GC and adjacent normal tissue. Images were obtained from the Human Protein Atlas (http://www.proteinatlas.org/).

Figure 7

A simple activity flowchart of the main differentially expressed proteins.

4. Discussion

In this study, most of the patients were already diagnosed with advanced gastric cancer. Their pathological differentiation was poor. Differentiated tumor cells have significant differences compared to normal gastric mucosa cells. The proliferation and differentiation abilities of these immature tumor cells were much higher than those of early gastric cancer cells. Thus, the overall condition and prognosis of the patients were poor. Due to recent progress in drug treatment, the therapeutic effect of chemotherapy, especially targeted drugs, in the treatment of gastric cancer has been improved. However, there are still many clinical problems that need to be solved. We collected the resected GC samples, tested the differentially expressed proteins and genes in response to individual treatments, and attempted to explore the occurrence and development of tumors from the perspective of proteomics and gene changes and to determine the interactions between proteins and genes. We found only an interaction between TOPO IIa and filamin A (FLNA), and no other intersection has yet been found. We speculate that the main reason for this is that we did not analyze these resected samples according to their Lauren classification. The GC samples from recruited patients were combined according to intestinal type, diffuse type, and mixed type for the iTRAQ analysis. Tan et al. [15] analyzed the gene expression profiles of 37 gastric cancer cell lines. They finally found 171 gene chips and divided them into the gastric intestinal (G-INT) and gastric diffuse subtypes (G-DIF). Further in vitro drug sensitivity tests demonstrated that cells of the G-INT type are sensitive to 5-FU and oxaliplatin. In addition, cells of the G-DIF type are sensitive to cisplatin. However, based on the pathological types of gastric cancer and the use of genotyping to guide evidence-based medicine, treatment options are very limited. However, this is the only method available for the individualized treatment of gastric cancer. To date, there are several mechanisms of multidrug resistance (MDR) in tumors that have been identified. (1) Intracellular drugs are discharged to the outside of the cell membrane by the ABC (ATP-binding cassette) transporter protein family, and the accumulation of intracellular drugs is reduced. (2) The cytotoxicity of chemotherapy drugs is reduced by multiple detoxification molecules. (3) The concentration of drugs is reduced by exocytosis in cells. (4) The abnormal distribution or the change in the number of molecular targets causes drugs to lose their function. (5) The antiapoptotic ability of tumors is enhanced by molecular apoptosis. FLNA, also called filamin A, plays important roles in the formation and function of the cytoskeleton. Studies have demonstrated that the FLNA protein may interact with multiple proteins and take part in the development of tumors [16, 17]. Our iTRAQ results showed that the expression of FLNA in GC samples was decreased by 0.502-fold compared with that in normal adjacent samples. Lv et al. [18] also showed that the expression of FLNA in GC tissues is lower than that in adjacent tissues, which is consistent with the results of our study. Their research also indicated that the survival of the FLNA low-expression group was significantly lower than that of the FLNA high-expression group. Zhai et al. [19] observed that the proliferation, invasion, and metastasis ability of hepatocellular carcinoma, colorectal cancer cells, and nasopharyngeal carcinoma cells were significantly reduced when FLNA was highly expressed. Zhao et al. [20] indicated that xenografted mice with FLNA knockdown showed an enhanced response to docetaxel compared with control xenografted mice with increased apoptosis. Topoisomerase II (TOPO II A) is located in the nucleus of human cells and is a critical enzyme involved in biological behavior, such as DNA replication, transcription, translation, repair, and recombination, chromosome segregation, and nucleic acid conformation [21]. Reports have indicated that the expression of TOPO II A is related to tumor growth and stage, the invasion of tumor cells into the surrounding tissue, and the metastasis of the tumor. Uesaka et al. [22] demonstrated that patients with high expression of TOPO II mRNA are more sensitive to etoposide. Lu et al. [23] showed that the gene is a crucial mediator of apoptosis triggered by doxorubicin. FILIP1L levels were increased markedly through transcriptional mechanisms following treatment with doxorubicin and other TOP2 inhibitors, including etoposide and mitoxantrone, but not the TOP2 catalytic inhibitors merbarone or dexrazoxane. These results indicate that the FILIP1L expression status in tumors may influence the response to anti-TOP2 chemotherapeutics. These studies imply that FLNA might participate in drug resistance to chemotherapy via its enhanced antiapoptosis ability. Vasodilator-stimulated phosphoprotein (VASP) plays an important role in the three-dimensional structure of actin protein and participates in the process of cell migration. VASP is involved in tumor invasion and/or metastasis progression [24]. COL4A2 is involved in tight junctions between a variety of human cells and plays a role in the adhesion of cancer cells [25]. Our results showed that the expression of VASP and COL4A2 in GC tissue was decreased compared with that in normal adjacent tissues. We speculate that the decreased expression of VASP reduced the adhesion and aggregation of tumor cells, which may lead to the invasion and metastasis of tumor cells into their surroundings. In addition, the expression of CAV1 (caveolin 1) and VCAM-1 (vascular cell adhesion molecule-1) in GC tissue was also consistent with that described in previous reports [26, 27]. However, the expression of PICK1 (protein kinase C alpha) was upregulated in our research, which is entirely different from the results of Sun et al.'s research [28]. This might be because the expression of PICK1 is related to the stage of GC, and the mixed samples used in our research were different from the samples they used, which led to different results. In conclusion, we investigated the differential protein expression in gastric cancer tissues and normal gastric mucosa using iTRAQ technology to explore the mechanism involved in gastric cancer. Six highly differentially expressed proteins were screened to identify the potential intersections between the differentially expressed proteins and abnormally expressed genes. This exploratory research may provide useful information for the clinical treatment of GC.
  25 in total

1.  Modification of topoisomerases in mammospheres derived from breast cancer cell line: clinical implications for combined treatments with tyrosine kinase inhibitors.

Authors:  Refael Peleg; Marianna Romzova; Inga Kogan-Zviagin; Ron N Apte; Esther Priel
Journal:  BMC Cancer       Date:  2014-12-03       Impact factor: 4.430

2.  [Expressions of COX-2, PKC-α and miR-101 in gastric cancer and their correlations].

Authors:  Haibing Sun; Yongchang Wei; Honglei Tu; Ning DU; Yang Zhao; Lijuan Hu; Hong Ren
Journal:  Nan Fang Yi Ke Da Xue Xue Bao       Date:  2013-04

Review 3.  Drug discovery strategies in the field of tumor energy metabolism: Limitations by metabolic flexibility and metabolic resistance to chemotherapy.

Authors:  N D Amoedo; E Obre; R Rossignol
Journal:  Biochim Biophys Acta Bioenerg       Date:  2017-02-16       Impact factor: 3.991

4.  Comparative epidemiology of gastric cancer between Japan and China.

Authors:  Yingsong Lin; Junko Ueda; Shogo Kikuchi; Yukari Totsuka; Wen-Qiang Wei; You-Lin Qiao; Manami Inoue
Journal:  World J Gastroenterol       Date:  2011-10-21       Impact factor: 5.742

5.  Intrinsic subtypes of gastric cancer, based on gene expression pattern, predict survival and respond differently to chemotherapy.

Authors:  Iain Beehuat Tan; Tatiana Ivanova; Kiat Hon Lim; Chee Wee Ong; Niantao Deng; Julian Lee; Sze Huey Tan; Jeanie Wu; Ming Hui Lee; Chia Huey Ooi; Sun Young Rha; Wai Keong Wong; Alex Boussioutas; Khay Guan Yeoh; Jimmy So; Wei Peng Yong; Akira Tsuburaya; Heike Grabsch; Han Chong Toh; Steven Rozen; Jae Ho Cheong; Sung Hoon Noh; Wei Kiat Wan; Jaffer A Ajani; Ju-Seog Lee; Manuel Salto Tellez; Patrick Tan
Journal:  Gastroenterology       Date:  2011-04-28       Impact factor: 22.682

6.  VCAM-1 directed immunoliposomes selectively target tumor vasculature in vivo.

Authors:  Sara Gosk; Torben Moos; Claudia Gottstein; Gerd Bendas
Journal:  Biochim Biophys Acta       Date:  2008-01-05

7.  STRING v10: protein-protein interaction networks, integrated over the tree of life.

Authors:  Damian Szklarczyk; Andrea Franceschini; Stefan Wyder; Kristoffer Forslund; Davide Heller; Jaime Huerta-Cepas; Milan Simonovic; Alexander Roth; Alberto Santos; Kalliopi P Tsafou; Michael Kuhn; Peer Bork; Lars J Jensen; Christian von Mering
Journal:  Nucleic Acids Res       Date:  2014-10-28       Impact factor: 16.971

8.  Resistance to cancer chemotherapy: failure in drug response from ADME to P-gp.

Authors:  Khalid O Alfarouk; Christian-Martin Stock; Sophie Taylor; Megan Walsh; Abdel Khalig Muddathir; Daniel Verduzco; Adil H H Bashir; Osama Y Mohammed; Gamal O Elhassan; Salvador Harguindey; Stephan J Reshkin; Muntaser E Ibrahim; Cyril Rauch
Journal:  Cancer Cell Int       Date:  2015-07-15       Impact factor: 5.722

9.  The patterns and timing of recurrence after curative resection for gastric cancer in China.

Authors:  Dan Liu; Ming Lu; Jian Li; Zuyao Yang; Qi Feng; Menglong Zhou; Zhen Zhang; Lin Shen
Journal:  World J Surg Oncol       Date:  2016-12-08       Impact factor: 2.754

10.  Determination of the protein expression profiles of breast cancer cell lines by quantitative proteomics using iTRAQ labelling and tandem mass spectrometry.

Authors:  Karla Grisel Calderón-González; Ma Luz Valero Rustarazo; Maria Luisa Labra-Barrios; César Isaac Bazán-Méndez; Alejandra Tavera-Tapia; Maria Esther Herrera-Aguirre; Manuel M Sánchez del Pino; José Luis Gallegos-Pérez; Humberto González-Márquez; Jose Manuel Hernández-Hernández; Gloria León-Ávila; Sergio Rodríguez-Cuevas; Fernando Guisa-Hohenstein; Juan Pedro Luna-Arias
Journal:  J Proteomics       Date:  2015-04-24       Impact factor: 4.044

View more
  2 in total

Review 1.  The Scaffold Protein PICK1 as a Target in Chronic Pain.

Authors:  Andreas Toft Sørensen; Joscha Rombach; Ulrik Gether; Kenneth Lindegaard Madsen
Journal:  Cells       Date:  2022-04-07       Impact factor: 7.666

2.  Identification and validation of PGLS as a metabolic target for early screening and prognostic monitoring of gastric cancer.

Authors:  Xiaoxia Yuan; Yang Xiao; Yaomin Luo; Chen Wei; Jiaxin Wang; Jinglin Huang; Weiliang Liao; Shenjie Song; Zhen Jiang
Journal:  J Clin Lab Anal       Date:  2021-12-24       Impact factor: 2.352

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.