ABSTRACT
Non-Hodgkin's lymphoma (NHL) is a group of malignancies of the immune system with variable clinical behaviors and diverse molecular features. Despite the progress made in classification of NHLs based on classical methods, molecular classifications are a work in progress. Toward this goal, we used an array-based technique called differential methylation hybridization (DMH) to study small B-cell lymphoma (SBCL) subtypes. A total of 43 genomic DMH experiments were performed. From these results, several statistical methods were used to generate a set of differentially methylated genes for further validation. Methylation of LHX2, POU3F3, HOXC10, NRP2, PRKCE, RAMP, MLLT2, NKX6.1, LRP1B and ARF4 was validated in cell lines and patient samples and demonstrated subtype-related preferential methylation patterns. For LHX2 and LRP1B, bisulfite sequencing, real-time reverse transcriptase-polymerase chain reaction and induction of gene expression following treatment with the demethylating agent, 5'-aza-2'-deoxycytidine, were confirmed. This new epigenetic information is helping to define molecular portraits of distinct subtypes of SBCL that are not recognized by current classification systems and provides valuable potential insights into the biology of these tumors.
Subject(s)
DNA Methylation , Gene Expression Regulation, Neoplastic , Leukemia, Lymphocytic, Chronic, B-Cell/classification , Leukemia, Lymphocytic, Chronic, B-Cell/genetics , Lymphoma, B-Cell/classification , Lymphoma, B-Cell/genetics , Adult , Cell Line, Tumor , Cluster Analysis , CpG Islands/physiology , Epigenesis, Genetic , Female , Genomics/methods , Homeodomain Proteins/genetics , Humans , LIM-Homeodomain Proteins , Leukemia, Lymphocytic, Chronic, B-Cell/metabolism , Lymphoma, B-Cell/metabolism , Male , Nucleic Acid Hybridization/methods , Oligonucleotide Array Sequence Analysis , RNA, Messenger/analysis , Receptors, LDL/genetics , Sulfites , Transcription Factors/geneticsABSTRACT
Fuzzy set methods have been used to model and manage uncertainty in various aspects of image processing, pattern recognition, and computer vision. High-level computer vision applications hold a great potential for fuzzy set theory because of its links to natural language. Linguistic scene description, a language-based interpretation of regions and their relationships, is one such application that is starting to bear the fruits of fuzzy set theoretic involvement. In this paper, we are expanding on two earlier endeavors. We introduce new families of fuzzy directional relations that rely on the computation of histograms of forces. These families preserve important relative position properties. They provide inputs to a fuzzy rule base that produces logical linguistic descriptions along with assessments as to the validity of the descriptions. Each linguistic output uses hedges from a dictionary of about 30 adverbs and other terms that can be tailored to individual users. Excellent results from several synthetic and real image examples show the applicability of this approach.
