A Metabolism-Related Gene Landscape Predicts Prostate Cancer Recurrence and Treatment Response

Lijie Zhou; Ruixin Fan; Yongbo Luo; Cai Zhang; Donghui Jia; Rongli Wang; Youmiao Zeng; Mengda Ren; Kaixuan Du; Wenbang Pan; Jinjian Yang; Fengyan Tian; Chaohui Gu

doi:10.3389/fimmu.2022.837991

A Metabolism-Related Gene Landscape Predicts Prostate Cancer Recurrence and Treatment Response

Front Immunol. 2022 Mar 10:13:837991. doi: 10.3389/fimmu.2022.837991. eCollection 2022.

Authors

Lijie Zhou^{1

2}, Ruixin Fan^{1

2}, Yongbo Luo^{1

2}, Cai Zhang³, Donghui Jia¹, Rongli Wang⁴, Youmiao Zeng^{1

2}, Mengda Ren^{1

2}, Kaixuan Du^{1

2}, Wenbang Pan^{1

2}, Jinjian Yang^{1

2}, Fengyan Tian⁵, Chaohui Gu^{1

2}

Affiliations

¹ Department of Urology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
² Department of Urology, Henan Institute of Urology and Zhengzhou Key Laboratory for Molecular Biology of Urological Tumor Research, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
³ Department of Clinical Laboratory, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
⁴ Department of Obstetrics and Gynecology, First Affiliated Hospital, Xi'an Jiao tong University, Xi'an, China.
⁵ Department of Pediatrics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.

Abstract

Background: Prostate cancer (PCa) is the most common malignant tumor in men. Although clinical treatments of PCa have made great progress in recent decades, once tolerance to treatments occurs, the disease progresses rapidly after recurrence. PCa exhibits a unique metabolic rewriting that changes from initial neoplasia to advanced neoplasia. However, systematic and comprehensive studies on the relationship of changes in the metabolic landscape of PCa with tumor recurrence and treatment response are lacking. We aimed to construct a metabolism-related gene landscape that predicts PCa recurrence and treatment response.

Methods: In the present study, we used differentially expressed gene analysis, protein-protein interaction (PPI) networks, univariate and multivariate Cox regression, and least absolute shrinkage and selection operator (LASSO) regression to construct and verify a metabolism-related risk model (MRM) to predict the disease-free survival (DFS) and response to treatment for PCa patients.

Results: The MRM predicted patient survival more accurately than the current clinical prognostic indicators. By using two independent PCa datasets (International Cancer Genome Consortium (ICGC) PCa and Taylor) and actual patients to test the model, we also confirmed that the metabolism-related risk score (MRS) was strongly related to PCa progression. Notably, patients in different MRS subgroups had significant differences in metabolic activity, mutant landscape, immune microenvironment, and drug sensitivity. Patients in the high-MRS group were more sensitive to immunotherapy and endocrine therapy, while patients in the low-MRS group were more sensitive to chemotherapy.

Conclusions: We developed an MRM, which might act as a clinical feature to more accurately assess prognosis and guide the selection of appropriate treatment for PCa patients. It is promising for further application in clinical practice.

Keywords: disease-free survival (DFS); drug sensitivity; immunotherapeutic response; metabolism; prostate cancer.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Gene Expression Regulation, Neoplastic*
Humans
Male
Neoplasm Recurrence, Local / genetics
Prognosis
Prostate / pathology
Prostatic Neoplasms* / genetics
Prostatic Neoplasms* / metabolism
Prostatic Neoplasms* / therapy
Tumor Microenvironment