A Classification Method Based on Principal Components of SELDI Spectra to Diagnose of Lung Adenocarcinoma

Lung cancer is the leading cause of cancer death worldwide, but techniques for effective early diagnosis are still lacking. Proteomics technology has been applied extensively to the study of the proteins involved in carcinogenesis. In this paper, a classification method was developed based on principal components of surface-enhanced laser desorption/ionization (SELDI) spectral data. This method was applied to SELDI spectral data from 71 lung adenocarcinoma patients and 24 healthy individuals. Unlike other peak-selection-based methods, this method takes each spectrum as a unity. The aim of this paper was to demonstrate that this unity-based classification method is more robust and powerful as a method of diagnosis than peak-selection-based methods.The results showed that this classification method, which is based on principal components, has outstanding performance with respect to distinguishing lung adenocarcinoma patients from normal individuals. Through leaving-one-out, 19-fold, 5-fold and 2-fold cross-validation studies, we found that this classification method based on principal components completely outperforms peak-selection-based methods, such as decision tree, classification and regression tree, support vector machine, and linear discriminant analysis.The classification method based on principal components of SELDI spectral data is a robust and powerful means of diagnosing lung adenocarcinoma. We assert that the high efficiency of this classification method renders it feasible for large-scale clinical use

<i>Hoya longlingensis</i> and <i>H. sichuanensis</i> (Apocynaceae), Two New Species from Southwestern China

Hoya longlingensis (E.F. Huang) and H. sichuanensis E.F. Huang are two new species of Apocynaceae from Southwestern China that are described in this study. Morphologically, the two species resemble H. tamdaoensis Rodda & T.B. Tran and H. lyi H. Lév., respectively. However, H. longlingensis differs from H. tamdaoensis by its elliptic leaves, mid-vein of leaf blades raised adaxially and depressed abaxially, lateral veins 2–4-paired, corolla yellow-green, outer angles of corona convex and spreading outside obviously. While H. sichuanensis differs from H. lyi by its obovate leaves, leaf apex rounded and base cuneate, petioles 1–3.5 cm long and ca. 3 mm in diameter, calyx lobes triangular, and corona whitish

Bimetallic AuPt alloy nanodendrites/reduced graphene oxide: One-pot ionic liquid-assisted synthesis and excellent electrocatalysis towards hydrogen evolution and methanol oxidation reactions

Herein, reduced graphene oxide supported well-dispersed bimetallic AuPt alloy nano dendrites (AuPt ANDs/rGO) were fabricated by a one-pot coreduction approach using ionic liquid (1-aminopropyl-3-methylimidazolium bromide, [APMIm]Br) as the stabilizer and capping agent. There is no any other polymer or seed involved. Characterized measurements include transmission electron microscopy (TEM), high angle annular dark-field scanning TEM (HAADF-STEM), X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS). The typical samples displayed excellent electrocatalytic activity and durability towards hydrogen evolution reaction (HER) and methanol oxidation reaction (MOR) in contrast with Pt nanocrystals/rGO and commercial Pt/C (50%) catalysts, which make it promising for practical catalysis in energy conversion and storage. (C) 2016 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.</p

Over-expression of extracellular matrix metalloproteinase inducer in prostate cancer is associated with high risk of prostate-specific antigen relapse after radical prostatectomy

Purpose: The prognostic efficiency of clinical grading and staging in patients with confined or moderately differentiated prostate cancer (PCa) has been markedly improved, which underscores the importance of new prognostic markers. Extracellular matrix metalloproteinase inducer (EMMPRIN) has been demonstrated to be involved in cancerangiogenesis, metastasis and invasion. EMMPRIN expression was evaluated by measuring mRNA and protein levels in a large cohort of patients with PCa following prostatectomy and the findings were compared with clinico-pathological parameters, including prostate-specific antigen (PSA) relapse time. Methods: EMMPRIN mRNA levels in 20 pairs of normal and cancerous prostate tissues were determined by quantitative real-time PCR. Protein expression in paraffin-embedded specimens of prostates gathered from 300 patients with PCa was detected by immunohistochemistry using a monoclonal antibody against EMMPRIN. The associations of EMMPRIN protein expression with the clinico-pathological parameters and PSA relapse-free time after radical prostatectomy were subsequently assessed. Results: Both EMMPRIN mRNA and protein levels were higher in PCa tissue, compared with adjacent normal tissue. In addition, the positive expression rates of EMMPRIN in PCa tissues were significantly associated with preoperative PSA levels (p=0.008), AJCC stage (p=0.006) and Gleason Score (p < 0.001), Risk classification (p < 0.001), lymph node status post-surgery (p < 0.001) and surgical margin status (p < 0.001) were also determined. Multivariate analysis, using the Cox proportional hazards model, revealed that positive EMMPRIN expression was an independent prognostic factor for an increased risk of PSA relapse. Conclusion: Over-expression of EMMPRIN correlated with the aggressiveness of PCa, and the PSA relapse-free time, and may be a novel and useful biomarker for follow-up and treatment decisions for PCa

Decision-tree-based classification model and experimental data.

<p>Two peaks that identified using a decision-tree-based classification model are shown, with 2 cases misclassified into control groups. The data used here are the peaks selected through baseline subtraction, normalization, peak detection, and peak alignment of SELDI data obtained from 71 lung adenocarcinoma patients and 24 normal individuals.</p

Candidate principal components.

a<p>Contribution of each PC to the whole variation.</p>b<p><i>P</i> value of the coefficient testing of logistic regression analysis on each PC.</p>c<p>Fitness index of each logistic regression model on single PC.</p

Features of lung adenocarcinoma patients.

a<p>Number of cases. The numbers in the parenthesis stand for the percentage.</p

Classification method based on principal components of SELDI spectral data and experimental data.

<p>Two cases and two normal individuals had been misclassified into opposite groups. The black squares indicate case individuals, and white squares with “V” shapes in the middle represent normal individuals. The data used here are the normalized SELDI data obtained from 71 lung adenocarcinoma patients and 24 normal individuals.</p

Cross-validation results of DT, SVM, LDA, CART, and our method.

<p>DT, decision-tree-based classification model; SVM, support vector machine; LDA, linear discriminant approach; CART, classification and regression tree.</p>a<p>The first line is the true positive rate (sensitivity); the second line is the true negative rate (specificity); and the third line is accuracy.</p