Abstract
BackgroundProstate 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.MethodsIn 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.ResultsThe 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.ConclusionsWe 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.
Highlights
Prostate cancer (PCa) is the most common malignant tumor in men worldwide [1]
We identified metabolism-related genes (MRGs) in PCa and constructed a prognostic indicator by Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses based on an MRG pair matrix and several PCa databases, which showed high accuracy in predicting recurrence and was associated with metabolic reprogramming, the immune microenvironment, and resistance to treatment
To systematically and comprehensively examine the tumor metabolic landscape at the gene level in PCa, we screened for metabolism-related differentially expressed genes in The Cancer Genome Atlas (TCGA) PCa database
Summary
Prostate cancer (PCa) is the most common malignant tumor in men worldwide [1]. Due to effective clinical interventions, including surgery, androgen deprivation therapy (ADT), and antiandrogen therapy, PCa has the highest 5-year survival rate (98%) among cancers. How to predict recurrence more and accurately, as well as to guide the selection of effective and sensitive treatments, is the focus of clinical research on PCa. Prostate cancer (PCa) is the most common malignant tumor in men. Clinical treatments of PCa have made great progress in recent decades, once tolerance to treatments occurs, the disease progresses rapidly after recurrence. 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
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