Identification of key pathways and genes from gastric precancerous lesions to gastric cancer by integrated bioinformatics analysis

Objective:This work aimed to illuminate the potential key genes and pathways in GC tumorigenesis based on bioinfOrmatics analysis.Methods:The differentially expressed genes(DEGs)between GPL tissue samples and GC tissue samples were investigated using the GSE55696 and GSE87666 microarray data from the Gene Expression Omnibus(GEO)database.DEGs were identified by an empirical Bayes method based on the Limma R package.Then,KEGG and GO enrichment analyses of DEGs were performed followed by protein-protein interaction(PPI)network construction.Finally,the overall survival(OS)analysis of key genes was performed by the Kaplan-Meier plotter online tool.Results:A total of 250 DEGs were obtained,of which 216 were up-regulated and 34 were down-regulated.KEGG pathways analysis showed that the up-regulated DEGs were enriched in cytokine-cytokine receptor interaction,chemokine signaling pathway,metabolic pathways,PI3K-Akt signaling pathway,NF-kappa B signaling pathway,and other signaling pathways about cancer,while no down-regulated pathways were enriched.A PPI network of DEGs was constructed with 117 nodes and 660 edges,and 20 genes were selected as hub genes owing to high degrees in the network.According to the Kaplan-Meier analysis,6 out of 20 hub genes including CCR7,FPR1,C3,CXCR5,GNB4,and PPBP with high mRNA expression were associated with poor OS for GC patients.Conclusion:The results of this study provide possible factors for the occurrence of GC,and the identification of the genes and pathways associated with the progression from GPL to GC provides valuable data for investigating the pathogenesis in future studies.

Opportunities for Computational Techniques for Multi-Omics Integrated Personalized Medicine

Personalized medicine is defined as ＂a model of healthcare that is predictive, personalized, preventive,and participator＂ and has very broad content. With the rapid development of high-throughput technologies, an explosive accumulation of biological information is collected from multiple layers of biological processes, including genomics, transcriptomics, proteomics, metabonomics, and interactomics（omics）. Implementing integrative analysis of these multiple omics data is the best way of deriving systematical and comprehensive views of living organisms, achieving better understanding of disease mechanisms, and finding operable personalized health treatments. With the help of computational methods, research in the field of biology and biomedicine has gained tremendous benefits over the past few decades. In the new era of personalized medicine, we will rely more on the assistance of computational analysis. In this paper, we briefly review the generation of multiple omics and their basic characteristics. And then the challenges and opportunities for computational analysis are discussed and some state-of-art analysis methods that were recently proposed by peers for integrative analysis of multiple omics data are reviewed. We foresee that further integrated omics data platform and computational tools would help to translate the biological knowledge to clinical usage and accelerate development of personalized medicine.

An Integrative Meta-analysis of MicroRNAs in Hepatocellular Carcinoma

We aimed to shed new light on the roles of microRNAs （miRNAs） in liver cancer using an integrative in silico bioinformatics analysis. A new protocol for target prediction and functional analysis is presented and applied to the 26 highly differentially deregulated miRNAs in hepatocellular carcinoma. This framework comprises： （1） the overlap of prediction results by four out of five target prediction tools, including TargetScan, PicTar, miRanda, DIANA-microT and miRDB （combining machine-learning, alignment, interaction energy and statistical tests in order to minimize false positives）, （2） evidence from previous microarray analysis on the expression of these targets, （3） gene ontology （GO） and pathway enrichment analysis of the miRNA targets and their pathways and （4） linking these results to oncogenesis and cancer hallmarks. This yielded new insights into the roles of miRNAs in cancer hallmarks. Here we presented several key targets and hundreds of new targets that are significantly enriched in many new cancer-related hallmarks. In addition, we also revealed some known and new oncogenic pathways for liver cancer. These included the famous MAPK, TGFβ and cell cycle pathways. New insights were also provided into Wnt signaling, prostate cancer, axon guidance and oocyte meiosis pathways. These signaling and developmental pathways crosstalk to regulate stem cell transformation and implicate a role of miRNAs in hepatic stem cell deregulation and cancer development. By analyzing their complete interactome, we proposed new categorization for some of these miRNAs as either tumor-suppressors or oncomiRs with dual roles. Therefore some of these miRNAs may be addressed as therapeutic targets or used as therapeutic agents. Such dual roles thus expand the view of miRNAs as active maintainers of cellular homeostasis.

Chinmedomics Builds a Bridge from Traditional to Modern Research of Traditional Chinese Medicine

＂Omics＂ is a new research field of integrative systems biology and bioinformatics.In the post genomic era,the core scientific problem is to study the relationship between different ＂omics＂ and functions based on bioinformatics.How to apply the omics method and technology to understand the complexity of traditional Chinese medicines（TCM）is one of the hot spots in the recent decade in China.