Discovery of cancer-specific and independent prognostic gene subsets of the slit-robo family using TCGA-PANCAN datasets

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buir.contributor.authorÖzhan, Ayşe
buir.contributor.authorTombaz, Melike
buir.contributor.authorKonu, Özlen
buir.contributor.orcidÖzhan, Ayşe|0000-0003-0282-0777
buir.contributor.orcidTombaz, Melike|0000-0002-0528-6680
buir.contributor.orcidKonu, Özlen|0000-0002-6223-5329
dc.citation.epage795en_US
dc.citation.issueNumber12en_US
dc.citation.spage782en_US
dc.citation.volumeNumber25en_US
dc.contributor.authorÖzhan, Ayşe
dc.contributor.authorTombaz, Melike
dc.contributor.authorKonu, Özlen
dc.date.accessioned2022-03-03T13:40:14Z
dc.date.available2022-03-03T13:40:14Z
dc.date.issued2021-12-08
dc.departmentDepartment of Molecular Biology and Geneticsen_US
dc.departmentInstitute of Materials Science and Nanotechnology (UNAM)en_US
dc.departmentInterdisciplinary Program in Neuroscience (NEUROSCIENCE)en_US
dc.description.abstractThe Slit-Robo family of axon guidance molecules works in concert, playing important roles in organ devel opment and cancer. Expressions of individual Slit-Robo genes have been used in calculating univariable hazard ratios (HRuni) for predicting cancer prognosis in the literature. However, Slit-Robo members do not act in dependently; hence, hazard ratios from multivariable Cox regression (HRmulti) on the whole gene set can further lead to identification of cancer-specific, novel, and independent prognostic gene pairs or modules. Herein, we obtained mRNA expressions of the Slit-Robo family consisting of four Robos (ROBO1/2/3/4) and three Slits (SLIT1/2/3), along with four types of survival outcome across cancers found in the Cancer Genome Atlas (TCGA). We used cluster heat maps to visualize closely associated pairs/modules of prognostic genes across 33 different cancers. We found a smaller number of significant genes in HRmulti than in HRuni, suggesting that the former analysis was less redundant. High ROBO4 expression emerged as relatively protective within the family, in both types of HR analyses. Multivariable Cox regression, on the other hand, revealed significantly more HR signatures containing Slit-Robo pairs acting in opposing directions than those containing Slit-Slit or Robo-Robo pairs for disease-specific survival. Furthermore, we discovered, through the online app SmulTCan’s lasso regression, Slit-Robo gene subsets that significantly differentiated between high- versus low-risk prog nosis patient groups, particularly for renal cancers and low-grade glioma. The statistical pipeline reported herein can help test independent and significant pairs/modules within a codependent gene family for cancer prog nostication, and thus should also prove useful in personalized/precision medicine research.en_US
dc.identifier.doi10.1089/omi.2021.0097en_US
dc.identifier.eissn1536-2310
dc.identifier.issn1557-8100
dc.identifier.urihttp://hdl.handle.net/11693/77679
dc.language.isoEnglishen_US
dc.publisherMary Ann Lieberten_US
dc.relation.isversionofhttps://doi.org/10.1089/omi.2021.0097en_US
dc.source.titleOMICS: A Journal of Integrative Biologyen_US
dc.subjectCanceren_US
dc.subjectBiomarkersen_US
dc.subjectSlit-Roboen_US
dc.subjectPrognosisen_US
dc.subjectGene expressionen_US
dc.subjectPersonalized medicineen_US
dc.titleDiscovery of cancer-specific and independent prognostic gene subsets of the slit-robo family using TCGA-PANCAN datasetsen_US
dc.typeArticleen_US
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Institute of Materials Science and Nanotechnology (UNAM)
Aysel Sabuncu Brain Research Center (BAM)
Department of Molecular Biology and Genetics