Jarkko Isotalo

miR-193b is an epigenetically regulated putative tumor suppressor in prostate cancer.

miRNAs have proven to be key regulators of gene expression and are differentially expressed in various diseases, including cancer. Our aim was to identify epigenetically dysregulated genes in prostate cancer. We performed miRNA expression profiling after relieving epigenetic modifications in 6 prostate cancer cell lines and nonmalignant prostate epithelial cells. Thirty‐eight miRNAs showed increased expression in any prostate cancer cell line after 5‐aza‐2′‐deoxycytidine (5azadC) and trichostatin A (TSA) treatments. Six of these also had decreased expression in clinical prostate cancer samples compared to benign prostatic hyperplasia. Among these, miR‐193b was methylated in 22Rv1 cell line at a CpG island ∼1 kb upstream of the miRNA locus. Expressing miR‐193b in 22Rv1 cells using pre‐miR‐193b oligonucleotides caused a significant growth reduction (p < 0.001) resulting from a decrease of cells in S‐phase of the cell cycle (p < 0.01). In addition, the anchorage independent growth was partially inhibited in transiently miR‐193b‐expressing 22Rv1 cells (p < 0.01). Altogether, our data suggest that miR‐193b is an epigenetically silenced putative tumor suppressor in prostate cancer.

Matrix Tricks for Linear Statistical Models

In teaching linear statistical models to first-year graduate students or to final-year undergraduate students there is no way to proceed smoothly without matrices and related concepts of linear algebra; their use is really essential. Our experience is that making some particular matrix tricks very familiar to students can substantially increase their insight into linear statistical models (and also multivariate statistical analysis). In matrix algebra, there are handy, sometimes even very simple “tricks” which simplify and clarify the treatment of a problem—both for the student and for the professor. Of course, the concept of a trick is not uniquely defined—by a trick we simply mean here a useful important handy result. In this book we collect together our Top Twenty favourite matrix tricks for linear statistical models.

Linear Prediction Sufficiency for New Observations in the General Gauss–Markov Model

We consider the prediction of new observations in a general Gauss–Markov model. We state the fundamental equations of the best linear unbiased prediction, BLUP, and consider some properties of the BLUP. Particularly, we focus on such linear statistics, which preserve enough information for obtaining the BLUP of new observations as a linear function of them. We call such statistics linearly prediction sufficient for new observations, and introduce some equivalent characterizations for this new concept.

TCEB1 promotes invasion of prostate cancer cells

Amplification of the long arm of chromosome 8 is one of the most recurrent findings in prostate cancer and it is associated with poor prognosis. Several minimal regions of amplification suggest multiple target genes which are yet to be identified. We have previously shown that TCEB1, EIF3S3, KIAA0196 and RAD21 are amplified and overexpressed in prostate cancer and they are located in the 8q area. In this study, we examined the functional effects of these genes to prostate cancer cell phenotype. We overexpressed and inhibited the genes by lentivirus mediated overexpression and RNA interference, respectively. shRNA mediated TCEB1 silencing decreased significantly cellular invasion of PC‐3 and DU145 cells through Matrigel. TCEB1 silencing reduced the anchorage‐independent growth of PC‐3 cells. Similar effects were not seen with any other genes. When overexpressed in NIH 3T3 cells, TCEB1 and EIF3S3 increased the growth rate of the cells. Transcriptional profiling of TCEB1 silenced PC‐3 cells revealed decrease of genes involved in invasion and metastasis. Finally, we also confirmed here the overexpression of TCEB1 in hormone‐refractory prostate tumors. This study indicates that TCEB1 promotes invasion of prostate cancer cells, is involved in development of hormone‐refractory prostate cancer and is thereby a strong candidate to be one of the target genes for the 8q gain.

Predicting knottiness of Pinus sylvestris for use in tree bucking procedures

Abstract The quality and accompanying value of Scots pine (Pinus sylvestris L.) lumber varies markedly in the Nordic countries. Consequently, tree bucking has a great impact on the economic result. The objectives of this study were (1) to analyse which knot characteristics usually reduce pine lumber sawn from the butt-logs to the next quality grade, (2) to determine the most appropriate characteristics that could be used in predicting lumber quality, and (3) to develop models that could be used in practice for predicting the probability of certain quality grades. The study was based on field experiments and test sawing data on 100 Scots pine stems from south-western Finland. Since the results showed that the maximum dead knot is the most crucial knot characteristic when the first cross-cutting point is determined, models were created that predict the height of the first dead knot that lowers grade A to B (B-grade dead knot). Both early growth rate and dead branch height should be measured to predict pine butt-log quality. Early growth rate seems to be appropriate in predicting between-stand variation, while dead branch height is appropriate for predicting within-stand variation.

A Useful Matrix Decomposition and Its Statistical Applications in Linear Regression

It is well known that if V is a symmetric positive definite n × n matrix, and (X: Z) is a partitioned orthogonal n × n matrix, then In this article, we show how useful we have found the formula (*), and in particular, its version and present several related formulas, as well as some generalized versions. We also include several statistical applications.

NMD and microRNA expression profiling of the HPCX1 locus reveal MAGEC1 as a candidate prostate cancer predisposition gene

BackgroundSeveral predisposition loci for hereditary prostate cancer (HPC) have been suggested, including HPCX1 at Xq27-q28, but due to the complex structure of the region, the susceptibility gene has not yet been identified.MethodsIn this study, nonsense-mediated mRNA decay (NMD) inhibition was used for the discovery of truncating mutations. Six prostate cancer (PC) patients and their healthy brothers were selected from a group of HPCX1-linked families. Expression analyses were done using Agilent 44 K oligoarrays, and selected genes were screened for mutations by direct sequencing. In addition, microRNA expression levels in the lymphoblastic cells were analyzed to trace variants that might alter miRNA expression and explain partly an inherited genetic predisposion to PC.ResultsSeventeen genes were selected for resequencing based on the NMD array, but no truncating mutations were found. The most interesting variant was MAGEC1 p.Met1?. An association was seen between the variant and unselected PC (OR = 2.35, 95% CI = 1.10-5.02) and HPC (OR = 3.38, 95% CI = 1.10-10.40). miRNA analysis revealed altogether 29 miRNAs with altered expression between the PC cases and controls. miRNA target analysis revealed that 12 of them also had possible target sites in the MAGEC1 gene. These miRNAs were selected for validation process including four miRNAs located in the X chromosome. The expressions of 14 miRNAs were validated in families that contributed to the significant signal differences in Agilent arrays.ConclusionsFurther functional studies are needed to fully understand the possible contribution of these miRNAs and MAGEC1 start codon variant to PC.

Singular Value Decomposition

While the eigenvalue decomposition \({\bf A} = \bf T{\bf \Lambda}T^{\prime},\) say, concerns only symmetric matrices, the singular value decomposition (SVD) \({\bf A} = \bf U{\bf \Delta}V^{\prime},\) say, concerns any n × m matrix. In this chapter we illustrate the usefulness of the SVD, particularly from the statistical point of view. Surprisingly many statistical methods have connections to the SVD.

Block-Diagonalization and the Schur Complement

In this chapter we present a block-diagonalization result for a symmetric nonnegative definite matrix. We may emphasize that the block-diagonalization result, sometimes called the Aitken block-diagonalization formula, due to Aitken (1939, Ch. 3, §29), is mathematically indeed quite simple just as it is. However, it is exceptionally handy and powerful tool for various situations arising in linear models and multivariate analysis, see, e.g., the derivation of the conditional multinormal distribution in Anderson (2003, §2.5); cf. also (9.21)–(9.22) (p. 193). We also consider the Schur complements whose usefulness in linear models and related areas can hardly be overestimated.

Formulas Useful for Linear Regression Analysis and Related Matrix Theory

The Model Matrix.- Fitted Values and Residuals.- Regression Coefficients.- Alternative Estimators.- Decompositions of Sums of Squares.- Partial Correlations.- Distributions.- Testing Hypotheses.- Diagnostics.- BLUE: Some Helpful Identities.- Estimability.- Best Linear Unbiased Estimator.- The Watson Efficiency.- Linear Sufficiency and Admissibility.- Best Linear Unbiased Predictor.- Mixed Model.- Multivariate Linear Model.- Inverse of a Partitioned Matrix.- Generalized Inverses.- Projectors.- Eigenvalues.- Discriminant Analysis.- Factor Analysis.- Canonical Correlations.- Matrix Decompositions.- Principal Component Analysis.- Lowner Ordering.- Rank Rules.- Inequalities.- Kronecker Product.- Matrix Derivatives.