System Biology Approach to Identify Potential Receptor for Targeting Cancer and Biomolecular Interaction Studies of Indole[2,1-a]Isoquinoline Derivative as Anticancerous Drug Candidate Against it

Identification of Potential Cytokinin Responsive Key Genes in Rice Treated With Trans-Zeatin Through Systems Biology Approach

Rice is an important staple food grain consumed by most of the population around the world. With climate and environmental changes, rice has undergone a tremendous stress state which has impacted crop production and productivity. Plant growth hormones are essential component that controls the overall outcome of the growth and development of the plant. Cytokinin is a hormone that plays an important role in plant immunity and defense systems. Trans-zeatin is an active form of cytokinin that can affect plant growth which is mediated by a multi-step two-component phosphorelay system that has different roles in various developmental stages. Systems biology is an approach for pathway analysis to trans-zeatin treated rice that could provide a deep understanding of different molecules associated with them. In this study, we have used a weighted gene co-expression network analysis method to identify the functional modules and hub genes involved in the cytokinin pathway. We have identified nine functional modules comprising of different hub genes which contribute to the cytokinin signaling route. The biological significance of these identified hub genes has been tested by applying well-proven statistical techniques to establish the association with the experimentally validated QTLs and annotated by the DAVID server. The establishment of key genes in different pathways has been confirmed. These results will be useful to design new stress-resistant cultivars which can provide sustainable yield in stress-specific conditions.

iODA: An integrated tool for analysis of cancer pathway consistency from heterogeneous multi-omics data

The latest advances in the next generation sequencing technology have greatly facilitated the extensive research of genomics and transcriptomics, thereby promoting the decoding of carcinogenesis with unprecedented resolution. Considering the contribution of analyzing high-throughput multi-omics data to the exploration of cancer molecular mechanisms, an integrated tool for heterogeneous multi-omics data analysis (iODA) is proposed for the systems-level interpretation of multi-omics data, i.e., transcriptomic profiles (mRNA or miRNA expression data) and protein-DNA interactions (ChIP-Seq data). Considering the data heterogeneity, iODA can compare six statistical algorithms in differential analysis for the selected sample data and assist users in choosing the globally optimal one for dysfunctional mRNA or miRNA identification. Since molecular signatures are more consistent at the pathway level than at the gene level, the tool is able to enrich the identified dysfunctional molecules onto the KEGG pathways and extracted the consistent items as key components for further pathogenesis investigation. Compared with other tools, iODA is multi-functional for the systematic analysis of different level of omics data, and its analytical power was demonstrated through case studies of single and cross-level prostate cancer omics data. iODA is open source under GNU GPL and can be downloaded from http://www.sysbio.org.cn/iODA.

Computational advances of tumor marker selection and sample classification in cancer proteomics

Cancer proteomics has become a powerful technique for characterizing the protein markers driving transformation of malignancy, tracing proteome variation triggered by therapeutics, and discovering the novel targets and drugs for the treatment of oncologic diseases. To facilitate cancer diagnosis/prognosis and accelerate drug target discovery, a variety of methods for tumor marker identification and sample classification have been developed and successfully applied to cancer proteomic studies. This review article describes the most recent advances in those various approaches together with their current applications in cancer-related studies. Firstly, a number of popular feature selection methods are overviewed with objective evaluation on their advantages and disadvantages. Secondly, these methods are grouped into three major classes based on their underlying algorithms. Finally, a variety of sample separation algorithms are discussed. This review provides a comprehensive overview of the advances on tumor maker identification and patients/samples/tissues separations, which could be guidance to the researches in cancer proteomics.

Discovery of novel and highly selective PI3Kδ inhibitors based on the p110δ crystal structure

Communicated by Ramaswamy H. Sarma.