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Author

Rork Kuick

Other affiliations: University of Florida
Bio: Rork Kuick is an academic researcher from University of Michigan. The author has contributed to research in topics: Gene expression profiling & Gene. The author has an hindex of 60, co-authored 166 publications receiving 16459 citations. Previous affiliations of Rork Kuick include University of Florida.


Papers
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Journal ArticleDOI
TL;DR: The identification of a set of genes that predict survival in early-stage lung adenocarcinoma allows delineation of a high-risk group that may benefit from adjuvant therapy.
Abstract: Histopathology is insufficient to predict disease progression and clinical outcome in lung adenocarcinoma. Here we show that gene-expression profiles based on microarray analysis can be used to predict patient survival in early-stage lung adenocarcinomas. Genes most related to survival were identified with univariate Cox analysis. Using either two equivalent but independent training and testing sets, or 'leave-one-out' cross-validation analysis with all tumors, a risk index based on the top 50 genes identified low-risk and high-risk stage I lung adenocarcinomas, which differed significantly with respect to survival. This risk index was then validated using an independent sample of lung adenocarcinomas that predicted high- and low-risk groups. This index included genes not previously associated with survival. The identification of a set of genes that predict survival in early-stage lung adenocarcinoma allows delineation of a high-risk group that may benefit from adjuvant therapy.

2,002 citations

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TL;DR: The data suggest the miRNA34s might be key effectors of p53 tumor-suppressor function, and their inactivation might contribute to certain cancers.

1,096 citations

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TL;DR: A large, training–testing, multi-site, blinded validation study to characterize the performance of several prognostic models based on gene expression for 442 lung adenocarcinomas, providing the largest available set of microarray data with extensive pathological and clinical annotation for lungAdenocARCinomas.
Abstract: Although prognostic gene expression signatures for survival in early-stage lung cancer have been proposed, for clinical application, it is critical to establish their performance across different subject populations and in different laboratories. Here we report a large, training-testing, multi-site, blinded validation study to characterize the performance of several prognostic models based on gene expression for 442 lung adenocarcinomas. The hypotheses proposed examined whether microarray measurements of gene expression either alone or combined with basic clinical covariates (stage, age, sex) could be used to predict overall survival in lung cancer subjects. Several models examined produced risk scores that substantially correlated with actual subject outcome. Most methods performed better with clinical data, supporting the combined use of clinical and molecular information when building prognostic models for early-stage lung cancer. This study also provides the largest available set of microarray data with extensive pathological and clinical annotation for lung adenocarcinomas.

1,020 citations

Journal Article
TL;DR: The data indicate that gene expression patterns in ovarian adenocarcinomas reflect both morphological features and biological behavior, and provide a foundation for the development of new type-specific diagnostic strategies and treatments for ovarian cancer.
Abstract: Biologically and clinically meaningful tumor classification schemes have long been sought. Some malignant epithelial neoplasms, such as those in the thyroid and endometrium, exhibit more than one pattern of differentiation, each associated with distinctive clinical features and treatments. In other tissues, all carcinomas, regardless of morphological type, are treated as though they represent a single disease. To better understand the biological and clinical features seen in the four major histological types of ovarian carcinoma (OvCa), we analyzed gene expression in 113 ovarian epithelial tumors using oligonucleotide microarrays. Global views of the variation in gene expression were obtained using PCA. These analyses show that mucinous and clear cell OvCas can be readily distinguished from serous OvCas based on their gene expression profiles, regardless of tumor stage and grade. In contrast, endometrioid adenocarcinomas show significant overlap with other histological types. Although high-stage/grade tumors are generally separable from low-stage/grade tumors, clear cell OvCa has a molecular signature that distinguishes it from other poor-prognosis OvCas. Indeed, 73 genes, expressed 2- to 29-fold higher in clear cell OvCas compared with each of the other OvCa types, were identified. Collectively, the data indicate that gene expression patterns in ovarian adenocarcinomas reflect both morphological features and biological behavior. Moreover, these studies provide a foundation for the development of new type-specific diagnostic strategies and treatments for ovarian cancer.

435 citations

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TL;DR: Five mutations in the maternal gene NALP7 in individuals with familial and recurrent HMs are reported, which is the first maternal effect gene identified in humans and is also responsible for recurrent spontaneous abortions, stillbirths and intrauterine growth retardation.
Abstract: Hydatidiform mole (HM) is an abnormal human pregnancy with no embryo and cystic degeneration of placental villi. We report five mutations in the maternal gene NALP7 in individuals with familial and recurrent HMs. NALP7 is a member of the CATERPILLER protein family involved in inflammation and apoptosis. NALP7 is the first maternal effect gene identified in humans and is also responsible for recurrent spontaneous abortions, stillbirths and intrauterine growth retardation.

412 citations


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TL;DR: The Gene Set Enrichment Analysis (GSEA) method as discussed by the authors focuses on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation.
Abstract: Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

34,830 citations

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TL;DR: Transcript expression in perigonadal adipose tissue from groups of mice in which adiposity varied due to sex, diet, and the obesity-related mutations agouti (Ay) and obese (Lepob) found that the expression of 1,304 transcripts correlated significantly with body mass.
Abstract: Obesity alters adipose tissue metabolic and endocrine function and leads to an increased release of fatty acids, hormones, and proinflammatory molecules that contribute to obesity associated complications. To further characterize the changes that occur in adipose tissue with increasing adiposity, we profiled transcript expression in perigonadal adipose tissue from groups of mice in which adiposity varied due to sex, diet, and the obesity-related mutations agouti (Ay) and obese (Lepob). We found that the expression of 1,304 transcripts correlated significantly with body mass. Of the 100 most significantly correlated genes, 30% encoded proteins that are characteristic of macrophages and are positively correlated with body mass. Immunohistochemical analysis of perigonadal, perirenal, mesenteric, and subcutaneous adipose tissue revealed that the percentage of cells expressing the macrophage marker F4/80 (F4/80+) was significantly and positively correlated with both adipocyte size and body mass. Similar relationships were found in human subcutaneous adipose tissue stained for the macrophage antigen CD68. Bone marrow transplant studies and quantitation of macrophage number in adipose tissue from macrophage-deficient (Csf1op/op) mice suggest that these F4/80+ cells are CSF-1 dependent, bone marrow-derived adipose tissue macrophages. Expression analysis of macrophage and nonmacrophage cell populations isolated from adipose tissue demonstrates that adipose tissue macrophages are responsible for almost all adipose tissue TNF-alpha expression and significant amounts of iNOS and IL-6 expression. Adipose tissue macrophage numbers increase in obesity and participate in inflammatory pathways that are activated in adipose tissues of obese individuals.

8,902 citations

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TL;DR: The 11th edition of Harrison's Principles of Internal Medicine welcomes Anthony Fauci to its editorial staff, in addition to more than 85 new contributors.
Abstract: The 11th edition of Harrison's Principles of Internal Medicine welcomes Anthony Fauci to its editorial staff, in addition to more than 85 new contributors. While the organization of the book is similar to previous editions, major emphasis has been placed on disorders that affect multiple organ systems. Important advances in genetics, immunology, and oncology are emphasized. Many chapters of the book have been rewritten and describe major advances in internal medicine. Subjects that received only a paragraph or two of attention in previous editions are now covered in entire chapters. Among the chapters that have been extensively revised are the chapters on infections in the compromised host, on skin rashes in infections, on many of the viral infections, including cytomegalovirus and Epstein-Barr virus, on sexually transmitted diseases, on diabetes mellitus, on disorders of bone and mineral metabolism, and on lymphadenopathy and splenomegaly. The major revisions in these chapters and many

6,968 citations

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TL;DR: The heritability of methylation states and the secondary nature of the decision to invite or exclude methylation support the idea that DNA methylation is adapted for a specific cellular memory function in development.
Abstract: The character of a cell is defined by its constituent proteins, which are the result of specific patterns of gene expression. Crucial determinants of gene expression patterns are DNA-binding transcription factors that choose genes for transcriptional activation or repression by recognizing the sequence of DNA bases in their promoter regions. Interaction of these factors with their cognate sequences triggers a chain of events, often involving changes in the structure of chromatin, that leads to the assembly of an active transcription complex (e.g., Cosma et al. 1999). But the types of transcription factors present in a cell are not alone sufficient to define its spectrum of gene activity, as the transcriptional potential of a genome can become restricted in a stable manner during development. The constraints imposed by developmental history probably account for the very low efficiency of cloning animals from the nuclei of differentiated cells (Rideout et al. 2001; Wakayama and Yanagimachi 2001). A “transcription factors only” model would predict that the gene expression pattern of a differentiated nucleus would be completely reversible upon exposure to a new spectrum of factors. Although many aspects of expression can be reprogrammed in this way (Gurdon 1999), some marks of differentiation are evidently so stable that immersion in an alien cytoplasm cannot erase the memory. The genomic sequence of a differentiated cell is thought to be identical in most cases to that of the zygote from which it is descended (mammalian B and T cells being an obvious exception). This means that the marks of developmental history are unlikely to be caused by widespread somatic mutation. Processes less irrevocable than mutation fall under the umbrella term “epigenetic” mechanisms. A current definition of epigenetics is: “The study of mitotically and/or meiotically heritable changes in gene function that cannot be explained by changes in DNA sequence” (Russo et al. 1996). There are two epigenetic systems that affect animal development and fulfill the criterion of heritability: DNA methylation and the Polycomb-trithorax group (Pc-G/trx) protein complexes. (Histone modification has some attributes of an epigenetic process, but the issue of heritability has yet to be resolved.) This review concerns DNA methylation, focusing on the generation, inheritance, and biological significance of genomic methylation patterns in the development of mammals. Data will be discussed favoring the notion that DNA methylation may only affect genes that are already silenced by other mechanisms in the embryo. Embryonic transcription, on the other hand, may cause the exclusion of the DNA methylation machinery. The heritability of methylation states and the secondary nature of the decision to invite or exclude methylation support the idea that DNA methylation is adapted for a specific cellular memory function in development. Indeed, the possibility will be discussed that DNA methylation and Pc-G/trx may represent alternative systems of epigenetic memory that have been interchanged over evolutionary time. Animal DNA methylation has been the subject of several recent reviews (Bird and Wolffe 1999; Bestor 2000; Hsieh 2000; Costello and Plass 2001; Jones and Takai 2001). For recent reviews of plant and fungal DNA methylation, see Finnegan et al. (2000), Martienssen and Colot (2001), and Matzke et al. (2001).

6,691 citations

Journal ArticleDOI
TL;DR: This work introduces Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner and constitutes a starting point to build pathway-centric models of biology.
Abstract: Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org .

6,125 citations