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Search Results: 1 - 10 of 8012 matches for " Dan Mercola "
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A Gradient Boosting Algorithm for Survival Analysis via Direct Optimization of Concordance Index
Yifei Chen,Zhenyu Jia,Dan Mercola,Xiaohui Xie
Computational and Mathematical Methods in Medicine , 2013, DOI: 10.1155/2013/873595
Abstract: Survival analysis focuses on modeling and predicting the time to an event of interest. Many statistical models have been proposed for survival analysis. They often impose strong assumptions on hazard functions, which describe how the risk of an event changes over time depending on covariates associated with each individual. In particular, the prevalent proportional hazards model assumes that covariates are multiplicatively related to the hazard. Here we propose a nonparametric model for survival analysis that does not explicitly assume particular forms of hazard functions. Our nonparametric model utilizes an ensemble of regression trees to determine how the hazard function varies according to the associated covariates. The ensemble model is trained using a gradient boosting method to optimize a smoothed approximation of the concordance index, which is one of the most widely used metrics in survival model performance evaluation. We implemented our model in a software package called GBMCI (gradient boosting machine for concordance index) and benchmarked the performance of our model against other popular survival models with a large-scale breast cancer prognosis dataset. Our experiment shows that GBMCI consistently outperforms other methods based on a number of covariate settings. GBMCI is implemented in R and is freely available online. 1. Introduction Survival analysis focuses on developing diagnostic and prognostic models to analyze the effect of covariates on the outcome of an event of interest, such as death or disease recurrence in disease studies. The analysis is often carried out using regression methods to estimate the relationship between the covariates and the time to event variable. In clinical trials, time to events is usually represented by survival times, which measure how long a patient with a localized disease is alive or disease-free after treatment, such as surgery or surgery plus adjuvant therapy. The covariates used in predicting survival times often include clinical features, such as age, disease status, and treatment type. More recently, molecular features, such as expression of genes, and genetic features, such as mutations in genes, are increasingly being included in the set of covariates. Survival analysis also has applications in many other fields. For instance, it is often used to model machine failure in mechanical systems. Depending on specific circumstances, survival times may also be referred to as failure times. A major complication for survival analysis is that the survival data are often incomplete due to censoring,
An Accurate Prostate Cancer Prognosticator Using a Seven-Gene Signature Plus Gleason Score and Taking Cell Type Heterogeneity into Account
Xin Chen, Shizhong Xu, Michael McClelland, Farah Rahmatpanah, Anne Sawyers, Zhenyu Jia, Dan Mercola
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0045178
Abstract: One of the major challenges in the development of prostate cancer prognostic biomarkers is the cellular heterogeneity in tissue samples. We developed an objective Cluster-Correlation (CC) analysis to identify gene expression changes in various cell types that are associated with progression. In the Cluster step, samples were clustered (unsupervised) based on the expression values of each gene through a mixture model combined with a multiple linear regression model in which cell-type percent data were used for decomposition. In the Correlation step, a Chi-square test was used to select potential prognostic genes. With CC analysis, we identified 324 significantly expressed genes (68 tumor and 256 stroma cell expressed genes) which were strongly associated with the observed biochemical relapse status. Significance Analysis of Microarray (SAM) was then utilized to develop a seven-gene classifier. The Classifier has been validated using two independent Data Sets. The overall prediction accuracy and sensitivity is 71% and 76%, respectively. The inclusion of the Gleason sum to the seven-gene classifier raised the prediction accuracy and sensitivity to 83% and 76% respectively based on independent testing. These results indicated that our prognostic model that includes cell type adjustments and using Gleason score and the seven-gene signature has some utility for predicting outcomes for prostate cancer for individual patients at the time of prognosis. The strategy could have applications for improving marker performance in other cancers and other diseases.
Early Growth Response 3 (Egr3) Is Highly Over-Expressed in Non-Relapsing Prostate Cancer but Not in Relapsing Prostate Cancer
Rebecca Pio, Zhenyu Jia, Veronique T. Baron, Dan Mercola, UCI NCI SPECS consortium of the Strategic Partners for the Evaluation of Cancer Signatures, Prostate Cancer
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0054096
Abstract: Members of the early growth response (EGR) family of transcription factors play diverse functions in response to many cellular stimuli, including growth, stress, and inflammation. Egr3 has gone relatively unstudied, but here through use of the SPECS (Strategic Partners for the Evaluation of Predictive Signatures of Prostate Cancer) Affymetrix whole genome gene expression database we report that Egr3 mRNA is significantly over-expressed in prostate cancer compared to normal prostate tissue (5-fold). The Human Protein Atlas (http://www.proteinatlas.org), a database of tissue microarrays labeled with antibodies against over 11,000 human proteins, was utilized to quantify Egr3 protein expression in normal prostate and prostate cancer patients. In agreement with the SPECS data, we found that Egr3 protein is significantly increased in prostate cancer. The SPECS database has the benefit of extensive clinical follow up for the prostate cancer patients. Analysis of Egr3 mRNA expression in relation to the relapse status reveals that Egr3 mRNA expression is increased in tumor cells of non-relapsed samples (n = 63) compared to normal prostate cells, but is significantly lower in relapsed samples (n = 38) compared to non-relapse. The observations were confirmed using an independent data set. A list of genes correlating with this unique expression pattern was determined. These Egr3-correlated genes were enriched with Egr binding sites in their promoters. The gene list contains inflammatory genes such as IL-6, IL-8, IL1β and COX-2, which have extensive connections to prostate cancer.
Expression Changes in the Stroma of Prostate Cancer Predict Subsequent Relapse
Zhenyu Jia, Farah B. Rahmatpanah, Xin Chen, Waldemar Lernhardt, Yipeng Wang, Xiao-Qin Xia, Anne Sawyers, Manuel Sutton, Michael McClelland, Dan Mercola
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0041371
Abstract: Biomarkers are needed to address overtreatment that occurs for the majority of prostate cancer patients that would not die of the disease but receive radical treatment. A possible barrier to biomarker discovery may be the polyclonal/multifocal nature of prostate tumors as well as cell-type heterogeneity between patient samples. Tumor-adjacent stroma (tumor microenvironment) is less affected by genetic alteration and might therefore yield more consistent biomarkers in response to tumor aggressiveness. To this end we compared Affymetrix gene expression profiles in stroma near tumor and identified a set of 115 probe sets for which the expression levels were significantly correlated with time-to-relapse. We also compared patients that chemically relapsed shortly after prostatectomy (<1 year), and patients that did not relapse in the first four years after prostatectomy. We identified 131 differentially expressed microarray probe sets between these two categories. 19 probe sets (15 genes overlapped between the two gene lists with p<0.0001). We developed a PAM-based classifier by training on samples containing stroma near tumor: 9 rapid relapse patient samples and 9 indolent patient samples. We then tested the classifier on 47 different samples, containing 90% or more stroma. The classifier predicted the risk status of patients with an average accuracy of 87%. This is the first general tumor microenvironment-based prognostic classifier. These results indicate that the prostate cancer microenvironment exhibits reproducible changes useful for predicting outcomes for patients.
Plasma-Derived Exosomal Survivin, a Plausible Biomarker for Early Detection of Prostate Cancer
Salma Khan, Jessica M. S. Jutzy, Malyn May A. Valenzuela, David Turay, Jonathan R. Aspe, Arjun Ashok, Saied Mirshahidi, Dan Mercola, Michael B. Lilly, Nathan R. Wall
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0046737
Abstract: Background Survivin is expressed in prostate cancer (PCa), and its downregulation sensitizes PCa cells to chemotherapeutic agents in vitro and in vivo. Small membrane-bound vesicles called exosomes, secreted from the endosomal membrane compartment, contain RNA and protein that they readily transport via exosome internalization into recipient cells. Recent progress has shown that tumor-derived exosomes play multiple roles in tumor growth and metastasis and may produce these functions via immune escape, tumor invasion and angiogenesis. Furthermore, exosome analysis may provide novel biomarkers to diagnose or monitor PCa treatment. Methods Exosomes were purified from the plasma and serum from 39 PCa patients, 20 BPH patients, 8 prostate cancer recurrent and 16 healthy controls using ultracentrifugation and their quantities and qualities were quantified and visualized from both the plasma and the purified exosomes using ELISA and Western blotting, respectively. Results Survivin was significantly increased in the tumor-derived samples, compared to those from BPH and controls with virtually no difference in the quantity of Survivin detected in exosomes collected from newly diagnosed patients exhibiting low (six) or high (nine) Gleason scores. Exosome Survivin levels were also higher in patients that had relapsed on chemotherapy compared to controls. Conclusions These studies demonstrate that Survivin exists in plasma exosomes from both normal, BPH and PCa subjects. The relative amounts of exosomal Survivin in PCa plasma was significantly higher than in those with pre-inflammatory BPH and control plasma. This differential expression of exosomal Survivin was seen with both newly diagnosed and advanced PCa subjects with high or low-grade cancers. Analysis of plasma exosomal Survivin levels may offer a convenient tool for diagnosing or monitoring PCa and may, as it is elevated in low as well as high Gleason scored samples, be used for early detection.
Prostate Cancer Postoperative Nomogram Scores and Obesity
Jacqueline M. Major,Hillary S. Klonoff-Cohen,John P. Pierce,Donald J. Slymen,Sidney L. Saltzstein,Caroline A. Macera,Dan Mercola,Michael W. Kattan
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0017382
Abstract: Nomograms are tools used in clinical practice to predict cancer outcomes and to help make decisions regarding management of disease. Since its conception, utility of the prostate cancer nomogram has more than tripled. Limited information is available on the relation between the nomograms' predicted probabilities and obesity. The purpose of this study was to examine whether the predictions from a validated postoperative prostate cancer nomogram were associated with obesity.
Egr1 regulates the coordinated expression of numerous EGF receptor target genes as identified by ChIP-on-chip
Shilpi Arora, Yipeng Wang, Zhenyu Jia, Saynur Vardar-Sengul, Ayla Munawar, Kutbuddin S Doctor, Michael Birrer, Michael McClelland, Eileen Adamson, Dan Mercola
Genome Biology , 2008, DOI: 10.1186/gb-2008-9-11-r166
Abstract: UV irradiation led to significant binding of 288 gene promoters by Egr1. A major functional subgroup consisted of apoptosis related genes. The largest subgroup of 24 genes belongs to the epidermal growth factor receptor-signal transduction pathway. Egr1 promoter binding had a significant impact on gene expression of target genes. Conventional chromatin immunoprecipitation and quantitative real time PCR were used to validate promoter binding and expression changes. Small interfering RNA experiments were used to demonstrate the specific role of Egr1 in gene regulation. UV stimulation promotes growth arrest and apoptosis of M12 cells and our data clearly show that a downstream target of the epidermal growth factor receptor, namely Egr1, mediates this apoptotic response. Our study also identified numerous previously unknown targets of Egr1. These include FasL, MAX and RRAS2, which may play a role in the apoptotic response/growth arrest.Our results indicate that M12 cells undergo Egr1-dependent apoptotic response upon UV stimulation and led to the identification of downstream targets of Egr1, which mediate epidermal growth factor receptor function.Early growth response-1 (Egr1) is a zinc-finger nuclear phosphoprotein and transcription factor [1,2]. The gene for Egr1 (also known as Zif/268, NGFI-A and Knox24) encodes a 533 amino acid protein with 6 Cys2-His2 zinc finger motifs that exhibit partial homology to the gene sequence encoding the DNA binding domain of the Wilms tumor-1 suppressor (WT1) [3]. Indeed, both Egr1 and WT1 bind the Egr1 consensus regulatory sequence CGCCCCCGC in a zinc-dependent manner. Egr1 was first cloned as NGFI-A [4] from NGF-induced PC12 cells, and as Egr1 from mouse cells [1]. Early studies indicated its potential roles in cardiac and neural differentiation in a pluripotent EC (endothelial cells) line [1] and a role in monocytic differentiation of myeloid leukemia cells [5]. Subsequent studies have identified roles of Egr1 in cell growth, differ
Inhibition of cell growth by EGR-1 in human primary cultures from malignant glioma
Antonella Calogero, Vincenza Lombari, Giorgia De Gregorio, Antonio Porcellini, Severine Ucci, Antonietta Arcella, Riccardo Caruso, Franco Gagliardi, Alberto Gulino, Gaetano Lanzetta, Luigi Frati, Dan Mercola, Giuseppe Ragona
Cancer Cell International , 2004, DOI: 10.1186/1475-2867-4-1
Abstract: Low levels of EGR-1 protein were found in all primary cultures examined, with lower values present in grade IV tumors and in cultures carrying wild-type copies of p53 gene. The levels of EGR-1 protein were significantly correlated to the amount of intracellular fibronectin, but only in tumors carrying wild-type copies of the p53 gene (R = 0,78, p = 0.0082). Duplication time, plating efficiency, colony formation in agarose, and contact inhibition were also altered in the p53 mutated tumor cultures compared to those carrying wild-type p53. Growth arrest was achieved in both types of tumor within 1–2 weeks following infection with a recombinant adenovirus overexpressing EGR-1 but not with the control adenovirus.Suppression of EGR-1 is a common event in gliomas and in most cases this is achieved through down-regulation of gene expression. Expression of EGR-1 by recombinant adenovirus infection almost completely abolishes the growth of tumor cells in vitro, regardless of the mutational status of the p53 gene.EGR-1 encodes a nuclear phosphoprotein that binds to DNA and regulates transcription through a GC-rich consensus sequence [1-4]. EGR-1 is involved in the regulation of cell responses to a wide array of stimuli such as mitogens, growth factors and stress stimuli [5-7]. Recent studies have shown that EGR-1 expression is altered in several types of neoplasia, compared to normal tissue [1,8,9]. Gene deletion or EGR-1 mutations have been reported in sporadic hematological malignancies [10]. EGR-1 expression has been found to be either decreased or undetectable in human breast cancer tissue and small cell lung carcinoma [11,12]. EGR-1 is altered in a different manner in prostate cancer, where higher levels of EGR-1 expression are found correlated to more advanced stages of malignancy [13]. Later studies confirmed in two independent mouse models that EGR-1 up-regulates tumor progression [14,15]. From these various studies it is clear that EGR-1 is involved in regulation of
The Transcription Factor EGR1 Localizes to the Nucleolus and Is Linked to Suppression of Ribosomal Precursor Synthesis
Donatella Ponti, Gian Carlo Bellenchi, Rosa Puca, Daniela Bastianelli, Marella Maroder, Giuseppe Ragona, Pascal Roussel, Marc Thiry, Dan Mercola, Antonella Calogero
PLOS ONE , 2014, DOI: 10.1371/journal.pone.0096037
Abstract: EGR1 is an immediate early gene with a wide range of activities as transcription factor, spanning from regulation of cell growth to differentiation. Numerous studies show that EGR1 either promotes the proliferation of stimulated cells or suppresses the tumorigenic growth of transformed cells. Upon interaction with ARF, EGR1 is sumoylated and acquires the ability to bind to specific targets such as PTEN and in turn to regulate cell growth. ARF is mainly localized to the periphery of nucleolus where is able to negatively regulate ribosome biogenesis. Since EGR1 colocalizes with ARF under IGF-1 stimulation we asked the question of whether EGR1 also relocate to the nucleolus to interact with ARF. Here we show that EGR1 colocalizes with nucleolar markers such as fibrillarin and B23 in the presence of ARF. Western analysis of nucleolar extracts from HeLa cells was used to confirm the presence of EGR1 in the nucleolus mainly as the 100 kDa sumoylated form. We also show that the level of the ribosomal RNA precursor 47S is inversely correlated to the level of EGR1 transcripts. The EGR1 iseffective to regulate the synthesis of the 47S rRNA precursor. Then we demonstrated that EGR1 binds to the Upstream Binding Factor (UBF) leading us to hypothesize that the regulating activity of EGR1 is mediated by its interaction within the transcriptional complex of RNA polymerase I. These results confirm the presence of EGR1 in the nucleolus and point to a role for EGR1 in the control of nucleolar metabolism.
Generation of “Virtual” Control Groups for Single Arm Prostate Cancer Adjuvant Trials
Zhenyu Jia, Michael B. Lilly, James A. Koziol, Xin Chen, Xiao-Qin Xia, Yipeng Wang, Douglas Skarecky, Manuel Sutton, Anne Sawyers, Herbert Ruckle, Philip M. Carpenter, Jessica Wang-Rodriguez, Jun Jiang, Mingsen Deng, Cong Pan, Jian-guo Zhu, Christine E. McLaren, Michael J. Gurley, Chung Lee, Michael McClelland, Thomas Ahlering, Michael W. Kattan, Dan Mercola
PLOS ONE , 2014, DOI: 10.1371/journal.pone.0085010
Abstract: It is difficult to construct a control group for trials of adjuvant therapy (Rx) of prostate cancer after radical prostatectomy (RP) due to ethical issues and patient acceptance. We utilized 8 curve-fitting models to estimate the time to 60%, 65%, … 95% chance of progression free survival (PFS) based on the data derived from Kattan post-RP nomogram. The 8 models were systematically applied to a training set of 153 post-RP cases without adjuvant Rx to develop 8 subsets of cases (reference case sets) whose observed PFS times were most accurately predicted by each model. To prepare a virtual control group for a single-arm adjuvant Rx trial, we first select the optimal model for the trial cases based on the minimum weighted Euclidean distance between the trial case set and the reference case set in terms of clinical features, and then compare the virtual PFS times calculated by the optimum model with the observed PFSs of the trial cases by the logrank test. The method was validated using an independent dataset of 155 post-RP patients without adjuvant Rx. We then applied the method to patients on a Phase II trial of adjuvant chemo-hormonal Rx post RP, which indicated that the adjuvant Rx is highly effective in prolonging PFS after RP in patients at high risk for prostate cancer recurrence. The method can accurately generate control groups for single-arm, post-RP adjuvant Rx trials for prostate cancer, facilitating development of new therapeutic strategies.
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