Background: Biomedical researchers often want to explore pathogenesis and pathways regulated by abnormally expressed genes, such as those identified by microarray analyses. Literature mining is an important way to assist in this task. Many literature mining tools are now available. However, few of them allows the user to make manual adjustments to zero in on what he/she wants to know in particular.

ObjectiveTo investigate whether lymph node ratio (LNR) and log odds ratio (LODDS) have prognostic significance for overall survival (OS) and disease-free survival (DFS) in patients with laryngeal squamous cell cancer (LSCC) treated with curative intent.Study DesignCase-control study.SettingUniversity hospital.Subjects and MethodsRecords of 229 patients with LSCC who underwent surgery with a curative intent with or without adjuvant treatment from 2000 to 2014 were reviewed. The clinicopathological parameters LNR and LODDS were analyzed; univariate and multivariate analysis was performed to evaluate the prognosis of each for OS and DFS.ResultsThe 5-year OS was 81.7% for LNR ≤0.233 and 47.1% for LNR >0.233, and the 5-year OS was 79.6% for LODDS ≤–0.1 and 51.8% for LODDS >–0.1, respectively. In the univariate analysis, the independent variables were subsites, pT stage (pT1 and pT2 vs pT3 and pT4), pN, pTNM, alcohol consumption, and LNR and LODDS (P < .05). By multivariate analysis, we determined that subsites, pT stage, alcohol consumption, LNR, and LODDS were independent prognostic predictors of survival (P < .05). Univariate and multivariate models identified that both LNR and LODDS were significant prognostic factors for survival. However, the hazard ratio (HR) for LNR >0.233 vs ≤0.233 was 8.95 (95% confidence interval [CI], 3.18-25.16; P < .001) in OS, and the HR was 11.37 (95% CI, 4.02-32.15; P < .001) in DFS. The risk of LNR was noticeably greater than other factors.ConclusionsLNR and LODDS were both prognostic factors for OS and DFS. However, LNR was confirmed as a more reliable indicator for evaluating the prognosis, and it can be used to increase the prognostic value of the traditional TNM classification of LSCC.

Introduction: Papillary thyroid cancer (PTC) is one of the most common malignancies involving the endocrine system. Aim: To explore the clinical value of ultrasound-based radiomics for predicting the recurrence of PTC after complete endoscopic resection. Material and methods: The general data of 361 PTC patients were collected. They were randomly assigned to the modeling group (n = 253) and the validation group (n = 108) according to the ratio of 7 : 3. In the modeling group, the PyRadiomics package was applied to extract radiomic features from preoperative ultrasound images, and least absolute shrinkage and selection operator (LASSO) was used to screen and to construct a radiomics score (Radscore). Independent prognostic predictors were identified using the Cox proportional hazards model, and a nomogram prediction model was constructed by R software. Results: Using the LASSO regression model, 7 radiomic features were screened and then the Rad-score was calculated. Based on the Rad-score, modeling and validation groups were divided into low-, medium-and high-risk groups, and the 10-year recurrence-free survival rates were 94.7% vs. 95.9%, 83.6% vs. 80.0%, and 50.0% vs. 66.6%, respectively (p < 0.001). Multivariate analysis revealed that age, lymph node metastasis and Rad-score were independent predictors for recurrence-free survival (p < 0.05). Conclusions: The ultrasound-based radiomics score can effectively predict the postoperative recurrence-free survival in patients with PTC. The nomogram prediction model is superior to the AJCC staging system in terms of predictive accuracy and consistency.

Background: This study explored whether laryngeal carcinoma could be divided into different subtypes based on molecular differences using a molecular subtype-prediction model. Methods:We extracted data from the Cancer Genome Atlas and Gene Expression Omnibus databases and then performed unsupervised cluster analysis to identify discrete molecular subtypes of laryngeal carcinoma. Significance analysis of microarrays was performed to detect differentially expressed genes for each subtype, and gene set enrichment analysis and the GenCliP3 software were used to label gene functions and identify key pathways. Results: We categorized 126 patients into C1 and C2 molecular subtypes associated with pathologic grade. The C2 subtype appeared more aggressive, with a worse prognosis. The most significant enrichment pathway of the C2 subtype was the Hedgehog pathway, and GLI1 was a core gene.Conclusions: Laryngeal carcinoma can be divided into two subtypes based on differences in molecular expression, which could identify key molecules associated with prognosis.

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Poster abstracts 72 ically organize them in biologically meaningful ways. There are many options for the implementation of feature extraction and gene clustering programs to help accomplish these goals. We have developed Matlab prototypes that emphasize flexibility, accuracy, transparency and the systematic incorporation of statistical analysis. These prototypes facilitate the development and refinement of algorithms as we converge on fully automatic analysis.There are many possible approaches to feature extraction, and the choices are influenced by factors including: feature morphology and uniformity, and positioning errors, array deposition methodologies and tolerances, labelling methods and scanner specifications. Our feature extraction software includes several methodologies and a number of adjustable parameters that can be modified to suit the application. For example, image file formats, image alignment procedures for two-colour images, irregular modifiable grids, feature locating, grid fitting, extraction, pixel-level outlier rejection, feature-level outlier rejection, background-subtraction and dye-normalization processes have all been incorporated. With this feature extraction prototype, we are can explore different methods of image analysis, outlier rejection, background subtraction and normalization while optimizing sensitivity and specificity. Relative expression levels are reported along with their respective statistical information.To analyse data from multiple array experiments we developed a package built around a clustering method based on a graph theoretic approach. This approach requires no assumptions as to the number of clusters expected and is flexible with regard to the selection of a similarity function. In particular, similarity functions that take into account statistical characteristics, such as confidence intervals, are supported. Results from applying both analysis tools to raw images and to processed gene expression data from humans, C. elegans and other organisms will be presented.The metabolic overlap syndromes hyperlipidaemia, diabetes mellitus, insulin resistance, obesity and hypertension together constitute the major risk factors for coronary heart disease. The genetic predisposition to these conditions is strong but complex, and many of the genes involved have not been identified. The identification of the genes or metabolic pathways involved in these disorders would lead to new diagnostic and therapeutic targets with wide applicability. The spontaneously hypertensive rat (SHR) is a model of essential hypertension which also displays abnormalities of lipid metabolism and insulin resistance similar to those found in the human metabolic syndromes. These include raised blood triglycerides and fatty acids, defective catecholamine-mediated lipolysis and excessive growth of intra-abdominal adipocytes. Similar abnormalities have been identified in combined hyperlipidaemia, maturity-onset diabetes mellitus and obesity in humans. We are using the SHR as a model to identify genes and pat...