| Literature DB >> 24742531 |
Saverio Candido1, Roberta Maestro, Jerry Polesel, Alessia Catania, Francesca Maira, Santo S Signorelli, James A McCubrey, Massimo Libra.
Abstract
Cancer remains one of the major cause of death in the Western world. Although, it has been demonstrated that new therapies can improve the outcome of cancer patients, still many patients relapse after treatment. Therefore, there is a need to identify novel factors involved in cancer development and/or progression. Recently, neutrophil gelatinase-associated lipocalin (NGAL) has been suggested as a key player in different cancer types. Its oncogenic effect may be related to the complex NGAL/MMP-9. In the present study, NGAL was analyzed at both transcript and protein levels in different cancer types by analysing 38 public available microarray datasets and the Human Protein Atlas tool. NGAL transcripts were significantly higher in the majority of solid tumors compared to the relative normal tissues for every dataset analyzed. Furthermore, concordance of NGAL at both mRNA and protein levels was observed for 6 cancer types including bladder, colorectal, liver, lung, ovarian, and pancreatic. All metastatic tumors showed a decrease of NGAL expression when compared to matched primary lesions. According to these results, NGAL is a candidate marker for tumor growth in a fraction of solid tumors. Further investigations are required to elucidate the function of NGAL in tumor development and metastatic processes.Entities:
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Year: 2014 PMID: 24742531 PMCID: PMC4039233 DOI: 10.18632/oncotarget.1738
Source DB: PubMed Journal: Oncotarget ISSN: 1949-2553
Figure 1Highly conserved lipocalin crystal structures consist of a single eight-stranded continuously hydrogen-bonded antiparallel β-barrel (A) delineating a calyx shape, which represents the internal ligand-binding site (B). Hydrophobicity surface (C)
Images were created from the RCSB PDB database (http://www.rcsb.org) (ID: 1NGL) using the UCSF Chimera package UFCS Chimera package that is developed by the Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco (supported by NIGMS P41-GM103311). Ref: The solution structure and dynamics of human neutrophil gelatinase-associated lipocalin by Coles M, et al. J Mol Biol. 1999; 289: 139-57.
Figure 2Effects of NGAL on survival, motility, angiogenic, apoptotic and glucose metabolism
NGAL: neutrophil gelatinase-associated lipocalin; MMP9: matrix metalloproteinase 9; NGAL-R: NGAL receptor; ErbB-2: v-Erb-B2 Avian Erythroblastic Leukemia Viral Oncogene Homolog = HER2; TNF-α: Tumor Necrosis Factor Alpha; IL-17: interleukin-17; IL-1β: interleukin-1 beta; FAK: focal adhesion kinase; NF-κB: nuclear factor kappa B cells; IκBζ: Inhibitor of NF-κB zeta subunit; HIF-1α: hypoxia inducible factor 1 alpha; VEGF: vascular endothelial growth factor. Fe++: Ferrous iron; Green lines with arrows indicate activation of pathway, red lines with blocked ends indicate inhibition of pathway, blue lines indicate transcriptional activation and additional events.
Gene expression patterns of NGAL in different cancer types from 29 datasets
| Cancer Type | # of samples | Fold Change (≤-2) or (≥2) | p < 0.01 (T-test) | Dataset | |||
| Cancer | Normal | Author [Ref.] | Year | Platform | |||
| Solid tumor | |||||||
| Bladder | 109 | 48 | 4.13 | 2.75E-05 | Sanchez-Carbayo M [ | 2006 | U133A |
| Cervix | 32 | 24 | -3.21 | 4.06E-04 | Scotto L [ | 2008 | U133A |
| Colon | 95 | 5 | 2.62 | 1.23E-02 | Kaiser S [ | 2007 | U133 Plus 2.0 |
| 81 | 24 | 4.15 | 7.04E-08 | Skrzypczak M [ | 2010 | U133 Plus 2.0 | |
| 70 | 12 | 4.78 | 7.06E-06 | Hong Y [ | 2010 | U133 Plus 2.0 | |
| Esophagus | 17 | 17 | -5.41 | 9.27E-05 | Hu N [ | 2010 | U133A |
| 53 | 53 | -2.92 | 1.05E-06 | Su H [ | 2011 | U133A/B | |
| Head and Neck | 6 | 4 | -18.58 | 7.33E-03 | Schlingemann J [ | 2005 | U133A |
| 31 | 10 | -12.44 | 3.60E-09 | Sengupta S [ | 2006 | U133 Plus 2.0 | |
| kidney | 51 | 5 | 4.15 | 4.98E-05 | Yusenko MV [ | 2009 | U133 Plus 2.0 |
| Liver | 35 | 10 | 8.66 | 2.32E-05 | Wurmbach E [ | 2007 | U133 Plus 2.0 |
| 22 | 21 | 3.48 | 5.14E-04 | Roessler S (1) [ | 2010 | U133 Plus 2.0 | |
| 225 | 220 | 2.94 | 6.86E-22 | Roessler S (2) [ | 2010 | HT U133A | |
| Lung | 30 | 30 | 2.79 | 1.72E-03 | Su LJ [ | 2007 | U133A |
| 58 | 49 | 2.28 | 2.64E-06 | Landi MT [ | 2008 | U133A | |
| 226 | 20 | 3.707 | 1.53E-7 | Okayama H [ | 2012 | U133 Plus 2.0 | |
| Ovary | 185 | 10 | 5.84 | 1.54E-06 | Bonome T [ | 2008 | U133A |
| 99 | 4 | 3.03 | 6.98E-04 | Hendrix ND [ | 2006 | U133A | |
| Pancreas | 36 | 16 | 14.05 | 5.15E-06 | Pei H [ | 2009 | U133 Plus 2.0 |
| 11 | 6 | 10.00 | 9.03E-05 | Segara D [ | 2005 | U133A | |
| 39 | 39 | 7.70 | 1.64E-10 | Badea L [ | 2008 | U133 Plus 2.0 | |
| Thyroid | 9 | 9 | 3.77 | 9.76E-4 | He H [ | 2005 | U133 Plus 2.0 |
| 14 | 4 | 2.33 | 0.001 | Vasko V [ | 2007 | U133 Plus 2.0 | |
| 26 | 4 | 2.05 | 9.34E-7 | Giordano TJ [ | 2006 | U133A | |
| Hematologic tumor | |||||||
| ALL | 750 | 74 | -11.77 | 7.57E-152 | Haferlach T [ | 2010 | U133 Plus 2.0 |
| AML | 542 | 74 | -16.94 | 2.32E-165 | Haferlach T [ | 2010 | U133 Plus 2.0 |
| 285 | 8 | -4.91 | 5.00E-03 | Valk PJ [ | 2004 | U133A | |
| CLL | 448 | 74 | -42.98 | 7.73E-194 | Haferlach T [ | 2010 | U133 Plus 2.0 |
| Myeloma | 9k | 5 | -3.90 | 0.002 | Agnelli L [ | 2009 | U133A |
Legend: Solid tumor: Cervix: aCervical Squamous Cell Carcinoma. Esophagus: bEsophageal Scquamous Cell Carcinoma; Head and Neck: cNasopharyngeal Carcinoma; dSquamous Cell Carcinoma. Kidney: eRenal Carcinoma; Liver: (1), dataset 1; (2) dataset 2; Lung: fLung adenocarcinoma; Ovary: gOvarian Carcinoma; Pancreas: hPancreatic Carcinoma; iPancreatic Ductal Adenocarcinoma; Thyroid: jThyroid Gland Papillary Carcinoma
Hematologic tumor: ALL, Acute Lymphoblastic Leukemia; AML, Acute Myeloid Leukemia; CLL, Chronic Lymphocytic Leukemia.
Myeloma: kPlasma Cell Leukemia.
NGAL transcripts in metastatic tissues compared to the relative primary tumor
| Cancer Type | # of samples | FC (≤ -1.5) or (≥ 1.5) | p < 0,05 (T-test) | Data set | |||
| metastasis | primary | Author [Ref.] | Year | Platform | |||
| Colorectal | 43 | 330 | -3.191 | 1.56E-06 | Bittner M[a] | 2005 | U133 Plus 2.0 |
| 27 | 56 | -5.437 | 1.04E-06 | Tsuji S [ | 2012 | U133 Plus 2.0 | |
| Kidney | 60 | 9 | -1.960 | 4.00E-03 | Jones J [ | 2005 | U133A |
| Melanoma | 40 | 16 | -2.475 | 3.00E-03 | Riker AI [ | 2007 | U133 Plus 2.0 |
| 52 | 31 | -4.849 | 1.12E-08 | Xu L [ | 2008 | U133A | |
| Ovarian | 75 | 166 | -1.999 | 2.00E-03 | Bittner M[a] | 2005 | U133 Plus 2.0 |
| 16 | 74 | -1.537 | 4.30E-03 | Anglesio MS [ | 2008 | U133 Plus 2.0 | |
| Prostate | 5 | 27 | -1.578 | 2.60E-02 | Vanaja DK [ | 2003 | U133A/B |
| 6 | 7 | -5.735 | 7.76E-04 | Varambally S [ | 2005 | U133 Plus 2.0 | |
[a] GEO Series GSE2109; FC, Fold change
Figure 3Distribution of NGAL transcript levels among cancer cases and normal samples
The percentage of tumor cases, indicated for each tumor setting, shows NGAL transcript levels below the 25th percentile (Cyan box) and above the 75th percentile (Magenta box) of the “normal” samples.
Figure 4Immunohistochemistry analysis of NGAL expression in human cancer
The data were obtained from the Human Protein Atlas. A single representative case for each cancer type (total 15) is shown along with its normal counterpart. Expression of NGAL in cancer sample was evaluated as strong, moderate, weak and negative immunostaining. The percentage is referred to the total cancer samples analyzed for each tumor type.
Clinical impact of NGAL expression pattern in different cancer type according to previous studies
| TUMOR TYPE | METHODS | CLINICAL IMPACT OF NGAL EXPRESSION | AUTHOR [REF.] |
| SOLID TUMORS | |||
| BLADDER | CM, GZ, MS | Diagnostic marker | Roy R [ |
| GZ, IHC, ELISA | Early marker of tumor progression | Monier F [ | |
| BRAIN | IHC, GZ, ELISA | Diagnostic marker | Smith [ |
| IHC | Positive correlation with high proliferation index in primary tumor | Barresi [ | |
| IHC | Associated with poor outcome | Liu MF [ | |
| BREAST | IHC | Associated with poor outcome | Stoesz SP [ |
| ELISA | Positive correlation with lymphatic node metastasis | Shen ZZ [ | |
| ELISA | Positive correlation with breast cancer aggressiveness | Provatopoulou X [ | |
| IHC | Positive correlation with poor prognosis in primary human breast cancer | Bauer M [ | |
| IHC | Positive correlation with poor outcome | Li [ | |
| IHC | Positive correlation with poor outcome | Wenners [ | |
| CERVICAL | IHC | Positive correlation with HPV type | Syrjänen [ |
| COLORECTAL | IHC, FISH | Positive correlation with tumor trasformation | Nielsen [ |
| ELISA | Prognostic utility in metastatic patients | Martì [ | |
| PCR, GZ | Diagnostic marker | Catalan [ | |
| ELISA | Not suitable as a diagnostic marker | Fung KY [ | |
| ELISA | Not useful marker of progression | McLean MH [ | |
| ESOPHAGEAL | GZ, IHC, WB | Positive correlation to cancer differentiation | Zhang [ |
| IHC | Positive correlation with progression | Du ZP [ | |
| GASTRIC | IHC, ELISA, WB | Positive correlation with poor outcome | Kubben [ |
| IHC, ELISA | Diagnostic marker and positive correlation with poor outcome | Wang HJ [ | |
| HEAD & NECK | ELISA | Positive correlation with poor outcome | Lin CW [ |
| HCC | IHC | Positive correlation with poor outcome | Zhang Y [ |
| LUNG | IHC | Positive correlation with poor outcome | Friedl A [ |
| PANCREATIC | ELISA | Diagnostic marker | Moniaux N [ |
| OVARIAN | IHC, ELISA, WB | Promotion of epithelial to mesenchymal transition | Lim R [ |
| PCR, IHC, ELISA | Positive correlation to cancer differentiation | Cho [ | |
| RENAL | IHC | Positive correlation with malignant phenotype | Barresi [ |
| ELISA | Positive correlation with poor outcome | Porta [ | |
| THYROID | IHC, Real time | Positive correlation with malignant phenotype | Iannetti [ |
| IHC | Diagnostic marker | Barresi [ | |
| HEMATOLOGICAL MALIGNANCIES | |||
| AML | RT-PCR | Positive correlation with better prognosis | Yang WC [ |
| CML | RT-PCR | Positive correlation with early stage of disease and BCR-ABL positivity | Villalva C [ |
AML, Acute Myeloid Leukemia, CM, Chromatography; CML, Chronic Myelogenous Leukemia; GZ, Gel zymography; HCC, Hepatocellular Carcinoma; IHC, Immunohistochemistry. MS, Mass Spectrometry.