Recently, Wang Weixu, a 2015 grass science major in the school of forestry and landscape architecture of our university, published the title of Pan cancer analysis identities associated signatures and cancer subtypes in the international journal Molecular Cancer (2019 if: 10.679, if 5 year: 7.898) with his co-author（ https://doi.org/10.1186/s12943-019-1035-x ）The paper. Among them, Wang Weixu from our university, Luo Zhenhua from Cincinnati University and Li Feng from Harvard University are the first authors of this paper; Dai Zhiming, associate professor of School of data science and computer science, Sun Yat sen University, and Xiong yuanyan, associate professor of School of life sciences are the co correspondents of this paper.
This paper suggests that cancer cells become immortalized through telomere maintenance mechanisms, such as telomerase reverse transcriptase (TERT) activation. In addition to maintaining telomere length, tert also activates various signaling pathways for cell survival. However, the characteristics of telomerase related genes in cancer are still elusive. The research team used the multidimensional data of Cancer Genome Atlas (TCGA) to systematically analyze the high and low expression of TERT in cancer. The results showed that: the high expression specific mRNA expression characteristics of TERT were related to the cell cycle related coexpression module of cancer type; the experimental screening of hub gene in cell cycle module showed that TPX2 and Exo1 were potential regulators of telomerase activity and cell survival; miRNA component Analysis showed that the high expression specific miR-17-92 cluster of TERT could target the biological process of cancer with high expression of TERT, and its expression was negatively correlated with tumor / normal telomere length ratio. Interestingly, cancer with high expression of TERT tends to mutate in extracellular matrix tissue genes and amplify MAPK signaling. By mining the drug target database, we found a lot of TERT high expression cancer specific somatic mutations, increased copy number and high expression genes with therapeutic targets. Finally, a random forest classifier integrating the multi-component features of telomerase was used to identify two cancer subtypes, showing significant differences in telomerase activity and patient survival. The results of this study delineate the molecular landscape of telomerase in cancer and provide therapeutic opportunities for cancer treatment.
Wang Weixu was influenced by experimental statistics, genetics, grass biotechnology, grass breeding and other courses in school, and was very interested in the application of high-dimensional statistics, pattern recognition and machine learning in cancer data. He participated in the research in the second half of 2016. During his participation, he was mainly responsible for the statistical analysis part of the project, including the use of weighted score tendency matching model The data are analyzed systematically by using the co expression network model and the unsupervised clustering model based on random forest. At present, Wang Weixu has been promoted to the Department of Biostatistics of Fudan University for postgraduate study.