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

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      Author(s)
      Özhan, Ayşe
      Tombaz, Melike
      Konu, Özlen
      Date
      2021-12-08
      Source Title
      OMICS: A Journal of Integrative Biology
      Print ISSN
      1557-8100
      Electronic ISSN
      1536-2310
      Publisher
      Mary Ann Liebert
      Volume
      25
      Issue
      12
      Pages
      782 - 795
      Language
      English
      Type
      Article
      Item Usage Stats
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      220
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      Abstract
      The 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.
      Keywords
      Cancer
      Biomarkers
      Slit-Robo
      Prognosis
      Gene expression
      Personalized medicine
      Permalink
      http://hdl.handle.net/11693/77679
      Published Version (Please cite this version)
      https://doi.org/10.1089/omi.2021.0097
      Collections
      • Aysel Sabuncu Brain Research Center (BAM) 228
      • Department of Molecular Biology and Genetics 512
      • Institute of Materials Science and Nanotechnology (UNAM) 2098
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