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Galectin-3 is a marker of favorable prognosis and a biologically relevant molecule in neuroblastic tumors.

ABSTRACT: Childhood neuroblastic tumors are characterized by heterogeneous clinical courses, ranging from benign ganglioneuroma (GN) to highly lethal neuroblastoma (NB). Although a refined prognostic evaluation and risk stratification of each tumor patient is becoming increasingly essential to personalize treatment options, currently only few biomolecular markers (essentially MYCN amplification, chromosome 11q status and DNA ploidy) are validated for this purpose in neuroblastic tumors. Here we report that Galectin-3 (Gal-3), a ?-galactoside-binding lectin involved in multiple biological functions that has already acquired diagnostic relevance in specific clinical settings, is variably expressed in most differentiated and less aggressive neuroblastic tumors, such as GN and ganglioneuroblastoma, as well as in a subset of NB cases. Gal-3 expression is associated with the INPC histopathological categorization (P<0.001) and Shimada favorable phenotype (P=0.001), but not with other prognostically relevant features. Importantly, Gal-3 expression was associated with a better 5-year overall survival (P=0.003), and with improved cumulative survival in patient subsets at worse prognosis, such as older age at diagnosis, advanced stages or NB histopathological classification. In vitro, Gal-3 expression and nuclear accumulation accompanied retinoic acid-induced cell differentiation in NB cell lines. Forced Gal-3 overexpression increased phenotypic differentiation and substrate adherence, while inhibiting proliferation. Altogether, these findings suggest that Gal-3 is a biologically relevant player for neuroblastic tumors, whose determination by conventional immunohistochemistry might be used for outcome assessment and patient's risk stratification in the clinical setting.

SUBMITTER: Veschi V

PROVIDER: S-EPMC3973198 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature

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Galectin-3 is a marker of favorable prognosis and a biologically relevant molecule in neuroblastic tumors.

Veschi V V Petroni M M Bartolazzi A A Altavista P P Dominici C C Capalbo C C Boldrini R R Castellano A A McDowell H P HP Pizer B B Frati L L Screpanti I I Gulino A A Giannini G G

Cell death & disease 20140306

Childhood neuroblastic tumors are characterized by heterogeneous clinical courses, ranging from benign ganglioneuroma (GN) to highly lethal neuroblastoma (NB). Although a refined prognostic evaluation and risk stratification of each tumor patient is becoming increasingly essential to personalize treatment options, currently only few biomolecular markers (essentially MYCN amplification, chromosome 11q status and DNA ploidy) are validated for this purpose in neuroblastic tumors. Here we report tha ...[more]

PMID: 24603328

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Project description:BackgroundGalectins (Gal) are a family of protein that bind to the β-galactoside of glycoproteins. It modulates a variety of biological functions, such as tumor growth, angiogenesis and tumor metastasis. A series of experimental and clinical evidences have been reported to support a correlation between galectin expressions and neoplastic transformation, progression and prognosis. The objective of this study was to estimate the expression of Gal-3 and Gal-9 in order to evaluate their relation to hepatocellular carcinoma (HCC) -related clinical features and their prognostic values.MethodsWe evaluated Gal-3 and Gal-9 expression in 247 HCC patients by a tissue microarray immunohistochemistry method, then analyzed the relationship between expression levels of Gal-3 and Gal-9 protein and tumor parameters or clinical outcomes.ResultsThe Gal-3 expression was significantly higher in tumor tissues compared with adjacent non-tumor tissues (P < 0.001), while no significant differences of Gal-9 was detected (P = 0.222). A higher Gal-3 expression was significantly associated with lymph-vascular invasion (P = 0.049), poor histological differentiation (P = 0.016), and no cirrhosis (P = 0.040). In contrast, a lower Gal-9 expression was related to lymph-vascular invasion (P = 0.012) and poor histological differentiation (P = 0.002). Survival analysis showed that patients with higher Gal-3 expression had worse overall survival (P = 0.012) , however no correlation was found between Gal-9 expression and survival (P = 0.185). Multivariate analysis showed that multiple tumor (HR = 1.94, 95% CI [1.36-2.78]), tumor size ≥ 5 cm (HR = 1.51, 95% CI [1.07-2.12]), Lymph-vascular invasion (HR = 1.45, 95% CI [1.00-2.10]) and Gal-3 expression (HR = 1.57, 95% CI [1.06-2.33]) were independent influencing factors of prognosis in patients with hepatocellular carcinoma.ConclusionGal-3 was expected to serve as a novel prognostic marker of hepatocellular carcinoma, while Gal-9 expression was only related to tumor progression.

Project description:Title: Array-based gene expression, CGH and tissue data define a 12q24 gain in neuroblastic tumors with prognostic implication. Background: Neuroblastoma has successfully served as a model system for the identification of neuroectoderm-derived oncogenes. However, in spite of various efforts, only a few clinically useful prognostic markers have been found. Here, we present a framework, which integrates DNA, RNA and tissue data to identify and prioritize genetic events that represent clinically relevant new therapeutic targets and prognostic biomarkers for neuroblastoma. Methods: A single-gene resolution aCGH profiling was integrated with microarray-based gene expression profiling data to distinguish genetic copy number alterations that were strongly associated with transcriptional changes in two neuroblastoma cell lines. FISH analysis using a hotspot tumour tissue microarray of 37 paraffin-embedded neuroblastoma samples and in silico data mining for gene expression information obtained from previously published studies including up to 445 healthy nervous system samples and 123 neuroblastoma samples were used to evaluate the clinical significance and transcriptional consequences of the detected alterations and to identify subsequently activated gene(s). Results: In addition to the anticipated high-level amplification and subsequent overexpression of MYCN, MEIS1, CDK4 and MDM2 oncogenes, the aCGH analysis revealed numerous other genetic alterations, including microamplifications at 2p and 12q24.11. Most interestingly, we identified and investigated the clinical relevance of a previously poorly characterized amplicon at 12q24.31. FISH analysis showed low-level gain of 12q24.31 in 14 of 33 (42%) neuroblastomas. Patients with the low-level gain had an intermediate prognosis in comparison to patients with MYCN amplification (poor prognosis) and to those with no MYCN amplification or 12q24.31 gain (good prognosis) (P = 0.001). Using the in silico data mining approach, we identified elevated expression of five genes located at the 12q24.31 amplicon in neuroblastoma (DIABLO, ZCCHC8, RSRC2, KNTC1 and MPHOSPH9). Among these, DIABLO showed the strongest activation suggesting a putative role in neuroblastoma progression. Conclusions: The presented systematic and rapid framework, which integrates aCGH, gene expression and tissue data to obtain novel targets and biomarkers for cancer, identified a low-level gain of the 12q24.31 as a potential new biomarker for neuroblastoma progression. Furthermore, results of in silico data mining suggest a new neuroblastoma target gene, DIABLO, within this region, whose functional and therapeutic role remains to be elucidated in follow-up studies.

Project description:BackgroundThe growth factor receptor network (GFRN) plays a significant role in driving key oncogenic processes. However, assessment of global GFRN activity is challenging due to complex crosstalk among GFRN components, or pathways, and the inability to study complex signaling networks in patient tumors. Here, pathway-specific genomic signatures were used to interrogate GFRN activity in breast tumors and the consequent phenotypic impact of GRFN activity patterns.MethodsNovel pathway signatures were generated in human primary mammary epithelial cells by overexpressing key genes from GFRN pathways (HER2, IGF1R, AKT1, EGFR, KRAS (G12V), RAF1, BAD). The pathway analysis toolkit Adaptive Signature Selection and InteGratioN (ASSIGN) was used to estimate pathway activity for GFRN components in 1119 breast tumors from The Cancer Genome Atlas (TCGA) and across 55 breast cancer cell lines from the Integrative Cancer Biology Program (ICBP43). These signatures were investigated for their relationship to pro- and anti-apoptotic protein expression and drug response in breast cancer cell lines.ResultsApplication of these signatures to breast tumor gene expression data identified two novel discrete phenotypes characterized by concordant, aberrant activation of either the HER2, IGF1R, and AKT pathways ("the survival phenotype") or the EGFR, KRAS (G12V), RAF1, and BAD pathways ("the growth phenotype"). These phenotypes described a significant amount of the variability in the total expression data across breast cancer tumors and characterized distinctive patterns in apoptosis evasion and drug response. The growth phenotype expressed lower levels of BIM and higher levels of MCL-1 proteins. Further, the growth phenotype was more sensitive to common chemotherapies and targeted therapies directed at EGFR and MEK. Alternatively, the survival phenotype was more sensitive to drugs inhibiting HER2, PI3K, AKT, and mTOR, but more resistant to chemotherapies.ConclusionsGene expression profiling revealed a bifurcation pattern in GFRN activity represented by two discrete phenotypes. These phenotypes correlate to unique mechanisms of apoptosis and drug response and have the potential of pinpointing targetable aberration(s) for more effective breast cancer treatments.

Project description:BackgroundThe use of 3-D similarity techniques in the analysis of biological data and virtual screening is pervasive, but what is a biologically meaningful 3-D similarity value? Can one find statistically significant separation between "active/active" and "active/inactive" spaces? These questions are explored using 734,486 biologically tested chemical structures, 1,389 biological assay data sets, and six different 3-D similarity types utilized by PubChem analysis tools.ResultsThe similarity value distributions of 269.7 billion unique conformer pairs from 734,486 biologically tested compounds (all-against-all) from PubChem were utilized to help work towards an answer to the question: what is a biologically meaningful 3-D similarity score? The average and standard deviation for the six similarity measures STST-opt, CTST-opt, ComboTST-opt, STCT-opt, CTCT-opt, and ComboTCT-opt were 0.54 ± 0.10, 0.07 ± 0.05, 0.62 ± 0.13, 0.41 ± 0.11, 0.18 ± 0.06, and 0.59 ± 0.14, respectively. Considering that this random distribution of biologically tested compounds was constructed using a single theoretical conformer per compound (the "default" conformer provided by PubChem), further study may be necessary using multiple diverse conformers per compound; however, given the breadth of the compound set, the single conformer per compound results may still apply to the case of multi-conformer per compound 3-D similarity value distributions. As such, this work is a critical step, covering a very wide corpus of chemical structures and biological assays, creating a statistical framework to build upon.The second part of this study explored the question of whether it was possible to realize a statistically meaningful 3-D similarity value separation between reputed biological assay "inactives" and "actives". Using the terminology of noninactive-noninactive (NN) pairs and the noninactive-inactive (NI) pairs to represent comparison of the "active/active" and "active/inactive" spaces, respectively, each of the 1,389 biological assays was examined by their 3-D similarity score differences between the NN and NI pairs and analyzed across all assays and by assay category types. While a consistent trend of separation was observed, this result was not statistically unambiguous after considering the respective standard deviations. While not all "actives" in a biological assay are amenable to this type of analysis, e.g., due to different mechanisms of action or binding configurations, the ambiguous separation may also be due to employing a single conformer per compound in this study. With that said, there were a subset of biological assays where a clear separation between the NN and NI pairs found. In addition, use of combo Tanimoto (ComboT) alone, independent of superposition optimization type, appears to be the most efficient 3-D score type in identifying these cases.ConclusionThis study provides a statistical guideline for analyzing biological assay data in terms of 3-D similarity and PubChem structure-activity analysis tools. When using a single conformer per compound, a relatively small number of assays appear to be able to separate "active/active" space from "active/inactive" space.

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Galectin-3 is a marker of favorable prognosis and a biologically relevant molecule in neuroblastic tumors.

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Galectin-3 is a marker of favorable prognosis and a biologically relevant molecule in neuroblastic tumors.

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