报告时间：2024年6月29日8:30-10:30
报告地点：管理楼413会议室
报告人：王林丛花
报告1标题：
Classification of Schizophrenia by Iterative Random Forest Feature Selection Based on DNA Methylation Array Data
报告1摘要：
Changes in DNA methylation are widely thought to be involved in the evolution of the disease, and most studies suggest that whole genome hypo-methylation levels are widespread in patients with schizophrenia. Since DNA methylation changes are important for the early detection and prevention of schizophrenia, it is crucial to identify differentially methylated sites with high specificity and sensitivity for disease classification. In this study, we present a comprehensive approach MethIRF for the DNA methylation-based classification of schizophrenia by iterative random forest feature selection. The results show that MethIRF has a powerful discrimination ability compared to the other four criteria methods for detecting differentially methylation sites. Moreover, the proposed method can discover significant CpG sites associated with schizophrenia and explore changes in the biological mechanisms of diseases.
报告2标题：
A polygenic methylation prediction model associated with response to chemotherapy in epithelial ovarian cancer
报告2摘要：
To identify potential aberrantly differentially methylated genes (DMGs) correlated with chemotherapy response (CR) and establish a polygenic methylation prediction model of CR in epithelial ovarian cancer (EOC), we accessed 177 (47 chemo-sensitive and 130 chemo-resistant) samples corresponding to three DNA-methylation microarray datasets from the Gene Expression Omnibus and 306 (290 chemo-sensitive and 16 chemo-resistant) samples from The Cancer Genome Atlas (TCGA) database. DMGs associated with chemotherapy sensitivity and chemotherapy resistance were identified by several packages of R software. Pathway enrichment and protein-protein interaction (PPI) network analyses were constructed by Metascape software. The key genes containing mRNA expressions associated with methylation levels were validated from the expression dataset by the GEO2R platform. The determination of the prognostic significance of key genes was performed by the Kaplan-Meier plotter database. The key genes-based polygenic methylation prediction model was established by binary logistic regression. Among accessed 483 samples, 457 (182 hypermethylated and 275 hypomethylated) DMGs correlated with chemo resistance. Twenty-nine hub genes were identified and further validated. Three genes, anterior gradient 2 (AGR2), heat shock-related 70-kDa protein 2 (HSPA2), and acetyltransferase 2 (ACAT2), showed a significantly negative correlation between their methylation levels and mRNA expressions, which also corresponded to prognostic significance. A polygenic methylation prediction model (0.5253 cutoff value) was established and validated with 0.659 sensitivity and 0.911 specificity.
报告3标题：
Identifying disease-associated genes based on network impulsive dynamics on multiplex and heterogeneous network
报告3摘要：
Multi-platforms biological data fusion has attracted extensive attention in the research of disease-gene prediction, but how to effectively integrate the multi-platforms biological data , especially heterogeneous biological network data , and mine the hidden features conducive to disease-gene prediction still need to be deeply explored . Therefore, the network impulse dynamics on multiplex-heterogeneous biological networks called NIDMH for multi-platforms heterogeneous biological network mining is further proposed. NIDMH can integrate multiple types of gene networks and disease networks. Based on the NIDMH model, the multiplex - heterogeneous network impulsive dynamical process is stimulated, and the disease-related genes are inferred according to the impulse dynamical signatures of genes. Through a series of experiments, NIDMH is confirmed to have good predictive ability under different conditions, proving that it is a more effective framework that can realize the joint mining of multi-source and heterogeneous biological networks.
报告4标题：
Multi-omics characterization of genome-wide abnormal DNA methylation reveals prognostic markers for acral melanoma
报告4摘要：
In this study, integrative analysis of acral melanoma with 74 patients was used to identify biomarkers associated with the prognosis of Acral melanoma. Two subgroups were identified among the 74 cases of acral melanomas: C1 and C2. Among two subgroups with different DNA methylation profile, there was significant difference of genomic characteristics and clinical prognostic features. Integrating genome-wide DNA methylation and RNA-seq data from the same samples, DMCs were found to be associated with the prognosis of acral melanoma. Using these featured DMCs, we developed a prognostic model to predict high-risk patients.
