Immune-Related Gene-Based Novel Subtypes to Establish a Model Predicting the Risk of Prostate Cancer
There is significant heterogeneity in prostate cancer (PCa), but immune status can reflect its prognosis. This study aimed to explore immune-related gene-based novel subtypes and to use them to create a model predicting the risk of PCa. We downloaded the data of 487 PCa patients from The Cancer Geno...
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Published in | Frontiers in genetics Vol. 11; p. 595657 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
Published |
Switzerland
Frontiers Media S.A
13.11.2020
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Subjects | |
Online Access | Get full text |
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Summary: | There is significant heterogeneity in prostate cancer (PCa), but immune status can reflect its prognosis. This study aimed to explore immune-related gene-based novel subtypes and to use them to create a model predicting the risk of PCa.
We downloaded the data of 487 PCa patients from The Cancer Genome Atlas (TCGA) database. We used immunologically relevant genes as input for consensus clustering and applied survival analysis and principal component analysis to determine the properties of the subtypes. We also explored differences of somatic variations, copy number variations,
fusion, and androgen receptor (AR) scores among the subtypes. Then, we examined the infiltration of different immune cells into the tumor microenvironment in each subtype. We next performed Gene Set Enrichment Analysis (GSEA) to illustrate the characteristics of the subtypes. Finally, based on the subtypes, we constructed a risk predictive model and verified it in TCGA, Gene Expression Omnibus (GEO), cBioPortal, and International Cancer Genome Consortium (ICGC) databases.
Four PCa subtypes (C1, C2, C3, and C4) were identified on immune status. Patients with the C3 subtype had the worst prognosis, while the other three groups did not differ significantly from each other in terms of their prognosis. Principal component analysis clearly distinguished high-risk (C3) and low-risk (C1 + 2 + 4) patients. Compared with the case in the low-risk subtype, the Speckle-type POZ Protein (
) had a higher mutation frequency and lower transcriptional level in the high-risk subtype. In C3, there was also a higher frequency of copy number alterations (CNA) of Clusterin (
) and lower
expression. In addition, C3 had a higher frequency of
fusion and higher AR scores. M2 macrophages also showed significantly higher infiltration in the high-risk subtype, while CD8
T cells and dendritic cells had significantly higher infiltration in the low-risk subtype. GSEA revealed that MYC, androgen, and KRAS were relatively activated and p53 was relatively suppressed in high-risk subtype, compared with the levels in the low-risk subtype. Finally, we trained a six-gene signature risk predictive model, which performed well in TCGA, GEO, cBioPortal, and ICGC databases.
PCa can be divided into four subtypes based on immune-related genes, among which the C3 subtype is associated with a poor prognosis. Based on these subtypes, a risk predictive model was developed, which could indicate patient prognosis. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Reviewed by: Ruidong Li, University of California, Riverside, United States; Abedalrhman Alkhateeb, University of Windsor, Canada This article was submitted to Computational Genomics, a section of the journal Frontiers in Genetics Edited by: Hua Zhong, College of Life Sciences, Wuhan University, China |
ISSN: | 1664-8021 1664-8021 |
DOI: | 10.3389/fgene.2020.595657 |