NDLI: Characterization of metabolism-associated molecular patterns in prostate cancer

Content Provider	Springer Nature : BioMed Central
Author	Yang, Bowei Jiang, Yongming Yang, Jun Zhou, Wenbo Yang, Tongxin Zhang, Rongchang Xu, Jinming Guo, Haixiang
Abstract	Background Metabolism is a hallmark of cancer and it involves in resistance to antitumor treatment. Therefore, the purposes of this study are to classify metabolism-related molecular pattern and to explore the molecular and tumor microenvironment characteristics for prognosis predicting in prostate cancer. Methods The mRNA expression profiles and the corresponding clinical information for prostate cancer patients from TCGA, cBioPortal, and GEO databases. Samples were classified using unsupervised non-negative matrix factorization (NMF) clustering based on differentially expressed metabolism-related genes (MAGs). The characteristics of disease-free survival (DFS), clinicopathological characteristics, pathways, TME, immune cell infiltration, response to immunotherapy, and sensitivity to chemotherapy between subclusters were explored. A prognostic signature was constructed by LASSO cox regression analysis based on differentially expressed MAGs and followed by the development for prognostic prediction. Results A total of 76 MAGs between prostate cancer samples and non-tumorous samples were found, then 489 patients were divided into two metabolism-related subclusters for prostate cancer. The significant differences in clinical characteristics (age, T/N stage, Gleason) and DFS between two subclusters. Cluster 1 was associated with cell cycle and metabolism-related pathways, and epithelial-mesenchymal transition (EMT), etc., involved in cluster 2. Moreover, lower ESTIMATE/immune/stromal scores, lower expression of HLAs and immune checkpoint-related genes, and lower half-maximal inhibitory concentration (IC50) values in cluster 1 compared with cluster 2. The 10 MAG signature was identified and constructed a risk model for DFS predicting. The patients with high-risk scores showed poorer DFS. The area under the curve (AUC) values for 1-, 3-, 5-year DFS were 0.744, 0.731, 0.735 in TCGA-PRAD dataset, and 0.668, 0.712, 0.809 in GSE70768 dataset, 0.763, 0.802, 0.772 in GSE70769 dataset. Besides, risk score and Gleason score were identified as independent factors for DFS predicting, and the AUC values of risk score and Gleason score were respectively 0.743 and 0.738. The nomogram showed a favorable performance in DFS predicting. Conclusion Our data identified two metabolism-related molecular subclusters for prostate cancer that were distinctly characterized in prostate cancer. Metabolism-related risk profiles were also constructed for prognostic prediction.
Related Links	https://bmcurol.biomedcentral.com/counter/pdf/10.1186/s12894-023-01275-w.pdf
Ending Page	14
Page Count	14
Starting Page	1
File Format	HTM / HTML
ISSN	14712490
DOI	10.1186/s12894-023-01275-w
Journal	BMC Urology
Issue Number	1
Volume Number	23
Language	English
Publisher	BioMed Central
Publisher Date	2023-06-06
Access Restriction	Open
Subject Keyword	Urology Internal Medicine Prostate cancer Metabolism Molecular subclusters Tumor environment Prognosis
Content Type	Text
Resource Type	Article
Subject	Urology Reproductive Medicine
Journal Impact Factor	1.7/2023
5-Year Journal Impact Factor	2/2023

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