BACKGROUND: Melanoma is a leading cause of cancer death. Thus, accurate prognostic biomarkers that will assist rational treatment planning need to be identified. METHODS: Microarray analysis of melanoma and normal tissue samples was performed to identify differentially expressed modules (DEMs) from the signaling network and ultimately detect molecular markers to support histological examination. Network motifs were extracted from the human signaling network. Then, significant expression-correlation differential modules were identified by comparing the network module expression-correlation differential scores under normal and disease conditions using the gene expression datasets. Finally, we obtained DEMs by the Wilcoxon rank test and considered the average gene expression level in these modules as the classification features for diagnosing melanoma. RESULTS: In total, 99 functional DEMs were identified from the signaling network and gene expression profiles. The area under the curve scores for cancer module genes, melanoma module genes, and whole network modules are 92.4%, 90.44%, and 88.45%, respectively. The classification efficiency rates for nonmodule features are 71.04% and 79.38%, which correspond to the features of cancer genes and melanoma cancer genes, respectively. Finally, we acquired six significant molecular biomarkers, namely, module 10 (CALM3, Ca 2+ , PKC, PDGFRA, phospholipase-g, PIB5PA, and phosphatidylinositol-3-kinase), module 14 (SRC, Src homology 2 domain-containing [SHC], SAM68, GIT1, transcription factor-4, CBLB, GRB2, VAV2, LCK, YES, PTCH2, downstream of tyrosine kinase [DOK], and KIT), module 16 (ELK3, p85beta, SHC, ZFYVE9, TGFBR1, TGFBR2, CITED1, SH3KBP1, HCK, DOK, and KIT), module 45 (RB, CCND3, CCNA2, CDK4, and CDK6), module 75 (PCNA, CDK4, and CCND1), and module 114 (PSD93, NMDAR, and FYN). CONCLUSION: We explored the gene expression profile and signaling network in a global view and identified DEMs that can be used as diagnostic or prognostic markers for melanoma.
BACKGROUND:Melanoma is a leading cause of cancer death. Thus, accurate prognostic biomarkers that will assist rational treatment planning need to be identified. METHODS: Microarray analysis of melanoma and normal tissue samples was performed to identify differentially expressed modules (DEMs) from the signaling network and ultimately detect molecular markers to support histological examination. Network motifs were extracted from the human signaling network. Then, significant expression-correlation differential modules were identified by comparing the network module expression-correlation differential scores under normal and disease conditions using the gene expression datasets. Finally, we obtained DEMs by the Wilcoxon rank test and considered the average gene expression level in these modules as the classification features for diagnosing melanoma. RESULTS: In total, 99 functional DEMs were identified from the signaling network and gene expression profiles. The area under the curve scores for cancer module genes, melanoma module genes, and whole network modules are 92.4%, 90.44%, and 88.45%, respectively. The classification efficiency rates for nonmodule features are 71.04% and 79.38%, which correspond to the features of cancer genes and melanoma cancer genes, respectively. Finally, we acquired six significant molecular biomarkers, namely, module 10 (CALM3, Ca 2+ , PKC, PDGFRA, phospholipase-g, PIB5PA, and phosphatidylinositol-3-kinase), module 14 (SRC, Src homology 2 domain-containing [SHC], SAM68, GIT1, transcription factor-4, CBLB, GRB2, VAV2, LCK, YES, PTCH2, downstream of tyrosine kinase [DOK], and KIT), module 16 (ELK3, p85beta, SHC, ZFYVE9, TGFBR1, TGFBR2, CITED1, SH3KBP1, HCK, DOK, and KIT), module 45 (RB, CCND3, CCNA2, CDK4, and CDK6), module 75 (PCNA, CDK4, and CCND1), and module 114 (PSD93, NMDAR, and FYN). CONCLUSION: We explored the gene expression profile and signaling network in a global view and identified DEMs that can be used as diagnostic or prognostic markers for melanoma.
Authors: Mitchell S Stark; Lisa N Tom; Glen M Boyle; Vanessa F Bonazzi; H Peter Soyer; Adrian C Herington; Pamela M Pollock; Nicholas K Hayward Journal: Oncotarget Date: 2016-08-02
Authors: Ibai Goicoechea; Ricardo Rezola; María Arestin; María M Caffarel; Ana Rosa Cortazar; Lorea Manterola; Marta Fernandez-Mercado; María Armesto; Carla Sole; Erika Larrea; Angela M Araujo; Nerea Ancizar; Arrate Plazaola; Ander Urruticoechea; Arkaitz Carracedo; Irune Ruiz; Isabel Alvarez Lopez; Charles H Lawrie Journal: F1000Res Date: 2017-08-30