Identification and molecular interaction mechanism of high abnormal regions of metabolic pathways regulated by noncoding RNAs

In recent years, our and other's experimental results showed that thousands of ncRNAs were significantly differentially expressed in malignant tumors such as esophageal cancer, and played an important role in regulating metabolic pathways of abnormal cancer cells. However, for so many ncRNAs, which one can directly target key enzymes and regulate metabolic pathways of abnormal cancer cells is still an important scientific problems in doubt. In the previous experiment, through developping the new algorithm of bioinformatics, we successfully identified the abnormal miRNAs regulating metabolic pathways of tumors. Based on these results, the project plan to firstly establish a direct model with ncRNAs as key regulatory nodes. Secondly, the significantly differential ncRNAs, protein-coding genes and metabolites in esophageal cancer are mapped to the above model of metabolic pathways. Through integrative analyses, the high abnormal regions and subpathways in metabolic pathways can be identified, which will further help to locate key ncRNAs in the metabolic pathways and the key enzymes directly regulated by them. Finally, through applying an integrative and comparative strategy to overexpression and knockdown experiment, the project will confirm sicentific hypothesis of abnormal molecular mechanism of metabolic pathways synergistically regulated by ncRNAs at the molecular/cell/animal/clinical tissue level.

Systematic identification of cancer risk pathway regions through integrating high-throughput gene, metabolite and pathway structure

The initiation and progression of cancer are closely related to functional abnormality of metabolic pathways. The high-throughput genomics and metabolomics techniques make us able to apply bioinformatics methods to disease risk metabolic pathway region identification. Moreover, the bioinformatics methods have become common strategy of identifying disease risk metabolic pathways. However, the existing methods usually only adopt single statistical evaluation and obviously lack integration, analysis and utilization for multiple molecular information in pathways associated with specific diseases. This leads to low accuracy and stability of identification for cancer risk pathway regions. In this project, we propose a bioinformatics method for accurately identifying high risk metabolic pathway regions associated with cancer, which is an attempt to more accurate level of pathway analysis through integrating information of cancer genes, metabolites and pathway structure in graph model of reconstructed pathways. Furthermore, gene expression data and metabolite concentration data are used to improve pathway identification. Through effectively integrating, analyzing and using gene, metabolite and pathway structure information, we expect that our method can accurately locate cancer risk metabolic pathway regions. In this project, we will also use multiple high-throughput data including different cancer classes to evaluate our method for effectively increasing accuracy and stability of the analysis results. Finally, we will construct the corresponding pathway analysis platform for providing the identification functions of risk pathway regions for multiple cancers. This project will be very meaningful for exploring mechanism of the initiation and progression of cancer from the viewpoint of pathways.

identification and functional research of crosstalk between high abnormal regions of pathways synergistically regulated by ncRNAs in complex diseases

Many studies showed that ncRNAs could regulate pathways and expression of key protein encoding genes with them by mainly synergistic effect. Meanwhile, our and other studies indicated that in complex diseases, the abnormal crosstalk between pathways often happens. Therefore, ncRNAs, as important regulators, can obviously regulate information transfer process between pathways. However, which ncRNAs can regulate abnormal crosstalk between key pathways in a specific complex disease is still an important scientific problem in doubt. Therefore, the project will construct new direct graph models based on pathway reconstruction with ncRNAs as key regulatory nodes. Through integrating target sequence information of ncRNAs and protein encoding genes, information of their co-expression and competing endogenous RNAs, and pathway structure information, we will develop new algorithms for effectively identifying crosstalk between high abnormal regions of pathways synergistically regulated by ncRNAs. Finally, through performing cross-validation among data in multiple diseases, ncRNAs and the regions between pathways regulated by them will be systematically analyzed. Then, key functional ncRNAs and regions will be mined. Furthermore, the clinical application value will be evaluated via analyzing relationships between these ncRNAs/regions and environment risk factors, disease diagnosis, prognosis and drug target.

Last updated at November, 2016

© 2016.Harbin Medical University Chunquan Li Lab