Univariate Bootstrap Research Articles

A novel, mitochondrial, internal tRNA-derived RNA fragment possesses clinical utility as a molecular prognostic biomarker in chronic lymphocytic leukemia

ObjectivesChronic lymphocytic leukemia (CLL) is the most common leukemia among adults. The prognosis of CLL patients varies considerably. Transfer RNA-derived RNA fragments (tRFs) constitute a class of small non-coding RNA fragments excised from mature tRNAs and pre-tRNAs located in nuclei as well as in mitochondria. In this study, the clinical utility of i-tRF-PheGAA, a novel mitochondrial tRF, was investigated in CLL. Design and methodsPeripheral blood mononuclear cells (PBMCs) were isolated from 91 CLL patients and 43 non-leukemic controls. Total RNA was isolated from each sample, polyadenylated at the 3′ end and reversely transcribed. An in-house developed real-time quantitative PCR assay was developed and applied, and the results were biostatistically analyzed. For the normalization of the i-tRF-PheGAA expression levels, the expression of a small nucleolar RNA (RNU48) was used as reference. ResultsMann-Whitney U test showed that i-tRF-PheGAA can distinguish between CLL samples and normal controls (p < 0.001). As determined by Kaplan-Meier survival analysis, overexpression of i-tRF-PheGAA was related to poor overall survival of the CLL patients (p < 0.001). Univariate bootstrap Cox regression analysis exhibited a higher hazard ratio of 7.95 (95% CI = 2.37–26.72, p < 0.001) for patients with positive i-tRF-PheGAA expression status. Multivariate bootstrap Cox regression analysis showed that the prognostic value of this tRF is independent of clinical stage, mutational status of the immunoglobulin heavy chain variable (IGHV) genetic locus, and CD38 expression status (p = 0.010). ConclusionsOur results show that i-tRF-PheGAA can serve as a molecular biomarker of poor prognosis in CLL, alongside with the existing factors for CLL prognosis.

A Simulation Study of Bootstrap Approaches to Estimate Confidence Intervals in DeFries-Fulker Regression Models (with Application to the Heritability of BMI Changes in the NLSY).

The univariate bootstrap is a relatively recently developed version of the bootstrap (Lee and Rodgers in Psychol Methods 3(1): 91, 1998). DeFries-Fulker (DF) analysis is a regression model used to estimate parameters in behavioral genetic models (DeFries and Fulker in Behav Genet 15(5): 467-473, 1985). It is appealing for its simplicity; however, it violates certain regression assumptions such as homogeneity of variance and independence of errors that make calculation of standard errors and confidence intervals problematic. Methods have been developed to account for these issues (Kohler and Rodgers in Behav Genet 31(2): 179-191, 2001), however the univariate bootstrap represents a unique means of doing so that is presaged by suggestions from previous DF research (e.g., Cherny et al. in Behav Genet 22(2): 153-162, 1992). In the present study we use simulations to examine the performance of the univariate bootstrap in the context of DF analysis. We compare a number of possible bootstrap schemes as well as more traditional confidence interval methods. We follow up with an empirical demonstration, applying results of the simulation to models estimated to investigate changes in body mass index in adults from the National Longitudinal Survey of Youth 1979 data.

Expressional profiling and clinical relevance of RNase κ in prostate cancer: a novel indicator of favorable progression-free survival.

Considering the unmet need for novel molecular tumor markers capable of improving prostate cancer (CaP) patients' management along with the fruitful results regarding the future use of ribonucleases (RNases) as molecular diagnostic and prognostic markers in CaP, we aimed to study the expressional profile of RNase κ in CaP and BPH and to investigate its clinical significance in CaP. Total RNA was extracted from 212 prostatic tissue samples (101 BPH and 111 CaP) and, following cDNA synthesis, quantitative real-time PCR (qPCR) was performed for the expressional quantification of RNase κ. Extensive statistical analysis, including bootstrap resampling, was performed to investigate the differential expression of RNase κ in patients with BPH and CaP and its associations with patients' clinicopathological and survival data. RNase κ was significantly downregulated (P = 0.002) in CaP patients compared to BPH ones. RNase κ overexpression was associated with decreased risk of CaP development and can discriminate between CaP and BPH independently of serum PSA levels (crude odds ratio = 0.93, P = 0.001). RNase κ upregulation was also associated with less advanced (P = 0.018) and less aggressive (P = 0.001) tumors as well as with longer progression-free survival (PFS) (P = 0.003). Finally univariate bootstrap Cox regression confirmed that RNase κ was associated with favorable prognosis (HR = 0.85, P = 0.002). RNase κ is a biomarker of favorable prognosis in CaP, which is significantly associated with less advanced and aggressive disease, as well as with enhanced PFS.

Recurrence Plot Based Damage Detection Method by Integrating Control Chart

Because of the importance of damage detection in manufacturing systems and other areas, many fault detection methods have been developed that are based on a vibration signal. Little work, however, has been reported in the literature on using a recurrence plot method to analyze the vibration signal for damage detection. In this paper, we develop a recurrence plot based fault detection method by integrating the statistical process control technique. The recurrence plots of the vibration signals are derived by using the recurrence plot (RP) method. Five types of features are extracted from the recurrence plots to quantify the vibration signals’ characteristic. Then, the control chart, a multivariate statistical process control technique, is used to monitor these features. The control chart technique, however, has the assumption that all the data should follow a normal distribution. The RP based bootstrap control chart is proposed to estimate the control chart parameters. The performance of the proposed RP based bootstrap control chart is evaluated by a simulation study and compared with other univariate bootstrap control charts based on recurrence plot features. A real case study of rolling element bearing fault detection demonstrates that the proposed fault detection method achieves a very good performance.

Downregulation and Prognostic Performance of MicroRNA 224 Expression in Prostate Cancer

The extensive use of prostate-specific antigen as a general prostate cancer biomarker has introduced the hazards of overdiagnosis and overtreatment. Recent studies have revealed the immense biomarker capacity of microRNAs (miRNAs) in prostate cancer. The aim of this study was to analyze the expression pattern of miR-224, a cancer-related miRNA, in prostate tumors and investigate its clinical utility. Total RNA was isolated from 139 prostate tissue samples. After the polyadenylation of total RNA by poly(A) polymerase, cDNA was synthesized with a suitable poly(T) adapter. miR-224 expression was assessed by quantitative real-time PCR and analyzed with the comparative quantification cycle method, C(q)(2(-ΔΔCq)). We performed comprehensive biostatistical analyses to explore the clinical value of miR-224 in prostate cancer. miR-224 expression was significantly downregulated in malignant samples compared with benign samples (P < 0.001). Higher miR-224 expression levels were found in prostate tumors that were less aggressive (P = 0.017) and in an earlier disease stage (P = 0.018). Patients with prostate cancer who were positive for miR-224 had significantly enhanced progression-free survival intervals compared with miR-224-negative patients (P = 0.021). Univariate bootstrap Cox regression confirmed that miR-224 was associated with favorable prognosis (hazard ratio 0.314, P = 0.013); nonetheless, multivariate analysis, adjusted for conventional markers, did not identify miR-224 as an independent prognostic indicator. miR-224 is aberrantly expressed in prostate cancer. Its assessment by cost-effective quantitative molecular methodologies could provide a useful biomarker for prostate cancer.

Extreme water levels of the Vistula River and Gdansk Harbour

Using Canonical Correlation Analysis (CCA), Rozynski et al. (2006) have demonstrated that there is a weak correlation between the water levels of Vistula River and Gdansk Harbour. Herein the CCA analysis serves as a first step for a univariate bootstrap resampling technique, applied to investigate coincident extreme water levels of Vistula River and Gdansk Harbour. The CCA-derived assumption of statistical independence is argued as being a suitable working approximation for (outline) engineering design. This allows the goodness-of-fit of different statistical models to be assessed in a quantitative manner with a bootstrap method. This also provides a convenient means of defining extreme levels together with their confidence intervals. The analysis with two statistical methods provides insight into the character of joint coastal extremes in an estuary of a large north European river. The rationale and methodology are at least partly applicable to similar estuaries of northern Europe.