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

Date
2021-12-08
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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
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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.

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Keywords
Cancer, Biomarkers, Slit-Robo, Prognosis, Gene expression, Personalized medicine
Citation
Published Version (Please cite this version)