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Year : 2014  |  Volume : 10  |  Issue : 1  |  Page : 89-96

Proteomic analysis reveals novel proteins associated with progression and differentiation of colorectal carcinoma

Department of General Surgery, The Third Affiliated XiangYa Hospital of Central South University,Changsha, Hunan 410013, China

Date of Web Publication23-Apr-2014

Correspondence Address:
Xiaorong Li
Department of General Surgery, The Third Affiliated XiangYa Hospital of Central South University, No. 138 Tongzipo Road, Yuelu District, Changsha, Hunan 410013
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/0973-1482.131396

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 > Abstract 

Aim: The objective of this study is to characterize differential proteomic expression among well-differentiation and poor-differentiation colorectal carcinoma tissues and normal mucous epithelium.
Materials and Methods: The study is based on quantitative 2-dimensional gel electrophoresis and analyzed by PDquest.
Results: Excluding redundancies due to proteolysis and posttranslational modified isoforms of over 600 protein spots, 11 proteins were revealed as regulated with statistical variance being within the 95 th confidence level and were identified by peptide mass fingerprinting in matrix assisted laser desorption/ionization time-of-flight mass spectrometry. Progression-associated proteins belong to the functional complexes of tumorigenesis, proliferation, differentiation, metabolism, and the regulation of major histocompatibility complex processing and other functions. Partial but significant overlap was revealed with previous proteomics and transcriptomics studies in CRC. Among various differentiation stage of CRC tissues, we identified calreticulin precursor, MHC class I antigen (human leukocyte antigen A ), glutathione S-transferase pi1, keratin 8, heat shock protein 27, tubulin beta chain, triosephosphate, fatty acid-binding protein, hemoglobin (deoxy) mutant with val b 1 replaced by met (HBB), and zinc finger protein 312 (FEZF2).
Conclusions: Their functional networks were analyzed by Ingenuity systems Ingenuity Pathways Analysis and revealed the potential roles as novel biomarkers for progression in various differentiation stages of CRC.

 > Abstract in Chinese 

结果:排除过多的由蛋白质水解和转译修饰后的亚型的600多蛋白质斑点,11种蛋白质具有统计学差异(95%可信区间),通过肽指纹基质辅助激光解吸/电离飞行时间质谱学辩认出。进展相关的蛋白属于功能复杂的肿瘤发生、增殖、分化、新陈代谢及重大组织适应整合和其它功能。先前结直肠癌的蛋白质组学和转录物组学研究揭示了部分但是有重要意义的重叠。在结直肠癌组织的不同分化阶段,我们找到了钙网织蛋白前体,MHC I类抗原(人白细胞抗原A),谷胱甘肽-S-转移酶pi1,角蛋白8,热激蛋白27,微管蛋白β链,磷酸丙糖,脂肪酸结合蛋白,血红蛋白(脱氧)变异体(val b1被met取代)(HBB),及锌指蛋白312(FEZF2)。

Keywords: Colorectal carcinoma, differentiation, mass spectrometry, proteome, 2-dimensional gel electrophoresis

How to cite this article:
Gan Y, Chen D, Li X. Proteomic analysis reveals novel proteins associated with progression and differentiation of colorectal carcinoma. J Can Res Ther 2014;10:89-96

How to cite this URL:
Gan Y, Chen D, Li X. Proteomic analysis reveals novel proteins associated with progression and differentiation of colorectal carcinoma. J Can Res Ther [serial online] 2014 [cited 2021 Jul 26];10:89-96. Available from: https://www.cancerjournal.net/text.asp?2014/10/1/89/131396

 > Introduction Top

Colorectal carcinoma is the third most common type of cancer diagnosed and the second largest cause of cancer-related deaths in much of the industrialized countries. As proposed in the genetic model of colorectal tumorigenesis, the oncogenesis and progression of CRC arise from colorectal epithelium as a result of the accumulation of genomic instability, genetic alterations in both oncogenes and tumor suppressor genes and epigenetic changes. [1],[2] Much work remains to be done to fully understand the nature and significance of the individual and collective genetic and epigenetic defects in CRC.

Although the genetic basis of cancer progression in CRC is well characterized by gene expression profiling, only few studies reported on profiling the levels of expressed proteins. [3],[4],[5] Stulik et al. first focused on the identification of proteins whose amount is altered in the course of malignant transformation of colon mucosa. [3] By using proteome analysis, Jungblut's group reveals novel proteins associated with proliferation and differentiation of the colorectal cancer cell line Caco-2. [4] Roth et al. further studied the differential expression proteomics on a human syngeneic cellular in vitro progression model of the colorectal adenoma-to-carcinoma sequence, the anchorage-dependent nontumorigenic adenoma derived cell line AA/C1 and the derived anchorage-independent and tumorigenic carcinoma cell line AA/C1/SB10C. [5] Recently, some cytokine plasma biomarkers were identified for predicting progression from colorectal adenoma to carcinoma. [6],[7],[8] However, the reproducibility and overlap of these proteomics studies and gene expression analyses are low in CRC. [9] In recent years, the development of research entailing the protein complement of the genome, the proteome, has evolved significantly as a result of improved technology for 2-dimensional gel electrophoresis, 2-DE image analysis, and mass spectrometry for protein identification. Using these technologies, it is now possible to obtain a more holistic view of physiological and pathological changes associated with CRC. Till now, there are several risk factors for recurrence after surgery in prognosis of CRC, including poor differentiation, lymphovascular invasion, perineural invasion, T4 tumor stage. The advanced differentiation stage of CRC is an important factor that independently affects the consequence of CRC prognosis. The difficulties in the poor prognosis associated with various differentiated stages have generated the need for research focusing on a proteomic marker for diagnostic and prognostic purposes. In the present study, we describe the comparative analysis on well-differentiated and poor-differentiated CRC tissues and normal mucous epithelium by using 2-DE and MS. This is the first experiment to study the differential expression proteomics on various differentiation stages of CRC tissues, thus may provide potential biomarkers for the pathological diagnosis and prognosis of CRC.

 > Materials and Methods Top

Equipment and chemicals

IPGphor, Multitemp III thermostatic circulator, Dry-Strip kit, Immobiline Dry-Strips with nonlinear pH gradient 3-10, 18 cm were procured from Amersham Pharmacia Biotech. Second-dimensional gels were cast and run in a Bio-Rad Protean II Xi Cell. Acrylamide, 1,6-Bis (acryloyl) piperazine (PDA) and urea were purchased from Amresco. CHAPS, TEMED, PMSF, RNase, Tris, SDS, ammonium persulfate, thiourea, iodoacetamide were purchased from Sigma. DTT was purchased from Progema. Standard medium size proteins kit and other reagents were purchased from SIBCB, China. Deionized water prepared with Milli-Q system (Millipore) was used for all buffers.

Patients and samples collection

All 12 cases of CRC and corresponding normal mucous epithelium (10-15 cm away from the edge of the tumor tissue) were obtained after resection in the Third Affiliated Xiangya Hospital of Central South University, China. The tissues were placed in liquid nitrogen immediately and stored at −80°C for use. The clinical and pathological characteristics of the recruited patients was determined and staged according to National Standards for Pathological Study of Colorectal Carcinoma in China, and recorded as: four males, eight females; seven well-differentiation CRCs with average age at 66.1 ± 12.0, five poor-differentiation CRCs with average age at 40.6 ± 13.6. In this study all the patients read and signed informed consents and this study was approved by the Ethics Committee of the Third Affiliated Xiangya Hospital of Central South University, China.


Commercial strips with nonlinear immobilized pH 3-10 gradient (IPG) were used for isoelectric focusing. These strips were swollen in rehydration buffer containing 2 M thiourea, 6 M urea, 4% CHAPS, 1mM PMSF, 60 mM DTT, and 0.4% Pharmalytes pH 3-10 overnight. Protein concentration in 8.0 M urea lysis buffer was determined by modified bicinchoninic acid (BCA) assay and for analytical 2-DE 400 μg was loaded in the first dimension. In the second dimension, 12% SDS-PAGE were used. Proteins were visualized by silver-staining and then scanned using a laser densitometer (300 dpi) linked to an Umax PowerLook III. The 2-DE image computer analysis was carried out using PD-quest 7.3 software (Bio-Rad, USA). The isoelectric points and molecular weights of individual proteins were evaluated using polypeptide SDS-PAGE-standards (Bio-Rad, USA).

Mass spectrometry

All mass spectra were acquired on a DE STR matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) mass spectrometer with Voyager technology (ABI, USA). Tryptic digests were prepared an AnchorChip sample plate according to the manufactory protocol. Both MS and MS/MS data were acquired with a N2 laser at 25 Hz sampling rate. PMF data and MS-MS data were combined using Data explorer 4.0 and Mascot Distiller, and the combined data set was submitted to MASCOT for protein identification. Matrixscience database (MSDB), with Homo sapiens as taxonomy was searched. The other parameters for searching were enzyme of trypsin; one missed cleavage; fixed modifications of carbamidomethyl (C); variable modifications of oxidation (Acetyl N-term); peptide tolerance of 300 ppm. Only significant hits, as defined by the MASCOT probability analysis (P < 0.05), were accepted.

Functional classification

The functional classification of the identified proteins from well-differentiated and poor-differentiated CRCs was based on Ingenuity Pathways Analysis (IPA) (Ingenuity Systems, http://www.ingenuity.com). IPA mapped each modulated protein to its corresponding gene object (e.g., genes, mRNAs, and proteins) in the Ingenuity Pathways Knowledge Base (IPKB). These gene objects, called focus genes, were overlaid onto a global molecular network developed from information contained in the IPKB and were used as the starting point to generate the anticipated biological networks based on their connectivity. A score of 3 indicates that there is a 1/1000 chance that the focus genes are in the network due to random chance. Therefore, genes network with scores of 3 was used as the cut-off for the networks significantly regulated in CRC.

Statistical analysis

Comparison between two characteristics was assessed using the nonparametric Mann-Whitney test and relationships were considered statistically significant when P < 0.05 (SPSS, USA). Proteins separated by 2-DE gels were quantitated in terms of their relative volume (%vol), that is, digitized staining intensity integrated over the area of an individual spot divided by the sum of integrated staining intensities of all spots and multiplied by 100.

 > Results Top

Analysis of protein distribution in matched sets of macroscopically normal mucous epithelium and well-differentiated and poor-differentiated CRC tissues

Whole cell lysates from normal mucous epithelium and CRC tissues obtained from seven well differentiated samples and five poor differentiated samples were analyzed by 2-DE with nonlinear IPG in the first dimension, then by 12% Tricine-SDS-PAGE in the second dimension. In both cases, approximately 680-950 proteins were resolved. The protein profiles of tumor tissue with all identified proteins are displayed in [Figure 1]a-c. The majority of proteins located at 20-90 kDa, pH 4-8, especially in region with 30-85 kDa, pH 4.5-7.7.
Figure 1: Comparison between proteome of well-differentiation and poor-differentiation CRC tissues. Map of the profile of protein spots separated in normal mucous epithelium (a), well-differentiation CRC (b) and poor-differentiation CRC (c) by 2-DE. Total protein (400 μg) was loaded onto an IGP strip (pH 3-10 NL), separated in the second dimension on a 12% polyacrylamide gel and visualized by silver staining. Molecular weight (kDa) and isoelectric values (pI) are shown on the image. Representative gels for three independent experiments derived from the same group of tissues (d)

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Differences in protein expression between normal tissue and differentiated CRC tissue

By comparing to their contribution, 816 ± 63 (n = 12) proteins spots were obtained in normal mucous epithelium, while 781 ± 74 (n = 7) spots in well-differentiated CRC, 766 ± 52 (n = 5) in poor-differentiated CRC. The matching rates were 86.1% and 82.7% for well-differentiation vs. normal and poor-differentiation vs. normal, respectively. To reduce the occasionality from the results, we repeated the same experiment for three times and obtained 682 ± 35 spots in average with 91.5% match rate. The same plots in these three independent experiments were observed in similar position [Figure 1]d, and their sequences were confirmed by MS (data not shown), indicating the reliable repeatability in this study. Among these genome-wide protein profiles, eight protein spots were identified to differentially express in well differentiated CRC, composing three specific proteins and five upregulated proteins; seven protein spots were identified to differentially express in poor differentiated CRC, composing four specific proteins and three upregulated proteins. There were four overlap protein spots among the well-differentiated and poor-differentiated CRC, suggesting the potential roles as biomarkers in both well-differentiated and poor-differentiated CRCs. Differentially expressed protein spots were subsequently subject to MS/MMS analysis. The MS/MS data were retrieved using the search algorithm MASCOT against the Matrix science protein sequence database. A total of 11 different proteins from 2-DE gels were successfully identified from the above altered proteins. The proteins identified in our study were listed by using a number of criteria including pI, MW, MOWSE score and expression level in [Table 1].
Table 1: Differentially expressed proteins identified by MALDI - TOF - MS after 2 - DE analysis of CRC and the corresponding normal mucous epithelium

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Validation of regulated proteins

A selected panel of six regulated proteins was analyzed to validate quantitative differences of protein expression in well-differentiated and poor-differentiated CRCs. These novel proteins had not been previously found in other studies to be regulated in various differentiation stage of CRC. Glutathione S-transferase pi1 (GSTP1) and KTR8 were upregulated in both well-differentiated and poor-differentiated CRCs, while tubulin beta chain (TUBB), triosephosphate (TPI), and fatty acid-binding protein (FABP1) were induced in well-differentiated CRC. Only human leukocyte antigen A (HLA-A) was found to upregulate in poor-differentiated CRC [Figure 2].
Figure 2: Validation of regulated proteins from various differentiation stage of CRC in 2-DE images. Asterisk means significant difference (P < 0.05)

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Functional context of regulated proteins

To further study the relationship and function of these altered proteins, functional classification was performed by using IPA (Ingenuity Systems, http://www.ingenuity.com). The 10 differentially expressed proteins were searched with their corresponding access numbers for their exact gene counterparts in IPA. The modulated proteins were overlaid onto a global molecular network developed from information contained in the IPKB highlighting the correlation with the known biomarkers in CRCs [Figure 3]. In agreement with previous proteomic studies on CRC, these regulated proteins identified by the current differential approach can be divided into four functional categories: I, Tumorigenesis, proliferation and differentiation; II, Metabolism; III, Major histocompatibility complex; and IV, other function [Figure 3].
Figure 3: Network analysis of differentially expressed proteins in CRC performed using the IPA software. The selected proteins are divided into four groups and their connections with known biomarkers in CRC are highlighted by IPKB

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 > Discussion Top

CRC is the common malignant tumors of the digestive tract caused by genetic lesions and stress from the environment. Previous studies have shown that the progression of CRC is a multi-factor and multi-step process, involving many signaling pathways like p53, KRAS, DCC, c-myc, CD44, nm23, etc. [1],[2],[10] Differential proteome analysis of tumor and normal tissues allows the identification of aberrantly expressed proteins in CRC that may provide valuable information for discovering candidate biomarkers for diagnosis, prognosis, and treatment of CRC as well as an understanding of carcinogenesis. [11],[12] The differentiation stages of CRC have a crucial impact on the biological characteristics of tumor, such as growth rate, invasion, and metastasis. In order to establish the protein expression profiles of CRC at various differentiation statuses and to characterize CRC progression and differentiation associated proteins, the comparative proteomic study was performed among the human well-differentiated CRC, poor-differentiated CRC and the corresponding normal mucous epithelium. Ten nonredundant differentially expressed proteins were identified by MALDI-TOF-MS. The method makes no assumption about known or unknown molecules, allowing the process to be independent of any presupposed hypotheses.

The differentially expressed proteins can be broadly divided into four categories. The first class is related to tumorigenesis, proliferation, and differentiation, including heat shock protein 27, TUBB, and zinc finger protein 312 (FEZF2). HSP27, an inducible heat shock protein is upregulated in cancer cells, where it facilitates multiple cellular processes pertinent to tumorigenesis. [13] In our study, HSP27 was found to specifically express in well-differentiation CRC and its functional analysis showed the connection with other prominent CRC biomarkers, for example, epidermal growth factor receptor (EGFR), protein kinase B (AKT), tumor necrosis factors, and human epidermal growth factor receptor 2 (ERBB2), etc., In cell culture-based studies, HSPs have been shown to promote cancer development by increasing cellular migration, [14] differentiation, [15] and drug resistance. [16] HSPs have also been shown to promote cell survival through the inhibition of apoptosis [17] and cell senescence. [18] Due to their involvement in cancer development, the inhibition of HSPs has been proposed as a potential cancer treatment strategy [19],[20] and increased HSP expression may also predict the response to some anticancer treatments. HSP27 has been implicated in the prognosis of specific cancers, including CRC. For example, the expression of HSP27 has been associated with distant metastasis [21] and poor survival. [22],[23] Recently, Ghosh et al. showed that HSP27 expression in CRCs was strongly associated with the copresence of wild-type KRAS and activated PI3K/AKT, indicating a possible role of HSP27 in overcoming PI3K/AKT induced senescence. [24]

TUBB is one of several members of a small family of globular proteins, which making up microtubules. Recently, genetic analysis of TUBB was found to be related to nonsmall-cell lung cancer. [25] In our study, we first identified TUBB as a potential biomarker in CRCs since it was upregulated in well-differentiated samples and showed relevance with other biomarkers like FOS, MDM2, PPARG, etc., We also first identified FEZF2 as a novel biomarker in low-differentiated CRC. FEZF2 is a transcription repressor, playing a role in the specification of corticospinal motor neurons and other subcerebral projection neurons. [26] Denkert et al. reveals the metabolic pattern of FEZF2 in invasive ovarian carcinomas and ovarian borderline tumors. [27] Only one biomarker HNF4A was shown the relevance of FEZF2 in CRC through our functional analysis.

The second category is related to protein and energy metabolism in tumor cells, including TPI, FABP1, and hemoglobin (deoxy) mutant with val b 1 replaced by met (HBB). In view of the role of FABP1 in cell growth and differentiation, it has been proposed that the alterations that occur in individual fatty acid binding protein expression during tumor development and progression [28],[29] may contribute to tumorigenesis. Additionally, it has been suggested that the expression of individual FABP1 in tumors may serve as useful diagnostic markers and novel therapeutic targets. [30] In prostate cancer FABP1 mRNA and protein has been found to show markedly increased expression in both primary tumors and in prostate cancer derived cell lines. [30] Inhibition of FABP1 by antisense oligonucleotides in prostate cancer decreases tumor cell proliferation and promotes apoptosis. Expression of FABP1 has also previously been observed in primary liver cancer with FABP1 specifically localized to tumor cells. Higher frequency of FABP1 expression occurs in hepatoblastomas compared with hepatomas. [31] Those observations in diverse types of tumors highlight tumor-specific expression patterns and presumably reflect tissue-specific regulatory mechanisms for FABP1. Although the roles of TPI and HBB in differentiation of CRC have not yet known, it provides the hint that poor-differentiated CRC links carbonhydrate metabolism and hemoglobin in production to tumor progression.

The third category is related to human leukocyte antigen, for example, HLA-A. HLA-A is a component of certain major histocompatibility complex (MHC) class I cell surface receptor isoforms that resides on the surface of all nucleated cells and platelets. Podack's group showed allogeneic vaccination with a B7.1 HLA-A gene-modified adenocarcinoma cell line in patients with advanced nonsmall-cell lung cancer, which suggests clinical benefit from vaccination. [32] Moreover, total loss of membranous HLA-A staining was significantly more frequent in MSI-H colorectal cancer. [33] Our result showed that HLA-A differentially expressed in both well-differentiated and poor-differentiated CRCs, and its interaction with BAX, BIRC5, and TERT, etc., indicating its potential role in advanced-stage of CRC.

The fourth category is a combination of proteins whose main functions are unknown in CRC, including CALR, GSTP1, and keratin 8 (KRT8). McCool et al. showed that CALR plays roles during mucin synthesis in LS180 and HT28/A1 human colonic adenocarcinoma cells. [34] Recent studied showed alterations of CALR expression in colon carcinomas containing both neoplastic and nonneoplastic cells. [35] The GSTP1 isoenzyme contributes around 80% of total GST enzyme activity within colonic tissue and is commonly over expressed by many tumors. [36] GSTP1 directly participates in the detoxification of platinum compounds and has been shown to be a factor responsible for intrinsic and acquired resistance of cancer cells to several other chemotherapeutic agents such as etoposide and adriamycin. [37] Furthermore, as radiotherapy exerts its effects via the generation of reactive oxygen species, GSTP1 may conjugate these with glutathione and so render radiotherapy inefficient. [38] A relationship has been suggested between decreased GSTP1 tissue expression and increased response to therapy in lung cancer, ovarian cancer, and cancers of the gastrointestinal tract. [39],[40],[41],[42] Therapy itself may also alter the expression of GSTP1 and therefore the way an individual responds may change during treatment. [43] KRT8 is a keratin protein, which can be used to differentiate lobular carcinoma of the breast from ductal carcinoma of the breast. [44] KRT8 is often used together with KRT18 and KRT19 to differentiate cells of epithelial origin from hematopoietic cells in tests that enumerate circulating tumor cells in blood. [45] The moderation of the effects of TNF may be the fundamental function of KRT8 common to liver regeneration, inflammatory bowel disease, hepatotoxin sensitivity, and the diagnostic, persistent expression of these keratins in many carcinomas. [46]

In summary, this study describes for the first time a proteomics approach employed to understand biochemical processes related to CRC differentiation and progression, highlighting alterations in proteins that involved in tumorigenesis, proliferation and differentiation, protein and energy metabolism and human leukocyte antigen. Of considerable significance in this context may be the strong upregulation of proteins like GSTP1, which may response to therapy in CRC, and KRT8, which related to differentiation of CRC and represents a potential target for the prevention and treatment of CRC.

 > References Top

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  [Figure 1], [Figure 2], [Figure 3]

  [Table 1]


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