Identification of plasma lipid species as promising diagnostic markers for prostate cancer

Autor: Xiaoli Chen, Yong Zhu, Mayumi Jijiwa, Masaki Nasu, Junmei Ai, Shengming Dai, Bin Jiang, Jicai Zhang, Gang Huang, Youping Deng
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: BMC Medical Informatics and Decision Making, Vol 20, Iss S9, Pp 1-10 (2020)
Druh dokumentu: article
ISSN: 1472-6947
DOI: 10.1186/s12911-020-01242-7
Popis: Abstract Background Prostate cancer is a very common and highly fatal in men. Current non-invasive detection methods like serum biomarker are unsatisfactory. Biomarkers with high accuracy for diagnostic of prostate cancer are urgently needed. Many lipid species have been found related to various cancers. The purpose of our study is to explore the diagnostic value of lipids for prostate cancer. Results Using triple quadruple liquid chromatography electrospray ionization tandem mass spectrometry, we performed lipidomics profiling of 367 lipids on a total 114 plasma samples from 30 patients with prostate cancer, 38 patients with benign prostatic hyperplasia (BPH), and 46 male healthy controls to evaluate the lipids as potential biomarkers in the diagnosis of prostate cancer. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database was used to construct the potential mechanism pathway. After statistical analysis, five lipids were identified as a panel of potential biomarkers for the detection of prostate cancer between prostate cancer group and the BPH group; the sensitivity, specificity, and area under curve (AUC) of the combination of these five lipids were 73.3, 81.6%, and 0.800, respectively. We also identified another panel of five lipids in distinguishing between prostate cancer group and the control group with predictive values of sensitivity at 76.7%, specificity at 80.4%, and AUC at 0.836, respectively. The glycerophospholipid metabolism pathway of the selected lipids was considered as the target pathway. Conclusions Our study indicated that the identified plasma lipid biomarkers have potential in the diagnosis of prostate cancer.
Databáze: Directory of Open Access Journals
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