A robust tool for discriminative analysis and feature selection in paired samples impacts the identification of the genes essential for reprogramming lung tissue to adenocarcinoma

<p>Abstract</p> <p>Background</p> <p>Lung cancer is the leading cause of cancer deaths in the world. The most common type of lung cancer is lung adenocarcinoma (AC). The genetic mechanisms of the early stages and lung AC progression steps are poorly understood. There is currently no clinically applicable gene test for the early diagnosis and AC aggressiveness. Among the major reasons for the lack of reliable diagnostic biomarkers are the extraordinary heterogeneity of the cancer cells, complex and poorly understudied interactions of the AC cells with adjacent tissue and immune system, gene variation across patient cohorts, measurement variability, small sample sizes and sub-optimal analytical methods. We suggest that gene expression profiling of the primary tumours and adjacent tissues (PT-AT) handled with a rational statistical and bioinformatics strategy of biomarker prediction and validation could provide significant progress in the identification of clinical biomarkers of AC. To minimise sample-to-sample variability, repeated multivariate measurements in the same object (organ or tissue, e.g. PT-AT in lung) across patients should be designed, but prediction and validation on the genome scale with small sample size is a great methodical challenge.</p> <p>Results</p> <p>To analyse PT-AT relationships efficiently in the statistical modelling, we propose an Extreme Class Discrimination (ECD) feature selection method that identifies a sub-set of the most discriminative variables (e.g. expressed genes). Our method consists of a paired Cross-normalization (CN) step followed by a modified sign Wilcoxon test with multivariate adjustment carried out for each variable. Using an Affymetrix U133A microarray paired dataset of 27 AC patients, we reviewed the global reprogramming of the transcriptome in human lung AC tissue versus normal lung tissue, which is associated with about 2,300 genes discriminating the tissues with 100% accuracy. Cluster analysis applied to these genes resulted in four distinct gene groups which we classified as associated with (i) up-regulated genes in the mitotic cell cycle lung AC, (ii) silenced/suppressed gene specific for normal lung tissue, (iii) cell communication and cell motility and (iv) the immune system features. The genes related to mutagenesis, specific lung cancers, early stage of AC development, tumour aggressiveness and metabolic pathway alterations and adaptations of cancer cells are strongly enriched in the AC PT-AT discriminative gene set. Two AC diagnostic biomarkers SPP1 and CENPA were successfully validated on RT-RCR tissue array. ECD method was systematically compared to several alternative methods and proved to be of better performance and as well as it was validated by comparison of the predicted gene set with literature meta-signature.</p> <p>Conclusions</p> <p>We developed a method that identifies and selects highly discriminative variables from high dimensional data spaces of potential biomarkers based on a statistical analysis of paired samples when the number of samples is small. This method provides superior selection in comparison to conventional methods and can be widely used in different applications. Our method revealed at least 23 hundreds patho-biologically essential genes associated with the global transcriptional reprogramming of human lung epithelium cells and lung AC aggressiveness. This gene set includes many previously published AC biomarkers reflecting inherent disease complexity and specifies the mechanisms of carcinogenesis in the lung AC. SPP1, CENPA and many other PT-AT discriminative genes could be considered as the prospective diagnostic and prognostic biomarkers of lung AC.</p

STRUCTURAL & MACRO-ENVIRONMENTAL ANALYSIS OF THE FACTORING INDUSTRY IN SINGAPORE

Bachelor'sBACHELOR OF BUSINESS ADMINISTRATION WITH HONOUR

Lung cancer in never‐smokers

The mystery behind never‐smokers being more prone to lung cancer is unlocked with regard to smoking status and se

HPLC-MS/MS coupled with equilibrium dialysis method for quantification of free drug concentration of pazopanib in plasma.

BackgroundThe selective occurrence of hepatotoxicity observed with use of pazopanib may be attributed to its high level of plasma protein binding and low hepatic extraction ratio. The primary objective was to investigate changes in free drug concentration amongst patients with varying albumin concentrations.MethodsA HPLC-MS/MS method using C18 column (4.6 × 150 mm, 5 μm) with ESI source in positive mode had been developed and validated for the quantitative determination of free pazaopanib concentration in human plasma. Prior to sample preparation, patient samples were subjected to 6-hour equilibrium dialysis with molecular weight cut-off set at 8000 Da.ResultsThe calibration curves were linear over the range of 5-1000 ng/mL, with a lower limit of quantification of 5 ng/mL. The intra-day and inter-day precisions and accuracies were all within ± 15 %, at 3 different quality controls. Higher median fraction unbound of pazopanib were observed in patients (n = 17) with lower than normal albumin concentrations.ConclusionWith the developed assay, monitoring of plasma free concentrations may be evaluated as an indicator of pazopanib exposure in patients

Applying Triz for Production Quality Improvement

This paper aims to provide a thorough analysis on the application of TRIZ in improving the quality of canned food production. TRIZ tools such as engineering systems analysis, function analysis, cause and effect chain analysis, By-separation model and 40 Inventive Principles are applied in order to discover some feasible and elegant solutions to alleviate the problem. Findings revealed that the rejected canned products on the conveyor belt will be isolated or picked up with other good condition canned products which are lined up very closely to the rejected cans; though the visioning system is able detect the fault printing on the canned product. The main root cause is that the rejected canned product is picked up with other canned products in good condition because all cans are lined up on the belt and are very close to each other or having no gaps between the cans. Conversely, all cans on the conveyor belts are required to be very close to each other to avoid collisions that may damage the cans. The root cause is solved by applying function analysis, By-separation tool and Inventive Principles. Therefore, it can be concluded that TRIZ is a powerful tool in inventive problem solving

Applying Triz for Production Quality Improvement

This paper aims to provide a thorough analysis on the application of TRIZ in improving the quality of canned food production. TRIZ tools such as engineering systems analysis, function analysis, cause and effect chain analysis, By-separation model and 40 Inventive Principles are applied in order to discover some feasible and elegant solutions to alleviate the problem. Findings revealed that the rejected canned products on the conveyor belt will be isolated or picked up with other good condition canned products which are lined up very closely to the rejected cans; though the visioning system is able detect the fault printing on the canned product. The main root cause is that the rejected canned product is picked up with other canned products in good condition because all cans are lined up on the belt and are very close to each other or having no gaps between the cans. Conversely, all cans on the conveyor belts are required to be very close to each other to avoid collisions that may damage the cans. The root cause is solved by applying function analysis, By-separation tool and Inventive Principles. Therefore, it can be concluded that TRIZ is a powerful tool in inventive problem solving