Exercise Overrides Blunted Hypoxic Ventilatory Response in Prematurely Born Men.

Pre-term birth provokes life-long anatomical and functional respiratory system sequelae. Although blunted hypoxic ventilatory response (HVR) is consistently observed in pre-term infants, it remains unclear if it persists with aging and, moreover, if it influences hypoxic exercise capacity. In addition, it remains unresolved whether the previously observed prematurity-related alterations in redox balance could contribute to HVR modulation. Twenty-one prematurely born adult males (gestational age = 29 ± 4 weeks], and 14 age matched controls born at full term (gestational age = 39 ± 2 weeks) underwent three tests in a randomized manner: (1) hypoxia chemo-sensitivity test to determine the resting and exercise poikilocapnic HVR and a graded exercise test to volitional exhaustion in (2) normoxia (F <sub>i</sub> O <sub>2</sub> = 0.21), and (3) normobaric hypoxia (F <sub>i</sub> O <sub>2</sub> = 0.13) to compare the hypoxia-related effects on maximal aerobic power (MAP). Selected prooxidant and antioxidant markers were analyzed from venous samples obtained before and after the HVR tests. Resting HVR was lower in the pre-term (0.21 ± 0.21 L ⋅ min <sup>-1</sup> ⋅ kg <sup>-1</sup> ) compared to full-term born individuals (0.47 ± 0.23 L ⋅ min <sup>-1</sup> ⋅ kg <sup>-1</sup> ; p < 0.05). No differences were noted in the exercise HVR or in any of the measured oxidative stress markers before or after the HVR test. Hypoxia-related reduction of MAP was comparable between the groups. These findings indicate that blunted resting HVR in prematurely born men persists into adulthood. Also, active adults born prematurely seem to tolerate hypoxic exercise well and should, hence, not be discouraged to engage in physical activities in hypoxic environments. Nevertheless, the blunted resting HVR and greater desaturation observed in the pre-term born individuals warrant caution especially during prolonged hypoxic exposures

Improving Cancer Classification Accuracy Using Gene Pairs

Recent studies suggest that the deregulation of pathways, rather than individual genes, may be critical in triggering carcinogenesis. The pathway deregulation is often caused by the simultaneous deregulation of more than one gene in the pathway. This suggests that robust gene pair combinations may exploit the underlying bio-molecular reactions that are relevant to the pathway deregulation and thus they could provide better biomarkers for cancer, as compared to individual genes. In order to validate this hypothesis, in this paper, we used gene pair combinations, called doublets, as input to the cancer classification algorithms, instead of the original expression values, and we showed that the classification accuracy was consistently improved across different datasets and classification algorithms. We validated the proposed approach using nine cancer datasets and five classification algorithms including Prediction Analysis for Microarrays (PAM), C4.5 Decision Trees (DT), Naive Bayesian (NB), Support Vector Machine (SVM), and k-Nearest Neighbor (k-NN)

Comparative study of unsupervised dimension reduction techniques for the visualization of microarray gene expression data

<p>Abstract</p> <p>Background</p> <p>Visualization of DNA microarray data in two or three dimensional spaces is an important exploratory analysis step in order to detect quality issues or to generate new hypotheses. Principal Component Analysis (PCA) is a widely used linear method to define the mapping between the high-dimensional data and its low-dimensional representation. During the last decade, many new nonlinear methods for dimension reduction have been proposed, but it is still unclear how well these methods capture the underlying structure of microarray gene expression data. In this study, we assessed the performance of the PCA approach and of six nonlinear dimension reduction methods, namely Kernel PCA, Locally Linear Embedding, Isomap, Diffusion Maps, Laplacian Eigenmaps and Maximum Variance Unfolding, in terms of visualization of microarray data.</p> <p>Results</p> <p>A systematic benchmark, consisting of Support Vector Machine classification, cluster validation and noise evaluations was applied to ten microarray and several simulated datasets. Significant differences between PCA and most of the nonlinear methods were observed in two and three dimensional target spaces. With an increasing number of dimensions and an increasing number of differentially expressed genes, all methods showed similar performance. PCA and Diffusion Maps responded less sensitive to noise than the other nonlinear methods.</p> <p>Conclusions</p> <p>Locally Linear Embedding and Isomap showed a superior performance on all datasets. In very low-dimensional representations and with few differentially expressed genes, these two methods preserve more of the underlying structure of the data than PCA, and thus are favorable alternatives for the visualization of microarray data.</p

Interaction among apoptosis-associated sequence variants and joint effects on aggressive prostate cancer

<p>Abstract</p> <p>Background</p> <p>Molecular and epidemiological evidence demonstrate that altered gene expression and single nucleotide polymorphisms in the apoptotic pathway are linked to many cancers. Yet, few studies emphasize the interaction of variant apoptotic genes and their joint modifying effects on prostate cancer (PCA) outcomes. An exhaustive assessment of all the possible two-, three- and four-way gene-gene interactions is computationally burdensome. This statistical conundrum stems from the prohibitive amount of data needed to account for multiple hypothesis testing.</p> <p>Methods</p> <p>To address this issue, we systematically prioritized and evaluated individual effects and complex interactions among 172 apoptotic SNPs in relation to PCA risk and aggressive disease (i.e., Gleason score ≥ 7 and tumor stages III/IV). Single and joint modifying effects on PCA outcomes among European-American men were analyzed using statistical epistasis networks coupled with multi-factor dimensionality reduction (SEN-guided MDR). The case-control study design included 1,175 incident PCA cases and 1,111 controls from the prostate, lung, colo-rectal, and ovarian (PLCO) cancer screening trial. Moreover, a subset analysis of PCA cases consisted of 688 aggressive and 488 non-aggressive PCA cases. SNP profiles were obtained using the NCI Cancer Genetic Markers of Susceptibility (CGEMS) data portal. Main effects were assessed using logistic regression (LR) models. Prior to modeling interactions, SEN was used to pre-process our genetic data. SEN used network science to reduce our analysis from > 36 million to < 13,000 SNP interactions. Interactions were visualized, evaluated, and validated using entropy-based MDR. All parametric and non-parametric models were adjusted for age, family history of PCA, and multiple hypothesis testing.</p> <p>Results</p> <p>Following LR modeling, eleven and thirteen sequence variants were associated with PCA risk and aggressive disease, respectively. However, none of these markers remained significant after we adjusted for multiple comparisons. Nevertheless, we detected a modest synergistic interaction between <it>AKT3 rs2125230-PRKCQ rs571715 </it>and disease aggressiveness using SEN-guided MDR (p = 0.011).</p> <p>Conclusions</p> <p>In summary, entropy-based SEN-guided MDR facilitated the logical prioritization and evaluation of apoptotic SNPs in relation to aggressive PCA. The suggestive interaction between <it>AKT3-PRKCQ </it>and aggressive PCA requires further validation using independent observational studies.</p

Visualization-based cancer microarray data classification analysis

MOTIVATION: Methods for analyzing cancer microarray data often face two distinct challenges: the models they infer need to perform well when classifying new tissue samples while at the same time providing an insight into the patterns and gene interactions hidden in the data. State-of-the-art supervised data mining methods often cover well only one of these aspects, motivating the development of methods where predictive models with a solid classification perform-ance would be easily communicated to the domain expert. RESULTS: Data visualization may provide for an excellent approach to knowledge discovery and analysis of class-labeled data. We have previously developed an approach called VizRank that can score and rank point-based visualizations according to degree of separa-tion of data instances of different class. We here extend VizRank with techniques to uncover outliers, score features (genes) and per-form classification, as well as to demonstrate that the proposed approach is well-suited for cancer microarray analysis. Using VizRank and radviz visualization on a set of previously published cancer microarray data sets, we were able to find simple, interpret-able data projections that include only a small subset of genes yet do clearly differentiate among different cancer types. We also report that our approach to classification through visualization achieves performance that is comparable to state-of-the-art supervised data mining techniques. AVAILABILITY: VizRank and radviz are implemented as part of the Orange data mining suite (http://www.ailab.si/orange). SUPPLEMENTARY MATERIAL: Supplementary material is available from http://www.ailab.si/supp/bi-cancer