bioRxiv2022
Identifying essential targets in genome-scale metabolic networks of cancer cells is a time-consuming process. This study proposed a fuzzy hierarchical optimization framework for identifying essential genes, metabolites and reactions. On the basis of four objectives, the framework can identify essential targets that lead to cancer cell death, and evaluate metabolic flux perturbations of normal cells due to treatment. Through fuzzy set theory, a multiobjective optimization problem was converted into a trilevel maximizing decision-making (MDM) problem. We applied nested hybrid differential evolution to solve the trilevel MDM problem to identify essential targets in the genome-scale metabolic models of five consensus molecular subtypes (CMSs) of colorectal cancers. We used various media to identify essential targets for each CMS, and discovered that most targets affected all five CMSs and that some genes belonged to a CMS-specific model. We used the experimental data for the lethality of cancer cell lines from the DepMap database to validate the identified essential genes. The results reveal that most of the identified essential genes were compatible to colorectal cancer cell lines from DepMap and that these genes could engender a high percentage of cell death when knocked out, except for EBP, LSS and SLC7A6. The identified essential genes were mostly involved in cholesterol biosynthesis, nucleotide metabolisms, and the glycerophospholipid biosynthetic pathway. The genes in the cholesterol biosynthetic pathway were also revealed to be determinable, if the medium used excluded a cholesterol uptake reaction. By contrast, the genes in the cholesterol biosynthetic pathway were non-essential, if a cholesterol uptake reaction was involved in the medium used. Furthermore, the essential gene CRLS1 was revealed as a medium-independent target for all CMSs irrespective of whether a medium involves a cholesterol uptake reaction. Author summaryEssential genes are indispensable genes for cells to grow and proliferate under certain physiological condition. Identifying essential genes in genome-scale metabolic networks of cancer cells is a time-consuming process. We develop an anticancer target discovery platform for identifying essential genes that conduct cell death when the genes of cancer cells are deleted. Meanwhile, the essential genes are also inactive on their healthy cells to maintain their cell viability and smaller metabolic alterations. We use fuzzy set theory to measure metabolic deviation of the perturbation of normal cells relative to healthy and cancer templates towards predicting side effects for treatment of each identified gene. The platform can identify essential genes, metabolites and reactions for treating five consensus molecular subtypes (CMS) of colorectal cancers with using various media. We discovered that most targets affected all five CMSs and that some genes belonged to a CMS-specific model. We found that the genes in the cholesterol biosynthetic pathway are nonessential for the cells that be compensated by a cholesterol uptake reaction from a medium. Furthermore, CRLS1 was revealed as an essential gene for all CMS colorectal cancer in a medium-independent manner that is unrelated to a cholesterol uptake reaction.