|dc.description.abstract||The development of neoplastic cells is hypothesized to be the result of cells responding to a stressful microenvironment such as chronic hypoxia, increased ROS and persistent immune attacks. Distinct levels of oxidative stress, estimated by gene markers in ROS-generating processes, are found to explain well the differences in disease incidences rates associated with different cancer types in different regions of the world. Further, increased levels of ROS could force the cells to induce higher antioxidant synthesis. This process could compete for sulfur resources with SAM synthesis used for DNA methylation, and eventually lead to a globally reduced level of DNA methylation. In metastatic cancer, oxidized cholesterol and its further metabolized derivatives are found to be a key driver of the explosive growth of post-metastatic cancers. My work suggests that it is the change in the O2 level between the metastasized and the primary sites, i.e., from O2 poor to O2 rich, that leads to the substantially increased uptake and de novo synthesis of cholesterol as well as oxidation and further metabolism of cholesterol towards the production of oxysterol and steroidal hormones, all powerful growth signals.
To understand how various stress types may drive the unique biology of cancer, we need to study cancer tissues rather than cancer cell line data since the former contains all the relevant information but the latter does not. Compared to the cell-based omic data, observed tissue-based gene-expression data are the results of gene-expression levels summed over all cell types, such as cancer cells, multiple immune cell types, fat cells, and normal cells in the tissues. A novel algorithm for de-convoluting tissue-based data to the cell-type specific contributions is developed based on the following information: (1) genes in each cell type are expressed in a coordinated manner, specifically they are grouped into pathways whose genes are co-expressed; and (2) different cell types tend to have different sets of pathways activated.||