Identification and validation of tumor microenvironment-related lnc RNA prognostic signature for uveal melanoma

AIM:To investigate the role of tumor microenvironment(TME)-related long non-coding RNA(lncRNA)in uveal melanoma(UM),probable prognostic signature and potential small molecule drugs using bioinformatics analysis.METHODS:UM expression profile data were downloaded from the Cancer Genome Atlas(TCGA)and bioinformatics methods were used to find prognostic lncRNAs related to UM immune cell infiltration.The gene expression profile data of 80 TCGA specimens were analyzed using the single sample Gene Set Enrichment Analysis(ss GSEA)method,and the immune cell infiltration of a single specimen was evaluated.Finally,the specimens were divided into high and low infiltration groups.The differential expression between the two groups was analyzed using the R package‘edge R’.Univariate,multivariate and Least Absolute Shrinkage and Selection Operator(LASSO)Cox regression analyses were performed to explore the prognostic value of TMErelated lncRNAs.Gene Ontology and Kyoto Encyclopedia of Genes and Genomes(KEGG)functional analyses were also performed.The Connectivity Map(CMap)data set was used to screen molecular drugs that may treat UM.RESULTS:A total of 2393 differentially expressed genes were identified and met the criteria for the low and high immune cell infiltration groups.Univariate Cox analysis of lncRNA genes with differential expression identified 186 genes associated with prognosis.Eight prognostic markers of TME-included lncRNA genes were established as potentially independent prognostic elements.Among 269 differentially expressed lncRNAs,69 were up-regulated and 200 were down-regulated.Univariate Cox regression analysis of the risk indicators and clinical characteristics of the 8 lncRNA gene constructs showed that age,TNM stage,tumor base diameter,and low and high risk indices had significant prognostic value.We screened the potential small-molecule drugs for UM,including W-13,AH-6809 and Imatinib.CONCLUSION:The prognostic markers identified in this study are reliable biomarkers of UM.This study expands our current understanding of the role of TME-related lncRNAs in UM genesis,which may lay the foundations for future treatment of this disease.

Second-order Optimality Conditions for Cone-subarcwise Connected Set-valued Optimization Problems

The concept of a cone subarcwise connected set-valued map is introduced. Several examples are given to illustrate that the cone subarcwise connected set-valued map is a proper generalization of the cone arcwise connected set-valued map, as well as the arcwise connected set is a proper generalization of the convex set,respectively. Then, by virtue of the generalized second-order contingent epiderivative, second-order necessary optimality conditions are established for a point pair to be a local global proper efficient element of set-valued optimization problems. When objective function is cone subarcwise connected, a second-order sufficient optimality condition is also obtained for a point pair to be a global proper efficient element of set-valued optimization problems.

Kernel-blending connection approximated by a neural network for image classification

This paper proposes a kernel-blending connection approximated by a neural network(KBNN)for image classification.A kernel mapping connection structure,guaranteed by the function approximation theorem,is devised to blend feature extraction and feature classification through neural network learning.First,a feature extractor learns features from the raw images.Next,an automatically constructed kernel mapping connection maps the feature vectors into a feature space.Finally,a linear classifier is used as an output layer of the neural network to provide classification results.Furthermore,a novel loss function involving a cross-entropy loss and a hinge loss is proposed to improve the generalizability of the neural network.Experimental results on three well-known image datasets illustrate that the proposed method has good classification accuracy and generalizability.

Application of Connectivity Map Database to Research on Chinese Materia Medica

The connectivity map（CMAP） database is established initially to connect biology, chemistry, and clinical conditions, which helps to discover the connection of disease-gene-drug. The CMAP approach has been applied in the field of drug discovery and development, which is widely recognized. In recently years, CMAP analysis has been applied in the research on Chinese materia medica（CMM）. The study of CMM is facing a wide range of challenges, such as complicated ingredients, multiple targets, multiple pathways of action and complex functioning mechanism. The idea of employing CMAP in the CMM research has brought a new perspective for researchers and provides a systematic method for elucidating the mechanism of CMM.The connectivity map （CMAP） database is established initially to connect biology, chemistry, and clinical conditions, which helps to discover the connection of disease-gene-drug. The CMAP approach has been applied in the field of drug discovery and development, which is widely recognized. In recently years, CMAP analysis has been applied in the research on Chinese materia medica （CMM）. The study of CMM is facing a wide range of challenges, such as complicated ingredients, multiple targets, multiple pathways of action and complex functioning mechanism. The idea of employing CMAP in the CMM research has brought a new perspective for researchers and provides a systematic method for elucidating the mechanism of CMM.