Rork Kuick

p53-Mediated Activation of miRNA34 Candidate Tumor-Suppressor Genes

BACKGROUND In response to varied cell stress signals, the p53 tumor-suppressor protein activates a multitude of genes encoding proteins with functions in cell-cycle control, DNA repair, senescence, and apoptosis. The role of p53 in transcription of other types of RNAs, such as microRNAs (miRNAs) is essentially unknown. RESULTS Using gene-expression analyses, reporter gene assays, and chromatin-immunoprecipitation approaches, we present definitive evidence that the abundance of the three-member miRNA34 family is directly regulated by p53 in cell lines and tissues. Using array-based approaches and algorithm predictions, we define genes likely to be directly regulated by miRNA34, with cell-cycle regulatory genes being the most prominent class. In addition, we provide functional evidence, obtained via antisense oligonucleotide transfection and the use of mouse embryonic stem cells with loss of miRNA34a function, that the BCL2 protein is regulated directly by miRNA34. Finally, we demonstrate that the expression of two miRNA34s is dramatically reduced in 6 of 14 (43%) non-small cell lung cancers (NSCLCs) and that the restoration of miRNA34 expression inhibits growth of NSCLC cells. CONCLUSIONS Taken together, the data suggest the miRNA34s might be key effectors of p53 tumor-suppressor function, and their inactivation might contribute to certain cancers.

Gene expression-based survival prediction in lung adenocarcinoma: A multi-site, blinded validation study

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.

Mutations in NALP7 cause recurrent hydatidiform moles and reproductive wastage in humans

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.

Distinct Transcriptional Profiles of Adrenocortical Tumors Uncovered by DNA Microarray Analysis

Comprehensive expression profiling of tumors using DNA microarrays has been used recently for molecular classification and biomarker discovery, as well as a tool to identify and investigate genes involved in tumorigenesis. Application of this approach to a cohort of benign and malignant adrenocortical tissues would be potentially informative in all of these aspects. In this study, we generated transcriptional profiles of 11 adrenocortical carcinomas (ACCs), 4 adrenocortical adenomas (ACAs), 3 normal adrenal cortices (NCs), and 1 macronodular hyperplasia (MNH) using Affymetrix HG_U95Av2 oligonucleotide arrays representing approximately 10,500 unique genes. The expression data set was used for unsupervised hierarchical cluster analysis as well as principal component analysis to visually represent the expression data. An analysis of variance on the three classes (NC, ACA plus MNH, and ACC) revealed 91 genes that displayed at least threefold differential expression between the ACC cohort and both the NC and ACA cohorts at a significance level of P < 0.01. Included in these 91 genes were those known to be up-regulated in adrenocortical tumors, such as insulin-like growth factor (IGF2), as well as novel differentially expressed genes such as osteopontin (SPP) and serine threonine kinase 15 (STK15). Increased expression of IGF2 was identified in 10 of 11 ACCs (90.9%) and was verified by quantitative reverse transcriptase-polymerase chain reaction. Select proliferation-related genes (TOP2A and Ki-67) were validated at the protein level using immunohistochemistry and adrenocortical tissue microarrays. Our results demonstrated significant and consistent gene expression changes in ACCs compared to benign adrenocortical lesions. Moreover, we identified several genes that represent potential diagnostic markers and may play a role in the pathogenesis of ACC.

Molecular classification of papillary thyroid carcinoma: distinct BRAF , RAS , and RET/PTC mutation-specific gene expression profiles discovered by DNA microarray analysis

Thyroid cancer poses a significant clinical challenge, and our understanding of its pathogenesis is incomplete. To gain insight into the pathogenesis of papillary thyroid carcinoma, transcriptional profiles of four normal thyroids and 51 papillary carcinomas (PCs) were generated using DNA microarrays. The tumors were genotyped for their common activating mutations: BRAF V600E point mutation, RET/PTC1 and 3 rearrangement and point mutations of KRAS, HRAS and NRAS. Principal component analysis based on the entire expression data set separated the PCs into three groups that were found to reflect tumor morphology and mutational status. By combining expression profiles with mutational status, we defined distinct expression profiles for the BRAF, RET/PTC and RAS mutation groups. Using small numbers of genes, a simple classifier was able to classify correctly the mutational status of all 40 tumors with known mutations. One tumor without a detectable mutation was predicted by the classifier to have a RET/PTC rearrangement and was shown to contain one by fluorescence in situ hybridization analysis. Among the mutation-specific expression signatures were genes whose differential expression was a direct consequence of the mutation, as well as genes involved in a variety of biological processes including immune response and signal transduction. Expression of one mutation-specific differentially expressed gene, TPO, was validated at the protein level using immunohistochemistry and tissue arrays containing an independent set of tumors. The results demonstrate that mutational status is the primary determinant of gene expression variation within these tumors, a finding that may have clinical and diagnostic significance and predicts success for therapies designed to prevent the consequences of these mutations.

Characterization of Gene Expression Profiles Associated with Glioma Progression Using Oligonucleotide-Based Microarray Analysis and Real-Time Reverse Transcription-Polymerase Chain Reaction

Diffuse astrocytoma of World Health Organization (WHO) grade II has an inherent tendency to spontaneously progress to anaplastic astrocytoma (WHO grade III) and/or glioblastoma (WHO grade IV). The molecular basis of astrocytoma progression is still poorly understood, in particular with respect to the progression-associated changes at the mRNA level. Therefore, we compared the transcriptional profile of approximately 6800 genes in primary WHO grade II gliomas and corresponding recurrent high-grade (WHO grade III or IV) gliomas from eight patients using oligonucleotide-based microarray analysis. We identified 66 genes whose mRNA levels differed significantly (P < 0.01, > or =2-fold change) between the primary and recurrent tumors. The microarray data were corroborated by real-time reverse transcription-polymerase chain reaction analysis of 12 selected genes, including 7 genes with increased expression and 5 genes with reduced expression on progression. In addition, the expression of these 12 genes was determined in an independent series of 43 astrocytic gliomas (9 diffuse astrocytomas, 10 anaplastic astrocytomas, 17 primary, and 7 secondary glioblastomas). These analyses confirmed that the transcript levels of nine of the selected genes (COL4A2, FOXM1, MGP, TOP2A, CENPF, IGFBP4, VEGFA, ADD3, and CAMK2G) differed significantly in WHO grade II astrocytomas as compared to anaplastic astrocytomas and/or glioblastomas. Thus, we identified and validated a set of interesting candidate genes whose differential expression likely plays a role in astrocytoma progression.

A biomarker panel for acute graft-versus-host disease.

No validated biomarkers exist for acute graft-versus-host disease (GVHD). We screened plasma with antibody microarrays for 120 proteins in a discovery set of 42 patients who underwent transplantation that revealed 8 potential biomarkers for diagnostic of GVHD. We then measured by enzyme-linked immunosorbent assay (ELISA) the levels of these biomarkers in samples from 424 patients who underwent transplantation randomly divided into training (n = 282) and validation (n = 142) sets. Logistic regression analysis of these 8 proteins determined a composite biomarker panel of 4 proteins (interleukin-2-receptor-alpha, tumor-necrosis-factor-receptor-1, interleukin-8, and hepatocyte growth factor) that optimally discriminated patients with and without GVHD. The area under the receiver operating characteristic curve distinguishing these 2 groups in the training set was 0.91 (95% confidence interval, 0.87-0.94) and 0.86 (95% confidence interval, 0.79-0.92) in the validation set. In patients with GVHD, Cox regression analysis revealed that the biomarker panel predicted survival independently of GVHD severity. A panel of 4 biomarkers can confirm the diagnosis of GVHD in patients at onset of clinical symptoms of GVHD and provide prognostic information independent of GVHD severity.

Protein profiles associated with survival in lung adenocarcinoma

Morphologic assessment of lung tumors is informative but insufficient to adequately predict patient outcome. We previously identified transcriptional profiles that predict patient survival, and here we identify proteins associated with patient survival in lung adenocarcinoma. A total of 682 individual protein spots were quantified in 90 lung adenocarcinomas by using quantitative two-dimensional polyacrylamide gel electrophoresis analysis. A leave-one-out cross-validation procedure using the top 20 survival-associated proteins identified by Cox modeling indicated that protein profiles as a whole can predict survival in stage I tumor patients (P = 0.01). Thirty-three of 46 survival-associated proteins were identified by using mass spectrometry. Expression of 12 candidate proteins was confirmed as tumor-derived with immunohistochemical analysis and tissue microarrays. Oligonucleotide microarray results from both the same tumors and from an independent study showed mRNAs associated with survival for 11 of 27 encoded genes. Combined analysis of protein and mRNA data revealed 11 components of the glycolysis pathway as associated with poor survival. Among these candidates, phosphoglycerate kinase 1 was associated with survival in the protein study, in both mRNA studies and in an independent validation set of 117 adenocarcinomas and squamous lung tumors using tissue microarrays. Elevated levels of phosphoglycerate kinase 1 in the serum were also significantly correlated with poor outcome in a validation set of 107 patients with lung adenocarcinomas using ELISA analysis. These studies identify new prognostic biomarkers and indicate that protein expression profiles can predict the outcome of patients with early-stage lung cancer.

Molecular Classification and Prognostication of Adrenocortical Tumors by Transcriptome Profiling

Purpose: Our understanding of adrenocortical carcinoma (ACC) has improved considerably, yet many unanswered questions remain. For instance, can molecular subtypes of ACC be identified? If so, what is their underlying pathogenetic basis and do they possess clinical significance? Experimental Design: We did a whole genome gene expression study of a large cohort of adrenocortical tissues annotated with clinicopathologic data. Using Affymetrix Human Genome U133 Plus 2.0 oligonucleotide arrays, transcriptional profiles were generated for 10 normal adrenal cortices (NC), 22 adrenocortical adenomas (ACA), and 33 ACCs. Results: The overall classification of adrenocortical tumors was recapitulated using principal component analysis of the entire data set. The NC and ACA cohorts showed little intragroup variation, whereas the ACC cohort revealed much greater variation in gene expression. A robust list of 2,875 differentially expressed genes in ACC compared with both NC and ACA was generated and used in functional enrichment analysis to find pathways and attributes of biological significance. Cluster analysis of the ACCs revealed two subtypes that reflected tumor proliferation, as measured by mitotic counts and cell cycle genes. Kaplan-Meier analysis of these ACC clusters showed a significant difference in survival (P < 0.020). Multivariate Cox modeling using stage, mitotic rate, and gene expression data as measured by the first principal component for ACC samples showed that gene expression data contains significant independent prognostic information (P < 0.017). Conclusions: This study lays the foundation for the molecular classification and prognostication of adrenocortical tumors and also provides a rich source of potential diagnostic and prognostic markers.

Microarray Analyses during Adipogenesis: Understanding the Effects of Wnt Signaling on Adipogenesis and the Roles of Liver X Receptor α in Adipocyte Metabolism

ABSTRACT Wnt signaling maintains preadipocytes in an undifferentiated state. When Wnt signaling is enforced, 3T3-L1 preadipocytes no longer undergo adipocyte conversion in response to adipogenic medium. Here we used microarray analyses to identify subsets of genes whose expression is aberrant when differentiation is blocked through enforced Wnt signaling. Furthermore, we used the microarray data to identify potentially important adipocyte genes and chose one of these, the liver X receptor α (LXRα), for further analyses. Our studies indicate that enforced Wnt signaling blunts the changes in gene expression that correspond to mitotic clonal expansion, suggesting that Wnt signaling inhibits adipogenesis in part through dysregulation of the cell cycle. Experiments designed to uncover the potential role of LXRα in adipogenesis revealed that this transcription factor, unlike CCAAT/enhancer binding protein α and peroxisome proliferator-activated receptor gamma, is not adipogenic but rather inhibits adipogenesis if inappropriately expressed and activated. However, LXRα has several important roles in adipocyte function. Our studies show that this nuclear receptor increases basal glucose uptake and glycogen synthesis in 3T3-L1 adipocytes. In addition, LXRα increases cholesterol synthesis and release of nonesterified fatty acids. Finally, treatment of mice with an LXRα agonist results in increased serum levels of glycerol and nonesterified fatty acids, consistent with increased lipolysis within adipose tissue. These findings demonstrate new metabolic roles for LXRα and increase our understanding of adipogenesis.