Gene Expression Analysis Reveals Prognostic Biomarkers of the Tyrosine Metabolism Reprogramming Pathway for Prostate Cancer

Wei Li; Zhe Lu; Dongqing Pan; Zejian Zhang; Hua He; Jiacheng Wu; Naixiong Peng

doi:10.1155/2022/5504173

Gene Expression Analysis Reveals Prognostic Biomarkers of the Tyrosine Metabolism Reprogramming Pathway for Prostate Cancer

J Oncol. 2022 Jul 6:2022:5504173. doi: 10.1155/2022/5504173. eCollection 2022.

Authors

Wei Li¹, Zhe Lu², Dongqing Pan³, Zejian Zhang¹, Hua He¹, Jiacheng Wu⁴, Naixiong Peng¹

Affiliations

¹ Department of Urology, Shenzhen Longhua District Central Hospital, The Affiliated Central Hospital of Shenzhen Longhua District, Guangdong Medical University, Shenzhen, Guangdong Province, China.
² Clinical Laboratory, Women and Children's Health Care Center of Hainan Province, Haikou, Hainan Province, China.
³ Dachong Health Service Center, Headquarters of Nanshan Medical Group, Shenzhen, Guangdong Province, China.
⁴ Department of Urology, Affiliated Tumor Hospital of Nantong University and Nantong Tumor Hospital, Nantong, Jiansu Province, China.

Abstract

Background: Tyrosine metabolism pathway-related genes were related to prostate cancer progression, which may be used as potential prognostic markers.

Aims: To dissect the dysregulation of tyrosine metabolism in prostate cancer and build a prognostic signature based on tyrosine metabolism-related genes for prostate cancer. Materials and Method. Cross-platform gene expression data of prostate cancer cohorts were collected from both The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Based on the expression of tyrosine metabolism-related enzymes (TMREs), an unsupervised consensus clustering method was used to classify prostate cancer patients into different molecular subtypes. We employed the least absolute shrinkage and selection operator (LASSO) Cox regression analysis to evaluate prognostic characteristics based on TMREs to obtain a prognostic effect. The nomogram model was established and used to synthesize molecular subtypes, prognostic characteristics, and clinicopathological features. Kaplan-Meier plots and logrank analysis were used to clarify survival differences between subtypes.

Results: Based on the hierarchical clustering method and the expression profiles of TMREs, prostate cancer samples were assigned into two subgroups (S1, subgroup 1; S2, subgroup 2), and the Kaplan-Meier plot and logrank analysis showed distinct survival outcomes between S1 and S2 subgroups. We further established a four-gene-based prognostic signature, and both in-group testing dataset and out-group testing dataset indicated the robustness of this model. By combining the four gene-based signatures and clinicopathological features, the nomogram model achieved better survival outcomes than any single classifier. Interestingly, we found that immune-related pathways were significantly concentrated on S1-upregulated genes, and the abundance of memory B cells, CD4+ resting memory T cells, M0 macrophages, resting dendritic cells, and resting mast cells were significantly different between S1 and S2 subgroups.

Conclusions: Our results indicate the prognostic value of genes related to tyrosine metabolism in prostate cancer and provide inspiration for treatment and prevention strategies.