hSAGEing: An Improved SAGE-Based Software for Identification of Human Tissue-Specific or Common Tumor Markers and Suppressors

SAGE (serial analysis of gene expression) is a powerful method of analyzing gene expression for the entire transcriptome. There are currently many well-developed SAGE tools. However, the cross-comparison of different tissues is seldom addressed, thus limiting the identification of common- and tissue-specific tumor markers.To improve the SAGE mining methods, we propose a novel function for cross-tissue comparison of SAGE data by combining the mathematical set theory and logic with a unique “multi-pool method” that analyzes multiple pools of pair-wise case controls individually. When all the settings are in “inclusion”, the common SAGE tag sequences are mined. When one tissue type is in “inclusion” and the other types of tissues are not in “inclusion”, the selected tissue-specific SAGE tag sequences are generated. They are displayed in tags-per-million (TPM) and fold values, as well as visually displayed in four kinds of scales in a color gradient pattern. In the fold visualization display, the top scores of the SAGE tag sequences are provided, along with cluster plots. A user-defined matrix file is designed for cross-tissue comparison by selecting libraries from publically available databases or user-defined libraries

Gene expression profiling may improve diagnosis in patients with carcinoma of unknown primary

Carcinomas of unknown primary (CUP) represent between 3 and 10% of malignancies. Treatment with nonspecific chemotherapy is commonly unhelpful and the median survival is between 3 and 6 months. Gene expression microarray (GEM) analysis has demonstrated that molecular signatures can aid in tumour classification and propose foster primaries. In this study, we demonstrate the clinical utility of a diagnostic gene expression profiling tool and discuss its potential implications for patient management strategies. Paraffin tumour samples from 21 cases of ‘true' CUP patients in whom standard investigation had failed to determine a primary site of malignancy were investigated using diagnostic gene profiling. The results were reviewed in the context of histology and clinical history. Classification of tumour origin using the GEM method confirmed the clinicians' suspicion in 16 out of 21 cases. There was a clinical/GEM inconsistency in 4 out of 21 patients and a pathological/GEM inconsistency in 1 patient. The improved diagnoses by the GEM method would have influenced the management in 12 out of 21 cases. Genomic profiling and cancer classification tools represent a promising analytical approach to assist with the management of CUP patients. We propose that GEM diagnosis be considered when the primary clinical algorithm has failed to provide a diagnosis

Clustering-based approaches to SAGE data mining

Serial analysis of gene expression (SAGE) is one of the most powerful tools for global gene expression profiling. It has led to several biological discoveries and biomedical applications, such as the prediction of new gene functions and the identification of biomarkers in human cancer research. Clustering techniques have become fundamental approaches in these applications. This paper reviews relevant clustering techniques specifically designed for this type of data. It places an emphasis on current limitations and opportunities in this area for supporting biologically-meaningful data mining and visualisation

The Recognition of N-Glycans by the Lectin ArtinM Mediates Cell Death of a Human Myeloid Leukemia Cell Line

ArtinM, a d-mannose-binding lectin from Artocarpus heterophyllus (jackfruit), interacts with N-glycosylated receptors on the surface of several cells of hematopoietic origin, triggering cell migration, degranulation, and cytokine release. Because malignant transformation is often associated with altered expression of cell surface glycans, we evaluated the interaction of ArtinM with human myelocytic leukemia cells and investigated cellular responses to lectin binding. The intensity of ArtinM binding varied across 3 leukemia cell lines: NB4>K562>U937. The binding, which was directly related to cell growth suppression, was inhibited in the presence of Manα1-3(Manα1-6)Manβ1, and was reverted in underglycosylated NB4 cells. ArtinM interaction with NB4 cells induced cell death (IC50 = 10 µg/mL), as indicated by cell surface exposure of phosphatidylserine and disruption of mitochondrial membrane potential unassociated with caspase activation or DNA fragmentation. Moreover, ArtinM treatment of NB4 cells strongly induced reactive oxygen species generation and autophagy, as indicated by the detection of acidic vesicular organelles in the treated cells. NB4 cell death was attributed to ArtinM recognition of the trimannosyl core of N-glycans containing a ß1,6-GlcNAc branch linked to α1,6-mannose. This modification correlated with higher levels of N-acetylglucosaminyltransferase V transcripts in NB4 cells than in K562 or U937 cells. Our results provide new insights into the potential of N-glycans containing a β1,6-GlcNAc branch linked to α1,6-mannose as a novel target for anti-leukemia treatment

Tumour-associated carbohydrate antigens in breast cancer

Glycosylation changes that occur in cancer often lead to the expression of tumour-associated carbohydrate antigens. In breast cancer, these antigens are usually associated with a poor prognosis and a reduced overall survival. Cellular models have shown the implication of these antigens in cell adhesion, migration, proliferation and tumour growth. The present review summarizes our current knowledge of glycosylation changes (structures, biosynthesis and occurrence) in breast cancer cell lines and primary tumours, and the consequences on disease progression and aggressiveness. The therapeutic strategies attempted to target tumour-associated carbohydrate antigens in breast cancer are also discussed

Transforming growth factor-beta-induced protein (TGFBI)/(Beta ig-H3): a matrix protein with dual functions in ovarian cancer

Transforming growth factor-beta-induced protein (TGFBI, also known as βig-H3 and keratoepithelin) is an extracellular matrix protein that plays a role in a wide range of physiological and pathological conditions including diabetes, corneal dystrophy and tumorigenesis. Many reports indicate that βig-H3 functions as a tumor suppressor. Loss of βig-H3 expression has been described in several cancers including ovarian cancer and promoter hypermethylation has been identified as an important mechanism for the silencing of the TGFBI gene. Our recent findings that βig-H3 is down-regulated in ovarian cancer and that high concentrations of βig-H3 can induce ovarian cancer cell death support a tumor suppressor role. However, there is also convincing data in the literature reporting a tumor-promoting role for βig-H3. We have shown βig-H3 to be abundantly expressed by peritoneal cells and increase the metastatic potential of ovarian cancer cells by promoting cell motility, invasion, and adhesion to peritoneal cells. Our findings suggest that βig-H3 has dual functions and can act both as a tumor suppressor or tumor promoter depending on the tumor microenvironment. This article reviews the current understanding of βig-H3 function in cancer cells with particular focus on ovarian cancer.Miranda P. Ween, Martin K. Oehler and Carmela Ricciardell