| 1. BackgroundEsophageal cancer (EC) is one of the six most common malignant diseases worldwide. Linzhou of Henan province, Northern China, has been recognized as the highest incidence area for EC in the world, and the predominant histologic type in this area is squamous cell carcinoma (SCC). The incidence of gastric cardia adenocarcinoma (GCA) is also very high in this area. At present, SCC and GCA are still the leading cause of cancer-related death in this area. The other phenomenon in Linzhou is that the primary SCC and GCA could occur together on the same patient, which has been named as concurrent carcinoma (CC) of the esophagus and gastric cardia by us. CC is not uncommon in this area. But reports about the incidence of CC are different from 0.4 to 2.5 percents. A retrospective investigation on 14805 SCC and GCA patients in Linzhou has demonstrated that the percentage of CC is 7 percents. Because of the similar genomic and environmental background in CC, to characterize the molecular changes in CC may not only facilitate our understanding in the molecular difference of GCA and SCC and elucidate the mechanism of carcinogenesis, but also provide important theoretical basis and information to identify the molecular biomarkers for high-risk subject screening and early diagnosis of SCC and GCA.However, the information for molecular changes in CC is largely unknown and the related reports are very limited because of that most of the CC patients is misdiagnosed, which results in the difficulties in CC. Sample collection, and that literature for CC is mostly case report. Resent studies from our laboratory on the expression of 11 kinds of proteins (P53, Rb, P16, mdm2, C-erb2, bax, MUC1, PCNA, CyclinD1, bcl-2, P21wafl) in CC tissues in Linzhou, Henan province have demonstrated the highest consistent expression rate for MUC1, bcl-2 and P53 in GCA and SCC tissues from the CC patients. Apparently, it is difficult to elucidate the expression of proteins in CC tissue and to identify the key proteins related with carcinogenesis of CC only by analyzing the changes of these proteins. It has been indicated that proteomics is an effective means for screening different proteins related with carcinogenesis. Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) is characterized by high-flux, high sensitivity and veracity in identifying the specific candidates of cancer-related proteins. Because of that SELDI-TOF-MS could mainly detect the different proteins in the serum, it can't reflect the changes of the proteins and genes in tissue. Comparative genomic hybridization (CGH) is a relatively new molecular cytogenetic technique. Combining fluorescent hybridization in situ with digital image analysis, CGH allows the identification of the entire genome for regional variations of DNA sequence copy number (gain, loss, and amplification of DNA sequences) in a single experiment.Apparently, it may facilitate the understanding of protein and gene alterations related with CC patients with proteomic analysis on patient serum together with CGH on cancer tissue. Thus, the present study was undertaken to characterize the molecular changes in the level of protein and genes on CC patients' blood and tissue with SELDI-TOF-MS and CGH methods, to compare the molecular changes among the patients with CC, only SCC and only GCA. The specific aim for this study is to identify candidate proteins and genes related with CC, to facilitate the understanding of mechanism of carcinogenesis in CC, and to provide important clues and information in identifying the biomarkers for high-risk subject screening and early detection in CC.2. Materials and methods2.1 patients 2.1.1 CC patients: All the nineteen cases patients with CC in this study were from Linzhou Center Hospital and Yaocun Esophageal Cancer Hospital in Linzhou, Henan. Of the patients, 15 were males and 4 were females, with a mean age of 62±11 years in male and 71±11 years in female. All the CC patients were conformed to primary SCC and GCA from the same patient by histopathology.2.1.2 SCC patients: Sixty one patients with only SCC in this study were from Linzhou Center Hospital and Yaocun Esophageal Cancer Hospital in Linzhou, Henan. Of the patients, 36 were males and 25 were females, with a mean age of 59±7 years in male and 56±9 years in female. All the patients were confirmed with only SCC, including 7 cases with T1, 8 with T2 and 44 with T3. Of the patients, 25 cases were confirmed with lymph node metastasis.2.1.3 GCA patients: Forty seven patients with GCA in this study were from Linzhou Center Hospital and Yaocun Esophageal Cancer Hospital in Linzhou, Henan. Of the patients, 36 were males and 11 were females, with a mean age of 61±8 years in male and 61±10 years in female. All the patients were confirmed with only GCA, including 32 cases with moderately differentiated adenocarcinoma, 15 patients with poorly differentiated adenocarcinoma. Of the patients, 23 cases were confirmed with lymph node metastasis.2.1.4 Healthy controls: Fifty healthy subjects as controls were inhabitants from Linzhou, the high-incidence area for SCC and GCA. Twenty were males and 21 were females, with a mean age of 56±11 years in male and 60±10 years in female.2.2 Tissues and blood sample collection and processing2.2.1 Serum: Five ml of fasting venous blood from each subject was collected into centrifuge tube in the field. Sera and blood clot were separated and stored in -80℃for further use. Before hemospasia every case was exclused with the disease of the acute or chronic inflammation (hepatitis, etc.).2.2.2 Tissue: The specimen from operation was at once divided into two parts along median line, and flattened, one part was fixed with 85% alcohol, and another part was preserved into liquid nitrogen, and put it into -80℃chest freezer after transferred to laboratory.2.3 IMAC-Cu proteinchip array and SELDI-TOF-MS2.3.1 A total of 10μL of each sample was diluted to 20μL with U9 buffer(9M Urea, 2% CHAPS, 50mM Tris-HC1, PH 9.0) and vortex at 4℃for 30 min. 10μL of the supernatants was mixed with 120μL binding buffer (50mM NaAC at PH3.5), and vortex for 5min.2.3.2 An eight-spot IMAC-Cu array was loaded with 50μl of 100mmolCuSO4, 50μl of 100mM Na Acetate 150μl 50mM HEPES (pH7.0), and 50μl of diluted serum sample was spotted on to each IMAC-Cu array spot and incubated for 60 min in 4℃on a shaker. Then washed with PBS solution (pH7.0). After washing, 0.5μl SPA was applied on each spot and allowed to air-dry.2.3.3 Mass spectrometry was performed in a PBS-Ⅱmass reader (Ciphergen Biosystems) Data were collected between 1500 and 20 000Da. Peaks with m/z＜1500 were excluded as the energy absorbing matrix signal generally interfered with peak detection in this region. Mass accuracy was calibrated externally using the All-in-1 peptide and All-in-1 Protein molecular mass standards (Ciphergen Biosystems). CipherGen ProteinChip software was used to read the data. Then the primary data was analyzed to draw the proteomic mass-spectrogram.2.4 The retrieval of related proteinsTagIdent tool from the ExPASy molecular biology server (http://us.expasy.org/tools/tagident.html) was used to retrieve the candidate protein based on protein molecular weight. By inputting the mass of an unknown protein, this tool will search in the SWISS-PROT protein database for proteins that could match with the requested mass. The extant of mass error was less than 0.5%.2.5 CGH2.5.1 Microdesection of tissuesThe frozen sample was embedded in OTC and cryosected into slides under-20℃and fixed with 95% alcohol. Tumor cells were selected under dissected microscopy. The tumor cells were moved to a centrifuge tube with 1.0ml. Genomic DNA was extracted from the dissected tumor cells by proteinase K/sodium dodecyl sulfate digestion followed by phenol/chloroform/alcohol extraction. Normal reference DNA was prepared from human placenta.2.5.2 Slide preparation with metaphase chromosome spreadsFive milliliter peripheral blood was extracted from healthy donors. The blood sample (lml) was cultured for 72h (5% CO2 incubator) in RPMl1640 (20% fetal bovine serum) 10ml, PHA 120μl (48μg). Blood cells were harvested by arresting with Colcemid (0.05mg/ml) for 30 min, followed by hypotonic treatment in KCl (0.075mM) for 20 min and fixation in cold methanol: acetic acid (3:1). Make up the cell suspension, drop slides, air dry, incubate at 37℃for 3-7 days in use. 2.5.3 Preparation of DNA probeThe protocal in chief for CGH: briefly, genomic DNA from a tumor sample and a normal reference was labeled directly with Spectrum Green-dUTP and Spectrum Red-dUTP (Vysis, Downers Grove, IL, USA) by nick translation, respectively.2.5.4 HybridizationTwo hundred nanograms of labeled tumor DNA and normal DNA probes were used in a 10μl hybridization mixture (containing 55% formamide, 2×SSC), and 10ug human CotI DNA, which was denatured at 75℃for 5 minutes. The slide containing normal metaphase spreads was treated with RNase (100μg/ml) at 37℃for 1 h and then denatured at 75℃in 70% formamide, and 2×SSC for 5 minutes. Hybridization with probes was then carded out at 37℃in a moist chamber for 72 hours. The slide was then washed in 0.4×SSC/0.3%NP-40 at 75℃for 2 min and then in 2×SSC/0.1%NP-40 at room temperature for 2 min. After the washing, the slide was counterstained with 1μg/ml DAPI in an ant fade solution.2.5.5 Digital image analysis in CGHThe hybridized metaphase chromosomes were analyzed using a digital image analysis system containing a Zeiss Axiophot microscope equipped with a Metachrome 2 cooled-charged device camera (Photometics, AZ). Three images of each metaphase were captured using filter wheel-mounted, single band excitation Rhodamine, FITC, and DAPI filters. The image analyses were carried out using Quips CGH Analysis software (Vysis). Five metaphases were analyzed to generate fluorescence ratio profiles in each case. Interpretation of the profiles was performed according to the program guidelines. The thresholds used for interpretation of gains and losses of a DNA sequence copy number was defined as a tumor/reference ratio greater than 1.25 or less than 0.75, respectively, with both the standard and the reverse hybridization methods2.6 Reverse transcription multiplex polymerase chain reaction (RT-PCR).Extraction of RNA was followed by introductions of SV Total RNA Isolation System. Reverse transcription was followed by introductions of ImProm-ⅡTM Reverse Transcription System. The primer was composed by the biological company in Shanghai. COX7C forward: 5'- CCA GAG TAT CCG GAG GTT CA-3', reverse: 5'- GAA AGG TGC GGC AAA CC -3', the length was 151bp, Beta-defensin1-2-3 forward: 5'-TGA TGC CTC TTC CAG GTG TT-3', reverse: 5'- GAT GAG GGA GCC CTT TCT GA-3', the length was 205bp. The reaction system was 10×Reaction buffer 2μl, 10mmol/L dNTPs 21μl, 25mmol/L MgCl2 1.6μl, forward primer 0.8μl, reverse primer 0.8μl, Taq DNA Polymerase 0.1μl, Template DNA 2μl, Sterile deionized Water 10.7μl. Reaction conditions: COX7C: the PCR procedure consisted of 40 cycles of denaturation at 94℃for 30s, annealing at 40℃for 2min, and extension at 72℃for 30s, with initial denaturation of sample cDNA at 95℃for 3min and an additional extension period of 5min after the last cycle. Beta-defensin1-2-3: the PCR procedure consisted of 30 cycles of denaturation at 94℃for 30s, annealing at 58℃for 15s, and extension at 72℃for 45s, with initial denaturation of sample cDNA at 94℃for 3min and an additional extension period of 8min after the last cycle.2.7 StatisticsAll the data were corrected by the Proteinchip Software 3.1 to make the ionic strength and molecular weight to be uniform. Then The Biomarker Wizard (BMW) software application (Ciphergen Biosystems) was used to autodeteet m/z peaks with a signal-to-noise ratio of at least 5. For validation purposes, peak clusters of the training set were applied in the validation set. Group differences were calculated with the same application, comparing mean intensities of all detected peaks between groups with non-parametric statistical tests. P values less than 0.05 were considered statistically significant. Chi-squared test and Kappa test were performed to compare the difference between different groups.3. Results3.1 Proteomie spectra in serum.3.1.1 Proteomic spectra of CC patients and healthy controls Ten different proteins between CC patients and healthy controls were statistical significance (P＜0.05), in which 5 proteins were up-regulated in CC and 5 were down-regulated in CC. Six tumor markers (M2866.21Da, M5103.30Da, M6597.65Da, M7931.55Da, M9311.96Da and M4113.99Da) were identified to discriminate the patients with CC and healthy controls with a veracity, sensitivity and specificity of 78.26%, 73.68% and 80%, respectively.3.1.2 Proteomic spectra of patients with CC, SCC, GCA and healthy controls: Twenty two different proteins were statistical significance (P＜0.05), in which, there were 11 (50%) proteins were coincident among CC patients, SCC patients and GCA patients; 14 (63%) proteins were coincident between CC and GCA patients and 19 (86%) proteins were coincident between SCC and GCA patients.3.1.3 Proteomic spectra of patients with CC, SCC and GCA and healthy controls: Thirteen tumor markers (M3154.10Da, M5077.56Da, M5340.52Da, M5849.26Da, M5869.54Da, M5897.23Da, M5950.50Da, M7770.81Da, M8157.72Da, M1519.88Da, M2889.83Da, M13872.6Da and M1486.54Da) were identified to discriminate the patients with tumor and healthy controls with a veracity, sensitivity and specificity of 85.6%, 88.5% and 82%, respectively.3.1.4 Proteomic spectra among the patients with CC, SCC and GCA: The proteins in serum of the 3 groups were different. Twenty different proteins were statistical significance between CC and SCC patients; 17 different proteins were statistical significance between CC and GCA patients and 9 different proteins were statistical significance between SCC and GCA patients.3.1.5 Proteomic spectra of SCC patients and healthy controls: Between SCC patients and healthy controls, 18 different proteins were statistical significance, in which 3 proteins were up-regulated and 15 proteins were down-regulated in SCC. Seven tumor markers (M9439.58Da, M6627.21Da, M2867.65Da, M4494.08Da, M7762.68Da, M6835.32Da and M4095.94Da) were identified to discriminate the patients with SCC and healthy controls with a veracity, sensitivity and specificity of 85.6%, 88.5% and 82%, respectively.3.1.6 Proteomic spectra of SCC patients with different stages of infiltration: Between T1 and T2 patients, there was no statistical significance. Between T1 and T3 patients, 2 different proteins (M4297.26Da, M4241.78Da) showed statistical significance, which were down-regulated in SCC. The contents of proteins in T3 group were higher than that in T1 group (P＜0.05). And between T2 and T3 patients, 2 different proteins (M6679.04Da, M6801.31Da) showed statistical significance, which were down-regulated in SCC. The contents of proteins in T3 group were higher than those in T2 group (P＜0.05).3.1.7 Proteomic spectra of SCC patients with and without lymph nodes metastasis: Two different proteins (M11730.18D and M9295.28Da) were verified to discriminat SCC patients with and without lymph nodes metastasis, which were down-regulated proteins in SCC. The contents of proteins in metastasis group were lower than those in without metastasis group (P＜0.05). 3.1.8 Proteomic spectra of GCA patients and healthy controls: Between GCA patients and healthy controls, 20 different proteins showed statistical significance, in which 12 proteins were up-regulated and 8 were down-regulated in GCA. Three tumor markers (M5908.48Da, M7943.64Da and M8938.70Da) were identified to discriminate the GCA patients and healthy controls with a veracity, sensitivity and specificity of 77.3%, 85.1% and 70%, respectively.3.1.9 Proteomic spectra in GCA patients with poorly and moderate differentiated GCA: Between the two groups, 15 different proteins showed statistical significance, in which 12 proteins were up-regulated and 3 were down-regulated in GCA. The content of up-regulated proteins in poorly differentiated group was higher than in moderately differentiated group (P＜0.05). The contents of down-regulated proteins in moderately differentiated group were lower than in poorly differentiated group.3.1.10 Compared with healthy controls, there were 10, 18 and 20 kinds of differential proteins identified in CC, SCC and solitary GCA patients. In which 6 candidate proteins were observed in each patient group. Seven proteins were the common differential proteins between the CC and SCC. Seven proteins were the common differential proteins between the GCA and CC. Nine kinds of proteins were the common differential proteins between the GCA and SCC.3.2 SWISS-PROT protein database retrieval and the expression of mRNA of the candidate proteins3.2.1 Screening candidate proteins by SWISS-PROT protein databaseTotal 80 candidate proteins were identified from SWISS-PROT protein database. And 21 proteins (Cytochrome c oxidase subunit 7C, Beta-defensin123, etc) was choosed as candidate tumor markers of CC according to the content of proteins in CC. There was no report about the relationship of these proteins and CC.3.2.2 The mRNA expression of the selected candidate proteins in CC tissuesTwo CC closely related proteins, COX7C and Beta-defensin123, were screened from the 21 CC candidate proteins based on the statistical analysis and the relationship with tumors for further mRNA expression analysis. The results of RT-PCR were as follows: the expression of in CC tissues: the positive rate was 60%, the positive rates in SCC and GCA were the same; the expression of Beta-defensin123 in CC tissues: of the 5 CC, the positive rate (60%) in SCC was higher than in GCA (40%). The mRNA expression for COX7C and Beta-defensin123 was not observed in the normal tissue. 3.3 CGH analysis in CCThe frequency of DNA copying number gains in SCC and GCA from CC patients were similar, which mainly located at 6p and 13q; but the chromosomal profile of DNA copy number losses were different: in SCC the most frequently detected loss were at 8p, 17p, 18q, in GCA the most frequently detected loss were at 1p, 16q, 17p and 21q.3.4 Comparative changes on CC patients by SELDI-TOF-MS and CGHThe chromosomal sites of the 21 candidate proteins by SELDI-TOF-MS were identified from the gene bank. The chromosomal sites of the 16 proteins were identical with the gain and loss of DNA sequences in CC detected by CGH (81%, 17/21). There were 8 chromosomal sites of the candidate proteins corresponded with the gain and loss of DNA sequences in CC detected by CGH. However, four candidate proteins by SELDI-TOF-MS were in consistent with CGH, i.e., 4 up-regulated proteins corresponded with loss of DNA copy number by CGH; and 4 down-regulated proteins corresponded with gain of DNA copy number by CGH.4. Conclusions4.1 Six candidate proteins are identified to occur identically in CC, SCC and GCA patients, and the proportion of these 6 identical candidate proteins to all the candidate proteins is highest in CC (60%), followed by SCC (33%) and GCA (30%), suggesting that these 6 candidate proteins may be the most promising biomarkers related with CC and that there are similar molecular basis involved in carcinogenesis of CC, SCC and GCA in Henan high risk area.4.2 In SCC, the most frequently detected gain sites are identified at 6p (75%) and 13q (50%), and loss sites at 8p (50%) and 17p (50%). The consistent sites with GCA are at 15q (50%) and 6p (50%) in gain and 17p (50%) in loss, suggesting that there are similar molecular mechanism involved in CC, SCC and GCA.4.3 The high coincident rate of candidate proteins and genes detected by SELDI-TOF-MS and CGH (76%) suggests that these candidate proteins and genes may play a key role in carcinogenesis of CC, SCC and GCA and that these two methods are of higher reproducibility and specificity in identifying cancer-related biomarkers. 4.4 A group of candidate proteins are identified with differentiation and lymph node metastasis in SCC and GCA, suggesting that these proteins may be biomarkers for SCC and GCA prognosis.4.5 The high mRNA expression rate of COX7C and Beta-defensin1-2-3 in CC tissue suggesting that these candidate proteins detected by SELDI-TOF-MS may play an important role in carcinogenesis of CC and that SELDI-TOF-MS is an important platform for screening tumor-related markers.
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