10.1186/1752-0509-7-12BMC Systems Biology7

Breen, E.J.

Kumar, G.

Ranganathan, S.

BMC Systems Biology

PubMed

Abstract
          
            Background
            Cancer is a complex disease where molecular mechanism remains elusive. A systems approach is needed to integrate diverse biological information for the prognosis and therapy risk assessment using mechanistic approach to understand gene interactions in pathways and networks and functional attributes to unravel the biological behaviour of tumors.
          
          
            Results
            We weighted the functional attributes based on various functional properties observed between cancerous and non-cancerous genes reported from literature. This weighing schema was then encoded in a Boolean logic framework to rank differentially expressed genes. We have identified 17 genes to be differentially expressed from a total of 11,173 genes, where ten genes are reported to be down-regulated via epigenetic inactivation and seven genes are up-regulated. Here, we report that the overexpressed genes IRAK1, CHEK1 and BUB1 may play an important role in ovarian cancer. We also show that these 17 genes can be used to form an ovarian cancer signature, to distinguish normal from ovarian cancer subjects and that the set of three genes, CHEK1, AR, and LYN, can be used to classify good and poor prognostic tumors.
          
          
            Conclusion
            We provided a workflow using a Boolean logic schema for the identification of differentially expressed genes by integrating diverse biological information. This integrated approach resulted in the identification of genes as potential biomarkers in ovarian cancer.
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Gaurav Kumar

Edmond J Breen

Shoba Ranganathan

Crossref

Identification of ovarian cancer associated genes using an integrated approach in a Boolean framework

Background Cancer is a complex disease where molecular mechanism remains elusive. A systems approach is needed to integrate diverse biological information for the prognosis and therapy risk assessment using mechanistic approach to understand gene interactions in pathways and networks and functional attributes to unravel the biological behaviour of tumors.
Results We weighted the functional attributes based on various functional properties observed between cancerous and non-cancerous genes reported from literature. This weighing schema was then encoded in a Boolean logic framework to rank differentially expressed genes. We have identified 17 genes to be differentially expressed from a total of 11,173 genes, where ten genes are reported to be down-regulated via epigenetic inactivation and seven genes are up-regulated. Here, we report that the overexpressed genes IRAK1, CHEK1 and BUB1 may play an important role in ovarian cancer. We also show that these 17 genes can be used to form an ovarian cancer signature, to distinguish normal from ovarian cancer subjects and that the set of three genes, CHEK1, AR, and LYN, can be used to classify good and poor prognostic tumors.
Conclusion We provided a workflow using a Boolean logic schema for the identification of differentially expressed genes by integrating diverse biological information. This integrated approach resulted in the identification of genes as potential biomarkers in ovarian cancer

Kumar, Gaurav

Breen, Edmond J.

Ranganathan, Shoba

VCU Scholars Compass

  1Identification of ovarian cancer associated genes using an integrated approach in a Boolean framework by Gaurav Kumar, Edmond J. Breen and Shoba Ranganathan Additional File 9: Relevant GO biological process characterization from GeneCards [72] for the 17 differentially expressed genes, mapped to cancer hallmarks (HM) in Table 3. Gene symbol GO ID GO term {HM} Evidence* PubMed IDs GO:0009611 response to wounding {10} NAS 12016211 GO:0042246 tissue regeneration {5} NAS 12878203 KLK6 GO:0045595 regulation of cell differentiation {2} NAS 11668196 GO:0031663 8. lipopolysaccharide-mediated signaling pathway {8} IMP 10383454 GO:0032088 negative regulation of NF-kappaB transcription factor activity {5} IMP 10383454 GO:0045893 positive regulation of transcription, DNA-dependent {4} NAS 11397809 GO:0008284 positive regulation of cell proliferation {2} IMP 17102137 IRAK1 GO:0070498 interleukin-1-mediated signaling pathway {10} IMP 10383454 GO:0000075 cell cycle checkpoint {7} TAS -- GO:0006260 DNA replication {4} TAS -- GO:0008284 positive regulation of cell proliferation {2} IMP 17102137 CDC7 GO:0033261 regulation of S phase {7} IMP 17102137 GO:0000077 DNA damage checkpoint {7} IMP 19716789 GO:0006260 DNA replication {7} TAS -- GO:0006281 DNA repair {7} IMP 19716789 CHEK1 GO:0010569 regulation of double-strand break repair via homologous recombination {7} IDA 15665856 GO:0007093 mitotic cell cycle checkpoint {7} TAS 9521327 BUB1 GO:0007094 mitotic cell cycle spindle assembly checkpoint {7} TAS 9790499 GO:0000077 DNA damage checkpoint {7} TAS 10617473 GO:0006302 double-strand break repair {7} IMP 18317453 GO:0006355 regulation of transcription, DNA-dependent {4} IDA 12717439 GO:0006974 response to DNA damage stimulus {7} TAS 9836640 GO:0008630 DNA damage response, signal transduction resulting in induction of apoptosis {7} IMP 12717439 CHEK2 GO:0045893 positive regulation of transcription, DNA-dependent {4} IDA 17101782 GO:0007166 cell surface receptor signaling pathway {2} TAS 9753616 GO:0007267 cell-cell signaling {1} TAS 9753616 GO:0008283 cell proliferation {4} TAS 23187001** STC2  GO:0016477 cell migration {3} TAS 23187001**   2Gene symbol GO ID GO term {HM} Evidence* PubMed IDs GO:0008283 cell proliferation {4} TAS 9620555 GO:0032436 positive regulation of proteasomal ubiquitin-dependent protein catabolic process {8} IMP 12805222 GO:0045892 negative regulation of transcription, DNA-dependent {5} IMP 12805222 GO:0045893 positive regulation of transcription, DNA-dependent {4} IMP 12805222 DAB2  GO:0090090 negative regulation of canonical Wnt receptor signaling pathway {1} IMP 12805222 GO:0006915 apoptotic process {6} TAS -- GO:0006921 cellular component disassembly involved in apoptotic process {6} TAS -- VIM GO:0006928 cellular component movement {3} TAS 16130169 GO:0006309 apoptotic DNA fragmentation {7} IMP 16153597 GO:0030154 cell differentiation {4} NAS 12471206 GO:0043065 positive regulation of apoptotic process {6} IMP 16153597 GO:0043280 positive regulation of cysteine-type endopeptidase activity involved in apoptotic process {6} IMP 16153597 FOXL2 GO:0045892 negative regulation of transcription, DNA-dependent {5} IDA 19744555 GO:0043069  negative regulation of programmed cell death {3} TAS  23056397**  LCN2 GO:0006916 anti-apoptosis {6} TAS 23056397** GO:0007267 cell-cell signaling {1} TAS 1557371 PGR  GO:0007165 signal transduction {1} TAS 1557371 GO:0008283 cell proliferation {4} NAS 10075738 GO:0008284 positive regulation of cell proliferation {2} IDA 17277772 AR GO:0016049 cell growth {1} NAS 15572661 GO:0006916 anti-apoptosis {6} TAS 8710868 GO:0006955 immune response {9} IMP 16886151 GO:0008284 positive regulation of cell proliferation {2} TAS 10749889 GO:0030335 positive regulation of cell migration {3} IMP 12138094 GO:0043066 negative regulation of apoptotic process {3} IDA 12556535 IGF1R GO:0045740      positive regulation of DNA replication {4}      IMP      12138094        3Gene symbol GO ID GO term {HM} Evidence* PubMed IDsGO:0002513 tolerance induction to self antigen {9} TAS 15489917 GO:0002768 immune response-regulating cell surface receptor signaling pathway {9} TAS 15489917 GO:0006974 response to DNA damage stimulus {7} IDA 10891478 GO:0008285 negative regulation of cell proliferation {1} IMP 14726379 GO:0033628 regulation of cell adhesion mediated by integrin {4} IMP 18802065 GO:0042981 regulation of apoptotic process {3/6} IDA 11517336 GO:0050777 negative regulation of immune response {9} TAS 15489917 GO:0051272 positive regulation of cellular component movement {3} IDA 16467205 LYN GO:0070668 positive regulation of mast cell proliferation {3} IMP 11435302 GO:0007155 cell adhesion {4} IDA 8117260  IGFBP7 GO:0008285 negative regulation of cell proliferation {1} TAS 8939990 GO:0006956 complement activation {9} TAS 1585460 GO:0032463 negative regulation of protein homooligomerization {4} IMP 16113678 CLU GO:0043066 negative regulation of apoptotic process {3} IMP 16113678  * Experimental Evidence Codes:  IDA: Inferred from Direct Assay  IMP: Inferred from Mutant Phenotype     Author Statement Evidence Codes:  TAS: Traceable Author Statement     ** Literature search by authors and not currently in GeneCards 

Background: Cancer is a complex disease where molecular mechanism remains elusive. A systems approach is needed to integrate diverse biological information for the prognosis and therapy risk assessment using mechanistic approach to understand gene interactions in pathways and networks and functional attributes to unravel the biological behaviour of tumors. Results: We weighted the functional attributes based on various functional properties observed between cancerous and non-cancerous genes reported from literature. This weighing schema was then encoded in a Boolean logic framework to rank differentially expressed genes. We have identified 17 genes to be differentially expressed from a total of 11,173 genes, where ten genes are reported to be down-regulated via epigenetic inactivation and seven genes are up-regulated. Here, we report that the overexpressed genes IRAK1, CHEK1 and BUB1 may play an important role in ovarian cancer. We also show that these 17 genes can be used to form an ovarian cancer signature, to distinguish normal from ovarian cancer subjects and that the set of three genes, CHEK1, AR, and LYN, can be used to classify good and poor prognostic tumors. Conclusion: We provided a workflow using a Boolean logic schema for the identification of differentially expressed genes by integrating diverse biological information. This integrated approach resulted in the identification of genes as potential biomarkers in ovarian cancer

Identification of ovarian cancer associated genes using an integrated approach in a Boolean framework

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