In:
PLOS ONE, Public Library of Science (PLoS), Vol. 18, No. 5 ( 2023-5-19), p. e0286032-
Abstract:
Identifying essential targets in the genome-scale metabolic networks of cancer cells is a time-consuming process. The present study proposed a fuzzy hierarchical optimization framework for identifying essential genes, metabolites and reactions. On the basis of four objectives, the present study developed a framework for identifying essential targets that lead to cancer cell death and evaluating metabolic flux perturbations in normal cells that have been caused by cancer 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 genome-scale metabolic models for five consensus molecular subtypes (CMSs) of colorectal cancer. We used various media to identify essential targets for each CMS and discovered that most targets affected all five CMSs and that some genes were CMS-specific. We obtained experimental data on 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 with the colorectal cancer cell lines obtained from DepMap and that these genes, with the exception of EBP, LSS, and SLC7A6, could generate a high level of cell death when knocked out. The identified essential genes were mostly involved in cholesterol biosynthesis, nucleotide metabolisms, and the glycerophospholipid biosynthetic pathway. The genes involved in the cholesterol biosynthetic pathway were also revealed to be determinable, if a cholesterol uptake reaction was not induced when the cells were in the culture medium. However, the genes involved in the cholesterol biosynthetic pathway became non-essential if such a reaction was induced. Furthermore, the essential gene CRLS1 was revealed as a medium-independent target for all CMSs.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0286032
DOI:
10.1371/journal.pone.0286032.g001
DOI:
10.1371/journal.pone.0286032.g002
DOI:
10.1371/journal.pone.0286032.g003
DOI:
10.1371/journal.pone.0286032.g004
DOI:
10.1371/journal.pone.0286032.g005
DOI:
10.1371/journal.pone.0286032.g006
DOI:
10.1371/journal.pone.0286032.t001
DOI:
10.1371/journal.pone.0286032.t002
DOI:
10.1371/journal.pone.0286032.t003
DOI:
10.1371/journal.pone.0286032.t004
DOI:
10.1371/journal.pone.0286032.s001
DOI:
10.1371/journal.pone.0286032.s002
DOI:
10.1371/journal.pone.0286032.s003
DOI:
10.1371/journal.pone.0286032.s004
DOI:
10.1371/journal.pone.0286032.s005
DOI:
10.1371/journal.pone.0286032.s006
DOI:
10.1371/journal.pone.0286032.s007
DOI:
10.1371/journal.pone.0286032.s008
DOI:
10.1371/journal.pone.0286032.r001
DOI:
10.1371/journal.pone.0286032.r002
DOI:
10.1371/journal.pone.0286032.r003
DOI:
10.1371/journal.pone.0286032.r004
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2023
detail.hit.zdb_id:
2267670-3
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