Article pubs.acs.org/jpr
Effect of Lung Squamous Cell Carcinoma Tumor Microenvironment on the CD105+ Endothelial Cell Proteome Huiqin Zhuo,†,¶ Zhi Lyu,‡,¶ Jing Su,§,∥,¶ Jian He,§,∥ Yihua Pei,† Xiao Cheng,‡ Nuo Zhou,§,∥ Xiaoling Lu,*,§,∥ Sufang Zhou,*,§,∥ and Yongxiang Zhao*,§,∥ †
Central Laboratory, The Affiliated Zhongshan Hospital, Xiamen University, Xiamen, Fujian 361004, China Respiratory Department, The Affiliated Zhongshan Hospital, Xiamen University, Xiamen, Fujian 361004, China § National Center for International Research of Biological Targeting Diagnosis and Therapy, Guangxi Medical University, Nanning, Guangxi 530021, China ∥ Guangxi Key Laboratory of Biological Targeting Diagnosis and Therapy Research, Guangxi Medical University, Nanning, Guangxi 530021, China ‡
S Supporting Information *
ABSTRACT: In lung cancer, antiangiogenic treatment targeting tumor endothelial cells (ECs) provides a survival advantage. To fully elucidate the behavior of ECs in a tumor microenvironment, high-purity (>98%) normal, paratumor-, and tumorderived CD105+ ECs were purified from lung squamous cell carcinoma by incubating cells with anti-CD105 antibody-coated magnetic beads. These cells exhibited typical EC characteristics. Totally, 1765 proteins were identified with high confidence by isobaric stable isotope tags and two-dimensional LC/MS/MS (iTRAQ-2DLC/MS/MS). In particular, 178 and 162 proteins were differentially expressed in paratumor- and tumor-derived ECs, respectively, compared to normal ECs. The up- and down-regulation trends showed good interassay correlation. Using gene ontology, they were classified into genes involved in major reprogramming of cellular metabolic processes, oxidative stress response, redox homeostasis, apoptosis, and platelet degranulation/activation. Moreover, tumor angiogenesis-initiating ECs appeared to acquire distinct properties. For example, cell migration and regulation of smooth muscle cell migration of paratumorderived ECs were significantly faster than that of normal and tumor-derived ECs. Among them, two migration-associated proteins, neuropilin 1 and platelet-derived growth factor receptor β predominantly expressed in ECs of paratumor from 16 patients with lung squamous cell carcinoma, were identified as potential biomarkers for antiangiogenic therapy. KEYWORDS: iTRAQ, lung squamous cell carcinoma, paratumor endothelial cells, proteomics, tumor endothelial cells
■
INTRODUCTION Lung cancer is the leading cause of cancer-related deaths worldwide. Recent studies have revealed that survival can be improved by treatment with antiangiogenic agents.1−3 Tumor angiogenesis involves proliferation, migration, differentiation of endothelial cells (ECs), modification of the extracellular matrix (ECM), and recruitment of accessory cells and is currently regarded as a potential therapeutic target for cancer.4 ECs as key determinants of the tumor microenvironment interact with tumor cells, ECM, and immune killer cells. To date, antiangiogenic strategies have largely focused on targeting ECs.5 Therefore, it is very important to fully understand the characteristics of ECs in tumor microenvironment. ECs within the tumor microenvironment undergo phenotypic and epigenetic changes during tumor initiation, progression, and metastasis. Cumulative evidence has indicated that cytogenetics, gene expression, morphology, pathophysiology, and atypical multipotent plasticity of tumor-derived endothelial cells (TECs) differ from those of normal endothelial cells (NECs).6−10 St Croix et al. first isolated © 2014 American Chemical Society
TECs from human colon carcinoma and found that a series of genes were specifically up-regulated in TECs (called tumor endothelial markers (TEMs)).6 TECs isolated from human hepatocellular carcinoma possess enhanced angiogenic activity and resistance to chemotherapeutic drugs and angiogenesis inhibitors.7 Moreover, 22 genes associated with apoptosis, extracellular matrix formation, bone remodeling, angiogenesis, cell adhesion, and cell proliferation were found to be substantially up-regulated in multiple myeloma ECs compared with ECs derived from patients with monoclonal gammopathy of undetermined significance.8 NECs are widely used to construct in vitro tumor EC models, using conditioned medium from cultured cancer cells for studies on tumor angiogenesis and antiangiogenic Special Issue: Proteomics of Human Diseases: Pathogenesis, Diagnosis, Prognosis, and Treatment Received: June 22, 2014 Published: September 10, 2014 4717
dx.doi.org/10.1021/pr5006229 | J. Proteome Res. 2014, 13, 4717−4729
Journal of Proteome Research
Article
therapy.11,12 TECs have rarely been isolated and cultured successfully. Moreover, NECs (obtained from tumor-adjacent normal tissues), human umbilical vein ECs, or human dermal microvascular ECs were the most commonly used controls for studies involving TECs obtained from human tumors or from tumor xenografts. However, the degree of modulation of ECs in response to tumor microenvironment varies, indicating that ECs obtained from tumor loci and paratumor ECs (PECs) are more relevant for elucidating the effects of the tumor environment on EC properties than are NECs. Compared with traditional vascular markers, for example, CD31, endoglin (CD105, also known as transforming growth factor β receptor) is a proliferation-associated and hypoxia-inducible protein that is abundantly expressed in angiogenic ECs.13 Anti-CD105 antibodies react only with microvascular ECs. Therefore, effective markers for better understanding the molecular differences among TECs, PECs, and NECs are required. Several studies have identified cancer-specific endothelial markers as diagnostic and potential therapeutic targets, such as markers for mapping of EC plasma membranes for elucidating molecular differences between in vitro and in vivo conditions,14 a functional antibody library-based screen of the TEC-surface proteome,11 and proteomic technologies used to profile ECs in human nonsmall cell lung cancer.15 However, the proteome map of lung squamous cell carcinoma (SCC) ECs, especially PECs and TECs, and the degree of modulation of ECs in response to tumor microenvironment in vivo remains unclear, largely because of analytical limitations. Here, we have successfully purified and characterized human CD105+ TECs (from cancer loci), PECs ( 5 cm away from the cancer loci) from fresh lung SCC tissues. We used these to create a large-scale proteomic map of the microvascular endothelium in human lung SCC tissues using iTRAQ-2DLC−MS/MS and observed the modulation of the tumor microenvironment by ECs.
■
material was then washed twice with PBS, suspended in complete M131 medium (Gibco) supplemented with 10% epidermal growth factor MVGS, 50 U/ml penicillin, and 50 μg/ mL streptomycin, and placed in flasks coated with attachment factor (Gibco) and cultured at 37 °C for 48 h. Then, tissues were removed and cells were further cultured to yield an enriched EC population. Cells were detached using 0.025% trypsin/0.01% EDTA (Gibco) and trypsin neutralizer solution (Gibco), and ECs were isolated from the cell suspension by magnetic cell sorting with the MACS system (Miltenyi Biotech Co., Gladbach, Germany), using anti-CD105 antibody-coupled magnetic beads according to the manufacturer’s instruction. After a few days, CD105-positive cells underwent a second isolation to eliminate contaminating cells. Gene Expression Using Real-Time RT-PCR
Total RNA was extracted using Trizol (Invitrogen, Carlsbad, CA, U.S.A.) to ensure the purity of the isolated ECs. RT-PCR analysis was performed using a SYBR Green I RT-PCR Master Mix kit from Bio-Rad Laboratories, Inc. (Hercules, CA, U.S.A.) on a Rotor-Gene 3000 (Corbett Robotics, Sydney, Australia). The relative expression levels of CD31, CD144, CD105, VEGFR1, APN, ITGAM (CD11b), CD45, and ACTA2 (αSMA) in TECs, PECs, and NECs were normalized to that of GAPDH, and NECs were used as the calibrator. Measurement of δ-Ct was performed at least in triplicate. RT-PCR data were analyzed for relative gene expression using the △△Ct method. The relative expression levels of 40 differentially expressed proteins were also analyzed using RT-PCR. The sequences of the forward and reverse primers are provided in Supporting Information Table S2. Flow Cytometric Analysis
Flow cytometric analysis was performed to verify endothelial phenotype. After detachment, cells were incubated with mouse antihuman CD31 and CD105 (BioLegend, San Diego, CA, U.S.A.) and sheep antihuman vWF polyclonal antibody (Abcam, Cambridge, U.K.) for 20 min at 4 °C. After washing, the cells were incubated with Alexa-Fluor 488 conjugated rat antimouse IgG, or Alexa-Fluor 555 donkey antisheep IgG for 20 min at 4 °C. The cells were analyzed on a FACSAria II (BD, San Jose, CA, U.S.A.). Endothelial phenotype was tested after purification, at least in triplicate. Representative data were analyzed using FlowJo software (Ashland, Dublin, OH, U.S.A.).
METHODS
EC Isolation and Culture
TECs, PECs, and NECs were obtained from fresh lung SCC specimens, collected by the Department of Chest Surgery in Zhongshan Hospital, Xiamen University. Patients did not receive any treatment prior to surgery and signed informed consent forms for sample collection (information on the participating patients [ID 1−6] is given in Supporting Information Table S1). The research specimens were obtained strictly according to the standard operating procedures of International Society for Biological and Environment Repositories. Specimens of cancer, paratumor (located 98% was used to confirm the quality of the preparations. The cell pools of NECs, PECs, and TECs immunoreacted with antiCD31, CD144, and CD105. The cells also reacted to antibodies directed against vWF, which is commonly used for EC identification (Figure 1A). CD31, CD105, and vWF expression, 4720
dx.doi.org/10.1021/pr5006229 | J. Proteome Res. 2014, 13, 4717−4729
Journal of Proteome Research
Article
Figure 2. Functional distribution of proteomically identified endothelial proteins involved in biological processes (A), and molecular function (B) (based on the Human Protein Reference Database [HPRD]).
Figure 3. Functional distribution of differentially expressed endothelial proteins identified by proteomic analysis involved in cellular components (A) (based on the Human Protein Reference Database [HPRD]). (B) The list of the top 10 gene ontology (GO) biological processes and KEGG pathways in which these proteins are involved (uploaded into Ingenuity Pathway analysis (IPA) software server) are shown.
EC Protein Expression Profiling Based on iTRAQ-2DLC−MS/MS Analysis
terms and KEGG pathways, ranked according to their significance level.
iTRAQ-based LC−MS/MS was used to profile protein expression. After redundancies were removed (see Methods), 11,195 unique peptides were identified by iTRAQ, resulting in the simultaneous identification and quantification of 1811 proteins with 99% confidence (see Supporting Information Identified Protein data). Of these 1811 proteins, 289 were identified as “uncharacterized protein”; corresponding genes could not be identified for only 18 proteins, and 1793 of the proteins were mapped to 1765 EntrezGene IDs. Based on HPRD analysis (Figures 2 and 3A), it was found that the biological processes in which 65.79% of the identified proteins were involved, included metabolism (28.34%), energy pathways (13.91%), signal transduction (12.22%), and cell communication (11.32%). The molecular function of 18.66% of the proteins remained unknown. Most of these proteins are present in cytoplasm, nucleus, and mitochondrion. Functional analysis of biological processes revealed that 511 GO terms (P < 0.01, 241 BP, 163 MF, and 107 CC), and 54 KEGG pathways (P < 0.05) corresponding to the 1765 proteins were enriched. Figure 3B displays the significant top 10 GO
Detection and Relative Quantification of Differentially Expressed Proteins
The 114/113 and 115/113 iTRAQ ratios indicate the relative abundance of proteins in PECs and TECs with respect to NECs, respectively. In relative quantification analysis by Proteome Discoverer software version 1.3, an additional 1.5fold change cutoff (ratio 1.5) was used to reduce false positives when classifying proteins as up- or downregulated. A total of 178 (66 up-regulated and 112 downregulated) and 162 (61 up-regulated and 101 down-regulated) differentially expressed PEC and TEC proteins were identified. Of these, 86 proteins were differentially expressed in both groups of cells, and all but one exhibited the same expression tends (Supporting Information Table S3−S5). Cluster analysis of the proteins identified in NECs, PECs, and TECs was conducted using heatmap.2 (Gplots package), and the proteins were grouped by their expression level. Protein expression in PECs and TECs was more similar than that in PECs and NECs (Figure 4A). Furthermore, the differentially expressed proteins were enriched in 16 KEGG Environmental 4721
dx.doi.org/10.1021/pr5006229 | J. Proteome Res. 2014, 13, 4717−4729
Journal of Proteome Research
Article
Figure 4. Protein expression patterns in normal (NECs), paratumor-derived (PECs), and tumor-derived endothelial cells (TECs). (A) Cluster map comparing protein expression patterns of NECs, PECs, and TECs. Functional category gene enrichment test using heatmap.2, and the gregmisc package in the R statistical environment (http://cran.r-project.org/web/packages/gplots). Red indicates higher expression, green indicates lower expression, and black indicates similar expression levels in the two cell types. (B) Pathway distribution of all proteomically identified endothelial proteins according to the KEGG Environmental Information Processing Pathway (http://www.kegg.jp), with statistical significance assigned to a corrected P value
Effect of Lung Squamous Cell Carcinoma Tumor Microenvironment on the CD105+ Endothelial Cell Proteome Huiqin Zhuo,†,¶ Zhi Lyu,‡,¶ Jing Su,§,∥,¶ Jian He,§,∥ Yihua Pei,† Xiao Cheng,‡ Nuo Zhou,§,∥ Xiaoling Lu,*,§,∥ Sufang Zhou,*,§,∥ and Yongxiang Zhao*,§,∥ †
Central Laboratory, The Affiliated Zhongshan Hospital, Xiamen University, Xiamen, Fujian 361004, China Respiratory Department, The Affiliated Zhongshan Hospital, Xiamen University, Xiamen, Fujian 361004, China § National Center for International Research of Biological Targeting Diagnosis and Therapy, Guangxi Medical University, Nanning, Guangxi 530021, China ∥ Guangxi Key Laboratory of Biological Targeting Diagnosis and Therapy Research, Guangxi Medical University, Nanning, Guangxi 530021, China ‡
S Supporting Information *
ABSTRACT: In lung cancer, antiangiogenic treatment targeting tumor endothelial cells (ECs) provides a survival advantage. To fully elucidate the behavior of ECs in a tumor microenvironment, high-purity (>98%) normal, paratumor-, and tumorderived CD105+ ECs were purified from lung squamous cell carcinoma by incubating cells with anti-CD105 antibody-coated magnetic beads. These cells exhibited typical EC characteristics. Totally, 1765 proteins were identified with high confidence by isobaric stable isotope tags and two-dimensional LC/MS/MS (iTRAQ-2DLC/MS/MS). In particular, 178 and 162 proteins were differentially expressed in paratumor- and tumor-derived ECs, respectively, compared to normal ECs. The up- and down-regulation trends showed good interassay correlation. Using gene ontology, they were classified into genes involved in major reprogramming of cellular metabolic processes, oxidative stress response, redox homeostasis, apoptosis, and platelet degranulation/activation. Moreover, tumor angiogenesis-initiating ECs appeared to acquire distinct properties. For example, cell migration and regulation of smooth muscle cell migration of paratumorderived ECs were significantly faster than that of normal and tumor-derived ECs. Among them, two migration-associated proteins, neuropilin 1 and platelet-derived growth factor receptor β predominantly expressed in ECs of paratumor from 16 patients with lung squamous cell carcinoma, were identified as potential biomarkers for antiangiogenic therapy. KEYWORDS: iTRAQ, lung squamous cell carcinoma, paratumor endothelial cells, proteomics, tumor endothelial cells
■
INTRODUCTION Lung cancer is the leading cause of cancer-related deaths worldwide. Recent studies have revealed that survival can be improved by treatment with antiangiogenic agents.1−3 Tumor angiogenesis involves proliferation, migration, differentiation of endothelial cells (ECs), modification of the extracellular matrix (ECM), and recruitment of accessory cells and is currently regarded as a potential therapeutic target for cancer.4 ECs as key determinants of the tumor microenvironment interact with tumor cells, ECM, and immune killer cells. To date, antiangiogenic strategies have largely focused on targeting ECs.5 Therefore, it is very important to fully understand the characteristics of ECs in tumor microenvironment. ECs within the tumor microenvironment undergo phenotypic and epigenetic changes during tumor initiation, progression, and metastasis. Cumulative evidence has indicated that cytogenetics, gene expression, morphology, pathophysiology, and atypical multipotent plasticity of tumor-derived endothelial cells (TECs) differ from those of normal endothelial cells (NECs).6−10 St Croix et al. first isolated © 2014 American Chemical Society
TECs from human colon carcinoma and found that a series of genes were specifically up-regulated in TECs (called tumor endothelial markers (TEMs)).6 TECs isolated from human hepatocellular carcinoma possess enhanced angiogenic activity and resistance to chemotherapeutic drugs and angiogenesis inhibitors.7 Moreover, 22 genes associated with apoptosis, extracellular matrix formation, bone remodeling, angiogenesis, cell adhesion, and cell proliferation were found to be substantially up-regulated in multiple myeloma ECs compared with ECs derived from patients with monoclonal gammopathy of undetermined significance.8 NECs are widely used to construct in vitro tumor EC models, using conditioned medium from cultured cancer cells for studies on tumor angiogenesis and antiangiogenic Special Issue: Proteomics of Human Diseases: Pathogenesis, Diagnosis, Prognosis, and Treatment Received: June 22, 2014 Published: September 10, 2014 4717
dx.doi.org/10.1021/pr5006229 | J. Proteome Res. 2014, 13, 4717−4729
Journal of Proteome Research
Article
therapy.11,12 TECs have rarely been isolated and cultured successfully. Moreover, NECs (obtained from tumor-adjacent normal tissues), human umbilical vein ECs, or human dermal microvascular ECs were the most commonly used controls for studies involving TECs obtained from human tumors or from tumor xenografts. However, the degree of modulation of ECs in response to tumor microenvironment varies, indicating that ECs obtained from tumor loci and paratumor ECs (PECs) are more relevant for elucidating the effects of the tumor environment on EC properties than are NECs. Compared with traditional vascular markers, for example, CD31, endoglin (CD105, also known as transforming growth factor β receptor) is a proliferation-associated and hypoxia-inducible protein that is abundantly expressed in angiogenic ECs.13 Anti-CD105 antibodies react only with microvascular ECs. Therefore, effective markers for better understanding the molecular differences among TECs, PECs, and NECs are required. Several studies have identified cancer-specific endothelial markers as diagnostic and potential therapeutic targets, such as markers for mapping of EC plasma membranes for elucidating molecular differences between in vitro and in vivo conditions,14 a functional antibody library-based screen of the TEC-surface proteome,11 and proteomic technologies used to profile ECs in human nonsmall cell lung cancer.15 However, the proteome map of lung squamous cell carcinoma (SCC) ECs, especially PECs and TECs, and the degree of modulation of ECs in response to tumor microenvironment in vivo remains unclear, largely because of analytical limitations. Here, we have successfully purified and characterized human CD105+ TECs (from cancer loci), PECs ( 5 cm away from the cancer loci) from fresh lung SCC tissues. We used these to create a large-scale proteomic map of the microvascular endothelium in human lung SCC tissues using iTRAQ-2DLC−MS/MS and observed the modulation of the tumor microenvironment by ECs.
■
material was then washed twice with PBS, suspended in complete M131 medium (Gibco) supplemented with 10% epidermal growth factor MVGS, 50 U/ml penicillin, and 50 μg/ mL streptomycin, and placed in flasks coated with attachment factor (Gibco) and cultured at 37 °C for 48 h. Then, tissues were removed and cells were further cultured to yield an enriched EC population. Cells were detached using 0.025% trypsin/0.01% EDTA (Gibco) and trypsin neutralizer solution (Gibco), and ECs were isolated from the cell suspension by magnetic cell sorting with the MACS system (Miltenyi Biotech Co., Gladbach, Germany), using anti-CD105 antibody-coupled magnetic beads according to the manufacturer’s instruction. After a few days, CD105-positive cells underwent a second isolation to eliminate contaminating cells. Gene Expression Using Real-Time RT-PCR
Total RNA was extracted using Trizol (Invitrogen, Carlsbad, CA, U.S.A.) to ensure the purity of the isolated ECs. RT-PCR analysis was performed using a SYBR Green I RT-PCR Master Mix kit from Bio-Rad Laboratories, Inc. (Hercules, CA, U.S.A.) on a Rotor-Gene 3000 (Corbett Robotics, Sydney, Australia). The relative expression levels of CD31, CD144, CD105, VEGFR1, APN, ITGAM (CD11b), CD45, and ACTA2 (αSMA) in TECs, PECs, and NECs were normalized to that of GAPDH, and NECs were used as the calibrator. Measurement of δ-Ct was performed at least in triplicate. RT-PCR data were analyzed for relative gene expression using the △△Ct method. The relative expression levels of 40 differentially expressed proteins were also analyzed using RT-PCR. The sequences of the forward and reverse primers are provided in Supporting Information Table S2. Flow Cytometric Analysis
Flow cytometric analysis was performed to verify endothelial phenotype. After detachment, cells were incubated with mouse antihuman CD31 and CD105 (BioLegend, San Diego, CA, U.S.A.) and sheep antihuman vWF polyclonal antibody (Abcam, Cambridge, U.K.) for 20 min at 4 °C. After washing, the cells were incubated with Alexa-Fluor 488 conjugated rat antimouse IgG, or Alexa-Fluor 555 donkey antisheep IgG for 20 min at 4 °C. The cells were analyzed on a FACSAria II (BD, San Jose, CA, U.S.A.). Endothelial phenotype was tested after purification, at least in triplicate. Representative data were analyzed using FlowJo software (Ashland, Dublin, OH, U.S.A.).
METHODS
EC Isolation and Culture
TECs, PECs, and NECs were obtained from fresh lung SCC specimens, collected by the Department of Chest Surgery in Zhongshan Hospital, Xiamen University. Patients did not receive any treatment prior to surgery and signed informed consent forms for sample collection (information on the participating patients [ID 1−6] is given in Supporting Information Table S1). The research specimens were obtained strictly according to the standard operating procedures of International Society for Biological and Environment Repositories. Specimens of cancer, paratumor (located 98% was used to confirm the quality of the preparations. The cell pools of NECs, PECs, and TECs immunoreacted with antiCD31, CD144, and CD105. The cells also reacted to antibodies directed against vWF, which is commonly used for EC identification (Figure 1A). CD31, CD105, and vWF expression, 4720
dx.doi.org/10.1021/pr5006229 | J. Proteome Res. 2014, 13, 4717−4729
Journal of Proteome Research
Article
Figure 2. Functional distribution of proteomically identified endothelial proteins involved in biological processes (A), and molecular function (B) (based on the Human Protein Reference Database [HPRD]).
Figure 3. Functional distribution of differentially expressed endothelial proteins identified by proteomic analysis involved in cellular components (A) (based on the Human Protein Reference Database [HPRD]). (B) The list of the top 10 gene ontology (GO) biological processes and KEGG pathways in which these proteins are involved (uploaded into Ingenuity Pathway analysis (IPA) software server) are shown.
EC Protein Expression Profiling Based on iTRAQ-2DLC−MS/MS Analysis
terms and KEGG pathways, ranked according to their significance level.
iTRAQ-based LC−MS/MS was used to profile protein expression. After redundancies were removed (see Methods), 11,195 unique peptides were identified by iTRAQ, resulting in the simultaneous identification and quantification of 1811 proteins with 99% confidence (see Supporting Information Identified Protein data). Of these 1811 proteins, 289 were identified as “uncharacterized protein”; corresponding genes could not be identified for only 18 proteins, and 1793 of the proteins were mapped to 1765 EntrezGene IDs. Based on HPRD analysis (Figures 2 and 3A), it was found that the biological processes in which 65.79% of the identified proteins were involved, included metabolism (28.34%), energy pathways (13.91%), signal transduction (12.22%), and cell communication (11.32%). The molecular function of 18.66% of the proteins remained unknown. Most of these proteins are present in cytoplasm, nucleus, and mitochondrion. Functional analysis of biological processes revealed that 511 GO terms (P < 0.01, 241 BP, 163 MF, and 107 CC), and 54 KEGG pathways (P < 0.05) corresponding to the 1765 proteins were enriched. Figure 3B displays the significant top 10 GO
Detection and Relative Quantification of Differentially Expressed Proteins
The 114/113 and 115/113 iTRAQ ratios indicate the relative abundance of proteins in PECs and TECs with respect to NECs, respectively. In relative quantification analysis by Proteome Discoverer software version 1.3, an additional 1.5fold change cutoff (ratio 1.5) was used to reduce false positives when classifying proteins as up- or downregulated. A total of 178 (66 up-regulated and 112 downregulated) and 162 (61 up-regulated and 101 down-regulated) differentially expressed PEC and TEC proteins were identified. Of these, 86 proteins were differentially expressed in both groups of cells, and all but one exhibited the same expression tends (Supporting Information Table S3−S5). Cluster analysis of the proteins identified in NECs, PECs, and TECs was conducted using heatmap.2 (Gplots package), and the proteins were grouped by their expression level. Protein expression in PECs and TECs was more similar than that in PECs and NECs (Figure 4A). Furthermore, the differentially expressed proteins were enriched in 16 KEGG Environmental 4721
dx.doi.org/10.1021/pr5006229 | J. Proteome Res. 2014, 13, 4717−4729
Journal of Proteome Research
Article
Figure 4. Protein expression patterns in normal (NECs), paratumor-derived (PECs), and tumor-derived endothelial cells (TECs). (A) Cluster map comparing protein expression patterns of NECs, PECs, and TECs. Functional category gene enrichment test using heatmap.2, and the gregmisc package in the R statistical environment (http://cran.r-project.org/web/packages/gplots). Red indicates higher expression, green indicates lower expression, and black indicates similar expression levels in the two cell types. (B) Pathway distribution of all proteomically identified endothelial proteins according to the KEGG Environmental Information Processing Pathway (http://www.kegg.jp), with statistical significance assigned to a corrected P value
