Identification of key genes underlying clinical features of hepatocellular carcinoma based on weighted gene co‑expression network analysis and bioinformatics analysis

Objective: To identify module genes that are closely related to clinical features of hepatocellular carcinoma (HCC) by weighted gene co‑expression network analysis, and to provide a reference for early clinical diagnosis and treatment. Methods: GSE84598 chip data were downloaded from the GEO database, and module genes closely related to the clinical features of HCC were extracted by comprehensive weighted gene co‑expression network analysis. Hub genes were identified through protein interaction network analysis by the maximum clique centrality (MCC) algorithm;Finally, the expression of hub genes was validated by TCGA database and the Kaplan Meier plotter online database was used to evaluate the prognostic relationship between hub genes and HCC patients. Results: By comparing the gene expression data between HCC tissue samples and normal liver tissue samples, a total of 6 262 differentially expressed genes were obtained, of which 2 207 were upregulated and 4 055 were downregulated. Weighted gene co‑expression network analysis was applied to identify 120 genes of key modules. By intersecting with the differentially expressed genes, 115 candidate hub genes were obtained. The results of enrichment analysis showed that the candidate hub genes were closely related to cell mitosis, p53 signaling pathway and so on. Further application of the MCC algorithm to the protein interaction network of 115 candidate hub genes identified five hub genes, namely NUF2, RRM2, UBE2C, CDC20 and MAD2L1. Validation of hub genes by TCGA database revealed that all five hub genes were significantly upregulated in HCC tissues compared to normal liver tissues;Moreover, survival analysis revealed that high expression of hub genes was closely associated with poor prognosis in HCC patients. Conclusions: This study identifies five hub genes by combining multiple databases, which may provide directions for the clinical diagnosis and treatment of HCC.

Quantitative identification of compounds-dependent on-modules and differential allosteric modules from homologous ischemic networks

Module-based methods have made much progress in deconstructing biological networks.However,it is a great challenge to quantitatively compare the topological structural variations of modules(allosteric modules,AMs)under different situations.A total of 23,42 and 15co-expression modules were identified in baicalin(BA),jasminoidin(JA)and ursodeoxycholic acid(UA)in a global anti-ischemic mice network,respectively.Then,we integrated the methods of module-based consensus ratio(MCR)and modified Z summary module statistic to validate 12 BA,22 JA and 8 UA on-modules based on comparing with vehicle.The MCRs for pairwise comparisons were 1.55%(BA vs JA),1.45%(BA vs UA),and1.27%(JA vs UA),respectively.Five conserved allosteric modules(CAMs)and 17 unique allosteric modules(UAMs)were identified among these groups.In conclusion,module-centric analysis may provide us a unique approach to understand multiple pharmacological mechanisms associated with differential phenotypes in the era of modular pharmacology.

Theory Analysis of the Handover Challenge in Express Train Access Networks （ETAN）

To handle the handover challenge in Express Train Access Networks(ETAN).mobility fading effects in high speed railway environments should be addressed first.Based on the investigation of fading effects in this paper,we obtain two theoretical bounds:HOTiming upper bound and HO-Margin lower bound,which are helpful guidelines to study the handover challenge today and in the future.Then,we apply them to analyze performance of conventional handover technologies and our proposal in ETAN.This follow-up theory analyses and simulation experiment results demonstrate that the proposed handover solution can minimize handover time up to 4ms(which is the fastest one so far),and reduce HO-Margin to 0.16 dB at a train speed of 350km/h.

癌蛋白质组的研究进展

癌症的发生是由于某些蛋白质的表达或变化引起的一系列的细胞无控制增殖的过程。癌蛋白质组学在研究癌症的发生发展、诊断治疗、药物筛选中起着越来越重要的作用。本文介绍了癌蛋白质组学研究进展。主要内容包括:常用的研究技术,癌发生发展的蛋白质网络作用机制,作为癌早期诊断的蛋白质分子标记的鉴定,癌症治疗新靶点的发现和治疗策略的制定等。

Rice Expression Database(RED)：An integrated RNA-Seq-derived gene expression database for rice

Rice is one of the most important stable food as well as a monocotyledonous model organism for the plant research community.Here,we present RED（Rice Expression Database;http：//expression.ic4r.org）,an integrated database of rice gene expression profiles derived entirely from RNA-Seq data.RED features a comprehensive collection of 284 high-quality RNA-Seq experiments,integrates a large number of gene expression profiles and covers a wide range of rice growth stages as well as various treatments.Based on massive expression profiles,RED provides a list of housekeeping and tissue-specific genes and dynamically constructs co-expression networks for gene（s） of interest.Besides,it provides user-friendly web interfaces for querying,browsing and visualizing expression profiles of concerned genes.Together,as a core resource in BIG Data Center,RED bears great utility for characterizing the function of rice genes and better understanding important biological processes and mechanisms underlying complex agronomic traits in rice.

Design Methodology of the Structure of Postal Express Mail Networks of Aviation Channels in China

On the basis of the postal area center office system, the continuous space approximation is used to study the structure of postal express mail networks of aviation channels in China. Tradeoffs among sorting, handling, transportation, administrative and facilities costs are examined. The optimizing design methodology proposed in this paper can be used to analyze and design the postal express mail network. The objective is to minimize the total system cost.