A new bioinformatics approach identifies overexpression of GRB2 as a poor prognostic biomarker for prostate cancer

Teppei Iwata,Stephen H H Chien,Ichiro Maeda,Sookhee Pae,Miki Yoshiike,Kazuo Yudo,Manabu Kubota,Rei Meguro,Kimino Minagawa,Toshio Kumai,Ko Sato,Sunny Cho,Wataru Usuba,Eiji Kikuchi,Shiro Urabe,Eleina Hames,Kishore Palanisamy,Shigeko Oonuma,Anna S Sedukhina

doi:10.1038/s41598-021-85086-9

Abstract

A subset of prostate cancer displays a poor clinical outcome. Therefore, identifying this poor prognostic subset within clinically aggressive groups (defined as a Gleason score (GS) ≧8) and developing effective treatments are essential if we are to improve prostate cancer survival. Here, we performed a bioinformatics analysis of a TCGA dataset (GS ≧8) to identify pathways upregulated in a prostate cancer cohort with short survival. When conducting bioinformatics analyses, the definition of factors such as “overexpression” and “shorter survival” is vital, as poor definition may lead to mis-estimations. To eliminate this possibility, we defined an expression cutoff value using an algorithm calculated by a Cox regression model, and the hazard ratio for each gene was set so as to identify genes whose expression levels were associated with shorter survival. Next, genes associated with shorter survival were entered into pathway analysis to identify pathways that were altered in a shorter survival cohort. We identified pathways involving upregulation of GRB2. Overexpression of GRB2 was linked to shorter survival in the TCGA dataset, a finding validated by histological examination of biopsy samples taken from the patients for diagnostic purposes. Thus, GRB2 is a novel biomarker that predicts shorter survival of patients with aggressive prostate cancer (GS ≧8).

Highlights

A subset of prostate cancer displays a poor clinical outcome
Prostate cancer with a Gleason Score (GS) ≥ 8 is defined as high-risk[11]; to identify pathways and molecules associated with shorter survival in a cohort with a poor clinical outcome, we selected patients with a GS ≥ 8 from the TCGA dataset (Table 1)
To define the cutoff point used in this study, we used an algorithm that identified the best cutoff value based on data from a Cox proportional hazard model[12]

Summary

Introduction

A subset of prostate cancer displays a poor clinical outcome. identifying this poor prognostic subset within clinically aggressive groups (defined as a Gleason score (GS) ≧8) and developing effective treatments are essential if we are to improve prostate cancer survival. We performed a bioinformatics analysis of a TCGA dataset (GS ≧8) to identify pathways upregulated in a prostate cancer cohort with short survival. The definition of factors such as “overexpression” and “shorter survival” is vital, as poor definition may lead to misestimations To eliminate this possibility, we defined an expression cutoff value using an algorithm calculated by a Cox regression model, and the hazard ratio for each gene was set so as to identify genes whose expression levels were associated with shorter survival. We investigated whether expression of the biomarker(s) identified by bioinformatics analysis was associated with histological diagnoses in biopsy section from a shorter survival clinical cohort. We show that overexpression of GRB2 is linked to shorter survival of patients with aggressive prostate cancer (defined as GS ≧8)

Methods

Results

Conclusion