Data Mining - Concepts and Techniques.

The hallmarks of cancer conceptualization is a heuristic tool for distilling the vast complexity of cancer phenotypes and genotypes into a provisional set of underlying principles. As knowledge of cancer mechanisms has progressed, other facets of the disease have emerged as potential refinements. Herein, the prospect is raised that phenotypic plasticity and disrupted differentiation is a discrete hallmark capability, and that nonmutational epigenetic reprogramming and polymorphic microbiomes both constitute distinctive enabling characteristics that facilitate the acquisition of hallmark capabilities. Additionally, senescent cells, of varying origins, may be added to the roster of functionally important cell types in the tumor microenvironment. SIGNIFICANCE: Cancer is daunting in the breadth and scope of its diversity, spanning genetics, cell and tissue biology, pathology, and response to therapy. Ever more powerful experimental and computational tools and technologies are providing an avalanche of "big data" about the myriad manifestations of the diseases that cancer encompasses. The integrative concept embodied in the hallmarks of cancer is helping to distill this complexity into an increasingly logical science, and the provisional new dimensions presented in this perspective may add value to that endeavor, to more fully understand mechanisms of cancer development and malignant progression, and apply that knowledge to cancer medicine. 

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Hallmarks of Cancer: New Dimensions

50 Years of Image Analysis

The hallmarks of cancer conceptualization is a heuristic tool for distilling the vast complexity of cancer phenotypes and genotypes into a provisional set of underlying principles. As knowledge of cancer mechanisms has progressed, other facets of the disease have emerged as potential refinements. Herein, the prospect is raised that phenotypic plasticity and disrupted differentiation is a discrete hallmark capability, and that nonmutational epigenetic reprogramming and polymorphic microbiomes both constitute distinctive enabling characteristics that facilitate the acquisition of hallmark capabilities. Additionally, senescent cells, of varying origins, may be added to the roster of functionally important cell types in the tumor microenvironment. SIGNIFICANCE: Cancer is daunting in the breadth and scope of its diversity, spanning genetics, cell and tissue biology, pathology, and response to therapy. Ever more powerful experimental and computational tools and technologies are providing an avalanche of "big data" about the myriad manifestations of the diseases that cancer encompasses. The integrative concept embodied in the hallmarks of cancer is helping to distill this complexity into an increasingly logical science, and the provisional new dimensions presented in this perspective may add value to that endeavor, to more fully understand mechanisms of cancer development and malignant progression, and apply that knowledge to cancer medicine.

Hallmarks of Cancer: New Dimensions.

From bench to bedside

Rough set based maximum relevance-maximum significance criterion and Gene selection from microarray data

Gene expression data clustering is one of the important tasks of functional genomics as it provides a powerful tool for studying functional relationships of genes in a biological process. Identifying coexpressed groups of genes represents the basic challenge in gene clustering problem. In this regard, a gene clustering algorithm, termed as robust rough-fuzzy $(c)$-means, is proposed judiciously integrating the merits of rough sets and fuzzy sets. While the concept of lower and upper approximations of rough sets deals with uncertainty, vagueness, and incompleteness in cluster definition, the integration of probabilistic and possibilistic memberships of fuzzy sets enables efficient handling of overlapping partitions in noisy environment. The concept of possibilistic lower bound and probabilistic boundary of a cluster, introduced in robust rough-fuzzy $(c)$-means, enables efficient selection of gene clusters. An efficient method is proposed to select initial prototypes of different gene clusters, which enables the proposed $(c)$-means algorithm to converge to an optimum or near optimum solutions and helps to discover coexpressed gene clusters. The effectiveness of the algorithm, along with a comparison with other algorithms, is demonstrated both qualitatively and quantitatively on 14 yeast microarray data sets.

Rough-Fuzzy Clustering for Grouping Functionally Similar Genes from Microarray Data

Activation of proinflammatory macrophages is associated with the inflammatory state of rheumatoid arthritis. Their polarization and activation are controlled by transcription factors such as NF-κB and the AP-1 transcription factor member c-Fos. Surprisingly, little is known about the role of the AP-1 transcription factor c-Jun in macrophage activation. In this study, we show that mRNA and protein levels of c-Jun are increased in macrophages following pro- or anti-inflammatory stimulations. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment cluster analyses of microarray data using wild-type and c-Jun-deleted macrophages highlight the central function of c-Jun in macrophages, in particular for immune responses, IL production, and hypoxia pathways. Mice deficient for c-Jun in macrophages show an amelioration of inflammation and bone destruction in the serum-induced arthritis model. In vivo and in vitro gene profiling, together with chromatin immunoprecipitation analysis of macrophages, revealed direct activation of the proinflammatory factor cyclooxygenase-2 and indirect inhibition of the anti-inflammatory factor arginase-1 by c-Jun. Thus, c-Jun regulates the activation state of macrophages and promotes arthritis via differentially regulating cyclooxygenase-2 and arginase-1 levels.

/pdf/the-ap-1-transcription-factor-c-jun-promotes-arthritis-by-3r39779osx.pdf

The AP-1 Transcription Factor c-Jun Promotes Arthritis by Regulating Cyclooxygenase-2 and Arginase-1 Expression in Macrophages.

Epigenetic deregulation remarkably triggers mechanisms associated with tumor aggressiveness like epithelial-mesenchymal transition (EMT). Since EMT is a highly complex, but also reversible event, epigenetic processes such as DNA methylation or chromatin alterations must be involved in its regulation. It was recently described that loss of the cell cycle regulator p21 was associated with a gain in EMT characteristics and an upregulation of the master EMT transcription factor ZEB1. In this study, in silico analysis was performed in combination with different in vitro and in vivo techniques to identify and verify novel epigenetic targets of ZEB1, and to proof the direct transcriptional regulation of SETD1B by ZEB1. The chorioallantoic-membrane assay served as an in vivo model to analyze the ZEB1/SETD1B interaction. Bioinformatical analysis of CRC patient data was used to examine the ZEB1/SETD1B network under clinical conditions and the ZEB1/SETD1B network was modeled under physiological and pathological conditions. Thus, we identified a self-reinforcing loop for ZEB1 expression and found that the SETD1B associated active chromatin mark H3K4me3 was enriched at the ZEB1 promoter in EMT cells. Moreover, clinical evaluation of CRC patient data showed that the simultaneous high expression of ZEB1 and SETD1B was correlated with the worst prognosis. Here we report that the expression of chromatin modifiers is remarkably dysregulated in EMT cells. SETD1B was identified as a new ZEB1 target in vitro and in vivo. Our study demonstrates a novel example of an activator role of ZEB1 for the epigenetic landscape in colorectal tumor cells.

/pdf/emt-transcription-factor-zeb1-alters-the-epigenetic-39r2b99mw4.pdf

EMT transcription factor ZEB1 alters the epigenetic landscape of colorectal cancer cells

Quantitative structure activity relationship (QSAR) is one of the important disciplines of computer-aided drug design that deals with the predictive modeling of properties of a molecule. In general, each QSAR dataset is small in size with large number of features or descriptors. Among the large amount of descriptors presented in the QSAR dataset, only a small fraction of them is effective for performing the predictive modeling task. In this paper, a new feature selection algorithm is presented, based on rough set theory, to select a set of effective molecular descriptors from a given QSAR dataset. The proposed algorithm selects the set of molecular descriptors by maximizing both relevance and significance of the descriptors. An important finding is that the proposed feature selection algorithm is shown to be effective in selecting relevant and significant molecular descriptors from the QSAR dataset for predictive modeling. The performance of the proposed algorithm is studied using R2 statistic of support vector regression method. The effectiveness of the proposed algorithm, along with a comparison with existing algorithms, is demonstrated on three QSAR datasets.

Sushmita Paul

Papers

Rough set based maximum relevance-maximum significance criterion and Gene selection from microarray data

Rough-Fuzzy Clustering for Grouping Functionally Similar Genes from Microarray Data

The AP-1 Transcription Factor c-Jun Promotes Arthritis by Regulating Cyclooxygenase-2 and Arginase-1 Expression in Macrophages.

EMT transcription factor ZEB1 alters the epigenetic landscape of colorectal cancer cells

Rough Sets for Selection of Molecular Descriptors to Predict Biological Activity of Molecules