Open Access

ARTICLE

Prognostic model for prostate cancer based on glycolysis-related genes and non-negative matrix factorization analysis

ZECHAO LU^1,#, FUCAI TANG^1,#, HAOBIN ZHOU^2,#, ZEGUANG LU^3,#, WANYAN CAI^4,#, JIAHAO ZHANG⁵, ZHICHENG TANG⁶, YONGCHANG LAI^1,*, ZHAOHUI HE^1,*

1 Department of Urology, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 518033, China
2 The First Clinical College of Guangzhou Medical University, Guangzhou, 511436, China
3 The Second Clinical College of Guangzhou Medical University, Guangzhou, 511436, China
4 Department of Social and Behavioural Sciences, City University of Hong Kong, Hong Kong, 999077, China
5 The Sixth Clinical College of Guangzhou Medical University, Guangzhou, 511436, China
6 The Third Clinical College of Guangzhou Medical University, Guangzhou, 511436, China

* Corresponding Authors: YONGCHANG LAI. Email: ; ZHAOHUI HE. Email:
# Contributed equally to this work

(This article belongs to the Special Issue: Bioinformatics Study of Diseases)

BIOCELL 2023, 47(2), 339-350. https://doi.org/10.32604/biocell.2023.023750

Received 13 May 2022; Accepted 22 August 2022; Issue published 18 November 2022

Abstract

Background: Establishing an appropriate prognostic model for PCa is essential for its effective treatment. Glycolysis is a vital energy-harvesting mechanism for tumors. Developing a prognostic model for PCa based on glycolysis-related genes is novel and has great potential. Methods: First, gene expression and clinical data of PCa patients were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), and glycolysis-related genes were obtained from the Molecular Signatures Database (MSigDB). Gene enrichment analysis was performed to verify that glycolysis functions were enriched in the genes we obtained, which were used in non-negative matrix factorization (NMF) to identify clusters. The correlation between clusters and clinical features was discussed, and the differentially expressed genes (DEGs) between the two clusters were investigated. Based on the DEGs, we investigated the biological differences between clusters, including immune cell infiltration, mutation, tumor immune dysfunction and exclusion, immune function, and checkpoint genes. To establish the prognostic model, the genes were filtered based on univariable Cox regression, LASSO, and multivariable Cox regression. Kaplan–Meier analysis and receiver operating characteristic analysis validated the prognostic value of the model. A nomogram of the risk score calculated by the prognostic model and clinical characteristics was constructed to quantitatively estimate the survival probability for PCa patients in the clinical setting. Result: The genes obtained from MSigDB were enriched in glycolysis functions. Two clusters were identified by NMF analysis based on 272 glycolysis-related genes, and a prognostic model based on DEGs between the two clusters was finally established. The prognostic model consisted of LAMPS, SPRN, ATOH1, TANC1, ETV1, TDRD1, KLK14, MESP2, POSTN, CRIP2, NAT1, AKR7A3, PODXL, CARTPT, and PCDHGB2. All sample, training, and test cohorts from The Cancer Genome Atlas (TCGA) and the external validation cohort from GEO showed significant differences between the high-risk and low-risk groups. The area under the ROC curve showed great performance of this prognostic model. Conclusion: A prognostic model based on glycolysis-related genes was established, with great performance and potential significance to the clinical application.

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