- Open Access
Enhanced frequency and potential mechanism of B regulatory cells in patients with lung cancer
© Zhou et al.; licensee BioMed Central Ltd. 2014
- Received: 8 September 2014
- Accepted: 21 October 2014
- Published: 11 November 2014
Regulatory T cells (Tregs) and B cells (Bregs) play an important role in the development of lung cancer. The present study aimed to investigate the phenotype of circulating Tregs and Bregs in patients with lung cancer and explore potential mechanism by which lung cancer cells act on the development of both.
Patients with lung cancer (n = 268) and healthy donors (n = 65) were enrolled in the study. Frequencies of Tregs and Bregs were measured by flow cytometry with antibodies against CD4, CD25, CD127, CD45RA, CD19, CD24, CD27 and IL-10 before and after co-cultures. qRT-PCR was performed to evaluate the mRNA levels of RANTES, MIP-1α, TGF-β, IFN-γ and IL-4.
We found a lower frequency of Tregs and a higher frequency of Bregs in patients with lung cancer compared to healthy donors. Co-culture of lung cancer cells with peripheral blood mononuclear cells could polarize the lymphocyte phenotype in the similar pattern. Lipopolysaccharide (LPS)-stimulated lung cancer cells significantly modulated regulatory cell number and function in an in vitro model.
We provide initial evidence that frequencies of peripheral Tregs decreased or Bregs increased in patients with lung cancer, which may be modulated directly by lung cancer cells. It seems cancer cells per se plays a crucial role in the development of tumor immunity.
- Regulatory T cells
- Regulatory B cells
- Lung cancer
Lung cancer is the most prevalent malignant tumor and the leading cause of cancer-associated morbidity and mortality . Over 1.4 million people were diagnosed with lung cancer in 2004 and about 1.3 million people die of lung cancer each year, according to the Global Burden of Disease study . Both tumor characteristics immune responses of patients with lung cancer could affect tumor development . Growing evidence has proposed an opposing role of the immune system in fostering tumor growth, in spite of the considerable evidence indicating that the immune system can recognize and destroy tumor cells -.
Regulatory T cells (Tregs) are a subpopulation of T cells with immune suppressive function. Recent studies demonstrated elevated percentages of Tregs in the total T cell population isolated from tumor tissues or peripheral blood in a variety of cancers, including lung cancer -. The accumulation of Tregs might be associated with advanced tumor growth and poor prognosis in lung cancer -. Regulatory B cells (Bregs) were also found to play a regulatory role in immune responses via the production of regulatory cytokines, such as IL-10 and TGF-β, and express inhibitory molecules to suppress pathogenic T cells and autoreactive B cells in a cell-to-cell contact-dependent manner ,. The absence or loss of Bregs may exacerbate disease symptoms in autoimmune diseases , chronic inflammatory diseases , or promot tumor progression. It was reported that Bregs played a critical role in pulmonary metastasis of breast cancer through inducing recruitment and expansion of Tregs . In developing tumors anti-tumorigenic and pro-tumorigenic immune and inflammatory mechanisms coexist, and the net effect of them affects tumor development .
However, there are few studies on the role of Bregs in lung cancer and the potential interaction of lung cancer cells on the development of Treg and Breg. The present study aimed to investigate the phenotype of circulating Tregs and Bregs in patients with lung cancer and explore potential mechanism by which lung cancer cells act on the antitumor immunity.
Blood samples collection
Peripheral blood samples were collected upon patient admission before any therapeutic intervention. The diagnosis of lung cancer was made on the basis of imaging or biopsy examination (n = 268). Control samples were obtained from healthy donors (n = 65). All blood samples were collected after informed consent was given. The present study was approved by the Ethical Evaluation Committee of Zhongshan Hospital.
Cell isolation and culture
Peripheral blood mononuclear cells (PBMC) were isolated as previously described . In brief, whole blood samples were overlaid onto Ficoll separation media (Tianjin Haoyang Biological Manufacture, China) after 1:1 dilution with Hank’s Balanced Salted Solution (Gibco, CA, USA). PBMCs were centrifuged for 15 min at × 2800 rpm, collected at the plasma interface and washed thrice after centrifugation at × 1500 rpm for 10 min. Human alveolar adenocarcinoma cell line A549, which were from our research center, and the isolated PBMCs were cultured in DMEM (high glucose, Hyclone, USA), supplemented with 10% FBS (Hyclone, USA), 100U/ml penicillin, and 100 μg/ml streptomycin at 37°C in a 5% CO2, 95% air environment in humidified incubators.
Twelve-well transwell chambers with a 0.4 μm porous membrane (Corning-Costar, USA) were used. A549 cells (5 × 105/well) were plated underneath the transwell chamber and stimulated with LPS, and then 0.5 ml of PBMC (2 × 106/ml) was added to the inner chamber at 24 hrs after LPS stimulation. After co-culturing for 48 hrs, PBMCs were harvested and stained by flow cytometry, while A549 cells were harvested and prepared for quantitative real time polymerase chain reaction (qRT-PCR). To investigate the role of LPS-related signal pathway, A549 cells were pretreated with NF-κB inhibitor PDTC at 10, 50, 100, 300, or 500 μM for 4 hrs.
Flow cytometry analysis
Flow cytometry analysis was conducted by FACS Aria II flow cytometry (BD Bioscience, USA). For surface staining, suspensions of PBMCs were stained on ice using predetermined optimal concentrations of each antibody for 30 min, and fixed using fixation buffer (BD PharMingen, USA). Tregs identified with CD4+CD25+CD127− expression were stained with human regulatory T cell Cocktail (BD PharMingen, USA)  and Bregs identified with CD19+CD24hiCD27+ expression were stained with human anti-CD19, human anti-CD24, and human anti-CD27 (BD PharMingen, USA) . Intracellular IL-10 analysis was performed by flow cytometry, as described previously . Briefly, cells were resuspended (2 × 106 cells/ml) in medium and stimulated with ODN2006 (10 μg/ml; Sangon Biotech, Shanghai, China) for 24 hrs with leukocyte activation cocktail (2 μl/ml; BD GolgiPlug™, BD Pharmingen, USA) added during the final 5 hrs before staining. After surface staining, cells were fixed, permeabilized using a Cytofix/Cytoperm™ Kit (BD PharMingen, USA), and stained with human anti-IL10 (BD PharMingen, USA) according to the manufacturer’s instructions. Results are expressed as frequency of Tregs or Bregs.
Quantitative real time polymerase chain reaction (qRT-PCR)
RNA extraction was performed using the TRIZOL™LS reagent (Invitrogen, Carlsbad, CA). cDNA was prepared using PrimeScript® RT reagent Kit (Takara, Shiga, Japan) following standard protocols. qRT-PCR was performed using SYBR® Premix Ex Taq™ (Takara, Shiga, Japan) on the ABI PRISM 7900 real-time PCR system (Applied Biosystems, Foster City, CA). All samples were run in triplicate. Results are shown as relative target mRNA levels.
To evaluate the frequency of peripheral Tregs and Bregs in patients with lung cancer, 268 patients were recruited from 800 patients with lung cancer under the restricted criteria.
To investigate the role of inflammation in shaping the phenotype of PBMC. To reveal the role that cell-cell-contact or cytokines play in phenotype alterations, A549 cells were stimulated with LPS at 10, 100, 1000 ng/ml or vehicle for 24 hrs, and LPS-stimulated A549 cells as activated LC cells and their supernatant as activated medium were then harvested. PBMCs from healthy donors were co-cultured with the harvested activated or non-activated A549 cells and medium for 48 hrs, respectively. The control group was PBMC from healthy donors without co-culture. Treg and Breg frequencies were enumerated by flow cytometry (Additional file 1: Figure S1A).
To reveal indirect effects of activated lung cancer cells on PBMC phenotypes and to investigate whether continuous stimulation by LPS will bears different effects on PBMC phenotype, A549 cells were planted in the lower chamber of the transwell and stimulated with LPS at 100 and 500 ng/ml or vehicle for 24 hrs. PBMCs from healthy donors were then added to the upper chamber of the transwell for co-culture for 48 hrs. The control group was PBMC from healthy donors without co-culture. Treg and Breg frequencies were enumerated by flow cytometry. The co-cultured A549 cells were also harvested for qPCR for mRNA expression of RANTES and MIP-1α, while the co-cultured PBMCs were harvested for mRNA expression of TGF-β, IFN-γ, and IL-4. The control group was A549 cell or PBMC from healthy donors without co-culture (Additional file 1: Figure S1B).
To investigate the role of LPS-related NF-κB signal pathway in the activation of lung cancer cells. A549 cells were planted in the lower chamber of the transwell and pretreated with NF-κB inhibitor PDTC at 10, 50, 100, 300, 500 μM or vehicle for 4 hrs, and then washed with fresh medium. After then, PDTC pre-treated A549 cells were stimulated with LPS at 500 ng/ml for 24 hrs and PBMCs from healthy donors were added to the upper chamber of the transwell for co-culture for 48 hrs. Treg frequencies were enumerated by flow cytometry (Additional file 1: Figure S1C).
To investigate the role of inflammation-activated lung cancer cells in phenotype alterations of PBMC obtained from patients with lung cancer and the phenotype difference between lung cancer patients and healthy individuals. A549 cells were stimulated with LPS at 100 and 500 ng/ml for 24 hrs, and LPS-stimulated A549 cells and their supernatant were then harvested. PBMC from lung cancer patients were co-cultured with harvested LPS-stimulated A549 cells and their supernatant for 48 hrs, respectively. The control group was PBMC from lung cancer patients without co-culture. Treg and Breg frequencies were enumerated by flow cytometry (Additional file 1: Figure S1D).
All values were expressed as mean ± SEM. Statistical analysis was performed using SPSS software (SPSS 20.0; SPSS Inc; Chicago, IL). Frequencies of peripheral Tregs and Bregs among groups were analyzed with one-way ANOVA, followed by an unpaired student’s t-test. P <0.05 was considered as statistically significant.
The immune system plays a significant role in the control of tumor progression, although the regulatory mechanism of interaction between two systems remains unclear. High proportions of Tregs were found in tumor-infiltrating lymphocytes of patients with lung cancer  and Tregs from patients with lung cancer directly inhibited autologous T cell proliferation . The percentage of Tregs might be correlated with the pathological stage in lung cancer or tumor burden . The present study demonstrated that peripheral frequencies of Tregs and CD45RA+Tregs in lung cancer patients was lower than those in healthy individuals, indicating a maturation-activation state of naïve Tregs and preferential homing of mature Tregs into the lungs of patients. Furthermore, the present study initially demonstrated that peripheral frequencies of Bregs cells were significantly higher in patients with lung cancer. Cancer-derived factors and the interaction of lung cancer cells with normal PBMCs may contribute to the expansion of Bregs, similar alterations of Tregs and Bregs observed in our clinical cohort.
Leukocytes within tumors play critical roles in the formation of inflammatory microenvironment and tumorigenesis, while little has been known about the potential mechanism to communicate between inflammation and cancer . The present study explored the relationship between inflammation and antitumor immunity adopting an in vitro model based on LPS-stimulated A549 cells. Inflammation-activated lung cancer cells or their products during the pretreatment could increase the frequencies of Tregs and CD45RA+Tregs from normal PBMCs. It seemed that the direct interaction between cells played a more important role in alterations of Treg phenotypes than their products which were more important in CD45RA+Treg phenotype alterations. Furthermore, continuous LPS stimulation during the interaction between cancer cells and PMBCs could increase frequencies of Tregs and CD45RA+Tregs. The increase of Tregs might also result from the natural Treg self-expansion promoted by inflammatory factors or the conversion of naïve CD4+ T cells.
Previous study demonstrated that the normal maturation-activation process of T cells was involved in the sequential expression of naïve T cells, mature T cells, or effector/cytotoxic T cells . CD45RA+Tregs in the periphery of humans express high levels of FOXP3 and manifest equivalent suppressive activity as compared to CD45RO+Tregs counterparts . Our observation of a higher proportion of CD45RA+Tregs indicates a final maturation-activation state of those cells promoted by cancer-related inflammatory factors. Inflammation-activated cancer cells could also play the initiators and/or secondary sources of the development of cancer microenvironment and alterations of local immunity through the direct interaction and products. The present study demonstrated that NF-κB inhibition of inflammation-activated cancer cells could decrease frequencies of Tregs and CD45RA+Tregs. Inflammation was also found to stimulate the production of chemo-attractants from lung cancer cells, responsible for the recruitment of infiltrated inflammatory cells.
Tumor cells play a crucial role in the conversion of naïve and/or effector T cells into Treg by providing antigenic stimulation and cytokines, although little has been known on the influence of cytokines on Treg proliferation or activation during the interaction between tumor and inflammatory cells. The previous study demonstrated that overexpression of RANTES was associated with improved prognosis in lung cancer . Lung cancer cells were found to produce MIP-1α which might affect the interaction between lung cancer and host inflammatory cells . The present study observed that mRNA expressions of RANTES and MIP-1α in cancer cells after co-culture of cancer cells and PBMCs in a concentration-dependent pattern, accompanied with the up-regulation of Tregs.
Interaction between PBMCs and inflammation-activated cancer cells or their products also increased the frequency of CD19+B cells and the frequency of CD19+CD24hiCD27+ B cells in a LPS-concentration dependent manner. Inflammation-activated cancer cells-driven products could induce the high expression of cytoplasmic IL-10 in B cells. It seems that the influencing roles of inflammation-activated cancer cells in the frequencies of CD19+CD24hiCD27+ and CD19+IL-10+ B cells are associated with the severities of inflammation. The interaction between inflammation-activated cancer cells or their products with PMBCs can play a critical role in the expansion of Bregs.
On basis of our finding that co-culture led to phenotype alterations of PBMCs from healthy individuals, we further investigated the role of inflammation-activated cancer cells in PBMCs from patients with lung cancer and found similar alterations of Treg and CD45RA+Treg phenotypes in PBMC from lung cancer patients to those in healthy donors. However, the interaction between PBMCs from lung cancer patients with inflammation-activated cancer cells decreased the frequency of Bregs, which might be explained by the immune state of cancer patients. Growing evidence has shown interaction between Tregs and Bregs in tumor microenvironment. A previous study revealed that Bregs in the lung metastasis from breast cancer were able to induce conversion of resting CD4+T cells to Tregs to support metastatic growth . The observation might also explain the expansion of Tregs in our co-culture-model. More investigations are needed to further explore the interactions between Tregs and Bregs and the underlying mechanism, involving mediators from both Tregs and Bregs or potential network biomarkers -.
In conclusion, we found decreased or increased frequencies of peripheral Tregs or Bregs in patients with lung cancer where the direct interaction of inflammation-activated cancer cells may play the critical and dominant roles (Additional file 2: Figure S2). Effects of lung cancer cells were associated with the severity of inflammation. Further studies are needed to reveal the underlying mechanisms leading to the alterations of lymphocyte phenotypes. Strategies against regulatory lymphocytes may be potential for tumor therapy in the future.
JBZ contributed to collection of information, analysis and interpretation of data and writing of the manuscript. ZHM contributed to collection of information. FM contributed to revision of the manuscript. XDW contributed to design and revision of the manuscript. All authors read and approved the final manuscript.
1Department of Pulmonary Medicine, Zhongshan Hospital, Fudan University, 2Shanghai Respiratory Research Institute, Zhongshan Hospital, Fudan University, 3Fudan University School Center for Clinical Bioinformatics, Shanghai, China; 4Sidra Medical and Research Centre, Doha, Qatar; 5Department of Respiratory Medicine, The First Hospital of Wenzhou Medical University, Wenzhou, China.
The work was supported by Shanghai Leading Academic Discipline Project (Project Number: B115), Zhongshan Distinguished Professor Grant (XDW), The National Nature Science Foundation of China (91230204, 81270099, 81320108001, 81270131), The Shanghai Committee of Science and Technology (12JC1402200, 12431900207, 11410708600, 14431905100), Zhejiang Provincial Natural Science Foundation (Z2080988), Zhejiang Provincial Science Technology Department Foundation (2010C14011), and Ministry of Education, Academic Special Science and Research Foundation for PhD Education (20130071110043).
- Siegel R, Naishadham D, Jemal A: Cancer statistics, 2013. CA Cancer J Clin. 2003, 63: 11-30. 10.3322/caac.21166.View ArticleGoogle Scholar
- World Health Organization: The global burden of disease: 2004 update. In [http://www.who.int/healthinfo/global_burden_disease/2004_report_update/en/]
- Johansson M, Denardo DG, Coussens LM: Polarized immune responses differentially regulate cancer development. Immunol Rev. 2008, 222: 145-154. 10.1111/j.1600-065X.2008.00600.x.PubMed CentralView ArticlePubMedGoogle Scholar
- Riddell SR: Finding a place for tumor-specific T cells in targeted cancer therapy. J Exp Med. 2004, 200: 1533-1537. 10.1084/jem.20042004.PubMed CentralView ArticlePubMedGoogle Scholar
- Dunn GP, Bruce AT, Ikeda H, Old LJ, Schreiber RD: Cancer immunoediting: from immunosurveillance to tumor escape. Nat Immunol. 2002, 3: 991-998. 10.1038/ni1102-991.View ArticlePubMedGoogle Scholar
- Smyth MJ, Cretney E, Kershaw MH, Hayakawa Y: Cytokines in cancer immunity and immunotherapy. Immunol Rev. 2004, 202: 275-293. 10.1111/j.0105-2896.2004.00199.x.View ArticlePubMedGoogle Scholar
- Woo EY, Chu CS, Goletz TJ, Schlienger K, Yeh H, Coukos G, Rubin SC, Kaiser LR, June CH: Regulatory CD4(+)CD25(+) T cells in tumors from patients with early-stage non-small cell lung cancer and late-stage ovarian cancer. Cancer Res. 2001, 61: 4766-4772.PubMedGoogle Scholar
- Okita R, Saeki T, Takashima S, Yamaguchi Y, Toge T: CD4 + CD25+ regulatory T cells in the peripheral blood of patients with breast cancer and non-small cell lung cancer. Oncol Rep. 2005, 14: 1269-1273.PubMedGoogle Scholar
- Taams LS, Smith J, Rustin MH, Salmon M, Poulter LW, Akbar AN: Human anergic/suppressive CD4(+)CD25(+) T cells: a highly differentiated and apoptosis-prone population. Eur J Immunol. 2001, 31: 1122-1131. 10.1002/1521-4141(200104)31:4<1122::AID-IMMU1122>3.0.CO;2-P.View ArticlePubMedGoogle Scholar
- Petersen RP, Campa MJ, Sperlazza J, Conlon D, Joshi MB, Harpole DH, Patz EF: Tumor infiltrating Foxp3+ regulatory T-cells are associated with recurrence in pathologic stage I NSCLC patients. Cancer. 2006, 107: 2866-2872. 10.1002/cncr.22282.View ArticlePubMedGoogle Scholar
- Shimizu K, Nakata M, Hirami Y, Yukawa T, Maeda A, Tanemoto K: Tumor-infiltrating Foxp3+ regulatory T cells are correlated with cyclooxygenase-2 expression and are associated with recurrence in resected non-small cell lung cancer. J Thorac Oncol. 2010, 5: 585-590.View ArticlePubMedGoogle Scholar
- Schneider T, Kimpfler S, Warth A, Schnabel PA, Dienemann H, Schadendorf D, Hoffmann H, Umansky V: FOXP3+ regulatory T cells and natural killer cells distinctly infiltrate primary tumors and draining lymph nodes in pulmonary adenocarcinoma. J Thorac Oncol. 2011, 6: 432-438. 10.1097/JTO.0b013e31820b80ca.View ArticlePubMedGoogle Scholar
- Lundy SK: Killer B lymphocytes: the evidence and the potential. Inflamm Res. 2009, 58: 345-357. 10.1007/s00011-009-0014-x.View ArticlePubMedGoogle Scholar
- Vitale G, Mion F, Pucillo C: Regulatory B cells: evidence, developmental origin and population diversity. Mol Immunol. 2010, 48: 1-8. 10.1016/j.molimm.2010.09.010.View ArticlePubMedGoogle Scholar
- Watanabe R, Ishiura N, Nakashima H, Kuwano Y, Okochi H, Tamaki K, Sato S, Tedder TF, Fujimoto M: Regulatory B cells (B10 cells) have a suppressive role in murine lupus: CD19 and B10 cell deficiency exacerbates systemic autoimmunity. J Immunol. 2010, 184: 4801-4809. 10.4049/jimmunol.0902385.PubMed CentralView ArticlePubMedGoogle Scholar
- DiLillo DJ, Matsushita T, Tedder TF: B10 cells and regulatory B cells balance immune responses during inflammation, autoimmunity, and cancer. Ann N Y Acad Sci. 2010, 1183: 38-57. 10.1111/j.1749-6632.2009.05137.x.View ArticlePubMedGoogle Scholar
- Olkhanud PB, Damdinsuren B, Bodogai M, Gress RE, Sen R, Wejksza K, Malchinkhuu E, Wersto RP, Biragyn A: Tumor-evoked regulatory B cells promote breast cancer metastasis by converting resting CD4 T cells to T-regulatory cells. Cancer Res. 2011, 71: 3505-3515. 10.1158/0008-5472.CAN-10-4316.PubMed CentralView ArticlePubMedGoogle Scholar
- Grivennikov SI, Greten FR, Karin M: Immunity, inflammation, and cancer. Cell. 2010, 140: 883-899. 10.1016/j.cell.2010.01.025.PubMed CentralView ArticlePubMedGoogle Scholar
- Bull M, Lee D, Stucky J, Chiu YL, Rubin A, Horton H, McElrath MJ: Defining blood processing parameters for optimal detection of cryopreserved antigen-specific responses for HIV vaccine trials. J Immunol Methods. 2007, 322: 57-69. 10.1016/j.jim.2007.02.003.PubMed CentralView ArticlePubMedGoogle Scholar
- Hein F, Massin F, Cravoisy-Popovic A, Barraud D, Levy B, Bollaert PE, Gibot S: The relationship between CD4 + CD25 + CD127- regulatory T cells and inflammatory response and outcome during shock states. Crit Care. 2010, 14: R19-10.1186/cc8876.PubMed CentralView ArticlePubMedGoogle Scholar
- Iwata Y, Matsushita T, Horikawa M, DiLillo DJ, Yanaba K, Venturi GM, Szabolcs PM, Magro CM, Williams AD, Hall RP, St Clair EW, Tedder TF: Characterization of a rare IL-10-competent B-cell subset in humans that parallels mouse regulatory B10 cells. Blood. 2011, 117: 530-541. 10.1182/blood-2010-07-294249.PubMed CentralView ArticlePubMedGoogle Scholar
- Yanaba K, Bouaziz J-D, Haas KM, Poe JC, Fujimoto M, Tedder TF: A regulatory B-cell subset with a unique CD1dhiCD5+ phenotype controls T cell-dependent inflammatory responses. Immunity. 2008, 28: 639-650. 10.1016/j.immuni.2008.03.017.View ArticlePubMedGoogle Scholar
- Woo EY, Yeh H, Chu CS, Schleinger K, Carroll RG, Riley JL, Kaiser LR, June CH: Cutting edge: Regulatory T cells from lung cancer patients directly inhibit autologous T cell proliferation. J Immunol. 2002, 168: 4272-4276. 10.4049/jimmunol.168.9.4272.View ArticlePubMedGoogle Scholar
- Ju S, Qiu H, Zhou X, Zhu B, Lv X, Huang X, Li J, Zhang Y, Ge Y, Johnson DE, Ju S, Shu Y: CD13 + CD4 + CD25hi regulatory T cells exhibit higher suppressive function and increase with tumor stage in non-small cell lung cancer patients. Cell Cycle. 2009, 8: 2578-2585. 10.4161/cc.8.16.9302.View ArticlePubMedGoogle Scholar
- Karin M: Nuclear factor-kappaB in cancer development and progression. Nature. 2006, 438: 820-827.Google Scholar
- Sallusto F, Geginat J, Lanzavecchia A: Central memory and effector memory T cell subsets: function, generation, and maintenance. Annu Rev Immunol. 2004, 22: 745-763. 10.1146/annurev.immunol.22.012703.104702.View ArticlePubMedGoogle Scholar
- Seddiki N, Santner-Nanan B, Tangye SG, Alexander SI, Solomon M, Lee S, Nanan R, Fazekas de Saint Groth B: Persistence of naïve CD45RA + regulatory T cells in adult life. Blood. 2006, 107: 2830-2838. 10.1182/blood-2005-06-2403.View ArticlePubMedGoogle Scholar
- Moran CJ, Arenberg DA, Huang CC, Giordano TJ, Thomas DG, Misek DE, Chen G, Iannettoni MD, Orringer MB, Hanash S, Beer DG: RANTES expression is a predictor of survival in stage I lung adenocarcinoma. Clin Cancer Res. 2002, 8: 3803-3812.PubMedGoogle Scholar
- Konishi T, Okabe H, Katoh H, Fujiyama Y, Mori A: Macrophage inflammatory protein-1 alpha expression in non-neoplastic and neoplastic lung tissue. Vichows Arch. 1996, 428: 107-111. 10.1007/BF00193938.View ArticleGoogle Scholar
- Wang XD, Peer D, Petersen B: Molecular and Cellular Therapies: New challenges and opportunities. Mol Cell Therap. 2013, 1: 1-10.1186/2052-8426-1-1.View ArticleGoogle Scholar
- Wu XD, Chen LN, Wang XD: Network biomarkers, interaction networks and dynamical network biomarkers in respiratory diseases. Clin Transl Med. 2014, 3: 16-10.1186/2001-1326-3-16.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhu Z, Wang DC, Popescu LM, Wang XD: Single-cell transcriptome in the identification of disease biomarkers: opportunities and challenges. J Transl Med. 2014, 12: 212-10.1186/s12967-014-0212-3.PubMed CentralView ArticlePubMedGoogle Scholar
- Wang XD: Role of clinical bioinformatics in the development of network-based Biomarkers. J Clin Bioinforma. 2011, 1: 28-10.1186/2043-9113-1-28.PubMed CentralView ArticlePubMedGoogle Scholar
- Wu DJ, Zhu BJ, Wang XD: Metabonomics-based omics study and atherosclerosis. J Clin Bioinforma. 2011, 1: 30-10.1186/2043-9113-1-30.PubMed CentralView ArticlePubMedGoogle Scholar
- Wang XD, Liotta L: Clinical bioinformatics: a new emerging science. J Clin Bioinforma. 2011, 1: 1-10.1186/2043-9113-1-1.PubMed CentralView ArticlePubMedGoogle Scholar
- Taylor MA, Schiemann WP: Therapeutic opportunities for targeting microRNAs in cancer. Mol Cell Therap. 2014, 2: 30-10.1186/2052-8426-2-30.View ArticleGoogle Scholar
- Donzelli S, Mori F, Biagioni F, Bellissimo T, Pulito C, Muti P, Strano S, Blandino G: MicroRNAs: short non-coding players in cancer chemoresistance. Mol Cell Therap. 2014, 2: 16-10.1186/2052-8426-2-16.View ArticleGoogle Scholar
- Frantzi M, Bhat A, Latosinska A: Clinical proteomic biomarkers: relevant issues on study design & technical considerations in biomarker development. Clin Transl Med. 2014, 3: 7-10.1186/2001-1326-3-7.PubMed CentralView ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.