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Study of differential proteins in lung adenocarcinoma using laser capture microdissection combined with liquid chip-mass spectrometry technology

BU, Li-na; YANG, Shuan-ying; LI, Feng-tao; SHANG, Wen-li; ZHANG, Wei; HUO, Shu-fen; NAN, Yan-dong; TIAN, Ying-xuan; DU, Jie; LIN, Xiu-li; LIU, Yan-feng; LIN, Yu-rong; RONG, Biao-xue

doi: 10.3760/cma.j.issn.0366-6999.2010.22.026
Original article
Free
SDC

Background In recent years the proportion of lung adenocarcinoma (adCA) which occurs in lung cancer patients has increased. Using laser capture microdissection (LCM) combined with liquid chip-mass spectrometry technology, we aimed to screen lung cancer biomarkers by studying the proteins in the tissues of adCA.

Methods We used LCM and magnetic bead based weak cation exchange (MB-WCX) to separate and purify the homogeneous adCA cells and normal cells from six cases of fresh adCA and matched normal lung tissues. The proteins were analyzed and identified by matrix assisted laser desorption/ionization time-of-fight mass spectrometry (MALDI-OF-MS). We screened for the best pattern using a radial basic function neural network algorithm.

Results About 2.895×106 and 1.584×106 cells were satisfactorily obtained by LCM from six cases of fresh lung adCA and matched normal lung tissues, respectively. The homogeneities of cell population were estimated to be over 95% as determined by microscopic visualization. Comparing the differentially expressed proteins between the lung adCA and the matched normal lung group, 221 and 239 protein peaks, respectively, were found in the mass-to-charge ration (M/Z) between 800 Da and 10 000 Da. According to t test, the expression of two protein peaks at 7521.5 M/Z and 5079.3 M/Z had the largest difference between tissues. They were more weakly expressed in the lung adCA compared to the matched normal group. The two protein peaks could accurately separate the lung adCA from the matched normal lung group by the sample distribution chart. A discriminatory pattern which can separate the lung adCA from the matched normal lung tissue consisting of three proteins at 3358.1 M/Z, 5079.3 M/Z and 7521.5 M/Z was established by a radial basic function neural network algorithm with a sensitivity of 100% and a specificity of 100%.

Conclusions Differential proteins in lung adCA were screened using LCM combined with liquid chip-mass spectrometry technology, and a biomarker model was established. It is possible that this technology is going to become a powerful tool in screening and early diagnosis of lung adCA.

Chin Med J 2010;123(22):3309–3313

Department of Respiratory Medicine (Bu LN, Yang SY, Li FT, Shang WL, Huo SF, Nan YD, Tian YX, Du J, Lin XL, Liu YF, Lin YR and Rong BX), Department of General Thoracic Surgery (Zhang W), Second Affiliated Hospital of Medical School, Xi'an Jiaotong University, Xi'an, Shaanxi 710004, China

Department of Respiratory Medicine, Xi'an Second Hospital, Xi'an, Shaanxi 710004, China (Bu LN)

Correspondence to: Dr. YANG Shuan-ying, Department of Respiratory Medicine, Second Affiliated Hospital of Medical School, Xi'an Jiaotong University, Xi'an, Shaanxi 710004, China (Tel: 86–13991392919. Fax: 86–29–87678599. Email: yangshuanying66@163.com)

This work was supported by grants from the National Natural Science Foundation of China (No. 30570795) and Program for New Century Excellent Talents in University (No. NECT-06–0845) and the Program in Science and Technology of Xi'an, Shaanxi Province (No. SF08009(1)).

(Received June 26, 2010)

Edited by WANG Mou-yue and LIU Huan

Adenocarcinoma (adCA) is one of the most common histological types in lung cancer. In recent years, the proportion of adCA of all lung cancers has increased.1 The incidence of lung adCA in females has increased more rapidly than in males, making up 68% lung cancers in women. The short five-year overall survival rate for patients with lung cancer is mainly due to unavailability of early diagnosis.2 Molecular markers to distinguish lung tumors from normal lung tissue have yet to be established.

Laser capture microdissection (LCM) technology can effectively separate tumor tissue from normal lung tissue, allowing the study of tissues markers for lung cancer. The liquid chip-mass spectrum technology is one of the most powerful tools for proteomics research, and can effectively screen for lung cancer biomarkers. This study was designed to screen for lung adCA biomarkers by studying protein expression in adCA and normal lung tissue using LCM combined with liquid chip-mass spectrometry technology.

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METHODS

Patients

Six patients suffering from lung adCA in this study were attended to the Second Affiliated Hospital of Medical School of Xi'an Jiaotong University from June 2006 to June 2008. The diagnosis of these cases was pathologically confirmed. No patients received chemotherapy or radiotherapy before operation. The average age of the six patients (two male and four female) was 58 years old (range 45–69 years). According to the International Union against Cancer (UICC, 2002) Staging System of Lung Cancer, one case was at stage IIa, two stage IIb, one stage IIIa, and two stage IIIb. One tumor was well differentiated, three were moderately differentiated, and two poorly differentiated. Informed consent was obtained from all patients and the study was approved by the Medical Ethics Committee of the hospital.

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Main reagents and instruments

The main reagents and instruments used in this study were as follows: 9.5 mol/L urea, 65 mmol/L DTT, 4% CHAPS, α-cyano-4-hydroxycinnamic acid and Eppendorf tubes (Xi'an Run-de Biotechnology Company Ltd., China); magnetic beads based weak cation exchange (MB-WCX) and magnetic bead separation device (Bruker Daltonics, Germany); freezing microtome (Microm/HM500, Germany); Arcturus PixCell II system and LCM caps (Arcturus, USA); optimal cutting temperatnre compound (SAKURA, USA); AutoflexIImass spectrometer (Bruker Daltonics).

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Tissue samples

The lung adCA and matched normal tissues were collected immediately after operation in the operation room. The size of each tissue block was about 1 cm × 1 cm ×1.5 cm. The tissues were washed three to four times to remove blood using 0.9% sodium chloride solution. The satisfied tissues were stored at -80°C until further use. The preparation of the tissue specimen was restricted to 30 minutes.

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Section and staining

The tissues were transported into a freezing microtome and embedded in optimal cutting temperature compound. An 8.0 μm-thick cryostat section was made and stained by improved hematoxylin and eosin (H&E) for LCM. The process of improved H&E was: staining with hematoxylin for 1 second; washing with distilled water for 10 seconds; staining with eosin for 10 seconds; dehydrating with 85% ethanol for 10 seconds, 90% ethanol for 10 seconds, and 100% ethanol for 10 seconds; clearing with xylene I for 2 minutes and xylene II for 2 minutes. The above dyeing process must be carried out in less than 6 minutes in an ice box at a temperature between 4 °C-10°C.3

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LCM

The stained sections were air dried and micro-dissected using the Arcturus PixCell II system. The conditions of LCM were: duration 15.5 ms, current 8.0 mA, sample thickness 8.0 μm, laser spot size 7.5 μm, pulse power 80 mW, pulse width 25.0 ms, threshold voltage 60 mV. Each stained section was shot with about 5000 pulses and the duration for LCM was restricted to 30 minutes. LCM caps were stored at -80°C until further use.

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Protein extraction and quantity

Cell lysate buffer (9.5 mol/L urea, 65 mmol/L DTT, 4% CHAPS, 0.2% IPG buffer) was added to all the samples in an ice-bath. All of the solutions were collected into an Eppendorf tube and crushed with ultrasound for 50 seconds at 80 W. Then, the extracted solution was recovered by centrifugation at 15 000 r/min for 45 minutes. Finally, the supernatant was taken and quantitated by the Bradford method. Average protein contents of adCA and matched normal cells were approximately 0.7 mg/ml and 0.6 mg/ml, respectively. Every 60 μg of protein was subpackaged into an Eppendorf tube and stored at -80°C until further use.

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Separation and purification

MB-WCX binding solution stored at -4°C was taken out and mixed for 1 minute. Then, 10 μl of MB-WCX binding solution, 10 μl of phosphate buffer solution and 30 μg of sample protein were added to a 200 μl sample tube to be mixed completely and incubated for 5 minutes at room temperature. The sample tube was placed on the magnetic bead separation device (Bruker Daltonics, Germany) for 1 minute. All samples were separated and purified according to the manufacturers’ instructions through a standard protocol (ClinProtTM, Bruker Daltonics).4,5

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Anchor chip spotting

Ten microliter of freshly prepared a-cyano-4-hydroxycinnamic acid (0.3 g/L in ethanol : acetone = 2:1) was mixed with 1 μl of the eluted sample. The eluted sample was not taken out of the freezer until analysis. One microliter of the mixture was spotted on a matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) target (Bruker Daltonics), and dried at room temperature for analysis.

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MALDI-TOF-MS

The system quality control was as follows: before data acquisition of every eight samples, the standard preparation would be calibrated. Eleven peptides were used as external standard preparation and the average molecular weight deviation was no more than 0.01%. Parameter settings were as follows: the ion source accelerating voltage was 20 kV, the ion detention withdraws 120 ns, and laser frequency 25 Hz. Peak M/Z values and intensities were collected in a mass range of 800 to 20 000 Da, focusing on a mass range of 800 to 10 000 Da. Profile spectra were acquired from an average of 400 laser shots per sample and averaged the value.

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Data acquisition

The primary data were analyzed by FlexAnalysis3.0 (Bruker Daltonics). The Top Hat Baseline and Savitsky Golay method were used for baseline processing and filter processing. The protein peaks were filtered to maintain the peaks with signal-to-noise ratios >4. We adjusted and normalized the processed data and measured the peak area and used it as a quantitative standard for the protein peak to obtain the protein multi-peptide atlas of the tissue.

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Statistical analysis

Difference between the maximal and minimal average peak area/intensities (Dave) and two kinds of comparison t tests (P value of t test, PTTA) were used for data analysis of protein peaks. According to statistical difference and distributed type, proteins were selected for sorting and compared. Using a radial basic function neural network algorithm to determine the sensitivity and specificity of proteins, we could screen for the best potential biomarkers for identifying lung adCA. A P <0.05 was considered statistically significant.

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RESULTS

LCM result

An 8 μm thick cryostat section of the adCA tissue was stained for LCM (Figure 1). About 2.895×106 and 1.584×106 cells were satisfactorily captured by LCM from the six samples of fresh lung adCA and matched normal lung tissues, respectively (Table 1). The homogeneities of cell population were estimated to be over 95% as determined by microscopic visualization.

Figure 1.

Figure 1.

Table 1

Table 1

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Protein multi-peptide atlas comparison

Totally 386 LCM caps were captured from the six tissue samples of fresh lung adCA and matched normal lung tissues. Separated and purified by magnetic beads, analyzed with MALDI-TOF-MS, the acquired data were analyzed and a protein multi-peptide atlas of the tissue was obtained. A total of 221 differential protein peaks were found in the lung adCA group and 239 protein peaks were found in the matched normal lung group in a mass range of 800 to 10 000 Da (Figure 2).

Figure 2.

Figure 2.

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Screening for different proteins

According to PTTA, the expression of protein peaks at 7521.5 M/Z and 5079.3 M/Z had the largest difference between the two tissue sources (Table 2). They were significantly less in the lung adCA compared to the matched normal lung group and their mean value and the standard deviations were smaller than those of the latter. The two protein peaks could accurately separate the lung adCA from the matched normal lung group by the sample distribution chart (Figure 3).

Table 2

Table 2

Figure 3.

Figure 3.

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Model setting

A discriminatory pattern consisting of three proteins at 3358.1 M/Z, 5079.3 M/Z and 7521.5 M/Z was established by radial direction primary function neural network algorithm with a sensitivity of 100% and a specificity of 100% to separate the lung adCA from the matched normal lung group.

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DISCUSSION

It is well known that the best method to cure lung cancer depends on early surgery. But when diagnosed, approximately 70% tumor patients had mostly been at mid- or late stage disease. Because of distant translation, most of the patients missed the best opportunity for surgery. The best strategy to cure cancer is early detection and individual treatment.6 Therefore, at present, finding an early diagnostic method for lung cancer is the best way to increase the cure rate of lung cancer and reduce the fatality rate of lung cancer patients. Since the postgenome time's advent, proteomic technology has become one of the most effective method on tumor researh.7 Two-dimensional gel electrophoresis (2-DE) is a major component of proteomics. It has been used widely for a decade. However, it is very time-consuming and has poor reproducibility, which reduces its value in the clinical practice. Difference in-gel electrophoresis, a new technology developed from 2-DE, has much better sensitivity, specificity, reproducibility and high flux characteristics compared to the traditional 2-DE. Difference in-gel electrophoresis requires fluorescence markers like Cy2, Cy3 and Cy5, and the marking process is too complicated and expensive for general use.8,9 Surface enhanced laser desorption/ionization time-of-flight mass spectrometry technology has many virtues, such as small spot-samples, easier handling, and higher sensitivity, but its reproducibility is disputed by some researchers.10,11

LCM, a revolutionary technology for tumor research, can separate single cell or groups of the same kind of cell population from complex tissues and effectively solve the problem of tissue heterogeneity in experiments.12,13 Specimens obtained by LCM are too small in amount to meet the need of 2-DE.14,15 Liquid chip-mass spectrometry technology is a powerful proteomic technology for screening potential distinctive proteins and tumor biomarkers, which has been applied to cancer research in recent years.4 It can detect about 2000 different proteins or polypeptides with high sensitivity, specificity and reproducibility. At the same time, these proteins or polypeptides can be identified through MS/MS.16,17 This technology has been used in the evaluation of many cancers, such as rectum cancer, bladder cancer, thyroid gland cancer, and lung cancer. Significant advances have made in the early diagnosis of cancer in recent years.5,18–21 In the study of pediatric acute lymphocyte leukemia, Pitts et al22 have established a biomarker model consisting of four proteins peaks (2437.7 M/Z, 6457.9 M/Z, 7772.2 M/Z and 9420.5 M/Z) by applying the liquid chip mass spectrum technology, which, hopefully, can become widely used as markers of acute lymphocyte leukemia. Currently, Germany is using this technical platform for early diagnosis of acute lymphocyte leukemia in children.

In this study, we collected six cases of lung adCA. The adCA and the matched normal lung cells were captured using LCM technology. The homogeneities of cell population were estimated to be over 95% as determined by microscopic visualization, which is similar to reported results.23 It effectively overcomes the problem of heterogeneity of the lung cancer tissues, reduces the background disturbance in the protein analysis and improves the results of the experiment.

In practical application, liquid chip technology using MB-WCX to separate and purify cells is very simple. It only needs several rounds of mixing, flushing and washing. The simplicity of operation makes it a prospective clinical proteomic sample preparation. MB-WCX is commonly used in the preparation of samples coming from tissues and serum. Its surface has weak negative charges which can bind proteins through positively charged amino acids, such as lysine, arginine, histidine. Through comparison of the expression of different proteins in lung adCA and matched normal lung tissue using MALDI-TOF-MS technology, 221 and 239 protein peaks with the M/Z ranging from 800 to 10 000 Da were found. The expression of two protein peaks at 7521.5 M/Z and 5079.3 M/Z showed the largest difference between the tumor and normal lung tissue. They were more weakly expressed in the protein multi-peptide atlas of the lung adCA than in the normal lung tissue. The two protein peaks were positioned at 7521.5 M/Z on the X-axis and 5079.3 M/Z on the Y-axis, and could accurately separate the lung adCA samples from the matched normal lung tissue with only a small overlapping area. A discriminatory pattern consisting of three proteins at 3358.1 M/Z, 5079.3 M/Z and 7521.5 M/Z was established by the RBF neural network algorithm with a sensitivity of 100% and a specificity of 100%, separating the lung adCA from the matched normal lung tissue. We suggest that the three proteins might serve as potential biomarkers for lung adCA, although further research is still needed. In the future we will enlarge the sample size, try other kinds of chips or combinations of chips, verify the reproducibility, and confirm the proteins by immunohistochemistry and Western blotting.

In conclusion, our study reports the use of LCM combined with liquid chip-mass spectrometry technology to screen the proteins in lung adCA and establish this diagnostic model. This technology may become a powerful diagnostic tool for lung cancer.

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Keywords:

lung adenocarcinoma; laser capture microdissection; magnetic beads; mass spectrometry

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